Update numpy to 1.25.2

737 files changed, 208018 insertions, 38960 deletions

diff --git a/contrib/python/numpy/include/numpy/core/include/numpy/_dtype_api.h b/contrib/python/numpy/include/numpy/core/include/numpy/_dtype_api.h
new file mode 100644
index 00000000000..449387567d1
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/include/numpy/_dtype_api.h
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/include/numpy/_dtype_api.h>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/include/numpy/_dtype_api.h>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/include/numpy/experimental_dtype_api.h b/contrib/python/numpy/include/numpy/core/include/numpy/experimental_dtype_api.h
new file mode 100644
index 00000000000..980091ba730
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/include/numpy/experimental_dtype_api.h
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/include/numpy/experimental_dtype_api.h>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/include/numpy/experimental_dtype_api.h>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/common/common.hpp b/contrib/python/numpy/include/numpy/core/src/common/common.hpp
new file mode 100644
index 00000000000..b803643e405
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/common/common.hpp
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/common/common.hpp>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/common/common.hpp>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/npysort/simd_qsort.dispatch.cpp b/contrib/python/numpy/include/numpy/core/src/npysort/simd_qsort.dispatch.cpp
new file mode 100644
index 00000000000..4e158241680
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/npysort/simd_qsort.dispatch.cpp
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.cpp>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/npysort/simd_qsort.dispatch.cpp>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp b/contrib/python/numpy/include/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp
new file mode 100644
index 00000000000..0e352add70f
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/npysort/x86-qsort.dispatch.cpp b/contrib/python/numpy/include/numpy/core/src/npysort/x86-qsort.dispatch.cpp
deleted file mode 100644
index bf871ae6943..00000000000
--- a/contrib/python/numpy/include/numpy/core/src/npysort/x86-qsort.dispatch.cpp
+++ /dev/null
@@ -1,5 +0,0 @@
-#ifdef USE_PYTHON3
-#include <contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.cpp>
-#else
-#error #include <contrib/python/numpy/py2/numpy/core/src/npysort/x86-qsort.dispatch.cpp>
-#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_autovec.dispatch.c b/contrib/python/numpy/include/numpy/core/src/umath/loops_autovec.dispatch.c
new file mode 100644
index 00000000000..6a72ddbf7a7
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_autovec.dispatch.c
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.c>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_autovec.dispatch.c>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_autovec.dispatch.h b/contrib/python/numpy/include/numpy/core/src/umath/loops_autovec.dispatch.h
new file mode 100644
index 00000000000..8aa140e9028
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_autovec.dispatch.h
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.h>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_autovec.dispatch.h>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_logical.dispatch.c b/contrib/python/numpy/include/numpy/core/src/umath/loops_logical.dispatch.c
new file mode 100644
index 00000000000..0ede76f59e9
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_logical.dispatch.c
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.c>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_logical.dispatch.c>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_logical.dispatch.h b/contrib/python/numpy/include/numpy/core/src/umath/loops_logical.dispatch.h
new file mode 100644
index 00000000000..4d07e71f60e
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_logical.dispatch.h
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.h>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_logical.dispatch.h>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_unary.dispatch.c b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary.dispatch.c
new file mode 100644
index 00000000000..c04c6104bdf
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary.dispatch.c
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.c>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_unary.dispatch.c>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_unary.dispatch.h b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary.dispatch.h
new file mode 100644
index 00000000000..be551a5dbc5
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary.dispatch.h
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.h>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_unary.dispatch.h>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_complex.dispatch.c b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_complex.dispatch.c
new file mode 100644
index 00000000000..22b14d773c9
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_complex.dispatch.c
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.c>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_unary_complex.dispatch.c>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_complex.dispatch.h b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_complex.dispatch.h
new file mode 100644
index 00000000000..41f56284862
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_complex.dispatch.h
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.h>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_unary_complex.dispatch.h>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_fp_le.dispatch.c b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_fp_le.dispatch.c
new file mode 100644
index 00000000000..c565f7b638c
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_fp_le.dispatch.c
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.c>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_unary_fp_le.dispatch.c>
+#endif
diff --git a/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_fp_le.dispatch.h b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_fp_le.dispatch.h
new file mode 100644
index 00000000000..896327ea9c4
--- /dev/null
+++ b/contrib/python/numpy/include/numpy/core/src/umath/loops_unary_fp_le.dispatch.h
@@ -0,0 +1,5 @@
+#ifdef USE_PYTHON3
+#include <contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.h>
+#else
+#error #include <contrib/python/numpy/py2/numpy/core/src/umath/loops_unary_fp_le.dispatch.h>
+#endif
diff --git a/contrib/python/numpy/py3/.dist-info/METADATA b/contrib/python/numpy/py3/.dist-info/METADATA
index 6137a937fd7..ed54cb49b3c 100644
--- a/contrib/python/numpy/py3/.dist-info/METADATA
+++ b/contrib/python/numpy/py3/.dist-info/METADATA
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: numpy
-Version: 1.24.4
+Version: 1.25.2
 Summary: Fundamental package for array computing in Python
 Home-page: https://www.numpy.org
 Author: Travis E. Oliphant et al.
@@ -9,7 +9,7 @@ Maintainer-email: numpy-discussion@python.org
 License: BSD-3-Clause
 Download-URL: https://pypi.python.org/pypi/numpy
 Project-URL: Bug Tracker, https://github.com/numpy/numpy/issues
-Project-URL: Documentation, https://numpy.org/doc/1.24
+Project-URL: Documentation, https://numpy.org/doc/1.25
 Project-URL: Source Code, https://github.com/numpy/numpy
 Platform: Windows
 Platform: Linux
@@ -23,7 +23,6 @@ Classifier: License :: OSI Approved :: BSD License
 Classifier: Programming Language :: C
 Classifier: Programming Language :: Python
 Classifier: Programming Language :: Python :: 3
-Classifier: Programming Language :: Python :: 3.8
 Classifier: Programming Language :: Python :: 3.9
 Classifier: Programming Language :: Python :: 3.10
 Classifier: Programming Language :: Python :: 3.11
@@ -36,13 +35,13 @@ Classifier: Operating System :: Microsoft :: Windows
 Classifier: Operating System :: POSIX
 Classifier: Operating System :: Unix
 Classifier: Operating System :: MacOS
-Requires-Python: >=3.8
+Requires-Python: >=3.9
 Description-Content-Type: text/markdown
 License-File: LICENSE.txt
 License-File: LICENSES_bundled.txt
 
 <h1 align="center">
-<img src="/branding/logo/primary/numpylogo.svg" width="300">
+<img src="https://raw.githubusercontent.com/numpy/numpy/main/branding/logo/primary/numpylogo.svg" width="300">
 </h1><br>
 
 
@@ -80,7 +79,7 @@ Testing:
 
 NumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:
 
-    python -c 'import numpy; numpy.test()'
+    python -c "import numpy, sys; sys.exit(numpy.test() is False)"
 
 Code of Conduct
 ----------------------
diff --git a/contrib/python/numpy/py3/LICENSE.txt b/contrib/python/numpy/py3/LICENSE.txt
index 23932bef25e..2dfbf61c056 100644
--- a/contrib/python/numpy/py3/LICENSE.txt
+++ b/contrib/python/numpy/py3/LICENSE.txt
@@ -1,4 +1,4 @@
-Copyright (c) 2005-2022, NumPy Developers.
+Copyright (c) 2005-2023, NumPy Developers.
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
diff --git a/contrib/python/numpy/py3/README.md b/contrib/python/numpy/py3/README.md
index 02f8f17fb95..c0e0b738891 100644
--- a/contrib/python/numpy/py3/README.md
+++ b/contrib/python/numpy/py3/README.md
@@ -1,5 +1,5 @@
 <h1 align="center">
-<img src="/branding/logo/primary/numpylogo.svg" width="300">
+<img src="https://raw.githubusercontent.com/numpy/numpy/main/branding/logo/primary/numpylogo.svg" width="300">
 </h1><br>
 
 
@@ -37,7 +37,7 @@ Testing:
 
 NumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:
 
-    python -c 'import numpy; numpy.test()'
+    python -c "import numpy, sys; sys.exit(numpy.test() is False)"
 
 Code of Conduct
 ----------------------
diff --git a/contrib/python/numpy/py3/numpy/__config__.py.in b/contrib/python/numpy/py3/numpy/__config__.py.in
new file mode 100644
index 00000000000..6c6c21cb85d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/__config__.py.in
@@ -0,0 +1,156 @@
+# This file is generated by numpy's build process
+# It contains system_info results at the time of building this package.
+from enum import Enum
+from numpy.core._multiarray_umath import (
+    __cpu_features__,
+    __cpu_baseline__,
+    __cpu_dispatch__,
+)
+
+__all__ = ["show"]
+_built_with_meson = True
+
+
+class DisplayModes(Enum):
+    stdout = "stdout"
+    dicts = "dicts"
+
+
+def _cleanup(d):
+    """
+    Removes empty values in a `dict` recursively
+    This ensures we remove values that Meson could not provide to CONFIG
+    """
+    if isinstance(d, dict):
+        return {k: _cleanup(v) for k, v in d.items() if v and _cleanup(v)}
+    else:
+        return d
+
+
+CONFIG = _cleanup(
+    {
+        "Compilers": {
+            "c": {
+                "name": "@C_COMP@",
+                "linker": "@C_COMP_LINKER_ID@",
+                "version": "@C_COMP_VERSION@",
+                "commands": "@C_COMP_CMD_ARRAY@",
+            },
+            "cython": {
+                "name": "@CYTHON_COMP@",
+                "linker": "@CYTHON_COMP_LINKER_ID@",
+                "version": "@CYTHON_COMP_VERSION@",
+                "commands": "@CYTHON_COMP_CMD_ARRAY@",
+            },
+            "c++": {
+                "name": "@CPP_COMP@",
+                "linker": "@CPP_COMP_LINKER_ID@",
+                "version": "@CPP_COMP_VERSION@",
+                "commands": "@CPP_COMP_CMD_ARRAY@",
+            },
+        },
+        "Machine Information": {
+            "host": {
+                "cpu": "@HOST_CPU@",
+                "family": "@HOST_CPU_FAMILY@",
+                "endian": "@HOST_CPU_ENDIAN@",
+                "system": "@HOST_CPU_SYSTEM@",
+            },
+            "build": {
+                "cpu": "@BUILD_CPU@",
+                "family": "@BUILD_CPU_FAMILY@",
+                "endian": "@BUILD_CPU_ENDIAN@",
+                "system": "@BUILD_CPU_SYSTEM@",
+            },
+            "cross-compiled": bool("@CROSS_COMPILED@".lower().replace("false", "")),
+        },
+        "Build Dependencies": {
+            "blas": {
+                "name": "@BLAS_NAME@",
+                "found": bool("@BLAS_FOUND@".lower().replace("false", "")),
+                "version": "@BLAS_VERSION@",
+                "detection method": "@BLAS_TYPE_NAME@",
+                "include directory": r"@BLAS_INCLUDEDIR@",
+                "lib directory": r"@BLAS_LIBDIR@",
+                "openblas configuration": "@BLAS_OPENBLAS_CONFIG@",
+                "pc file directory": r"@BLAS_PCFILEDIR@",
+            },
+            "lapack": {
+                "name": "@LAPACK_NAME@",
+                "found": bool("@LAPACK_FOUND@".lower().replace("false", "")),
+                "version": "@LAPACK_VERSION@",
+                "detection method": "@LAPACK_TYPE_NAME@",
+                "include directory": r"@LAPACK_INCLUDEDIR@",
+                "lib directory": r"@LAPACK_LIBDIR@",
+                "openblas configuration": "@LAPACK_OPENBLAS_CONFIG@",
+                "pc file directory": r"@LAPACK_PCFILEDIR@",
+            },
+        },
+        "Python Information": {
+            "path": r"@PYTHON_PATH@",
+            "version": "@PYTHON_VERSION@",
+        },
+        "SIMD Extensions": {
+            "baseline": __cpu_baseline__,
+            "found": [
+                feature for feature in __cpu_dispatch__ if __cpu_features__[feature]
+            ],
+            "not found": [
+                feature for feature in __cpu_dispatch__ if not __cpu_features__[feature]
+            ],
+        },
+    }
+)
+
+
+def _check_pyyaml():
+    import yaml
+
+    return yaml
+
+
+def show(mode=DisplayModes.stdout.value):
+    """
+    Show libraries and system information on which NumPy was built
+    and is being used
+
+    Parameters
+    ----------
+    mode : {`'stdout'`, `'dicts'`}, optional.
+        Indicates how to display the config information.
+        `'stdout'` prints to console, `'dicts'` returns a dictionary
+        of the configuration.
+
+    Returns
+    -------
+    out : {`dict`, `None`}
+        If mode is `'dicts'`, a dict is returned, else None
+
+    See Also
+    --------
+    get_include : Returns the directory containing NumPy C
+                  header files.
+
+    Notes
+    -----
+    1. The `'stdout'` mode will give more readable
+       output if ``pyyaml`` is installed
+
+    """
+    if mode == DisplayModes.stdout.value:
+        try:  # Non-standard library, check import
+            yaml = _check_pyyaml()
+
+            print(yaml.dump(CONFIG))
+        except ModuleNotFoundError:
+            import warnings
+            import json
+
+            warnings.warn("Install `pyyaml` for better output", stacklevel=1)
+            print(json.dumps(CONFIG, indent=2))
+    elif mode == DisplayModes.dicts.value:
+        return CONFIG
+    else:
+        raise AttributeError(
+            f"Invalid `mode`, use one of: {', '.join([e.value for e in DisplayModes])}"
+        )
diff --git a/contrib/python/numpy/py3/numpy/__init__.cython-30.pxd b/contrib/python/numpy/py3/numpy/__init__.cython-30.pxd
index 5fd6086e070..0dd2fff2b87 100644
--- a/contrib/python/numpy/py3/numpy/__init__.cython-30.pxd
+++ b/contrib/python/numpy/py3/numpy/__init__.cython-30.pxd
@@ -401,7 +401,7 @@ cdef extern from "numpy/arrayobject.h":
     int PyArray_TYPE(ndarray arr) nogil
 
     object PyArray_GETITEM(ndarray arr, void *itemptr)
-    int PyArray_SETITEM(ndarray arr, void *itemptr, object obj)
+    int PyArray_SETITEM(ndarray arr, void *itemptr, object obj) except -1
 
     bint PyTypeNum_ISBOOL(int) nogil
     bint PyTypeNum_ISUNSIGNED(int) nogil
@@ -452,7 +452,7 @@ cdef extern from "numpy/arrayobject.h":
 
     bint PyArray_SAFEALIGNEDCOPY(ndarray) nogil
     bint PyArray_ISNBO(char) nogil              # works on ndarray.byteorder
-    bint PyArray_IsNativeByteOrder(char) nogil  # works on ndarray.byteorder
+    bint PyArray_IsNativeByteOrder(char) nogil # works on ndarray.byteorder
     bint PyArray_ISNOTSWAPPED(ndarray) nogil
     bint PyArray_ISBYTESWAPPED(ndarray) nogil
 
@@ -515,7 +515,6 @@ cdef extern from "numpy/arrayobject.h":
     void* PyArray_GETPTR3(ndarray m, npy_intp i, npy_intp j, npy_intp k) nogil
     void* PyArray_GETPTR4(ndarray m, npy_intp i, npy_intp j, npy_intp k, npy_intp l) nogil
 
-    void PyArray_XDECREF_ERR(ndarray)
     # Cannot be supported due to out arg
     # void PyArray_DESCR_REPLACE(descr)
 
@@ -549,21 +548,21 @@ cdef extern from "numpy/arrayobject.h":
     # Functions taking dtype and returning object/ndarray are disabled
     # for now as they steal dtype references. I'm conservative and disable
     # more than is probably needed until it can be checked further.
-    int PyArray_SetNumericOps        (object)
+    int PyArray_SetNumericOps (object) except -1
     object PyArray_GetNumericOps ()
-    int PyArray_INCREF (ndarray)
-    int PyArray_XDECREF (ndarray)
+    int PyArray_INCREF (ndarray) except *  # uses PyArray_Item_INCREF...
+    int PyArray_XDECREF (ndarray) except *  # uses PyArray_Item_DECREF...
     void PyArray_SetStringFunction (object, int)
     dtype PyArray_DescrFromType (int)
     object PyArray_TypeObjectFromType (int)
     char * PyArray_Zero (ndarray)
     char * PyArray_One (ndarray)
     #object PyArray_CastToType (ndarray, dtype, int)
-    int PyArray_CastTo (ndarray, ndarray)
-    int PyArray_CastAnyTo (ndarray, ndarray)
-    int PyArray_CanCastSafely (int, int)
-    npy_bool PyArray_CanCastTo (dtype, dtype)
-    int PyArray_ObjectType (object, int)
+    int PyArray_CastTo (ndarray, ndarray) except -1
+    int PyArray_CastAnyTo (ndarray, ndarray) except -1
+    int PyArray_CanCastSafely (int, int)  # writes errors
+    npy_bool PyArray_CanCastTo (dtype, dtype)  # writes errors
+    int PyArray_ObjectType (object, int) except 0
     dtype PyArray_DescrFromObject (object, dtype)
     #ndarray* PyArray_ConvertToCommonType (object, int *)
     dtype PyArray_DescrFromScalar (object)
@@ -587,34 +586,34 @@ cdef extern from "numpy/arrayobject.h":
     #object PyArray_FromIter (object, dtype, npy_intp)
     object PyArray_Return (ndarray)
     #object PyArray_GetField (ndarray, dtype, int)
-    #int PyArray_SetField (ndarray, dtype, int, object)
+    #int PyArray_SetField (ndarray, dtype, int, object) except -1
     object PyArray_Byteswap (ndarray, npy_bool)
     object PyArray_Resize (ndarray, PyArray_Dims *, int, NPY_ORDER)
-    int PyArray_MoveInto (ndarray, ndarray)
-    int PyArray_CopyInto (ndarray, ndarray)
-    int PyArray_CopyAnyInto (ndarray, ndarray)
-    int PyArray_CopyObject (ndarray, object)
+    int PyArray_MoveInto (ndarray, ndarray) except -1
+    int PyArray_CopyInto (ndarray, ndarray) except -1
+    int PyArray_CopyAnyInto (ndarray, ndarray) except -1
+    int PyArray_CopyObject (ndarray, object) except -1
     object PyArray_NewCopy (ndarray, NPY_ORDER)
     object PyArray_ToList (ndarray)
     object PyArray_ToString (ndarray, NPY_ORDER)
-    int PyArray_ToFile (ndarray, stdio.FILE *, char *, char *)
-    int PyArray_Dump (object, object, int)
+    int PyArray_ToFile (ndarray, stdio.FILE *, char *, char *) except -1
+    int PyArray_Dump (object, object, int) except -1
     object PyArray_Dumps (object, int)
-    int PyArray_ValidType (int)
+    int PyArray_ValidType (int)  # Cannot error
     void PyArray_UpdateFlags (ndarray, int)
     object PyArray_New (type, int, npy_intp *, int, npy_intp *, void *, int, int, object)
     #object PyArray_NewFromDescr (type, dtype, int, npy_intp *, npy_intp *, void *, int, object)
     #dtype PyArray_DescrNew (dtype)
     dtype PyArray_DescrNewFromType (int)
-    double PyArray_GetPriority (object, double)
+    double PyArray_GetPriority (object, double)  # clears errors as of 1.25
     object PyArray_IterNew (object)
     object PyArray_MultiIterNew (int, ...)
 
-    int PyArray_PyIntAsInt (object)
+    int PyArray_PyIntAsInt (object) except? -1
     npy_intp PyArray_PyIntAsIntp (object)
-    int PyArray_Broadcast (broadcast)
-    void PyArray_FillObjectArray (ndarray, object)
-    int PyArray_FillWithScalar (ndarray, object)
+    int PyArray_Broadcast (broadcast) except -1
+    void PyArray_FillObjectArray (ndarray, object) except *
+    int PyArray_FillWithScalar (ndarray, object) except -1
     npy_bool PyArray_CheckStrides (int, int, npy_intp, npy_intp, npy_intp *, npy_intp *)
     dtype PyArray_DescrNewByteorder (dtype, char)
     object PyArray_IterAllButAxis (object, int *)
@@ -628,10 +627,10 @@ cdef extern from "numpy/arrayobject.h":
     object PyArray_NewFlagsObject (object)
     npy_bool PyArray_CanCastScalar (type, type)
     #int PyArray_CompareUCS4 (npy_ucs4 *, npy_ucs4 *, register size_t)
-    int PyArray_RemoveSmallest (broadcast)
+    int PyArray_RemoveSmallest (broadcast) except -1
     int PyArray_ElementStrides (object)
-    void PyArray_Item_INCREF (char *, dtype)
-    void PyArray_Item_XDECREF (char *, dtype)
+    void PyArray_Item_INCREF (char *, dtype) except *
+    void PyArray_Item_XDECREF (char *, dtype) except *
     object PyArray_FieldNames (object)
     object PyArray_Transpose (ndarray, PyArray_Dims *)
     object PyArray_TakeFrom (ndarray, object, int, ndarray, NPY_CLIPMODE)
@@ -639,7 +638,7 @@ cdef extern from "numpy/arrayobject.h":
     object PyArray_PutMask (ndarray, object, object)
     object PyArray_Repeat (ndarray, object, int)
     object PyArray_Choose (ndarray, object, ndarray, NPY_CLIPMODE)
-    int PyArray_Sort (ndarray, int, NPY_SORTKIND)
+    int PyArray_Sort (ndarray, int, NPY_SORTKIND) except -1
     object PyArray_ArgSort (ndarray, int, NPY_SORTKIND)
     object PyArray_SearchSorted (ndarray, object, NPY_SEARCHSIDE, PyObject *)
     object PyArray_ArgMax (ndarray, int, ndarray)
@@ -677,49 +676,49 @@ cdef extern from "numpy/arrayobject.h":
     #int PyArray_As2D (object*, char ***, int *, int *, int)
     int PyArray_Free (object, void *)
     #int PyArray_Converter (object, object*)
-    int PyArray_IntpFromSequence (object, npy_intp *, int)
+    int PyArray_IntpFromSequence (object, npy_intp *, int) except -1
     object PyArray_Concatenate (object, int)
     object PyArray_InnerProduct (object, object)
     object PyArray_MatrixProduct (object, object)
     object PyArray_CopyAndTranspose (object)
     object PyArray_Correlate (object, object, int)
     int PyArray_TypestrConvert (int, int)
-    #int PyArray_DescrConverter (object, dtype*)
-    #int PyArray_DescrConverter2 (object, dtype*)
-    int PyArray_IntpConverter (object, PyArray_Dims *)
-    #int PyArray_BufferConverter (object, chunk)
-    int PyArray_AxisConverter (object, int *)
-    int PyArray_BoolConverter (object, npy_bool *)
-    int PyArray_ByteorderConverter (object, char *)
-    int PyArray_OrderConverter (object, NPY_ORDER *)
-    unsigned char PyArray_EquivTypes (dtype, dtype)
+    #int PyArray_DescrConverter (object, dtype*) except 0
+    #int PyArray_DescrConverter2 (object, dtype*) except 0
+    int PyArray_IntpConverter (object, PyArray_Dims *) except 0
+    #int PyArray_BufferConverter (object, chunk) except 0
+    int PyArray_AxisConverter (object, int *) except 0
+    int PyArray_BoolConverter (object, npy_bool *) except 0
+    int PyArray_ByteorderConverter (object, char *) except 0
+    int PyArray_OrderConverter (object, NPY_ORDER *) except 0
+    unsigned char PyArray_EquivTypes (dtype, dtype)  # clears errors
     #object PyArray_Zeros (int, npy_intp *, dtype, int)
     #object PyArray_Empty (int, npy_intp *, dtype, int)
     object PyArray_Where (object, object, object)
     object PyArray_Arange (double, double, double, int)
     #object PyArray_ArangeObj (object, object, object, dtype)
-    int PyArray_SortkindConverter (object, NPY_SORTKIND *)
+    int PyArray_SortkindConverter (object, NPY_SORTKIND *) except 0
     object PyArray_LexSort (object, int)
     object PyArray_Round (ndarray, int, ndarray)
     unsigned char PyArray_EquivTypenums (int, int)
-    int PyArray_RegisterDataType (dtype)
-    int PyArray_RegisterCastFunc (dtype, int, PyArray_VectorUnaryFunc *)
-    int PyArray_RegisterCanCast (dtype, int, NPY_SCALARKIND)
+    int PyArray_RegisterDataType (dtype) except -1
+    int PyArray_RegisterCastFunc (dtype, int, PyArray_VectorUnaryFunc *) except -1
+    int PyArray_RegisterCanCast (dtype, int, NPY_SCALARKIND) except -1
     #void PyArray_InitArrFuncs (PyArray_ArrFuncs *)
     object PyArray_IntTupleFromIntp (int, npy_intp *)
     int PyArray_TypeNumFromName (char *)
-    int PyArray_ClipmodeConverter (object, NPY_CLIPMODE *)
-    #int PyArray_OutputConverter (object, ndarray*)
+    int PyArray_ClipmodeConverter (object, NPY_CLIPMODE *) except 0
+    #int PyArray_OutputConverter (object, ndarray*) except 0
     object PyArray_BroadcastToShape (object, npy_intp *, int)
     void _PyArray_SigintHandler (int)
     void* _PyArray_GetSigintBuf ()
-    #int PyArray_DescrAlignConverter (object, dtype*)
-    #int PyArray_DescrAlignConverter2 (object, dtype*)
-    int PyArray_SearchsideConverter (object, void *)
+    #int PyArray_DescrAlignConverter (object, dtype*) except 0
+    #int PyArray_DescrAlignConverter2 (object, dtype*) except 0
+    int PyArray_SearchsideConverter (object, void *) except 0
     object PyArray_CheckAxis (ndarray, int *, int)
     npy_intp PyArray_OverflowMultiplyList (npy_intp *, int)
     int PyArray_CompareString (char *, char *, size_t)
-    int PyArray_SetBaseObject(ndarray, base)  # NOTE: steals a reference to base! Use "set_array_base()" instead.
+    int PyArray_SetBaseObject(ndarray, base) except -1 # NOTE: steals a reference to base! Use "set_array_base()" instead.
 
 
 # Typedefs that matches the runtime dtype objects in
@@ -753,11 +752,9 @@ ctypedef double complex complex128_t
 # The int types are mapped a bit surprising --
 # numpy.int corresponds to 'l' and numpy.long to 'q'
 ctypedef npy_long       int_t
-ctypedef npy_longlong   long_t
 ctypedef npy_longlong   longlong_t
 
 ctypedef npy_ulong      uint_t
-ctypedef npy_ulonglong  ulong_t
 ctypedef npy_ulonglong  ulonglong_t
 
 ctypedef npy_intp       intp_t
@@ -908,7 +905,7 @@ cdef extern from "numpy/ufuncobject.h":
     object PyUFunc_FromFuncAndData(PyUFuncGenericFunction *,
           void **, char *, int, int, int, int, char *, char *, int)
     int PyUFunc_RegisterLoopForType(ufunc, int,
-                                    PyUFuncGenericFunction, int *, void *)
+                                    PyUFuncGenericFunction, int *, void *) except -1
     void PyUFunc_f_f_As_d_d \
          (char **, npy_intp *, npy_intp *, void *)
     void PyUFunc_d_d \
@@ -958,7 +955,7 @@ cdef extern from "numpy/ufuncobject.h":
     void PyUFunc_clearfperr()
     int PyUFunc_getfperr()
     int PyUFunc_handlefperr \
-        (int, PyObject *, int, int *)
+        (int, PyObject *, int, int *) except -1
     int PyUFunc_ReplaceLoopBySignature \
         (ufunc, PyUFuncGenericFunction, int *, PyUFuncGenericFunction *)
     object PyUFunc_FromFuncAndDataAndSignature \
diff --git a/contrib/python/numpy/py3/numpy/__init__.pxd b/contrib/python/numpy/py3/numpy/__init__.pxd
index 03db9a0c12f..47d9294c1c4 100644
--- a/contrib/python/numpy/py3/numpy/__init__.pxd
+++ b/contrib/python/numpy/py3/numpy/__init__.pxd
@@ -359,7 +359,7 @@ cdef extern from "numpy/arrayobject.h":
     int PyArray_TYPE(ndarray arr) nogil
 
     object PyArray_GETITEM(ndarray arr, void *itemptr)
-    int PyArray_SETITEM(ndarray arr, void *itemptr, object obj)
+    int PyArray_SETITEM(ndarray arr, void *itemptr, object obj) except -1
 
     bint PyTypeNum_ISBOOL(int) nogil
     bint PyTypeNum_ISUNSIGNED(int) nogil
@@ -473,7 +473,6 @@ cdef extern from "numpy/arrayobject.h":
     void* PyArray_GETPTR3(ndarray m, npy_intp i, npy_intp j, npy_intp k) nogil
     void* PyArray_GETPTR4(ndarray m, npy_intp i, npy_intp j, npy_intp k, npy_intp l) nogil
 
-    void PyArray_XDECREF_ERR(ndarray)
     # Cannot be supported due to out arg
     # void PyArray_DESCR_REPLACE(descr)
 
@@ -507,21 +506,21 @@ cdef extern from "numpy/arrayobject.h":
     # Functions taking dtype and returning object/ndarray are disabled
     # for now as they steal dtype references. I'm conservative and disable
     # more than is probably needed until it can be checked further.
-    int PyArray_SetNumericOps        (object)
+    int PyArray_SetNumericOps (object) except -1
     object PyArray_GetNumericOps ()
-    int PyArray_INCREF (ndarray)
-    int PyArray_XDECREF (ndarray)
+    int PyArray_INCREF (ndarray) except *  # uses PyArray_Item_INCREF...
+    int PyArray_XDECREF (ndarray) except *  # uses PyArray_Item_DECREF...
     void PyArray_SetStringFunction (object, int)
     dtype PyArray_DescrFromType (int)
     object PyArray_TypeObjectFromType (int)
     char * PyArray_Zero (ndarray)
     char * PyArray_One (ndarray)
     #object PyArray_CastToType (ndarray, dtype, int)
-    int PyArray_CastTo (ndarray, ndarray)
-    int PyArray_CastAnyTo (ndarray, ndarray)
-    int PyArray_CanCastSafely (int, int)
-    npy_bool PyArray_CanCastTo (dtype, dtype)
-    int PyArray_ObjectType (object, int)
+    int PyArray_CastTo (ndarray, ndarray) except -1
+    int PyArray_CastAnyTo (ndarray, ndarray) except -1
+    int PyArray_CanCastSafely (int, int)  # writes errors
+    npy_bool PyArray_CanCastTo (dtype, dtype)  # writes errors
+    int PyArray_ObjectType (object, int) except 0
     dtype PyArray_DescrFromObject (object, dtype)
     #ndarray* PyArray_ConvertToCommonType (object, int *)
     dtype PyArray_DescrFromScalar (object)
@@ -545,34 +544,34 @@ cdef extern from "numpy/arrayobject.h":
     #object PyArray_FromIter (object, dtype, npy_intp)
     object PyArray_Return (ndarray)
     #object PyArray_GetField (ndarray, dtype, int)
-    #int PyArray_SetField (ndarray, dtype, int, object)
+    #int PyArray_SetField (ndarray, dtype, int, object) except -1
     object PyArray_Byteswap (ndarray, npy_bool)
     object PyArray_Resize (ndarray, PyArray_Dims *, int, NPY_ORDER)
-    int PyArray_MoveInto (ndarray, ndarray)
-    int PyArray_CopyInto (ndarray, ndarray)
-    int PyArray_CopyAnyInto (ndarray, ndarray)
-    int PyArray_CopyObject (ndarray, object)
+    int PyArray_MoveInto (ndarray, ndarray) except -1
+    int PyArray_CopyInto (ndarray, ndarray) except -1
+    int PyArray_CopyAnyInto (ndarray, ndarray) except -1
+    int PyArray_CopyObject (ndarray, object) except -1
     object PyArray_NewCopy (ndarray, NPY_ORDER)
     object PyArray_ToList (ndarray)
     object PyArray_ToString (ndarray, NPY_ORDER)
-    int PyArray_ToFile (ndarray, stdio.FILE *, char *, char *)
-    int PyArray_Dump (object, object, int)
+    int PyArray_ToFile (ndarray, stdio.FILE *, char *, char *) except -1
+    int PyArray_Dump (object, object, int) except -1
     object PyArray_Dumps (object, int)
-    int PyArray_ValidType (int)
+    int PyArray_ValidType (int)  # Cannot error
     void PyArray_UpdateFlags (ndarray, int)
     object PyArray_New (type, int, npy_intp *, int, npy_intp *, void *, int, int, object)
     #object PyArray_NewFromDescr (type, dtype, int, npy_intp *, npy_intp *, void *, int, object)
     #dtype PyArray_DescrNew (dtype)
     dtype PyArray_DescrNewFromType (int)
-    double PyArray_GetPriority (object, double)
+    double PyArray_GetPriority (object, double)  # clears errors as of 1.25
     object PyArray_IterNew (object)
     object PyArray_MultiIterNew (int, ...)
 
-    int PyArray_PyIntAsInt (object)
+    int PyArray_PyIntAsInt (object) except? -1
     npy_intp PyArray_PyIntAsIntp (object)
-    int PyArray_Broadcast (broadcast)
-    void PyArray_FillObjectArray (ndarray, object)
-    int PyArray_FillWithScalar (ndarray, object)
+    int PyArray_Broadcast (broadcast) except -1
+    void PyArray_FillObjectArray (ndarray, object) except *
+    int PyArray_FillWithScalar (ndarray, object) except -1
     npy_bool PyArray_CheckStrides (int, int, npy_intp, npy_intp, npy_intp *, npy_intp *)
     dtype PyArray_DescrNewByteorder (dtype, char)
     object PyArray_IterAllButAxis (object, int *)
@@ -586,10 +585,10 @@ cdef extern from "numpy/arrayobject.h":
     object PyArray_NewFlagsObject (object)
     npy_bool PyArray_CanCastScalar (type, type)
     #int PyArray_CompareUCS4 (npy_ucs4 *, npy_ucs4 *, register size_t)
-    int PyArray_RemoveSmallest (broadcast)
+    int PyArray_RemoveSmallest (broadcast) except -1
     int PyArray_ElementStrides (object)
-    void PyArray_Item_INCREF (char *, dtype)
-    void PyArray_Item_XDECREF (char *, dtype)
+    void PyArray_Item_INCREF (char *, dtype) except *
+    void PyArray_Item_XDECREF (char *, dtype) except *
     object PyArray_FieldNames (object)
     object PyArray_Transpose (ndarray, PyArray_Dims *)
     object PyArray_TakeFrom (ndarray, object, int, ndarray, NPY_CLIPMODE)
@@ -597,7 +596,7 @@ cdef extern from "numpy/arrayobject.h":
     object PyArray_PutMask (ndarray, object, object)
     object PyArray_Repeat (ndarray, object, int)
     object PyArray_Choose (ndarray, object, ndarray, NPY_CLIPMODE)
-    int PyArray_Sort (ndarray, int, NPY_SORTKIND)
+    int PyArray_Sort (ndarray, int, NPY_SORTKIND) except -1
     object PyArray_ArgSort (ndarray, int, NPY_SORTKIND)
     object PyArray_SearchSorted (ndarray, object, NPY_SEARCHSIDE, PyObject *)
     object PyArray_ArgMax (ndarray, int, ndarray)
@@ -635,49 +634,49 @@ cdef extern from "numpy/arrayobject.h":
     #int PyArray_As2D (object*, char ***, int *, int *, int)
     int PyArray_Free (object, void *)
     #int PyArray_Converter (object, object*)
-    int PyArray_IntpFromSequence (object, npy_intp *, int)
+    int PyArray_IntpFromSequence (object, npy_intp *, int) except -1
     object PyArray_Concatenate (object, int)
     object PyArray_InnerProduct (object, object)
     object PyArray_MatrixProduct (object, object)
     object PyArray_CopyAndTranspose (object)
     object PyArray_Correlate (object, object, int)
     int PyArray_TypestrConvert (int, int)
-    #int PyArray_DescrConverter (object, dtype*)
-    #int PyArray_DescrConverter2 (object, dtype*)
-    int PyArray_IntpConverter (object, PyArray_Dims *)
-    #int PyArray_BufferConverter (object, chunk)
-    int PyArray_AxisConverter (object, int *)
-    int PyArray_BoolConverter (object, npy_bool *)
-    int PyArray_ByteorderConverter (object, char *)
-    int PyArray_OrderConverter (object, NPY_ORDER *)
-    unsigned char PyArray_EquivTypes (dtype, dtype)
+    #int PyArray_DescrConverter (object, dtype*) except 0
+    #int PyArray_DescrConverter2 (object, dtype*) except 0
+    int PyArray_IntpConverter (object, PyArray_Dims *) except 0
+    #int PyArray_BufferConverter (object, chunk) except 0
+    int PyArray_AxisConverter (object, int *) except 0
+    int PyArray_BoolConverter (object, npy_bool *) except 0
+    int PyArray_ByteorderConverter (object, char *) except 0
+    int PyArray_OrderConverter (object, NPY_ORDER *) except 0
+    unsigned char PyArray_EquivTypes (dtype, dtype)  # clears errors
     #object PyArray_Zeros (int, npy_intp *, dtype, int)
     #object PyArray_Empty (int, npy_intp *, dtype, int)
     object PyArray_Where (object, object, object)
     object PyArray_Arange (double, double, double, int)
     #object PyArray_ArangeObj (object, object, object, dtype)
-    int PyArray_SortkindConverter (object, NPY_SORTKIND *)
+    int PyArray_SortkindConverter (object, NPY_SORTKIND *) except 0
     object PyArray_LexSort (object, int)
     object PyArray_Round (ndarray, int, ndarray)
     unsigned char PyArray_EquivTypenums (int, int)
-    int PyArray_RegisterDataType (dtype)
-    int PyArray_RegisterCastFunc (dtype, int, PyArray_VectorUnaryFunc *)
-    int PyArray_RegisterCanCast (dtype, int, NPY_SCALARKIND)
+    int PyArray_RegisterDataType (dtype) except -1
+    int PyArray_RegisterCastFunc (dtype, int, PyArray_VectorUnaryFunc *) except -1
+    int PyArray_RegisterCanCast (dtype, int, NPY_SCALARKIND) except -1
     #void PyArray_InitArrFuncs (PyArray_ArrFuncs *)
     object PyArray_IntTupleFromIntp (int, npy_intp *)
     int PyArray_TypeNumFromName (char *)
-    int PyArray_ClipmodeConverter (object, NPY_CLIPMODE *)
-    #int PyArray_OutputConverter (object, ndarray*)
+    int PyArray_ClipmodeConverter (object, NPY_CLIPMODE *) except 0
+    #int PyArray_OutputConverter (object, ndarray*) except 0
     object PyArray_BroadcastToShape (object, npy_intp *, int)
     void _PyArray_SigintHandler (int)
     void* _PyArray_GetSigintBuf ()
-    #int PyArray_DescrAlignConverter (object, dtype*)
-    #int PyArray_DescrAlignConverter2 (object, dtype*)
-    int PyArray_SearchsideConverter (object, void *)
+    #int PyArray_DescrAlignConverter (object, dtype*) except 0
+    #int PyArray_DescrAlignConverter2 (object, dtype*) except 0
+    int PyArray_SearchsideConverter (object, void *) except 0
     object PyArray_CheckAxis (ndarray, int *, int)
     npy_intp PyArray_OverflowMultiplyList (npy_intp *, int)
     int PyArray_CompareString (char *, char *, size_t)
-    int PyArray_SetBaseObject(ndarray, base)  # NOTE: steals a reference to base! Use "set_array_base()" instead.
+    int PyArray_SetBaseObject(ndarray, base) except -1 # NOTE: steals a reference to base! Use "set_array_base()" instead.
 
 
 # Typedefs that matches the runtime dtype objects in
@@ -711,11 +710,9 @@ ctypedef double complex complex128_t
 # The int types are mapped a bit surprising --
 # numpy.int corresponds to 'l' and numpy.long to 'q'
 ctypedef npy_long       int_t
-ctypedef npy_longlong   long_t
 ctypedef npy_longlong   longlong_t
 
 ctypedef npy_ulong      uint_t
-ctypedef npy_ulonglong  ulong_t
 ctypedef npy_ulonglong  ulonglong_t
 
 ctypedef npy_intp       intp_t
@@ -866,7 +863,7 @@ cdef extern from "numpy/ufuncobject.h":
     object PyUFunc_FromFuncAndData(PyUFuncGenericFunction *,
           void **, char *, int, int, int, int, char *, char *, int)
     int PyUFunc_RegisterLoopForType(ufunc, int,
-                                    PyUFuncGenericFunction, int *, void *)
+                                    PyUFuncGenericFunction, int *, void *) except -1
     void PyUFunc_f_f_As_d_d \
          (char **, npy_intp *, npy_intp *, void *)
     void PyUFunc_d_d \
@@ -916,7 +913,7 @@ cdef extern from "numpy/ufuncobject.h":
     void PyUFunc_clearfperr()
     int PyUFunc_getfperr()
     int PyUFunc_handlefperr \
-        (int, PyObject *, int, int *)
+        (int, PyObject *, int, int *) except -1
     int PyUFunc_ReplaceLoopBySignature \
         (ufunc, PyUFuncGenericFunction, int *, PyUFuncGenericFunction *)
     object PyUFunc_FromFuncAndDataAndSignature \
diff --git a/contrib/python/numpy/py3/numpy/__init__.py b/contrib/python/numpy/py3/numpy/__init__.py
index 12e6bbac303..a6c9fe16032 100644
--- a/contrib/python/numpy/py3/numpy/__init__.py
+++ b/contrib/python/numpy/py3/numpy/__init__.py
@@ -74,10 +74,6 @@ test
     Run numpy unittests
 show_config
     Show numpy build configuration
-dual
-    Overwrite certain functions with high-performance SciPy tools.
-    Note: `numpy.dual` is deprecated.  Use the functions from NumPy or Scipy
-    directly instead of importing them from `numpy.dual`.
 matlib
     Make everything matrices.
 __version__
@@ -106,10 +102,11 @@ Exceptions to this rule are documented.
 import sys
 import warnings
 
-from ._globals import (
-    ModuleDeprecationWarning, VisibleDeprecationWarning,
-    _NoValue, _CopyMode
-)
+from ._globals import _NoValue, _CopyMode
+# These exceptions were moved in 1.25 and are hidden from __dir__()
+from .exceptions import (
+    ComplexWarning, ModuleDeprecationWarning, VisibleDeprecationWarning,
+    TooHardError, AxisError)
 
 # We first need to detect if we're being called as part of the numpy setup
 # procedure itself in a reliable manner.
@@ -121,6 +118,9 @@ except NameError:
 if __NUMPY_SETUP__:
     sys.stderr.write('Running from numpy source directory.\n')
 else:
+    # Allow distributors to run custom init code before importing numpy.core
+    from . import _distributor_init
+
     try:
         from numpy.__config__ import show as show_config
     except ImportError as e:
@@ -129,18 +129,18 @@ else:
         your python interpreter from there."""
         raise ImportError(msg) from e
 
-    __all__ = ['ModuleDeprecationWarning',
-               'VisibleDeprecationWarning']
+    __all__ = [
+        'exceptions', 'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
+        'ComplexWarning', 'TooHardError', 'AxisError']
 
     # mapping of {name: (value, deprecation_msg)}
     __deprecated_attrs__ = {}
 
-    # Allow distributors to run custom init code
-    from . import _distributor_init
-
     from . import core
     from .core import *
     from . import compat
+    from . import exceptions
+    from . import dtypes
     from . import lib
     # NOTE: to be revisited following future namespace cleanup.
     # See gh-14454 and gh-15672 for discussion.
@@ -194,7 +194,7 @@ else:
         "`np.{n}` is a deprecated alias for `{an}`.  (Deprecated NumPy 1.24)")
 
     # Some of these are awkward (since `np.str` may be preferable in the long
-    # term), but overall the names ending in 0 seem undesireable
+    # term), but overall the names ending in 0 seem undesirable
     _type_info = [
         ("bool8", bool_, "np.bool_"),
         ("int0", intp, "np.intp"),
@@ -216,9 +216,16 @@ else:
     __deprecated_attrs__.update({
         n: (alias, _msg.format(n=n, an=an)) for n, alias, an in _type_info})
 
-    del _msg, _type_info
+    import math
 
-    from .core import round, abs, max, min
+    __deprecated_attrs__['math'] = (math,
+        "`np.math` is a deprecated alias for the standard library `math` "
+        "module (Deprecated Numpy 1.25). Replace usages of `np.math` with "
+        "`math`")
+
+    del math, _msg, _type_info
+
+    from .core import abs
     # now that numpy modules are imported, can initialize limits
     core.getlimits._register_known_types()
 
@@ -228,6 +235,12 @@ else:
     __all__.extend(lib.__all__)
     __all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
 
+    # Remove min and max from __all__ to avoid `from numpy import *` override
+    # the builtins min/max. Temporary fix for 1.25.x/1.26.x, see gh-24229.
+    __all__.remove('min')
+    __all__.remove('max')
+    __all__.remove('round')
+
     # Remove one of the two occurrences of `issubdtype`, which is exposed as
     # both `numpy.core.issubdtype` and `numpy.lib.issubdtype`.
     __all__.remove('issubdtype')
@@ -273,6 +286,7 @@ else:
         # Warn for expired attributes, and return a dummy function
         # that always raises an exception.
         import warnings
+        import math
         try:
             msg = __expired_functions__[attr]
         except KeyError:
@@ -304,26 +318,23 @@ else:
         if attr in __former_attrs__:
             raise AttributeError(__former_attrs__[attr])
 
-        # Importing Tester requires importing all of UnitTest which is not a
-        # cheap import Since it is mainly used in test suits, we lazy import it
-        # here to save on the order of 10 ms of import time for most users
-        #
-        # The previous way Tester was imported also had a side effect of adding
-        # the full `numpy.testing` namespace
         if attr == 'testing':
             import numpy.testing as testing
             return testing
         elif attr == 'Tester':
-            from .testing import Tester
-            return Tester
+            "Removed in NumPy 1.25.0"
+            raise RuntimeError("Tester was removed in NumPy 1.25.")
 
         raise AttributeError("module {!r} has no attribute "
                              "{!r}".format(__name__, attr))
 
     def __dir__():
-        public_symbols = globals().keys() | {'Tester', 'testing'}
+        public_symbols = globals().keys() | {'testing'}
         public_symbols -= {
             "core", "matrixlib",
+            # These were moved in 1.25 and may be deprecated eventually:
+            "ModuleDeprecationWarning", "VisibleDeprecationWarning",
+            "ComplexWarning", "TooHardError", "AxisError"
         }
         return list(public_symbols)
 
@@ -415,13 +426,17 @@ else:
 
     # Note that this will currently only make a difference on Linux
     core.multiarray._set_madvise_hugepage(use_hugepage)
+    del use_hugepage
 
     # Give a warning if NumPy is reloaded or imported on a sub-interpreter
     # We do this from python, since the C-module may not be reloaded and
     # it is tidier organized.
     core.multiarray._multiarray_umath._reload_guard()
 
-    core._set_promotion_state(os.environ.get("NPY_PROMOTION_STATE", "legacy"))
+    # default to "weak" promotion for "NumPy 2".
+    core._set_promotion_state(
+        os.environ.get("NPY_PROMOTION_STATE",
+                       "weak" if _using_numpy2_behavior() else "legacy"))
 
     # Tell PyInstaller where to find hook-numpy.py
     def _pyinstaller_hooks_dir():
diff --git a/contrib/python/numpy/py3/numpy/__init__.pyi b/contrib/python/numpy/py3/numpy/__init__.pyi
index 99a96f96ec1..45c3023c6a0 100644
--- a/contrib/python/numpy/py3/numpy/__init__.pyi
+++ b/contrib/python/numpy/py3/numpy/__init__.pyi
@@ -1,19 +1,15 @@
 import builtins
 import os
-import sys
 import mmap
 import ctypes as ct
 import array as _array
 import datetime as dt
 import enum
 from abc import abstractmethod
-from types import TracebackType, MappingProxyType
+from types import TracebackType, MappingProxyType, GenericAlias
 from contextlib import ContextDecorator
 from contextlib import contextmanager
 
-if sys.version_info >= (3, 9):
-    from types import GenericAlias
-
 from numpy._pytesttester import PytestTester
 from numpy.core._internal import _ctypes
 
@@ -204,6 +200,7 @@ from typing import (
 # Ensures that the stubs are picked up
 from numpy import (
     ctypeslib as ctypeslib,
+    exceptions as exceptions,
     fft as fft,
     lib as lib,
     linalg as linalg,
@@ -212,6 +209,8 @@ from numpy import (
     random as random,
     testing as testing,
     version as version,
+    exceptions as exceptions,
+    dtypes as dtypes,
 )
 
 from numpy.core import defchararray, records
@@ -253,6 +252,8 @@ from numpy.core.fromnumeric import (
     any as any,
     cumsum as cumsum,
     ptp as ptp,
+    max as max,
+    min as min,
     amax as amax,
     amin as amin,
     prod as prod,
@@ -260,6 +261,7 @@ from numpy.core.fromnumeric import (
     ndim as ndim,
     size as size,
     around as around,
+    round as round,
     mean as mean,
     std as std,
     var as var,
@@ -394,7 +396,6 @@ from numpy.core.numerictypes import (
     issubsctype as issubsctype,
     issubdtype as issubdtype,
     sctype2char as sctype2char,
-    find_common_type as find_common_type,
     nbytes as nbytes,
     cast as cast,
     ScalarType as ScalarType,
@@ -411,6 +412,15 @@ from numpy.core.shape_base import (
     vstack as vstack,
 )
 
+from numpy.exceptions import (
+    ComplexWarning as ComplexWarning,
+    ModuleDeprecationWarning as ModuleDeprecationWarning,
+    VisibleDeprecationWarning as VisibleDeprecationWarning,
+    TooHardError as TooHardError,
+    DTypePromotionError as DTypePromotionError,
+    AxisError as AxisError,
+)
+
 from numpy.lib import (
     emath as emath,
 )
@@ -664,19 +674,9 @@ test: PytestTester
 #
 # Placeholders for classes
 
-# Some of these are aliases; others are wrappers with an identical signature
-round = around
-round_ = around
-max = amax
-min = amin
-product = prod
-cumproduct = cumprod
-sometrue = any
-alltrue = all
-
 def show_config() -> None: ...
 
-_NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray)
+_NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray[Any, Any])
 _DTypeScalar_co = TypeVar("_DTypeScalar_co", covariant=True, bound=generic)
 _ByteOrder = L["S", "<", ">", "=", "|", "L", "B", "N", "I"]
 
@@ -850,8 +850,7 @@ class dtype(Generic[_DTypeScalar_co]):
         metadata: dict[builtins.str, Any] = ...,
     ) -> dtype[object_]: ...
 
-    if sys.version_info >= (3, 9):
-        def __class_getitem__(self, item: Any) -> GenericAlias: ...
+    def __class_getitem__(self, item: Any) -> GenericAlias: ...
 
     @overload
     def __getitem__(self: dtype[void], key: list[builtins.str]) -> dtype[void]: ...
@@ -933,13 +932,13 @@ class dtype(Generic[_DTypeScalar_co]):
 
 _ArrayLikeInt = Union[
     int,
-    integer,
-    Sequence[Union[int, integer]],
+    integer[Any],
+    Sequence[Union[int, integer[Any]]],
     Sequence[Sequence[Any]],  # TODO: wait for support for recursive types
-    ndarray
+    ndarray[Any, Any]
 ]
 
-_FlatIterSelf = TypeVar("_FlatIterSelf", bound=flatiter)
+_FlatIterSelf = TypeVar("_FlatIterSelf", bound=flatiter[Any])
 
 @final
 class flatiter(Generic[_NdArraySubClass]):
@@ -957,7 +956,7 @@ class flatiter(Generic[_NdArraySubClass]):
     @overload
     def __getitem__(
         self: flatiter[ndarray[Any, dtype[_ScalarType]]],
-        key: int | integer | tuple[int | integer],
+        key: int | integer[Any] | tuple[int | integer[Any]],
     ) -> _ScalarType: ...
     @overload
     def __getitem__(
@@ -1152,7 +1151,7 @@ class _ArrayOrScalarCommon:
         axis: None | SupportsIndex = ...,
         kind: None | _SortKind = ...,
         order: None | str | Sequence[str] = ...,
-    ) -> ndarray: ...
+    ) -> ndarray[Any, Any]: ...
 
     @overload
     def choose(
@@ -1160,7 +1159,7 @@ class _ArrayOrScalarCommon:
         choices: ArrayLike,
         out: None = ...,
         mode: _ModeKind = ...,
-    ) -> ndarray: ...
+    ) -> ndarray[Any, Any]: ...
     @overload
     def choose(
         self,
@@ -1176,7 +1175,7 @@ class _ArrayOrScalarCommon:
         max: None | ArrayLike = ...,
         out: None = ...,
         **kwargs: Any,
-    ) -> ndarray: ...
+    ) -> ndarray[Any, Any]: ...
     @overload
     def clip(
         self,
@@ -1184,7 +1183,7 @@ class _ArrayOrScalarCommon:
         max: ArrayLike = ...,
         out: None = ...,
         **kwargs: Any,
-    ) -> ndarray: ...
+    ) -> ndarray[Any, Any]: ...
     @overload
     def clip(
         self,
@@ -1208,7 +1207,7 @@ class _ArrayOrScalarCommon:
         a: ArrayLike,
         axis: None | SupportsIndex = ...,
         out: None = ...,
-    ) -> ndarray: ...
+    ) -> ndarray[Any, Any]: ...
     @overload
     def compress(
         self,
@@ -1227,7 +1226,7 @@ class _ArrayOrScalarCommon:
         axis: None | SupportsIndex = ...,
         dtype: DTypeLike = ...,
         out: None = ...,
-    ) -> ndarray: ...
+    ) -> ndarray[Any, Any]: ...
     @overload
     def cumprod(
         self,
@@ -1242,7 +1241,7 @@ class _ArrayOrScalarCommon:
         axis: None | SupportsIndex = ...,
         dtype: DTypeLike = ...,
         out: None = ...,
-    ) -> ndarray: ...
+    ) -> ndarray[Any, Any]: ...
     @overload
     def cumsum(
         self,
@@ -1487,7 +1486,7 @@ class _SupportsImag(Protocol[_T_co]):
 class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
     __hash__: ClassVar[None]
     @property
-    def base(self) -> None | ndarray: ...
+    def base(self) -> None | ndarray[Any, Any]: ...
     @property
     def ndim(self) -> int: ...
     @property
@@ -1514,8 +1513,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
         order: _OrderKACF = ...,
     ) -> _ArraySelf: ...
 
-    if sys.version_info >= (3, 9):
-        def __class_getitem__(self, item: Any) -> GenericAlias: ...
+    def __class_getitem__(self, item: Any) -> GenericAlias: ...
 
     @overload
     def __array__(self, dtype: None = ..., /) -> ndarray[Any, _DType_co]: ...
@@ -1540,7 +1538,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
 
     # NOTE: In practice any object is accepted by `obj`, but as `__array_finalize__`
     # is a pseudo-abstract method the type has been narrowed down in order to
-    # grant subclasses a bit more flexiblity
+    # grant subclasses a bit more flexibility
     def __array_finalize__(self, obj: None | NDArray[Any], /) -> None: ...
 
     def __array_wrap__(
@@ -1655,7 +1653,7 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
     # 1D + 1D returns a scalar;
     # all other with at least 1 non-0D array return an ndarray.
     @overload
-    def dot(self, b: _ScalarLike_co, out: None = ...) -> ndarray: ...
+    def dot(self, b: _ScalarLike_co, out: None = ...) -> ndarray[Any, Any]: ...
     @overload
     def dot(self, b: ArrayLike, out: None = ...) -> Any: ...  # type: ignore[misc]
     @overload
@@ -1932,7 +1930,6 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
     def __neg__(self: NDArray[object_]) -> Any: ...
 
     # Binary ops
-    # NOTE: `ndarray` does not implement `__imatmul__`
     @overload
     def __matmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ...  # type: ignore[misc]
     @overload
@@ -2519,6 +2516,19 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeType, _DType_co]):
     @overload
     def __ior__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
 
+    @overload
+    def __imatmul__(self: NDArray[bool_], other: _ArrayLikeBool_co) -> NDArray[bool_]: ...
+    @overload
+    def __imatmul__(self: NDArray[unsignedinteger[_NBit1]], other: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[_NBit1]]: ...
+    @overload
+    def __imatmul__(self: NDArray[signedinteger[_NBit1]], other: _ArrayLikeInt_co) -> NDArray[signedinteger[_NBit1]]: ...
+    @overload
+    def __imatmul__(self: NDArray[floating[_NBit1]], other: _ArrayLikeFloat_co) -> NDArray[floating[_NBit1]]: ...
+    @overload
+    def __imatmul__(self: NDArray[complexfloating[_NBit1, _NBit1]], other: _ArrayLikeComplex_co) -> NDArray[complexfloating[_NBit1, _NBit1]]: ...
+    @overload
+    def __imatmul__(self: NDArray[object_], other: Any) -> NDArray[object_]: ...
+
     def __dlpack__(self: NDArray[number[Any]], *, stream: None = ...) -> _PyCapsule: ...
     def __dlpack_device__(self) -> tuple[int, L[0]]: ...
 
@@ -2678,8 +2688,7 @@ class number(generic, Generic[_NBit1]):  # type: ignore
     def real(self: _ArraySelf) -> _ArraySelf: ...
     @property
     def imag(self: _ArraySelf) -> _ArraySelf: ...
-    if sys.version_info >= (3, 9):
-        def __class_getitem__(self, item: Any) -> GenericAlias: ...
+    def __class_getitem__(self, item: Any) -> GenericAlias: ...
     def __int__(self) -> int: ...
     def __float__(self) -> float: ...
     def __complex__(self) -> complex: ...
@@ -2833,20 +2842,20 @@ class integer(number[_NBit1]):  # type: ignore
     def __index__(self) -> int: ...
     __truediv__: _IntTrueDiv[_NBit1]
     __rtruediv__: _IntTrueDiv[_NBit1]
-    def __mod__(self, value: _IntLike_co) -> integer: ...
-    def __rmod__(self, value: _IntLike_co) -> integer: ...
+    def __mod__(self, value: _IntLike_co) -> integer[Any]: ...
+    def __rmod__(self, value: _IntLike_co) -> integer[Any]: ...
     def __invert__(self: _IntType) -> _IntType: ...
     # Ensure that objects annotated as `integer` support bit-wise operations
-    def __lshift__(self, other: _IntLike_co) -> integer: ...
-    def __rlshift__(self, other: _IntLike_co) -> integer: ...
-    def __rshift__(self, other: _IntLike_co) -> integer: ...
-    def __rrshift__(self, other: _IntLike_co) -> integer: ...
-    def __and__(self, other: _IntLike_co) -> integer: ...
-    def __rand__(self, other: _IntLike_co) -> integer: ...
-    def __or__(self, other: _IntLike_co) -> integer: ...
-    def __ror__(self, other: _IntLike_co) -> integer: ...
-    def __xor__(self, other: _IntLike_co) -> integer: ...
-    def __rxor__(self, other: _IntLike_co) -> integer: ...
+    def __lshift__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __rlshift__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __rshift__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __rrshift__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __and__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __rand__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __or__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __ror__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __xor__(self, other: _IntLike_co) -> integer[Any]: ...
+    def __rxor__(self, other: _IntLike_co) -> integer[Any]: ...
 
 class signedinteger(integer[_NBit1]):
     def __init__(self, value: _IntValue = ..., /) -> None: ...
@@ -2971,8 +2980,8 @@ ulonglong = unsignedinteger[_NBitLongLong]
 class inexact(number[_NBit1]):  # type: ignore
     def __getnewargs__(self: inexact[_64Bit]) -> tuple[float, ...]: ...
 
-_IntType = TypeVar("_IntType", bound=integer)
-_FloatType = TypeVar('_FloatType', bound=floating)
+_IntType = TypeVar("_IntType", bound=integer[Any])
+_FloatType = TypeVar('_FloatType', bound=floating[Any])
 
 class floating(inexact[_NBit1]):
     def __init__(self, value: _FloatValue = ..., /) -> None: ...
@@ -2986,9 +2995,8 @@ class floating(inexact[_NBit1]):
     @classmethod
     def fromhex(cls: type[float64], string: str, /) -> float64: ...
     def as_integer_ratio(self) -> tuple[int, int]: ...
-    if sys.version_info >= (3, 9):
-        def __ceil__(self: float64) -> int: ...
-        def __floor__(self: float64) -> int: ...
+    def __ceil__(self: float64) -> int: ...
+    def __floor__(self: float64) -> int: ...
     def __trunc__(self: float64) -> int: ...
     def __getnewargs__(self: float64) -> tuple[float]: ...
     def __getformat__(self: float64, typestr: L["double", "float"], /) -> str: ...
@@ -3320,22 +3328,8 @@ class _CopyMode(enum.Enum):
     NEVER: L[2]
 
 # Warnings
-class ModuleDeprecationWarning(DeprecationWarning): ...
-class VisibleDeprecationWarning(UserWarning): ...
-class ComplexWarning(RuntimeWarning): ...
 class RankWarning(UserWarning): ...
 
-# Errors
-class TooHardError(RuntimeError): ...
-
-class AxisError(ValueError, IndexError):
-    axis: None | int
-    ndim: None | int
-    @overload
-    def __init__(self, axis: str, ndim: None = ..., msg_prefix: None = ...) -> None: ...
-    @overload
-    def __init__(self, axis: int, ndim: int, msg_prefix: None | str = ...) -> None: ...
-
 _CallType = TypeVar("_CallType", bound=_ErrFunc | _SupportsWrite[str])
 
 class errstate(Generic[_CallType], ContextDecorator):
diff --git a/contrib/python/numpy/py3/numpy/_build_utils/__init__.py b/contrib/python/numpy/py3/numpy/_build_utils/__init__.py
new file mode 100644
index 00000000000..ac4908957ad
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/_build_utils/__init__.py
@@ -0,0 +1,22 @@
+# Don't use the deprecated NumPy C API. Define this to a fixed version
+# instead of NPY_API_VERSION in order not to break compilation for
+# released SciPy versions when NumPy introduces a new deprecation. Use
+# in setup.py::
+#
+#   config.add_extension('_name', sources=['source_fname'], **numpy_nodepr_api)
+#
+numpy_nodepr_api = dict(
+    define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_9_API_VERSION")]
+)
+
+
+def import_file(folder, module_name):
+    """Import a file directly, avoiding importing scipy"""
+    import importlib
+    import pathlib
+
+    fname = pathlib.Path(folder) / f'{module_name}.py'
+    spec = importlib.util.spec_from_file_location(module_name, str(fname))
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
diff --git a/contrib/python/numpy/py3/numpy/_build_utils/gcc_build_bitness.py b/contrib/python/numpy/py3/numpy/_build_utils/gcc_build_bitness.py
new file mode 100644
index 00000000000..fcad237e98b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/_build_utils/gcc_build_bitness.py
@@ -0,0 +1,21 @@
+#!python
+""" Detect bitness (32 or 64) of Mingw-w64 gcc build target on Windows.
+"""
+
+import re
+from subprocess import run, PIPE
+
+
+def main():
+    res = run(['gcc', '-v'], check=True, text=True, capture_output=True)
+    target = re.search(r'^Target: (.*)$', res.stderr, flags=re.M).groups()[0]
+    if target.startswith('i686'):
+        print('32')
+    elif target.startswith('x86_64'):
+        print('64')
+    else:
+        raise RuntimeError('Could not detect Mingw-w64 bitness')
+
+
+if __name__ == "__main__":
+    main()
diff --git a/contrib/python/numpy/py3/numpy/_build_utils/process_src_template.py b/contrib/python/numpy/py3/numpy/_build_utils/process_src_template.py
new file mode 100644
index 00000000000..4a0915e2525
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/_build_utils/process_src_template.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+import sys
+import os
+import argparse
+import importlib.util
+
+
+def get_processor():
+    # Convoluted because we can't import from numpy.distutils
+    # (numpy is not yet built)
+    conv_template_path = os.path.join(
+        os.path.dirname(__file__),
+        '..', 'distutils', 'conv_template.py'
+    )
+    spec = importlib.util.spec_from_file_location(
+        'conv_template', conv_template_path
+    )
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod.process_file
+
+
+def process_and_write_file(fromfile, outfile):
+    """Process tempita templated file and write out the result.
+
+    The template file is expected to end in `.src`
+    (e.g., `.c.src` or `.h.src`).
+    Processing `npy_somefile.c.src` generates `npy_somefile.c`.
+
+    """
+    process_file = get_processor()
+    content = process_file(fromfile)
+    with open(outfile, 'w') as f:
+        f.write(content)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "infile",
+        type=str,
+        help="Path to the input file"
+    )
+    parser.add_argument(
+        "-o",
+        "--outfile",
+        type=str,
+        help="Path to the output file"
+    )
+    parser.add_argument(
+        "-i",
+        "--ignore",
+        type=str,
+        help="An ignored input - may be useful to add a "
+        "dependency between custom targets",
+    )
+    args = parser.parse_args()
+
+    if not args.infile.endswith('.src'):
+        raise ValueError(f"Unexpected extension: {args.infile}")
+
+    outfile_abs = os.path.join(os.getcwd(), args.outfile)
+    process_and_write_file(args.infile, outfile_abs)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/contrib/python/numpy/py3/numpy/_build_utils/setup.py b/contrib/python/numpy/py3/numpy/_build_utils/setup.py
new file mode 100644
index 00000000000..73a8f2fff58
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/_build_utils/setup.py
@@ -0,0 +1,12 @@
+def configuration(parent_package='', top_path=None):
+    from numpy.distutils.misc_util import Configuration
+
+    config = Configuration('_build_utils', parent_package, top_path)
+    config.add_data_dir('tests')
+    return config
+
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+
+    setup(**configuration(top_path='').todict())
diff --git a/contrib/python/numpy/py3/numpy/_build_utils/tempita.py b/contrib/python/numpy/py3/numpy/_build_utils/tempita.py
new file mode 100755
index 00000000000..3bcc789c56e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/_build_utils/tempita.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+import sys
+import os
+import argparse
+
+from Cython import Tempita as tempita
+
+# XXX: If this import ever fails (does it really?), vendor either
+# cython.tempita or numpy/npy_tempita.
+
+
+def process_tempita(fromfile, outfile=None):
+    """Process tempita templated file and write out the result.
+
+    The template file is expected to end in `.c.in` or `.pyx.in`:
+    E.g. processing `template.c.in` generates `template.c`.
+
+    """
+    if outfile is None:
+        # We're dealing with a distutils build here, write in-place
+        outfile = os.path.splitext(fromfile)[0]
+
+    from_filename = tempita.Template.from_filename
+    template = from_filename(fromfile, encoding=sys.getdefaultencoding())
+
+    content = template.substitute()
+
+    with open(outfile, 'w') as f:
+        f.write(content)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "infile",
+        type=str,
+        help="Path to the input file"
+    )
+    parser.add_argument(
+        "-o",
+        "--outfile",
+        type=str,
+        help="Path to the output file"
+    )
+    parser.add_argument(
+        "-i",
+        "--ignore",
+        type=str,
+        help="An ignored input - may be useful to add a "
+        "dependency between custom targets",
+    )
+    args = parser.parse_args()
+
+    if not args.infile.endswith('.in'):
+        raise ValueError(f"Unexpected extension: {args.infile}")
+
+    outfile_abs = os.path.join(os.getcwd(), args.outfile)
+    process_tempita(args.infile, outfile_abs)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/contrib/python/numpy/py3/numpy/_globals.py b/contrib/python/numpy/py3/numpy/_globals.py
index 1e4f26cd86a..416a20f5e11 100644
--- a/contrib/python/numpy/py3/numpy/_globals.py
+++ b/contrib/python/numpy/py3/numpy/_globals.py
@@ -17,10 +17,9 @@ motivated this module.
 """
 import enum
 
-__ALL__ = [
-    'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
-    '_NoValue', '_CopyMode'
-    ]
+from ._utils import set_module as _set_module
+
+__all__ = ['_NoValue', '_CopyMode']
 
 
 # Disallow reloading this module so as to preserve the identities of the
@@ -30,33 +29,6 @@ if '_is_loaded' in globals():
 _is_loaded = True
 
 
-class ModuleDeprecationWarning(DeprecationWarning):
-    """Module deprecation warning.
-
-    The nose tester turns ordinary Deprecation warnings into test failures.
-    That makes it hard to deprecate whole modules, because they get
-    imported by default. So this is a special Deprecation warning that the
-    nose tester will let pass without making tests fail.
-
-    """
-
-
-ModuleDeprecationWarning.__module__ = 'numpy'
-
-
-class VisibleDeprecationWarning(UserWarning):
-    """Visible deprecation warning.
-
-    By default, python will not show deprecation warnings, so this class
-    can be used when a very visible warning is helpful, for example because
-    the usage is most likely a user bug.
-
-    """
-
-
-VisibleDeprecationWarning.__module__ = 'numpy'
-
-
 class _NoValueType:
     """Special keyword value.
 
@@ -90,6 +62,7 @@ class _NoValueType:
 _NoValue = _NoValueType()
 
 
+@_set_module("numpy")
 class _CopyMode(enum.Enum):
     """
     An enumeration for the copy modes supported
@@ -120,6 +93,3 @@ class _CopyMode(enum.Enum):
             return False
 
         raise ValueError(f"{self} is neither True nor False.")
-
-
-_CopyMode.__module__ = 'numpy'
diff --git a/contrib/python/numpy/py3/numpy/_pyinstaller/hook-numpy.py b/contrib/python/numpy/py3/numpy/_pyinstaller/hook-numpy.py
index a08b7c96304..6f24318adf5 100644
--- a/contrib/python/numpy/py3/numpy/_pyinstaller/hook-numpy.py
+++ b/contrib/python/numpy/py3/numpy/_pyinstaller/hook-numpy.py
@@ -20,18 +20,15 @@ if is_pure_conda:
     from PyInstaller.utils.hooks import conda_support
     datas = conda_support.collect_dynamic_libs("numpy", dependencies=True)
 
-# Submodules PyInstaller cannot detect (probably because they are only imported
-# by extension modules, which PyInstaller cannot read).
-hiddenimports = ['numpy.core._dtype_ctypes']
-if is_conda:
-    hiddenimports.append("six")
+# Submodules PyInstaller cannot detect.  `_dtype_ctypes` is only imported
+# from C and `_multiarray_tests` is used in tests (which are not packed).
+hiddenimports = ['numpy.core._dtype_ctypes', 'numpy.core._multiarray_tests']
 
 # Remove testing and building code and packages that are referenced throughout
 # NumPy but are not really dependencies.
 excludedimports = [
     "scipy",
     "pytest",
-    "nose",
     "f2py",
     "setuptools",
     "numpy.f2py",
diff --git a/contrib/python/numpy/py3/numpy/_typing/__init__.py b/contrib/python/numpy/py3/numpy/_typing/__init__.py
index b800c54b6fe..29922d958ef 100644
--- a/contrib/python/numpy/py3/numpy/_typing/__init__.py
+++ b/contrib/python/numpy/py3/numpy/_typing/__init__.py
@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
-from numpy import ufunc
-from numpy.core.overrides import set_module
+from .. import ufunc
+from .._utils import set_module
 from typing import TYPE_CHECKING, final
 
 
@@ -180,6 +180,7 @@ from ._dtype_like import (
     _DTypeLikeComplex_co as _DTypeLikeComplex_co,
 )
 from ._array_like import (
+    NDArray as NDArray,
     ArrayLike as ArrayLike,
     _ArrayLike as _ArrayLike,
     _FiniteNestedSequence as _FiniteNestedSequence,
@@ -201,11 +202,6 @@ from ._array_like import (
     _ArrayLikeUnknown as _ArrayLikeUnknown,
     _UnknownType as _UnknownType,
 )
-from ._generic_alias import (
-    NDArray as NDArray,
-    _DType as _DType,
-    _GenericAlias as _GenericAlias,
-)
 
 if TYPE_CHECKING:
     from ._ufunc import (
diff --git a/contrib/python/numpy/py3/numpy/_typing/_add_docstring.py b/contrib/python/numpy/py3/numpy/_typing/_add_docstring.py
index 10d77f5161d..f84d19271c2 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_add_docstring.py
+++ b/contrib/python/numpy/py3/numpy/_typing/_add_docstring.py
@@ -3,7 +3,7 @@
 import re
 import textwrap
 
-from ._generic_alias import NDArray
+from ._array_like import NDArray
 
 _docstrings_list = []
 
diff --git a/contrib/python/numpy/py3/numpy/_typing/_array_like.py b/contrib/python/numpy/py3/numpy/_typing/_array_like.py
index 16c9defcf8b..cba6fffaf95 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_array_like.py
+++ b/contrib/python/numpy/py3/numpy/_typing/_array_like.py
@@ -1,10 +1,7 @@
 from __future__ import annotations
 
-# NOTE: Import `Sequence` from `typing` as we it is needed for a type-alias,
-# not an annotation
-import sys
-from collections.abc import Collection, Callable
-from typing import Any, Sequence, Protocol, Union, TypeVar, runtime_checkable
+from collections.abc import Collection, Callable, Sequence
+from typing import Any, Protocol, Union, TypeVar, runtime_checkable
 from numpy import (
     ndarray,
     dtype,
@@ -26,8 +23,11 @@ from ._nested_sequence import _NestedSequence
 
 _T = TypeVar("_T")
 _ScalarType = TypeVar("_ScalarType", bound=generic)
-_DType = TypeVar("_DType", bound="dtype[Any]")
-_DType_co = TypeVar("_DType_co", covariant=True, bound="dtype[Any]")
+_ScalarType_co = TypeVar("_ScalarType_co", bound=generic, covariant=True)
+_DType = TypeVar("_DType", bound=dtype[Any])
+_DType_co = TypeVar("_DType_co", covariant=True, bound=dtype[Any])
+
+NDArray = ndarray[Any, dtype[_ScalarType_co]]
 
 # The `_SupportsArray` protocol only cares about the default dtype
 # (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned
@@ -62,8 +62,8 @@ _FiniteNestedSequence = Union[
 
 # A subset of `npt.ArrayLike` that can be parametrized w.r.t. `np.generic`
 _ArrayLike = Union[
-    _SupportsArray["dtype[_ScalarType]"],
-    _NestedSequence[_SupportsArray["dtype[_ScalarType]"]],
+    _SupportsArray[dtype[_ScalarType]],
+    _NestedSequence[_SupportsArray[dtype[_ScalarType]]],
 ]
 
 # A union representing array-like objects; consists of two typevars:
@@ -83,71 +83,70 @@ _DualArrayLike = Union[
 # is resolved. See also the mypy issue:
 #
 # https://github.com/python/typing/issues/593
-if sys.version_info[:2] < (3, 9):
-    ArrayLike = _DualArrayLike[
-        dtype,
-        Union[bool, int, float, complex, str, bytes],
-    ]
-else:
-    ArrayLike = _DualArrayLike[
-        dtype[Any],
-        Union[bool, int, float, complex, str, bytes],
-    ]
+ArrayLike = _DualArrayLike[
+    dtype[Any],
+    Union[bool, int, float, complex, str, bytes],
+]
 
 # `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
 # given the casting rules `same_kind`
 _ArrayLikeBool_co = _DualArrayLike[
-    "dtype[bool_]",
+    dtype[bool_],
     bool,
 ]
 _ArrayLikeUInt_co = _DualArrayLike[
-    "dtype[Union[bool_, unsignedinteger[Any]]]",
+    dtype[Union[bool_, unsignedinteger[Any]]],
     bool,
 ]
 _ArrayLikeInt_co = _DualArrayLike[
-    "dtype[Union[bool_, integer[Any]]]",
+    dtype[Union[bool_, integer[Any]]],
     Union[bool, int],
 ]
 _ArrayLikeFloat_co = _DualArrayLike[
-    "dtype[Union[bool_, integer[Any], floating[Any]]]",
+    dtype[Union[bool_, integer[Any], floating[Any]]],
     Union[bool, int, float],
 ]
 _ArrayLikeComplex_co = _DualArrayLike[
-    "dtype[Union[bool_, integer[Any], floating[Any], complexfloating[Any, Any]]]",
+    dtype[Union[
+        bool_,
+        integer[Any],
+        floating[Any],
+        complexfloating[Any, Any],
+    ]],
     Union[bool, int, float, complex],
 ]
 _ArrayLikeNumber_co = _DualArrayLike[
-    "dtype[Union[bool_, number[Any]]]",
+    dtype[Union[bool_, number[Any]]],
     Union[bool, int, float, complex],
 ]
 _ArrayLikeTD64_co = _DualArrayLike[
-    "dtype[Union[bool_, integer[Any], timedelta64]]",
+    dtype[Union[bool_, integer[Any], timedelta64]],
     Union[bool, int],
 ]
 _ArrayLikeDT64_co = Union[
-    _SupportsArray["dtype[datetime64]"],
-    _NestedSequence[_SupportsArray["dtype[datetime64]"]],
+    _SupportsArray[dtype[datetime64]],
+    _NestedSequence[_SupportsArray[dtype[datetime64]]],
 ]
 _ArrayLikeObject_co = Union[
-    _SupportsArray["dtype[object_]"],
-    _NestedSequence[_SupportsArray["dtype[object_]"]],
+    _SupportsArray[dtype[object_]],
+    _NestedSequence[_SupportsArray[dtype[object_]]],
 ]
 
 _ArrayLikeVoid_co = Union[
-    _SupportsArray["dtype[void]"],
-    _NestedSequence[_SupportsArray["dtype[void]"]],
+    _SupportsArray[dtype[void]],
+    _NestedSequence[_SupportsArray[dtype[void]]],
 ]
 _ArrayLikeStr_co = _DualArrayLike[
-    "dtype[str_]",
+    dtype[str_],
     str,
 ]
 _ArrayLikeBytes_co = _DualArrayLike[
-    "dtype[bytes_]",
+    dtype[bytes_],
     bytes,
 ]
 
 _ArrayLikeInt = _DualArrayLike[
-    "dtype[integer[Any]]",
+    dtype[integer[Any]],
     int,
 ]
 
@@ -160,6 +159,6 @@ class _UnknownType:
 
 
 _ArrayLikeUnknown = _DualArrayLike[
-    "dtype[_UnknownType]",
+    dtype[_UnknownType],
     _UnknownType,
 ]
diff --git a/contrib/python/numpy/py3/numpy/_typing/_callable.pyi b/contrib/python/numpy/py3/numpy/_typing/_callable.pyi
index 5dfe9e7c234..ee818e90575 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_callable.pyi
+++ b/contrib/python/numpy/py3/numpy/_typing/_callable.pyi
@@ -43,7 +43,7 @@ from ._scalars import (
     _NumberLike_co,
 )
 from . import NBitBase
-from ._generic_alias import NDArray
+from ._array_like import NDArray
 from ._nested_sequence import _NestedSequence
 
 _T1 = TypeVar("_T1")
diff --git a/contrib/python/numpy/py3/numpy/_typing/_dtype_like.py b/contrib/python/numpy/py3/numpy/_typing/_dtype_like.py
index e92e17dd20c..207a99c56b3 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_dtype_like.py
+++ b/contrib/python/numpy/py3/numpy/_typing/_dtype_like.py
@@ -1,10 +1,8 @@
+from collections.abc import Sequence
 from typing import (
     Any,
-    List,
     Sequence,
-    Tuple,
     Union,
-    Type,
     TypeVar,
     Protocol,
     TypedDict,
@@ -14,7 +12,6 @@ from typing import (
 import numpy as np
 
 from ._shape import _ShapeLike
-from ._generic_alias import _DType as DType
 
 from ._char_codes import (
     _BoolCodes,
@@ -59,7 +56,7 @@ from ._char_codes import (
 )
 
 _SCT = TypeVar("_SCT", bound=np.generic)
-_DType_co = TypeVar("_DType_co", covariant=True, bound=DType[Any])
+_DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any])
 
 _DTypeLikeNested = Any  # TODO: wait for support for recursive types
 
@@ -89,41 +86,41 @@ class _SupportsDType(Protocol[_DType_co]):
 
 # A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`
 _DTypeLike = Union[
-    "np.dtype[_SCT]",
-    Type[_SCT],
-    _SupportsDType["np.dtype[_SCT]"],
+    np.dtype[_SCT],
+    type[_SCT],
+    _SupportsDType[np.dtype[_SCT]],
 ]
 
 
 # Would create a dtype[np.void]
 _VoidDTypeLike = Union[
     # (flexible_dtype, itemsize)
-    Tuple[_DTypeLikeNested, int],
+    tuple[_DTypeLikeNested, int],
     # (fixed_dtype, shape)
-    Tuple[_DTypeLikeNested, _ShapeLike],
+    tuple[_DTypeLikeNested, _ShapeLike],
     # [(field_name, field_dtype, field_shape), ...]
     #
     # The type here is quite broad because NumPy accepts quite a wide
     # range of inputs inside the list; see the tests for some
     # examples.
-    List[Any],
+    list[Any],
     # {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
     #  'itemsize': ...}
     _DTypeDict,
     # (base_dtype, new_dtype)
-    Tuple[_DTypeLikeNested, _DTypeLikeNested],
+    tuple[_DTypeLikeNested, _DTypeLikeNested],
 ]
 
 # Anything that can be coerced into numpy.dtype.
 # Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
 DTypeLike = Union[
-    DType[Any],
+    np.dtype[Any],
     # default data type (float64)
     None,
     # array-scalar types and generic types
-    Type[Any],  # NOTE: We're stuck with `Type[Any]` due to object dtypes
+    type[Any],  # NOTE: We're stuck with `type[Any]` due to object dtypes
     # anything with a dtype attribute
-    _SupportsDType[DType[Any]],
+    _SupportsDType[np.dtype[Any]],
     # character codes, type strings or comma-separated fields, e.g., 'float64'
     str,
     _VoidDTypeLike,
@@ -139,16 +136,16 @@ DTypeLike = Union[
 # Aliases for commonly used dtype-like objects.
 # Note that the precision of `np.number` subclasses is ignored herein.
 _DTypeLikeBool = Union[
-    Type[bool],
-    Type[np.bool_],
-    DType[np.bool_],
-    _SupportsDType[DType[np.bool_]],
+    type[bool],
+    type[np.bool_],
+    np.dtype[np.bool_],
+    _SupportsDType[np.dtype[np.bool_]],
     _BoolCodes,
 ]
 _DTypeLikeUInt = Union[
-    Type[np.unsignedinteger],
-    DType[np.unsignedinteger],
-    _SupportsDType[DType[np.unsignedinteger]],
+    type[np.unsignedinteger],
+    np.dtype[np.unsignedinteger],
+    _SupportsDType[np.dtype[np.unsignedinteger]],
     _UInt8Codes,
     _UInt16Codes,
     _UInt32Codes,
@@ -161,10 +158,10 @@ _DTypeLikeUInt = Union[
     _ULongLongCodes,
 ]
 _DTypeLikeInt = Union[
-    Type[int],
-    Type[np.signedinteger],
-    DType[np.signedinteger],
-    _SupportsDType[DType[np.signedinteger]],
+    type[int],
+    type[np.signedinteger],
+    np.dtype[np.signedinteger],
+    _SupportsDType[np.dtype[np.signedinteger]],
     _Int8Codes,
     _Int16Codes,
     _Int32Codes,
@@ -177,10 +174,10 @@ _DTypeLikeInt = Union[
     _LongLongCodes,
 ]
 _DTypeLikeFloat = Union[
-    Type[float],
-    Type[np.floating],
-    DType[np.floating],
-    _SupportsDType[DType[np.floating]],
+    type[float],
+    type[np.floating],
+    np.dtype[np.floating],
+    _SupportsDType[np.dtype[np.floating]],
     _Float16Codes,
     _Float32Codes,
     _Float64Codes,
@@ -190,10 +187,10 @@ _DTypeLikeFloat = Union[
     _LongDoubleCodes,
 ]
 _DTypeLikeComplex = Union[
-    Type[complex],
-    Type[np.complexfloating],
-    DType[np.complexfloating],
-    _SupportsDType[DType[np.complexfloating]],
+    type[complex],
+    type[np.complexfloating],
+    np.dtype[np.complexfloating],
+    _SupportsDType[np.dtype[np.complexfloating]],
     _Complex64Codes,
     _Complex128Codes,
     _CSingleCodes,
@@ -201,42 +198,42 @@ _DTypeLikeComplex = Union[
     _CLongDoubleCodes,
 ]
 _DTypeLikeDT64 = Union[
-    Type[np.timedelta64],
-    DType[np.timedelta64],
-    _SupportsDType[DType[np.timedelta64]],
+    type[np.timedelta64],
+    np.dtype[np.timedelta64],
+    _SupportsDType[np.dtype[np.timedelta64]],
     _TD64Codes,
 ]
 _DTypeLikeTD64 = Union[
-    Type[np.datetime64],
-    DType[np.datetime64],
-    _SupportsDType[DType[np.datetime64]],
+    type[np.datetime64],
+    np.dtype[np.datetime64],
+    _SupportsDType[np.dtype[np.datetime64]],
     _DT64Codes,
 ]
 _DTypeLikeStr = Union[
-    Type[str],
-    Type[np.str_],
-    DType[np.str_],
-    _SupportsDType[DType[np.str_]],
+    type[str],
+    type[np.str_],
+    np.dtype[np.str_],
+    _SupportsDType[np.dtype[np.str_]],
     _StrCodes,
 ]
 _DTypeLikeBytes = Union[
-    Type[bytes],
-    Type[np.bytes_],
-    DType[np.bytes_],
-    _SupportsDType[DType[np.bytes_]],
+    type[bytes],
+    type[np.bytes_],
+    np.dtype[np.bytes_],
+    _SupportsDType[np.dtype[np.bytes_]],
     _BytesCodes,
 ]
 _DTypeLikeVoid = Union[
-    Type[np.void],
-    DType[np.void],
-    _SupportsDType[DType[np.void]],
+    type[np.void],
+    np.dtype[np.void],
+    _SupportsDType[np.dtype[np.void]],
     _VoidCodes,
     _VoidDTypeLike,
 ]
 _DTypeLikeObject = Union[
     type,
-    DType[np.object_],
-    _SupportsDType[DType[np.object_]],
+    np.dtype[np.object_],
+    _SupportsDType[np.dtype[np.object_]],
     _ObjectCodes,
 ]
 
diff --git a/contrib/python/numpy/py3/numpy/_typing/_extended_precision.py b/contrib/python/numpy/py3/numpy/_typing/_extended_precision.py
index 4bb8d05aca1..7246b47d0ee 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_extended_precision.py
+++ b/contrib/python/numpy/py3/numpy/_typing/_extended_precision.py
@@ -5,9 +5,6 @@ The subclasses are defined here (instead of ``__init__.pyi``) such
 that they can be imported conditionally via the numpy's mypy plugin.
 """
 
-from typing import Any
-from typing import TYPE_CHECKING
-
 import numpy as np
 from . import (
     _80Bit,
@@ -16,29 +13,15 @@ from . import (
     _256Bit,
 )
 
-if TYPE_CHECKING:
-    uint128 = np.unsignedinteger[_128Bit]
-    uint256 = np.unsignedinteger[_256Bit]
-    int128 = np.signedinteger[_128Bit]
-    int256 = np.signedinteger[_256Bit]
-    float80 = np.floating[_80Bit]
-    float96 = np.floating[_96Bit]
-    float128 = np.floating[_128Bit]
-    float256 = np.floating[_256Bit]
-    complex160 = np.complexfloating[_80Bit, _80Bit]
-    complex192 = np.complexfloating[_96Bit, _96Bit]
-    complex256 = np.complexfloating[_128Bit, _128Bit]
-    complex512 = np.complexfloating[_256Bit, _256Bit]
-else:
-    uint128 = Any
-    uint256 = Any
-    int128 = Any
-    int256 = Any
-    float80 = Any
-    float96 = Any
-    float128 = Any
-    float256 = Any
-    complex160 = Any
-    complex192 = Any
-    complex256 = Any
-    complex512 = Any
+uint128 = np.unsignedinteger[_128Bit]
+uint256 = np.unsignedinteger[_256Bit]
+int128 = np.signedinteger[_128Bit]
+int256 = np.signedinteger[_256Bit]
+float80 = np.floating[_80Bit]
+float96 = np.floating[_96Bit]
+float128 = np.floating[_128Bit]
+float256 = np.floating[_256Bit]
+complex160 = np.complexfloating[_80Bit, _80Bit]
+complex192 = np.complexfloating[_96Bit, _96Bit]
+complex256 = np.complexfloating[_128Bit, _128Bit]
+complex512 = np.complexfloating[_256Bit, _256Bit]
diff --git a/contrib/python/numpy/py3/numpy/_typing/_generic_alias.py b/contrib/python/numpy/py3/numpy/_typing/_generic_alias.py
deleted file mode 100644
index 01cd224adbe..00000000000
--- a/contrib/python/numpy/py3/numpy/_typing/_generic_alias.py
+++ /dev/null
@@ -1,245 +0,0 @@
-from __future__ import annotations
-
-import sys
-import types
-from collections.abc import Generator, Iterable, Iterator
-from typing import (
-    Any,
-    ClassVar,
-    NoReturn,
-    TypeVar,
-    TYPE_CHECKING,
-)
-
-import numpy as np
-
-__all__ = ["_GenericAlias", "NDArray"]
-
-_T = TypeVar("_T", bound="_GenericAlias")
-
-
-def _to_str(obj: object) -> str:
-    """Helper function for `_GenericAlias.__repr__`."""
-    if obj is Ellipsis:
-        return '...'
-    elif isinstance(obj, type) and not isinstance(obj, _GENERIC_ALIAS_TYPE):
-        if obj.__module__ == 'builtins':
-            return obj.__qualname__
-        else:
-            return f'{obj.__module__}.{obj.__qualname__}'
-    else:
-        return repr(obj)
-
-
-def _parse_parameters(args: Iterable[Any]) -> Generator[TypeVar, None, None]:
-    """Search for all typevars and typevar-containing objects in `args`.
-
-    Helper function for `_GenericAlias.__init__`.
-
-    """
-    for i in args:
-        if hasattr(i, "__parameters__"):
-            yield from i.__parameters__
-        elif isinstance(i, TypeVar):
-            yield i
-
-
-def _reconstruct_alias(alias: _T, parameters: Iterator[TypeVar]) -> _T:
-    """Recursively replace all typevars with those from `parameters`.
-
-    Helper function for `_GenericAlias.__getitem__`.
-
-    """
-    args = []
-    for i in alias.__args__:
-        if isinstance(i, TypeVar):
-            value: Any = next(parameters)
-        elif isinstance(i, _GenericAlias):
-            value = _reconstruct_alias(i, parameters)
-        elif hasattr(i, "__parameters__"):
-            prm_tup = tuple(next(parameters) for _ in i.__parameters__)
-            value = i[prm_tup]
-        else:
-            value = i
-        args.append(value)
-
-    cls = type(alias)
-    return cls(alias.__origin__, tuple(args), alias.__unpacked__)
-
-
-class _GenericAlias:
-    """A python-based backport of the `types.GenericAlias` class.
-
-    E.g. for ``t = list[int]``, ``t.__origin__`` is ``list`` and
-    ``t.__args__`` is ``(int,)``.
-
-    See Also
-    --------
-    :pep:`585`
-        The PEP responsible for introducing `types.GenericAlias`.
-
-    """
-
-    __slots__ = (
-        "__weakref__",
-        "_origin",
-        "_args",
-        "_parameters",
-        "_hash",
-        "_starred",
-    )
-
-    @property
-    def __origin__(self) -> type:
-        return super().__getattribute__("_origin")
-
-    @property
-    def __args__(self) -> tuple[object, ...]:
-        return super().__getattribute__("_args")
-
-    @property
-    def __parameters__(self) -> tuple[TypeVar, ...]:
-        """Type variables in the ``GenericAlias``."""
-        return super().__getattribute__("_parameters")
-
-    @property
-    def __unpacked__(self) -> bool:
-        return super().__getattribute__("_starred")
-
-    @property
-    def __typing_unpacked_tuple_args__(self) -> tuple[object, ...] | None:
-        # NOTE: This should return `__args__` if `__origin__` is a tuple,
-        # which should never be the case with how `_GenericAlias` is used
-        # within numpy
-        return None
-
-    def __init__(
-        self,
-        origin: type,
-        args: object | tuple[object, ...],
-        starred: bool = False,
-    ) -> None:
-        self._origin = origin
-        self._args = args if isinstance(args, tuple) else (args,)
-        self._parameters = tuple(_parse_parameters(self.__args__))
-        self._starred = starred
-
-    @property
-    def __call__(self) -> type[Any]:
-        return self.__origin__
-
-    def __reduce__(self: _T) -> tuple[
-        type[_T],
-        tuple[type[Any], tuple[object, ...], bool],
-    ]:
-        cls = type(self)
-        return cls, (self.__origin__, self.__args__, self.__unpacked__)
-
-    def __mro_entries__(self, bases: Iterable[object]) -> tuple[type[Any]]:
-        return (self.__origin__,)
-
-    def __dir__(self) -> list[str]:
-        """Implement ``dir(self)``."""
-        cls = type(self)
-        dir_origin = set(dir(self.__origin__))
-        return sorted(cls._ATTR_EXCEPTIONS | dir_origin)
-
-    def __hash__(self) -> int:
-        """Return ``hash(self)``."""
-        # Attempt to use the cached hash
-        try:
-            return super().__getattribute__("_hash")
-        except AttributeError:
-            self._hash: int = (
-                hash(self.__origin__) ^
-                hash(self.__args__) ^
-                hash(self.__unpacked__)
-            )
-            return super().__getattribute__("_hash")
-
-    def __instancecheck__(self, obj: object) -> NoReturn:
-        """Check if an `obj` is an instance."""
-        raise TypeError("isinstance() argument 2 cannot be a "
-                        "parameterized generic")
-
-    def __subclasscheck__(self, cls: type) -> NoReturn:
-        """Check if a `cls` is a subclass."""
-        raise TypeError("issubclass() argument 2 cannot be a "
-                        "parameterized generic")
-
-    def __repr__(self) -> str:
-        """Return ``repr(self)``."""
-        args = ", ".join(_to_str(i) for i in self.__args__)
-        origin = _to_str(self.__origin__)
-        prefix = "*" if self.__unpacked__ else ""
-        return f"{prefix}{origin}[{args}]"
-
-    def __getitem__(self: _T, key: object | tuple[object, ...]) -> _T:
-        """Return ``self[key]``."""
-        key_tup = key if isinstance(key, tuple) else (key,)
-
-        if len(self.__parameters__) == 0:
-            raise TypeError(f"There are no type variables left in {self}")
-        elif len(key_tup) > len(self.__parameters__):
-            raise TypeError(f"Too many arguments for {self}")
-        elif len(key_tup) < len(self.__parameters__):
-            raise TypeError(f"Too few arguments for {self}")
-
-        key_iter = iter(key_tup)
-        return _reconstruct_alias(self, key_iter)
-
-    def __eq__(self, value: object) -> bool:
-        """Return ``self == value``."""
-        if not isinstance(value, _GENERIC_ALIAS_TYPE):
-            return NotImplemented
-        return (
-            self.__origin__ == value.__origin__ and
-            self.__args__ == value.__args__ and
-            self.__unpacked__ == getattr(
-                value, "__unpacked__", self.__unpacked__
-            )
-        )
-
-    def __iter__(self: _T) -> Generator[_T, None, None]:
-        """Return ``iter(self)``."""
-        cls = type(self)
-        yield cls(self.__origin__, self.__args__, True)
-
-    _ATTR_EXCEPTIONS: ClassVar[frozenset[str]] = frozenset({
-        "__origin__",
-        "__args__",
-        "__parameters__",
-        "__mro_entries__",
-        "__reduce__",
-        "__reduce_ex__",
-        "__copy__",
-        "__deepcopy__",
-        "__unpacked__",
-        "__typing_unpacked_tuple_args__",
-        "__class__",
-    })
-
-    def __getattribute__(self, name: str) -> Any:
-        """Return ``getattr(self, name)``."""
-        # Pull the attribute from `__origin__` unless its
-        # name is in `_ATTR_EXCEPTIONS`
-        cls = type(self)
-        if name in cls._ATTR_EXCEPTIONS:
-            return super().__getattribute__(name)
-        return getattr(self.__origin__, name)
-
-
-# See `_GenericAlias.__eq__`
-if sys.version_info >= (3, 9):
-    _GENERIC_ALIAS_TYPE = (_GenericAlias, types.GenericAlias)
-else:
-    _GENERIC_ALIAS_TYPE = (_GenericAlias,)
-
-ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True)
-
-if TYPE_CHECKING or sys.version_info >= (3, 9):
-    _DType = np.dtype[ScalarType]
-    NDArray = np.ndarray[Any, np.dtype[ScalarType]]
-else:
-    _DType = _GenericAlias(np.dtype, (ScalarType,))
-    NDArray = _GenericAlias(np.ndarray, (Any, _DType))
diff --git a/contrib/python/numpy/py3/numpy/_typing/_nested_sequence.py b/contrib/python/numpy/py3/numpy/_typing/_nested_sequence.py
index 789bf384443..4b6cafc5100 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_nested_sequence.py
+++ b/contrib/python/numpy/py3/numpy/_typing/_nested_sequence.py
@@ -2,9 +2,9 @@
 
 from __future__ import annotations
 
+from collections.abc import Iterator
 from typing import (
     Any,
-    Iterator,
     overload,
     TypeVar,
     Protocol,
diff --git a/contrib/python/numpy/py3/numpy/_typing/_scalars.py b/contrib/python/numpy/py3/numpy/_typing/_scalars.py
index 516b996dc00..e46ff04a00d 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_scalars.py
+++ b/contrib/python/numpy/py3/numpy/_typing/_scalars.py
@@ -1,4 +1,4 @@
-from typing import Union, Tuple, Any
+from typing import Union, Any
 
 import numpy as np
 
@@ -10,13 +10,13 @@ _CharLike_co = Union[str, bytes]
 # The 6 `<X>Like_co` type-aliases below represent all scalars that can be
 # coerced into `<X>` (with the casting rule `same_kind`)
 _BoolLike_co = Union[bool, np.bool_]
-_UIntLike_co = Union[_BoolLike_co, np.unsignedinteger]
-_IntLike_co = Union[_BoolLike_co, int, np.integer]
-_FloatLike_co = Union[_IntLike_co, float, np.floating]
-_ComplexLike_co = Union[_FloatLike_co, complex, np.complexfloating]
+_UIntLike_co = Union[_BoolLike_co, np.unsignedinteger[Any]]
+_IntLike_co = Union[_BoolLike_co, int, np.integer[Any]]
+_FloatLike_co = Union[_IntLike_co, float, np.floating[Any]]
+_ComplexLike_co = Union[_FloatLike_co, complex, np.complexfloating[Any, Any]]
 _TD64Like_co = Union[_IntLike_co, np.timedelta64]
 
-_NumberLike_co = Union[int, float, complex, np.number, np.bool_]
+_NumberLike_co = Union[int, float, complex, np.number[Any], np.bool_]
 _ScalarLike_co = Union[
     int,
     float,
@@ -27,4 +27,4 @@ _ScalarLike_co = Union[
 ]
 
 # `_VoidLike_co` is technically not a scalar, but it's close enough
-_VoidLike_co = Union[Tuple[Any, ...], np.void]
+_VoidLike_co = Union[tuple[Any, ...], np.void]
diff --git a/contrib/python/numpy/py3/numpy/_typing/_shape.py b/contrib/python/numpy/py3/numpy/_typing/_shape.py
index c28859b19ba..4f1204e47c6 100644
--- a/contrib/python/numpy/py3/numpy/_typing/_shape.py
+++ b/contrib/python/numpy/py3/numpy/_typing/_shape.py
@@ -1,6 +1,7 @@
-from typing import Sequence, Tuple, Union, SupportsIndex
+from collections.abc import Sequence
+from typing import Union, SupportsIndex
 
-_Shape = Tuple[int, ...]
+_Shape = tuple[int, ...]
 
 # Anything that can be coerced to a shape tuple
 _ShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]
diff --git a/contrib/python/numpy/py3/numpy/_utils/__init__.py b/contrib/python/numpy/py3/numpy/_utils/__init__.py
new file mode 100644
index 00000000000..60703f145af
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/_utils/__init__.py
@@ -0,0 +1,27 @@
+"""
+This is a module for defining private helpers which do not depend on the
+rest of NumPy.
+
+Everything in here must be self-contained so that it can be
+imported anywhere else without creating circular imports.
+If a utility requires the import of NumPy, it probably belongs
+in ``numpy.core``.
+"""
+
+
+def set_module(module):
+    """Private decorator for overriding __module__ on a function or class.
+
+    Example usage::
+
+        @set_module('numpy')
+        def example():
+            pass
+
+        assert example.__module__ == 'numpy'
+    """
+    def decorator(func):
+        if module is not None:
+            func.__module__ = module
+        return func
+    return decorator
diff --git a/contrib/python/numpy/py3/numpy/compat/_inspect.py b/contrib/python/numpy/py3/numpy/_utils/_inspect.py
index 9a874a71dd0..9a874a71dd0 100644
--- a/contrib/python/numpy/py3/numpy/compat/_inspect.py
+++ b/contrib/python/numpy/py3/numpy/_utils/_inspect.py
diff --git a/contrib/python/numpy/py3/numpy/compat/_pep440.py b/contrib/python/numpy/py3/numpy/_utils/_pep440.py
index 73d0afb5e95..73d0afb5e95 100644
--- a/contrib/python/numpy/py3/numpy/compat/_pep440.py
+++ b/contrib/python/numpy/py3/numpy/_utils/_pep440.py
diff --git a/contrib/python/numpy/py3/numpy/_version.py b/contrib/python/numpy/py3/numpy/_version.py
index 9a7f017ac41..27510850787 100644
--- a/contrib/python/numpy/py3/numpy/_version.py
+++ b/contrib/python/numpy/py3/numpy/_version.py
@@ -8,11 +8,11 @@ import json
 
 version_json = '''
 {
- "date": "2023-06-25T20:31:30-0600",
+ "date": "2023-07-30T18:52:43-0600",
  "dirty": false,
  "error": null,
- "full-revisionid": "9315a9072b2636f75c831b4eca9f42a5f67ca2fb",
- "version": "1.24.4"
+ "full-revisionid": "ea677928332c37e8052b4d599bf6ee52cf363cf9",
+ "version": "1.25.2"
 }
 '''  # END VERSION_JSON
 
diff --git a/contrib/python/numpy/py3/numpy/array_api/__init__.py b/contrib/python/numpy/py3/numpy/array_api/__init__.py
index 5e58ee0a899..964873faab2 100644
--- a/contrib/python/numpy/py3/numpy/array_api/__init__.py
+++ b/contrib/python/numpy/py3/numpy/array_api/__init__.py
@@ -121,7 +121,7 @@ warnings.warn(
     "The numpy.array_api submodule is still experimental. See NEP 47.", stacklevel=2
 )
 
-__array_api_version__ = "2021.12"
+__array_api_version__ = "2022.12"
 
 __all__ = ["__array_api_version__"]
 
@@ -173,6 +173,7 @@ from ._data_type_functions import (
     broadcast_to,
     can_cast,
     finfo,
+    isdtype,
     iinfo,
     result_type,
 )
@@ -198,6 +199,8 @@ from ._dtypes import (
     uint64,
     float32,
     float64,
+    complex64,
+    complex128,
     bool,
 )
 
@@ -232,6 +235,7 @@ from ._elementwise_functions import (
     bitwise_right_shift,
     bitwise_xor,
     ceil,
+    conj,
     cos,
     cosh,
     divide,
@@ -242,6 +246,7 @@ from ._elementwise_functions import (
     floor_divide,
     greater,
     greater_equal,
+    imag,
     isfinite,
     isinf,
     isnan,
@@ -261,6 +266,7 @@ from ._elementwise_functions import (
     not_equal,
     positive,
     pow,
+    real,
     remainder,
     round,
     sign,
@@ -333,6 +339,10 @@ __all__ += [
     "trunc",
 ]
 
+from ._indexing_functions import take
+
+__all__ += ["take"]
+
 # linalg is an extension in the array API spec, which is a sub-namespace. Only
 # a subset of functions in it are imported into the top-level namespace.
 from . import linalg
diff --git a/contrib/python/numpy/py3/numpy/array_api/_array_object.py b/contrib/python/numpy/py3/numpy/array_api/_array_object.py
index c4746fad96e..ec465208e8b 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_array_object.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_array_object.py
@@ -24,6 +24,7 @@ from ._dtypes import (
     _integer_dtypes,
     _integer_or_boolean_dtypes,
     _floating_dtypes,
+    _complex_floating_dtypes,
     _numeric_dtypes,
     _result_type,
     _dtype_categories,
@@ -56,7 +57,7 @@ class Array:
     functions, such as asarray().
 
     """
-    _array: np.ndarray
+    _array: np.ndarray[Any, Any]
 
     # Use a custom constructor instead of __init__, as manually initializing
     # this class is not supported API.
@@ -139,7 +140,7 @@ class Array:
 
         if self.dtype not in _dtype_categories[dtype_category]:
             raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}")
-        if isinstance(other, (int, float, bool)):
+        if isinstance(other, (int, complex, float, bool)):
             other = self._promote_scalar(other)
         elif isinstance(other, Array):
             if other.dtype not in _dtype_categories[dtype_category]:
@@ -189,11 +190,23 @@ class Array:
                 raise TypeError(
                     "Python int scalars cannot be promoted with bool arrays"
                 )
+            if self.dtype in _integer_dtypes:
+                info = np.iinfo(self.dtype)
+                if not (info.min <= scalar <= info.max):
+                    raise OverflowError(
+                        "Python int scalars must be within the bounds of the dtype for integer arrays"
+                    )
+            # int + array(floating) is allowed
         elif isinstance(scalar, float):
             if self.dtype not in _floating_dtypes:
                 raise TypeError(
                     "Python float scalars can only be promoted with floating-point arrays."
                 )
+        elif isinstance(scalar, complex):
+            if self.dtype not in _complex_floating_dtypes:
+                raise TypeError(
+                    "Python complex scalars can only be promoted with complex floating-point arrays."
+                )
         else:
             raise TypeError("'scalar' must be a Python scalar")
 
@@ -454,11 +467,19 @@ class Array:
         # Note: This is an error here.
         if self._array.ndim != 0:
             raise TypeError("bool is only allowed on arrays with 0 dimensions")
-        if self.dtype not in _boolean_dtypes:
-            raise ValueError("bool is only allowed on boolean arrays")
         res = self._array.__bool__()
         return res
 
+    def __complex__(self: Array, /) -> complex:
+        """
+        Performs the operation __complex__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("complex is only allowed on arrays with 0 dimensions")
+        res = self._array.__complex__()
+        return res
+
     def __dlpack__(self: Array, /, *, stream: None = None) -> PyCapsule:
         """
         Performs the operation __dlpack__.
@@ -492,8 +513,8 @@ class Array:
         # Note: This is an error here.
         if self._array.ndim != 0:
             raise TypeError("float is only allowed on arrays with 0 dimensions")
-        if self.dtype not in _floating_dtypes:
-            raise ValueError("float is only allowed on floating-point arrays")
+        if self.dtype in _complex_floating_dtypes:
+            raise TypeError("float is not allowed on complex floating-point arrays")
         res = self._array.__float__()
         return res
 
@@ -501,7 +522,7 @@ class Array:
         """
         Performs the operation __floordiv__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__floordiv__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__floordiv__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
@@ -512,7 +533,7 @@ class Array:
         """
         Performs the operation __ge__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__ge__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__ge__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
@@ -542,7 +563,7 @@ class Array:
         """
         Performs the operation __gt__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__gt__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__gt__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
@@ -556,8 +577,8 @@ class Array:
         # Note: This is an error here.
         if self._array.ndim != 0:
             raise TypeError("int is only allowed on arrays with 0 dimensions")
-        if self.dtype not in _integer_dtypes:
-            raise ValueError("int is only allowed on integer arrays")
+        if self.dtype in _complex_floating_dtypes:
+            raise TypeError("int is not allowed on complex floating-point arrays")
         res = self._array.__int__()
         return res
 
@@ -581,7 +602,7 @@ class Array:
         """
         Performs the operation __le__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__le__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__le__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
@@ -603,7 +624,7 @@ class Array:
         """
         Performs the operation __lt__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__lt__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__lt__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
@@ -626,7 +647,7 @@ class Array:
         """
         Performs the operation __mod__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__mod__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__mod__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
@@ -808,7 +829,7 @@ class Array:
         """
         Performs the operation __ifloordiv__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__ifloordiv__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__ifloordiv__")
         if other is NotImplemented:
             return other
         self._array.__ifloordiv__(other._array)
@@ -818,7 +839,7 @@ class Array:
         """
         Performs the operation __rfloordiv__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__rfloordiv__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__rfloordiv__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
@@ -850,23 +871,13 @@ class Array:
         """
         Performs the operation __imatmul__.
         """
-        # Note: NumPy does not implement __imatmul__.
-
         # matmul is not defined for scalars, but without this, we may get
         # the wrong error message from asarray.
         other = self._check_allowed_dtypes(other, "numeric", "__imatmul__")
         if other is NotImplemented:
             return other
-
-        # __imatmul__ can only be allowed when it would not change the shape
-        # of self.
-        other_shape = other.shape
-        if self.shape == () or other_shape == ():
-            raise ValueError("@= requires at least one dimension")
-        if len(other_shape) == 1 or other_shape[-1] != other_shape[-2]:
-            raise ValueError("@= cannot change the shape of the input array")
-        self._array[:] = self._array.__matmul__(other._array)
-        return self
+        res = self._array.__imatmul__(other._array)
+        return self.__class__._new(res)
 
     def __rmatmul__(self: Array, other: Array, /) -> Array:
         """
@@ -884,7 +895,7 @@ class Array:
         """
         Performs the operation __imod__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__imod__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__imod__")
         if other is NotImplemented:
             return other
         self._array.__imod__(other._array)
@@ -894,7 +905,7 @@ class Array:
         """
         Performs the operation __rmod__.
         """
-        other = self._check_allowed_dtypes(other, "numeric", "__rmod__")
+        other = self._check_allowed_dtypes(other, "real numeric", "__rmod__")
         if other is NotImplemented:
             return other
         self, other = self._normalize_two_args(self, other)
diff --git a/contrib/python/numpy/py3/numpy/array_api/_data_type_functions.py b/contrib/python/numpy/py3/numpy/array_api/_data_type_functions.py
index 7026bd48956..6f972c3b542 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_data_type_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_data_type_functions.py
@@ -1,7 +1,17 @@
 from __future__ import annotations
 
 from ._array_object import Array
-from ._dtypes import _all_dtypes, _result_type
+from ._dtypes import (
+    _all_dtypes,
+    _boolean_dtypes,
+    _signed_integer_dtypes,
+    _unsigned_integer_dtypes,
+    _integer_dtypes,
+    _real_floating_dtypes,
+    _complex_floating_dtypes,
+    _numeric_dtypes,
+    _result_type,
+)
 
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, List, Tuple, Union
@@ -80,6 +90,7 @@ class finfo_object:
     max: float
     min: float
     smallest_normal: float
+    dtype: Dtype
 
 
 @dataclass
@@ -87,6 +98,7 @@ class iinfo_object:
     bits: int
     max: int
     min: int
+    dtype: Dtype
 
 
 def finfo(type: Union[Dtype, Array], /) -> finfo_object:
@@ -104,6 +116,7 @@ def finfo(type: Union[Dtype, Array], /) -> finfo_object:
         float(fi.max),
         float(fi.min),
         float(fi.smallest_normal),
+        fi.dtype,
     )
 
 
@@ -114,9 +127,47 @@ def iinfo(type: Union[Dtype, Array], /) -> iinfo_object:
     See its docstring for more information.
     """
     ii = np.iinfo(type)
-    return iinfo_object(ii.bits, ii.max, ii.min)
+    return iinfo_object(ii.bits, ii.max, ii.min, ii.dtype)
 
 
+# Note: isdtype is a new function from the 2022.12 array API specification.
+def isdtype(
+    dtype: Dtype, kind: Union[Dtype, str, Tuple[Union[Dtype, str], ...]]
+) -> bool:
+    """
+    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
+    for more details
+    """
+    if isinstance(kind, tuple):
+        # Disallow nested tuples
+        if any(isinstance(k, tuple) for k in kind):
+            raise TypeError("'kind' must be a dtype, str, or tuple of dtypes and strs")
+        return any(isdtype(dtype, k) for k in kind)
+    elif isinstance(kind, str):
+        if kind == 'bool':
+            return dtype in _boolean_dtypes
+        elif kind == 'signed integer':
+            return dtype in _signed_integer_dtypes
+        elif kind == 'unsigned integer':
+            return dtype in _unsigned_integer_dtypes
+        elif kind == 'integral':
+            return dtype in _integer_dtypes
+        elif kind == 'real floating':
+            return dtype in _real_floating_dtypes
+        elif kind == 'complex floating':
+            return dtype in _complex_floating_dtypes
+        elif kind == 'numeric':
+            return dtype in _numeric_dtypes
+        else:
+            raise ValueError(f"Unrecognized data type kind: {kind!r}")
+    elif kind in _all_dtypes:
+        return dtype == kind
+    else:
+        raise TypeError(f"'kind' must be a dtype, str, or tuple of dtypes and strs, not {type(kind).__name__}")
+
 def result_type(*arrays_and_dtypes: Union[Array, Dtype]) -> Dtype:
     """
     Array API compatible wrapper for :py:func:`np.result_type <numpy.result_type>`.
diff --git a/contrib/python/numpy/py3/numpy/array_api/_dtypes.py b/contrib/python/numpy/py3/numpy/array_api/_dtypes.py
index 476d619fee6..0e8f666eeed 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_dtypes.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_dtypes.py
@@ -12,6 +12,8 @@ uint32 = np.dtype("uint32")
 uint64 = np.dtype("uint64")
 float32 = np.dtype("float32")
 float64 = np.dtype("float64")
+complex64 = np.dtype("complex64")
+complex128 = np.dtype("complex128")
 # Note: This name is changed
 bool = np.dtype("bool")
 
@@ -26,11 +28,17 @@ _all_dtypes = (
     uint64,
     float32,
     float64,
+    complex64,
+    complex128,
     bool,
 )
 _boolean_dtypes = (bool,)
-_floating_dtypes = (float32, float64)
+_real_floating_dtypes = (float32, float64)
+_floating_dtypes = (float32, float64, complex64, complex128)
+_complex_floating_dtypes = (complex64, complex128)
 _integer_dtypes = (int8, int16, int32, int64, uint8, uint16, uint32, uint64)
+_signed_integer_dtypes = (int8, int16, int32, int64)
+_unsigned_integer_dtypes = (uint8, uint16, uint32, uint64)
 _integer_or_boolean_dtypes = (
     bool,
     int8,
@@ -42,9 +50,23 @@ _integer_or_boolean_dtypes = (
     uint32,
     uint64,
 )
+_real_numeric_dtypes = (
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
 _numeric_dtypes = (
     float32,
     float64,
+    complex64,
+    complex128,
     int8,
     int16,
     int32,
@@ -57,10 +79,13 @@ _numeric_dtypes = (
 
 _dtype_categories = {
     "all": _all_dtypes,
+    "real numeric": _real_numeric_dtypes,
     "numeric": _numeric_dtypes,
     "integer": _integer_dtypes,
     "integer or boolean": _integer_or_boolean_dtypes,
     "boolean": _boolean_dtypes,
+    "real floating-point": _floating_dtypes,
+    "complex floating-point": _complex_floating_dtypes,
     "floating-point": _floating_dtypes,
 }
 
@@ -133,6 +158,18 @@ _promotion_table = {
     (float32, float64): float64,
     (float64, float32): float64,
     (float64, float64): float64,
+    (complex64, complex64): complex64,
+    (complex64, complex128): complex128,
+    (complex128, complex64): complex128,
+    (complex128, complex128): complex128,
+    (float32, complex64): complex64,
+    (float32, complex128): complex128,
+    (float64, complex64): complex128,
+    (float64, complex128): complex128,
+    (complex64, float32): complex64,
+    (complex64, float64): complex128,
+    (complex128, float32): complex128,
+    (complex128, float64): complex128,
     (bool, bool): bool,
 }
 
diff --git a/contrib/python/numpy/py3/numpy/array_api/_elementwise_functions.py b/contrib/python/numpy/py3/numpy/array_api/_elementwise_functions.py
index c758a09447a..8b696772f6d 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_elementwise_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_elementwise_functions.py
@@ -3,8 +3,11 @@ from __future__ import annotations
 from ._dtypes import (
     _boolean_dtypes,
     _floating_dtypes,
+    _real_floating_dtypes,
+    _complex_floating_dtypes,
     _integer_dtypes,
     _integer_or_boolean_dtypes,
+    _real_numeric_dtypes,
     _numeric_dtypes,
     _result_type,
 )
@@ -105,8 +108,8 @@ def atan2(x1: Array, x2: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
-        raise TypeError("Only floating-point dtypes are allowed in atan2")
+    if x1.dtype not in _real_floating_dtypes or x2.dtype not in _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in atan2")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
@@ -230,14 +233,25 @@ def ceil(x: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in ceil")
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in ceil")
     if x.dtype in _integer_dtypes:
         # Note: The return dtype of ceil is the same as the input
         return x
     return Array._new(np.ceil(x._array))
 
 
+def conj(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.conj <numpy.conj>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _complex_floating_dtypes:
+        raise TypeError("Only complex floating-point dtypes are allowed in conj")
+    return Array._new(np.conj(x))
+
+
 def cos(x: Array, /) -> Array:
     """
     Array API compatible wrapper for :py:func:`np.cos <numpy.cos>`.
@@ -314,8 +328,8 @@ def floor(x: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in floor")
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in floor")
     if x.dtype in _integer_dtypes:
         # Note: The return dtype of floor is the same as the input
         return x
@@ -328,8 +342,8 @@ def floor_divide(x1: Array, x2: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in floor_divide")
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in floor_divide")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
@@ -342,8 +356,8 @@ def greater(x1: Array, x2: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in greater")
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in greater")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
@@ -356,14 +370,25 @@ def greater_equal(x1: Array, x2: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in greater_equal")
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in greater_equal")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
     return Array._new(np.greater_equal(x1._array, x2._array))
 
 
+def imag(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.imag <numpy.imag>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _complex_floating_dtypes:
+        raise TypeError("Only complex floating-point dtypes are allowed in imag")
+    return Array._new(np.imag(x))
+
+
 def isfinite(x: Array, /) -> Array:
     """
     Array API compatible wrapper for :py:func:`np.isfinite <numpy.isfinite>`.
@@ -403,8 +428,8 @@ def less(x1: Array, x2: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in less")
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in less")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
@@ -417,8 +442,8 @@ def less_equal(x1: Array, x2: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in less_equal")
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in less_equal")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
@@ -475,8 +500,8 @@ def logaddexp(x1: Array, x2: Array) -> Array:
 
     See its docstring for more information.
     """
-    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
-        raise TypeError("Only floating-point dtypes are allowed in logaddexp")
+    if x1.dtype not in _real_floating_dtypes or x2.dtype not in _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in logaddexp")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
@@ -599,14 +624,25 @@ def pow(x1: Array, x2: Array, /) -> Array:
     return Array._new(np.power(x1._array, x2._array))
 
 
+def real(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.real <numpy.real>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _complex_floating_dtypes:
+        raise TypeError("Only complex floating-point dtypes are allowed in real")
+    return Array._new(np.real(x))
+
+
 def remainder(x1: Array, x2: Array, /) -> Array:
     """
     Array API compatible wrapper for :py:func:`np.remainder <numpy.remainder>`.
 
     See its docstring for more information.
     """
-    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in remainder")
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in remainder")
     # Call result type here just to raise on disallowed type combinations
     _result_type(x1.dtype, x2.dtype)
     x1, x2 = Array._normalize_two_args(x1, x2)
@@ -721,8 +757,8 @@ def trunc(x: Array, /) -> Array:
 
     See its docstring for more information.
     """
-    if x.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in trunc")
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in trunc")
     if x.dtype in _integer_dtypes:
         # Note: The return dtype of trunc is the same as the input
         return x
diff --git a/contrib/python/numpy/py3/numpy/array_api/_indexing_functions.py b/contrib/python/numpy/py3/numpy/array_api/_indexing_functions.py
new file mode 100644
index 00000000000..baf23f7f0b6
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/array_api/_indexing_functions.py
@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _integer_dtypes
+
+import numpy as np
+
+def take(x: Array, indices: Array, /, *, axis: Optional[int] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.take <numpy.take>`.
+
+    See its docstring for more information.
+    """
+    if axis is None and x.ndim != 1:
+        raise ValueError("axis must be specified when ndim > 1")
+    if indices.dtype not in _integer_dtypes:
+        raise TypeError("Only integer dtypes are allowed in indexing")
+    if indices.ndim != 1:
+        raise ValueError("Only 1-dim indices array is supported")
+    return Array._new(np.take(x._array, indices._array, axis=axis))
diff --git a/contrib/python/numpy/py3/numpy/array_api/_manipulation_functions.py b/contrib/python/numpy/py3/numpy/array_api/_manipulation_functions.py
index 7991f46a272..556bde7d0b0 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_manipulation_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_manipulation_functions.py
@@ -53,13 +53,27 @@ def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array:
 
 
 # Note: the optional argument is called 'shape', not 'newshape'
-def reshape(x: Array, /, shape: Tuple[int, ...]) -> Array:
+def reshape(x: Array, 
+            /, 
+            shape: Tuple[int, ...],
+            *,
+            copy: Optional[Bool] = None) -> Array:
     """
     Array API compatible wrapper for :py:func:`np.reshape <numpy.reshape>`.
 
     See its docstring for more information.
     """
-    return Array._new(np.reshape(x._array, shape))
+
+    data = x._array
+    if copy:
+        data = np.copy(data)
+
+    reshaped = np.reshape(data, shape)
+
+    if copy is False and not np.shares_memory(data, reshaped):
+        raise AttributeError("Incompatible shape for in-place modification.")
+
+    return Array._new(reshaped)
 
 
 def roll(
diff --git a/contrib/python/numpy/py3/numpy/array_api/_searching_functions.py b/contrib/python/numpy/py3/numpy/array_api/_searching_functions.py
index 40f5a4d2e8f..a1f4b0c904c 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_searching_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_searching_functions.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from ._array_object import Array
-from ._dtypes import _result_type
+from ._dtypes import _result_type, _real_numeric_dtypes
 
 from typing import Optional, Tuple
 
@@ -14,6 +14,8 @@ def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -
 
     See its docstring for more information.
     """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in argmax")
     return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
 
 
@@ -23,6 +25,8 @@ def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -
 
     See its docstring for more information.
     """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in argmin")
     return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
 
 
diff --git a/contrib/python/numpy/py3/numpy/array_api/_sorting_functions.py b/contrib/python/numpy/py3/numpy/array_api/_sorting_functions.py
index afbb412f7f5..9b8cb044d88 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_sorting_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_sorting_functions.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from ._array_object import Array
+from ._dtypes import _real_numeric_dtypes
 
 import numpy as np
 
@@ -14,6 +15,8 @@ def argsort(
 
     See its docstring for more information.
     """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in argsort")
     # Note: this keyword argument is different, and the default is different.
     kind = "stable" if stable else "quicksort"
     if not descending:
@@ -41,6 +44,8 @@ def sort(
 
     See its docstring for more information.
     """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in sort")
     # Note: this keyword argument is different, and the default is different.
     kind = "stable" if stable else "quicksort"
     res = np.sort(x._array, axis=axis, kind=kind)
diff --git a/contrib/python/numpy/py3/numpy/array_api/_statistical_functions.py b/contrib/python/numpy/py3/numpy/array_api/_statistical_functions.py
index 5bc831ac296..98e31b51c11 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_statistical_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_statistical_functions.py
@@ -1,12 +1,12 @@
 from __future__ import annotations
 
 from ._dtypes import (
-    _floating_dtypes,
+    _real_floating_dtypes,
+    _real_numeric_dtypes,
     _numeric_dtypes,
 )
 from ._array_object import Array
-from ._creation_functions import asarray
-from ._dtypes import float32, float64
+from ._dtypes import float32, float64, complex64, complex128
 
 from typing import TYPE_CHECKING, Optional, Tuple, Union
 
@@ -23,8 +23,8 @@ def max(
     axis: Optional[Union[int, Tuple[int, ...]]] = None,
     keepdims: bool = False,
 ) -> Array:
-    if x.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in max")
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in max")
     return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))
 
 
@@ -35,8 +35,8 @@ def mean(
     axis: Optional[Union[int, Tuple[int, ...]]] = None,
     keepdims: bool = False,
 ) -> Array:
-    if x.dtype not in _floating_dtypes:
-        raise TypeError("Only floating-point dtypes are allowed in mean")
+    if x.dtype not in _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in mean")
     return Array._new(np.mean(x._array, axis=axis, keepdims=keepdims))
 
 
@@ -47,8 +47,8 @@ def min(
     axis: Optional[Union[int, Tuple[int, ...]]] = None,
     keepdims: bool = False,
 ) -> Array:
-    if x.dtype not in _numeric_dtypes:
-        raise TypeError("Only numeric dtypes are allowed in min")
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in min")
     return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))
 
 
@@ -62,10 +62,14 @@ def prod(
 ) -> Array:
     if x.dtype not in _numeric_dtypes:
         raise TypeError("Only numeric dtypes are allowed in prod")
-    # Note: sum() and prod() always upcast float32 to float64 for dtype=None
-    # We need to do so here before computing the product to avoid overflow
-    if dtype is None and x.dtype == float32:
-        dtype = float64
+    # Note: sum() and prod() always upcast for dtype=None. `np.prod` does that
+    # for integers, but not for float32 or complex64, so we need to
+    # special-case it here
+    if dtype is None:
+        if x.dtype == float32:
+            dtype = float64
+        elif x.dtype == complex64:
+            dtype = complex128
     return Array._new(np.prod(x._array, dtype=dtype, axis=axis, keepdims=keepdims))
 
 
@@ -78,8 +82,8 @@ def std(
     keepdims: bool = False,
 ) -> Array:
     # Note: the keyword argument correction is different here
-    if x.dtype not in _floating_dtypes:
-        raise TypeError("Only floating-point dtypes are allowed in std")
+    if x.dtype not in _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in std")
     return Array._new(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims))
 
 
@@ -93,11 +97,14 @@ def sum(
 ) -> Array:
     if x.dtype not in _numeric_dtypes:
         raise TypeError("Only numeric dtypes are allowed in sum")
-    # Note: sum() and prod() always upcast integers to (u)int64 and float32 to
-    # float64 for dtype=None. `np.sum` does that too for integers, but not for
-    # float32, so we need to special-case it here
-    if dtype is None and x.dtype == float32:
-        dtype = float64
+    # Note: sum() and prod() always upcast for dtype=None. `np.sum` does that
+    # for integers, but not for float32 or complex64, so we need to
+    # special-case it here
+    if dtype is None:
+        if x.dtype == float32:
+            dtype = float64
+        elif x.dtype == complex64:
+            dtype = complex128
     return Array._new(np.sum(x._array, axis=axis, dtype=dtype, keepdims=keepdims))
 
 
@@ -110,6 +117,6 @@ def var(
     keepdims: bool = False,
 ) -> Array:
     # Note: the keyword argument correction is different here
-    if x.dtype not in _floating_dtypes:
-        raise TypeError("Only floating-point dtypes are allowed in var")
+    if x.dtype not in _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in var")
     return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))
diff --git a/contrib/python/numpy/py3/numpy/array_api/_typing.py b/contrib/python/numpy/py3/numpy/array_api/_typing.py
index dfa87b3584b..3f9b7186aeb 100644
--- a/contrib/python/numpy/py3/numpy/array_api/_typing.py
+++ b/contrib/python/numpy/py3/numpy/array_api/_typing.py
@@ -17,14 +17,12 @@ __all__ = [
     "PyCapsule",
 ]
 
-import sys
 from typing import (
     Any,
     Literal,
     Sequence,
     Type,
     Union,
-    TYPE_CHECKING,
     TypeVar,
     Protocol,
 )
@@ -51,21 +49,20 @@ class NestedSequence(Protocol[_T_co]):
     def __len__(self, /) -> int: ...
 
 Device = Literal["cpu"]
-if TYPE_CHECKING or sys.version_info >= (3, 9):
-    Dtype = dtype[Union[
-        int8,
-        int16,
-        int32,
-        int64,
-        uint8,
-        uint16,
-        uint32,
-        uint64,
-        float32,
-        float64,
-    ]]
-else:
-    Dtype = dtype
+
+Dtype = dtype[Union[
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+]]
+
 
 SupportsBufferProtocol = Any
 PyCapsule = Any
diff --git a/contrib/python/numpy/py3/numpy/array_api/linalg.py b/contrib/python/numpy/py3/numpy/array_api/linalg.py
index d214046effd..58320db55ce 100644
--- a/contrib/python/numpy/py3/numpy/array_api/linalg.py
+++ b/contrib/python/numpy/py3/numpy/array_api/linalg.py
@@ -1,6 +1,13 @@
 from __future__ import annotations
 
-from ._dtypes import _floating_dtypes, _numeric_dtypes
+from ._dtypes import (
+    _floating_dtypes,
+    _numeric_dtypes,
+    float32,
+    float64,
+    complex64,
+    complex128
+)
 from ._manipulation_functions import reshape
 from ._array_object import Array
 
@@ -8,7 +15,7 @@ from ..core.numeric import normalize_axis_tuple
 
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
-    from ._typing import Literal, Optional, Sequence, Tuple, Union
+    from ._typing import Literal, Optional, Sequence, Tuple, Union, Dtype
 
 from typing import NamedTuple
 
@@ -363,7 +370,7 @@ def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int],
     return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
 
 # Note: trace is the numpy top-level namespace, not np.linalg
-def trace(x: Array, /, *, offset: int = 0) -> Array:
+def trace(x: Array, /, *, offset: int = 0, dtype: Optional[Dtype] = None) -> Array:
     """
     Array API compatible wrapper for :py:func:`np.trace <numpy.trace>`.
 
@@ -371,9 +378,17 @@ def trace(x: Array, /, *, offset: int = 0) -> Array:
     """
     if x.dtype not in _numeric_dtypes:
         raise TypeError('Only numeric dtypes are allowed in trace')
+
+    # Note: trace() works the same as sum() and prod() (see
+    # _statistical_functions.py)
+    if dtype is None:
+        if x.dtype == float32:
+            dtype = float64
+        elif x.dtype == complex64:
+            dtype = complex128
     # Note: trace always operates on the last two axes, whereas np.trace
     # operates on the first two axes by default
-    return Array._new(np.asarray(np.trace(x._array, offset=offset, axis1=-2, axis2=-1)))
+    return Array._new(np.asarray(np.trace(x._array, offset=offset, axis1=-2, axis2=-1, dtype=dtype)))
 
 # Note: vecdot is not in NumPy
 def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
diff --git a/contrib/python/numpy/py3/numpy/array_api/tests/test_array_object.py b/contrib/python/numpy/py3/numpy/array_api/tests/test_array_object.py
index c6407bd6976..de8082458f5 100644
--- a/contrib/python/numpy/py3/numpy/array_api/tests/test_array_object.py
+++ b/contrib/python/numpy/py3/numpy/array_api/tests/test_array_object.py
@@ -1,6 +1,6 @@
 import operator
 
-from numpy.testing import assert_raises
+from numpy.testing import assert_raises, suppress_warnings
 import numpy as np
 import pytest
 
@@ -9,9 +9,12 @@ from numpy.array_api._array_object import Array
 from numpy.array_api._dtypes import (
     _all_dtypes,
     _boolean_dtypes,
+    _real_floating_dtypes,
     _floating_dtypes,
+    _complex_floating_dtypes,
     _integer_dtypes,
     _integer_or_boolean_dtypes,
+    _real_numeric_dtypes,
     _numeric_dtypes,
     int8,
     int16,
@@ -85,13 +88,13 @@ def test_operators():
         "__add__": "numeric",
         "__and__": "integer_or_boolean",
         "__eq__": "all",
-        "__floordiv__": "numeric",
-        "__ge__": "numeric",
-        "__gt__": "numeric",
-        "__le__": "numeric",
+        "__floordiv__": "real numeric",
+        "__ge__": "real numeric",
+        "__gt__": "real numeric",
+        "__le__": "real numeric",
         "__lshift__": "integer",
-        "__lt__": "numeric",
-        "__mod__": "numeric",
+        "__lt__": "real numeric",
+        "__mod__": "real numeric",
         "__mul__": "numeric",
         "__ne__": "all",
         "__or__": "integer_or_boolean",
@@ -101,7 +104,6 @@ def test_operators():
         "__truediv__": "floating",
         "__xor__": "integer_or_boolean",
     }
-
     # Recompute each time because of in-place ops
     def _array_vals():
         for d in _integer_dtypes:
@@ -111,13 +113,15 @@ def test_operators():
         for d in _floating_dtypes:
             yield asarray(1.0, dtype=d)
 
+
+    BIG_INT = int(1e30)
     for op, dtypes in binary_op_dtypes.items():
         ops = [op]
         if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]:
             rop = "__r" + op[2:]
             iop = "__i" + op[2:]
             ops += [rop, iop]
-        for s in [1, 1.0, False]:
+        for s in [1, 1.0, 1j, BIG_INT, False]:
             for _op in ops:
                 for a in _array_vals():
                     # Test array op scalar. From the spec, the following combinations
@@ -125,13 +129,12 @@ def test_operators():
 
                     # - Python bool for a bool array dtype,
                     # - a Python int within the bounds of the given dtype for integer array dtypes,
-                    # - a Python int or float for floating-point array dtypes
-
-                    # We do not do bounds checking for int scalars, but rather use the default
-                    # NumPy behavior for casting in that case.
+                    # - a Python int or float for real floating-point array dtypes
+                    # - a Python int, float, or complex for complex floating-point array dtypes
 
                     if ((dtypes == "all"
                          or dtypes == "numeric" and a.dtype in _numeric_dtypes
+                         or dtypes == "real numeric" and a.dtype in _real_numeric_dtypes
                          or dtypes == "integer" and a.dtype in _integer_dtypes
                          or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
                          or dtypes == "boolean" and a.dtype in _boolean_dtypes
@@ -141,10 +144,18 @@ def test_operators():
                         # isinstance here.
                         and (a.dtype in _boolean_dtypes and type(s) == bool
                              or a.dtype in _integer_dtypes and type(s) == int
-                             or a.dtype in _floating_dtypes and type(s) in [float, int]
+                             or a.dtype in _real_floating_dtypes and type(s) in [float, int]
+                             or a.dtype in _complex_floating_dtypes and type(s) in [complex, float, int]
                         )):
-                        # Only test for no error
-                        getattr(a, _op)(s)
+                        if a.dtype in _integer_dtypes and s == BIG_INT:
+                            assert_raises(OverflowError, lambda: getattr(a, _op)(s))
+                        else:
+                            # Only test for no error
+                            with suppress_warnings() as sup:
+                                # ignore warnings from pow(BIG_INT)
+                                sup.filter(RuntimeWarning,
+                                           "invalid value encountered in power")
+                                getattr(a, _op)(s)
                     else:
                         assert_raises(TypeError, lambda: getattr(a, _op)(s))
 
@@ -174,8 +185,9 @@ def test_operators():
                             # Ensure only those dtypes that are required for every operator are allowed.
                             elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
                                                       or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or (dtypes == "real numeric" and x.dtype in _real_numeric_dtypes and y.dtype in _real_numeric_dtypes)
                                 or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
-                                or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _numeric_dtypes
+                                or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
                                 or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
                                                                        or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes)
                                 or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
@@ -263,31 +275,39 @@ def test_python_scalar_construtors():
     b = asarray(False)
     i = asarray(0)
     f = asarray(0.0)
+    c = asarray(0j)
 
     assert bool(b) == False
     assert int(i) == 0
     assert float(f) == 0.0
     assert operator.index(i) == 0
 
-    # bool/int/float should only be allowed on 0-D arrays.
+    # bool/int/float/complex should only be allowed on 0-D arrays.
     assert_raises(TypeError, lambda: bool(asarray([False])))
     assert_raises(TypeError, lambda: int(asarray([0])))
     assert_raises(TypeError, lambda: float(asarray([0.0])))
+    assert_raises(TypeError, lambda: complex(asarray([0j])))
     assert_raises(TypeError, lambda: operator.index(asarray([0])))
 
-    # bool/int/float should only be allowed on arrays of the corresponding
-    # dtype
-    assert_raises(ValueError, lambda: bool(i))
-    assert_raises(ValueError, lambda: bool(f))
+    # bool should work on all types of arrays
+    assert bool(b) is bool(i) is bool(f) is bool(c) is False
+
+    # int should fail on complex arrays
+    assert int(b) == int(i) == int(f) == 0
+    assert_raises(TypeError, lambda: int(c))
 
-    assert_raises(ValueError, lambda: int(b))
-    assert_raises(ValueError, lambda: int(f))
+    # float should fail on complex arrays
+    assert float(b) == float(i) == float(f) == 0.0
+    assert_raises(TypeError, lambda: float(c))
 
-    assert_raises(ValueError, lambda: float(b))
-    assert_raises(ValueError, lambda: float(i))
+    # complex should work on all types of arrays
+    assert complex(b) == complex(i) == complex(f) == complex(c) == 0j
 
+    # index should only work on integer arrays
+    assert operator.index(i) == 0
     assert_raises(TypeError, lambda: operator.index(b))
     assert_raises(TypeError, lambda: operator.index(f))
+    assert_raises(TypeError, lambda: operator.index(c))
 
 
 def test_device_property():
diff --git a/contrib/python/numpy/py3/numpy/array_api/tests/test_data_type_functions.py b/contrib/python/numpy/py3/numpy/array_api/tests/test_data_type_functions.py
index efe3d0abd4b..61d56ca45b1 100644
--- a/contrib/python/numpy/py3/numpy/array_api/tests/test_data_type_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/tests/test_data_type_functions.py
@@ -1,7 +1,8 @@
 import pytest
 
+from numpy.testing import assert_raises
 from numpy import array_api as xp
-
+import numpy as np
 
 @pytest.mark.parametrize(
     "from_, to, expected",
@@ -17,3 +18,14 @@ def test_can_cast(from_, to, expected):
     can_cast() returns correct result
     """
     assert xp.can_cast(from_, to) == expected
+
+def test_isdtype_strictness():
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, 64))
+    assert_raises(ValueError, lambda: xp.isdtype(xp.float64, 'f8'))
+
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, (('integral',),)))
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, np.object_))
+
+    # TODO: These will require https://github.com/numpy/numpy/issues/23883
+    # assert_raises(TypeError, lambda: xp.isdtype(xp.float64, None))
+    # assert_raises(TypeError, lambda: xp.isdtype(xp.float64, np.float64))
diff --git a/contrib/python/numpy/py3/numpy/array_api/tests/test_elementwise_functions.py b/contrib/python/numpy/py3/numpy/array_api/tests/test_elementwise_functions.py
index 0fd258a6ca5..208cafab819 100644
--- a/contrib/python/numpy/py3/numpy/array_api/tests/test_elementwise_functions.py
+++ b/contrib/python/numpy/py3/numpy/array_api/tests/test_elementwise_functions.py
@@ -29,7 +29,7 @@ def test_function_types():
         "asin": "floating-point",
         "asinh": "floating-point",
         "atan": "floating-point",
-        "atan2": "floating-point",
+        "atan2": "real floating-point",
         "atanh": "floating-point",
         "bitwise_and": "integer or boolean",
         "bitwise_invert": "integer or boolean",
@@ -37,24 +37,26 @@ def test_function_types():
         "bitwise_or": "integer or boolean",
         "bitwise_right_shift": "integer",
         "bitwise_xor": "integer or boolean",
-        "ceil": "numeric",
+        "ceil": "real numeric",
+        "conj": "complex floating-point",
         "cos": "floating-point",
         "cosh": "floating-point",
         "divide": "floating-point",
         "equal": "all",
         "exp": "floating-point",
         "expm1": "floating-point",
-        "floor": "numeric",
-        "floor_divide": "numeric",
-        "greater": "numeric",
-        "greater_equal": "numeric",
+        "floor": "real numeric",
+        "floor_divide": "real numeric",
+        "greater": "real numeric",
+        "greater_equal": "real numeric",
+        "imag": "complex floating-point",
         "isfinite": "numeric",
         "isinf": "numeric",
         "isnan": "numeric",
-        "less": "numeric",
-        "less_equal": "numeric",
+        "less": "real numeric",
+        "less_equal": "real numeric",
         "log": "floating-point",
-        "logaddexp": "floating-point",
+        "logaddexp": "real floating-point",
         "log10": "floating-point",
         "log1p": "floating-point",
         "log2": "floating-point",
@@ -67,7 +69,8 @@ def test_function_types():
         "not_equal": "all",
         "positive": "numeric",
         "pow": "numeric",
-        "remainder": "numeric",
+        "real": "complex floating-point",
+        "remainder": "real numeric",
         "round": "numeric",
         "sign": "numeric",
         "sin": "floating-point",
@@ -77,7 +80,7 @@ def test_function_types():
         "subtract": "numeric",
         "tan": "floating-point",
         "tanh": "floating-point",
-        "trunc": "numeric",
+        "trunc": "real numeric",
     }
 
     def _array_vals():
diff --git a/contrib/python/numpy/py3/numpy/array_api/tests/test_indexing_functions.py b/contrib/python/numpy/py3/numpy/array_api/tests/test_indexing_functions.py
new file mode 100644
index 00000000000..9e05c63863a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/array_api/tests/test_indexing_functions.py
@@ -0,0 +1,24 @@
+import pytest
+
+from numpy import array_api as xp
+
+
+@pytest.mark.parametrize(
+    "x, indices, axis, expected",
+    [
+        ([2, 3], [1, 1, 0], 0,  [3, 3, 2]),
+        ([2, 3], [1, 1, 0], -1, [3, 3, 2]),
+        ([[2, 3]], [1], -1, [[3]]),
+        ([[2, 3]], [0, 0], 0, [[2, 3], [2, 3]]),
+    ],
+)
+def test_take_function(x, indices, axis, expected):
+    """
+    Indices respect relative order of a descending stable-sort
+
+    See https://github.com/numpy/numpy/issues/20778
+    """
+    x = xp.asarray(x)
+    indices = xp.asarray(indices)
+    out = xp.take(x, indices, axis=axis)
+    assert xp.all(out == xp.asarray(expected))
diff --git a/contrib/python/numpy/py3/numpy/array_api/tests/test_manipulation_functions.py b/contrib/python/numpy/py3/numpy/array_api/tests/test_manipulation_functions.py
new file mode 100644
index 00000000000..46f760c5847
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/array_api/tests/test_manipulation_functions.py
@@ -0,0 +1,37 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from numpy.array_api import all
+from numpy.array_api._creation_functions import asarray
+from numpy.array_api._dtypes import float64, int8
+from numpy.array_api._manipulation_functions import (
+        concat,
+        reshape,
+        stack
+)
+
+
+def test_concat_errors():
+    assert_raises(TypeError, lambda: concat((1, 1), axis=None))
+    assert_raises(TypeError, lambda: concat([asarray([1], dtype=int8),
+                                             asarray([1], dtype=float64)]))
+
+
+def test_stack_errors():
+    assert_raises(TypeError, lambda: stack([asarray([1, 1], dtype=int8),
+                                            asarray([2, 2], dtype=float64)]))
+
+
+def test_reshape_copy():
+    a = asarray(np.ones((2, 3)))
+    b = reshape(a, (3, 2), copy=True)
+    assert not np.shares_memory(a._array, b._array)
+    
+    a = asarray(np.ones((2, 3)))
+    b = reshape(a, (3, 2), copy=False)
+    assert np.shares_memory(a._array, b._array)
+
+    a = asarray(np.ones((2, 3)).T)
+    b = reshape(a, (3, 2), copy=True)
+    assert_raises(AttributeError, lambda: reshape(a, (2, 3), copy=False))
+
diff --git a/contrib/python/numpy/py3/numpy/compat/__init__.py b/contrib/python/numpy/py3/numpy/compat/__init__.py
index afee621b872..504f8b0030b 100644
--- a/contrib/python/numpy/py3/numpy/compat/__init__.py
+++ b/contrib/python/numpy/py3/numpy/compat/__init__.py
@@ -8,9 +8,10 @@ extensions, which may be included for the following reasons:
   * we may only need a small subset of the copied library/module
 
 """
-from . import _inspect
+
+from .._utils import _inspect
+from .._utils._inspect import getargspec, formatargspec
 from . import py3k
-from ._inspect import getargspec, formatargspec
 from .py3k import *
 
 __all__ = []
diff --git a/contrib/python/numpy/py3/numpy/compat/py3k.py b/contrib/python/numpy/py3/numpy/compat/py3k.py
index 6392421c846..d02c9f8fe34 100644
--- a/contrib/python/numpy/py3/numpy/compat/py3k.py
+++ b/contrib/python/numpy/py3/numpy/compat/py3k.py
@@ -47,9 +47,6 @@ def asstr(s):
     return str(s)
 
 def isfileobj(f):
-    return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter))
-
-def _isfileobj(f):
     if not isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter)):
         return False
     try:
diff --git a/contrib/python/numpy/py3/numpy/compat/tests/test_compat.py b/contrib/python/numpy/py3/numpy/compat/tests/test_compat.py
index 64430032926..d4391565ee0 100644
--- a/contrib/python/numpy/py3/numpy/compat/tests/test_compat.py
+++ b/contrib/python/numpy/py3/numpy/compat/tests/test_compat.py
@@ -1,7 +1,7 @@
 from os.path import join
 from io import BufferedReader, BytesIO
 
-from numpy.compat.py3k import isfileobj, _isfileobj
+from numpy.compat import isfileobj
 from numpy.testing import assert_
 from numpy.testing import tempdir
 
@@ -19,17 +19,4 @@ def test_isfileobj():
         with open(filename, 'rb') as f:
             assert_(isfileobj(f))
 
-def test__isfileobj():
-    with tempdir(prefix="numpy_test_compat_") as folder:
-        filename = join(folder, 'a.bin')
-
-        with open(filename, 'wb') as f:
-            assert_(_isfileobj(f))
-
-        with open(filename, 'ab') as f:
-            assert_(_isfileobj(f))
-
-        with open(filename, 'rb') as f:
-            assert_(_isfileobj(f))
-        
-        assert_(_isfileobj(BufferedReader(BytesIO())) is False)
+        assert_(isfileobj(BufferedReader(BytesIO())) is False)
diff --git a/contrib/python/numpy/py3/numpy/conftest.py b/contrib/python/numpy/py3/numpy/conftest.py
index 3d110c87488..f1a3eda9890 100644
--- a/contrib/python/numpy/py3/numpy/conftest.py
+++ b/contrib/python/numpy/py3/numpy/conftest.py
@@ -40,6 +40,8 @@ hypothesis.settings.load_profile(
     "numpy-profile" if os.path.isfile(_pytest_ini) else "np.test() profile"
 )
 
+# The experimentalAPI is used in _umath_tests
+os.environ["NUMPY_EXPERIMENTAL_DTYPE_API"] = "1"
 
 def pytest_configure(config):
     config.addinivalue_line("markers",
diff --git a/contrib/python/numpy/py3/numpy/core/__init__.py b/contrib/python/numpy/py3/numpy/core/__init__.py
index 748705e3321..08e71736363 100644
--- a/contrib/python/numpy/py3/numpy/core/__init__.py
+++ b/contrib/python/numpy/py3/numpy/core/__init__.py
@@ -84,7 +84,6 @@ from .defchararray import chararray
 from . import function_base
 from .function_base import *
 from . import _machar
-from ._machar import *
 from . import getlimits
 from .getlimits import *
 from . import shape_base
@@ -93,7 +92,6 @@ from . import einsumfunc
 from .einsumfunc import *
 del nt
 
-from .fromnumeric import amax as max, amin as min, round_ as round
 from .numeric import absolute as abs
 
 # do this after everything else, to minimize the chance of this misleadingly
@@ -144,22 +142,25 @@ def _DType_reconstruct(scalar_type):
 
 
 def _DType_reduce(DType):
-    # To pickle a DType without having to add top-level names, pickle the
-    # scalar type for now (and assume that reconstruction will be possible).
-    if DType is dtype:
-        return "dtype"  # must pickle `np.dtype` as a singleton.
-    scalar_type = DType.type  # pickle the scalar type for reconstruction
+    # As types/classes, most DTypes can simply be pickled by their name:
+    if not DType._legacy or DType.__module__ == "numpy.dtypes":
+        return DType.__name__
+
+    # However, user defined legacy dtypes (like rational) do not end up in
+    # `numpy.dtypes` as module and do not have a public class at all.
+    # For these, we pickle them by reconstructing them from the scalar type:
+    scalar_type = DType.type
     return _DType_reconstruct, (scalar_type,)
 
 
 def __getattr__(name):
-    # Deprecated 2021-10-20, NumPy 1.22
-    if name == "machar":
+    # Deprecated 2022-11-22, NumPy 1.25.
+    if name == "MachAr":
         warnings.warn(
-            "The `np.core.machar` module is deprecated (NumPy 1.22)",
+            "The `np.core.MachAr` is considered private API (NumPy 1.24)",
             DeprecationWarning, stacklevel=2,
         )
-        return _machar
+        return _machar.MachAr
     raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
 
 
diff --git a/contrib/python/numpy/py3/numpy/core/_add_newdocs.py b/contrib/python/numpy/py3/numpy/core/_add_newdocs.py
index 09f47380f77..6e29fcf59f2 100644
--- a/contrib/python/numpy/py3/numpy/core/_add_newdocs.py
+++ b/contrib/python/numpy/py3/numpy/core/_add_newdocs.py
@@ -12,6 +12,7 @@ NOTE: Many of the methods of ndarray have corresponding functions.
 from numpy.core.function_base import add_newdoc
 from numpy.core.overrides import array_function_like_doc
 
+
 ###############################################################################
 #
 # flatiter
@@ -798,18 +799,18 @@ add_newdoc('numpy.core.multiarray', 'array',
     ----------
     object : array_like
         An array, any object exposing the array interface, an object whose
-        __array__ method returns an array, or any (nested) sequence.
+        ``__array__`` method returns an array, or any (nested) sequence.
         If object is a scalar, a 0-dimensional array containing object is
         returned.
     dtype : data-type, optional
-        The desired data-type for the array.  If not given, then the type will
-        be determined as the minimum type required to hold the objects in the
-        sequence.
+        The desired data-type for the array. If not given, NumPy will try to use
+        a default ``dtype`` that can represent the values (by applying promotion
+        rules when necessary.)
     copy : bool, optional
         If true (default), then the object is copied.  Otherwise, a copy will
-        only be made if __array__ returns a copy, if obj is a nested sequence,
-        or if a copy is needed to satisfy any of the other requirements
-        (`dtype`, `order`, etc.).
+        only be made if ``__array__`` returns a copy, if obj is a nested
+        sequence, or if a copy is needed to satisfy any of the other
+        requirements (``dtype``, ``order``, etc.).
     order : {'K', 'A', 'C', 'F'}, optional
         Specify the memory layout of the array. If object is not an array, the
         newly created array will be in C order (row major) unless 'F' is
@@ -858,7 +859,7 @@ add_newdoc('numpy.core.multiarray', 'array',
 
     Notes
     -----
-    When order is 'A' and `object` is an array in neither 'C' nor 'F' order,
+    When order is 'A' and ``object`` is an array in neither 'C' nor 'F' order,
     and a copy is forced by a change in dtype, then the order of the result is
     not necessarily 'C' as expected. This is likely a bug.
 
@@ -2703,6 +2704,12 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('nbytes',
     Does not include memory consumed by non-element attributes of the
     array object.
 
+    See Also
+    --------
+    sys.getsizeof
+        Memory consumed by the object itself without parents in case view.
+        This does include memory consumed by non-element attributes.
+
     Examples
     --------
     >>> x = np.zeros((3,5,2), dtype=np.complex128)
@@ -2928,7 +2935,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('T',
 
 
 add_newdoc('numpy.core.multiarray', 'ndarray', ('__array__',
-    """ a.__array__([dtype], /) -> reference if type unchanged, copy otherwise.
+    """ a.__array__([dtype], /)
 
     Returns either a new reference to self if dtype is not given or a new array
     of provided data type if dtype is different from the current dtype of the
@@ -2991,10 +2998,6 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('__class_getitem__',
     >>> np.ndarray[Any, np.dtype[Any]]
     numpy.ndarray[typing.Any, numpy.dtype[typing.Any]]
 
-    Notes
-    -----
-    This method is only available for python 3.9 and later.
-
     See Also
     --------
     :pep:`585` : Type hinting generics in standard collections.
@@ -3005,7 +3008,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('__class_getitem__',
 
 
 add_newdoc('numpy.core.multiarray', 'ndarray', ('__deepcopy__',
-    """a.__deepcopy__(memo, /) -> Deep copy of array.
+    """a.__deepcopy__(memo, /)
 
     Used if :func:`copy.deepcopy` is called on an array.
 
@@ -6008,8 +6011,8 @@ add_newdoc('numpy.core.multiarray', 'dtype', ('byteorder',
     >>> # '=' is the byteorder
     >>> import sys
     >>> sys_is_le = sys.byteorder == 'little'
-    >>> native_code = sys_is_le and '<' or '>'
-    >>> swapped_code = sys_is_le and '>' or '<'
+    >>> native_code = '<' if sys_is_le else '>'
+    >>> swapped_code = '>' if sys_is_le else '<'
     >>> dt = np.dtype(native_code + 'i2')
     >>> dt.byteorder
     '='
@@ -6448,8 +6451,8 @@ add_newdoc('numpy.core.multiarray', 'dtype', ('newbyteorder',
     --------
     >>> import sys
     >>> sys_is_le = sys.byteorder == 'little'
-    >>> native_code = sys_is_le and '<' or '>'
-    >>> swapped_code = sys_is_le and '>' or '<'
+    >>> native_code = '<' if sys_is_le else '>'
+    >>> swapped_code = '>' if sys_is_le else '<'
     >>> native_dt = np.dtype(native_code+'i2')
     >>> swapped_dt = np.dtype(swapped_code+'i2')
     >>> native_dt.newbyteorder('S') == swapped_dt
@@ -6495,10 +6498,6 @@ add_newdoc('numpy.core.multiarray', 'dtype', ('__class_getitem__',
     >>> np.dtype[np.int64]
     numpy.dtype[numpy.int64]
 
-    Notes
-    -----
-    This method is only available for python 3.9 and later.
-
     See Also
     --------
     :pep:`585` : Type hinting generics in standard collections.
@@ -7008,10 +7007,6 @@ add_newdoc('numpy.core.numerictypes', 'number', ('__class_getitem__',
     >>> np.signedinteger[Any]
     numpy.signedinteger[typing.Any]
 
-    Notes
-    -----
-    This method is only available for python 3.9 and later.
-
     See Also
     --------
     :pep:`585` : Type hinting generics in standard collections.
diff --git a/contrib/python/numpy/py3/numpy/core/_add_newdocs_scalars.py b/contrib/python/numpy/py3/numpy/core/_add_newdocs_scalars.py
index 15d37522ad6..f9a6ad963ec 100644
--- a/contrib/python/numpy/py3/numpy/core/_add_newdocs_scalars.py
+++ b/contrib/python/numpy/py3/numpy/core/_add_newdocs_scalars.py
@@ -93,7 +93,7 @@ def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
     add_newdoc('numpy.core.numerictypes', obj, docstring)
 
 
-add_newdoc_for_scalar_type('bool_', ['bool8'],
+add_newdoc_for_scalar_type('bool_', [],
     """
     Boolean type (True or False), stored as a byte.
 
@@ -204,7 +204,11 @@ add_newdoc_for_scalar_type('str_', ['unicode_'],
     r"""
     A unicode string.
 
-    When used in arrays, this type strips trailing null codepoints.
+    This type strips trailing null codepoints.
+
+    >>> s = np.str_("abc\x00")
+    >>> s
+    'abc'
 
     Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
     contents as UCS4:
@@ -255,7 +259,7 @@ add_newdoc_for_scalar_type('void', [],
        ``\0`` bytes.  The 5 can be a Python or NumPy integer.
     2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
        The dtype itemsize will match the byte string length, here ``"V10"``.
-    3. When a ``dtype=`` is passed the call is rougly the same as an
+    3. When a ``dtype=`` is passed the call is roughly the same as an
        array creation.  However, a void scalar rather than array is returned.
 
     Please see the examples which show all three different conventions.
diff --git a/contrib/python/numpy/py3/numpy/core/_asarray.py b/contrib/python/numpy/py3/numpy/core/_asarray.py
index 28f1fe6fa72..a9abc5a88ca 100644
--- a/contrib/python/numpy/py3/numpy/core/_asarray.py
+++ b/contrib/python/numpy/py3/numpy/core/_asarray.py
@@ -24,10 +24,6 @@ POSSIBLE_FLAGS = {
 }
 
 
-def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
-    return (like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def require(a, dtype=None, requirements=None, *, like=None):
@@ -100,10 +96,10 @@ def require(a, dtype=None, requirements=None, *, like=None):
     """
     if like is not None:
         return _require_with_like(
+            like,
             a,
             dtype=dtype,
             requirements=requirements,
-            like=like,
         )
 
     if not requirements:
@@ -135,6 +131,4 @@ def require(a, dtype=None, requirements=None, *, like=None):
     return arr
 
 
-_require_with_like = array_function_dispatch(
-    _require_dispatcher, use_like=True
-)(require)
+_require_with_like = array_function_dispatch()(require)
diff --git a/contrib/python/numpy/py3/numpy/core/_asarray.pyi b/contrib/python/numpy/py3/numpy/core/_asarray.pyi
index 473bc037cef..69d1528d43e 100644
--- a/contrib/python/numpy/py3/numpy/core/_asarray.pyi
+++ b/contrib/python/numpy/py3/numpy/core/_asarray.pyi
@@ -1,10 +1,10 @@
 from collections.abc import Iterable
-from typing import TypeVar, Union, overload, Literal
+from typing import Any, TypeVar, Union, overload, Literal
 
 from numpy import ndarray
 from numpy._typing import DTypeLike, _SupportsArrayFunc
 
-_ArrayType = TypeVar("_ArrayType", bound=ndarray)
+_ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any])
 
 _Requirements = Literal[
     "C", "C_CONTIGUOUS", "CONTIGUOUS",
@@ -31,7 +31,7 @@ def require(
     requirements: _E | Iterable[_RequirementsWithE] = ...,
     *,
     like: _SupportsArrayFunc = ...
-) -> ndarray: ...
+) -> ndarray[Any, Any]: ...
 @overload
 def require(
     a: object,
@@ -39,4 +39,4 @@ def require(
     requirements: None | _Requirements | Iterable[_Requirements] = ...,
     *,
     like: _SupportsArrayFunc = ...
-) -> ndarray: ...
+) -> ndarray[Any, Any]: ...
diff --git a/contrib/python/numpy/py3/numpy/core/_dtype.py b/contrib/python/numpy/py3/numpy/core/_dtype.py
index 3db80c17eeb..ff50f5199a5 100644
--- a/contrib/python/numpy/py3/numpy/core/_dtype.py
+++ b/contrib/python/numpy/py3/numpy/core/_dtype.py
@@ -114,13 +114,13 @@ def _scalar_str(dtype, short):
         # platforms, so it should never include the itemsize here.
         return "'O'"
 
-    elif dtype.type == np.string_:
+    elif dtype.type == np.bytes_:
         if _isunsized(dtype):
             return "'S'"
         else:
             return "'S%d'" % dtype.itemsize
 
-    elif dtype.type == np.unicode_:
+    elif dtype.type == np.str_:
         if _isunsized(dtype):
             return "'%sU'" % byteorder
         else:
@@ -334,6 +334,8 @@ def _name_includes_bit_suffix(dtype):
     elif dtype.type == np.bool_:
         # implied
         return False
+    elif dtype.type is None:
+        return True
     elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
         # unspecified
         return False
@@ -348,7 +350,9 @@ def _name_get(dtype):
         # user dtypes don't promise to do anything special
         return dtype.type.__name__
 
-    if issubclass(dtype.type, np.void):
+    if dtype.kind == '\x00':
+        name = type(dtype).__name__
+    elif issubclass(dtype.type, np.void):
         # historically, void subclasses preserve their name, eg `record64`
         name = dtype.type.__name__
     else:
diff --git a/contrib/python/numpy/py3/numpy/core/_exceptions.py b/contrib/python/numpy/py3/numpy/core/_exceptions.py
index be3aa8beeae..87d4213a6d4 100644
--- a/contrib/python/numpy/py3/numpy/core/_exceptions.py
+++ b/contrib/python/numpy/py3/numpy/core/_exceptions.py
@@ -5,7 +5,7 @@ in python where it's easier.
 By putting the formatting in `__str__`, we also avoid paying the cost for
 users who silence the exceptions.
 """
-from numpy.core.overrides import set_module
+from .._utils import set_module
 
 def _unpack_tuple(tup):
     if len(tup) == 1:
@@ -36,36 +36,35 @@ class UFuncTypeError(TypeError):
 
 
 @_display_as_base
-class _UFuncBinaryResolutionError(UFuncTypeError):
-    """ Thrown when a binary resolution fails """
+class _UFuncNoLoopError(UFuncTypeError):
+    """ Thrown when a ufunc loop cannot be found """
     def __init__(self, ufunc, dtypes):
         super().__init__(ufunc)
         self.dtypes = tuple(dtypes)
-        assert len(self.dtypes) == 2
 
     def __str__(self):
         return (
-            "ufunc {!r} cannot use operands with types {!r} and {!r}"
+            "ufunc {!r} did not contain a loop with signature matching types "
+            "{!r} -> {!r}"
         ).format(
-            self.ufunc.__name__, *self.dtypes
+            self.ufunc.__name__,
+            _unpack_tuple(self.dtypes[:self.ufunc.nin]),
+            _unpack_tuple(self.dtypes[self.ufunc.nin:])
         )
 
 
 @_display_as_base
-class _UFuncNoLoopError(UFuncTypeError):
-    """ Thrown when a ufunc loop cannot be found """
+class _UFuncBinaryResolutionError(_UFuncNoLoopError):
+    """ Thrown when a binary resolution fails """
     def __init__(self, ufunc, dtypes):
-        super().__init__(ufunc)
-        self.dtypes = tuple(dtypes)
+        super().__init__(ufunc, dtypes)
+        assert len(self.dtypes) == 2
 
     def __str__(self):
         return (
-            "ufunc {!r} did not contain a loop with signature matching types "
-            "{!r} -> {!r}"
+            "ufunc {!r} cannot use operands with types {!r} and {!r}"
         ).format(
-            self.ufunc.__name__,
-            _unpack_tuple(self.dtypes[:self.ufunc.nin]),
-            _unpack_tuple(self.dtypes[self.ufunc.nin:])
+            self.ufunc.__name__, *self.dtypes
         )
 
 
@@ -114,113 +113,6 @@ class _UFuncOutputCastingError(_UFuncCastingError):
         )
 
 
-# Exception used in shares_memory()
-@set_module('numpy')
-class TooHardError(RuntimeError):
-    """max_work was exceeded.
-
-    This is raised whenever the maximum number of candidate solutions 
-    to consider specified by the ``max_work`` parameter is exceeded.
-    Assigning a finite number to max_work may have caused the operation 
-    to fail.
-
-    """
-    
-    pass
-
-
-@set_module('numpy')
-class AxisError(ValueError, IndexError):
-    """Axis supplied was invalid.
-
-    This is raised whenever an ``axis`` parameter is specified that is larger
-    than the number of array dimensions.
-    For compatibility with code written against older numpy versions, which
-    raised a mixture of `ValueError` and `IndexError` for this situation, this
-    exception subclasses both to ensure that ``except ValueError`` and
-    ``except IndexError`` statements continue to catch `AxisError`.
-
-    .. versionadded:: 1.13
-
-    Parameters
-    ----------
-    axis : int or str
-        The out of bounds axis or a custom exception message.
-        If an axis is provided, then `ndim` should be specified as well.
-    ndim : int, optional
-        The number of array dimensions.
-    msg_prefix : str, optional
-        A prefix for the exception message.
-
-    Attributes
-    ----------
-    axis : int, optional
-        The out of bounds axis or ``None`` if a custom exception
-        message was provided. This should be the axis as passed by
-        the user, before any normalization to resolve negative indices.
-
-        .. versionadded:: 1.22
-    ndim : int, optional
-        The number of array dimensions or ``None`` if a custom exception
-        message was provided.
-
-        .. versionadded:: 1.22
-
-
-    Examples
-    --------
-    >>> array_1d = np.arange(10)
-    >>> np.cumsum(array_1d, axis=1)
-    Traceback (most recent call last):
-      ...
-    numpy.AxisError: axis 1 is out of bounds for array of dimension 1
-
-    Negative axes are preserved:
-
-    >>> np.cumsum(array_1d, axis=-2)
-    Traceback (most recent call last):
-      ...
-    numpy.AxisError: axis -2 is out of bounds for array of dimension 1
-
-    The class constructor generally takes the axis and arrays'
-    dimensionality as arguments:
-
-    >>> print(np.AxisError(2, 1, msg_prefix='error'))
-    error: axis 2 is out of bounds for array of dimension 1
-
-    Alternatively, a custom exception message can be passed:
-
-    >>> print(np.AxisError('Custom error message'))
-    Custom error message
-
-    """
-
-    __slots__ = ("axis", "ndim", "_msg")
-
-    def __init__(self, axis, ndim=None, msg_prefix=None):
-        if ndim is msg_prefix is None:
-            # single-argument form: directly set the error message
-            self._msg = axis
-            self.axis = None
-            self.ndim = None
-        else:
-            self._msg = msg_prefix
-            self.axis = axis
-            self.ndim = ndim
-
-    def __str__(self):
-        axis = self.axis
-        ndim = self.ndim
-
-        if axis is ndim is None:
-            return self._msg
-        else:
-            msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
-            if self._msg is not None:
-                msg = f"{self._msg}: {msg}"
-            return msg
-
-
 @_display_as_base
 class _ArrayMemoryError(MemoryError):
     """ Thrown when an array cannot be allocated"""
diff --git a/contrib/python/numpy/py3/numpy/core/_internal.py b/contrib/python/numpy/py3/numpy/core/_internal.py
index 85076f3e1e7..c7838588040 100644
--- a/contrib/python/numpy/py3/numpy/core/_internal.py
+++ b/contrib/python/numpy/py3/numpy/core/_internal.py
@@ -9,6 +9,7 @@ import re
 import sys
 import warnings
 
+from ..exceptions import DTypePromotionError
 from .multiarray import dtype, array, ndarray, promote_types
 try:
     import ctypes
@@ -454,7 +455,8 @@ def _promote_fields(dt1, dt2):
     """
     # Both must be structured and have the same names in the same order
     if (dt1.names is None or dt2.names is None) or dt1.names != dt2.names:
-        raise TypeError("invalid type promotion")
+        raise DTypePromotionError(
+                f"field names `{dt1.names}` and `{dt2.names}` mismatch.")
 
     # if both are identical, we can (maybe!) just return the same dtype.
     identical = dt1 is dt2
@@ -467,7 +469,8 @@ def _promote_fields(dt1, dt2):
 
         # Check that the titles match (if given):
         if field1[2:] != field2[2:]:
-            raise TypeError("invalid type promotion")
+            raise DTypePromotionError(
+                    f"field titles of field '{name}' mismatch")
         if len(field1) == 2:
             new_fields.append((name, new_descr))
         else:
diff --git a/contrib/python/numpy/py3/numpy/core/_machar.py b/contrib/python/numpy/py3/numpy/core/_machar.py
index 3cc7db2784d..59d71014ff2 100644
--- a/contrib/python/numpy/py3/numpy/core/_machar.py
+++ b/contrib/python/numpy/py3/numpy/core/_machar.py
@@ -7,14 +7,13 @@ Author: Pearu Peterson, September 2003
 """
 __all__ = ['MachAr']
 
-from numpy.core.fromnumeric import any
-from numpy.core._ufunc_config import errstate
-from numpy.core.overrides import set_module
+from .fromnumeric import any
+from ._ufunc_config import errstate
+from .._utils import set_module
 
 # Need to speed this up...especially for longfloat
 
 # Deprecated 2021-10-20, NumPy 1.22
-@set_module('numpy')
 class MachAr:
     """
     Diagnosing machine parameters.
diff --git a/contrib/python/numpy/py3/numpy/core/_methods.py b/contrib/python/numpy/py3/numpy/core/_methods.py
index 040f02a9d84..0fc070b34c3 100644
--- a/contrib/python/numpy/py3/numpy/core/_methods.py
+++ b/contrib/python/numpy/py3/numpy/core/_methods.py
@@ -87,79 +87,16 @@ def _count_reduce_items(arr, axis, keepdims=False, where=True):
                         keepdims)
     return items
 
-# Numpy 1.17.0, 2019-02-24
-# Various clip behavior deprecations, marked with _clip_dep as a prefix.
-
-def _clip_dep_is_scalar_nan(a):
-    # guarded to protect circular imports
-    from numpy.core.fromnumeric import ndim
-    if ndim(a) != 0:
-        return False
-    try:
-        return um.isnan(a)
-    except TypeError:
-        return False
-
-def _clip_dep_is_byte_swapped(a):
-    if isinstance(a, mu.ndarray):
-        return not a.dtype.isnative
-    return False
-
-def _clip_dep_invoke_with_casting(ufunc, *args, out=None, casting=None, **kwargs):
-    # normal path
-    if casting is not None:
-        return ufunc(*args, out=out, casting=casting, **kwargs)
-
-    # try to deal with broken casting rules
-    try:
-        return ufunc(*args, out=out, **kwargs)
-    except _exceptions._UFuncOutputCastingError as e:
-        # Numpy 1.17.0, 2019-02-24
-        warnings.warn(
-            "Converting the output of clip from {!r} to {!r} is deprecated. "
-            "Pass `casting=\"unsafe\"` explicitly to silence this warning, or "
-            "correct the type of the variables.".format(e.from_, e.to),
-            DeprecationWarning,
-            stacklevel=2
-        )
-        return ufunc(*args, out=out, casting="unsafe", **kwargs)
-
-def _clip(a, min=None, max=None, out=None, *, casting=None, **kwargs):
+def _clip(a, min=None, max=None, out=None, **kwargs):
     if min is None and max is None:
         raise ValueError("One of max or min must be given")
 
-    # Numpy 1.17.0, 2019-02-24
-    # This deprecation probably incurs a substantial slowdown for small arrays,
-    # it will be good to get rid of it.
-    if not _clip_dep_is_byte_swapped(a) and not _clip_dep_is_byte_swapped(out):
-        using_deprecated_nan = False
-        if _clip_dep_is_scalar_nan(min):
-            min = -float('inf')
-            using_deprecated_nan = True
-        if _clip_dep_is_scalar_nan(max):
-            max = float('inf')
-            using_deprecated_nan = True
-        if using_deprecated_nan:
-            warnings.warn(
-                "Passing `np.nan` to mean no clipping in np.clip has always "
-                "been unreliable, and is now deprecated. "
-                "In future, this will always return nan, like it already does "
-                "when min or max are arrays that contain nan. "
-                "To skip a bound, pass either None or an np.inf of an "
-                "appropriate sign.",
-                DeprecationWarning,
-                stacklevel=2
-            )
-
     if min is None:
-        return _clip_dep_invoke_with_casting(
-            um.minimum, a, max, out=out, casting=casting, **kwargs)
+        return um.minimum(a, max, out=out, **kwargs)
     elif max is None:
-        return _clip_dep_invoke_with_casting(
-            um.maximum, a, min, out=out, casting=casting, **kwargs)
+        return um.maximum(a, min, out=out, **kwargs)
     else:
-        return _clip_dep_invoke_with_casting(
-            um.clip, a, min, max, out=out, casting=casting, **kwargs)
+        return um.clip(a, min, max, out=out, **kwargs)
 
 def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
     arr = asanyarray(a)
diff --git a/contrib/python/numpy/py3/numpy/core/_string_helpers.py b/contrib/python/numpy/py3/numpy/core/_string_helpers.py
index 45e6a739ee5..1f757cc0734 100644
--- a/contrib/python/numpy/py3/numpy/core/_string_helpers.py
+++ b/contrib/python/numpy/py3/numpy/core/_string_helpers.py
@@ -6,7 +6,7 @@ Used primarily to generate type name aliases.
 # "import string" is costly to import!
 # Construct the translation tables directly
 #   "A" = chr(65), "a" = chr(97)
-_all_chars = [chr(_m) for _m in range(256)]
+_all_chars = tuple(map(chr, range(256)))
 _ascii_upper = _all_chars[65:65+26]
 _ascii_lower = _all_chars[97:97+26]
 LOWER_TABLE = "".join(_all_chars[:65] + _ascii_lower + _all_chars[65+26:])
diff --git a/contrib/python/numpy/py3/numpy/core/_type_aliases.pyi b/contrib/python/numpy/py3/numpy/core/_type_aliases.pyi
index bbead0cb5b9..c0b6f1a80c5 100644
--- a/contrib/python/numpy/py3/numpy/core/_type_aliases.pyi
+++ b/contrib/python/numpy/py3/numpy/core/_type_aliases.pyi
@@ -1,12 +1,12 @@
-from typing import TypedDict
+from typing import Any, TypedDict
 
 from numpy import generic, signedinteger, unsignedinteger, floating, complexfloating
 
 class _SCTypes(TypedDict):
-    int: list[type[signedinteger]]
-    uint: list[type[unsignedinteger]]
-    float: list[type[floating]]
-    complex: list[type[complexfloating]]
+    int: list[type[signedinteger[Any]]]
+    uint: list[type[unsignedinteger[Any]]]
+    float: list[type[floating[Any]]]
+    complex: list[type[complexfloating[Any, Any]]]
     others: list[type]
 
 sctypeDict: dict[int | str, type[generic]]
diff --git a/contrib/python/numpy/py3/numpy/core/_ufunc_config.py b/contrib/python/numpy/py3/numpy/core/_ufunc_config.py
index 5aac7ab091e..df821309581 100644
--- a/contrib/python/numpy/py3/numpy/core/_ufunc_config.py
+++ b/contrib/python/numpy/py3/numpy/core/_ufunc_config.py
@@ -7,7 +7,7 @@ import collections.abc
 import contextlib
 import contextvars
 
-from .overrides import set_module
+from .._utils import set_module
 from .umath import (
     UFUNC_BUFSIZE_DEFAULT,
     ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,
diff --git a/contrib/python/numpy/py3/numpy/core/arrayprint.py b/contrib/python/numpy/py3/numpy/core/arrayprint.py
index 957cecf1cae..62cd527073a 100644
--- a/contrib/python/numpy/py3/numpy/core/arrayprint.py
+++ b/contrib/python/numpy/py3/numpy/core/arrayprint.py
@@ -169,7 +169,7 @@ def set_printoptions(precision=None, threshold=None, edgeitems=None,
         - 'longfloat' : 128-bit floats
         - 'complexfloat'
         - 'longcomplexfloat' : composed of two 128-bit floats
-        - 'numpystr' : types `numpy.string_` and `numpy.unicode_`
+        - 'numpystr' : types `numpy.bytes_` and `numpy.str_`
         - 'object' : `np.object_` arrays
 
         Other keys that can be used to set a group of types at once are:
@@ -475,7 +475,7 @@ def _get_format_function(data, **options):
             return formatdict['longcomplexfloat']()
         else:
             return formatdict['complexfloat']()
-    elif issubclass(dtypeobj, (_nt.unicode_, _nt.string_)):
+    elif issubclass(dtypeobj, (_nt.str_, _nt.bytes_)):
         return formatdict['numpystr']()
     elif issubclass(dtypeobj, _nt.datetime64):
         return formatdict['datetime']()
@@ -616,7 +616,7 @@ def array2string(a, max_line_width=None, precision=None,
         - 'complexfloat'
         - 'longcomplexfloat' : composed of two 128-bit floats
         - 'void' : type `numpy.void`
-        - 'numpystr' : types `numpy.string_` and `numpy.unicode_`
+        - 'numpystr' : types `numpy.bytes_` and `numpy.str_`
 
         Other keys that can be used to set a group of types at once are:
 
@@ -724,7 +724,7 @@ def array2string(a, max_line_width=None, precision=None,
         # Deprecation 11-9-2017  v1.14
         warnings.warn("'style' argument is deprecated and no longer functional"
                       " except in 1.13 'legacy' mode",
-                      DeprecationWarning, stacklevel=3)
+                      DeprecationWarning, stacklevel=2)
 
     if options['legacy'] > 113:
         options['linewidth'] -= len(suffix)
@@ -1096,7 +1096,7 @@ def format_float_scientific(x, precision=None, unique=True, trim='k',
         identify the value may be printed and rounded unbiased.
 
         -- versionadded:: 1.21.0
-        
+
     Returns
     -------
     rep : string
@@ -1181,7 +1181,7 @@ def format_float_positional(x, precision=None, unique=True,
         Minimum number of digits to print. Only has an effect if `unique=True`
         in which case additional digits past those necessary to uniquely
         identify the value may be printed, rounding the last additional digit.
-        
+
         -- versionadded:: 1.21.0
 
     Returns
@@ -1339,13 +1339,29 @@ class TimedeltaFormat(_TimelikeFormat):
 
 
 class SubArrayFormat:
-    def __init__(self, format_function):
+    def __init__(self, format_function, **options):
         self.format_function = format_function
+        self.threshold = options['threshold']
+        self.edge_items = options['edgeitems']
+
+    def __call__(self, a):
+        self.summary_insert = "..." if a.size > self.threshold else ""
+        return self.format_array(a)
+
+    def format_array(self, a):
+        if np.ndim(a) == 0:
+            return self.format_function(a)
+
+        if self.summary_insert and a.shape[0] > 2*self.edge_items:
+            formatted = (
+                [self.format_array(a_) for a_ in a[:self.edge_items]]
+                + [self.summary_insert]
+                + [self.format_array(a_) for a_ in a[-self.edge_items:]]
+            )
+        else:
+            formatted = [self.format_array(a_) for a_ in a]
 
-    def __call__(self, arr):
-        if arr.ndim <= 1:
-            return "[" + ", ".join(self.format_function(a) for a in arr) + "]"
-        return "[" + ", ".join(self.__call__(a) for a in arr) + "]"
+        return "[" + ", ".join(formatted) + "]"
 
 
 class StructuredVoidFormat:
@@ -1369,7 +1385,7 @@ class StructuredVoidFormat:
         for field_name in data.dtype.names:
             format_function = _get_format_function(data[field_name], **options)
             if data.dtype[field_name].shape != ():
-                format_function = SubArrayFormat(format_function)
+                format_function = SubArrayFormat(format_function, **options)
             format_functions.append(format_function)
         return cls(format_functions)
 
@@ -1429,6 +1445,10 @@ def dtype_is_implied(dtype):
     if dtype.names is not None:
         return False
 
+    # should care about endianness *unless size is 1* (e.g., int8, bool)
+    if not dtype.isnative:
+        return False
+
     return dtype.type in _typelessdata
 
 
@@ -1453,10 +1473,14 @@ def dtype_short_repr(dtype):
         return "'%s'" % str(dtype)
 
     typename = dtype.name
+    if not dtype.isnative:
+        # deal with cases like dtype('<u2') that are identical to an
+        # established dtype (in this case uint16)
+        # except that they have a different endianness.
+        return "'%s'" % str(dtype)
     # quote typenames which can't be represented as python variable names
     if typename and not (typename[0].isalpha() and typename.isalnum()):
         typename = repr(typename)
-
     return typename
 
 
diff --git a/contrib/python/numpy/py3/numpy/core/check_longdouble.c b/contrib/python/numpy/py3/numpy/core/check_longdouble.c
new file mode 100644
index 00000000000..756ef3fc6b5
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/check_longdouble.c
@@ -0,0 +1,15 @@
+/* "before" is 16 bytes to ensure there's no padding between it and "x".
+ *    We're not expecting any "long double" bigger than 16 bytes or with
+ *       alignment requirements stricter than 16 bytes.  */
+typedef long double test_type;
+
+struct {
+        char         before[16];
+        test_type    x;
+        char         after[8];
+} foo = {
+        { '\0', '\0', '\0', '\0', '\0', '\0', '\0', '\0',
+          '\001', '\043', '\105', '\147', '\211', '\253', '\315', '\357' },
+        -123456789.0,
+        { '\376', '\334', '\272', '\230', '\166', '\124', '\062', '\020' }
+};
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/cversions.txt b/contrib/python/numpy/py3/numpy/core/code_generators/cversions.txt
index dddcc148c00..e52193d7a46 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/cversions.txt
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/cversions.txt
@@ -68,3 +68,6 @@
 # NonNull attributes removed from numpy_api.py
 # Version 16 (NumPy 1.24) No change.
 0x00000010 = 04a7bf1e65350926a0e528798da263c0
+
+# Version 17 (NumPy 1.25) No actual change.
+0x00000011 = ca1aebdad799358149567d9d93cbca09
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/genapi.py b/contrib/python/numpy/py3/numpy/core/code_generators/genapi.py
index 68ae30d5bef..2cdaba52d9e 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/genapi.py
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/genapi.py
@@ -6,17 +6,35 @@ See ``find_function`` for how functions should be formatted, and
 specified.
 
 """
-from numpy.distutils.conv_template import process_file as process_c_file
-
 import hashlib
 import io
 import os
 import re
 import sys
+import importlib.util
 import textwrap
 
 from os.path import join
 
+
+def get_processor():
+    # Convoluted because we can't import from numpy.distutils
+    # (numpy is not yet built)
+    conv_template_path = os.path.join(
+        os.path.dirname(__file__),
+        '..', '..', 'distutils', 'conv_template.py'
+    )
+    spec = importlib.util.spec_from_file_location(
+        'conv_template', conv_template_path
+    )
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod.process_file
+
+
+process_c_file = get_processor()
+
+
 __docformat__ = 'restructuredtext'
 
 # The files under src/ that are scanned for API functions
@@ -81,6 +99,27 @@ def _repl(str):
     return str.replace('Bool', 'npy_bool')
 
 
+class MinVersion:
+    def __init__(self, version):
+        """ Version should be the normal NumPy version, e.g. "1.25" """
+        major, minor = version.split(".")
+        self.version = f"NPY_{major}_{minor}_API_VERSION"
+
+    def __str__(self):
+        # Used by version hashing:
+        return self.version
+
+    def add_guard(self, name, normal_define):
+        """Wrap a definition behind a version guard"""
+        wrap = textwrap.dedent(f"""
+            #if NPY_FEATURE_VERSION >= {self.version}
+            {{define}}
+            #endif""")
+
+        # we only insert `define` later to avoid confusing dedent:
+        return wrap.format(define=normal_define)
+
+
 class StealRef:
     def __init__(self, arg):
         self.arg = arg # counting from 1
@@ -113,21 +152,6 @@ class Function:
             doccomment = ''
         return '%s%s %s(%s)' % (doccomment, self.return_type, self.name, argstr)
 
-    def to_ReST(self):
-        lines = ['::', '', '  ' + self.return_type]
-        argstr = ',\000'.join([self._format_arg(*a) for a in self.args])
-        name = '  %s' % (self.name,)
-        s = textwrap.wrap('(%s)' % (argstr,), width=72,
-                          initial_indent=name,
-                          subsequent_indent=' ' * (len(name)+1),
-                          break_long_words=False)
-        for l in s:
-            lines.append(l.replace('\000', ' ').rstrip())
-        lines.append('')
-        if self.doc:
-            lines.append(textwrap.dedent(self.doc))
-        return '\n'.join(lines)
-
     def api_hash(self):
         m = hashlib.md5()
         m.update(remove_whitespace(self.return_type))
@@ -389,7 +413,20 @@ class FunctionApi:
     def __init__(self, name, index, annotations, return_type, args, api_name):
         self.name = name
         self.index = index
-        self.annotations = annotations
+
+        self.min_version = None
+        self.annotations = []
+        for annotation in annotations:
+            # String checks, because manual import breaks isinstance
+            if type(annotation).__name__ == "StealRef":
+                self.annotations.append(annotation)
+            elif type(annotation).__name__ == "MinVersion":
+                if self.min_version is not None:
+                    raise ValueError("Two minimum versions specified!")
+                self.min_version = annotation
+            else:
+                raise ValueError(f"unknown annotation {annotation}")
+
         self.return_type = return_type
         self.args = args
         self.api_name = api_name
@@ -401,13 +438,14 @@ class FunctionApi:
         return argstr
 
     def define_from_array_api_string(self):
-        define = """\
-#define %s \\\n        (*(%s (*)(%s)) \\
-         %s[%d])""" % (self.name,
-                                self.return_type,
-                                self._argtypes_string(),
-                                self.api_name,
-                                self.index)
+        arguments = self._argtypes_string()
+        define = textwrap.dedent(f"""\
+            #define {self.name} \\
+                    (*({self.return_type} (*)({arguments})) \\
+                {self.api_name}[{self.index}])""")
+
+        if self.min_version is not None:
+            define = self.min_version.add_guard(self.name, define)
         return define
 
     def array_api_define(self):
@@ -498,7 +536,7 @@ def get_versions_hash():
     d = []
 
     file = os.path.join(os.path.dirname(__file__), 'cversions.txt')
-    with open(file, 'r') as fid:
+    with open(file) as fid:
         for line in fid:
             m = VERRE.match(line)
             if m:
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/generate_numpy_api.py b/contrib/python/numpy/py3/numpy/core/code_generators/generate_numpy_api.py
index a57a36a7821..bfcb0d0e544 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/generate_numpy_api.py
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/generate_numpy_api.py
@@ -1,6 +1,8 @@
+#!/usr/bin/env python3
 import os
-import genapi
+import argparse
 
+import genapi
 from genapi import \
         TypeApi, GlobalVarApi, FunctionApi, BoolValuesApi
 
@@ -139,19 +141,12 @@ void *PyArray_API[] = {
 };
 """
 
-c_api_header = """
-===========
-NumPy C-API
-===========
-"""
-
 def generate_api(output_dir, force=False):
     basename = 'multiarray_api'
 
     h_file = os.path.join(output_dir, '__%s.h' % basename)
     c_file = os.path.join(output_dir, '__%s.c' % basename)
-    d_file = os.path.join(output_dir, '%s.txt' % basename)
-    targets = (h_file, c_file, d_file)
+    targets = (h_file, c_file)
 
     sources = numpy_api.multiarray_api
 
@@ -165,7 +160,6 @@ def generate_api(output_dir, force=False):
 def do_generate_api(targets, sources):
     header_file = targets[0]
     c_file = targets[1]
-    doc_file = targets[2]
 
     global_vars = sources[0]
     scalar_bool_values = sources[1]
@@ -234,11 +228,30 @@ def do_generate_api(targets, sources):
     s = c_template % ',\n'.join(init_list)
     genapi.write_file(c_file, s)
 
-    # write to documentation
-    s = c_api_header
-    for func in numpyapi_list:
-        s += func.to_ReST()
-        s += '\n\n'
-    genapi.write_file(doc_file, s)
-
     return targets
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-o",
+        "--outdir",
+        type=str,
+        help="Path to the output directory"
+    )
+    parser.add_argument(
+        "-i",
+        "--ignore",
+        type=str,
+        help="An ignored input - may be useful to add a "
+             "dependency between custom targets"
+    )
+    args = parser.parse_args()
+
+    outdir_abs = os.path.join(os.getcwd(), args.outdir)
+
+    generate_api(outdir_abs)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/generate_ufunc_api.py b/contrib/python/numpy/py3/numpy/core/code_generators/generate_ufunc_api.py
index 04c023675fa..e03299a52cf 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/generate_ufunc_api.py
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/generate_ufunc_api.py
@@ -1,9 +1,9 @@
 import os
-import genapi
-
-import numpy_api
+import argparse
 
+import genapi
 from genapi import TypeApi, FunctionApi
+import numpy_api
 
 h_template = r"""
 #ifdef _UMATHMODULE
@@ -30,7 +30,7 @@ static void **PyUFunc_API=NULL;
 
 %s
 
-static NPY_INLINE int
+static inline int
 _import_umath(void)
 {
   PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
@@ -122,8 +122,7 @@ def generate_api(output_dir, force=False):
 
     h_file = os.path.join(output_dir, '__%s.h' % basename)
     c_file = os.path.join(output_dir, '__%s.c' % basename)
-    d_file = os.path.join(output_dir, '%s.txt' % basename)
-    targets = (h_file, c_file, d_file)
+    targets = (h_file, c_file)
 
     sources = ['ufunc_api_order.txt']
 
@@ -137,7 +136,6 @@ def generate_api(output_dir, force=False):
 def do_generate_api(targets, sources):
     header_file = targets[0]
     c_file = targets[1]
-    doc_file = targets[2]
 
     ufunc_api_index = genapi.merge_api_dicts((
             numpy_api.ufunc_funcs_api,
@@ -179,15 +177,23 @@ def do_generate_api(targets, sources):
     s = c_template % ',\n'.join(init_list)
     genapi.write_file(c_file, s)
 
-    # Write to documentation
-    s = '''
-=================
-NumPy Ufunc C-API
-=================
-'''
-    for func in ufunc_api_list:
-        s += func.to_ReST()
-        s += '\n\n'
-    genapi.write_file(doc_file, s)
-
     return targets
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-o",
+        "--outdir",
+        type=str,
+        help="Path to the output directory"
+    )
+    args = parser.parse_args()
+
+    outdir_abs = os.path.join(os.getcwd(), args.outdir)
+
+    generate_api(outdir_abs)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath.py b/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath.py
index 378ab3b7b96..a170f83be62 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath.py
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath.py
@@ -1,9 +1,16 @@
+"""
+Generate the code to build all the internal ufuncs. At the base is the defdict:
+a dictionary ofUfunc classes. This is fed to make_code to generate
+__umath_generated.c
+"""
 import os
 import re
 import struct
 import sys
 import textwrap
+import argparse
 
+# identity objects
 Zero = "PyLong_FromLong(0)"
 One = "PyLong_FromLong(1)"
 True_ = "(Py_INCREF(Py_True), Py_True)"
@@ -54,9 +61,6 @@ class TypeDescription:
     cfunc_alias : str or none, optional
         Appended to inner loop C function name, e.g., FLOAT_{cfunc_alias}. See make_arrays.
         NOTE: it doesn't support 'astype'
-    simd : list
-        Available SIMD ufunc loops, dispatched at runtime in specified order
-        Currently only supported for simples types (see make_arrays)
     dispatch : str or None, optional
         Dispatch-able source name without its extension '.dispatch.c' that
         contains the definition of ufunc, dispatched at runtime depending on the
@@ -64,7 +68,7 @@ class TypeDescription:
         NOTE: it doesn't support 'astype'
     """
     def __init__(self, type, f=None, in_=None, out=None, astype=None, cfunc_alias=None,
-                 simd=None, dispatch=None):
+                 dispatch=None):
         self.type = type
         self.func_data = f
         if astype is None:
@@ -77,7 +81,6 @@ class TypeDescription:
             out = out.replace('P', type)
         self.out = out
         self.cfunc_alias = cfunc_alias
-        self.simd = simd
         self.dispatch = dispatch
 
     def finish_signature(self, nin, nout):
@@ -106,6 +109,11 @@ def _check_order(types1, types2):
         if t2i > t1i:
             break
 
+    if types1 == "QQ?" and types2 == "qQ?":
+        # Explicitly allow this mixed case, rather than figure out what order
+        # is nicer or how to encode it.
+        return
+
     raise TypeError(
             f"Input dtypes are unsorted or duplicate: {types1} and {types2}")
 
@@ -120,11 +128,11 @@ def check_td_order(tds):
     for prev_i, sign in enumerate(signatures[1:]):
         if sign in signatures[:prev_i+1]:
             continue  # allow duplicates...
-        
+
         _check_order(signatures[prev_i], sign)
 
 
-_fdata_map = dict(
+_floatformat_map = dict(
     e='npy_%sf',
     f='npy_%sf',
     d='npy_%s',
@@ -135,11 +143,14 @@ _fdata_map = dict(
 )
 
 def build_func_data(types, f):
-    func_data = [_fdata_map.get(t, '%s') % (f,) for t in types]
+    func_data = [_floatformat_map.get(t, '%s') % (f,) for t in types]
     return func_data
 
 def TD(types, f=None, astype=None, in_=None, out=None, cfunc_alias=None,
-       simd=None, dispatch=None):
+       dispatch=None):
+    """
+    Generate a TypeDescription instance for each item in types
+    """
     if f is not None:
         if isinstance(f, str):
             func_data = build_func_data(types, f)
@@ -163,12 +174,6 @@ def TD(types, f=None, astype=None, in_=None, out=None, cfunc_alias=None,
         raise ValueError("Number of types and outputs do not match")
     tds = []
     for t, fd, i, o in zip(types, func_data, in_, out):
-        # [(simd-name, list of types)]
-        if simd is not None:
-            simdt = [k for k, v in simd if t in v]
-        else:
-            simdt = []
-
         # [(dispatch file name without extension '.dispatch.c*', list of types)]
         if dispatch:
             dispt = ([k for k, v in dispatch if t in v]+[None])[0]
@@ -176,7 +181,7 @@ def TD(types, f=None, astype=None, in_=None, out=None, cfunc_alias=None,
             dispt = None
         tds.append(TypeDescription(
             t, f=fd, in_=i, out=o, astype=astype, cfunc_alias=cfunc_alias,
-            simd=simdt, dispatch=dispt
+            dispatch=dispt
         ))
     return tds
 
@@ -187,12 +192,15 @@ class Ufunc:
     ----------
     nin : number of input arguments
     nout : number of output arguments
-    identity : identity element for a two-argument function
+    identity : identity element for a two-argument function (like Zero)
     docstring : docstring for the ufunc
-    type_descriptions : list of TypeDescription objects
+    typereso: type resolver function of type PyUFunc_TypeResolutionFunc
+    type_descriptions : TypeDescription objects
+    signature: a generalized ufunc signature (like for matmul)
+    indexed: add indexed loops (ufunc.at) for these type characters
     """
     def __init__(self, nin, nout, identity, docstring, typereso,
-                 *type_descriptions, signature=None):
+                 *type_descriptions, signature=None, indexed=''):
         self.nin = nin
         self.nout = nout
         if identity is None:
@@ -202,6 +210,7 @@ class Ufunc:
         self.typereso = typereso
         self.type_descriptions = []
         self.signature = signature
+        self.indexed = indexed
         for td in type_descriptions:
             self.type_descriptions.extend(td)
         for td in self.type_descriptions:
@@ -283,7 +292,8 @@ chartoname = {
     'P': 'OBJECT',
 }
 
-noobj = '?bBhHiIlLqQefdgFDGmM'
+no_obj_bool = 'bBhHiIlLqQefdgFDGmM'
+noobj = '?' + no_obj_bool
 all = '?bBhHiIlLqQefdgFDGOmM'
 
 O = 'O'
@@ -317,6 +327,7 @@ nocmplxO = nocmplx+O
 nocmplxP = nocmplx+P
 notimes_or_obj = bints + inexact
 nodatetime_or_obj = bints + inexact
+no_bool_times_obj = ints + inexact
 
 # Find which code corresponds to int64.
 int64 = ''
@@ -336,35 +347,50 @@ defdict = {
     Ufunc(2, 1, Zero,
           docstrings.get('numpy.core.umath.add'),
           'PyUFunc_AdditionTypeResolver',
-          TD(notimes_or_obj, simd=[('avx2', ints)], dispatch=[('loops_arithm_fp', 'fdFD')]),
+          TD('?', cfunc_alias='logical_or', dispatch=[('loops_logical', '?')]),
+          TD(no_bool_times_obj, dispatch=[
+              ('loops_arithm_fp', 'fdFD'),
+              ('loops_autovec', ints),
+          ]),
           [TypeDescription('M', FullTypeDescr, 'Mm', 'M'),
            TypeDescription('m', FullTypeDescr, 'mm', 'm'),
            TypeDescription('M', FullTypeDescr, 'mM', 'M'),
           ],
           TD(O, f='PyNumber_Add'),
+          indexed=intfltcmplx
           ),
 'subtract':
     Ufunc(2, 1, None, # Zero is only a unit to the right, not the left
           docstrings.get('numpy.core.umath.subtract'),
           'PyUFunc_SubtractionTypeResolver',
-          TD(ints + inexact, simd=[('avx2', ints)], dispatch=[('loops_arithm_fp', 'fdFD')]),
+          TD(no_bool_times_obj, dispatch=[
+              ('loops_arithm_fp', 'fdFD'),
+              ('loops_autovec', ints),
+          ]),
           [TypeDescription('M', FullTypeDescr, 'Mm', 'M'),
            TypeDescription('m', FullTypeDescr, 'mm', 'm'),
            TypeDescription('M', FullTypeDescr, 'MM', 'm'),
           ],
           TD(O, f='PyNumber_Subtract'),
+          indexed=intfltcmplx
           ),
 'multiply':
     Ufunc(2, 1, One,
           docstrings.get('numpy.core.umath.multiply'),
           'PyUFunc_MultiplicationTypeResolver',
-          TD(notimes_or_obj, simd=[('avx2', ints)], dispatch=[('loops_arithm_fp', 'fdFD')]),
+          TD('?', cfunc_alias='logical_and',
+                  dispatch=[('loops_logical', '?')]),
+          TD(no_bool_times_obj, dispatch=[
+              ('loops_arithm_fp', 'fdFD'),
+              ('loops_autovec', ints),
+          ]),
           [TypeDescription('m', FullTypeDescr, 'mq', 'm'),
            TypeDescription('m', FullTypeDescr, 'qm', 'm'),
            TypeDescription('m', FullTypeDescr, 'md', 'm'),
            TypeDescription('m', FullTypeDescr, 'dm', 'm'),
           ],
           TD(O, f='PyNumber_Multiply'),
+          indexed=intfltcmplx
           ),
 #'true_divide' : aliased to divide in umathmodule.c:initumath
 'floor_divide':
@@ -379,6 +405,7 @@ defdict = {
            TypeDescription('m', FullTypeDescr, 'mm', 'q'),
           ],
           TD(O, f='PyNumber_FloorDivide'),
+          indexed=flts + ints
           ),
 'divide':
     Ufunc(2, 1, None, # One is only a unit to the right, not the left
@@ -390,12 +417,16 @@ defdict = {
            TypeDescription('m', FullTypeDescr, 'mm', 'd', cfunc_alias='divide'),
           ],
           TD(O, f='PyNumber_TrueDivide'),
+          indexed=flts
           ),
 'conjugate':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.conjugate'),
           None,
-          TD(ints+flts+cmplx, simd=[('avx2', ints), ('avx512f', cmplxvec)]),
+          TD(ints+flts+cmplx, dispatch=[
+              ('loops_arithm_fp', 'FD'),
+              ('loops_autovec', ints),
+          ]),
           TD(P, f='conjugate'),
           ),
 'fmod':
@@ -410,14 +441,21 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.square'),
           None,
-          TD(ints+inexact, simd=[('avx2', ints), ('avx512f', 'FD')], dispatch=[('loops_unary_fp', 'fd')]),
+          TD(ints+inexact, dispatch=[
+              ('loops_unary_fp', 'fd'),
+              ('loops_arithm_fp', 'FD'),
+              ('loops_autovec', ints),
+          ]),
           TD(O, f='Py_square'),
           ),
 'reciprocal':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.reciprocal'),
           None,
-          TD(ints+inexact, simd=[('avx2', ints)], dispatch=[('loops_unary_fp', 'fd')]),
+          TD(ints+inexact, dispatch=[
+              ('loops_unary_fp', 'fd'),
+              ('loops_autovec', ints),
+          ]),
           TD(O, f='Py_reciprocal'),
           ),
 # This is no longer used as numpy.ones_like, however it is
@@ -449,8 +487,13 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.absolute'),
           'PyUFunc_AbsoluteTypeResolver',
-          TD(bints+flts+timedeltaonly, dispatch=[('loops_unary_fp', 'fd')]),
-          TD(cmplx, simd=[('avx512f', cmplxvec)], out=('f', 'd', 'g')),
+          TD(bints+flts+timedeltaonly, dispatch=[
+              ('loops_unary_fp', 'fd'),
+              ('loops_logical', '?'),
+              ('loops_autovec', ints + 'e'),
+          ]),
+          TD(cmplx, dispatch=[('loops_unary_complex', 'FD')],
+             out=('f', 'd', 'g')),
           TD(O, f='PyNumber_Absolute'),
           ),
 '_arg':
@@ -463,7 +506,7 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.negative'),
           'PyUFunc_NegativeTypeResolver',
-          TD(ints+flts+timedeltaonly, simd=[('avx2', ints)]),
+          TD(ints+flts+timedeltaonly, dispatch=[('loops_unary', ints+'fdg')]),
           TD(cmplx, f='neg'),
           TD(O, f='PyNumber_Negative'),
           ),
@@ -479,82 +522,113 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.sign'),
           'PyUFunc_SimpleUniformOperationTypeResolver',
-          TD(nobool_or_datetime),
+          TD(nobool_or_datetime, dispatch=[('loops_autovec', ints)]),
           ),
 'greater':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.greater'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(all, out='?', dispatch=[('loops_comparison', bints+'fd')]),
-          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
+          TD(bints, out='?'),
+          [TypeDescription('q', FullTypeDescr, 'qQ', '?'),
+           TypeDescription('q', FullTypeDescr, 'Qq', '?')],
+          TD(inexact+times, out='?', dispatch=[('loops_comparison', bints+'fd')]),
           TD('O', out='?'),
+          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
           ),
 'greater_equal':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.greater_equal'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(all, out='?', dispatch=[('loops_comparison', bints+'fd')]),
-          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
+          TD(bints, out='?'),
+          [TypeDescription('q', FullTypeDescr, 'qQ', '?'),
+           TypeDescription('q', FullTypeDescr, 'Qq', '?')],
+          TD(inexact+times, out='?', dispatch=[('loops_comparison', bints+'fd')]),
           TD('O', out='?'),
+          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
           ),
 'less':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.less'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(all, out='?', dispatch=[('loops_comparison', bints+'fd')]),
-          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
+          TD(bints, out='?'),
+          [TypeDescription('q', FullTypeDescr, 'qQ', '?'),
+           TypeDescription('q', FullTypeDescr, 'Qq', '?')],
+          TD(inexact+times, out='?', dispatch=[('loops_comparison', bints+'fd')]),
           TD('O', out='?'),
+          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
           ),
 'less_equal':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.less_equal'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(all, out='?', dispatch=[('loops_comparison', bints+'fd')]),
-          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
+          TD(bints, out='?'),
+          [TypeDescription('q', FullTypeDescr, 'qQ', '?'),
+           TypeDescription('q', FullTypeDescr, 'Qq', '?')],
+          TD(inexact+times, out='?', dispatch=[('loops_comparison', bints+'fd')]),
           TD('O', out='?'),
+          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
           ),
 'equal':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.equal'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(all, out='?', dispatch=[('loops_comparison', bints+'fd')]),
-          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
+          TD(bints, out='?'),
+          [TypeDescription('q', FullTypeDescr, 'qQ', '?'),
+           TypeDescription('q', FullTypeDescr, 'Qq', '?')],
+          TD(inexact+times, out='?', dispatch=[('loops_comparison', bints+'fd')]),
           TD('O', out='?'),
+          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
           ),
 'not_equal':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.not_equal'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(all, out='?', dispatch=[('loops_comparison', bints+'fd')]),
-          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
+          TD(bints, out='?'),
+          [TypeDescription('q', FullTypeDescr, 'qQ', '?'),
+           TypeDescription('q', FullTypeDescr, 'Qq', '?')],
+          TD(inexact+times, out='?', dispatch=[('loops_comparison', bints+'fd')]),
           TD('O', out='?'),
+          [TypeDescription('O', FullTypeDescr, 'OO', 'O')],
           ),
 'logical_and':
     Ufunc(2, 1, True_,
           docstrings.get('numpy.core.umath.logical_and'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(nodatetime_or_obj, out='?', simd=[('avx2', ints)]),
+          TD(nodatetime_or_obj, out='?', dispatch=[
+              ('loops_logical', '?'),
+              ('loops_autovec', ints),
+          ]),
           TD(O, f='npy_ObjectLogicalAnd'),
           ),
 'logical_not':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.logical_not'),
           None,
-          TD(nodatetime_or_obj, out='?', simd=[('avx2', ints)]),
+          TD(nodatetime_or_obj, out='?', dispatch=[
+              ('loops_logical', '?'),
+              ('loops_autovec', ints),
+          ]),
           TD(O, f='npy_ObjectLogicalNot'),
           ),
 'logical_or':
     Ufunc(2, 1, False_,
           docstrings.get('numpy.core.umath.logical_or'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(nodatetime_or_obj, out='?', simd=[('avx2', ints)]),
+          TD(nodatetime_or_obj, out='?', dispatch=[
+              ('loops_logical', '?'),
+              ('loops_autovec', ints),
+          ]),
           TD(O, f='npy_ObjectLogicalOr'),
           ),
 'logical_xor':
     Ufunc(2, 1, False_,
           docstrings.get('numpy.core.umath.logical_xor'),
           'PyUFunc_SimpleBinaryComparisonTypeResolver',
-          TD(nodatetime_or_obj, out='?'),
+          TD('?', out='?', cfunc_alias='not_equal',
+                           dispatch=[('loops_comparison', '?')]),
+          TD(no_bool_times_obj, out='?', dispatch=[
+              ('loops_autovec', ints),
+          ]),
           # TODO: using obj.logical_xor() seems pretty much useless:
           TD(P, f='logical_xor'),
           ),
@@ -562,15 +636,20 @@ defdict = {
     Ufunc(2, 1, ReorderableNone,
           docstrings.get('numpy.core.umath.maximum'),
           'PyUFunc_SimpleUniformOperationTypeResolver',
-          TD(noobj, dispatch=[('loops_minmax', ints+'fdg')]),
-          TD(O, f='npy_ObjectMax')
+          TD('?', cfunc_alias='logical_or', dispatch=[('loops_logical', '?')]),
+          TD(no_obj_bool, dispatch=[('loops_minmax', ints+'fdg')]),
+          TD(O, f='npy_ObjectMax'),
+          indexed=flts + ints,
           ),
 'minimum':
     Ufunc(2, 1, ReorderableNone,
           docstrings.get('numpy.core.umath.minimum'),
           'PyUFunc_SimpleUniformOperationTypeResolver',
-          TD(noobj, dispatch=[('loops_minmax', ints+'fdg')]),
-          TD(O, f='npy_ObjectMin')
+          TD('?', cfunc_alias='logical_and',
+                  dispatch=[('loops_logical', '?')]),
+          TD(no_obj_bool, dispatch=[('loops_minmax', ints+'fdg')]),
+          TD(O, f='npy_ObjectMin'),
+          indexed=flts + ints,
           ),
 'clip':
     Ufunc(3, 1, ReorderableNone,
@@ -583,15 +662,20 @@ defdict = {
     Ufunc(2, 1, ReorderableNone,
           docstrings.get('numpy.core.umath.fmax'),
           'PyUFunc_SimpleUniformOperationTypeResolver',
-          TD(noobj, dispatch=[('loops_minmax', 'fdg')]),
-          TD(O, f='npy_ObjectMax')
+          TD('?', cfunc_alias='logical_or', dispatch=[('loops_logical', '?')]),
+          TD(no_obj_bool, dispatch=[('loops_minmax', 'fdg')]),
+          TD(O, f='npy_ObjectMax'),
+          indexed=flts + ints,
           ),
 'fmin':
     Ufunc(2, 1, ReorderableNone,
           docstrings.get('numpy.core.umath.fmin'),
           'PyUFunc_SimpleUniformOperationTypeResolver',
-          TD(noobj, dispatch=[('loops_minmax', 'fdg')]),
-          TD(O, f='npy_ObjectMin')
+          TD('?', cfunc_alias='logical_and',
+                  dispatch=[('loops_logical', '?')]),
+          TD(no_obj_bool, dispatch=[('loops_minmax', 'fdg')]),
+          TD(O, f='npy_ObjectMin'),
+          indexed=flts + ints,
           ),
 'logaddexp':
     Ufunc(2, 1, MinusInfinity,
@@ -609,42 +693,49 @@ defdict = {
     Ufunc(2, 1, AllOnes,
           docstrings.get('numpy.core.umath.bitwise_and'),
           None,
-          TD(bints, simd=[('avx2', ints)]),
+          TD('?', cfunc_alias='logical_and',
+                  dispatch=[('loops_logical', '?')]),
+          TD(ints, dispatch=[('loops_autovec', ints)]),
           TD(O, f='PyNumber_And'),
           ),
 'bitwise_or':
     Ufunc(2, 1, Zero,
           docstrings.get('numpy.core.umath.bitwise_or'),
           None,
-          TD(bints, simd=[('avx2', ints)]),
+          TD('?', cfunc_alias='logical_or', dispatch=[('loops_logical', '?')]),
+          TD(ints, dispatch=[('loops_autovec', ints)]),
           TD(O, f='PyNumber_Or'),
           ),
 'bitwise_xor':
     Ufunc(2, 1, Zero,
           docstrings.get('numpy.core.umath.bitwise_xor'),
           None,
-          TD(bints, simd=[('avx2', ints)]),
+          TD('?', cfunc_alias='not_equal',
+                  dispatch=[('loops_comparison', '?')]),
+          TD(ints, dispatch=[('loops_autovec', ints)]),
           TD(O, f='PyNumber_Xor'),
           ),
 'invert':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.invert'),
           None,
-          TD(bints, simd=[('avx2', ints)]),
+          TD('?', cfunc_alias='logical_not',
+                  dispatch=[('loops_logical', '?')]),
+          TD(ints, dispatch=[('loops_autovec', ints)]),
           TD(O, f='PyNumber_Invert'),
           ),
 'left_shift':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.left_shift'),
           None,
-          TD(ints, simd=[('avx2', ints)]),
+          TD(ints, dispatch=[('loops_autovec', ints)]),
           TD(O, f='PyNumber_Lshift'),
           ),
 'right_shift':
     Ufunc(2, 1, None,
           docstrings.get('numpy.core.umath.right_shift'),
           None,
-          TD(ints, simd=[('avx2', ints)]),
+          TD(ints, dispatch=[('loops_autovec', ints)]),
           TD(O, f='PyNumber_Rshift'),
           ),
 'heaviside':
@@ -731,7 +822,7 @@ defdict = {
           None,
           TD('e', dispatch=[('loops_umath_fp', 'e')]),
           TD('f', dispatch=[('loops_trigonometric', 'f')]),
-          TD('d', dispatch=[('loops_umath_fp', 'd')]),
+          TD('d', dispatch=[('loops_trigonometric', 'd')]),
           TD('g' + cmplx, f='cos'),
           TD(P, f='cos'),
           ),
@@ -741,7 +832,7 @@ defdict = {
           None,
           TD('e', dispatch=[('loops_umath_fp', 'e')]),
           TD('f', dispatch=[('loops_trigonometric', 'f')]),
-          TD('d', dispatch=[('loops_umath_fp', 'd')]),
+          TD('d', dispatch=[('loops_trigonometric', 'd')]),
           TD('g' + cmplx, f='sin'),
           TD(P, f='sin'),
           ),
@@ -934,7 +1025,10 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.isnan'),
           'PyUFunc_IsFiniteTypeResolver',
-          TD(noobj, simd=[('avx512_skx', 'fd')], out='?'),
+          TD(noobj, out='?', dispatch=[
+              ('loops_unary_fp_le', inexactvec),
+              ('loops_autovec', bints),
+          ]),
           ),
 'isnat':
     Ufunc(1, 1, None,
@@ -946,19 +1040,25 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.isinf'),
           'PyUFunc_IsFiniteTypeResolver',
-          TD(noobj, simd=[('avx512_skx', 'fd')], out='?'),
+          TD(noobj, out='?', dispatch=[
+              ('loops_unary_fp_le', inexactvec),
+              ('loops_autovec', bints + 'mM'),
+          ]),
           ),
 'isfinite':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.isfinite'),
           'PyUFunc_IsFiniteTypeResolver',
-          TD(noobj, simd=[('avx512_skx', 'fd')], out='?'),
+          TD(noobj, out='?', dispatch=[
+              ('loops_unary_fp_le', inexactvec),
+              ('loops_autovec', bints),
+          ]),
           ),
 'signbit':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.signbit'),
           None,
-          TD(flts, simd=[('avx512_skx', 'fd')], out='?'),
+          TD(flts, out='?', dispatch=[('loops_unary_fp_le', inexactvec)]),
           ),
 'copysign':
     Ufunc(2, 1, None,
@@ -1095,7 +1195,10 @@ def make_arrays(funcdict):
             if t.func_data is FullTypeDescr:
                 tname = english_upper(chartoname[t.type])
                 datalist.append('(void *)NULL')
-                cfunc_fname = f"{tname}_{t.in_}_{t.out}_{cfunc_alias}"
+                if t.out == "?":
+                    cfunc_fname = f"{tname}_{t.in_}_bool_{cfunc_alias}"
+                else:
+                    cfunc_fname = f"{tname}_{t.in_}_{t.out}_{cfunc_alias}"
             elif isinstance(t.func_data, FuncNameSuffix):
                 datalist.append('(void *)NULL')
                 tname = english_upper(chartoname[t.type])
@@ -1104,18 +1207,6 @@ def make_arrays(funcdict):
                 datalist.append('(void *)NULL')
                 tname = english_upper(chartoname[t.type])
                 cfunc_fname = f"{tname}_{cfunc_alias}"
-                if t.simd is not None:
-                    for vt in t.simd:
-                        code2list.append(textwrap.dedent("""\
-                        #ifdef HAVE_ATTRIBUTE_TARGET_{ISA}
-                        if (NPY_CPU_HAVE({ISA})) {{
-                            {fname}_functions[{idx}] = {cname}_{isa};
-                        }}
-                        #endif
-                        """).format(
-                            ISA=vt.upper(), isa=vt,
-                            fname=name, cname=cfunc_fname, idx=k
-                        ))
             else:
                 try:
                     thedict = arity_lookup[uf.nin, uf.nout]
@@ -1227,6 +1318,40 @@ def make_ufuncs(funcdict):
         if uf.typereso is not None:
             mlist.append(
                 r"((PyUFuncObject *)f)->type_resolver = &%s;" % uf.typereso)
+        for c in uf.indexed:
+            # Handle indexed loops by getting the underlying ArrayMethodObject
+            # from the list in f._loops and setting its field appropriately
+            fmt = textwrap.dedent("""
+            {{
+                PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum({typenum});
+                PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                                   dtype, {count});
+                if (info == NULL) {{
+                    return -1;
+                }}
+                if (info == Py_None) {{
+                    PyErr_SetString(PyExc_RuntimeError,
+                        "cannot add indexed loop to ufunc "
+                        "{name} with {typenum}");
+                    return -1;
+                }}
+                if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {{
+                    PyErr_SetString(PyExc_RuntimeError,
+                        "Not a PyArrayMethodObject in ufunc "
+                        "{name} with {typenum}");
+                }}
+                ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                                 {funcname};
+                /* info is borrowed, no need to decref*/
+            }}
+            """)
+            mlist.append(fmt.format(
+                typenum=f"NPY_{english_upper(chartoname[c])}",
+                count=uf.nin+uf.nout,
+                name=name,
+                funcname = f"{english_upper(chartoname[c])}_{name}_indexed",
+            ))
+
         mlist.append(r"""PyDict_SetItemString(dictionary, "%s", f);""" % name)
         mlist.append(r"""Py_DECREF(f);""")
         code3list.append('\n'.join(mlist))
@@ -1249,8 +1374,14 @@ def make_code(funcdict, filename):
     #include "loops.h"
     #include "matmul.h"
     #include "clip.h"
+    #include "dtypemeta.h"
     #include "_umath_doc_generated.h"
+
     %s
+    /* Returns a borrowed ref of the second value in the matching info tuple */
+    PyObject *
+    get_info_no_cast(PyUFuncObject *ufunc, PyArray_DTypeMeta *op_dtype,
+                     int ndtypes);
 
     static int
     InitOperators(PyObject *dictionary) {
@@ -1265,8 +1396,29 @@ def make_code(funcdict, filename):
     return code
 
 
-if __name__ == "__main__":
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-o",
+        "--outfile",
+        type=str,
+        help="Path to the output directory"
+    )
+    args = parser.parse_args()
+
+    # used to insert the name of this file into the generated file
     filename = __file__
     code = make_code(defdict, filename)
-    with open('__umath_generated.c', 'w') as fid:
-        fid.write(code)
+
+    if not args.outfile:
+        # This is the distutils-based build
+        outfile = '__umath_generated.c'
+    else:
+        outfile = os.path.join(os.getcwd(), args.outfile)
+
+    with open(outfile, 'w') as f:
+        f.write(code)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath_doc.py b/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath_doc.py
index 9888730fd03..fc0c2a1381c 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath_doc.py
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/generate_umath_doc.py
@@ -1,6 +1,7 @@
 import sys
 import os
 import textwrap
+import argparse
 
 sys.path.insert(0, os.path.dirname(__file__))
 import ufunc_docstrings as docstrings
@@ -28,3 +29,21 @@ def write_code(target):
             cdef_str = normalize_doc(string)
             fid.write(f"#define {cdef_name} \"{cdef_str}\"\n")
         fid.write("#endif //NUMPY_CORE_INCLUDE__UMATH_DOC_GENERATED_H\n")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-o",
+        "--outfile",
+        type=str,
+        help="Path to the output directory"
+    )
+    args = parser.parse_args()
+
+    outfile = os.path.join(os.getcwd(), args.outfile)
+    write_code(outfile)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/numpy_api.py b/contrib/python/numpy/py3/numpy/core/code_generators/numpy_api.py
index fa28f92b7ca..bb3b15806a5 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/numpy_api.py
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/numpy_api.py
@@ -13,7 +13,23 @@ When adding a function, make sure to use the next integer not used as an index
 exception, so it should hopefully not get unnoticed).
 
 """
-from code_generators.genapi import StealRef
+
+import os
+import importlib.util
+
+
+def get_annotations():
+    # Convoluted because we can't import from numpy.distutils
+    # (numpy is not yet built)
+    genapi_py = os.path.join(os.path.dirname(__file__), 'genapi.py')
+    spec = importlib.util.spec_from_file_location('conv_template', genapi_py)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod.StealRef, mod.MinVersion
+
+
+StealRef, MinVersion = get_annotations()
+#from code_generators.genapi import StealRef
 
 # index, type
 multiarray_global_vars = {
@@ -351,8 +367,8 @@ multiarray_funcs_api = {
     'PyArray_ResolveWritebackIfCopy':       (302,),
     'PyArray_SetWritebackIfCopyBase':       (303,),
     # End 1.14 API
-    'PyDataMem_SetHandler':                 (304,),
-    'PyDataMem_GetHandler':                 (305,),
+    'PyDataMem_SetHandler':                 (304, MinVersion("1.22")),
+    'PyDataMem_GetHandler':                 (305, MinVersion("1.22")),
     # End 1.22 API
 }
 
@@ -406,7 +422,7 @@ ufunc_funcs_api = {
     # End 1.7 API
     'PyUFunc_RegisterLoopForDescr':             (41,),
     # End 1.8 API
-    'PyUFunc_FromFuncAndDataAndSignatureAndIdentity': (42,),
+    'PyUFunc_FromFuncAndDataAndSignatureAndIdentity': (42, MinVersion("1.16")),
     # End 1.16 API
 }
 
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/ufunc_docstrings.py b/contrib/python/numpy/py3/numpy/core/code_generators/ufunc_docstrings.py
index efc43b01c79..1695b4d2736 100644
--- a/contrib/python/numpy/py3/numpy/core/code_generators/ufunc_docstrings.py
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/ufunc_docstrings.py
@@ -667,7 +667,7 @@ add_newdoc('numpy.core.umath', 'bitwise_or',
     --------
     The number 13 has the binary representation ``00001101``. Likewise,
     16 is represented by ``00010000``.  The bit-wise OR of 13 and 16 is
-    then ``000111011``, or 29:
+    then ``00011101``, or 29:
 
     >>> np.bitwise_or(13, 16)
     29
@@ -2021,7 +2021,7 @@ add_newdoc('numpy.core.umath', 'log',
     has a branch cut `[-inf, 0]` and is continuous from above on it. `log`
     handles the floating-point negative zero as an infinitesimal negative
     number, conforming to the C99 standard.
-    
+
     In the cases where the input has a negative real part and a very small
     negative complex part (approaching 0), the result is so close to `-pi`
     that it evaluates to exactly `-pi`.
@@ -2457,7 +2457,7 @@ add_newdoc('numpy.core.umath', 'maximum',
     """
     Element-wise maximum of array elements.
 
-    Compare two arrays and returns a new array containing the element-wise
+    Compare two arrays and return a new array containing the element-wise
     maxima. If one of the elements being compared is a NaN, then that
     element is returned. If both elements are NaNs then the first is
     returned. The latter distinction is important for complex NaNs, which
@@ -2516,7 +2516,7 @@ add_newdoc('numpy.core.umath', 'minimum',
     """
     Element-wise minimum of array elements.
 
-    Compare two arrays and returns a new array containing the element-wise
+    Compare two arrays and return a new array containing the element-wise
     minima. If one of the elements being compared is a NaN, then that
     element is returned. If both elements are NaNs then the first is
     returned. The latter distinction is important for complex NaNs, which
@@ -2575,7 +2575,7 @@ add_newdoc('numpy.core.umath', 'fmax',
     """
     Element-wise maximum of array elements.
 
-    Compare two arrays and returns a new array containing the element-wise
+    Compare two arrays and return a new array containing the element-wise
     maxima. If one of the elements being compared is a NaN, then the
     non-nan element is returned. If both elements are NaNs then the first
     is returned.  The latter distinction is important for complex NaNs,
@@ -2633,7 +2633,7 @@ add_newdoc('numpy.core.umath', 'fmin',
     """
     Element-wise minimum of array elements.
 
-    Compare two arrays and returns a new array containing the element-wise
+    Compare two arrays and return a new array containing the element-wise
     minima. If one of the elements being compared is a NaN, then the
     non-nan element is returned. If both elements are NaNs then the first
     is returned.  The latter distinction is important for complex NaNs,
@@ -3833,7 +3833,7 @@ add_newdoc('numpy.core.umath', 'sqrt',
     --------
     emath.sqrt
         A version which returns complex numbers when given negative reals.
-        Note: 0.0 and -0.0 are handled differently for complex inputs.
+        Note that 0.0 and -0.0 are handled differently for complex inputs.
 
     Notes
     -----
diff --git a/contrib/python/numpy/py3/numpy/core/code_generators/verify_c_api_version.py b/contrib/python/numpy/py3/numpy/core/code_generators/verify_c_api_version.py
new file mode 100644
index 00000000000..282e5e7d325
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/code_generators/verify_c_api_version.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+import os
+import sys
+import argparse
+
+
+class MismatchCAPIError(ValueError):
+    pass
+
+
+def get_api_versions(apiversion):
+    """
+    Return current C API checksum and the recorded checksum.
+
+    Return current C API checksum and the recorded checksum for the given
+    version of the C API version.
+
+    """
+    # Compute the hash of the current API as defined in the .txt files in
+    # code_generators
+    sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
+    try:
+        m = __import__('genapi')
+        numpy_api = __import__('numpy_api')
+        curapi_hash = m.fullapi_hash(numpy_api.full_api)
+        apis_hash = m.get_versions_hash()
+    finally:
+        del sys.path[0]
+
+    return curapi_hash, apis_hash[apiversion]
+
+
+def check_api_version(apiversion):
+    """Emits a MismatchCAPIWarning if the C API version needs updating."""
+    curapi_hash, api_hash = get_api_versions(apiversion)
+
+    # If different hash, it means that the api .txt files in
+    # codegen_dir have been updated without the API version being
+    # updated. Any modification in those .txt files should be reflected
+    # in the api and eventually abi versions.
+    # To compute the checksum of the current API, use numpy/core/cversions.py
+    if not curapi_hash == api_hash:
+        msg = ("API mismatch detected, the C API version "
+               "numbers have to be updated. Current C api version is "
+               f"{apiversion}, with checksum {curapi_hash}, but recorded "
+               f"checksum in core/codegen_dir/cversions.txt is {api_hash}. If "
+               "functions were added in the C API, you have to update "
+               f"C_API_VERSION in {__file__}."
+               )
+        raise MismatchCAPIError(msg)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--api-version",
+        type=str,
+        help="C API version to verify (as a hex string)"
+    )
+    args = parser.parse_args()
+
+    check_api_version(int(args.api_version, base=16))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/contrib/python/numpy/py3/numpy/core/config.h.in b/contrib/python/numpy/py3/numpy/core/config.h.in
new file mode 100644
index 00000000000..e3b55975352
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/config.h.in
@@ -0,0 +1,116 @@
+#mesondefine SIZEOF_PY_INTPTR_T
+#mesondefine SIZEOF_OFF_T
+#mesondefine SIZEOF_PY_LONG_LONG
+
+#mesondefine HAVE_BACKTRACE
+#mesondefine HAVE_MADVISE
+#mesondefine HAVE_FTELLO
+#mesondefine HAVE_FSEEKO
+#mesondefine HAVE_FALLOCATE
+#mesondefine HAVE_STRTOLD_L
+#mesondefine HAVE__THREAD
+#mesondefine HAVE___DECLSPEC_THREAD_
+
+/* Optional headers */
+#mesondefine HAVE_XLOCALE_H
+#mesondefine HAVE_DLFCN_H
+#mesondefine HAVE_EXECINFO_H
+#mesondefine HAVE_LIBUNWIND_H
+#mesondefine HAVE_SYS_MMAN_H
+#mesondefine HAVE_XMMINTRIN_H
+#mesondefine HAVE_EMMINTRIN_H
+#mesondefine HAVE_IMMINTRIN_H
+
+/* Optional intrinsics */
+#mesondefine HAVE___BUILTIN_ISNAN
+#mesondefine HAVE___BUILTIN_ISINF
+#mesondefine HAVE___BUILTIN_ISFINITE
+#mesondefine HAVE___BUILTIN_BSWAP32
+#mesondefine HAVE___BUILTIN_BSWAP64
+#mesondefine HAVE___BUILTIN_EXPECT
+#mesondefine HAVE___BUILTIN_MUL_OVERFLOW
+#mesondefine HAVE___BUILTIN_PREFETCH
+
+#mesondefine HAVE_ATTRIBUTE_OPTIMIZE_UNROLL_LOOPS
+#mesondefine HAVE_ATTRIBUTE_OPTIMIZE_OPT_3
+#mesondefine HAVE_ATTRIBUTE_OPTIMIZE_OPT_2
+#mesondefine HAVE_ATTRIBUTE_NONNULL
+
+/* C99 complex support and complex.h are not universal */
+#mesondefine HAVE_COMPLEX_H
+#mesondefine HAVE_CABS
+#mesondefine HAVE_CACOS
+#mesondefine HAVE_CACOSH
+#mesondefine HAVE_CARG
+#mesondefine HAVE_CASIN
+#mesondefine HAVE_CASINH
+#mesondefine HAVE_CATAN
+#mesondefine HAVE_CATANH
+#mesondefine HAVE_CEXP
+#mesondefine HAVE_CLOG
+#mesondefine HAVE_CPOW
+#mesondefine HAVE_CSQRT
+#mesondefine HAVE_CABSF
+#mesondefine HAVE_CACOSF
+#mesondefine HAVE_CACOSHF
+#mesondefine HAVE_CARGF
+#mesondefine HAVE_CASINF
+#mesondefine HAVE_CASINHF
+#mesondefine HAVE_CATANF
+#mesondefine HAVE_CATANHF
+#mesondefine HAVE_CEXPF
+#mesondefine HAVE_CLOGF
+#mesondefine HAVE_CPOWF
+#mesondefine HAVE_CSQRTF
+#mesondefine HAVE_CABSL
+#mesondefine HAVE_CACOSL
+#mesondefine HAVE_CACOSHL
+#mesondefine HAVE_CARGL
+#mesondefine HAVE_CASINL
+#mesondefine HAVE_CASINHL
+#mesondefine HAVE_CATANL
+#mesondefine HAVE_CATANHL
+#mesondefine HAVE_CEXPL
+#mesondefine HAVE_CLOGL
+#mesondefine HAVE_CPOWL
+#mesondefine HAVE_CSQRTL
+/* FreeBSD */
+#mesondefine HAVE_CSINF
+#mesondefine HAVE_CSINHF
+#mesondefine HAVE_CCOSF
+#mesondefine HAVE_CCOSHF
+#mesondefine HAVE_CTANF
+#mesondefine HAVE_CTANHF
+#mesondefine HAVE_CSIN
+#mesondefine HAVE_CSINH
+#mesondefine HAVE_CCOS
+#mesondefine HAVE_CCOSH
+#mesondefine HAVE_CTAN
+#mesondefine HAVE_CTANH
+#mesondefine HAVE_CSINL
+#mesondefine HAVE_CSINHL
+#mesondefine HAVE_CCOSL
+#mesondefine HAVE_CCOSHL
+#mesondefine HAVE_CTANL
+#mesondefine HAVE_CTANHL
+
+#mesondefine NPY_CAN_LINK_SVML
+#mesondefine NPY_RELAXED_STRIDES_DEBUG
+
+#mesondefine HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE
+#mesondefine HAVE_LDOUBLE_INTEL_EXTENDED_12_BYTES_LE
+#mesondefine HAVE_LDOUBLE_MOTOROLA_EXTENDED_12_BYTES_BE
+#mesondefine HAVE_LDOUBLE_IEEE_DOUBLE_LE
+#mesondefine HAVE_LDOUBLE_IEEE_DOUBLE_BE
+#mesondefine HAVE_LDOUBLE_IEEE_QUAD_LE
+#mesondefine HAVE_LDOUBLE_IEEE_QUAD_BE
+#mesondefine HAVE_LDOUBLE_IBM_DOUBLE_DOUBLE_LE
+#mesondefine HAVE_LDOUBLE_IBM_DOUBLE_DOUBLE_BE
+
+#ifndef __cplusplus
+/* #undef inline */
+#endif
+
+#ifndef NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_
+#error config.h should never be included directly, include npy_config.h instead
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/defchararray.py b/contrib/python/numpy/py3/numpy/core/defchararray.py
index 6750e497a86..11c5a30bff7 100644
--- a/contrib/python/numpy/py3/numpy/core/defchararray.py
+++ b/contrib/python/numpy/py3/numpy/core/defchararray.py
@@ -6,7 +6,7 @@ operations and methods.
    The `chararray` class exists for backwards compatibility with
    Numarray, it is not recommended for new development. Starting from numpy
    1.4, if one needs arrays of strings, it is recommended to use arrays of
-   `dtype` `object_`, `string_` or `unicode_`, and use the free functions
+   `dtype` `object_`, `bytes_` or `str_`, and use the free functions
    in the `numpy.char` module for fast vectorized string operations.
 
 Some methods will only be available if the corresponding string method is
@@ -16,12 +16,13 @@ The preferred alias for `defchararray` is `numpy.char`.
 
 """
 import functools
+
+from .._utils import set_module
 from .numerictypes import (
-    string_, unicode_, integer, int_, object_, bool_, character)
+    bytes_, str_, integer, int_, object_, bool_, character)
 from .numeric import ndarray, compare_chararrays
 from .numeric import array as narray
 from numpy.core.multiarray import _vec_string
-from numpy.core.overrides import set_module
 from numpy.core import overrides
 from numpy.compat import asbytes
 import numpy
@@ -45,26 +46,29 @@ array_function_dispatch = functools.partial(
     overrides.array_function_dispatch, module='numpy.char')
 
 
-def _use_unicode(*args):
-    """
-    Helper function for determining the output type of some string
-    operations.
+def _is_unicode(arr):
+    """Returns True if arr is a string or a string array with a dtype that
+    represents a unicode string, otherwise returns False.
 
-    For an operation on two ndarrays, if at least one is unicode, the
-    result should be unicode.
     """
-    for x in args:
-        if (isinstance(x, str) or
-                issubclass(numpy.asarray(x).dtype.type, unicode_)):
-            return unicode_
-    return string_
+    if (isinstance(arr, str) or
+            issubclass(numpy.asarray(arr).dtype.type, str)):
+        return True
+    return False
+
 
-def _to_string_or_unicode_array(result):
+def _to_bytes_or_str_array(result, output_dtype_like=None):
     """
-    Helper function to cast a result back into a string or unicode array
-    if an object array must be used as an intermediary.
+    Helper function to cast a result back into an array
+    with the appropriate dtype if an object array must be used
+    as an intermediary.
     """
-    return numpy.asarray(result.tolist())
+    ret = numpy.asarray(result.tolist())
+    dtype = getattr(output_dtype_like, 'dtype', None)
+    if dtype is not None:
+        return ret.astype(type(dtype)(_get_num_chars(ret)), copy=False)
+    return ret
+
 
 def _clean_args(*args):
     """
@@ -88,7 +92,7 @@ def _get_num_chars(a):
     a string or unicode array.  This is to abstract out the fact that
     for a unicode array this is itemsize / 4.
     """
-    if issubclass(a.dtype.type, unicode_):
+    if issubclass(a.dtype.type, str_):
         return a.itemsize // 4
     return a.itemsize
 
@@ -274,7 +278,7 @@ def str_len(a):
 
     See Also
     --------
-    builtins.len
+    len
 
     Examples
     --------
@@ -311,16 +315,26 @@ def add(x1, x2):
     Returns
     -------
     add : ndarray
-        Output array of `string_` or `unicode_`, depending on input types
+        Output array of `bytes_` or `str_`, depending on input types
         of the same shape as `x1` and `x2`.
 
     """
     arr1 = numpy.asarray(x1)
     arr2 = numpy.asarray(x2)
     out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
-    dtype = _use_unicode(arr1, arr2)
-    return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
 
+    if type(arr1.dtype) != type(arr2.dtype):
+        # Enforce this for now.  The solution to it will be implement add
+        # as a ufunc.  It never worked right on Python 3: bytes + unicode gave
+        # nonsense unicode + bytes errored, and unicode + object used the
+        # object dtype itemsize as num chars (worked on short strings).
+        # bytes + void worked but promoting void->bytes is dubious also.
+        raise TypeError(
+            "np.char.add() requires both arrays of the same dtype kind, but "
+            f"got dtypes: '{arr1.dtype}' and '{arr2.dtype}' (the few cases "
+            "where this used to work often lead to incorrect results).")
+
+    return _vec_string(arr1, type(arr1.dtype)(out_size), '__add__', (arr2,))
 
 def _multiply_dispatcher(a, i):
     return (a,)
@@ -370,7 +384,7 @@ def multiply(a, i):
         raise ValueError("Can only multiply by integers")
     out_size = _get_num_chars(a_arr) * max(int(i_arr.max()), 0)
     return _vec_string(
-        a_arr, (a_arr.dtype.type, out_size), '__mul__', (i_arr,))
+        a_arr, type(a_arr.dtype)(out_size), '__mul__', (i_arr,))
 
 
 def _mod_dispatcher(a, values):
@@ -401,8 +415,8 @@ def mod(a, values):
     str.__mod__
 
     """
-    return _to_string_or_unicode_array(
-        _vec_string(a, object_, '__mod__', (values,)))
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, '__mod__', (values,)), a)
 
 
 @array_function_dispatch(_unary_op_dispatcher)
@@ -495,10 +509,10 @@ def center(a, width, fillchar=' '):
     a_arr = numpy.asarray(a)
     width_arr = numpy.asarray(width)
     size = int(numpy.max(width_arr.flat))
-    if numpy.issubdtype(a_arr.dtype, numpy.string_):
+    if numpy.issubdtype(a_arr.dtype, numpy.bytes_):
         fillchar = asbytes(fillchar)
     return _vec_string(
-        a_arr, (a_arr.dtype.type, size), 'center', (width_arr, fillchar))
+        a_arr, type(a_arr.dtype)(size), 'center', (width_arr, fillchar))
 
 
 def _count_dispatcher(a, sub, start=None, end=None):
@@ -597,7 +611,7 @@ def decode(a, encoding=None, errors=None):
     array(['aAaAaA', '  aA  ', 'abBABba'], dtype='<U7')
 
     """
-    return _to_string_or_unicode_array(
+    return _to_bytes_or_str_array(
         _vec_string(a, object_, 'decode', _clean_args(encoding, errors)))
 
 
@@ -633,7 +647,7 @@ def encode(a, encoding=None, errors=None):
     The type of the result will depend on the encoding specified.
 
     """
-    return _to_string_or_unicode_array(
+    return _to_bytes_or_str_array(
         _vec_string(a, object_, 'encode', _clean_args(encoding, errors)))
 
 
@@ -721,8 +735,8 @@ def expandtabs(a, tabsize=8):
     str.expandtabs
 
     """
-    return _to_string_or_unicode_array(
-        _vec_string(a, object_, 'expandtabs', (tabsize,)))
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'expandtabs', (tabsize,)), a)
 
 
 @array_function_dispatch(_count_dispatcher)
@@ -1041,8 +1055,8 @@ def join(sep, seq):
     array(['g-h-c', 'o.s.d'], dtype='<U5')
 
     """
-    return _to_string_or_unicode_array(
-        _vec_string(sep, object_, 'join', (seq,)))
+    return _to_bytes_or_str_array(
+        _vec_string(sep, object_, 'join', (seq,)), seq)
 
 
 
@@ -1080,10 +1094,10 @@ def ljust(a, width, fillchar=' '):
     a_arr = numpy.asarray(a)
     width_arr = numpy.asarray(width)
     size = int(numpy.max(width_arr.flat))
-    if numpy.issubdtype(a_arr.dtype, numpy.string_):
+    if numpy.issubdtype(a_arr.dtype, numpy.bytes_):
         fillchar = asbytes(fillchar)
     return _vec_string(
-        a_arr, (a_arr.dtype.type, size), 'ljust', (width_arr, fillchar))
+        a_arr, type(a_arr.dtype)(size), 'ljust', (width_arr, fillchar))
 
 
 @array_function_dispatch(_unary_op_dispatcher)
@@ -1216,8 +1230,8 @@ def partition(a, sep):
     str.partition
 
     """
-    return _to_string_or_unicode_array(
-        _vec_string(a, object_, 'partition', (sep,)))
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'partition', (sep,)), a)
 
 
 def _replace_dispatcher(a, old, new, count=None):
@@ -1261,9 +1275,8 @@ def replace(a, old, new, count=None):
     >>> np.char.replace(a, 'is', 'was')
     array(['The dwash was fresh', 'Thwas was it'], dtype='<U19')
     """
-    return _to_string_or_unicode_array(
-        _vec_string(
-            a, object_, 'replace', [old, new] + _clean_args(count)))
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'replace', [old, new] + _clean_args(count)), a)
 
 
 @array_function_dispatch(_count_dispatcher)
@@ -1359,10 +1372,10 @@ def rjust(a, width, fillchar=' '):
     a_arr = numpy.asarray(a)
     width_arr = numpy.asarray(width)
     size = int(numpy.max(width_arr.flat))
-    if numpy.issubdtype(a_arr.dtype, numpy.string_):
+    if numpy.issubdtype(a_arr.dtype, numpy.bytes_):
         fillchar = asbytes(fillchar)
     return _vec_string(
-        a_arr, (a_arr.dtype.type, size), 'rjust', (width_arr, fillchar))
+        a_arr, type(a_arr.dtype)(size), 'rjust', (width_arr, fillchar))
 
 
 @array_function_dispatch(_partition_dispatcher)
@@ -1397,8 +1410,8 @@ def rpartition(a, sep):
     str.rpartition
 
     """
-    return _to_string_or_unicode_array(
-        _vec_string(a, object_, 'rpartition', (sep,)))
+    return _to_bytes_or_str_array(
+        _vec_string(a, object_, 'rpartition', (sep,)), a)
 
 
 def _split_dispatcher(a, sep=None, maxsplit=None):
@@ -1753,7 +1766,7 @@ def translate(a, table, deletechars=None):
 
     """
     a_arr = numpy.asarray(a)
-    if issubclass(a_arr.dtype.type, unicode_):
+    if issubclass(a_arr.dtype.type, str_):
         return _vec_string(
             a_arr, a_arr.dtype, 'translate', (table,))
     else:
@@ -1828,7 +1841,7 @@ def zfill(a, width):
     width_arr = numpy.asarray(width)
     size = int(numpy.max(width_arr.flat))
     return _vec_string(
-        a_arr, (a_arr.dtype.type, size), 'zfill', (width_arr,))
+        a_arr, type(a_arr.dtype)(size), 'zfill', (width_arr,))
 
 
 @array_function_dispatch(_unary_op_dispatcher)
@@ -1837,7 +1850,7 @@ def isnumeric(a):
     For each element, return True if there are only numeric
     characters in the element.
 
-    Calls `unicode.isnumeric` element-wise.
+    Calls `str.isnumeric` element-wise.
 
     Numeric characters include digit characters, and all characters
     that have the Unicode numeric value property, e.g. ``U+2155,
@@ -1855,7 +1868,7 @@ def isnumeric(a):
 
     See Also
     --------
-    unicode.isnumeric
+    str.isnumeric
 
     Examples
     --------
@@ -1863,7 +1876,7 @@ def isnumeric(a):
     array([ True, False, False, False, False])
 
     """
-    if _use_unicode(a) != unicode_:
+    if not _is_unicode(a):
         raise TypeError("isnumeric is only available for Unicode strings and arrays")
     return _vec_string(a, bool_, 'isnumeric')
 
@@ -1874,7 +1887,7 @@ def isdecimal(a):
     For each element, return True if there are only decimal
     characters in the element.
 
-    Calls `unicode.isdecimal` element-wise.
+    Calls `str.isdecimal` element-wise.
 
     Decimal characters include digit characters, and all characters
     that can be used to form decimal-radix numbers,
@@ -1892,7 +1905,7 @@ def isdecimal(a):
 
     See Also
     --------
-    unicode.isdecimal
+    str.isdecimal
 
     Examples
     --------
@@ -1900,8 +1913,9 @@ def isdecimal(a):
     array([ True, False, False, False])
 
     """ 
-    if _use_unicode(a) != unicode_:
-        raise TypeError("isnumeric is only available for Unicode strings and arrays")
+    if not _is_unicode(a):
+        raise TypeError(
+            "isdecimal is only available for Unicode strings and arrays")
     return _vec_string(a, bool_, 'isdecimal')
 
 
@@ -1917,7 +1931,7 @@ class chararray(ndarray):
        The `chararray` class exists for backwards compatibility with
        Numarray, it is not recommended for new development. Starting from numpy
        1.4, if one needs arrays of strings, it is recommended to use arrays of
-       `dtype` `object_`, `string_` or `unicode_`, and use the free functions
+       `dtype` `object_`, `bytes_` or `str_`, and use the free functions
        in the `numpy.char` module for fast vectorized string operations.
 
     Versus a regular NumPy array of type `str` or `unicode`, this
@@ -2051,9 +2065,9 @@ class chararray(ndarray):
         global _globalvar
 
         if unicode:
-            dtype = unicode_
+            dtype = str_
         else:
-            dtype = string_
+            dtype = bytes_
 
         # force itemsize to be a Python int, since using NumPy integer
         # types results in itemsize.itemsize being used as the size of
@@ -2177,7 +2191,7 @@ class chararray(ndarray):
     def __radd__(self, other):
         """
         Return (other + self), that is string concatenation,
-        element-wise for a pair of array_likes of `string_` or `unicode_`.
+        element-wise for a pair of array_likes of `bytes_` or `str_`.
 
         See Also
         --------
@@ -2210,8 +2224,8 @@ class chararray(ndarray):
     def __mod__(self, i):
         """
         Return (self % i), that is pre-Python 2.6 string formatting
-        (interpolation), element-wise for a pair of array_likes of `string_`
-        or `unicode_`.
+        (interpolation), element-wise for a pair of array_likes of `bytes_`
+        or `str_`.
 
         See Also
         --------
@@ -2721,7 +2735,7 @@ def array(obj, itemsize=None, copy=True, unicode=None, order=None):
     .. note::
        This class is provided for numarray backward-compatibility.
        New code (not concerned with numarray compatibility) should use
-       arrays of type `string_` or `unicode_` and use the free functions
+       arrays of type `bytes_` or `str_` and use the free functions
        in :mod:`numpy.char <numpy.core.defchararray>` for fast
        vectorized string operations instead.
 
@@ -2804,26 +2818,26 @@ def array(obj, itemsize=None, copy=True, unicode=None, order=None):
             # itemsize is in 8-bit chars, so for Unicode, we need
             # to divide by the size of a single Unicode character,
             # which for NumPy is always 4
-            if issubclass(obj.dtype.type, unicode_):
+            if issubclass(obj.dtype.type, str_):
                 itemsize //= 4
 
         if unicode is None:
-            if issubclass(obj.dtype.type, unicode_):
+            if issubclass(obj.dtype.type, str_):
                 unicode = True
             else:
                 unicode = False
 
         if unicode:
-            dtype = unicode_
+            dtype = str_
         else:
-            dtype = string_
+            dtype = bytes_
 
         if order is not None:
             obj = numpy.asarray(obj, order=order)
         if (copy or
                 (itemsize != obj.itemsize) or
-                (not unicode and isinstance(obj, unicode_)) or
-                (unicode and isinstance(obj, string_))):
+                (not unicode and isinstance(obj, str_)) or
+                (unicode and isinstance(obj, bytes_))):
             obj = obj.astype((dtype, int(itemsize)))
         return obj
 
@@ -2836,9 +2850,9 @@ def array(obj, itemsize=None, copy=True, unicode=None, order=None):
             # Fall through to the default case
 
     if unicode:
-        dtype = unicode_
+        dtype = str_
     else:
-        dtype = string_
+        dtype = bytes_
 
     if itemsize is None:
         val = narray(obj, dtype=dtype, order=order, subok=True)
diff --git a/contrib/python/numpy/py3/numpy/core/einsumfunc.py b/contrib/python/numpy/py3/numpy/core/einsumfunc.py
index d6c5885b810..01966f0fe75 100644
--- a/contrib/python/numpy/py3/numpy/core/einsumfunc.py
+++ b/contrib/python/numpy/py3/numpy/core/einsumfunc.py
@@ -728,7 +728,7 @@ def einsum_path(*operands, optimize='greedy', einsum_call=False):
         * if False no optimization is taken
         * if True defaults to the 'greedy' algorithm
         * 'optimal' An algorithm that combinatorially explores all possible
-          ways of contracting the listed tensors and choosest the least costly
+          ways of contracting the listed tensors and chooses the least costly
           path. Scales exponentially with the number of terms in the
           contraction.
         * 'greedy' An algorithm that chooses the best pair contraction
diff --git a/contrib/python/numpy/py3/numpy/core/einsumfunc.pyi b/contrib/python/numpy/py3/numpy/core/einsumfunc.pyi
index c811a57838d..ad483bb9035 100644
--- a/contrib/python/numpy/py3/numpy/core/einsumfunc.pyi
+++ b/contrib/python/numpy/py3/numpy/core/einsumfunc.pyi
@@ -5,10 +5,6 @@ from numpy import (
     ndarray,
     dtype,
     bool_,
-    unsignedinteger,
-    signedinteger,
-    floating,
-    complexfloating,
     number,
     _OrderKACF,
 )
@@ -18,12 +14,14 @@ from numpy._typing import (
     _ArrayLikeInt_co,
     _ArrayLikeFloat_co,
     _ArrayLikeComplex_co,
+    _ArrayLikeObject_co,
     _DTypeLikeBool,
     _DTypeLikeUInt,
     _DTypeLikeInt,
     _DTypeLikeFloat,
     _DTypeLikeComplex,
     _DTypeLikeComplex_co,
+    _DTypeLikeObject,
 )
 
 _ArrayType = TypeVar(
@@ -132,6 +130,51 @@ def einsum(
     optimize: _OptimizeKind = ...,
 ) -> _ArrayType: ...
 
+@overload
+def einsum(
+    subscripts: str | _ArrayLikeInt_co,
+    /,
+    *operands: _ArrayLikeObject_co,
+    out: None = ...,
+    dtype: None | _DTypeLikeObject = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    subscripts: str | _ArrayLikeInt_co,
+    /,
+    *operands: Any,
+    casting: _CastingUnsafe,
+    dtype: None | _DTypeLikeObject = ...,
+    out: None = ...,
+    order: _OrderKACF = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    subscripts: str | _ArrayLikeInt_co,
+    /,
+    *operands: _ArrayLikeObject_co,
+    out: _ArrayType,
+    dtype: None | _DTypeLikeObject = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> _ArrayType: ...
+@overload
+def einsum(
+    subscripts: str | _ArrayLikeInt_co,
+    /,
+    *operands: Any,
+    out: _ArrayType,
+    casting: _CastingUnsafe,
+    dtype: None | _DTypeLikeObject = ...,
+    order: _OrderKACF = ...,
+    optimize: _OptimizeKind = ...,
+) -> _ArrayType: ...
+
 # NOTE: `einsum_call` is a hidden kwarg unavailable for public use.
 # It is therefore excluded from the signatures below.
 # NOTE: In practice the list consists of a `str` (first element)
@@ -139,6 +182,6 @@ def einsum(
 def einsum_path(
     subscripts: str | _ArrayLikeInt_co,
     /,
-    *operands: _ArrayLikeComplex_co,
+    *operands: _ArrayLikeComplex_co | _DTypeLikeObject,
     optimize: _OptimizeKind = ...,
 ) -> tuple[list[Any], str]: ...
diff --git a/contrib/python/numpy/py3/numpy/core/fromnumeric.py b/contrib/python/numpy/py3/numpy/core/fromnumeric.py
index ca9d55cb758..69cabb33e57 100644
--- a/contrib/python/numpy/py3/numpy/core/fromnumeric.py
+++ b/contrib/python/numpy/py3/numpy/core/fromnumeric.py
@@ -6,6 +6,7 @@ import types
 import warnings
 
 import numpy as np
+from .._utils import set_module
 from . import multiarray as mu
 from . import overrides
 from . import umath as um
@@ -20,8 +21,9 @@ __all__ = [
     'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
     'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
     'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
+    'max', 'min',
     'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
-    'ravel', 'repeat', 'reshape', 'resize', 'round_',
+    'ravel', 'repeat', 'reshape', 'resize', 'round', 'round_',
     'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
     'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
 ]
@@ -236,24 +238,9 @@ def reshape(a, newshape, order='C'):
 
     Notes
     -----
-    It is not always possible to change the shape of an array without
-    copying the data. If you want an error to be raised when the data is copied,
-    you should assign the new shape to the shape attribute of the array::
-
-     >>> a = np.zeros((10, 2))
-
-     # A transpose makes the array non-contiguous
-     >>> b = a.T
-
-     # Taking a view makes it possible to modify the shape without modifying
-     # the initial object.
-     >>> c = b.view()
-     >>> c.shape = (20)
-     Traceback (most recent call last):
-        ...
-     AttributeError: Incompatible shape for in-place modification. Use
-     `.reshape()` to make a copy with the desired shape.
-
+    It is not always possible to change the shape of an array without copying
+    the data.
+    
     The `order` keyword gives the index ordering both for *fetching* the values
     from `a`, and then *placing* the values into the output array.
     For example, let's say you have an array:
@@ -436,7 +423,7 @@ def _repeat_dispatcher(a, repeats, axis=None):
 @array_function_dispatch(_repeat_dispatcher)
 def repeat(a, repeats, axis=None):
     """
-    Repeat elements of an array.
+    Repeat each element of an array after themselves
 
     Parameters
     ----------
@@ -1814,9 +1801,10 @@ def ravel(a, order='C'):
     Returns
     -------
     y : array_like
-        y is an array of the same subtype as `a`, with shape ``(a.size,)``.
-        Note that matrices are special cased for backward compatibility, if `a`
-        is a matrix, then y is a 1-D ndarray.
+        y is a contiguous 1-D array of the same subtype as `a`,
+        with shape ``(a.size,)``.
+        Note that matrices are special cased for backward compatibility,
+        if `a` is a matrix, then y is a 1-D ndarray.
 
     See Also
     --------
@@ -1835,7 +1823,8 @@ def ravel(a, order='C'):
     column-major, Fortran-style index ordering.
 
     When a view is desired in as many cases as possible, ``arr.reshape(-1)``
-    may be preferable.
+    may be preferable. However, ``ravel`` supports ``K`` in the optional
+    ``order`` argument while ``reshape`` does not.
 
     Examples
     --------
@@ -2313,7 +2302,7 @@ def sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
         warnings.warn(
             "Calling np.sum(generator) is deprecated, and in the future will give a different result. "
             "Use np.sum(np.fromiter(generator)) or the python sum builtin instead.",
-            DeprecationWarning, stacklevel=3)
+            DeprecationWarning, stacklevel=2)
 
         res = _sum_(a)
         if out is not None:
@@ -2695,13 +2684,14 @@ def ptp(a, axis=None, out=None, keepdims=np._NoValue):
     return _methods._ptp(a, axis=axis, out=out, **kwargs)
 
 
-def _amax_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,
-                     where=None):
+def _max_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,
+                    where=None):
     return (a, out)
 
 
-@array_function_dispatch(_amax_dispatcher)
-def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
+@array_function_dispatch(_max_dispatcher)
+@set_module('numpy')
+def max(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
          where=np._NoValue):
     """
     Return the maximum of an array or maximum along an axis.
@@ -2729,7 +2719,7 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         the result will broadcast correctly against the input array.
 
         If the default value is passed, then `keepdims` will not be
-        passed through to the `amax` method of sub-classes of
+        passed through to the ``max`` method of sub-classes of
         `ndarray`, however any non-default value will be.  If the
         sub-class' method does not implement `keepdims` any
         exceptions will be raised.
@@ -2748,7 +2738,7 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
 
     Returns
     -------
-    amax : ndarray or scalar
+    max : ndarray or scalar
         Maximum of `a`. If `axis` is None, the result is a scalar value.
         If `axis` is an int, the result is an array of dimension
         ``a.ndim - 1``. If `axis` is a tuple, the result is an array of 
@@ -2775,9 +2765,9 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
     corresponding max value will be NaN as well. To ignore NaN values
     (MATLAB behavior), please use nanmax.
 
-    Don't use `amax` for element-wise comparison of 2 arrays; when
+    Don't use `~numpy.max` for element-wise comparison of 2 arrays; when
     ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
-    ``amax(a, axis=0)``.
+    ``max(a, axis=0)``.
 
     Examples
     --------
@@ -2785,19 +2775,19 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
     >>> a
     array([[0, 1],
            [2, 3]])
-    >>> np.amax(a)           # Maximum of the flattened array
+    >>> np.max(a)           # Maximum of the flattened array
     3
-    >>> np.amax(a, axis=0)   # Maxima along the first axis
+    >>> np.max(a, axis=0)   # Maxima along the first axis
     array([2, 3])
-    >>> np.amax(a, axis=1)   # Maxima along the second axis
+    >>> np.max(a, axis=1)   # Maxima along the second axis
     array([1, 3])
-    >>> np.amax(a, where=[False, True], initial=-1, axis=0)
+    >>> np.max(a, where=[False, True], initial=-1, axis=0)
     array([-1,  3])
     >>> b = np.arange(5, dtype=float)
     >>> b[2] = np.NaN
-    >>> np.amax(b)
+    >>> np.max(b)
     nan
-    >>> np.amax(b, where=~np.isnan(b), initial=-1)
+    >>> np.max(b, where=~np.isnan(b), initial=-1)
     4.0
     >>> np.nanmax(b)
     4.0
@@ -2805,14 +2795,14 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
     You can use an initial value to compute the maximum of an empty slice, or
     to initialize it to a different value:
 
-    >>> np.amax([[-50], [10]], axis=-1, initial=0)
+    >>> np.max([[-50], [10]], axis=-1, initial=0)
     array([ 0, 10])
 
     Notice that the initial value is used as one of the elements for which the
     maximum is determined, unlike for the default argument Python's max
     function, which is only used for empty iterables.
 
-    >>> np.amax([5], initial=6)
+    >>> np.max([5], initial=6)
     6
     >>> max([5], default=6)
     5
@@ -2821,14 +2811,31 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
                           keepdims=keepdims, initial=initial, where=where)
 
 
-def _amin_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,
-                     where=None):
+@array_function_dispatch(_max_dispatcher)
+def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
+         where=np._NoValue):
+    """
+    Return the maximum of an array or maximum along an axis.
+
+    `amax` is an alias of `~numpy.max`.
+
+    See Also
+    --------
+    max : alias of this function
+    ndarray.max : equivalent method
+    """
+    return _wrapreduction(a, np.maximum, 'max', axis, None, out,
+                          keepdims=keepdims, initial=initial, where=where)
+
+
+def _min_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,
+                    where=None):
     return (a, out)
 
 
-@array_function_dispatch(_amin_dispatcher)
-def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
-         where=np._NoValue):
+@array_function_dispatch(_min_dispatcher)
+def min(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
+        where=np._NoValue):
     """
     Return the minimum of an array or minimum along an axis.
 
@@ -2855,7 +2862,7 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         the result will broadcast correctly against the input array.
 
         If the default value is passed, then `keepdims` will not be
-        passed through to the `amin` method of sub-classes of
+        passed through to the ``min`` method of sub-classes of
         `ndarray`, however any non-default value will be.  If the
         sub-class' method does not implement `keepdims` any
         exceptions will be raised.
@@ -2874,7 +2881,7 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
 
     Returns
     -------
-    amin : ndarray or scalar
+    min : ndarray or scalar
         Minimum of `a`. If `axis` is None, the result is a scalar value.
         If `axis` is an int, the result is an array of dimension
         ``a.ndim - 1``.  If `axis` is a tuple, the result is an array of 
@@ -2901,9 +2908,9 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
     corresponding min value will be NaN as well. To ignore NaN values
     (MATLAB behavior), please use nanmin.
 
-    Don't use `amin` for element-wise comparison of 2 arrays; when
+    Don't use `~numpy.min` for element-wise comparison of 2 arrays; when
     ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
-    ``amin(a, axis=0)``.
+    ``min(a, axis=0)``.
 
     Examples
     --------
@@ -2911,25 +2918,25 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
     >>> a
     array([[0, 1],
            [2, 3]])
-    >>> np.amin(a)           # Minimum of the flattened array
+    >>> np.min(a)           # Minimum of the flattened array
     0
-    >>> np.amin(a, axis=0)   # Minima along the first axis
+    >>> np.min(a, axis=0)   # Minima along the first axis
     array([0, 1])
-    >>> np.amin(a, axis=1)   # Minima along the second axis
+    >>> np.min(a, axis=1)   # Minima along the second axis
     array([0, 2])
-    >>> np.amin(a, where=[False, True], initial=10, axis=0)
+    >>> np.min(a, where=[False, True], initial=10, axis=0)
     array([10,  1])
 
     >>> b = np.arange(5, dtype=float)
     >>> b[2] = np.NaN
-    >>> np.amin(b)
+    >>> np.min(b)
     nan
-    >>> np.amin(b, where=~np.isnan(b), initial=10)
+    >>> np.min(b, where=~np.isnan(b), initial=10)
     0.0
     >>> np.nanmin(b)
     0.0
 
-    >>> np.amin([[-50], [10]], axis=-1, initial=0)
+    >>> np.min([[-50], [10]], axis=-1, initial=0)
     array([-50,   0])
 
     Notice that the initial value is used as one of the elements for which the
@@ -2938,7 +2945,7 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
 
     Notice that this isn't the same as Python's ``default`` argument.
 
-    >>> np.amin([6], initial=5)
+    >>> np.min([6], initial=5)
     5
     >>> min([6], default=5)
     6
@@ -2947,6 +2954,23 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
                           keepdims=keepdims, initial=initial, where=where)
 
 
+@array_function_dispatch(_min_dispatcher)
+def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
+         where=np._NoValue):
+    """
+    Return the minimum of an array or minimum along an axis.
+
+    `amin` is an alias of `~numpy.min`.
+
+    See Also
+    --------
+    min : alias of this function
+    ndarray.min : equivalent method
+    """
+    return _wrapreduction(a, np.minimum, 'min', axis, None, out,
+                          keepdims=keepdims, initial=initial, where=where)
+
+
 def _prod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
                      initial=None, where=None):
     return (a, out)
@@ -3238,12 +3262,12 @@ def size(a, axis=None):
             return asarray(a).shape[axis]
 
 
-def _around_dispatcher(a, decimals=None, out=None):
+def _round_dispatcher(a, decimals=None, out=None):
     return (a, out)
 
 
-@array_function_dispatch(_around_dispatcher)
-def around(a, decimals=0, out=None):
+@array_function_dispatch(_round_dispatcher)
+def round(a, decimals=0, out=None):
     """
     Evenly round to the given number of decimals.
 
@@ -3274,18 +3298,17 @@ def around(a, decimals=0, out=None):
     See Also
     --------
     ndarray.round : equivalent method
+    around : an alias for this function
     ceil, fix, floor, rint, trunc
 
 
     Notes
     -----
-    `~numpy.round` is often used as an alias for `~numpy.around`.
-    
     For values exactly halfway between rounded decimal values, NumPy
     rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
     -0.5 and 0.5 round to 0.0, etc.
 
-    ``np.around`` uses a fast but sometimes inexact algorithm to round
+    ``np.round`` uses a fast but sometimes inexact algorithm to round
     floating-point datatypes. For positive `decimals` it is equivalent to
     ``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has
     error due to the inexact representation of decimal fractions in the IEEE
@@ -3322,21 +3345,38 @@ def around(a, decimals=0, out=None):
 
     Examples
     --------
-    >>> np.around([0.37, 1.64])
+    >>> np.round([0.37, 1.64])
     array([0., 2.])
-    >>> np.around([0.37, 1.64], decimals=1)
+    >>> np.round([0.37, 1.64], decimals=1)
     array([0.4, 1.6])
-    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
+    >>> np.round([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
     array([0., 2., 2., 4., 4.])
-    >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
+    >>> np.round([1,2,3,11], decimals=1) # ndarray of ints is returned
     array([ 1,  2,  3, 11])
-    >>> np.around([1,2,3,11], decimals=-1)
+    >>> np.round([1,2,3,11], decimals=-1)
     array([ 0,  0,  0, 10])
 
     """
     return _wrapfunc(a, 'round', decimals=decimals, out=out)
 
 
+@array_function_dispatch(_round_dispatcher)
+def around(a, decimals=0, out=None):
+    """
+    Round an array to the given number of decimals.
+
+    `around` is an alias of `~numpy.round`.
+
+    See Also
+    --------
+    ndarray.round : equivalent method
+    round : alias for this function
+    ceil, fix, floor, rint, trunc
+
+    """
+    return _wrapfunc(a, 'round', decimals=decimals, out=out)
+
+
 def _mean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None, *,
                      where=None):
     return (a, where, out)
@@ -3748,14 +3788,31 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, *,
                          **kwargs)
 
 
-# Aliases of other functions. These have their own definitions only so that
-# they can have unique docstrings.
+# Aliases of other functions. Provided unique docstrings 
+# are for reference purposes only. Wherever possible,
+# avoid using them.
+
+
+def _round__dispatcher(a, decimals=None, out=None):
+    # 2023-02-28, 1.25.0
+    warnings.warn("`round_` is deprecated as of NumPy 1.25.0, and will be "
+                  "removed in NumPy 2.0. Please use `round` instead.",
+                  DeprecationWarning, stacklevel=3)
+    return (a, out)
+
 
-@array_function_dispatch(_around_dispatcher)
+@array_function_dispatch(_round__dispatcher)
 def round_(a, decimals=0, out=None):
     """
     Round an array to the given number of decimals.
 
+    `~numpy.round_` is a disrecommended backwards-compatibility
+    alias of `~numpy.around` and `~numpy.round`.
+
+    .. deprecated:: 1.25.0
+        ``round_`` is deprecated as of NumPy 1.25.0, and will be
+        removed in NumPy 2.0. Please use `round` instead.
+
     See Also
     --------
     around : equivalent function; see for details.
@@ -3763,11 +3820,24 @@ def round_(a, decimals=0, out=None):
     return around(a, decimals=decimals, out=out)
 
 
-@array_function_dispatch(_prod_dispatcher, verify=False)
+def _product_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
+                        initial=None, where=None):
+    # 2023-03-02, 1.25.0
+    warnings.warn("`product` is deprecated as of NumPy 1.25.0, and will be "
+                  "removed in NumPy 2.0. Please use `prod` instead.",
+                  DeprecationWarning, stacklevel=3)
+    return (a, out)
+
+
+@array_function_dispatch(_product_dispatcher, verify=False)
 def product(*args, **kwargs):
     """
     Return the product of array elements over a given axis.
 
+    .. deprecated:: 1.25.0
+        ``product`` is deprecated as of NumPy 1.25.0, and will be
+        removed in NumPy 2.0. Please use `prod` instead.
+
     See Also
     --------
     prod : equivalent function; see for details.
@@ -3775,11 +3845,23 @@ def product(*args, **kwargs):
     return prod(*args, **kwargs)
 
 
-@array_function_dispatch(_cumprod_dispatcher, verify=False)
+def _cumproduct_dispatcher(a, axis=None, dtype=None, out=None):
+    # 2023-03-02, 1.25.0
+    warnings.warn("`cumproduct` is deprecated as of NumPy 1.25.0, and will be "
+                  "removed in NumPy 2.0. Please use `cumprod` instead.",
+                  DeprecationWarning, stacklevel=3)
+    return (a, out)
+
+
+@array_function_dispatch(_cumproduct_dispatcher, verify=False)
 def cumproduct(*args, **kwargs):
     """
     Return the cumulative product over the given axis.
 
+    .. deprecated:: 1.25.0
+        ``cumproduct`` is deprecated as of NumPy 1.25.0, and will be
+        removed in NumPy 2.0. Please use `cumprod` instead.
+
     See Also
     --------
     cumprod : equivalent function; see for details.
@@ -3787,13 +3869,26 @@ def cumproduct(*args, **kwargs):
     return cumprod(*args, **kwargs)
 
 
-@array_function_dispatch(_any_dispatcher, verify=False)
+def _sometrue_dispatcher(a, axis=None, out=None, keepdims=None, *,
+                         where=np._NoValue):
+    # 2023-03-02, 1.25.0
+    warnings.warn("`sometrue` is deprecated as of NumPy 1.25.0, and will be "
+                  "removed in NumPy 2.0. Please use `any` instead.",
+                  DeprecationWarning, stacklevel=3)
+    return (a, where, out)
+
+
+@array_function_dispatch(_sometrue_dispatcher, verify=False)
 def sometrue(*args, **kwargs):
     """
     Check whether some values are true.
 
     Refer to `any` for full documentation.
 
+    .. deprecated:: 1.25.0
+        ``sometrue`` is deprecated as of NumPy 1.25.0, and will be
+        removed in NumPy 2.0. Please use `any` instead.
+
     See Also
     --------
     any : equivalent function; see for details.
@@ -3801,11 +3896,23 @@ def sometrue(*args, **kwargs):
     return any(*args, **kwargs)
 
 
-@array_function_dispatch(_all_dispatcher, verify=False)
+def _alltrue_dispatcher(a, axis=None, out=None, keepdims=None, *, where=None):
+    # 2023-03-02, 1.25.0
+    warnings.warn("`alltrue` is deprecated as of NumPy 1.25.0, and will be "
+                  "removed in NumPy 2.0. Please use `all` instead.",
+                  DeprecationWarning, stacklevel=3)
+    return (a, where, out)
+
+
+@array_function_dispatch(_alltrue_dispatcher, verify=False)
 def alltrue(*args, **kwargs):
     """
     Check if all elements of input array are true.
 
+    .. deprecated:: 1.25.0
+        ``alltrue`` is deprecated as of NumPy 1.25.0, and will be
+        removed in NumPy 2.0. Please use `all` instead.
+
     See Also
     --------
     numpy.all : Equivalent function; see for details.
diff --git a/contrib/python/numpy/py3/numpy/core/fromnumeric.pyi b/contrib/python/numpy/py3/numpy/core/fromnumeric.pyi
index 17b17819d70..43d1785578d 100644
--- a/contrib/python/numpy/py3/numpy/core/fromnumeric.pyi
+++ b/contrib/python/numpy/py3/numpy/core/fromnumeric.pyi
@@ -1047,3 +1047,7 @@ def var(
     *,
     where: _ArrayLikeBool_co = ...,
 ) -> _ArrayType: ...
+
+max = amax
+min = amin
+round = around
diff --git a/contrib/python/numpy/py3/numpy/core/function_base.py b/contrib/python/numpy/py3/numpy/core/function_base.py
index 3dc51a81b18..00e4e6b0ea8 100644
--- a/contrib/python/numpy/py3/numpy/core/function_base.py
+++ b/contrib/python/numpy/py3/numpy/core/function_base.py
@@ -3,6 +3,7 @@ import warnings
 import operator
 import types
 
+import numpy as np
 from . import numeric as _nx
 from .numeric import result_type, NaN, asanyarray, ndim
 from numpy.core.multiarray import add_docstring
@@ -167,7 +168,7 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
     y += start
 
     if endpoint and num > 1:
-        y[-1] = stop
+        y[-1, ...] = stop
 
     if axis != 0:
         y = _nx.moveaxis(y, 0, axis)
@@ -183,7 +184,7 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
 
 def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None,
                          dtype=None, axis=None):
-    return (start, stop)
+    return (start, stop, base)
 
 
 @array_function_dispatch(_logspace_dispatcher)
@@ -199,6 +200,9 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
     .. versionchanged:: 1.16.0
         Non-scalar `start` and `stop` are now supported.
 
+    .. versionchanged:: 1.25.0
+        Non-scalar 'base` is now supported
+
     Parameters
     ----------
     start : array_like
@@ -223,9 +227,10 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
         an integer; `float` is chosen even if the arguments would produce an
         array of integers.
     axis : int, optional
-        The axis in the result to store the samples.  Relevant only if start
-        or stop are array-like.  By default (0), the samples will be along a
-        new axis inserted at the beginning. Use -1 to get an axis at the end.
+        The axis in the result to store the samples.  Relevant only if start,
+        stop, or base are array-like.  By default (0), the samples will be
+        along a new axis inserted at the beginning. Use -1 to get an axis at
+        the end.
 
         .. versionadded:: 1.16.0
 
@@ -247,7 +252,7 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
 
     Notes
     -----
-    Logspace is equivalent to the code
+    If base is a scalar, logspace is equivalent to the code
 
     >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
     ... # doctest: +SKIP
@@ -262,6 +267,9 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
     array([100.        ,  177.827941  ,  316.22776602,  562.34132519])
     >>> np.logspace(2.0, 3.0, num=4, base=2.0)
     array([4.        ,  5.0396842 ,  6.34960421,  8.        ])
+    >>> np.logspace(2.0, 3.0, num=4, base=[2.0, 3.0], axis=-1)
+    array([[ 4.        ,  5.0396842 ,  6.34960421,  8.        ],
+           [ 9.        , 12.98024613, 18.72075441, 27.        ]])
 
     Graphical illustration:
 
@@ -279,7 +287,13 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
     >>> plt.show()
 
     """
+    ndmax = np.broadcast(start, stop, base).ndim
+    start, stop, base = (
+        np.array(a, copy=False, subok=True, ndmin=ndmax)
+        for a in (start, stop, base)
+    )
     y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
+    base = np.expand_dims(base, axis=axis)
     if dtype is None:
         return _nx.power(base, y)
     return _nx.power(base, y).astype(dtype, copy=False)
diff --git a/contrib/python/numpy/py3/numpy/core/getlimits.py b/contrib/python/numpy/py3/numpy/core/getlimits.py
index 2c0f462cc49..13414c2a64d 100644
--- a/contrib/python/numpy/py3/numpy/core/getlimits.py
+++ b/contrib/python/numpy/py3/numpy/core/getlimits.py
@@ -5,8 +5,8 @@ __all__ = ['finfo', 'iinfo']
 
 import warnings
 
+from .._utils import set_module
 from ._machar import MachAr
-from .overrides import set_module
 from . import numeric
 from . import numerictypes as ntypes
 from .numeric import array, inf, NaN
@@ -147,8 +147,12 @@ _MACHAR_PARAMS = {
 
 # Key to identify the floating point type.  Key is result of
 # ftype('-0.1').newbyteorder('<').tobytes()
+#
+# 20230201 - use (ftype(-1.0) / ftype(10.0)).newbyteorder('<').tobytes()
+#            instead because stold may have deficiencies on some platforms.
 # See:
 # https://perl5.git.perl.org/perl.git/blob/3118d7d684b56cbeb702af874f4326683c45f045:/Configure
+
 _KNOWN_TYPES = {}
 def _register_type(machar, bytepat):
     _KNOWN_TYPES[bytepat] = machar
@@ -240,8 +244,6 @@ def _register_known_types():
     # IEEE 754 128-bit binary float
     _register_type(float128_ma,
         b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
-    _register_type(float128_ma,
-        b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
     _float_ma[128] = float128_ma
 
     # Known parameters for float80 (Intel 80-bit extended precision)
@@ -329,7 +331,9 @@ def _get_machar(ftype):
     if params is None:
         raise ValueError(repr(ftype))
     # Detect known / suspected types
-    key = ftype('-0.1').newbyteorder('<').tobytes()
+    # ftype(-1.0) / ftype(10.0) is better than ftype('-0.1') because stold
+    # may be deficient
+    key = (ftype(-1.0) / ftype(10.)).newbyteorder('<').tobytes()
     ma_like = None
     if ftype == ntypes.longdouble:
         # Could be 80 bit == 10 byte extended precision, where last bytes can
@@ -338,7 +342,14 @@ def _get_machar(ftype):
         # random garbage.
         ma_like = _KNOWN_TYPES.get(key[:10])
     if ma_like is None:
+        # see if the full key is known.
         ma_like = _KNOWN_TYPES.get(key)
+    if ma_like is None and len(key) == 16:
+        # machine limits could be f80 masquerading as np.float128,
+        # find all keys with length 16 and make new dict, but make the keys
+        # only 10 bytes long, the last bytes can be random garbage
+        _kt = {k[:10]: v for k, v in _KNOWN_TYPES.items() if len(k) == 16}
+        ma_like = _kt.get(key[:10])
     if ma_like is not None:
         return ma_like
     # Fall back to parameter discovery
@@ -352,6 +363,9 @@ def _get_machar(ftype):
 
 def _discovered_machar(ftype):
     """ Create MachAr instance with found information on float types
+
+    TODO: MachAr should be retired completely ideally.  We currently only
+          ever use it system with broken longdouble (valgrind, WSL).
     """
     params = _MACHAR_PARAMS[ftype]
     return MachAr(lambda v: array([v], ftype),
@@ -387,11 +401,6 @@ class finfo:
     iexp : int
         The number of bits in the exponent portion of the floating point
         representation.
-    machar : MachAr
-        The object which calculated these parameters and holds more
-        detailed information.
-
-        .. deprecated:: 1.22
     machep : int
         The exponent that yields `eps`.
     max : floating point number of the appropriate type
@@ -432,7 +441,6 @@ class finfo:
 
     See Also
     --------
-    MachAr : The implementation of the tests that produce this information.
     iinfo : The equivalent for integer data types.
     spacing : The distance between a value and the nearest adjacent number
     nextafter : The next floating point value after x1 towards x2
@@ -475,12 +483,28 @@ class finfo:
 
     def __new__(cls, dtype):
         try:
+            obj = cls._finfo_cache.get(dtype)  # most common path
+            if obj is not None:
+                return obj
+        except TypeError:
+            pass
+
+        if dtype is None:
+            # Deprecated in NumPy 1.25, 2023-01-16
+            warnings.warn(
+                "finfo() dtype cannot be None. This behavior will "
+                "raise an error in the future. (Deprecated in NumPy 1.25)",
+                DeprecationWarning,
+                stacklevel=2
+            )
+
+        try:
             dtype = numeric.dtype(dtype)
         except TypeError:
             # In case a float instance was given
             dtype = numeric.dtype(type(dtype))
 
-        obj = cls._finfo_cache.get(dtype, None)
+        obj = cls._finfo_cache.get(dtype)
         if obj is not None:
             return obj
         dtypes = [dtype]
@@ -490,17 +514,24 @@ class finfo:
             dtype = newdtype
         if not issubclass(dtype, numeric.inexact):
             raise ValueError("data type %r not inexact" % (dtype))
-        obj = cls._finfo_cache.get(dtype, None)
+        obj = cls._finfo_cache.get(dtype)
         if obj is not None:
             return obj
         if not issubclass(dtype, numeric.floating):
             newdtype = _convert_to_float[dtype]
             if newdtype is not dtype:
+                # dtype changed, for example from complex128 to float64
                 dtypes.append(newdtype)
                 dtype = newdtype
-        obj = cls._finfo_cache.get(dtype, None)
-        if obj is not None:
-            return obj
+
+                obj = cls._finfo_cache.get(dtype, None)
+                if obj is not None:
+                    # the original dtype was not in the cache, but the new
+                    # dtype is in the cache. we add the original dtypes to
+                    # the cache and return the result
+                    for dt in dtypes:
+                        cls._finfo_cache[dt] = obj
+                    return obj
         obj = object.__new__(cls)._init(dtype)
         for dt in dtypes:
             cls._finfo_cache[dt] = obj
@@ -595,20 +626,6 @@ class finfo:
         """
         return self.smallest_normal
 
-    @property
-    def machar(self):
-        """The object which calculated these parameters and holds more
-        detailed information.
-
-        .. deprecated:: 1.22
-        """
-        # Deprecated 2021-10-27, NumPy 1.22
-        warnings.warn(
-            "`finfo.machar` is deprecated (NumPy 1.22)",
-            DeprecationWarning, stacklevel=2,
-        )
-        return self._machar
-
 
 @set_module('numpy')
 class iinfo:
diff --git a/contrib/python/numpy/py3/numpy/core/include/meson.build b/contrib/python/numpy/py3/numpy/core/include/meson.build
new file mode 100644
index 00000000000..be07a07b8a5
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/include/meson.build
@@ -0,0 +1,52 @@
+installed_headers = [
+  'numpy/_neighborhood_iterator_imp.h',
+  'numpy/arrayobject.h',
+  'numpy/arrayscalars.h',
+  'numpy/_dtype_api.h',
+  'numpy/experimental_dtype_api.h',
+  'numpy/halffloat.h',
+  'numpy/ndarrayobject.h',
+  'numpy/ndarraytypes.h',
+  'numpy/noprefix.h',
+  'numpy/npy_1_7_deprecated_api.h',
+  'numpy/npy_3kcompat.h',
+  'numpy/npy_common.h',
+  'numpy/npy_cpu.h',
+  'numpy/npy_endian.h',
+  'numpy/npy_interrupt.h',
+  'numpy/npy_math.h',
+  'numpy/npy_no_deprecated_api.h',
+  'numpy/npy_os.h',
+  'numpy/numpyconfig.h',
+  'numpy/old_defines.h',
+  'numpy/ufuncobject.h',
+  'numpy/utils.h',
+]
+
+# Note that oldnumeric.h is not installed on purpose. After investigation,
+# there are only two active repos left which use it, and those got a heads up
+# about removal of this header in numpy 1.25.0:
+#   https://github.com/enthought/enable/issues/1005
+#   https://github.com/fhs/pyhdf/issues/63
+
+py.install_sources(
+  installed_headers,
+  subdir: 'numpy/core/include/numpy'
+)
+
+py.install_sources(
+  [
+    'numpy/random/bitgen.h',
+    'numpy/random/distributions.h',
+  ],
+  subdir: 'numpy/core/include/numpy/random'
+)
+
+
+py.install_sources(
+  [
+    'numpy/libdivide/libdivide.h',
+    'numpy/libdivide/LICENSE.txt',
+  ],
+  subdir: 'numpy/core/include/numpy/random'
+)
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/.doxyfile b/contrib/python/numpy/py3/numpy/core/include/numpy/.doxyfile
new file mode 100644
index 00000000000..ed2aefff78c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/.doxyfile
@@ -0,0 +1,2 @@
+INCLUDE_PATH += @CUR_DIR
+PREDEFINED += NPY_INTERNAL_BUILD
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/__multiarray_api.h b/contrib/python/numpy/py3/numpy/core/include/numpy/__multiarray_api.h
index 6485d07317f..3123dc8ab3b 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/__multiarray_api.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/__multiarray_api.h
@@ -647,7 +647,7 @@ static void **PyArray_API=NULL;
 
 #define PyArray_GetNDArrayCVersion \
         (*(unsigned int (*)(void)) \
-         PyArray_API[0])
+    PyArray_API[0])
 #define PyBigArray_Type (*(PyTypeObject *)PyArray_API[1])
 #define PyArray_Type (*(PyTypeObject *)PyArray_API[2])
 #define PyArrayDescr_Type (*(PyTypeObject *)PyArray_API[3])
@@ -689,526 +689,526 @@ static void **PyArray_API=NULL;
 #define PyVoidArrType_Type (*(PyTypeObject *)PyArray_API[39])
 #define PyArray_SetNumericOps \
         (*(int (*)(PyObject *)) \
-         PyArray_API[40])
+    PyArray_API[40])
 #define PyArray_GetNumericOps \
         (*(PyObject * (*)(void)) \
-         PyArray_API[41])
+    PyArray_API[41])
 #define PyArray_INCREF \
         (*(int (*)(PyArrayObject *)) \
-         PyArray_API[42])
+    PyArray_API[42])
 #define PyArray_XDECREF \
         (*(int (*)(PyArrayObject *)) \
-         PyArray_API[43])
+    PyArray_API[43])
 #define PyArray_SetStringFunction \
         (*(void (*)(PyObject *, int)) \
-         PyArray_API[44])
+    PyArray_API[44])
 #define PyArray_DescrFromType \
         (*(PyArray_Descr * (*)(int)) \
-         PyArray_API[45])
+    PyArray_API[45])
 #define PyArray_TypeObjectFromType \
         (*(PyObject * (*)(int)) \
-         PyArray_API[46])
+    PyArray_API[46])
 #define PyArray_Zero \
         (*(char * (*)(PyArrayObject *)) \
-         PyArray_API[47])
+    PyArray_API[47])
 #define PyArray_One \
         (*(char * (*)(PyArrayObject *)) \
-         PyArray_API[48])
+    PyArray_API[48])
 #define PyArray_CastToType \
         (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \
-         PyArray_API[49])
+    PyArray_API[49])
 #define PyArray_CastTo \
         (*(int (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[50])
+    PyArray_API[50])
 #define PyArray_CastAnyTo \
         (*(int (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[51])
+    PyArray_API[51])
 #define PyArray_CanCastSafely \
         (*(int (*)(int, int)) \
-         PyArray_API[52])
+    PyArray_API[52])
 #define PyArray_CanCastTo \
         (*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *)) \
-         PyArray_API[53])
+    PyArray_API[53])
 #define PyArray_ObjectType \
         (*(int (*)(PyObject *, int)) \
-         PyArray_API[54])
+    PyArray_API[54])
 #define PyArray_DescrFromObject \
         (*(PyArray_Descr * (*)(PyObject *, PyArray_Descr *)) \
-         PyArray_API[55])
+    PyArray_API[55])
 #define PyArray_ConvertToCommonType \
         (*(PyArrayObject ** (*)(PyObject *, int *)) \
-         PyArray_API[56])
+    PyArray_API[56])
 #define PyArray_DescrFromScalar \
         (*(PyArray_Descr * (*)(PyObject *)) \
-         PyArray_API[57])
+    PyArray_API[57])
 #define PyArray_DescrFromTypeObject \
         (*(PyArray_Descr * (*)(PyObject *)) \
-         PyArray_API[58])
+    PyArray_API[58])
 #define PyArray_Size \
         (*(npy_intp (*)(PyObject *)) \
-         PyArray_API[59])
+    PyArray_API[59])
 #define PyArray_Scalar \
         (*(PyObject * (*)(void *, PyArray_Descr *, PyObject *)) \
-         PyArray_API[60])
+    PyArray_API[60])
 #define PyArray_FromScalar \
         (*(PyObject * (*)(PyObject *, PyArray_Descr *)) \
-         PyArray_API[61])
+    PyArray_API[61])
 #define PyArray_ScalarAsCtype \
         (*(void (*)(PyObject *, void *)) \
-         PyArray_API[62])
+    PyArray_API[62])
 #define PyArray_CastScalarToCtype \
         (*(int (*)(PyObject *, void *, PyArray_Descr *)) \
-         PyArray_API[63])
+    PyArray_API[63])
 #define PyArray_CastScalarDirect \
         (*(int (*)(PyObject *, PyArray_Descr *, void *, int)) \
-         PyArray_API[64])
+    PyArray_API[64])
 #define PyArray_ScalarFromObject \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[65])
+    PyArray_API[65])
 #define PyArray_GetCastFunc \
         (*(PyArray_VectorUnaryFunc * (*)(PyArray_Descr *, int)) \
-         PyArray_API[66])
+    PyArray_API[66])
 #define PyArray_FromDims \
         (*(PyObject * (*)(int NPY_UNUSED(nd), int *NPY_UNUSED(d), int NPY_UNUSED(type))) \
-         PyArray_API[67])
+    PyArray_API[67])
 #define PyArray_FromDimsAndDataAndDescr \
         (*(PyObject * (*)(int NPY_UNUSED(nd), int *NPY_UNUSED(d), PyArray_Descr *, char *NPY_UNUSED(data))) \
-         PyArray_API[68])
+    PyArray_API[68])
 #define PyArray_FromAny \
         (*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \
-         PyArray_API[69])
+    PyArray_API[69])
 #define PyArray_EnsureArray \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[70])
+    PyArray_API[70])
 #define PyArray_EnsureAnyArray \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[71])
+    PyArray_API[71])
 #define PyArray_FromFile \
         (*(PyObject * (*)(FILE *, PyArray_Descr *, npy_intp, char *)) \
-         PyArray_API[72])
+    PyArray_API[72])
 #define PyArray_FromString \
         (*(PyObject * (*)(char *, npy_intp, PyArray_Descr *, npy_intp, char *)) \
-         PyArray_API[73])
+    PyArray_API[73])
 #define PyArray_FromBuffer \
         (*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp, npy_intp)) \
-         PyArray_API[74])
+    PyArray_API[74])
 #define PyArray_FromIter \
         (*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp)) \
-         PyArray_API[75])
+    PyArray_API[75])
 #define PyArray_Return \
         (*(PyObject * (*)(PyArrayObject *)) \
-         PyArray_API[76])
+    PyArray_API[76])
 #define PyArray_GetField \
         (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \
-         PyArray_API[77])
+    PyArray_API[77])
 #define PyArray_SetField \
         (*(int (*)(PyArrayObject *, PyArray_Descr *, int, PyObject *)) \
-         PyArray_API[78])
+    PyArray_API[78])
 #define PyArray_Byteswap \
         (*(PyObject * (*)(PyArrayObject *, npy_bool)) \
-         PyArray_API[79])
+    PyArray_API[79])
 #define PyArray_Resize \
         (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, int, NPY_ORDER NPY_UNUSED(order))) \
-         PyArray_API[80])
+    PyArray_API[80])
 #define PyArray_MoveInto \
         (*(int (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[81])
+    PyArray_API[81])
 #define PyArray_CopyInto \
         (*(int (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[82])
+    PyArray_API[82])
 #define PyArray_CopyAnyInto \
         (*(int (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[83])
+    PyArray_API[83])
 #define PyArray_CopyObject \
         (*(int (*)(PyArrayObject *, PyObject *)) \
-         PyArray_API[84])
+    PyArray_API[84])
 #define PyArray_NewCopy \
         (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
-         PyArray_API[85])
+    PyArray_API[85])
 #define PyArray_ToList \
         (*(PyObject * (*)(PyArrayObject *)) \
-         PyArray_API[86])
+    PyArray_API[86])
 #define PyArray_ToString \
         (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
-         PyArray_API[87])
+    PyArray_API[87])
 #define PyArray_ToFile \
         (*(int (*)(PyArrayObject *, FILE *, char *, char *)) \
-         PyArray_API[88])
+    PyArray_API[88])
 #define PyArray_Dump \
         (*(int (*)(PyObject *, PyObject *, int)) \
-         PyArray_API[89])
+    PyArray_API[89])
 #define PyArray_Dumps \
         (*(PyObject * (*)(PyObject *, int)) \
-         PyArray_API[90])
+    PyArray_API[90])
 #define PyArray_ValidType \
         (*(int (*)(int)) \
-         PyArray_API[91])
+    PyArray_API[91])
 #define PyArray_UpdateFlags \
         (*(void (*)(PyArrayObject *, int)) \
-         PyArray_API[92])
+    PyArray_API[92])
 #define PyArray_New \
         (*(PyObject * (*)(PyTypeObject *, int, npy_intp const *, int, npy_intp const *, void *, int, int, PyObject *)) \
-         PyArray_API[93])
+    PyArray_API[93])
 #define PyArray_NewFromDescr \
         (*(PyObject * (*)(PyTypeObject *, PyArray_Descr *, int, npy_intp const *, npy_intp const *, void *, int, PyObject *)) \
-         PyArray_API[94])
+    PyArray_API[94])
 #define PyArray_DescrNew \
         (*(PyArray_Descr * (*)(PyArray_Descr *)) \
-         PyArray_API[95])
+    PyArray_API[95])
 #define PyArray_DescrNewFromType \
         (*(PyArray_Descr * (*)(int)) \
-         PyArray_API[96])
+    PyArray_API[96])
 #define PyArray_GetPriority \
         (*(double (*)(PyObject *, double)) \
-         PyArray_API[97])
+    PyArray_API[97])
 #define PyArray_IterNew \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[98])
+    PyArray_API[98])
 #define PyArray_MultiIterNew \
         (*(PyObject* (*)(int, ...)) \
-         PyArray_API[99])
+    PyArray_API[99])
 #define PyArray_PyIntAsInt \
         (*(int (*)(PyObject *)) \
-         PyArray_API[100])
+    PyArray_API[100])
 #define PyArray_PyIntAsIntp \
         (*(npy_intp (*)(PyObject *)) \
-         PyArray_API[101])
+    PyArray_API[101])
 #define PyArray_Broadcast \
         (*(int (*)(PyArrayMultiIterObject *)) \
-         PyArray_API[102])
+    PyArray_API[102])
 #define PyArray_FillObjectArray \
         (*(void (*)(PyArrayObject *, PyObject *)) \
-         PyArray_API[103])
+    PyArray_API[103])
 #define PyArray_FillWithScalar \
         (*(int (*)(PyArrayObject *, PyObject *)) \
-         PyArray_API[104])
+    PyArray_API[104])
 #define PyArray_CheckStrides \
         (*(npy_bool (*)(int, int, npy_intp, npy_intp, npy_intp const *, npy_intp const *)) \
-         PyArray_API[105])
+    PyArray_API[105])
 #define PyArray_DescrNewByteorder \
         (*(PyArray_Descr * (*)(PyArray_Descr *, char)) \
-         PyArray_API[106])
+    PyArray_API[106])
 #define PyArray_IterAllButAxis \
         (*(PyObject * (*)(PyObject *, int *)) \
-         PyArray_API[107])
+    PyArray_API[107])
 #define PyArray_CheckFromAny \
         (*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \
-         PyArray_API[108])
+    PyArray_API[108])
 #define PyArray_FromArray \
         (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \
-         PyArray_API[109])
+    PyArray_API[109])
 #define PyArray_FromInterface \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[110])
+    PyArray_API[110])
 #define PyArray_FromStructInterface \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[111])
+    PyArray_API[111])
 #define PyArray_FromArrayAttr \
         (*(PyObject * (*)(PyObject *, PyArray_Descr *, PyObject *)) \
-         PyArray_API[112])
+    PyArray_API[112])
 #define PyArray_ScalarKind \
         (*(NPY_SCALARKIND (*)(int, PyArrayObject **)) \
-         PyArray_API[113])
+    PyArray_API[113])
 #define PyArray_CanCoerceScalar \
         (*(int (*)(int, int, NPY_SCALARKIND)) \
-         PyArray_API[114])
+    PyArray_API[114])
 #define PyArray_NewFlagsObject \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[115])
+    PyArray_API[115])
 #define PyArray_CanCastScalar \
         (*(npy_bool (*)(PyTypeObject *, PyTypeObject *)) \
-         PyArray_API[116])
+    PyArray_API[116])
 #define PyArray_CompareUCS4 \
         (*(int (*)(npy_ucs4 const *, npy_ucs4 const *, size_t)) \
-         PyArray_API[117])
+    PyArray_API[117])
 #define PyArray_RemoveSmallest \
         (*(int (*)(PyArrayMultiIterObject *)) \
-         PyArray_API[118])
+    PyArray_API[118])
 #define PyArray_ElementStrides \
         (*(int (*)(PyObject *)) \
-         PyArray_API[119])
+    PyArray_API[119])
 #define PyArray_Item_INCREF \
         (*(void (*)(char *, PyArray_Descr *)) \
-         PyArray_API[120])
+    PyArray_API[120])
 #define PyArray_Item_XDECREF \
         (*(void (*)(char *, PyArray_Descr *)) \
-         PyArray_API[121])
+    PyArray_API[121])
 #define PyArray_FieldNames \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[122])
+    PyArray_API[122])
 #define PyArray_Transpose \
         (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *)) \
-         PyArray_API[123])
+    PyArray_API[123])
 #define PyArray_TakeFrom \
         (*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *, NPY_CLIPMODE)) \
-         PyArray_API[124])
+    PyArray_API[124])
 #define PyArray_PutTo \
         (*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject *, NPY_CLIPMODE)) \
-         PyArray_API[125])
+    PyArray_API[125])
 #define PyArray_PutMask \
         (*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject*)) \
-         PyArray_API[126])
+    PyArray_API[126])
 #define PyArray_Repeat \
         (*(PyObject * (*)(PyArrayObject *, PyObject *, int)) \
-         PyArray_API[127])
+    PyArray_API[127])
 #define PyArray_Choose \
         (*(PyObject * (*)(PyArrayObject *, PyObject *, PyArrayObject *, NPY_CLIPMODE)) \
-         PyArray_API[128])
+    PyArray_API[128])
 #define PyArray_Sort \
         (*(int (*)(PyArrayObject *, int, NPY_SORTKIND)) \
-         PyArray_API[129])
+    PyArray_API[129])
 #define PyArray_ArgSort \
         (*(PyObject * (*)(PyArrayObject *, int, NPY_SORTKIND)) \
-         PyArray_API[130])
+    PyArray_API[130])
 #define PyArray_SearchSorted \
         (*(PyObject * (*)(PyArrayObject *, PyObject *, NPY_SEARCHSIDE, PyObject *)) \
-         PyArray_API[131])
+    PyArray_API[131])
 #define PyArray_ArgMax \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[132])
+    PyArray_API[132])
 #define PyArray_ArgMin \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[133])
+    PyArray_API[133])
 #define PyArray_Reshape \
         (*(PyObject * (*)(PyArrayObject *, PyObject *)) \
-         PyArray_API[134])
+    PyArray_API[134])
 #define PyArray_Newshape \
         (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, NPY_ORDER)) \
-         PyArray_API[135])
+    PyArray_API[135])
 #define PyArray_Squeeze \
         (*(PyObject * (*)(PyArrayObject *)) \
-         PyArray_API[136])
+    PyArray_API[136])
 #define PyArray_View \
         (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, PyTypeObject *)) \
-         PyArray_API[137])
+    PyArray_API[137])
 #define PyArray_SwapAxes \
         (*(PyObject * (*)(PyArrayObject *, int, int)) \
-         PyArray_API[138])
+    PyArray_API[138])
 #define PyArray_Max \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[139])
+    PyArray_API[139])
 #define PyArray_Min \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[140])
+    PyArray_API[140])
 #define PyArray_Ptp \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[141])
+    PyArray_API[141])
 #define PyArray_Mean \
         (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
-         PyArray_API[142])
+    PyArray_API[142])
 #define PyArray_Trace \
         (*(PyObject * (*)(PyArrayObject *, int, int, int, int, PyArrayObject *)) \
-         PyArray_API[143])
+    PyArray_API[143])
 #define PyArray_Diagonal \
         (*(PyObject * (*)(PyArrayObject *, int, int, int)) \
-         PyArray_API[144])
+    PyArray_API[144])
 #define PyArray_Clip \
         (*(PyObject * (*)(PyArrayObject *, PyObject *, PyObject *, PyArrayObject *)) \
-         PyArray_API[145])
+    PyArray_API[145])
 #define PyArray_Conjugate \
         (*(PyObject * (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[146])
+    PyArray_API[146])
 #define PyArray_Nonzero \
         (*(PyObject * (*)(PyArrayObject *)) \
-         PyArray_API[147])
+    PyArray_API[147])
 #define PyArray_Std \
         (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *, int)) \
-         PyArray_API[148])
+    PyArray_API[148])
 #define PyArray_Sum \
         (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
-         PyArray_API[149])
+    PyArray_API[149])
 #define PyArray_CumSum \
         (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
-         PyArray_API[150])
+    PyArray_API[150])
 #define PyArray_Prod \
         (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
-         PyArray_API[151])
+    PyArray_API[151])
 #define PyArray_CumProd \
         (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \
-         PyArray_API[152])
+    PyArray_API[152])
 #define PyArray_All \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[153])
+    PyArray_API[153])
 #define PyArray_Any \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[154])
+    PyArray_API[154])
 #define PyArray_Compress \
         (*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *)) \
-         PyArray_API[155])
+    PyArray_API[155])
 #define PyArray_Flatten \
         (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
-         PyArray_API[156])
+    PyArray_API[156])
 #define PyArray_Ravel \
         (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \
-         PyArray_API[157])
+    PyArray_API[157])
 #define PyArray_MultiplyList \
         (*(npy_intp (*)(npy_intp const *, int)) \
-         PyArray_API[158])
+    PyArray_API[158])
 #define PyArray_MultiplyIntList \
         (*(int (*)(int const *, int)) \
-         PyArray_API[159])
+    PyArray_API[159])
 #define PyArray_GetPtr \
         (*(void * (*)(PyArrayObject *, npy_intp const*)) \
-         PyArray_API[160])
+    PyArray_API[160])
 #define PyArray_CompareLists \
         (*(int (*)(npy_intp const *, npy_intp const *, int)) \
-         PyArray_API[161])
+    PyArray_API[161])
 #define PyArray_AsCArray \
         (*(int (*)(PyObject **, void *, npy_intp *, int, PyArray_Descr*)) \
-         PyArray_API[162])
+    PyArray_API[162])
 #define PyArray_As1D \
         (*(int (*)(PyObject **NPY_UNUSED(op), char **NPY_UNUSED(ptr), int *NPY_UNUSED(d1), int NPY_UNUSED(typecode))) \
-         PyArray_API[163])
+    PyArray_API[163])
 #define PyArray_As2D \
         (*(int (*)(PyObject **NPY_UNUSED(op), char ***NPY_UNUSED(ptr), int *NPY_UNUSED(d1), int *NPY_UNUSED(d2), int NPY_UNUSED(typecode))) \
-         PyArray_API[164])
+    PyArray_API[164])
 #define PyArray_Free \
         (*(int (*)(PyObject *, void *)) \
-         PyArray_API[165])
+    PyArray_API[165])
 #define PyArray_Converter \
         (*(int (*)(PyObject *, PyObject **)) \
-         PyArray_API[166])
+    PyArray_API[166])
 #define PyArray_IntpFromSequence \
         (*(int (*)(PyObject *, npy_intp *, int)) \
-         PyArray_API[167])
+    PyArray_API[167])
 #define PyArray_Concatenate \
         (*(PyObject * (*)(PyObject *, int)) \
-         PyArray_API[168])
+    PyArray_API[168])
 #define PyArray_InnerProduct \
         (*(PyObject * (*)(PyObject *, PyObject *)) \
-         PyArray_API[169])
+    PyArray_API[169])
 #define PyArray_MatrixProduct \
         (*(PyObject * (*)(PyObject *, PyObject *)) \
-         PyArray_API[170])
+    PyArray_API[170])
 #define PyArray_CopyAndTranspose \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[171])
+    PyArray_API[171])
 #define PyArray_Correlate \
         (*(PyObject * (*)(PyObject *, PyObject *, int)) \
-         PyArray_API[172])
+    PyArray_API[172])
 #define PyArray_TypestrConvert \
         (*(int (*)(int, int)) \
-         PyArray_API[173])
+    PyArray_API[173])
 #define PyArray_DescrConverter \
         (*(int (*)(PyObject *, PyArray_Descr **)) \
-         PyArray_API[174])
+    PyArray_API[174])
 #define PyArray_DescrConverter2 \
         (*(int (*)(PyObject *, PyArray_Descr **)) \
-         PyArray_API[175])
+    PyArray_API[175])
 #define PyArray_IntpConverter \
         (*(int (*)(PyObject *, PyArray_Dims *)) \
-         PyArray_API[176])
+    PyArray_API[176])
 #define PyArray_BufferConverter \
         (*(int (*)(PyObject *, PyArray_Chunk *)) \
-         PyArray_API[177])
+    PyArray_API[177])
 #define PyArray_AxisConverter \
         (*(int (*)(PyObject *, int *)) \
-         PyArray_API[178])
+    PyArray_API[178])
 #define PyArray_BoolConverter \
         (*(int (*)(PyObject *, npy_bool *)) \
-         PyArray_API[179])
+    PyArray_API[179])
 #define PyArray_ByteorderConverter \
         (*(int (*)(PyObject *, char *)) \
-         PyArray_API[180])
+    PyArray_API[180])
 #define PyArray_OrderConverter \
         (*(int (*)(PyObject *, NPY_ORDER *)) \
-         PyArray_API[181])
+    PyArray_API[181])
 #define PyArray_EquivTypes \
         (*(unsigned char (*)(PyArray_Descr *, PyArray_Descr *)) \
-         PyArray_API[182])
+    PyArray_API[182])
 #define PyArray_Zeros \
         (*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \
-         PyArray_API[183])
+    PyArray_API[183])
 #define PyArray_Empty \
         (*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \
-         PyArray_API[184])
+    PyArray_API[184])
 #define PyArray_Where \
         (*(PyObject * (*)(PyObject *, PyObject *, PyObject *)) \
-         PyArray_API[185])
+    PyArray_API[185])
 #define PyArray_Arange \
         (*(PyObject * (*)(double, double, double, int)) \
-         PyArray_API[186])
+    PyArray_API[186])
 #define PyArray_ArangeObj \
         (*(PyObject * (*)(PyObject *, PyObject *, PyObject *, PyArray_Descr *)) \
-         PyArray_API[187])
+    PyArray_API[187])
 #define PyArray_SortkindConverter \
         (*(int (*)(PyObject *, NPY_SORTKIND *)) \
-         PyArray_API[188])
+    PyArray_API[188])
 #define PyArray_LexSort \
         (*(PyObject * (*)(PyObject *, int)) \
-         PyArray_API[189])
+    PyArray_API[189])
 #define PyArray_Round \
         (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \
-         PyArray_API[190])
+    PyArray_API[190])
 #define PyArray_EquivTypenums \
         (*(unsigned char (*)(int, int)) \
-         PyArray_API[191])
+    PyArray_API[191])
 #define PyArray_RegisterDataType \
         (*(int (*)(PyArray_Descr *)) \
-         PyArray_API[192])
+    PyArray_API[192])
 #define PyArray_RegisterCastFunc \
         (*(int (*)(PyArray_Descr *, int, PyArray_VectorUnaryFunc *)) \
-         PyArray_API[193])
+    PyArray_API[193])
 #define PyArray_RegisterCanCast \
         (*(int (*)(PyArray_Descr *, int, NPY_SCALARKIND)) \
-         PyArray_API[194])
+    PyArray_API[194])
 #define PyArray_InitArrFuncs \
         (*(void (*)(PyArray_ArrFuncs *)) \
-         PyArray_API[195])
+    PyArray_API[195])
 #define PyArray_IntTupleFromIntp \
         (*(PyObject * (*)(int, npy_intp const *)) \
-         PyArray_API[196])
+    PyArray_API[196])
 #define PyArray_TypeNumFromName \
         (*(int (*)(char const *)) \
-         PyArray_API[197])
+    PyArray_API[197])
 #define PyArray_ClipmodeConverter \
         (*(int (*)(PyObject *, NPY_CLIPMODE *)) \
-         PyArray_API[198])
+    PyArray_API[198])
 #define PyArray_OutputConverter \
         (*(int (*)(PyObject *, PyArrayObject **)) \
-         PyArray_API[199])
+    PyArray_API[199])
 #define PyArray_BroadcastToShape \
         (*(PyObject * (*)(PyObject *, npy_intp *, int)) \
-         PyArray_API[200])
+    PyArray_API[200])
 #define _PyArray_SigintHandler \
         (*(void (*)(int)) \
-         PyArray_API[201])
+    PyArray_API[201])
 #define _PyArray_GetSigintBuf \
         (*(void* (*)(void)) \
-         PyArray_API[202])
+    PyArray_API[202])
 #define PyArray_DescrAlignConverter \
         (*(int (*)(PyObject *, PyArray_Descr **)) \
-         PyArray_API[203])
+    PyArray_API[203])
 #define PyArray_DescrAlignConverter2 \
         (*(int (*)(PyObject *, PyArray_Descr **)) \
-         PyArray_API[204])
+    PyArray_API[204])
 #define PyArray_SearchsideConverter \
         (*(int (*)(PyObject *, void *)) \
-         PyArray_API[205])
+    PyArray_API[205])
 #define PyArray_CheckAxis \
         (*(PyObject * (*)(PyArrayObject *, int *, int)) \
-         PyArray_API[206])
+    PyArray_API[206])
 #define PyArray_OverflowMultiplyList \
         (*(npy_intp (*)(npy_intp const *, int)) \
-         PyArray_API[207])
+    PyArray_API[207])
 #define PyArray_CompareString \
         (*(int (*)(const char *, const char *, size_t)) \
-         PyArray_API[208])
+    PyArray_API[208])
 #define PyArray_MultiIterFromObjects \
         (*(PyObject* (*)(PyObject **, int, int, ...)) \
-         PyArray_API[209])
+    PyArray_API[209])
 #define PyArray_GetEndianness \
         (*(int (*)(void)) \
-         PyArray_API[210])
+    PyArray_API[210])
 #define PyArray_GetNDArrayCFeatureVersion \
         (*(unsigned int (*)(void)) \
-         PyArray_API[211])
+    PyArray_API[211])
 #define PyArray_Correlate2 \
         (*(PyObject * (*)(PyObject *, PyObject *, int)) \
-         PyArray_API[212])
+    PyArray_API[212])
 #define PyArray_NeighborhoodIterNew \
         (*(PyObject* (*)(PyArrayIterObject *, const npy_intp *, int, PyArrayObject*)) \
-         PyArray_API[213])
+    PyArray_API[213])
 #define PyTimeIntegerArrType_Type (*(PyTypeObject *)PyArray_API[214])
 #define PyDatetimeArrType_Type (*(PyTypeObject *)PyArray_API[215])
 #define PyTimedeltaArrType_Type (*(PyTypeObject *)PyArray_API[216])
@@ -1216,263 +1216,269 @@ static void **PyArray_API=NULL;
 #define NpyIter_Type (*(PyTypeObject *)PyArray_API[218])
 #define PyArray_SetDatetimeParseFunction \
         (*(void (*)(PyObject *NPY_UNUSED(op))) \
-         PyArray_API[219])
+    PyArray_API[219])
 #define PyArray_DatetimeToDatetimeStruct \
         (*(void (*)(npy_datetime NPY_UNUSED(val), NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_datetimestruct *)) \
-         PyArray_API[220])
+    PyArray_API[220])
 #define PyArray_TimedeltaToTimedeltaStruct \
         (*(void (*)(npy_timedelta NPY_UNUSED(val), NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_timedeltastruct *)) \
-         PyArray_API[221])
+    PyArray_API[221])
 #define PyArray_DatetimeStructToDatetime \
         (*(npy_datetime (*)(NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_datetimestruct *NPY_UNUSED(d))) \
-         PyArray_API[222])
+    PyArray_API[222])
 #define PyArray_TimedeltaStructToTimedelta \
         (*(npy_datetime (*)(NPY_DATETIMEUNIT NPY_UNUSED(fr), npy_timedeltastruct *NPY_UNUSED(d))) \
-         PyArray_API[223])
+    PyArray_API[223])
 #define NpyIter_New \
         (*(NpyIter * (*)(PyArrayObject *, npy_uint32, NPY_ORDER, NPY_CASTING, PyArray_Descr*)) \
-         PyArray_API[224])
+    PyArray_API[224])
 #define NpyIter_MultiNew \
         (*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **)) \
-         PyArray_API[225])
+    PyArray_API[225])
 #define NpyIter_AdvancedNew \
         (*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **, int, int **, npy_intp *, npy_intp)) \
-         PyArray_API[226])
+    PyArray_API[226])
 #define NpyIter_Copy \
         (*(NpyIter * (*)(NpyIter *)) \
-         PyArray_API[227])
+    PyArray_API[227])
 #define NpyIter_Deallocate \
         (*(int (*)(NpyIter *)) \
-         PyArray_API[228])
+    PyArray_API[228])
 #define NpyIter_HasDelayedBufAlloc \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[229])
+    PyArray_API[229])
 #define NpyIter_HasExternalLoop \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[230])
+    PyArray_API[230])
 #define NpyIter_EnableExternalLoop \
         (*(int (*)(NpyIter *)) \
-         PyArray_API[231])
+    PyArray_API[231])
 #define NpyIter_GetInnerStrideArray \
         (*(npy_intp * (*)(NpyIter *)) \
-         PyArray_API[232])
+    PyArray_API[232])
 #define NpyIter_GetInnerLoopSizePtr \
         (*(npy_intp * (*)(NpyIter *)) \
-         PyArray_API[233])
+    PyArray_API[233])
 #define NpyIter_Reset \
         (*(int (*)(NpyIter *, char **)) \
-         PyArray_API[234])
+    PyArray_API[234])
 #define NpyIter_ResetBasePointers \
         (*(int (*)(NpyIter *, char **, char **)) \
-         PyArray_API[235])
+    PyArray_API[235])
 #define NpyIter_ResetToIterIndexRange \
         (*(int (*)(NpyIter *, npy_intp, npy_intp, char **)) \
-         PyArray_API[236])
+    PyArray_API[236])
 #define NpyIter_GetNDim \
         (*(int (*)(NpyIter *)) \
-         PyArray_API[237])
+    PyArray_API[237])
 #define NpyIter_GetNOp \
         (*(int (*)(NpyIter *)) \
-         PyArray_API[238])
+    PyArray_API[238])
 #define NpyIter_GetIterNext \
         (*(NpyIter_IterNextFunc * (*)(NpyIter *, char **)) \
-         PyArray_API[239])
+    PyArray_API[239])
 #define NpyIter_GetIterSize \
         (*(npy_intp (*)(NpyIter *)) \
-         PyArray_API[240])
+    PyArray_API[240])
 #define NpyIter_GetIterIndexRange \
         (*(void (*)(NpyIter *, npy_intp *, npy_intp *)) \
-         PyArray_API[241])
+    PyArray_API[241])
 #define NpyIter_GetIterIndex \
         (*(npy_intp (*)(NpyIter *)) \
-         PyArray_API[242])
+    PyArray_API[242])
 #define NpyIter_GotoIterIndex \
         (*(int (*)(NpyIter *, npy_intp)) \
-         PyArray_API[243])
+    PyArray_API[243])
 #define NpyIter_HasMultiIndex \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[244])
+    PyArray_API[244])
 #define NpyIter_GetShape \
         (*(int (*)(NpyIter *, npy_intp *)) \
-         PyArray_API[245])
+    PyArray_API[245])
 #define NpyIter_GetGetMultiIndex \
         (*(NpyIter_GetMultiIndexFunc * (*)(NpyIter *, char **)) \
-         PyArray_API[246])
+    PyArray_API[246])
 #define NpyIter_GotoMultiIndex \
         (*(int (*)(NpyIter *, npy_intp const *)) \
-         PyArray_API[247])
+    PyArray_API[247])
 #define NpyIter_RemoveMultiIndex \
         (*(int (*)(NpyIter *)) \
-         PyArray_API[248])
+    PyArray_API[248])
 #define NpyIter_HasIndex \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[249])
+    PyArray_API[249])
 #define NpyIter_IsBuffered \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[250])
+    PyArray_API[250])
 #define NpyIter_IsGrowInner \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[251])
+    PyArray_API[251])
 #define NpyIter_GetBufferSize \
         (*(npy_intp (*)(NpyIter *)) \
-         PyArray_API[252])
+    PyArray_API[252])
 #define NpyIter_GetIndexPtr \
         (*(npy_intp * (*)(NpyIter *)) \
-         PyArray_API[253])
+    PyArray_API[253])
 #define NpyIter_GotoIndex \
         (*(int (*)(NpyIter *, npy_intp)) \
-         PyArray_API[254])
+    PyArray_API[254])
 #define NpyIter_GetDataPtrArray \
         (*(char ** (*)(NpyIter *)) \
-         PyArray_API[255])
+    PyArray_API[255])
 #define NpyIter_GetDescrArray \
         (*(PyArray_Descr ** (*)(NpyIter *)) \
-         PyArray_API[256])
+    PyArray_API[256])
 #define NpyIter_GetOperandArray \
         (*(PyArrayObject ** (*)(NpyIter *)) \
-         PyArray_API[257])
+    PyArray_API[257])
 #define NpyIter_GetIterView \
         (*(PyArrayObject * (*)(NpyIter *, npy_intp)) \
-         PyArray_API[258])
+    PyArray_API[258])
 #define NpyIter_GetReadFlags \
         (*(void (*)(NpyIter *, char *)) \
-         PyArray_API[259])
+    PyArray_API[259])
 #define NpyIter_GetWriteFlags \
         (*(void (*)(NpyIter *, char *)) \
-         PyArray_API[260])
+    PyArray_API[260])
 #define NpyIter_DebugPrint \
         (*(void (*)(NpyIter *)) \
-         PyArray_API[261])
+    PyArray_API[261])
 #define NpyIter_IterationNeedsAPI \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[262])
+    PyArray_API[262])
 #define NpyIter_GetInnerFixedStrideArray \
         (*(void (*)(NpyIter *, npy_intp *)) \
-         PyArray_API[263])
+    PyArray_API[263])
 #define NpyIter_RemoveAxis \
         (*(int (*)(NpyIter *, int)) \
-         PyArray_API[264])
+    PyArray_API[264])
 #define NpyIter_GetAxisStrideArray \
         (*(npy_intp * (*)(NpyIter *, int)) \
-         PyArray_API[265])
+    PyArray_API[265])
 #define NpyIter_RequiresBuffering \
         (*(npy_bool (*)(NpyIter *)) \
-         PyArray_API[266])
+    PyArray_API[266])
 #define NpyIter_GetInitialDataPtrArray \
         (*(char ** (*)(NpyIter *)) \
-         PyArray_API[267])
+    PyArray_API[267])
 #define NpyIter_CreateCompatibleStrides \
         (*(int (*)(NpyIter *, npy_intp, npy_intp *)) \
-         PyArray_API[268])
+    PyArray_API[268])
 #define PyArray_CastingConverter \
         (*(int (*)(PyObject *, NPY_CASTING *)) \
-         PyArray_API[269])
+    PyArray_API[269])
 #define PyArray_CountNonzero \
         (*(npy_intp (*)(PyArrayObject *)) \
-         PyArray_API[270])
+    PyArray_API[270])
 #define PyArray_PromoteTypes \
         (*(PyArray_Descr * (*)(PyArray_Descr *, PyArray_Descr *)) \
-         PyArray_API[271])
+    PyArray_API[271])
 #define PyArray_MinScalarType \
         (*(PyArray_Descr * (*)(PyArrayObject *)) \
-         PyArray_API[272])
+    PyArray_API[272])
 #define PyArray_ResultType \
         (*(PyArray_Descr * (*)(npy_intp, PyArrayObject *arrs[], npy_intp, PyArray_Descr *descrs[])) \
-         PyArray_API[273])
+    PyArray_API[273])
 #define PyArray_CanCastArrayTo \
         (*(npy_bool (*)(PyArrayObject *, PyArray_Descr *, NPY_CASTING)) \
-         PyArray_API[274])
+    PyArray_API[274])
 #define PyArray_CanCastTypeTo \
         (*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *, NPY_CASTING)) \
-         PyArray_API[275])
+    PyArray_API[275])
 #define PyArray_EinsteinSum \
         (*(PyArrayObject * (*)(char *, npy_intp, PyArrayObject **, PyArray_Descr *, NPY_ORDER, NPY_CASTING, PyArrayObject *)) \
-         PyArray_API[276])
+    PyArray_API[276])
 #define PyArray_NewLikeArray \
         (*(PyObject * (*)(PyArrayObject *, NPY_ORDER, PyArray_Descr *, int)) \
-         PyArray_API[277])
+    PyArray_API[277])
 #define PyArray_GetArrayParamsFromObject \
         (*(int (*)(PyObject *NPY_UNUSED(op), PyArray_Descr *NPY_UNUSED(requested_dtype), npy_bool NPY_UNUSED(writeable), PyArray_Descr **NPY_UNUSED(out_dtype), int *NPY_UNUSED(out_ndim), npy_intp *NPY_UNUSED(out_dims), PyArrayObject **NPY_UNUSED(out_arr), PyObject *NPY_UNUSED(context))) \
-         PyArray_API[278])
+    PyArray_API[278])
 #define PyArray_ConvertClipmodeSequence \
         (*(int (*)(PyObject *, NPY_CLIPMODE *, int)) \
-         PyArray_API[279])
+    PyArray_API[279])
 #define PyArray_MatrixProduct2 \
         (*(PyObject * (*)(PyObject *, PyObject *, PyArrayObject*)) \
-         PyArray_API[280])
+    PyArray_API[280])
 #define NpyIter_IsFirstVisit \
         (*(npy_bool (*)(NpyIter *, int)) \
-         PyArray_API[281])
+    PyArray_API[281])
 #define PyArray_SetBaseObject \
         (*(int (*)(PyArrayObject *, PyObject *)) \
-         PyArray_API[282])
+    PyArray_API[282])
 #define PyArray_CreateSortedStridePerm \
         (*(void (*)(int, npy_intp const *, npy_stride_sort_item *)) \
-         PyArray_API[283])
+    PyArray_API[283])
 #define PyArray_RemoveAxesInPlace \
         (*(void (*)(PyArrayObject *, const npy_bool *)) \
-         PyArray_API[284])
+    PyArray_API[284])
 #define PyArray_DebugPrint \
         (*(void (*)(PyArrayObject *)) \
-         PyArray_API[285])
+    PyArray_API[285])
 #define PyArray_FailUnlessWriteable \
         (*(int (*)(PyArrayObject *, const char *)) \
-         PyArray_API[286])
+    PyArray_API[286])
 #define PyArray_SetUpdateIfCopyBase \
         (*(int (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[287])
+    PyArray_API[287])
 #define PyDataMem_NEW \
         (*(void * (*)(size_t)) \
-         PyArray_API[288])
+    PyArray_API[288])
 #define PyDataMem_FREE \
         (*(void (*)(void *)) \
-         PyArray_API[289])
+    PyArray_API[289])
 #define PyDataMem_RENEW \
         (*(void * (*)(void *, size_t)) \
-         PyArray_API[290])
+    PyArray_API[290])
 #define PyDataMem_SetEventHook \
         (*(PyDataMem_EventHookFunc * (*)(PyDataMem_EventHookFunc *, void *, void **)) \
-         PyArray_API[291])
+    PyArray_API[291])
 #define NPY_DEFAULT_ASSIGN_CASTING (*(NPY_CASTING *)PyArray_API[292])
 #define PyArray_MapIterSwapAxes \
         (*(void (*)(PyArrayMapIterObject *, PyArrayObject **, int)) \
-         PyArray_API[293])
+    PyArray_API[293])
 #define PyArray_MapIterArray \
         (*(PyObject * (*)(PyArrayObject *, PyObject *)) \
-         PyArray_API[294])
+    PyArray_API[294])
 #define PyArray_MapIterNext \
         (*(void (*)(PyArrayMapIterObject *)) \
-         PyArray_API[295])
+    PyArray_API[295])
 #define PyArray_Partition \
         (*(int (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \
-         PyArray_API[296])
+    PyArray_API[296])
 #define PyArray_ArgPartition \
         (*(PyObject * (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \
-         PyArray_API[297])
+    PyArray_API[297])
 #define PyArray_SelectkindConverter \
         (*(int (*)(PyObject *, NPY_SELECTKIND *)) \
-         PyArray_API[298])
+    PyArray_API[298])
 #define PyDataMem_NEW_ZEROED \
         (*(void * (*)(size_t, size_t)) \
-         PyArray_API[299])
+    PyArray_API[299])
 #define PyArray_CheckAnyScalarExact \
         (*(int (*)(PyObject *)) \
-         PyArray_API[300])
+    PyArray_API[300])
 #define PyArray_MapIterArrayCopyIfOverlap \
         (*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *)) \
-         PyArray_API[301])
+    PyArray_API[301])
 #define PyArray_ResolveWritebackIfCopy \
         (*(int (*)(PyArrayObject *)) \
-         PyArray_API[302])
+    PyArray_API[302])
 #define PyArray_SetWritebackIfCopyBase \
         (*(int (*)(PyArrayObject *, PyArrayObject *)) \
-         PyArray_API[303])
+    PyArray_API[303])
+
+#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
 #define PyDataMem_SetHandler \
         (*(PyObject * (*)(PyObject *)) \
-         PyArray_API[304])
+    PyArray_API[304])
+#endif
+
+#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
 #define PyDataMem_GetHandler \
         (*(PyObject * (*)(void)) \
-         PyArray_API[305])
+    PyArray_API[305])
+#endif
 #define PyDataMem_DefaultHandler (*(PyObject* *)PyArray_API[306])
 
 #if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT)
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/__ufunc_api.h b/contrib/python/numpy/py3/numpy/core/include/numpy/__ufunc_api.h
index 5b5a9f4dfa8..e2efe29e863 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/__ufunc_api.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/__ufunc_api.h
@@ -109,132 +109,135 @@ static void **PyUFunc_API=NULL;
 #define PyUFunc_Type (*(PyTypeObject *)PyUFunc_API[0])
 #define PyUFunc_FromFuncAndData \
         (*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int)) \
-         PyUFunc_API[1])
+    PyUFunc_API[1])
 #define PyUFunc_RegisterLoopForType \
         (*(int (*)(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *)) \
-         PyUFunc_API[2])
+    PyUFunc_API[2])
 #define PyUFunc_GenericFunction \
         (*(int (*)(PyUFuncObject *NPY_UNUSED(ufunc), PyObject *NPY_UNUSED(args), PyObject *NPY_UNUSED(kwds), PyArrayObject **NPY_UNUSED(op))) \
-         PyUFunc_API[3])
+    PyUFunc_API[3])
 #define PyUFunc_f_f_As_d_d \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[4])
+    PyUFunc_API[4])
 #define PyUFunc_d_d \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[5])
+    PyUFunc_API[5])
 #define PyUFunc_f_f \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[6])
+    PyUFunc_API[6])
 #define PyUFunc_g_g \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[7])
+    PyUFunc_API[7])
 #define PyUFunc_F_F_As_D_D \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[8])
+    PyUFunc_API[8])
 #define PyUFunc_F_F \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[9])
+    PyUFunc_API[9])
 #define PyUFunc_D_D \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[10])
+    PyUFunc_API[10])
 #define PyUFunc_G_G \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[11])
+    PyUFunc_API[11])
 #define PyUFunc_O_O \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[12])
+    PyUFunc_API[12])
 #define PyUFunc_ff_f_As_dd_d \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[13])
+    PyUFunc_API[13])
 #define PyUFunc_ff_f \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[14])
+    PyUFunc_API[14])
 #define PyUFunc_dd_d \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[15])
+    PyUFunc_API[15])
 #define PyUFunc_gg_g \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[16])
+    PyUFunc_API[16])
 #define PyUFunc_FF_F_As_DD_D \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[17])
+    PyUFunc_API[17])
 #define PyUFunc_DD_D \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[18])
+    PyUFunc_API[18])
 #define PyUFunc_FF_F \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[19])
+    PyUFunc_API[19])
 #define PyUFunc_GG_G \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[20])
+    PyUFunc_API[20])
 #define PyUFunc_OO_O \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[21])
+    PyUFunc_API[21])
 #define PyUFunc_O_O_method \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[22])
+    PyUFunc_API[22])
 #define PyUFunc_OO_O_method \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[23])
+    PyUFunc_API[23])
 #define PyUFunc_On_Om \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[24])
+    PyUFunc_API[24])
 #define PyUFunc_GetPyValues \
         (*(int (*)(char *, int *, int *, PyObject **)) \
-         PyUFunc_API[25])
+    PyUFunc_API[25])
 #define PyUFunc_checkfperr \
         (*(int (*)(int, PyObject *, int *)) \
-         PyUFunc_API[26])
+    PyUFunc_API[26])
 #define PyUFunc_clearfperr \
         (*(void (*)(void)) \
-         PyUFunc_API[27])
+    PyUFunc_API[27])
 #define PyUFunc_getfperr \
         (*(int (*)(void)) \
-         PyUFunc_API[28])
+    PyUFunc_API[28])
 #define PyUFunc_handlefperr \
         (*(int (*)(int, PyObject *, int, int *)) \
-         PyUFunc_API[29])
+    PyUFunc_API[29])
 #define PyUFunc_ReplaceLoopBySignature \
         (*(int (*)(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *)) \
-         PyUFunc_API[30])
+    PyUFunc_API[30])
 #define PyUFunc_FromFuncAndDataAndSignature \
         (*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int, const char *)) \
-         PyUFunc_API[31])
+    PyUFunc_API[31])
 #define PyUFunc_SetUsesArraysAsData \
         (*(int (*)(void **NPY_UNUSED(data), size_t NPY_UNUSED(i))) \
-         PyUFunc_API[32])
+    PyUFunc_API[32])
 #define PyUFunc_e_e \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[33])
+    PyUFunc_API[33])
 #define PyUFunc_e_e_As_f_f \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[34])
+    PyUFunc_API[34])
 #define PyUFunc_e_e_As_d_d \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[35])
+    PyUFunc_API[35])
 #define PyUFunc_ee_e \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[36])
+    PyUFunc_API[36])
 #define PyUFunc_ee_e_As_ff_f \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[37])
+    PyUFunc_API[37])
 #define PyUFunc_ee_e_As_dd_d \
         (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
-         PyUFunc_API[38])
+    PyUFunc_API[38])
 #define PyUFunc_DefaultTypeResolver \
         (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **)) \
-         PyUFunc_API[39])
+    PyUFunc_API[39])
 #define PyUFunc_ValidateCasting \
         (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr **)) \
-         PyUFunc_API[40])
+    PyUFunc_API[40])
 #define PyUFunc_RegisterLoopForDescr \
         (*(int (*)(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *)) \
-         PyUFunc_API[41])
+    PyUFunc_API[41])
+
+#if NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION
 #define PyUFunc_FromFuncAndDataAndSignatureAndIdentity \
         (*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *)) \
-         PyUFunc_API[42])
+    PyUFunc_API[42])
+#endif
 
-static NPY_INLINE int
+static inline int
 _import_umath(void)
 {
   PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/__umath_generated.c b/contrib/python/numpy/py3/numpy/core/include/numpy/__umath_generated.c
index 0e12deb98a1..bebeef60349 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/__umath_generated.c
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/__umath_generated.c
@@ -9,7 +9,9 @@
 #include "loops.h"
 #include "matmul.h"
 #include "clip.h"
+#include "dtypemeta.h"
 #include "_umath_doc_generated.h"
+
 static PyUFuncGenericFunction _arg_functions[] = {CFLOAT__arg, CDOUBLE__arg, CLONGDOUBLE__arg};
 static void * _arg_data[] = {(void *)NULL, (void *)NULL, (void *)NULL};
 static char _arg_signatures[] = {NPY_CFLOAT, NPY_FLOAT, NPY_CDOUBLE, NPY_DOUBLE, NPY_CLONGDOUBLE, NPY_LONGDOUBLE};
@@ -19,7 +21,7 @@ static char _ones_like_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, N
 static PyUFuncGenericFunction absolute_functions[] = {BOOL_absolute, BYTE_absolute, UBYTE_absolute, SHORT_absolute, USHORT_absolute, INT_absolute, UINT_absolute, LONG_absolute, ULONG_absolute, LONGLONG_absolute, ULONGLONG_absolute, HALF_absolute, FLOAT_absolute, DOUBLE_absolute, LONGDOUBLE_absolute, TIMEDELTA_absolute, CFLOAT_absolute, CDOUBLE_absolute, CLONGDOUBLE_absolute, NULL};
 static void * absolute_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char absolute_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_CFLOAT, NPY_FLOAT, NPY_CDOUBLE, NPY_DOUBLE, NPY_CLONGDOUBLE, NPY_LONGDOUBLE, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction add_functions[] = {BOOL_add, BYTE_add, UBYTE_add, SHORT_add, USHORT_add, INT_add, UINT_add, LONG_add, ULONG_add, LONGLONG_add, ULONGLONG_add, HALF_add, FLOAT_add, DOUBLE_add, LONGDOUBLE_add, CFLOAT_add, CDOUBLE_add, CLONGDOUBLE_add, DATETIME_Mm_M_add, TIMEDELTA_mm_m_add, DATETIME_mM_M_add, NULL};
+static PyUFuncGenericFunction add_functions[] = {BOOL_logical_or, BYTE_add, UBYTE_add, SHORT_add, USHORT_add, INT_add, UINT_add, LONG_add, ULONG_add, LONGLONG_add, ULONGLONG_add, HALF_add, FLOAT_add, DOUBLE_add, LONGDOUBLE_add, CFLOAT_add, CDOUBLE_add, CLONGDOUBLE_add, DATETIME_Mm_M_add, TIMEDELTA_mm_m_add, DATETIME_mM_M_add, NULL};
 static void * add_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char add_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_DATETIME, NPY_TIMEDELTA, NPY_DATETIME, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_DATETIME, NPY_DATETIME, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction arccos_functions[] = {HALF_arccos, FLOAT_arccos, DOUBLE_arccos, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL};
@@ -43,13 +45,13 @@ static char arctan2_signatures[] = {NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY
 static PyUFuncGenericFunction arctanh_functions[] = {HALF_arctanh, FLOAT_arctanh, DOUBLE_arctanh, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL};
 static void * arctanh_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)"arctanh"};
 static char arctanh_signatures[] = {NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction bitwise_and_functions[] = {BOOL_bitwise_and, BYTE_bitwise_and, UBYTE_bitwise_and, SHORT_bitwise_and, USHORT_bitwise_and, INT_bitwise_and, UINT_bitwise_and, LONG_bitwise_and, ULONG_bitwise_and, LONGLONG_bitwise_and, ULONGLONG_bitwise_and, NULL};
+static PyUFuncGenericFunction bitwise_and_functions[] = {BOOL_logical_and, BYTE_bitwise_and, UBYTE_bitwise_and, SHORT_bitwise_and, USHORT_bitwise_and, INT_bitwise_and, UINT_bitwise_and, LONG_bitwise_and, ULONG_bitwise_and, LONGLONG_bitwise_and, ULONGLONG_bitwise_and, NULL};
 static void * bitwise_and_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char bitwise_and_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction bitwise_or_functions[] = {BOOL_bitwise_or, BYTE_bitwise_or, UBYTE_bitwise_or, SHORT_bitwise_or, USHORT_bitwise_or, INT_bitwise_or, UINT_bitwise_or, LONG_bitwise_or, ULONG_bitwise_or, LONGLONG_bitwise_or, ULONGLONG_bitwise_or, NULL};
+static PyUFuncGenericFunction bitwise_or_functions[] = {BOOL_logical_or, BYTE_bitwise_or, UBYTE_bitwise_or, SHORT_bitwise_or, USHORT_bitwise_or, INT_bitwise_or, UINT_bitwise_or, LONG_bitwise_or, ULONG_bitwise_or, LONGLONG_bitwise_or, ULONGLONG_bitwise_or, NULL};
 static void * bitwise_or_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char bitwise_or_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction bitwise_xor_functions[] = {BOOL_bitwise_xor, BYTE_bitwise_xor, UBYTE_bitwise_xor, SHORT_bitwise_xor, USHORT_bitwise_xor, INT_bitwise_xor, UINT_bitwise_xor, LONG_bitwise_xor, ULONG_bitwise_xor, LONGLONG_bitwise_xor, ULONGLONG_bitwise_xor, NULL};
+static PyUFuncGenericFunction bitwise_xor_functions[] = {BOOL_not_equal, BYTE_bitwise_xor, UBYTE_bitwise_xor, SHORT_bitwise_xor, USHORT_bitwise_xor, INT_bitwise_xor, UINT_bitwise_xor, LONG_bitwise_xor, ULONG_bitwise_xor, LONGLONG_bitwise_xor, ULONGLONG_bitwise_xor, NULL};
 static void * bitwise_xor_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char bitwise_xor_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction cbrt_functions[] = {HALF_cbrt, FLOAT_cbrt, DOUBLE_cbrt, NULL, NULL, NULL, NULL, NULL};
@@ -85,9 +87,9 @@ static char divide_signatures[] = {NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_
 static PyUFuncGenericFunction divmod_functions[] = {BYTE_divmod, UBYTE_divmod, SHORT_divmod, USHORT_divmod, INT_divmod, UINT_divmod, LONG_divmod, ULONG_divmod, LONGLONG_divmod, ULONGLONG_divmod, HALF_divmod, FLOAT_divmod, DOUBLE_divmod, LONGDOUBLE_divmod, TIMEDELTA_mm_qm_divmod};
 static void * divmod_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char divmod_signatures[] = {NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_LONGLONG, NPY_TIMEDELTA};
-static PyUFuncGenericFunction equal_functions[] = {BOOL_equal, BYTE_equal, UBYTE_equal, SHORT_equal, USHORT_equal, INT_equal, UINT_equal, LONG_equal, ULONG_equal, LONGLONG_equal, ULONGLONG_equal, HALF_equal, FLOAT_equal, DOUBLE_equal, LONGDOUBLE_equal, CFLOAT_equal, CDOUBLE_equal, CLONGDOUBLE_equal, OBJECT_equal, TIMEDELTA_equal, DATETIME_equal, OBJECT_OO_O_equal, OBJECT_equal};
-static void * equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
-static char equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_BOOL};
+static PyUFuncGenericFunction equal_functions[] = {BOOL_equal, BYTE_equal, UBYTE_equal, SHORT_equal, USHORT_equal, INT_equal, UINT_equal, LONG_equal, ULONG_equal, LONGLONG_equal, ULONGLONG_equal, LONGLONG_qQ_bool_equal, LONGLONG_Qq_bool_equal, HALF_equal, FLOAT_equal, DOUBLE_equal, LONGDOUBLE_equal, CFLOAT_equal, CDOUBLE_equal, CLONGDOUBLE_equal, DATETIME_equal, TIMEDELTA_equal, OBJECT_equal, OBJECT_OO_O_equal};
+static void * equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
+static char equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_LONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction exp_functions[] = {HALF_exp, FLOAT_exp, DOUBLE_exp, NULL, NULL, NULL, NULL, NULL, NULL, NULL};
 static void * exp_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)"exp"};
 static char exp_signatures[] = {NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_OBJECT, NPY_OBJECT};
@@ -109,10 +111,10 @@ static char floor_signatures[] = {NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_
 static PyUFuncGenericFunction floor_divide_functions[] = {BYTE_divide, UBYTE_divide, SHORT_divide, USHORT_divide, INT_divide, UINT_divide, LONG_divide, ULONG_divide, LONGLONG_divide, ULONGLONG_divide, HALF_floor_divide, FLOAT_floor_divide, DOUBLE_floor_divide, LONGDOUBLE_floor_divide, TIMEDELTA_mq_m_floor_divide, TIMEDELTA_md_m_floor_divide, TIMEDELTA_mm_q_floor_divide, NULL};
 static void * floor_divide_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char floor_divide_signatures[] = {NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_TIMEDELTA, NPY_LONGLONG, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_DOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_LONGLONG, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction fmax_functions[] = {BOOL_fmax, BYTE_fmax, UBYTE_fmax, SHORT_fmax, USHORT_fmax, INT_fmax, UINT_fmax, LONG_fmax, ULONG_fmax, LONGLONG_fmax, ULONGLONG_fmax, HALF_fmax, FLOAT_fmax, DOUBLE_fmax, LONGDOUBLE_fmax, CFLOAT_fmax, CDOUBLE_fmax, CLONGDOUBLE_fmax, TIMEDELTA_fmax, DATETIME_fmax, NULL};
+static PyUFuncGenericFunction fmax_functions[] = {BOOL_logical_or, BYTE_fmax, UBYTE_fmax, SHORT_fmax, USHORT_fmax, INT_fmax, UINT_fmax, LONG_fmax, ULONG_fmax, LONGLONG_fmax, ULONGLONG_fmax, HALF_fmax, FLOAT_fmax, DOUBLE_fmax, LONGDOUBLE_fmax, CFLOAT_fmax, CDOUBLE_fmax, CLONGDOUBLE_fmax, TIMEDELTA_fmax, DATETIME_fmax, NULL};
 static void * fmax_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char fmax_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_DATETIME, NPY_DATETIME, NPY_DATETIME, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction fmin_functions[] = {BOOL_fmin, BYTE_fmin, UBYTE_fmin, SHORT_fmin, USHORT_fmin, INT_fmin, UINT_fmin, LONG_fmin, ULONG_fmin, LONGLONG_fmin, ULONGLONG_fmin, HALF_fmin, FLOAT_fmin, DOUBLE_fmin, LONGDOUBLE_fmin, CFLOAT_fmin, CDOUBLE_fmin, CLONGDOUBLE_fmin, TIMEDELTA_fmin, DATETIME_fmin, NULL};
+static PyUFuncGenericFunction fmin_functions[] = {BOOL_logical_and, BYTE_fmin, UBYTE_fmin, SHORT_fmin, USHORT_fmin, INT_fmin, UINT_fmin, LONG_fmin, ULONG_fmin, LONGLONG_fmin, ULONGLONG_fmin, HALF_fmin, FLOAT_fmin, DOUBLE_fmin, LONGDOUBLE_fmin, CFLOAT_fmin, CDOUBLE_fmin, CLONGDOUBLE_fmin, TIMEDELTA_fmin, DATETIME_fmin, NULL};
 static void * fmin_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char fmin_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_DATETIME, NPY_DATETIME, NPY_DATETIME, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction fmod_functions[] = {BYTE_fmod, UBYTE_fmod, SHORT_fmod, USHORT_fmod, INT_fmod, UINT_fmod, LONG_fmod, ULONG_fmod, LONGLONG_fmod, ULONGLONG_fmod, NULL, NULL, NULL, NULL, NULL};
@@ -124,19 +126,19 @@ static char frexp_signatures[] = {NPY_HALF, NPY_HALF, NPY_INT, NPY_FLOAT, NPY_FL
 static PyUFuncGenericFunction gcd_functions[] = {BYTE_gcd, UBYTE_gcd, SHORT_gcd, USHORT_gcd, INT_gcd, UINT_gcd, LONG_gcd, ULONG_gcd, LONGLONG_gcd, ULONGLONG_gcd, NULL};
 static void * gcd_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char gcd_signatures[] = {NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction greater_functions[] = {BOOL_greater, BYTE_greater, UBYTE_greater, SHORT_greater, USHORT_greater, INT_greater, UINT_greater, LONG_greater, ULONG_greater, LONGLONG_greater, ULONGLONG_greater, HALF_greater, FLOAT_greater, DOUBLE_greater, LONGDOUBLE_greater, CFLOAT_greater, CDOUBLE_greater, CLONGDOUBLE_greater, OBJECT_greater, TIMEDELTA_greater, DATETIME_greater, OBJECT_OO_O_greater, OBJECT_greater};
-static void * greater_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
-static char greater_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_BOOL};
-static PyUFuncGenericFunction greater_equal_functions[] = {BOOL_greater_equal, BYTE_greater_equal, UBYTE_greater_equal, SHORT_greater_equal, USHORT_greater_equal, INT_greater_equal, UINT_greater_equal, LONG_greater_equal, ULONG_greater_equal, LONGLONG_greater_equal, ULONGLONG_greater_equal, HALF_greater_equal, FLOAT_greater_equal, DOUBLE_greater_equal, LONGDOUBLE_greater_equal, CFLOAT_greater_equal, CDOUBLE_greater_equal, CLONGDOUBLE_greater_equal, OBJECT_greater_equal, TIMEDELTA_greater_equal, DATETIME_greater_equal, OBJECT_OO_O_greater_equal, OBJECT_greater_equal};
-static void * greater_equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
-static char greater_equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_BOOL};
+static PyUFuncGenericFunction greater_functions[] = {BOOL_greater, BYTE_greater, UBYTE_greater, SHORT_greater, USHORT_greater, INT_greater, UINT_greater, LONG_greater, ULONG_greater, LONGLONG_greater, ULONGLONG_greater, LONGLONG_qQ_bool_greater, LONGLONG_Qq_bool_greater, HALF_greater, FLOAT_greater, DOUBLE_greater, LONGDOUBLE_greater, CFLOAT_greater, CDOUBLE_greater, CLONGDOUBLE_greater, DATETIME_greater, TIMEDELTA_greater, OBJECT_greater, OBJECT_OO_O_greater};
+static void * greater_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
+static char greater_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_LONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
+static PyUFuncGenericFunction greater_equal_functions[] = {BOOL_greater_equal, BYTE_greater_equal, UBYTE_greater_equal, SHORT_greater_equal, USHORT_greater_equal, INT_greater_equal, UINT_greater_equal, LONG_greater_equal, ULONG_greater_equal, LONGLONG_greater_equal, ULONGLONG_greater_equal, LONGLONG_qQ_bool_greater_equal, LONGLONG_Qq_bool_greater_equal, HALF_greater_equal, FLOAT_greater_equal, DOUBLE_greater_equal, LONGDOUBLE_greater_equal, CFLOAT_greater_equal, CDOUBLE_greater_equal, CLONGDOUBLE_greater_equal, DATETIME_greater_equal, TIMEDELTA_greater_equal, OBJECT_greater_equal, OBJECT_OO_O_greater_equal};
+static void * greater_equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
+static char greater_equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_LONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction heaviside_functions[] = {NULL, NULL, NULL, NULL};
 static void * heaviside_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char heaviside_signatures[] = {NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE};
 static PyUFuncGenericFunction hypot_functions[] = {NULL, NULL, NULL, NULL, NULL};
 static void * hypot_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)"hypot"};
 static char hypot_signatures[] = {NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction invert_functions[] = {BOOL_invert, BYTE_invert, UBYTE_invert, SHORT_invert, USHORT_invert, INT_invert, UINT_invert, LONG_invert, ULONG_invert, LONGLONG_invert, ULONGLONG_invert, NULL};
+static PyUFuncGenericFunction invert_functions[] = {BOOL_logical_not, BYTE_invert, UBYTE_invert, SHORT_invert, USHORT_invert, INT_invert, UINT_invert, LONG_invert, ULONG_invert, LONGLONG_invert, ULONGLONG_invert, NULL};
 static void * invert_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char invert_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction isfinite_functions[] = {BOOL_isfinite, BYTE_isfinite, UBYTE_isfinite, SHORT_isfinite, USHORT_isfinite, INT_isfinite, UINT_isfinite, LONG_isfinite, ULONG_isfinite, LONGLONG_isfinite, ULONGLONG_isfinite, HALF_isfinite, FLOAT_isfinite, DOUBLE_isfinite, LONGDOUBLE_isfinite, CFLOAT_isfinite, CDOUBLE_isfinite, CLONGDOUBLE_isfinite, TIMEDELTA_isfinite, DATETIME_isfinite};
@@ -160,12 +162,12 @@ static char ldexp_signatures[] = {NPY_HALF, NPY_INT, NPY_HALF, NPY_FLOAT, NPY_IN
 static PyUFuncGenericFunction left_shift_functions[] = {BYTE_left_shift, UBYTE_left_shift, SHORT_left_shift, USHORT_left_shift, INT_left_shift, UINT_left_shift, LONG_left_shift, ULONG_left_shift, LONGLONG_left_shift, ULONGLONG_left_shift, NULL};
 static void * left_shift_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char left_shift_signatures[] = {NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction less_functions[] = {BOOL_less, BYTE_less, UBYTE_less, SHORT_less, USHORT_less, INT_less, UINT_less, LONG_less, ULONG_less, LONGLONG_less, ULONGLONG_less, HALF_less, FLOAT_less, DOUBLE_less, LONGDOUBLE_less, CFLOAT_less, CDOUBLE_less, CLONGDOUBLE_less, OBJECT_less, TIMEDELTA_less, DATETIME_less, OBJECT_OO_O_less, OBJECT_less};
-static void * less_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
-static char less_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_BOOL};
-static PyUFuncGenericFunction less_equal_functions[] = {BOOL_less_equal, BYTE_less_equal, UBYTE_less_equal, SHORT_less_equal, USHORT_less_equal, INT_less_equal, UINT_less_equal, LONG_less_equal, ULONG_less_equal, LONGLONG_less_equal, ULONGLONG_less_equal, HALF_less_equal, FLOAT_less_equal, DOUBLE_less_equal, LONGDOUBLE_less_equal, CFLOAT_less_equal, CDOUBLE_less_equal, CLONGDOUBLE_less_equal, OBJECT_less_equal, TIMEDELTA_less_equal, DATETIME_less_equal, OBJECT_OO_O_less_equal, OBJECT_less_equal};
-static void * less_equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
-static char less_equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_BOOL};
+static PyUFuncGenericFunction less_functions[] = {BOOL_less, BYTE_less, UBYTE_less, SHORT_less, USHORT_less, INT_less, UINT_less, LONG_less, ULONG_less, LONGLONG_less, ULONGLONG_less, LONGLONG_qQ_bool_less, LONGLONG_Qq_bool_less, HALF_less, FLOAT_less, DOUBLE_less, LONGDOUBLE_less, CFLOAT_less, CDOUBLE_less, CLONGDOUBLE_less, DATETIME_less, TIMEDELTA_less, OBJECT_less, OBJECT_OO_O_less};
+static void * less_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
+static char less_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_LONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
+static PyUFuncGenericFunction less_equal_functions[] = {BOOL_less_equal, BYTE_less_equal, UBYTE_less_equal, SHORT_less_equal, USHORT_less_equal, INT_less_equal, UINT_less_equal, LONG_less_equal, ULONG_less_equal, LONGLONG_less_equal, ULONGLONG_less_equal, LONGLONG_qQ_bool_less_equal, LONGLONG_Qq_bool_less_equal, HALF_less_equal, FLOAT_less_equal, DOUBLE_less_equal, LONGDOUBLE_less_equal, CFLOAT_less_equal, CDOUBLE_less_equal, CLONGDOUBLE_less_equal, DATETIME_less_equal, TIMEDELTA_less_equal, OBJECT_less_equal, OBJECT_OO_O_less_equal};
+static void * less_equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
+static char less_equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_LONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction log_functions[] = {HALF_log, FLOAT_log, DOUBLE_log, NULL, NULL, NULL, NULL, NULL, NULL, NULL};
 static void * log_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)"log"};
 static char log_signatures[] = {NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_OBJECT, NPY_OBJECT};
@@ -193,22 +195,22 @@ static char logical_not_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BOOL,
 static PyUFuncGenericFunction logical_or_functions[] = {BOOL_logical_or, BYTE_logical_or, UBYTE_logical_or, SHORT_logical_or, USHORT_logical_or, INT_logical_or, UINT_logical_or, LONG_logical_or, ULONG_logical_or, LONGLONG_logical_or, ULONGLONG_logical_or, HALF_logical_or, FLOAT_logical_or, DOUBLE_logical_or, LONGDOUBLE_logical_or, CFLOAT_logical_or, CDOUBLE_logical_or, CLONGDOUBLE_logical_or, NULL};
 static void * logical_or_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char logical_or_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction logical_xor_functions[] = {BOOL_logical_xor, BYTE_logical_xor, UBYTE_logical_xor, SHORT_logical_xor, USHORT_logical_xor, INT_logical_xor, UINT_logical_xor, LONG_logical_xor, ULONG_logical_xor, LONGLONG_logical_xor, ULONGLONG_logical_xor, HALF_logical_xor, FLOAT_logical_xor, DOUBLE_logical_xor, LONGDOUBLE_logical_xor, CFLOAT_logical_xor, CDOUBLE_logical_xor, CLONGDOUBLE_logical_xor, NULL};
+static PyUFuncGenericFunction logical_xor_functions[] = {BOOL_not_equal, BYTE_logical_xor, UBYTE_logical_xor, SHORT_logical_xor, USHORT_logical_xor, INT_logical_xor, UINT_logical_xor, LONG_logical_xor, ULONG_logical_xor, LONGLONG_logical_xor, ULONGLONG_logical_xor, HALF_logical_xor, FLOAT_logical_xor, DOUBLE_logical_xor, LONGDOUBLE_logical_xor, CFLOAT_logical_xor, CDOUBLE_logical_xor, CLONGDOUBLE_logical_xor, NULL};
 static void * logical_xor_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)"logical_xor"};
 static char logical_xor_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction matmul_functions[] = {BOOL_matmul, BYTE_matmul, UBYTE_matmul, SHORT_matmul, USHORT_matmul, INT_matmul, UINT_matmul, LONG_matmul, ULONG_matmul, LONGLONG_matmul, ULONGLONG_matmul, HALF_matmul, FLOAT_matmul, DOUBLE_matmul, LONGDOUBLE_matmul, CFLOAT_matmul, CDOUBLE_matmul, CLONGDOUBLE_matmul, OBJECT_matmul};
 static void * matmul_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char matmul_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction maximum_functions[] = {BOOL_maximum, BYTE_maximum, UBYTE_maximum, SHORT_maximum, USHORT_maximum, INT_maximum, UINT_maximum, LONG_maximum, ULONG_maximum, LONGLONG_maximum, ULONGLONG_maximum, HALF_maximum, FLOAT_maximum, DOUBLE_maximum, LONGDOUBLE_maximum, CFLOAT_maximum, CDOUBLE_maximum, CLONGDOUBLE_maximum, TIMEDELTA_maximum, DATETIME_maximum, NULL};
+static PyUFuncGenericFunction maximum_functions[] = {BOOL_logical_or, BYTE_maximum, UBYTE_maximum, SHORT_maximum, USHORT_maximum, INT_maximum, UINT_maximum, LONG_maximum, ULONG_maximum, LONGLONG_maximum, ULONGLONG_maximum, HALF_maximum, FLOAT_maximum, DOUBLE_maximum, LONGDOUBLE_maximum, CFLOAT_maximum, CDOUBLE_maximum, CLONGDOUBLE_maximum, TIMEDELTA_maximum, DATETIME_maximum, NULL};
 static void * maximum_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char maximum_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_DATETIME, NPY_DATETIME, NPY_DATETIME, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
-static PyUFuncGenericFunction minimum_functions[] = {BOOL_minimum, BYTE_minimum, UBYTE_minimum, SHORT_minimum, USHORT_minimum, INT_minimum, UINT_minimum, LONG_minimum, ULONG_minimum, LONGLONG_minimum, ULONGLONG_minimum, HALF_minimum, FLOAT_minimum, DOUBLE_minimum, LONGDOUBLE_minimum, CFLOAT_minimum, CDOUBLE_minimum, CLONGDOUBLE_minimum, TIMEDELTA_minimum, DATETIME_minimum, NULL};
+static PyUFuncGenericFunction minimum_functions[] = {BOOL_logical_and, BYTE_minimum, UBYTE_minimum, SHORT_minimum, USHORT_minimum, INT_minimum, UINT_minimum, LONG_minimum, ULONG_minimum, LONGLONG_minimum, ULONGLONG_minimum, HALF_minimum, FLOAT_minimum, DOUBLE_minimum, LONGDOUBLE_minimum, CFLOAT_minimum, CDOUBLE_minimum, CLONGDOUBLE_minimum, TIMEDELTA_minimum, DATETIME_minimum, NULL};
 static void * minimum_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char minimum_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_DATETIME, NPY_DATETIME, NPY_DATETIME, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction modf_functions[] = {HALF_modf, FLOAT_modf, DOUBLE_modf, LONGDOUBLE_modf};
 static void * modf_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char modf_signatures[] = {NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE};
-static PyUFuncGenericFunction multiply_functions[] = {BOOL_multiply, BYTE_multiply, UBYTE_multiply, SHORT_multiply, USHORT_multiply, INT_multiply, UINT_multiply, LONG_multiply, ULONG_multiply, LONGLONG_multiply, ULONGLONG_multiply, HALF_multiply, FLOAT_multiply, DOUBLE_multiply, LONGDOUBLE_multiply, CFLOAT_multiply, CDOUBLE_multiply, CLONGDOUBLE_multiply, TIMEDELTA_mq_m_multiply, TIMEDELTA_qm_m_multiply, TIMEDELTA_md_m_multiply, TIMEDELTA_dm_m_multiply, NULL};
+static PyUFuncGenericFunction multiply_functions[] = {BOOL_logical_and, BYTE_multiply, UBYTE_multiply, SHORT_multiply, USHORT_multiply, INT_multiply, UINT_multiply, LONG_multiply, ULONG_multiply, LONGLONG_multiply, ULONGLONG_multiply, HALF_multiply, FLOAT_multiply, DOUBLE_multiply, LONGDOUBLE_multiply, CFLOAT_multiply, CDOUBLE_multiply, CLONGDOUBLE_multiply, TIMEDELTA_mq_m_multiply, TIMEDELTA_qm_m_multiply, TIMEDELTA_md_m_multiply, TIMEDELTA_dm_m_multiply, NULL};
 static void * multiply_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char multiply_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_TIMEDELTA, NPY_LONGLONG, NPY_TIMEDELTA, NPY_LONGLONG, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_DOUBLE, NPY_TIMEDELTA, NPY_DOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction negative_functions[] = {BYTE_negative, UBYTE_negative, SHORT_negative, USHORT_negative, INT_negative, UINT_negative, LONG_negative, ULONG_negative, LONGLONG_negative, ULONGLONG_negative, HALF_negative, FLOAT_negative, DOUBLE_negative, LONGDOUBLE_negative, TIMEDELTA_negative, NULL, NULL, NULL, NULL};
@@ -217,9 +219,9 @@ static char negative_signatures[] = {NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, N
 static PyUFuncGenericFunction nextafter_functions[] = {HALF_nextafter, FLOAT_nextafter, DOUBLE_nextafter, LONGDOUBLE_nextafter};
 static void * nextafter_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char nextafter_signatures[] = {NPY_HALF, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE};
-static PyUFuncGenericFunction not_equal_functions[] = {BOOL_not_equal, BYTE_not_equal, UBYTE_not_equal, SHORT_not_equal, USHORT_not_equal, INT_not_equal, UINT_not_equal, LONG_not_equal, ULONG_not_equal, LONGLONG_not_equal, ULONGLONG_not_equal, HALF_not_equal, FLOAT_not_equal, DOUBLE_not_equal, LONGDOUBLE_not_equal, CFLOAT_not_equal, CDOUBLE_not_equal, CLONGDOUBLE_not_equal, OBJECT_not_equal, TIMEDELTA_not_equal, DATETIME_not_equal, OBJECT_OO_O_not_equal, OBJECT_not_equal};
-static void * not_equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
-static char not_equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT, NPY_BOOL};
+static PyUFuncGenericFunction not_equal_functions[] = {BOOL_not_equal, BYTE_not_equal, UBYTE_not_equal, SHORT_not_equal, USHORT_not_equal, INT_not_equal, UINT_not_equal, LONG_not_equal, ULONG_not_equal, LONGLONG_not_equal, ULONGLONG_not_equal, LONGLONG_qQ_bool_not_equal, LONGLONG_Qq_bool_not_equal, HALF_not_equal, FLOAT_not_equal, DOUBLE_not_equal, LONGDOUBLE_not_equal, CFLOAT_not_equal, CDOUBLE_not_equal, CLONGDOUBLE_not_equal, DATETIME_not_equal, TIMEDELTA_not_equal, OBJECT_not_equal, OBJECT_OO_O_not_equal};
+static void * not_equal_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
+static char not_equal_signatures[] = {NPY_BOOL, NPY_BOOL, NPY_BOOL, NPY_BYTE, NPY_BYTE, NPY_BOOL, NPY_UBYTE, NPY_UBYTE, NPY_BOOL, NPY_SHORT, NPY_SHORT, NPY_BOOL, NPY_USHORT, NPY_USHORT, NPY_BOOL, NPY_INT, NPY_INT, NPY_BOOL, NPY_UINT, NPY_UINT, NPY_BOOL, NPY_LONG, NPY_LONG, NPY_BOOL, NPY_ULONG, NPY_ULONG, NPY_BOOL, NPY_LONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_LONGLONG, NPY_ULONGLONG, NPY_BOOL, NPY_ULONGLONG, NPY_LONGLONG, NPY_BOOL, NPY_HALF, NPY_HALF, NPY_BOOL, NPY_FLOAT, NPY_FLOAT, NPY_BOOL, NPY_DOUBLE, NPY_DOUBLE, NPY_BOOL, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_BOOL, NPY_CFLOAT, NPY_CFLOAT, NPY_BOOL, NPY_CDOUBLE, NPY_CDOUBLE, NPY_BOOL, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_BOOL, NPY_DATETIME, NPY_DATETIME, NPY_BOOL, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_BOOL, NPY_OBJECT, NPY_OBJECT, NPY_OBJECT};
 static PyUFuncGenericFunction positive_functions[] = {BYTE_positive, UBYTE_positive, SHORT_positive, USHORT_positive, INT_positive, UINT_positive, LONG_positive, ULONG_positive, LONGLONG_positive, ULONGLONG_positive, HALF_positive, FLOAT_positive, DOUBLE_positive, LONGDOUBLE_positive, TIMEDELTA_positive, NULL, NULL, NULL, NULL};
 static void * positive_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char positive_signatures[] = {NPY_BYTE, NPY_BYTE, NPY_UBYTE, NPY_UBYTE, NPY_SHORT, NPY_SHORT, NPY_USHORT, NPY_USHORT, NPY_INT, NPY_INT, NPY_UINT, NPY_UINT, NPY_LONG, NPY_LONG, NPY_ULONG, NPY_ULONG, NPY_LONGLONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_ULONGLONG, NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_TIMEDELTA, NPY_TIMEDELTA, NPY_CFLOAT, NPY_CFLOAT, NPY_CDOUBLE, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_CLONGDOUBLE, NPY_OBJECT, NPY_OBJECT};
@@ -277,6 +279,10 @@ static char tanh_signatures[] = {NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_D
 static PyUFuncGenericFunction trunc_functions[] = {NULL, FLOAT_trunc, DOUBLE_trunc, NULL, NULL, NULL, NULL};
 static void * trunc_data[] = {(void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL, (void *)NULL};
 static char trunc_signatures[] = {NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_FLOAT, NPY_FLOAT, NPY_DOUBLE, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_OBJECT, NPY_OBJECT};
+/* Returns a borrowed ref of the second value in the matching info tuple */
+PyObject *
+get_info_no_cast(PyUFuncObject *ufunc, PyArray_DTypeMeta *op_dtype,
+                 int ndtypes);
 
 static int
 InitOperators(PyObject *dictionary) {
@@ -284,80 +290,8 @@ InitOperators(PyObject *dictionary) {
 
     _ones_like_functions[20] = PyUFunc_O_O;
     _ones_like_data[20] = (void *) Py_get_one;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512F
-    if (NPY_CPU_HAVE(AVX512F)) {
-        absolute_functions[16] = CFLOAT_absolute_avx512f;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512F
-    if (NPY_CPU_HAVE(AVX512F)) {
-        absolute_functions[17] = CDOUBLE_absolute_avx512f;
-    }
-    #endif
-    
     absolute_functions[19] = PyUFunc_O_O;
     absolute_data[19] = (void *) PyNumber_Absolute;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[1] = BYTE_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[2] = UBYTE_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[3] = SHORT_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[4] = USHORT_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[5] = INT_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[6] = UINT_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[7] = LONG_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[8] = ULONG_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[9] = LONGLONG_add_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        add_functions[10] = ULONGLONG_add_avx2;
-    }
-    #endif
-    
     add_functions[21] = PyUFunc_OO_O;
     add_data[21] = (void *) PyNumber_Add;
     arccos_functions[3] = PyUFunc_e_e_As_f_f;
@@ -455,190 +389,10 @@ InitOperators(PyObject *dictionary) {
     arctanh_functions[9] = PyUFunc_G_G;
     arctanh_data[9] = (void *) nc_atanhl;
     arctanh_functions[10] = PyUFunc_O_O_method;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[1] = BYTE_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[2] = UBYTE_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[3] = SHORT_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[4] = USHORT_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[5] = INT_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[6] = UINT_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[7] = LONG_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[8] = ULONG_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[9] = LONGLONG_bitwise_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_and_functions[10] = ULONGLONG_bitwise_and_avx2;
-    }
-    #endif
-    
     bitwise_and_functions[11] = PyUFunc_OO_O;
     bitwise_and_data[11] = (void *) PyNumber_And;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[1] = BYTE_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[2] = UBYTE_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[3] = SHORT_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[4] = USHORT_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[5] = INT_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[6] = UINT_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[7] = LONG_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[8] = ULONG_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[9] = LONGLONG_bitwise_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_or_functions[10] = ULONGLONG_bitwise_or_avx2;
-    }
-    #endif
-    
     bitwise_or_functions[11] = PyUFunc_OO_O;
     bitwise_or_data[11] = (void *) PyNumber_Or;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[1] = BYTE_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[2] = UBYTE_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[3] = SHORT_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[4] = USHORT_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[5] = INT_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[6] = UINT_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[7] = LONG_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[8] = ULONG_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[9] = LONGLONG_bitwise_xor_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        bitwise_xor_functions[10] = ULONGLONG_bitwise_xor_avx2;
-    }
-    #endif
-    
     bitwise_xor_functions[11] = PyUFunc_OO_O;
     bitwise_xor_data[11] = (void *) PyNumber_Xor;
     cbrt_functions[3] = PyUFunc_e_e_As_f_f;
@@ -662,78 +416,6 @@ InitOperators(PyObject *dictionary) {
     ceil_data[6] = (void *) npy_ObjectCeil;
     clip_functions[20] = PyUFunc_OOO_O;
     clip_data[20] = (void *) npy_ObjectClip;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[0] = BYTE_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[1] = UBYTE_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[2] = SHORT_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[3] = USHORT_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[4] = INT_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[5] = UINT_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[6] = LONG_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[7] = ULONG_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[8] = LONGLONG_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        conjugate_functions[9] = ULONGLONG_conjugate_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512F
-    if (NPY_CPU_HAVE(AVX512F)) {
-        conjugate_functions[14] = CFLOAT_conjugate_avx512f;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512F
-    if (NPY_CPU_HAVE(AVX512F)) {
-        conjugate_functions[15] = CDOUBLE_conjugate_avx512f;
-    }
-    #endif
-    
     conjugate_functions[17] = PyUFunc_O_O_method;
     cos_functions[3] = PyUFunc_g_g;
     cos_data[3] = (void *) npy_cosl;
@@ -883,166 +565,10 @@ InitOperators(PyObject *dictionary) {
     hypot_functions[3] = PyUFunc_gg_g;
     hypot_data[3] = (void *) npy_hypotl;
     hypot_functions[4] = PyUFunc_OO_O_method;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[1] = BYTE_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[2] = UBYTE_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[3] = SHORT_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[4] = USHORT_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[5] = INT_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[6] = UINT_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[7] = LONG_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[8] = ULONG_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[9] = LONGLONG_invert_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        invert_functions[10] = ULONGLONG_invert_avx2;
-    }
-    #endif
-    
     invert_functions[11] = PyUFunc_O_O;
     invert_data[11] = (void *) PyNumber_Invert;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        isfinite_functions[12] = FLOAT_isfinite_avx512_skx;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        isfinite_functions[13] = DOUBLE_isfinite_avx512_skx;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        isinf_functions[12] = FLOAT_isinf_avx512_skx;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        isinf_functions[13] = DOUBLE_isinf_avx512_skx;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        isnan_functions[12] = FLOAT_isnan_avx512_skx;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        isnan_functions[13] = DOUBLE_isnan_avx512_skx;
-    }
-    #endif
-    
     lcm_functions[10] = PyUFunc_OO_O;
     lcm_data[10] = (void *) npy_ObjectLCM;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[0] = BYTE_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[1] = UBYTE_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[2] = SHORT_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[3] = USHORT_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[4] = INT_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[5] = UINT_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[6] = LONG_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[7] = ULONG_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[8] = LONGLONG_left_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        left_shift_functions[9] = ULONGLONG_left_shift_avx2;
-    }
-    #endif
-    
     left_shift_functions[10] = PyUFunc_OO_O;
     left_shift_data[10] = (void *) PyNumber_Lshift;
     log_functions[3] = PyUFunc_f_f;
@@ -1119,190 +645,10 @@ InitOperators(PyObject *dictionary) {
     logaddexp2_data[2] = (void *) npy_logaddexp2;
     logaddexp2_functions[3] = PyUFunc_gg_g;
     logaddexp2_data[3] = (void *) npy_logaddexp2l;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[1] = BYTE_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[2] = UBYTE_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[3] = SHORT_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[4] = USHORT_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[5] = INT_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[6] = UINT_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[7] = LONG_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[8] = ULONG_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[9] = LONGLONG_logical_and_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_and_functions[10] = ULONGLONG_logical_and_avx2;
-    }
-    #endif
-    
     logical_and_functions[18] = PyUFunc_OO_O;
     logical_and_data[18] = (void *) npy_ObjectLogicalAnd;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[1] = BYTE_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[2] = UBYTE_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[3] = SHORT_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[4] = USHORT_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[5] = INT_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[6] = UINT_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[7] = LONG_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[8] = ULONG_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[9] = LONGLONG_logical_not_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_not_functions[10] = ULONGLONG_logical_not_avx2;
-    }
-    #endif
-    
     logical_not_functions[18] = PyUFunc_O_O;
     logical_not_data[18] = (void *) npy_ObjectLogicalNot;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[1] = BYTE_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[2] = UBYTE_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[3] = SHORT_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[4] = USHORT_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[5] = INT_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[6] = UINT_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[7] = LONG_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[8] = ULONG_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[9] = LONGLONG_logical_or_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        logical_or_functions[10] = ULONGLONG_logical_or_avx2;
-    }
-    #endif
-    
     logical_or_functions[18] = PyUFunc_OO_O;
     logical_or_data[18] = (void *) npy_ObjectLogicalOr;
     logical_xor_functions[18] = PyUFunc_OO_O_method;
@@ -1310,128 +656,8 @@ InitOperators(PyObject *dictionary) {
     maximum_data[20] = (void *) npy_ObjectMax;
     minimum_functions[20] = PyUFunc_OO_O;
     minimum_data[20] = (void *) npy_ObjectMin;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[1] = BYTE_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[2] = UBYTE_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[3] = SHORT_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[4] = USHORT_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[5] = INT_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[6] = UINT_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[7] = LONG_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[8] = ULONG_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[9] = LONGLONG_multiply_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        multiply_functions[10] = ULONGLONG_multiply_avx2;
-    }
-    #endif
-    
     multiply_functions[22] = PyUFunc_OO_O;
     multiply_data[22] = (void *) PyNumber_Multiply;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[0] = BYTE_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[1] = UBYTE_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[2] = SHORT_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[3] = USHORT_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[4] = INT_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[5] = UINT_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[6] = LONG_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[7] = ULONG_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[8] = LONGLONG_negative_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        negative_functions[9] = ULONGLONG_negative_avx2;
-    }
-    #endif
-    
     negative_functions[15] = PyUFunc_F_F;
     negative_data[15] = (void *) nc_negf;
     negative_functions[16] = PyUFunc_D_D;
@@ -1484,130 +710,10 @@ InitOperators(PyObject *dictionary) {
     radians_functions[3] = PyUFunc_g_g;
     radians_data[3] = (void *) npy_radiansl;
     radians_functions[4] = PyUFunc_O_O_method;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[0] = BYTE_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[1] = UBYTE_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[2] = SHORT_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[3] = USHORT_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[4] = INT_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[5] = UINT_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[6] = LONG_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[7] = ULONG_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[8] = LONGLONG_reciprocal_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        reciprocal_functions[9] = ULONGLONG_reciprocal_avx2;
-    }
-    #endif
-    
     reciprocal_functions[17] = PyUFunc_O_O;
     reciprocal_data[17] = (void *) Py_reciprocal;
     remainder_functions[15] = PyUFunc_OO_O;
     remainder_data[15] = (void *) PyNumber_Remainder;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[0] = BYTE_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[1] = UBYTE_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[2] = SHORT_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[3] = USHORT_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[4] = INT_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[5] = UINT_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[6] = LONG_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[7] = ULONG_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[8] = LONGLONG_right_shift_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        right_shift_functions[9] = ULONGLONG_right_shift_avx2;
-    }
-    #endif
-    
     right_shift_functions[10] = PyUFunc_OO_O;
     right_shift_data[10] = (void *) PyNumber_Rshift;
     rint_functions[0] = PyUFunc_e_e_As_f_f;
@@ -1625,18 +731,6 @@ InitOperators(PyObject *dictionary) {
     rint_functions[8] = PyUFunc_G_G;
     rint_data[8] = (void *) nc_rintl;
     rint_functions[9] = PyUFunc_O_O_method;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        signbit_functions[1] = FLOAT_signbit_avx512_skx;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-    if (NPY_CPU_HAVE(AVX512_SKX)) {
-        signbit_functions[2] = DOUBLE_signbit_avx512_skx;
-    }
-    #endif
-    
     sin_functions[3] = PyUFunc_g_g;
     sin_data[3] = (void *) npy_sinl;
     sin_functions[4] = PyUFunc_F_F;
@@ -1676,140 +770,8 @@ InitOperators(PyObject *dictionary) {
     sqrt_functions[8] = PyUFunc_G_G;
     sqrt_data[8] = (void *) nc_sqrtl;
     sqrt_functions[9] = PyUFunc_O_O_method;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[0] = BYTE_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[1] = UBYTE_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[2] = SHORT_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[3] = USHORT_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[4] = INT_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[5] = UINT_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[6] = LONG_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[7] = ULONG_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[8] = LONGLONG_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        square_functions[9] = ULONGLONG_square_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512F
-    if (NPY_CPU_HAVE(AVX512F)) {
-        square_functions[14] = CFLOAT_square_avx512f;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX512F
-    if (NPY_CPU_HAVE(AVX512F)) {
-        square_functions[15] = CDOUBLE_square_avx512f;
-    }
-    #endif
-    
     square_functions[17] = PyUFunc_O_O;
     square_data[17] = (void *) Py_square;
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[0] = BYTE_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[1] = UBYTE_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[2] = SHORT_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[3] = USHORT_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[4] = INT_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[5] = UINT_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[6] = LONG_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[7] = ULONG_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[8] = LONGLONG_subtract_avx2;
-    }
-    #endif
-    
-    #ifdef HAVE_ATTRIBUTE_TARGET_AVX2
-    if (NPY_CPU_HAVE(AVX2)) {
-        subtract_functions[9] = ULONGLONG_subtract_avx2;
-    }
-    #endif
-    
     subtract_functions[20] = PyUFunc_OO_O;
     subtract_data[20] = (void *) PyNumber_Subtract;
     tan_functions[3] = PyUFunc_e_e_As_f_f;
@@ -1854,6 +816,474 @@ InitOperators(PyObject *dictionary) {
     trunc_data[6] = (void *) npy_ObjectTrunc;
     
     #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_logical.dispatch.h"
+    #endif
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[0] = BOOL_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[0] = BOOL_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[0] = BOOL_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[0] = BOOL_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(fmax_functions[0] = BOOL_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(fmin_functions[0] = BOOL_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[0] = BOOL_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[0] = BOOL_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[0] = BOOL_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[0] = BOOL_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(maximum_functions[0] = BOOL_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(minimum_functions[0] = BOOL_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[0] = BOOL_logical_and);
+    
+    
+    #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+    #endif
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[1] = BYTE_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[2] = UBYTE_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[3] = SHORT_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[4] = USHORT_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[5] = INT_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[6] = UINT_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[7] = LONG_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[8] = ULONG_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[9] = LONGLONG_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[10] = ULONGLONG_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[11] = HALF_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[1] = BYTE_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[2] = UBYTE_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[3] = SHORT_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[4] = USHORT_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[5] = INT_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[6] = UINT_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[7] = LONG_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[8] = ULONG_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[9] = LONGLONG_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(add_functions[10] = ULONGLONG_add);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[1] = BYTE_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[2] = UBYTE_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[3] = SHORT_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[4] = USHORT_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[5] = INT_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[6] = UINT_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[7] = LONG_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[8] = ULONG_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[9] = LONGLONG_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_and_functions[10] = ULONGLONG_bitwise_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[1] = BYTE_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[2] = UBYTE_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[3] = SHORT_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[4] = USHORT_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[5] = INT_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[6] = UINT_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[7] = LONG_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[8] = ULONG_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[9] = LONGLONG_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_or_functions[10] = ULONGLONG_bitwise_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[1] = BYTE_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[2] = UBYTE_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[3] = SHORT_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[4] = USHORT_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[5] = INT_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[6] = UINT_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[7] = LONG_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[8] = ULONG_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[9] = LONGLONG_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[10] = ULONGLONG_bitwise_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[0] = BYTE_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[1] = UBYTE_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[2] = SHORT_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[3] = USHORT_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[4] = INT_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[5] = UINT_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[6] = LONG_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[7] = ULONG_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[8] = LONGLONG_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[9] = ULONGLONG_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[1] = BYTE_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[2] = UBYTE_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[3] = SHORT_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[4] = USHORT_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[5] = INT_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[6] = UINT_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[7] = LONG_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[8] = ULONG_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[9] = LONGLONG_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(invert_functions[10] = ULONGLONG_invert);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[0] = BOOL_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[1] = BYTE_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[2] = UBYTE_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[3] = SHORT_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[4] = USHORT_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[5] = INT_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[6] = UINT_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[7] = LONG_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[8] = ULONG_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[9] = LONGLONG_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[10] = ULONGLONG_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[0] = BOOL_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[1] = BYTE_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[2] = UBYTE_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[3] = SHORT_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[4] = USHORT_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[5] = INT_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[6] = UINT_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[7] = LONG_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[8] = ULONG_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[9] = LONGLONG_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[10] = ULONGLONG_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[18] = TIMEDELTA_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[19] = DATETIME_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[0] = BOOL_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[1] = BYTE_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[2] = UBYTE_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[3] = SHORT_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[4] = USHORT_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[5] = INT_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[6] = UINT_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[7] = LONG_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[8] = ULONG_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[9] = LONGLONG_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[10] = ULONGLONG_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[0] = BYTE_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[1] = UBYTE_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[2] = SHORT_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[3] = USHORT_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[4] = INT_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[5] = UINT_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[6] = LONG_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[7] = ULONG_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[8] = LONGLONG_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(left_shift_functions[9] = ULONGLONG_left_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[1] = BYTE_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[2] = UBYTE_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[3] = SHORT_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[4] = USHORT_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[5] = INT_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[6] = UINT_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[7] = LONG_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[8] = ULONG_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[9] = LONGLONG_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_and_functions[10] = ULONGLONG_logical_and);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[1] = BYTE_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[2] = UBYTE_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[3] = SHORT_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[4] = USHORT_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[5] = INT_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[6] = UINT_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[7] = LONG_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[8] = ULONG_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[9] = LONGLONG_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_not_functions[10] = ULONGLONG_logical_not);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[1] = BYTE_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[2] = UBYTE_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[3] = SHORT_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[4] = USHORT_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[5] = INT_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[6] = UINT_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[7] = LONG_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[8] = ULONG_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[9] = LONGLONG_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_or_functions[10] = ULONGLONG_logical_or);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[1] = BYTE_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[2] = UBYTE_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[3] = SHORT_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[4] = USHORT_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[5] = INT_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[6] = UINT_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[7] = LONG_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[8] = ULONG_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[9] = LONGLONG_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[10] = ULONGLONG_logical_xor);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[1] = BYTE_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[2] = UBYTE_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[3] = SHORT_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[4] = USHORT_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[5] = INT_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[6] = UINT_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[7] = LONG_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[8] = ULONG_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[9] = LONGLONG_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(multiply_functions[10] = ULONGLONG_multiply);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[0] = BYTE_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[1] = UBYTE_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[2] = SHORT_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[3] = USHORT_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[4] = INT_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[5] = UINT_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[6] = LONG_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[7] = ULONG_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[8] = LONGLONG_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(reciprocal_functions[9] = ULONGLONG_reciprocal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[0] = BYTE_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[1] = UBYTE_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[2] = SHORT_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[3] = USHORT_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[4] = INT_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[5] = UINT_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[6] = LONG_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[7] = ULONG_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[8] = LONGLONG_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(right_shift_functions[9] = ULONGLONG_right_shift);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[0] = BYTE_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[1] = UBYTE_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[2] = SHORT_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[3] = USHORT_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[4] = INT_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[5] = UINT_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[6] = LONG_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[7] = ULONG_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[8] = LONGLONG_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(sign_functions[9] = ULONGLONG_sign);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[0] = BYTE_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[1] = UBYTE_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[2] = SHORT_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[3] = USHORT_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[4] = INT_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[5] = UINT_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[6] = LONG_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[7] = ULONG_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[8] = LONGLONG_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[9] = ULONGLONG_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[0] = BYTE_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[1] = UBYTE_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[2] = SHORT_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[3] = USHORT_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[4] = INT_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[5] = UINT_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[6] = LONG_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[7] = ULONG_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[8] = LONGLONG_subtract);
+    
+    NPY_CPU_DISPATCH_CALL_XB(subtract_functions[9] = ULONGLONG_subtract);
+    
+    
+    #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_unary_fp.dispatch.h"
     #endif
     
@@ -1891,6 +1321,15 @@ InitOperators(PyObject *dictionary) {
     
     
     #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary_complex.dispatch.h"
+    #endif
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[16] = CFLOAT_absolute);
+    
+    NPY_CPU_DISPATCH_CALL_XB(absolute_functions[17] = CDOUBLE_absolute);
+    
+    
+    #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_arithm_fp.dispatch.h"
     #endif
     
@@ -1902,6 +1341,10 @@ InitOperators(PyObject *dictionary) {
     
     NPY_CPU_DISPATCH_CALL_XB(add_functions[16] = CDOUBLE_add);
     
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[14] = CFLOAT_conjugate);
+    
+    NPY_CPU_DISPATCH_CALL_XB(conjugate_functions[15] = CDOUBLE_conjugate);
+    
     NPY_CPU_DISPATCH_CALL_XB(divide_functions[1] = FLOAT_divide);
     
     NPY_CPU_DISPATCH_CALL_XB(divide_functions[2] = DOUBLE_divide);
@@ -1914,6 +1357,10 @@ InitOperators(PyObject *dictionary) {
     
     NPY_CPU_DISPATCH_CALL_XB(multiply_functions[16] = CDOUBLE_multiply);
     
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[14] = CFLOAT_square);
+    
+    NPY_CPU_DISPATCH_CALL_XB(square_functions[15] = CDOUBLE_square);
+    
     NPY_CPU_DISPATCH_CALL_XB(subtract_functions[11] = FLOAT_subtract);
     
     NPY_CPU_DISPATCH_CALL_XB(subtract_functions[12] = DOUBLE_subtract);
@@ -1975,8 +1422,6 @@ InitOperators(PyObject *dictionary) {
     
     NPY_CPU_DISPATCH_CALL_XB(cos_functions[0] = HALF_cos);
     
-    NPY_CPU_DISPATCH_CALL_XB(cos_functions[2] = DOUBLE_cos);
-    
     NPY_CPU_DISPATCH_CALL_XB(cosh_functions[0] = HALF_cosh);
     
     NPY_CPU_DISPATCH_CALL_XB(cosh_functions[1] = FLOAT_cosh);
@@ -2023,8 +1468,6 @@ InitOperators(PyObject *dictionary) {
     
     NPY_CPU_DISPATCH_CALL_XB(sin_functions[0] = HALF_sin);
     
-    NPY_CPU_DISPATCH_CALL_XB(sin_functions[2] = DOUBLE_sin);
-    
     NPY_CPU_DISPATCH_CALL_XB(sinh_functions[0] = HALF_sinh);
     
     NPY_CPU_DISPATCH_CALL_XB(sinh_functions[1] = FLOAT_sinh);
@@ -2041,13 +1484,50 @@ InitOperators(PyObject *dictionary) {
     
     
     #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_comparison.dispatch.h"
+    #endif
+    
+    NPY_CPU_DISPATCH_CALL_XB(bitwise_xor_functions[0] = BOOL_not_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(equal_functions[14] = FLOAT_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(equal_functions[15] = DOUBLE_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(greater_functions[14] = FLOAT_greater);
+    
+    NPY_CPU_DISPATCH_CALL_XB(greater_functions[15] = DOUBLE_greater);
+    
+    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[14] = FLOAT_greater_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[15] = DOUBLE_greater_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(less_functions[14] = FLOAT_less);
+    
+    NPY_CPU_DISPATCH_CALL_XB(less_functions[15] = DOUBLE_less);
+    
+    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[14] = FLOAT_less_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[15] = DOUBLE_less_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(logical_xor_functions[0] = BOOL_not_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[14] = FLOAT_not_equal);
+    
+    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[15] = DOUBLE_not_equal);
+    
+    
+    #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_trigonometric.dispatch.h"
     #endif
     
     NPY_CPU_DISPATCH_CALL_XB(cos_functions[1] = FLOAT_cos);
     
+    NPY_CPU_DISPATCH_CALL_XB(cos_functions[2] = DOUBLE_cos);
+    
     NPY_CPU_DISPATCH_CALL_XB(sin_functions[1] = FLOAT_sin);
     
+    NPY_CPU_DISPATCH_CALL_XB(sin_functions[2] = DOUBLE_sin);
+    
     
     #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_modulo.dispatch.h"
@@ -2115,167 +1595,6 @@ InitOperators(PyObject *dictionary) {
     
     
     #ifndef NPY_DISABLE_OPTIMIZATION
-    #include "loops_comparison.dispatch.h"
-    #endif
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[0] = BOOL_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[1] = BYTE_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[2] = UBYTE_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[3] = SHORT_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[4] = USHORT_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[5] = INT_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[6] = UINT_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[7] = LONG_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[8] = ULONG_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[9] = LONGLONG_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[10] = ULONGLONG_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[12] = FLOAT_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(equal_functions[13] = DOUBLE_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[0] = BOOL_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[1] = BYTE_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[2] = UBYTE_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[3] = SHORT_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[4] = USHORT_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[5] = INT_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[6] = UINT_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[7] = LONG_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[8] = ULONG_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[9] = LONGLONG_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[10] = ULONGLONG_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[12] = FLOAT_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_functions[13] = DOUBLE_greater);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[0] = BOOL_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[1] = BYTE_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[2] = UBYTE_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[3] = SHORT_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[4] = USHORT_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[5] = INT_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[6] = UINT_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[7] = LONG_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[8] = ULONG_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[9] = LONGLONG_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[10] = ULONGLONG_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[12] = FLOAT_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(greater_equal_functions[13] = DOUBLE_greater_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[0] = BOOL_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[1] = BYTE_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[2] = UBYTE_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[3] = SHORT_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[4] = USHORT_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[5] = INT_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[6] = UINT_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[7] = LONG_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[8] = ULONG_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[9] = LONGLONG_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[10] = ULONGLONG_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[12] = FLOAT_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_functions[13] = DOUBLE_less);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[0] = BOOL_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[1] = BYTE_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[2] = UBYTE_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[3] = SHORT_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[4] = USHORT_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[5] = INT_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[6] = UINT_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[7] = LONG_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[8] = ULONG_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[9] = LONGLONG_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[10] = ULONGLONG_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[12] = FLOAT_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(less_equal_functions[13] = DOUBLE_less_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[0] = BOOL_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[1] = BYTE_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[2] = UBYTE_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[3] = SHORT_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[4] = USHORT_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[5] = INT_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[6] = UINT_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[7] = LONG_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[8] = ULONG_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[9] = LONGLONG_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[10] = ULONGLONG_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[12] = FLOAT_not_equal);
-    
-    NPY_CPU_DISPATCH_CALL_XB(not_equal_functions[13] = DOUBLE_not_equal);
-    
-    
-    #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_exponent_log.dispatch.h"
     #endif
     
@@ -2391,6 +1710,58 @@ InitOperators(PyObject *dictionary) {
     
     
     #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary_fp_le.dispatch.h"
+    #endif
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[12] = FLOAT_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isfinite_functions[13] = DOUBLE_isfinite);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[12] = FLOAT_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isinf_functions[13] = DOUBLE_isinf);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[12] = FLOAT_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(isnan_functions[13] = DOUBLE_isnan);
+    
+    NPY_CPU_DISPATCH_CALL_XB(signbit_functions[1] = FLOAT_signbit);
+    
+    NPY_CPU_DISPATCH_CALL_XB(signbit_functions[2] = DOUBLE_signbit);
+    
+    
+    #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary.dispatch.h"
+    #endif
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[0] = BYTE_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[1] = UBYTE_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[2] = SHORT_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[3] = USHORT_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[4] = INT_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[5] = UINT_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[6] = LONG_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[7] = ULONG_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[8] = LONGLONG_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[9] = ULONGLONG_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[11] = FLOAT_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[12] = DOUBLE_negative);
+    
+    NPY_CPU_DISPATCH_CALL_XB(negative_functions[13] = LONGDOUBLE_negative);
+    
+    
+    #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_hyperbolic.dispatch.h"
     #endif
     
@@ -2471,6 +1842,414 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_AdditionTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CFLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_CFLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_CFLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CFLOAT_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_CDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_CDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CDOUBLE_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CLONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "add with NPY_CLONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "add with NPY_CLONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CLONGDOUBLE_add_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "add", f);
     Py_DECREF(f);
     identity = NULL;
@@ -2833,6 +2612,102 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_TrueDivisionTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "divide with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "divide with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "divide with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "divide with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "divide with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "divide with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "divide with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "divide with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "divide", f);
     Py_DECREF(f);
     identity = NULL;
@@ -2859,7 +2734,7 @@ InitOperators(PyObject *dictionary) {
         return -1;
     }
     f = PyUFunc_FromFuncAndDataAndSignatureAndIdentity(
-        equal_functions, equal_data, equal_signatures, 23,
+        equal_functions, equal_data, equal_signatures, 24,
         2, 1, PyUFunc_None, "equal",
         DOC_NUMPY_CORE_UMATH_EQUAL, 0, NULL, identity
     );
@@ -2998,6 +2873,342 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_DivisionTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "floor_divide with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "floor_divide with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_floor_divide_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "floor_divide", f);
     Py_DECREF(f);
     identity = (Py_INCREF(Py_None), Py_None);
@@ -3017,6 +3228,342 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_SimpleUniformOperationTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmax with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmax with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_fmax_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "fmax", f);
     Py_DECREF(f);
     identity = (Py_INCREF(Py_None), Py_None);
@@ -3036,6 +3583,342 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_SimpleUniformOperationTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "fmin with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "fmin with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_fmin_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "fmin", f);
     Py_DECREF(f);
     identity = NULL;
@@ -3098,7 +3981,7 @@ InitOperators(PyObject *dictionary) {
         return -1;
     }
     f = PyUFunc_FromFuncAndDataAndSignatureAndIdentity(
-        greater_functions, greater_data, greater_signatures, 23,
+        greater_functions, greater_data, greater_signatures, 24,
         2, 1, PyUFunc_None, "greater",
         DOC_NUMPY_CORE_UMATH_GREATER, 0, NULL, identity
     );
@@ -3117,7 +4000,7 @@ InitOperators(PyObject *dictionary) {
         return -1;
     }
     f = PyUFunc_FromFuncAndDataAndSignatureAndIdentity(
-        greater_equal_functions, greater_equal_data, greater_equal_signatures, 23,
+        greater_equal_functions, greater_equal_data, greater_equal_signatures, 24,
         2, 1, PyUFunc_None, "greater_equal",
         DOC_NUMPY_CORE_UMATH_GREATER_EQUAL, 0, NULL, identity
     );
@@ -3321,7 +4204,7 @@ InitOperators(PyObject *dictionary) {
         return -1;
     }
     f = PyUFunc_FromFuncAndDataAndSignatureAndIdentity(
-        less_functions, less_data, less_signatures, 23,
+        less_functions, less_data, less_signatures, 24,
         2, 1, PyUFunc_None, "less",
         DOC_NUMPY_CORE_UMATH_LESS, 0, NULL, identity
     );
@@ -3340,7 +4223,7 @@ InitOperators(PyObject *dictionary) {
         return -1;
     }
     f = PyUFunc_FromFuncAndDataAndSignatureAndIdentity(
-        less_equal_functions, less_equal_data, less_equal_signatures, 23,
+        less_equal_functions, less_equal_data, less_equal_signatures, 24,
         2, 1, PyUFunc_None, "less_equal",
         DOC_NUMPY_CORE_UMATH_LESS_EQUAL, 0, NULL, identity
     );
@@ -3573,6 +4456,342 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_SimpleUniformOperationTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "maximum with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "maximum with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_maximum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "maximum", f);
     Py_DECREF(f);
     identity = (Py_INCREF(Py_None), Py_None);
@@ -3592,6 +4811,342 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_SimpleUniformOperationTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "minimum with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "minimum with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_minimum_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "minimum", f);
     Py_DECREF(f);
     identity = NULL;
@@ -3629,6 +5184,414 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_MultiplicationTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CFLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_CFLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_CFLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CFLOAT_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_CDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_CDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CDOUBLE_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CLONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "multiply with NPY_CLONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "multiply with NPY_CLONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CLONGDOUBLE_multiply_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "multiply", f);
     Py_DECREF(f);
     identity = NULL;
@@ -3673,7 +5636,7 @@ InitOperators(PyObject *dictionary) {
         return -1;
     }
     f = PyUFunc_FromFuncAndDataAndSignatureAndIdentity(
-        not_equal_functions, not_equal_data, not_equal_signatures, 23,
+        not_equal_functions, not_equal_data, not_equal_signatures, 24,
         2, 1, PyUFunc_None, "not_equal",
         DOC_NUMPY_CORE_UMATH_NOT_EQUAL, 0, NULL, identity
     );
@@ -3977,6 +5940,414 @@ InitOperators(PyObject *dictionary) {
     }
     
     ((PyUFuncObject *)f)->type_resolver = &PyUFunc_SubtractionTypeResolver;
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_BYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_BYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_BYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         BYTE_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UBYTE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_UBYTE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_UBYTE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UBYTE_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_SHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_SHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_SHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         SHORT_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_USHORT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_USHORT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_USHORT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         USHORT_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_INT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_INT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_INT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         INT_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_UINT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_UINT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_UINT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         UINT_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_LONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_LONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONG_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_ULONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_ULONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONG_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_LONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_LONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGLONG_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_ULONGLONG);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_ULONGLONG");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_ULONGLONG");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         ULONGLONG_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_HALF);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_HALF");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_HALF");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         HALF_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_FLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_FLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_FLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         FLOAT_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_DOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_DOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         DOUBLE_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_LONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_LONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_LONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         LONGDOUBLE_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CFLOAT);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_CFLOAT");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_CFLOAT");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CFLOAT_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_CDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_CDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CDOUBLE_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
+    
+    {
+        PyArray_DTypeMeta *dtype = PyArray_DTypeFromTypeNum(NPY_CLONGDOUBLE);
+        PyObject *info = get_info_no_cast((PyUFuncObject *)f,
+                                           dtype, 3);
+        if (info == NULL) {
+            return -1;
+        }
+        if (info == Py_None) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "cannot add indexed loop to ufunc "
+                "subtract with NPY_CLONGDOUBLE");
+            return -1;
+        }
+        if (!PyObject_TypeCheck(info, &PyArrayMethod_Type)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                "Not a PyArrayMethodObject in ufunc "
+                "subtract with NPY_CLONGDOUBLE");
+        }
+        ((PyArrayMethodObject*)info)->contiguous_indexed_loop =
+                                                         CLONGDOUBLE_subtract_indexed;
+        /* info is borrowed, no need to decref*/
+    }
+    
     PyDict_SetItemString(dictionary, "subtract", f);
     Py_DECREF(f);
     identity = NULL;
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_dtype_api.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_dtype_api.h
new file mode 100644
index 00000000000..39fbc500b0f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_dtype_api.h
@@ -0,0 +1,408 @@
+/*
+ * DType related API shared by the (experimental) public API And internal API.
+ */
+
+#ifndef NUMPY_CORE_INCLUDE_NUMPY___DTYPE_API_H_
+#define NUMPY_CORE_INCLUDE_NUMPY___DTYPE_API_H_
+
+#define __EXPERIMENTAL_DTYPE_API_VERSION 11
+
+struct PyArrayMethodObject_tag;
+
+/*
+ * Largely opaque struct for DType classes (i.e. metaclass instances).
+ * The internal definition is currently in `ndarraytypes.h` (export is a bit
+ * more complex because `PyArray_Descr` is a DTypeMeta internally but not
+ * externally).
+ */
+#if !(defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD)
+
+    typedef struct PyArray_DTypeMeta_tag {
+        PyHeapTypeObject super;
+
+        /*
+        * Most DTypes will have a singleton default instance, for the
+        * parametric legacy DTypes (bytes, string, void, datetime) this
+        * may be a pointer to the *prototype* instance?
+        */
+        PyArray_Descr *singleton;
+        /* Copy of the legacy DTypes type number, usually invalid. */
+        int type_num;
+
+        /* The type object of the scalar instances (may be NULL?) */
+        PyTypeObject *scalar_type;
+        /*
+        * DType flags to signal legacy, parametric, or
+        * abstract.  But plenty of space for additional information/flags.
+        */
+        npy_uint64 flags;
+
+        /*
+        * Use indirection in order to allow a fixed size for this struct.
+        * A stable ABI size makes creating a static DType less painful
+        * while also ensuring flexibility for all opaque API (with one
+        * indirection due the pointer lookup).
+        */
+        void *dt_slots;
+        /* Allow growing (at the moment also beyond this) */
+        void *reserved[3];
+    } PyArray_DTypeMeta;
+
+#endif  /* not internal build */
+
+/*
+ * ******************************************************
+ *         ArrayMethod API (Casting and UFuncs)
+ * ******************************************************
+ */
+/*
+ * NOTE: Expected changes:
+ *       * probably split runtime and general flags into two
+ *       * should possibly not use an enum for typedef for more stable ABI?
+ */
+typedef enum {
+    /* Flag for whether the GIL is required */
+    NPY_METH_REQUIRES_PYAPI = 1 << 0,
+    /*
+     * Some functions cannot set floating point error flags, this flag
+     * gives us the option (not requirement) to skip floating point error
+     * setup/check. No function should set error flags and ignore them
+     * since it would interfere with chaining operations (e.g. casting).
+     */
+    NPY_METH_NO_FLOATINGPOINT_ERRORS = 1 << 1,
+    /* Whether the method supports unaligned access (not runtime) */
+    NPY_METH_SUPPORTS_UNALIGNED = 1 << 2,
+    /*
+     * Used for reductions to allow reordering the operation.  At this point
+     * assume that if set, it also applies to normal operations though!
+     */
+    NPY_METH_IS_REORDERABLE = 1 << 3,
+    /*
+     * Private flag for now for *logic* functions.  The logical functions
+     * `logical_or` and `logical_and` can always cast the inputs to booleans
+     * "safely" (because that is how the cast to bool is defined).
+     * @seberg: I am not sure this is the best way to handle this, so its
+     * private for now (also it is very limited anyway).
+     * There is one "exception". NA aware dtypes cannot cast to bool
+     * (hopefully), so the `??->?` loop should error even with this flag.
+     * But a second NA fallback loop will be necessary.
+     */
+    _NPY_METH_FORCE_CAST_INPUTS = 1 << 17,
+
+    /* All flags which can change at runtime */
+    NPY_METH_RUNTIME_FLAGS = (
+            NPY_METH_REQUIRES_PYAPI |
+            NPY_METH_NO_FLOATINGPOINT_ERRORS),
+} NPY_ARRAYMETHOD_FLAGS;
+
+
+typedef struct PyArrayMethod_Context_tag {
+    /* The caller, which is typically the original ufunc.  May be NULL */
+    PyObject *caller;
+    /* The method "self".  Publically currentl an opaque object. */
+    struct PyArrayMethodObject_tag *method;
+
+    /* Operand descriptors, filled in by resolve_descriptors */
+    PyArray_Descr **descriptors;
+    /* Structure may grow (this is harmless for DType authors) */
+} PyArrayMethod_Context;
+
+
+/*
+ * The main object for creating a new ArrayMethod. We use the typical `slots`
+ * mechanism used by the Python limited API (see below for the slot defs).
+ */
+typedef struct {
+    const char *name;
+    int nin, nout;
+    NPY_CASTING casting;
+    NPY_ARRAYMETHOD_FLAGS flags;
+    PyArray_DTypeMeta **dtypes;
+    PyType_Slot *slots;
+} PyArrayMethod_Spec;
+
+
+/*
+ * ArrayMethod slots
+ * -----------------
+ *
+ * SLOTS IDs For the ArrayMethod creation, once fully public, IDs are fixed
+ * but can be deprecated and arbitrarily extended.
+ */
+#define NPY_METH_resolve_descriptors 1
+/* We may want to adapt the `get_loop` signature a bit: */
+#define _NPY_METH_get_loop 2
+#define NPY_METH_get_reduction_initial 3
+/* specific loops for constructions/default get_loop: */
+#define NPY_METH_strided_loop 4
+#define NPY_METH_contiguous_loop 5
+#define NPY_METH_unaligned_strided_loop 6
+#define NPY_METH_unaligned_contiguous_loop 7
+#define NPY_METH_contiguous_indexed_loop 8
+
+/*
+ * The resolve descriptors function, must be able to handle NULL values for
+ * all output (but not input) `given_descrs` and fill `loop_descrs`.
+ * Return -1 on error or 0 if the operation is not possible without an error
+ * set.  (This may still be in flux.)
+ * Otherwise must return the "casting safety", for normal functions, this is
+ * almost always "safe" (or even "equivalent"?).
+ *
+ * `resolve_descriptors` is optional if all output DTypes are non-parametric.
+ */
+typedef NPY_CASTING (resolve_descriptors_function)(
+        /* "method" is currently opaque (necessary e.g. to wrap Python) */
+        struct PyArrayMethodObject_tag *method,
+        /* DTypes the method was created for */
+        PyArray_DTypeMeta **dtypes,
+        /* Input descriptors (instances).  Outputs may be NULL. */
+        PyArray_Descr **given_descrs,
+        /* Exact loop descriptors to use, must not hold references on error */
+        PyArray_Descr **loop_descrs,
+        npy_intp *view_offset);
+
+
+typedef int (PyArrayMethod_StridedLoop)(PyArrayMethod_Context *context,
+        char *const *data, const npy_intp *dimensions, const npy_intp *strides,
+        NpyAuxData *transferdata);
+
+
+typedef int (get_loop_function)(
+        PyArrayMethod_Context *context,
+        int aligned, int move_references,
+        const npy_intp *strides,
+        PyArrayMethod_StridedLoop **out_loop,
+        NpyAuxData **out_transferdata,
+        NPY_ARRAYMETHOD_FLAGS *flags);
+
+/**
+ * Query an ArrayMethod for the initial value for use in reduction.
+ *
+ * @param context The arraymethod context, mainly to access the descriptors.
+ * @param reduction_is_empty Whether the reduction is empty. When it is, the
+ *     value returned may differ.  In this case it is a "default" value that
+ *     may differ from the "identity" value normally used.  For example:
+ *     - `0.0` is the default for `sum([])`.  But `-0.0` is the correct
+ *       identity otherwise as it preserves the sign for `sum([-0.0])`.
+ *     - We use no identity for object, but return the default of `0` and `1`
+ *       for the empty `sum([], dtype=object)` and `prod([], dtype=object)`.
+ *       This allows `np.sum(np.array(["a", "b"], dtype=object))` to work.
+ *     - `-inf` or `INT_MIN` for `max` is an identity, but at least `INT_MIN`
+ *       not a good *default* when there are no items.
+ * @param initial Pointer to initial data to be filled (if possible)
+ *
+ * @returns -1, 0, or 1 indicating error, no initial value, and initial being
+ *     successfully filled.  Errors must not be given where 0 is correct, NumPy
+ *     may call this even when not strictly necessary.
+ */
+typedef int (get_reduction_initial_function)(
+        PyArrayMethod_Context *context, npy_bool reduction_is_empty,
+        char *initial);
+
+/*
+ * The following functions are only used by the wrapping array method defined
+ * in umath/wrapping_array_method.c
+ */
+
+/*
+ * The function to convert the given descriptors (passed in to
+ * `resolve_descriptors`) and translates them for the wrapped loop.
+ * The new descriptors MUST be viewable with the old ones, `NULL` must be
+ * supported (for outputs) and should normally be forwarded.
+ *
+ * The function must clean up on error.
+ *
+ * NOTE: We currently assume that this translation gives "viewable" results.
+ *       I.e. there is no additional casting related to the wrapping process.
+ *       In principle that could be supported, but not sure it is useful.
+ *       This currently also means that e.g. alignment must apply identically
+ *       to the new dtypes.
+ *
+ * TODO: Due to the fact that `resolve_descriptors` is also used for `can_cast`
+ *       there is no way to "pass out" the result of this function.  This means
+ *       it will be called twice for every ufunc call.
+ *       (I am considering including `auxdata` as an "optional" parameter to
+ *       `resolve_descriptors`, so that it can be filled there if not NULL.)
+ */
+typedef int translate_given_descrs_func(int nin, int nout,
+        PyArray_DTypeMeta *wrapped_dtypes[],
+        PyArray_Descr *given_descrs[], PyArray_Descr *new_descrs[]);
+
+/**
+ * The function to convert the actual loop descriptors (as returned by the
+ * original `resolve_descriptors` function) to the ones the output array
+ * should use.
+ * This function must return "viewable" types, it must not mutate them in any
+ * form that would break the inner-loop logic.  Does not need to support NULL.
+ *
+ * The function must clean up on error.
+ *
+ * @param nargs Number of arguments
+ * @param new_dtypes The DTypes of the output (usually probably not needed)
+ * @param given_descrs Original given_descrs to the resolver, necessary to
+ *        fetch any information related to the new dtypes from the original.
+ * @param original_descrs The `loop_descrs` returned by the wrapped loop.
+ * @param loop_descrs The output descriptors, compatible to `original_descrs`.
+ *
+ * @returns 0 on success, -1 on failure.
+ */
+typedef int translate_loop_descrs_func(int nin, int nout,
+        PyArray_DTypeMeta *new_dtypes[], PyArray_Descr *given_descrs[],
+        PyArray_Descr *original_descrs[], PyArray_Descr *loop_descrs[]);
+
+
+/*
+ * A traverse loop working on a single array. This is similar to the general
+ * strided-loop function. This is designed for loops that need to visit every
+ * element of a single array.
+ *
+ * Currently this is used for array clearing, via the NPY_DT_get_clear_loop
+ * API hook, and zero-filling, via the NPY_DT_get_fill_zero_loop API hook.
+ * These are most useful for handling arrays storing embedded references to
+ * python objects or heap-allocated data.
+ *
+ * The `void *traverse_context` is passed in because we may need to pass in
+ * Intepreter state or similar in the future, but we don't want to pass in
+ * a full context (with pointers to dtypes, method, caller which all make
+ * no sense for a traverse function).
+ *
+ * We assume for now that this context can be just passed through in the
+ * the future (for structured dtypes).
+ *
+ */
+typedef int (traverse_loop_function)(
+        void *traverse_context, PyArray_Descr *descr, char *data,
+        npy_intp size, npy_intp stride, NpyAuxData *auxdata);
+
+
+/*
+ * Simplified get_loop function specific to dtype traversal
+ *
+ * It should set the flags needed for the traversal loop and set out_loop to the
+ * loop function, which must be a valid traverse_loop_function
+ * pointer. Currently this is used for zero-filling and clearing arrays storing
+ * embedded references.
+ *
+ */
+typedef int (get_traverse_loop_function)(
+        void *traverse_context, PyArray_Descr *descr,
+        int aligned, npy_intp fixed_stride,
+        traverse_loop_function **out_loop, NpyAuxData **out_auxdata,
+        NPY_ARRAYMETHOD_FLAGS *flags);
+
+
+/*
+ * ****************************
+ *          DTYPE API
+ * ****************************
+ */
+
+#define NPY_DT_ABSTRACT 1 << 1
+#define NPY_DT_PARAMETRIC 1 << 2
+#define NPY_DT_NUMERIC 1 << 3
+
+/*
+ * These correspond to slots in the NPY_DType_Slots struct and must
+ * be in the same order as the members of that struct. If new slots
+ * get added or old slots get removed NPY_NUM_DTYPE_SLOTS must also
+ * be updated
+ */
+
+#define NPY_DT_discover_descr_from_pyobject 1
+// this slot is considered private because its API hasn't beed decided
+#define _NPY_DT_is_known_scalar_type 2
+#define NPY_DT_default_descr 3
+#define NPY_DT_common_dtype 4
+#define NPY_DT_common_instance 5
+#define NPY_DT_ensure_canonical 6
+#define NPY_DT_setitem 7
+#define NPY_DT_getitem 8
+#define NPY_DT_get_clear_loop 9
+#define NPY_DT_get_fill_zero_loop 10
+
+// These PyArray_ArrFunc slots will be deprecated and replaced eventually
+// getitem and setitem can be defined as a performance optimization;
+// by default the user dtypes call `legacy_getitem_using_DType` and
+// `legacy_setitem_using_DType`, respectively. This functionality is
+// only supported for basic NumPy DTypes.
+
+
+// used to separate dtype slots from arrfuncs slots
+// intended only for internal use but defined here for clarity
+#define _NPY_DT_ARRFUNCS_OFFSET (1 << 10)
+
+// Cast is disabled
+// #define NPY_DT_PyArray_ArrFuncs_cast 0 + _NPY_DT_ARRFUNCS_OFFSET
+
+#define NPY_DT_PyArray_ArrFuncs_getitem 1 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_setitem 2 + _NPY_DT_ARRFUNCS_OFFSET
+
+#define NPY_DT_PyArray_ArrFuncs_copyswapn 3 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_copyswap 4 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_compare 5 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_argmax 6 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_dotfunc 7 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_scanfunc 8 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_fromstr 9 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_nonzero 10 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_fill 11 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_fillwithscalar 12 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_sort 13 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_argsort 14 + _NPY_DT_ARRFUNCS_OFFSET
+
+// Casting related slots are disabled. See
+// https://github.com/numpy/numpy/pull/23173#discussion_r1101098163
+// #define NPY_DT_PyArray_ArrFuncs_castdict 15 + _NPY_DT_ARRFUNCS_OFFSET
+// #define NPY_DT_PyArray_ArrFuncs_scalarkind 16 + _NPY_DT_ARRFUNCS_OFFSET
+// #define NPY_DT_PyArray_ArrFuncs_cancastscalarkindto 17 + _NPY_DT_ARRFUNCS_OFFSET
+// #define NPY_DT_PyArray_ArrFuncs_cancastto 18 + _NPY_DT_ARRFUNCS_OFFSET
+
+// These are deprecated in NumPy 1.19, so are disabled here.
+// #define NPY_DT_PyArray_ArrFuncs_fastclip 19 + _NPY_DT_ARRFUNCS_OFFSET
+// #define NPY_DT_PyArray_ArrFuncs_fastputmask 20 + _NPY_DT_ARRFUNCS_OFFSET
+// #define NPY_DT_PyArray_ArrFuncs_fasttake 21 + _NPY_DT_ARRFUNCS_OFFSET
+#define NPY_DT_PyArray_ArrFuncs_argmin 22 + _NPY_DT_ARRFUNCS_OFFSET
+
+// TODO: These slots probably still need some thought, and/or a way to "grow"?
+typedef struct {
+    PyTypeObject *typeobj;    /* type of python scalar or NULL */
+    int flags;                /* flags, including parametric and abstract */
+    /* NULL terminated cast definitions. Use NULL for the newly created DType */
+    PyArrayMethod_Spec **casts;
+    PyType_Slot *slots;
+    /* Baseclass or NULL (will always subclass `np.dtype`) */
+    PyTypeObject *baseclass;
+} PyArrayDTypeMeta_Spec;
+
+
+typedef PyArray_Descr *(discover_descr_from_pyobject_function)(
+        PyArray_DTypeMeta *cls, PyObject *obj);
+
+/*
+ * Before making this public, we should decide whether it should pass
+ * the type, or allow looking at the object. A possible use-case:
+ * `np.array(np.array([0]), dtype=np.ndarray)`
+ * Could consider arrays that are not `dtype=ndarray` "scalars".
+ */
+typedef int (is_known_scalar_type_function)(
+        PyArray_DTypeMeta *cls, PyTypeObject *obj);
+
+typedef PyArray_Descr *(default_descr_function)(PyArray_DTypeMeta *cls);
+typedef PyArray_DTypeMeta *(common_dtype_function)(
+        PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtype2);
+typedef PyArray_Descr *(common_instance_function)(
+        PyArray_Descr *dtype1, PyArray_Descr *dtype2);
+typedef PyArray_Descr *(ensure_canonical_function)(PyArray_Descr *dtype);
+
+/*
+ * TODO: These two functions are currently only used for experimental DType
+ *       API support.  Their relation should be "reversed": NumPy should
+ *       always use them internally.
+ *       There are open points about "casting safety" though, e.g. setting
+ *       elements is currently always unsafe.
+ */
+typedef int(setitemfunction)(PyArray_Descr *, PyObject *, char *);
+typedef PyObject *(getitemfunction)(PyArray_Descr *, char *);
+
+
+#endif  /* NUMPY_CORE_INCLUDE_NUMPY___DTYPE_API_H_ */
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_neighborhood_iterator_imp.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_neighborhood_iterator_imp.h
index 07e2363d00c..b365cb50854 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/_neighborhood_iterator_imp.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_neighborhood_iterator_imp.h
@@ -4,7 +4,7 @@
 /*
  * Private API (here for inline)
  */
-static NPY_INLINE int
+static inline int
 _PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter);
 
 /*
@@ -34,7 +34,7 @@ _PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter);
         iter->coordinates[c] = iter->bounds[c][0]; \
     }
 
-static NPY_INLINE int
+static inline int
 _PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter)
 {
     npy_intp i, wb;
@@ -49,7 +49,7 @@ _PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter)
 /*
  * Version optimized for 2d arrays, manual loop unrolling
  */
-static NPY_INLINE int
+static inline int
 _PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter)
 {
     npy_intp wb;
@@ -64,7 +64,7 @@ _PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter)
 /*
  * Advance to the next neighbour
  */
-static NPY_INLINE int
+static inline int
 PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter)
 {
     _PyArrayNeighborhoodIter_IncrCoord (iter);
@@ -76,7 +76,7 @@ PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter)
 /*
  * Reset functions
  */
-static NPY_INLINE int
+static inline int
 PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter)
 {
     npy_intp i;
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.linux.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-linux.h
index 2d226a88c66..429ec0909e8 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.linux.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-linux.h
@@ -13,10 +13,6 @@
 #define NPY_SIZEOF_PY_LONG_LONG 8
 #define NPY_SIZEOF_LONGLONG 8
 #define NPY_NO_SMP 0
-#define NPY_HAVE_DECL_ISNAN
-#define NPY_HAVE_DECL_ISINF
-#define NPY_HAVE_DECL_SIGNBIT
-#define NPY_HAVE_DECL_ISFINITE
 #define NPY_USE_C99_COMPLEX 1
 #define NPY_HAVE_COMPLEX_DOUBLE 1
 #define NPY_HAVE_COMPLEX_FLOAT 1
@@ -24,7 +20,7 @@
 #define NPY_USE_C99_FORMATS 1
 #define NPY_VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
 #define NPY_ABI_VERSION 0x01000009
-#define NPY_API_VERSION 0x00000010
+#define NPY_API_VERSION 0x00000011
 
 #ifndef __STDC_FORMAT_MACROS
 #define __STDC_FORMAT_MACROS 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-osx-arm64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-osx-arm64.h
new file mode 100644
index 00000000000..e05111210dc
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-osx-arm64.h
@@ -0,0 +1,26 @@
+#define NPY_SIZEOF_SHORT SIZEOF_SHORT
+#define NPY_SIZEOF_INT SIZEOF_INT
+#define NPY_SIZEOF_LONG SIZEOF_LONG
+#define NPY_SIZEOF_FLOAT 4
+#define NPY_SIZEOF_COMPLEX_FLOAT 8
+#define NPY_SIZEOF_DOUBLE 8
+#define NPY_SIZEOF_COMPLEX_DOUBLE 16
+#define NPY_SIZEOF_LONGDOUBLE 8
+#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16
+#define NPY_SIZEOF_PY_INTPTR_T 8
+#define NPY_SIZEOF_OFF_T 8
+#define NPY_SIZEOF_PY_LONG_LONG 8
+#define NPY_SIZEOF_LONGLONG 8
+#define NPY_NO_SMP 0
+#define NPY_USE_C99_COMPLEX 1
+#define NPY_HAVE_COMPLEX_DOUBLE 1
+#define NPY_HAVE_COMPLEX_FLOAT 1
+#define NPY_HAVE_COMPLEX_LONG_DOUBLE 1
+#define NPY_USE_C99_FORMATS 1
+#define NPY_VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
+#define NPY_ABI_VERSION 0x01000009
+#define NPY_API_VERSION 0x00000011
+
+#ifndef __STDC_FORMAT_MACROS
+#define __STDC_FORMAT_MACROS 1
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.darwin.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-osx-x86_64.h
index 3f14faf4ff9..3e1cda2b866 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.darwin.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-osx-x86_64.h
@@ -12,10 +12,6 @@
 #define NPY_SIZEOF_PY_LONG_LONG 8
 #define NPY_SIZEOF_LONGLONG 8
 #define NPY_NO_SMP 0
-#define NPY_HAVE_DECL_ISNAN
-#define NPY_HAVE_DECL_ISINF
-#define NPY_HAVE_DECL_SIGNBIT
-#define NPY_HAVE_DECL_ISFINITE
 #define NPY_USE_C99_COMPLEX 1
 #define NPY_HAVE_COMPLEX_DOUBLE 1
 #define NPY_HAVE_COMPLEX_FLOAT 1
@@ -23,7 +19,7 @@
 #define NPY_USE_C99_FORMATS 1
 #define NPY_VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
 #define NPY_ABI_VERSION 0x01000009
-#define NPY_API_VERSION 0x00000010
+#define NPY_API_VERSION 0x00000011
 
 #ifndef __STDC_FORMAT_MACROS
 #define __STDC_FORMAT_MACROS 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.windows.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-win.h
index 94baab88dd7..ae428859405 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.windows.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig-win.h
@@ -13,15 +13,11 @@
 #define NPY_SIZEOF_LONGLONG 8
 #define NPY_NO_SIGNAL 1
 #define NPY_NO_SMP 0
-#define NPY_HAVE_DECL_ISNAN
-#define NPY_HAVE_DECL_ISINF
-#define NPY_HAVE_DECL_SIGNBIT
-#define NPY_HAVE_DECL_ISFINITE
 #define NPY_USE_C99_COMPLEX 1
 #define NPY_USE_C99_FORMATS 1
 #define NPY_VISIBILITY_HIDDEN
 #define NPY_ABI_VERSION 0x01000009
-#define NPY_API_VERSION 0x00000010
+#define NPY_API_VERSION 0x00000011
 
 #ifndef __STDC_FORMAT_MACROS
 #define __STDC_FORMAT_MACROS 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.h
index 625bd0f404a..2c5c95a5c2a 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.h
@@ -1,9 +1,11 @@
-#include <util/system/platform.h>
+#pragma once
 
-#if defined(_windows_)
-#   include "_numpyconfig.windows.h"
-#elif defined(_darwin_)
-#   include "_numpyconfig.darwin.h"
+#if defined(__APPLE__) && (defined(__aarch64__) || defined(_M_ARM64))
+#   include "_numpyconfig-osx-arm64.h"
+#elif defined(__APPLE__) && (defined(__x86_64__) || defined(_M_X64))
+#   include "_numpyconfig-osx-x86_64.h"
+#elif defined(_MSC_VER)
+#   include "_numpyconfig-win.h"
 #else
-#   include "_numpyconfig.linux.h"
+#   include "_numpyconfig-linux.h"
 #endif
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.h.in b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.h.in
new file mode 100644
index 00000000000..a218200264e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_numpyconfig.h.in
@@ -0,0 +1,32 @@
+#mesondefine NPY_HAVE_ENDIAN_H
+
+#mesondefine NPY_SIZEOF_SHORT
+#mesondefine NPY_SIZEOF_INT
+#mesondefine NPY_SIZEOF_LONG
+#mesondefine NPY_SIZEOF_FLOAT
+#mesondefine NPY_SIZEOF_COMPLEX_FLOAT
+#mesondefine NPY_SIZEOF_DOUBLE
+#mesondefine NPY_SIZEOF_COMPLEX_DOUBLE
+#mesondefine NPY_SIZEOF_LONGDOUBLE
+#mesondefine NPY_SIZEOF_COMPLEX_LONGDOUBLE
+#mesondefine NPY_SIZEOF_PY_INTPTR_T
+#mesondefine NPY_SIZEOF_OFF_T
+#mesondefine NPY_SIZEOF_PY_LONG_LONG
+#mesondefine NPY_SIZEOF_LONGLONG
+
+#mesondefine NPY_USE_C99_COMPLEX
+#mesondefine NPY_HAVE_COMPLEX_DOUBLE
+#mesondefine NPY_HAVE_COMPLEX_FLOAT
+#mesondefine NPY_HAVE_COMPLEX_LONG_DOUBLE
+#mesondefine NPY_USE_C99_FORMATS
+
+#mesondefine NPY_NO_SIGNAL
+#mesondefine NPY_NO_SMP
+
+#mesondefine NPY_VISIBILITY_HIDDEN
+#mesondefine NPY_ABI_VERSION
+#mesondefine NPY_API_VERSION
+
+#ifndef __STDC_FORMAT_MACROS
+#define __STDC_FORMAT_MACROS 1
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/_umath_doc_generated.h b/contrib/python/numpy/py3/numpy/core/include/numpy/_umath_doc_generated.h
index c6b8545375b..98e184eca5c 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/_umath_doc_generated.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/_umath_doc_generated.h
@@ -11,7 +11,7 @@
 #define DOC_NUMPY_CORE_UMATH__ARG "DO NOT USE, ONLY FOR TESTING"
 #define DOC_NUMPY_CORE_UMATH_ARCTANH "Inverse hyperbolic tangent element-wise.\n""\n""Parameters\n""----------\n""x : array_like\n""    Input array.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""out : ndarray or scalar\n""    Array of the same shape as `x`.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""emath.arctanh\n""\n""Notes\n""-----\n""`arctanh` is a multivalued function: for each `x` there are infinitely\n""many numbers `z` such that ``tanh(z) = x``. The convention is to return\n""the `z` whose imaginary part lies in `[-pi/2, pi/2]`.\n""\n""For real-valued input data types, `arctanh` always returns real output.\n""For each value that cannot be expressed as a real number or infinity,\n""it yields ``nan`` and sets the `invalid` floating point error flag.\n""\n""For complex-valued input, `arctanh` is a complex analytical function\n""that has branch cuts `[-1, -inf]` and `[1, inf]` and is continuous from\n""above on the former and from below on the latter.\n""\n""The inverse hyperbolic tangent is also known as `atanh` or ``tanh^-1``.\n""\n""References\n""----------\n"".. [1] M. Abramowitz and I.A. Stegun, \"Handbook of Mathematical Functions\",\n""       10th printing, 1964, pp. 86.\n""       https://personal.math.ubc.ca/~cbm/aands/page_86.htm\n"".. [2] Wikipedia, \"Inverse hyperbolic function\",\n""       https://en.wikipedia.org/wiki/Arctanh\n""\n""Examples\n""--------\n"">>> np.arctanh([0, -0.5])\n""array([ 0.        , -0.54930614])"
 #define DOC_NUMPY_CORE_UMATH_BITWISE_AND "Compute the bit-wise AND of two arrays element-wise.\n""\n""Computes the bit-wise AND of the underlying binary representation of\n""the integers in the input arrays. This ufunc implements the C/Python\n""operator ``&``.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    Only integer and boolean types are handled.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""out : ndarray or scalar\n""    Result.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""logical_and\n""bitwise_or\n""bitwise_xor\n""binary_repr :\n""    Return the binary representation of the input number as a string.\n""\n""Examples\n""--------\n""The number 13 is represented by ``00001101``.  Likewise, 17 is\n""represented by ``00010001``.  The bit-wise AND of 13 and 17 is\n""therefore ``000000001``, or 1:\n""\n"">>> np.bitwise_and(13, 17)\n""1\n""\n"">>> np.bitwise_and(14, 13)\n""12\n"">>> np.binary_repr(12)\n""\'1100\'\n"">>> np.bitwise_and([14,3], 13)\n""array([12,  1])\n""\n"">>> np.bitwise_and([11,7], [4,25])\n""array([0, 1])\n"">>> np.bitwise_and(np.array([2,5,255]), np.array([3,14,16]))\n""array([ 2,  4, 16])\n"">>> np.bitwise_and([True, True], [False, True])\n""array([False,  True])\n""\n""The ``&`` operator can be used as a shorthand for ``np.bitwise_and`` on\n""ndarrays.\n""\n"">>> x1 = np.array([2, 5, 255])\n"">>> x2 = np.array([3, 14, 16])\n"">>> x1 & x2\n""array([ 2,  4, 16])"
-#define DOC_NUMPY_CORE_UMATH_BITWISE_OR "Compute the bit-wise OR of two arrays element-wise.\n""\n""Computes the bit-wise OR of the underlying binary representation of\n""the integers in the input arrays. This ufunc implements the C/Python\n""operator ``|``.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    Only integer and boolean types are handled.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""out : ndarray or scalar\n""    Result.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""logical_or\n""bitwise_and\n""bitwise_xor\n""binary_repr :\n""    Return the binary representation of the input number as a string.\n""\n""Examples\n""--------\n""The number 13 has the binary representation ``00001101``. Likewise,\n""16 is represented by ``00010000``.  The bit-wise OR of 13 and 16 is\n""then ``000111011``, or 29:\n""\n"">>> np.bitwise_or(13, 16)\n""29\n"">>> np.binary_repr(29)\n""\'11101\'\n""\n"">>> np.bitwise_or(32, 2)\n""34\n"">>> np.bitwise_or([33, 4], 1)\n""array([33,  5])\n"">>> np.bitwise_or([33, 4], [1, 2])\n""array([33,  6])\n""\n"">>> np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))\n""array([  6,   5, 255])\n"">>> np.array([2, 5, 255]) | np.array([4, 4, 4])\n""array([  6,   5, 255])\n"">>> np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),\n""...               np.array([4, 4, 4, 2147483647], dtype=np.int32))\n""array([         6,          5,        255, 2147483647])\n"">>> np.bitwise_or([True, True], [False, True])\n""array([ True,  True])\n""\n""The ``|`` operator can be used as a shorthand for ``np.bitwise_or`` on\n""ndarrays.\n""\n"">>> x1 = np.array([2, 5, 255])\n"">>> x2 = np.array([4, 4, 4])\n"">>> x1 | x2\n""array([  6,   5, 255])"
+#define DOC_NUMPY_CORE_UMATH_BITWISE_OR "Compute the bit-wise OR of two arrays element-wise.\n""\n""Computes the bit-wise OR of the underlying binary representation of\n""the integers in the input arrays. This ufunc implements the C/Python\n""operator ``|``.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    Only integer and boolean types are handled.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""out : ndarray or scalar\n""    Result.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""logical_or\n""bitwise_and\n""bitwise_xor\n""binary_repr :\n""    Return the binary representation of the input number as a string.\n""\n""Examples\n""--------\n""The number 13 has the binary representation ``00001101``. Likewise,\n""16 is represented by ``00010000``.  The bit-wise OR of 13 and 16 is\n""then ``00011101``, or 29:\n""\n"">>> np.bitwise_or(13, 16)\n""29\n"">>> np.binary_repr(29)\n""\'11101\'\n""\n"">>> np.bitwise_or(32, 2)\n""34\n"">>> np.bitwise_or([33, 4], 1)\n""array([33,  5])\n"">>> np.bitwise_or([33, 4], [1, 2])\n""array([33,  6])\n""\n"">>> np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))\n""array([  6,   5, 255])\n"">>> np.array([2, 5, 255]) | np.array([4, 4, 4])\n""array([  6,   5, 255])\n"">>> np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),\n""...               np.array([4, 4, 4, 2147483647], dtype=np.int32))\n""array([         6,          5,        255, 2147483647])\n"">>> np.bitwise_or([True, True], [False, True])\n""array([ True,  True])\n""\n""The ``|`` operator can be used as a shorthand for ``np.bitwise_or`` on\n""ndarrays.\n""\n"">>> x1 = np.array([2, 5, 255])\n"">>> x2 = np.array([4, 4, 4])\n"">>> x1 | x2\n""array([  6,   5, 255])"
 #define DOC_NUMPY_CORE_UMATH_BITWISE_XOR "Compute the bit-wise XOR of two arrays element-wise.\n""\n""Computes the bit-wise XOR of the underlying binary representation of\n""the integers in the input arrays. This ufunc implements the C/Python\n""operator ``^``.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    Only integer and boolean types are handled.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""out : ndarray or scalar\n""    Result.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""logical_xor\n""bitwise_and\n""bitwise_or\n""binary_repr :\n""    Return the binary representation of the input number as a string.\n""\n""Examples\n""--------\n""The number 13 is represented by ``00001101``. Likewise, 17 is\n""represented by ``00010001``.  The bit-wise XOR of 13 and 17 is\n""therefore ``00011100``, or 28:\n""\n"">>> np.bitwise_xor(13, 17)\n""28\n"">>> np.binary_repr(28)\n""\'11100\'\n""\n"">>> np.bitwise_xor(31, 5)\n""26\n"">>> np.bitwise_xor([31,3], 5)\n""array([26,  6])\n""\n"">>> np.bitwise_xor([31,3], [5,6])\n""array([26,  5])\n"">>> np.bitwise_xor([True, True], [False, True])\n""array([ True, False])\n""\n""The ``^`` operator can be used as a shorthand for ``np.bitwise_xor`` on\n""ndarrays.\n""\n"">>> x1 = np.array([True, True])\n"">>> x2 = np.array([False, True])\n"">>> x1 ^ x2\n""array([ True, False])"
 #define DOC_NUMPY_CORE_UMATH_CEIL "Return the ceiling of the input, element-wise.\n""\n""The ceil of the scalar `x` is the smallest integer `i`, such that\n""``i >= x``.  It is often denoted as :math:`\\lceil x \\rceil`.\n""\n""Parameters\n""----------\n""x : array_like\n""    Input data.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The ceiling of each element in `x`, with `float` dtype.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""floor, trunc, rint, fix\n""\n""Examples\n""--------\n"">>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])\n"">>> np.ceil(a)\n""array([-1., -1., -0.,  1.,  2.,  2.,  2.])"
 #define DOC_NUMPY_CORE_UMATH_TRUNC "Return the truncated value of the input, element-wise.\n""\n""The truncated value of the scalar `x` is the nearest integer `i` which\n""is closer to zero than `x` is. In short, the fractional part of the\n""signed number `x` is discarded.\n""\n""Parameters\n""----------\n""x : array_like\n""    Input data.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The truncated value of each element in `x`.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""ceil, floor, rint, fix\n""\n""Notes\n""-----\n"".. versionadded:: 1.3.0\n""\n""Examples\n""--------\n"">>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])\n"">>> np.trunc(a)\n""array([-1., -1., -0.,  0.,  1.,  1.,  2.])"
@@ -51,10 +51,10 @@
 #define DOC_NUMPY_CORE_UMATH_LOGICAL_NOT "Compute the truth value of NOT x element-wise.\n""\n""Parameters\n""----------\n""x : array_like\n""    Logical NOT is applied to the elements of `x`.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : bool or ndarray of bool\n""    Boolean result with the same shape as `x` of the NOT operation\n""    on elements of `x`.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""logical_and, logical_or, logical_xor\n""\n""Examples\n""--------\n"">>> np.logical_not(3)\n""False\n"">>> np.logical_not([True, False, 0, 1])\n""array([False,  True,  True, False])\n""\n"">>> x = np.arange(5)\n"">>> np.logical_not(x<3)\n""array([False, False, False,  True,  True])"
 #define DOC_NUMPY_CORE_UMATH_LOGICAL_OR "Compute the truth value of x1 OR x2 element-wise.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    Logical OR is applied to the elements of `x1` and `x2`.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or bool\n""    Boolean result of the logical OR operation applied to the elements\n""    of `x1` and `x2`; the shape is determined by broadcasting.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""logical_and, logical_not, logical_xor\n""bitwise_or\n""\n""Examples\n""--------\n"">>> np.logical_or(True, False)\n""True\n"">>> np.logical_or([True, False], [False, False])\n""array([ True, False])\n""\n"">>> x = np.arange(5)\n"">>> np.logical_or(x < 1, x > 3)\n""array([ True, False, False, False,  True])\n""\n""The ``|`` operator can be used as a shorthand for ``np.logical_or`` on\n""boolean ndarrays.\n""\n"">>> a = np.array([True, False])\n"">>> b = np.array([False, False])\n"">>> a | b\n""array([ True, False])"
 #define DOC_NUMPY_CORE_UMATH_LOGICAL_XOR "Compute the truth value of x1 XOR x2, element-wise.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    Logical XOR is applied to the elements of `x1` and `x2`.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : bool or ndarray of bool\n""    Boolean result of the logical XOR operation applied to the elements\n""    of `x1` and `x2`; the shape is determined by broadcasting.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""logical_and, logical_or, logical_not, bitwise_xor\n""\n""Examples\n""--------\n"">>> np.logical_xor(True, False)\n""True\n"">>> np.logical_xor([True, True, False, False], [True, False, True, False])\n""array([False,  True,  True, False])\n""\n"">>> x = np.arange(5)\n"">>> np.logical_xor(x < 1, x > 3)\n""array([ True, False, False, False,  True])\n""\n""Simple example showing support of broadcasting\n""\n"">>> np.logical_xor(0, np.eye(2))\n""array([[ True, False],\n""       [False,  True]])"
-#define DOC_NUMPY_CORE_UMATH_MAXIMUM "Element-wise maximum of array elements.\n""\n""Compare two arrays and returns a new array containing the element-wise\n""maxima. If one of the elements being compared is a NaN, then that\n""element is returned. If both elements are NaNs then the first is\n""returned. The latter distinction is important for complex NaNs, which\n""are defined as at least one of the real or imaginary parts being a NaN.\n""The net effect is that NaNs are propagated.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The maximum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""minimum :\n""    Element-wise minimum of two arrays, propagates NaNs.\n""fmax :\n""    Element-wise maximum of two arrays, ignores NaNs.\n""amax :\n""    The maximum value of an array along a given axis, propagates NaNs.\n""nanmax :\n""    The maximum value of an array along a given axis, ignores NaNs.\n""\n""fmin, amin, nanmin\n""\n""Notes\n""-----\n""The maximum is equivalent to ``np.where(x1 >= x2, x1, x2)`` when\n""neither x1 nor x2 are nans, but it is faster and does proper\n""broadcasting.\n""\n""Examples\n""--------\n"">>> np.maximum([2, 3, 4], [1, 5, 2])\n""array([2, 5, 4])\n""\n"">>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting\n""array([[ 1. ,  2. ],\n""       [ 0.5,  2. ]])\n""\n"">>> np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])\n""array([nan, nan, nan])\n"">>> np.maximum(np.Inf, 1)\n""inf"
-#define DOC_NUMPY_CORE_UMATH_MINIMUM "Element-wise minimum of array elements.\n""\n""Compare two arrays and returns a new array containing the element-wise\n""minima. If one of the elements being compared is a NaN, then that\n""element is returned. If both elements are NaNs then the first is\n""returned. The latter distinction is important for complex NaNs, which\n""are defined as at least one of the real or imaginary parts being a NaN.\n""The net effect is that NaNs are propagated.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The minimum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""maximum :\n""    Element-wise maximum of two arrays, propagates NaNs.\n""fmin :\n""    Element-wise minimum of two arrays, ignores NaNs.\n""amin :\n""    The minimum value of an array along a given axis, propagates NaNs.\n""nanmin :\n""    The minimum value of an array along a given axis, ignores NaNs.\n""\n""fmax, amax, nanmax\n""\n""Notes\n""-----\n""The minimum is equivalent to ``np.where(x1 <= x2, x1, x2)`` when\n""neither x1 nor x2 are NaNs, but it is faster and does proper\n""broadcasting.\n""\n""Examples\n""--------\n"">>> np.minimum([2, 3, 4], [1, 5, 2])\n""array([1, 3, 2])\n""\n"">>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting\n""array([[ 0.5,  0. ],\n""       [ 0. ,  1. ]])\n""\n"">>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])\n""array([nan, nan, nan])\n"">>> np.minimum(-np.Inf, 1)\n""-inf"
-#define DOC_NUMPY_CORE_UMATH_FMAX "Element-wise maximum of array elements.\n""\n""Compare two arrays and returns a new array containing the element-wise\n""maxima. If one of the elements being compared is a NaN, then the\n""non-nan element is returned. If both elements are NaNs then the first\n""is returned.  The latter distinction is important for complex NaNs,\n""which are defined as at least one of the real or imaginary parts being\n""a NaN. The net effect is that NaNs are ignored when possible.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The maximum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""fmin :\n""    Element-wise minimum of two arrays, ignores NaNs.\n""maximum :\n""    Element-wise maximum of two arrays, propagates NaNs.\n""amax :\n""    The maximum value of an array along a given axis, propagates NaNs.\n""nanmax :\n""    The maximum value of an array along a given axis, ignores NaNs.\n""\n""minimum, amin, nanmin\n""\n""Notes\n""-----\n"".. versionadded:: 1.3.0\n""\n""The fmax is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither\n""x1 nor x2 are NaNs, but it is faster and does proper broadcasting.\n""\n""Examples\n""--------\n"">>> np.fmax([2, 3, 4], [1, 5, 2])\n""array([ 2.,  5.,  4.])\n""\n"">>> np.fmax(np.eye(2), [0.5, 2])\n""array([[ 1. ,  2. ],\n""       [ 0.5,  2. ]])\n""\n"">>> np.fmax([np.nan, 0, np.nan],[0, np.nan, np.nan])\n""array([ 0.,  0., nan])"
-#define DOC_NUMPY_CORE_UMATH_FMIN "Element-wise minimum of array elements.\n""\n""Compare two arrays and returns a new array containing the element-wise\n""minima. If one of the elements being compared is a NaN, then the\n""non-nan element is returned. If both elements are NaNs then the first\n""is returned.  The latter distinction is important for complex NaNs,\n""which are defined as at least one of the real or imaginary parts being\n""a NaN. The net effect is that NaNs are ignored when possible.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The minimum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""fmax :\n""    Element-wise maximum of two arrays, ignores NaNs.\n""minimum :\n""    Element-wise minimum of two arrays, propagates NaNs.\n""amin :\n""    The minimum value of an array along a given axis, propagates NaNs.\n""nanmin :\n""    The minimum value of an array along a given axis, ignores NaNs.\n""\n""maximum, amax, nanmax\n""\n""Notes\n""-----\n"".. versionadded:: 1.3.0\n""\n""The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither\n""x1 nor x2 are NaNs, but it is faster and does proper broadcasting.\n""\n""Examples\n""--------\n"">>> np.fmin([2, 3, 4], [1, 5, 2])\n""array([1, 3, 2])\n""\n"">>> np.fmin(np.eye(2), [0.5, 2])\n""array([[ 0.5,  0. ],\n""       [ 0. ,  1. ]])\n""\n"">>> np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])\n""array([ 0.,  0., nan])"
+#define DOC_NUMPY_CORE_UMATH_MAXIMUM "Element-wise maximum of array elements.\n""\n""Compare two arrays and return a new array containing the element-wise\n""maxima. If one of the elements being compared is a NaN, then that\n""element is returned. If both elements are NaNs then the first is\n""returned. The latter distinction is important for complex NaNs, which\n""are defined as at least one of the real or imaginary parts being a NaN.\n""The net effect is that NaNs are propagated.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The maximum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""minimum :\n""    Element-wise minimum of two arrays, propagates NaNs.\n""fmax :\n""    Element-wise maximum of two arrays, ignores NaNs.\n""amax :\n""    The maximum value of an array along a given axis, propagates NaNs.\n""nanmax :\n""    The maximum value of an array along a given axis, ignores NaNs.\n""\n""fmin, amin, nanmin\n""\n""Notes\n""-----\n""The maximum is equivalent to ``np.where(x1 >= x2, x1, x2)`` when\n""neither x1 nor x2 are nans, but it is faster and does proper\n""broadcasting.\n""\n""Examples\n""--------\n"">>> np.maximum([2, 3, 4], [1, 5, 2])\n""array([2, 5, 4])\n""\n"">>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting\n""array([[ 1. ,  2. ],\n""       [ 0.5,  2. ]])\n""\n"">>> np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])\n""array([nan, nan, nan])\n"">>> np.maximum(np.Inf, 1)\n""inf"
+#define DOC_NUMPY_CORE_UMATH_MINIMUM "Element-wise minimum of array elements.\n""\n""Compare two arrays and return a new array containing the element-wise\n""minima. If one of the elements being compared is a NaN, then that\n""element is returned. If both elements are NaNs then the first is\n""returned. The latter distinction is important for complex NaNs, which\n""are defined as at least one of the real or imaginary parts being a NaN.\n""The net effect is that NaNs are propagated.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The minimum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""maximum :\n""    Element-wise maximum of two arrays, propagates NaNs.\n""fmin :\n""    Element-wise minimum of two arrays, ignores NaNs.\n""amin :\n""    The minimum value of an array along a given axis, propagates NaNs.\n""nanmin :\n""    The minimum value of an array along a given axis, ignores NaNs.\n""\n""fmax, amax, nanmax\n""\n""Notes\n""-----\n""The minimum is equivalent to ``np.where(x1 <= x2, x1, x2)`` when\n""neither x1 nor x2 are NaNs, but it is faster and does proper\n""broadcasting.\n""\n""Examples\n""--------\n"">>> np.minimum([2, 3, 4], [1, 5, 2])\n""array([1, 3, 2])\n""\n"">>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting\n""array([[ 0.5,  0. ],\n""       [ 0. ,  1. ]])\n""\n"">>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])\n""array([nan, nan, nan])\n"">>> np.minimum(-np.Inf, 1)\n""-inf"
+#define DOC_NUMPY_CORE_UMATH_FMAX "Element-wise maximum of array elements.\n""\n""Compare two arrays and return a new array containing the element-wise\n""maxima. If one of the elements being compared is a NaN, then the\n""non-nan element is returned. If both elements are NaNs then the first\n""is returned.  The latter distinction is important for complex NaNs,\n""which are defined as at least one of the real or imaginary parts being\n""a NaN. The net effect is that NaNs are ignored when possible.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The maximum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""fmin :\n""    Element-wise minimum of two arrays, ignores NaNs.\n""maximum :\n""    Element-wise maximum of two arrays, propagates NaNs.\n""amax :\n""    The maximum value of an array along a given axis, propagates NaNs.\n""nanmax :\n""    The maximum value of an array along a given axis, ignores NaNs.\n""\n""minimum, amin, nanmin\n""\n""Notes\n""-----\n"".. versionadded:: 1.3.0\n""\n""The fmax is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither\n""x1 nor x2 are NaNs, but it is faster and does proper broadcasting.\n""\n""Examples\n""--------\n"">>> np.fmax([2, 3, 4], [1, 5, 2])\n""array([ 2.,  5.,  4.])\n""\n"">>> np.fmax(np.eye(2), [0.5, 2])\n""array([[ 1. ,  2. ],\n""       [ 0.5,  2. ]])\n""\n"">>> np.fmax([np.nan, 0, np.nan],[0, np.nan, np.nan])\n""array([ 0.,  0., nan])"
+#define DOC_NUMPY_CORE_UMATH_FMIN "Element-wise minimum of array elements.\n""\n""Compare two arrays and return a new array containing the element-wise\n""minima. If one of the elements being compared is a NaN, then the\n""non-nan element is returned. If both elements are NaNs then the first\n""is returned.  The latter distinction is important for complex NaNs,\n""which are defined as at least one of the real or imaginary parts being\n""a NaN. The net effect is that NaNs are ignored when possible.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays holding the elements to be compared.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray or scalar\n""    The minimum of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""See Also\n""--------\n""fmax :\n""    Element-wise maximum of two arrays, ignores NaNs.\n""minimum :\n""    Element-wise minimum of two arrays, propagates NaNs.\n""amin :\n""    The minimum value of an array along a given axis, propagates NaNs.\n""nanmin :\n""    The minimum value of an array along a given axis, ignores NaNs.\n""\n""maximum, amax, nanmax\n""\n""Notes\n""-----\n"".. versionadded:: 1.3.0\n""\n""The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither\n""x1 nor x2 are NaNs, but it is faster and does proper broadcasting.\n""\n""Examples\n""--------\n"">>> np.fmin([2, 3, 4], [1, 5, 2])\n""array([1, 3, 2])\n""\n"">>> np.fmin(np.eye(2), [0.5, 2])\n""array([[ 0.5,  0. ],\n""       [ 0. ,  1. ]])\n""\n"">>> np.fmin([np.nan, 0, np.nan],[0, np.nan, np.nan])\n""array([ 0.,  0., nan])"
 #define DOC_NUMPY_CORE_UMATH_CLIP "Clip (limit) the values in an array.\n""\n""Given an interval, values outside the interval are clipped to\n""the interval edges.  For example, if an interval of ``[0, 1]``\n""is specified, values smaller than 0 become 0, and values larger\n""than 1 become 1.\n""\n""Equivalent to but faster than ``np.minimum(np.maximum(a, a_min), a_max)``.\n""\n""Parameters\n""----------\n""a : array_like\n""    Array containing elements to clip.\n""a_min : array_like\n""    Minimum value.\n""a_max : array_like\n""    Maximum value.\n""out : ndarray, optional\n""    The results will be placed in this array. It may be the input\n""    array for in-place clipping.  `out` must be of the right shape\n""    to hold the output.  Its type is preserved.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""See Also\n""--------\n""numpy.clip :\n""    Wrapper that makes the ``a_min`` and ``a_max`` arguments optional,\n""    dispatching to one of `~numpy.core.umath.clip`,\n""    `~numpy.core.umath.minimum`, and `~numpy.core.umath.maximum`.\n""\n""Returns\n""-------\n""clipped_array : ndarray\n""    An array with the elements of `a`, but where values\n""    < `a_min` are replaced with `a_min`, and those > `a_max`\n""    with `a_max`."
 #define DOC_NUMPY_CORE_UMATH_MATMUL "Matrix product of two arrays.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    Input arrays, scalars not allowed.\n""out : ndarray, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that matches the signature `(n,k),(k,m)->(n,m)`. If not\n""    provided or None, a freshly-allocated array is returned.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""    .. versionadded:: 1.16\n""       Now handles ufunc kwargs\n""\n""Returns\n""-------\n""y : ndarray\n""    The matrix product of the inputs.\n""    This is a scalar only when both x1, x2 are 1-d vectors.\n""\n""Raises\n""------\n""ValueError\n""    If the last dimension of `x1` is not the same size as\n""    the second-to-last dimension of `x2`.\n""\n""    If a scalar value is passed in.\n""\n""See Also\n""--------\n""vdot : Complex-conjugating dot product.\n""tensordot : Sum products over arbitrary axes.\n""einsum : Einstein summation convention.\n""dot : alternative matrix product with different broadcasting rules.\n""\n""Notes\n""-----\n""\n""The behavior depends on the arguments in the following way.\n""\n""- If both arguments are 2-D they are multiplied like conventional\n""  matrices.\n""- If either argument is N-D, N > 2, it is treated as a stack of\n""  matrices residing in the last two indexes and broadcast accordingly.\n""- If the first argument is 1-D, it is promoted to a matrix by\n""  prepending a 1 to its dimensions. After matrix multiplication\n""  the prepended 1 is removed.\n""- If the second argument is 1-D, it is promoted to a matrix by\n""  appending a 1 to its dimensions. After matrix multiplication\n""  the appended 1 is removed.\n""\n""``matmul`` differs from ``dot`` in two important ways:\n""\n""- Multiplication by scalars is not allowed, use ``*`` instead.\n""- Stacks of matrices are broadcast together as if the matrices\n""  were elements, respecting the signature ``(n,k),(k,m)->(n,m)``:\n""\n""  >>> a = np.ones([9, 5, 7, 4])\n""  >>> c = np.ones([9, 5, 4, 3])\n""  >>> np.dot(a, c).shape\n""  (9, 5, 7, 9, 5, 3)\n""  >>> np.matmul(a, c).shape\n""  (9, 5, 7, 3)\n""  >>> # n is 7, k is 4, m is 3\n""\n""The matmul function implements the semantics of the ``@`` operator\n""introduced in Python 3.5 following :pep:`465`.\n""\n""It uses an optimized BLAS library when possible (see `numpy.linalg`).\n""\n""Examples\n""--------\n""For 2-D arrays it is the matrix product:\n""\n"">>> a = np.array([[1, 0],\n""...               [0, 1]])\n"">>> b = np.array([[4, 1],\n""...               [2, 2]])\n"">>> np.matmul(a, b)\n""array([[4, 1],\n""       [2, 2]])\n""\n""For 2-D mixed with 1-D, the result is the usual.\n""\n"">>> a = np.array([[1, 0],\n""...               [0, 1]])\n"">>> b = np.array([1, 2])\n"">>> np.matmul(a, b)\n""array([1, 2])\n"">>> np.matmul(b, a)\n""array([1, 2])\n""\n""\n""Broadcasting is conventional for stacks of arrays\n""\n"">>> a = np.arange(2 * 2 * 4).reshape((2, 2, 4))\n"">>> b = np.arange(2 * 2 * 4).reshape((2, 4, 2))\n"">>> np.matmul(a,b).shape\n""(2, 2, 2)\n"">>> np.matmul(a, b)[0, 1, 1]\n""98\n"">>> sum(a[0, 1, :] * b[0 , :, 1])\n""98\n""\n""Vector, vector returns the scalar inner product, but neither argument\n""is complex-conjugated:\n""\n"">>> np.matmul([2j, 3j], [2j, 3j])\n""(-13+0j)\n""\n""Scalar multiplication raises an error.\n""\n"">>> np.matmul([1,2], 3)\n""Traceback (most recent call last):\n""...\n""ValueError: matmul: Input operand 1 does not have enough dimensions ...\n""\n""The ``@`` operator can be used as a shorthand for ``np.matmul`` on\n""ndarrays.\n""\n"">>> x1 = np.array([2j, 3j])\n"">>> x2 = np.array([2j, 3j])\n"">>> x1 @ x2\n""(-13+0j)\n""\n"".. versionadded:: 1.10.0"
 #define DOC_NUMPY_CORE_UMATH_MODF "Return the fractional and integral parts of an array, element-wise.\n""\n""The fractional and integral parts are negative if the given number is\n""negative.\n""\n""Parameters\n""----------\n""x : array_like\n""    Input array.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y1 : ndarray\n""    Fractional part of `x`.\n""    This is a scalar if `x` is a scalar.\n""y2 : ndarray\n""    Integral part of `x`.\n""    This is a scalar if `x` is a scalar.\n""\n""Notes\n""-----\n""For integer input the return values are floats.\n""\n""See Also\n""--------\n""divmod : ``divmod(x, 1)`` is equivalent to ``modf`` with the return values\n""         switched, except it always has a positive remainder.\n""\n""Examples\n""--------\n"">>> np.modf([0, 3.5])\n""(array([ 0. ,  0.5]), array([ 0.,  3.]))\n"">>> np.modf(-0.5)\n""(-0.5, -0)"
@@ -79,7 +79,7 @@
 #define DOC_NUMPY_CORE_UMATH_SPACING "Return the distance between x and the nearest adjacent number.\n""\n""Parameters\n""----------\n""x : array_like\n""    Values to find the spacing of.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""out : ndarray or scalar\n""    The spacing of values of `x`.\n""    This is a scalar if `x` is a scalar.\n""\n""Notes\n""-----\n""It can be considered as a generalization of EPS:\n""``spacing(np.float64(1)) == np.finfo(np.float64).eps``, and there\n""should not be any representable number between ``x + spacing(x)`` and\n""x for any finite x.\n""\n""Spacing of +- inf and NaN is NaN.\n""\n""Examples\n""--------\n"">>> np.spacing(1) == np.finfo(np.float64).eps\n""True"
 #define DOC_NUMPY_CORE_UMATH_SIN "Trigonometric sine, element-wise.\n""\n""Parameters\n""----------\n""x : array_like\n""    Angle, in radians (:math:`2 \\pi` rad equals 360 degrees).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : array_like\n""    The sine of each element of x.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""arcsin, sinh, cos\n""\n""Notes\n""-----\n""The sine is one of the fundamental functions of trigonometry (the\n""mathematical study of triangles).  Consider a circle of radius 1\n""centered on the origin.  A ray comes in from the :math:`+x` axis, makes\n""an angle at the origin (measured counter-clockwise from that axis), and\n""departs from the origin.  The :math:`y` coordinate of the outgoing\n""ray\'s intersection with the unit circle is the sine of that angle.  It\n""ranges from -1 for :math:`x=3\\pi / 2` to +1 for :math:`\\pi / 2.`  The\n""function has zeroes where the angle is a multiple of :math:`\\pi`.\n""Sines of angles between :math:`\\pi` and :math:`2\\pi` are negative.\n""The numerous properties of the sine and related functions are included\n""in any standard trigonometry text.\n""\n""Examples\n""--------\n""Print sine of one angle:\n""\n"">>> np.sin(np.pi/2.)\n""1.0\n""\n""Print sines of an array of angles given in degrees:\n""\n"">>> np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180. )\n""array([ 0.        ,  0.5       ,  0.70710678,  0.8660254 ,  1.        ])\n""\n""Plot the sine function:\n""\n"">>> import matplotlib.pylab as plt\n"">>> x = np.linspace(-np.pi, np.pi, 201)\n"">>> plt.plot(x, np.sin(x))\n"">>> plt.xlabel(\'Angle [rad]\')\n"">>> plt.ylabel(\'sin(x)\')\n"">>> plt.axis(\'tight\')\n"">>> plt.show()"
 #define DOC_NUMPY_CORE_UMATH_SINH "Hyperbolic sine, element-wise.\n""\n""Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or\n""``-1j * np.sin(1j*x)``.\n""\n""Parameters\n""----------\n""x : array_like\n""    Input array.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray\n""    The corresponding hyperbolic sine values.\n""    This is a scalar if `x` is a scalar.\n""\n""Notes\n""-----\n""If `out` is provided, the function writes the result into it,\n""and returns a reference to `out`.  (See Examples)\n""\n""References\n""----------\n""M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.\n""New York, NY: Dover, 1972, pg. 83.\n""\n""Examples\n""--------\n"">>> np.sinh(0)\n""0.0\n"">>> np.sinh(np.pi*1j/2)\n""1j\n"">>> np.sinh(np.pi*1j) # (exact value is 0)\n""1.2246063538223773e-016j\n"">>> # Discrepancy due to vagaries of floating point arithmetic.\n""\n"">>> # Example of providing the optional output parameter\n"">>> out1 = np.array([0], dtype=\'d\')\n"">>> out2 = np.sinh([0.1], out1)\n"">>> out2 is out1\n""True\n""\n"">>> # Example of ValueError due to provision of shape mis-matched `out`\n"">>> np.sinh(np.zeros((3,3)),np.zeros((2,2)))\n""Traceback (most recent call last):\n""  File \"<stdin>\", line 1, in <module>\n""ValueError: operands could not be broadcast together with shapes (3,3) (2,2)"
-#define DOC_NUMPY_CORE_UMATH_SQRT "Return the non-negative square-root of an array, element-wise.\n""\n""Parameters\n""----------\n""x : array_like\n""    The values whose square-roots are required.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray\n""    An array of the same shape as `x`, containing the positive\n""    square-root of each element in `x`.  If any element in `x` is\n""    complex, a complex array is returned (and the square-roots of\n""    negative reals are calculated).  If all of the elements in `x`\n""    are real, so is `y`, with negative elements returning ``nan``.\n""    If `out` was provided, `y` is a reference to it.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""emath.sqrt\n""    A version which returns complex numbers when given negative reals.\n""    Note: 0.0 and -0.0 are handled differently for complex inputs.\n""\n""Notes\n""-----\n""*sqrt* has--consistent with common convention--as its branch cut the\n""real \"interval\" [`-inf`, 0), and is continuous from above on it.\n""A branch cut is a curve in the complex plane across which a given\n""complex function fails to be continuous.\n""\n""Examples\n""--------\n"">>> np.sqrt([1,4,9])\n""array([ 1.,  2.,  3.])\n""\n"">>> np.sqrt([4, -1, -3+4J])\n""array([ 2.+0.j,  0.+1.j,  1.+2.j])\n""\n"">>> np.sqrt([4, -1, np.inf])\n""array([ 2., nan, inf])"
+#define DOC_NUMPY_CORE_UMATH_SQRT "Return the non-negative square-root of an array, element-wise.\n""\n""Parameters\n""----------\n""x : array_like\n""    The values whose square-roots are required.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray\n""    An array of the same shape as `x`, containing the positive\n""    square-root of each element in `x`.  If any element in `x` is\n""    complex, a complex array is returned (and the square-roots of\n""    negative reals are calculated).  If all of the elements in `x`\n""    are real, so is `y`, with negative elements returning ``nan``.\n""    If `out` was provided, `y` is a reference to it.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""emath.sqrt\n""    A version which returns complex numbers when given negative reals.\n""    Note that 0.0 and -0.0 are handled differently for complex inputs.\n""\n""Notes\n""-----\n""*sqrt* has--consistent with common convention--as its branch cut the\n""real \"interval\" [`-inf`, 0), and is continuous from above on it.\n""A branch cut is a curve in the complex plane across which a given\n""complex function fails to be continuous.\n""\n""Examples\n""--------\n"">>> np.sqrt([1,4,9])\n""array([ 1.,  2.,  3.])\n""\n"">>> np.sqrt([4, -1, -3+4J])\n""array([ 2.+0.j,  0.+1.j,  1.+2.j])\n""\n"">>> np.sqrt([4, -1, np.inf])\n""array([ 2., nan, inf])"
 #define DOC_NUMPY_CORE_UMATH_CBRT "Return the cube-root of an array, element-wise.\n""\n"".. versionadded:: 1.10.0\n""\n""Parameters\n""----------\n""x : array_like\n""    The values whose cube-roots are required.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray\n""    An array of the same shape as `x`, containing the cube\n""    cube-root of each element in `x`.\n""    If `out` was provided, `y` is a reference to it.\n""    This is a scalar if `x` is a scalar.\n""\n""\n""Examples\n""--------\n"">>> np.cbrt([1,8,27])\n""array([ 1.,  2.,  3.])"
 #define DOC_NUMPY_CORE_UMATH_SQUARE "Return the element-wise square of the input.\n""\n""Parameters\n""----------\n""x : array_like\n""    Input data.\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""out : ndarray or scalar\n""    Element-wise `x*x`, of the same shape and dtype as `x`.\n""    This is a scalar if `x` is a scalar.\n""\n""See Also\n""--------\n""numpy.linalg.matrix_power\n""sqrt\n""power\n""\n""Examples\n""--------\n"">>> np.square([-1j, 1])\n""array([-1.-0.j,  1.+0.j])"
 #define DOC_NUMPY_CORE_UMATH_SUBTRACT "Subtract arguments, element-wise.\n""\n""Parameters\n""----------\n""x1, x2 : array_like\n""    The arrays to be subtracted from each other.\n""    If ``x1.shape != x2.shape``, they must be broadcastable to a common\n""    shape (which becomes the shape of the output).\n""out : ndarray, None, or tuple of ndarray and None, optional\n""    A location into which the result is stored. If provided, it must have\n""    a shape that the inputs broadcast to. If not provided or None,\n""    a freshly-allocated array is returned. A tuple (possible only as a\n""    keyword argument) must have length equal to the number of outputs.\n""where : array_like, optional\n""    This condition is broadcast over the input. At locations where the\n""    condition is True, the `out` array will be set to the ufunc result.\n""    Elsewhere, the `out` array will retain its original value.\n""    Note that if an uninitialized `out` array is created via the default\n""    ``out=None``, locations within it where the condition is False will\n""    remain uninitialized.\n""**kwargs\n""    For other keyword-only arguments, see the\n""    :ref:`ufunc docs <ufuncs.kwargs>`.\n""\n""Returns\n""-------\n""y : ndarray\n""    The difference of `x1` and `x2`, element-wise.\n""    This is a scalar if both `x1` and `x2` are scalars.\n""\n""Notes\n""-----\n""Equivalent to ``x1 - x2`` in terms of array broadcasting.\n""\n""Examples\n""--------\n"">>> np.subtract(1.0, 4.0)\n""-3.0\n""\n"">>> x1 = np.arange(9.0).reshape((3, 3))\n"">>> x2 = np.arange(3.0)\n"">>> np.subtract(x1, x2)\n""array([[ 0.,  0.,  0.],\n""       [ 3.,  3.,  3.],\n""       [ 6.,  6.,  6.]])\n""\n""The ``-`` operator can be used as a shorthand for ``np.subtract`` on\n""ndarrays.\n""\n"">>> x1 = np.arange(9.0).reshape((3, 3))\n"">>> x2 = np.arange(3.0)\n"">>> x1 - x2\n""array([[0., 0., 0.],\n""       [3., 3., 3.],\n""       [6., 6., 6.]])"
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/arrayscalars.h b/contrib/python/numpy/py3/numpy/core/include/numpy/arrayscalars.h
index a20a6801686..258bf95b62c 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/arrayscalars.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/arrayscalars.h
@@ -139,7 +139,9 @@ typedef struct {
         /* note that the PyObject_HEAD macro lives right here */
         PyUnicodeObject base;
         Py_UCS4 *obval;
+    #if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION
         char *buffer_fmt;
+    #endif
 } PyUnicodeScalarObject;
 
 
@@ -149,7 +151,9 @@ typedef struct {
         PyArray_Descr *descr;
         int flags;
         PyObject *base;
+    #if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION
         void *_buffer_info;  /* private buffer info, tagged to allow warning */
+    #endif
 } PyVoidScalarObject;
 
 /* Macros
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config-darwin-arm64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config-darwin-arm64.h
deleted file mode 100644
index 430b3fb05db..00000000000
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/config-darwin-arm64.h
+++ /dev/null
@@ -1,18 +0,0 @@
-#pragma once
-
-#include "config-darwin-x86_64.h"
-
-#undef HAVE_XMMINTRIN_H
-#undef HAVE_EMMINTRIN_H
-#undef HAVE_IMMINTRIN_H
-
-#undef HAVE_ATTRIBUTE_TARGET_AVX
-#undef HAVE_ATTRIBUTE_TARGET_AVX2
-#undef HAVE_ATTRIBUTE_TARGET_AVX512F
-#undef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-#undef HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
-#undef HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
-#undef HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS
-
-#undef HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE
-#define HAVE_LDOUBLE_IEEE_DOUBLE_LE 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-aarch64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-aarch64.h
index 81e16a20d30..7fc48ca662f 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-aarch64.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-aarch64.h
@@ -6,13 +6,5 @@
 #undef HAVE_EMMINTRIN_H
 #undef HAVE_IMMINTRIN_H
 
-#undef HAVE_ATTRIBUTE_TARGET_AVX
-#undef HAVE_ATTRIBUTE_TARGET_AVX2
-#undef HAVE_ATTRIBUTE_TARGET_AVX512F
-#undef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-#undef HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
-#undef HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
-#undef HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS
-
 #undef HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE
 #define HAVE_LDOUBLE_IEEE_QUAD_LE 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-ppc64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-ppc64.h
index 81e16a20d30..7fc48ca662f 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-ppc64.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-ppc64.h
@@ -6,13 +6,5 @@
 #undef HAVE_EMMINTRIN_H
 #undef HAVE_IMMINTRIN_H
 
-#undef HAVE_ATTRIBUTE_TARGET_AVX
-#undef HAVE_ATTRIBUTE_TARGET_AVX2
-#undef HAVE_ATTRIBUTE_TARGET_AVX512F
-#undef HAVE_ATTRIBUTE_TARGET_AVX512_SKX
-#undef HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
-#undef HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
-#undef HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS
-
 #undef HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE
 #define HAVE_LDOUBLE_IEEE_QUAD_LE 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-x86_64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-x86_64.h
index a155958ca83..0f9ad5e88be 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-x86_64.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/config-linux-x86_64.h
@@ -8,11 +8,9 @@
 #define HAVE_BACKTRACE 1
 */
 #define HAVE_MADVISE 1
-#if !defined(__aarch64__) && !defined(__powerpc__)
 #define HAVE_XMMINTRIN_H 1
 #define HAVE_EMMINTRIN_H 1
 #define HAVE_IMMINTRIN_H 1
-#endif
 #define HAVE_FEATURES_H 1
 #define HAVE_DLFCN_H 1
 #define HAVE_EXECINFO_H 1
@@ -25,32 +23,12 @@
 #define HAVE___BUILTIN_BSWAP64 1
 #define HAVE___BUILTIN_EXPECT 1
 #define HAVE___BUILTIN_MUL_OVERFLOW 1
-#define HAVE__M_FROM_INT64 1
-#define HAVE__MM_LOAD_PS 1
-#define HAVE__MM_PREFETCH 1
-#define HAVE__MM_LOAD_PD 1
 #define HAVE___BUILTIN_PREFETCH 1
-#define HAVE_LINK_AVX 1
-#define HAVE_LINK_AVX2 1
-#define HAVE_LINK_AVX512F 1
-#define HAVE_LINK_AVX512_SKX 1
-#define HAVE_XGETBV 1
 #define HAVE_ATTRIBUTE_OPTIMIZE_UNROLL_LOOPS 1
 #define HAVE_ATTRIBUTE_OPTIMIZE_OPT_3 1
 #define HAVE_ATTRIBUTE_OPTIMIZE_OPT_2 1
 #define HAVE_ATTRIBUTE_NONNULL 1
-#define HAVE_ATTRIBUTE_TARGET_AVX 1
-#define HAVE_ATTRIBUTE_TARGET_AVX2 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512F 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512_SKX 1
-#define HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS 1
 #define HAVE___THREAD 1
-#define HAVE_DECL_ISNAN
-#define HAVE_DECL_ISINF
-#define HAVE_DECL_SIGNBIT
-#define HAVE_DECL_ISFINITE
 #define HAVE_COMPLEX_H 1
 #define HAVE_CABS 1
 #define HAVE_CACOS 1
@@ -107,6 +85,7 @@
 #define HAVE_CTANL 1
 #define HAVE_CTANHL 1
 #define NPY_RESTRICT restrict
+//#define NPY_CAN_LINK_SVML 1
 #define NPY_RELAXED_STRIDES_DEBUG 0
 #define HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE 1
 #ifndef __cplusplus
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config-osx-arm64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config-osx-arm64.h
new file mode 100644
index 00000000000..881aab07a7e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/config-osx-arm64.h
@@ -0,0 +1,10 @@
+#pragma once
+
+#include "config-osx-x86_64.h"
+
+#undef HAVE_XMMINTRIN_H
+#undef HAVE_EMMINTRIN_H
+#undef HAVE_IMMINTRIN_H
+
+#undef HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE
+#define HAVE_LDOUBLE_IEEE_DOUBLE_LE 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config-darwin-x86_64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config-osx-x86_64.h
index 6c5229ae3d1..26360788450 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/config-darwin-x86_64.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/config-osx-x86_64.h
@@ -1,8 +1,11 @@
+/* may be overridden by numpyconfig.h on darwin */
 #define SIZEOF_PY_INTPTR_T 8
 #define SIZEOF_OFF_T 8
 #define SIZEOF_PY_LONG_LONG 8
-#define MATHLIB
+#define MATHLIB 
+/* breake test_extension_incref_elide and test_extension_incref_elide_stack
 #define HAVE_BACKTRACE 1
+*/
 #define HAVE_MADVISE 1
 #define HAVE_XMMINTRIN_H 1
 #define HAVE_EMMINTRIN_H 1
@@ -20,29 +23,9 @@
 #define HAVE___BUILTIN_BSWAP64 1
 #define HAVE___BUILTIN_EXPECT 1
 #define HAVE___BUILTIN_MUL_OVERFLOW 1
-#define HAVE__M_FROM_INT64 1
-#define HAVE__MM_LOAD_PS 1
-#define HAVE__MM_PREFETCH 1
-#define HAVE__MM_LOAD_PD 1
 #define HAVE___BUILTIN_PREFETCH 1
-#define HAVE_LINK_AVX 1
-#define HAVE_LINK_AVX2 1
-#define HAVE_LINK_AVX512F 1
-#define HAVE_LINK_AVX512_SKX 1
-#define HAVE_XGETBV 1
 #define HAVE_ATTRIBUTE_NONNULL 1
-#define HAVE_ATTRIBUTE_TARGET_AVX 1
-#define HAVE_ATTRIBUTE_TARGET_AVX2 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512F 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512_SKX 1
-#define HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS 1
-#define HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS 1
 #define HAVE___THREAD 1
-#define HAVE_DECL_ISNAN
-#define HAVE_DECL_ISINF
-#define HAVE_DECL_SIGNBIT
-#define HAVE_DECL_ISFINITE
 #define HAVE_COMPLEX_H 1
 #define HAVE_CABS 1
 #define HAVE_CACOS 1
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config-windows-x86_64.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config-win-x86_64.h
index 78a55c90acd..78a55c90acd 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/config-windows-x86_64.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/config-win-x86_64.h
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/config.h b/contrib/python/numpy/py3/numpy/core/include/numpy/config.h
index 90163f18d52..a252eedf2e0 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/config.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/config.h
@@ -1,9 +1,11 @@
-#if defined(_MSC_VER) && (defined(__x86_64__) || defined(_M_X64))
-#   include "config-windows-x86_64.h"
+#pragma once
+
+#if defined(__APPLE__) && (defined(__aarch64__) || defined(_M_ARM64))
+#   include "config-osx-arm64.h"
 #elif defined(__APPLE__) && (defined(__x86_64__) || defined(_M_X64))
-#   include "config-darwin-x86_64.h"
-#elif defined(__APPLE__) && (defined(__aarch64__) || defined(_M_ARM64))
-#   include "config-darwin-arm64.h"
+#   include "config-osx-x86_64.h"
+#elif defined(_MSC_VER) && (defined(__x86_64__) || defined(_M_X64))
+#   include "config-win-x86_64.h"
 #elif defined(__linux__) && (defined(__x86_64__) || defined(_M_X64))
 #   include "config-linux-x86_64.h"
 #elif defined(__linux__) && (defined(__aarch64__) || defined(_M_ARM64))
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/experimental_dtype_api.h b/contrib/python/numpy/py3/numpy/core/include/numpy/experimental_dtype_api.h
index 1fbd4198102..19088dabdce 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/experimental_dtype_api.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/experimental_dtype_api.h
@@ -3,6 +3,9 @@
  * NEPs 41 to 43.  For background, please check these NEPs.  Otherwise,
  * this header also serves as documentation for the time being.
  *
+ * The header includes `_dtype_api.h` which holds most definition while this
+ * header mainly wraps functions for public consumption.
+ *
  * Please do not hesitate to contact @seberg with questions.  This is
  * developed together with https://github.com/seberg/experimental_user_dtypes
  * and those interested in experimenting are encouraged to contribute there.
@@ -114,7 +117,13 @@
 
 #include <Python.h>
 #include "ndarraytypes.h"
+#include "_dtype_api.h"
 
+/*
+ * The contents of PyArrayMethodObject are currently opaque (is there a way
+ * good way to make them be `PyObject *`?)
+ */
+typedef struct PyArrayMethodObject_tag PyArrayMethodObject;
 
 /*
  * There must be a better way?! -- Oh well, this is experimental
@@ -154,66 +163,6 @@
 #endif
 
 
-/*
- * DTypeMeta struct, the content may be made fully opaque (except the size).
- * We may also move everything into a single `void *dt_slots`.
- */
-typedef struct {
-    PyHeapTypeObject super;
-    PyArray_Descr *singleton;
-    int type_num;
-    PyTypeObject *scalar_type;
-    npy_uint64 flags;
-    void *dt_slots;
-    void *reserved[3];
-} PyArray_DTypeMeta;
-
-
-/*
- * ******************************************************
- *         ArrayMethod API (Casting and UFuncs)
- * ******************************************************
- */
-/*
- * NOTE: Expected changes:
- *       * invert logic of floating point error flag
- *       * probably split runtime and general flags into two
- *       * should possibly not use an enum for typedef for more stable ABI?
- */
-typedef enum {
-    /* Flag for whether the GIL is required */
-    NPY_METH_REQUIRES_PYAPI = 1 << 1,
-    /*
-     * Some functions cannot set floating point error flags, this flag
-     * gives us the option (not requirement) to skip floating point error
-     * setup/check. No function should set error flags and ignore them
-     * since it would interfere with chaining operations (e.g. casting).
-     */
-    NPY_METH_NO_FLOATINGPOINT_ERRORS = 1 << 2,
-    /* Whether the method supports unaligned access (not runtime) */
-    NPY_METH_SUPPORTS_UNALIGNED = 1 << 3,
-
-    /* All flags which can change at runtime */
-    NPY_METH_RUNTIME_FLAGS = (
-            NPY_METH_REQUIRES_PYAPI |
-            NPY_METH_NO_FLOATINGPOINT_ERRORS),
-} NPY_ARRAYMETHOD_FLAGS;
-
-
-/*
- * The main object for creating a new ArrayMethod. We use the typical `slots`
- * mechanism used by the Python limited API (see below for the slot defs).
- */
-typedef struct {
-    const char *name;
-    int nin, nout;
-    NPY_CASTING casting;
-    NPY_ARRAYMETHOD_FLAGS flags;
-    PyArray_DTypeMeta **dtypes;
-    PyType_Slot *slots;
-} PyArrayMethod_Spec;
-
-
 typedef int _ufunc_addloop_fromspec_func(
         PyObject *ufunc, PyArrayMethod_Spec *spec);
 /*
@@ -267,92 +216,6 @@ typedef int _ufunc_addpromoter_func(
 #define PyUFunc_AddPromoter \
     (*(_ufunc_addpromoter_func *)(__experimental_dtype_api_table[1]))
 
-
-/*
- * The resolve descriptors function, must be able to handle NULL values for
- * all output (but not input) `given_descrs` and fill `loop_descrs`.
- * Return -1 on error or 0 if the operation is not possible without an error
- * set.  (This may still be in flux.)
- * Otherwise must return the "casting safety", for normal functions, this is
- * almost always "safe" (or even "equivalent"?).
- *
- * `resolve_descriptors` is optional if all output DTypes are non-parametric.
- */
-#define NPY_METH_resolve_descriptors 1
-typedef NPY_CASTING (resolve_descriptors_function)(
-        /* "method" is currently opaque (necessary e.g. to wrap Python) */
-        PyObject *method,
-        /* DTypes the method was created for */
-        PyObject **dtypes,
-        /* Input descriptors (instances).  Outputs may be NULL. */
-        PyArray_Descr **given_descrs,
-        /* Exact loop descriptors to use, must not hold references on error */
-        PyArray_Descr **loop_descrs,
-        npy_intp *view_offset);
-
-/* NOT public yet: Signature needs adapting as external API. */
-#define _NPY_METH_get_loop 2
-
-/*
- * Current public API to define fast inner-loops.  You must provide a
- * strided loop.  If this is a cast between two "versions" of the same dtype
- * you must also provide an unaligned strided loop.
- * Other loops are useful to optimize the very common contiguous case.
- *
- * NOTE: As of now, NumPy will NOT use unaligned loops in ufuncs!
- */
-#define NPY_METH_strided_loop 3
-#define NPY_METH_contiguous_loop 4
-#define NPY_METH_unaligned_strided_loop 5
-#define NPY_METH_unaligned_contiguous_loop 6
-
-
-typedef struct {
-    PyObject *caller;  /* E.g. the original ufunc, may be NULL */
-    PyObject *method;  /* The method "self". Currently an opaque object */
-
-    /* Operand descriptors, filled in by resolve_descriptors */
-    PyArray_Descr **descriptors;
-    /* Structure may grow (this is harmless for DType authors) */
-} PyArrayMethod_Context;
-
-typedef int (PyArrayMethod_StridedLoop)(PyArrayMethod_Context *context,
-        char *const *data, const npy_intp *dimensions, const npy_intp *strides,
-        NpyAuxData *transferdata);
-
-
-
-/*
- * ****************************
- *          DTYPE API
- * ****************************
- */
-
-#define NPY_DT_ABSTRACT 1 << 1
-#define NPY_DT_PARAMETRIC 1 << 2
-
-#define NPY_DT_discover_descr_from_pyobject 1
-#define _NPY_DT_is_known_scalar_type 2
-#define NPY_DT_default_descr 3
-#define NPY_DT_common_dtype 4
-#define NPY_DT_common_instance 5
-#define NPY_DT_ensure_canonical 6
-#define NPY_DT_setitem 7
-#define NPY_DT_getitem 8
-
-
-// TODO: These slots probably still need some thought, and/or a way to "grow"?
-typedef struct{
-    PyTypeObject *typeobj;    /* type of python scalar or NULL */
-    int flags;                /* flags, including parametric and abstract */
-    /* NULL terminated cast definitions. Use NULL for the newly created DType */
-    PyArrayMethod_Spec **casts;
-    PyType_Slot *slots;
-    /* Baseclass or NULL (will always subclass `np.dtype`) */
-    PyTypeObject *baseclass;
-} PyArrayDTypeMeta_Spec;
-
-
 #define PyArrayDTypeMeta_Type \
     (*(PyTypeObject *)__experimental_dtype_api_table[2])
 typedef int __dtypemeta_fromspec(
@@ -392,7 +255,7 @@ typedef PyArray_Descr *__get_default_descr(
 #define _PyArray_GetDefaultDescr \
     ((__get_default_descr *)(__experimental_dtype_api_table[6]))
 
-static NPY_INLINE PyArray_Descr *
+static inline PyArray_Descr *
 PyArray_GetDefaultDescr(PyArray_DTypeMeta *DType)
 {
     if (DType->singleton != NULL) {
@@ -444,7 +307,9 @@ PyArray_GetDefaultDescr(PyArray_DTypeMeta *DType)
 /* Datetime/Timedelta */
 #define PyArray_DatetimeDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[40])
 #define PyArray_TimedeltaDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[41])
-
+/* Object/Void */
+#define PyArray_ObjectDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[42])
+#define PyArray_VoidDType (*(PyArray_DTypeMeta *)__experimental_dtype_api_table[43])
 
 /*
  * ********************************
@@ -458,16 +323,14 @@ PyArray_GetDefaultDescr(PyArray_DTypeMeta *DType)
  */
 #if !defined(NO_IMPORT) && !defined(NO_IMPORT_ARRAY)
 
-#define __EXPERIMENTAL_DTYPE_VERSION 5
-
 static int
 import_experimental_dtype_api(int version)
 {
-    if (version != __EXPERIMENTAL_DTYPE_VERSION) {
+    if (version != __EXPERIMENTAL_DTYPE_API_VERSION) {
         PyErr_Format(PyExc_RuntimeError,
                 "DType API version %d did not match header version %d. Please "
                 "update the import statement and check for API changes.",
-                version, __EXPERIMENTAL_DTYPE_VERSION);
+                version, __EXPERIMENTAL_DTYPE_API_VERSION);
         return -1;
     }
     if (__experimental_dtype_api_table != __uninitialized_table) {
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/multiarray_api.txt b/contrib/python/numpy/py3/numpy/core/include/numpy/multiarray_api.txt
deleted file mode 100644
index 3f56b4374d3..00000000000
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/multiarray_api.txt
+++ /dev/null
@@ -1,2501 +0,0 @@
-
-===========
-NumPy C-API
-===========
-::
-
-  unsigned int
-  PyArray_GetNDArrayCVersion(void )
-
-
-Included at the very first so not auto-grabbed and thus not labeled.
-
-::
-
-  int
-  PyArray_SetNumericOps(PyObject *dict)
-
-Set internal structure with number functions that all arrays will use
-
-::
-
-  PyObject *
-  PyArray_GetNumericOps(void )
-
-Get dictionary showing number functions that all arrays will use
-
-::
-
-  int
-  PyArray_INCREF(PyArrayObject *mp)
-
-For object arrays, increment all internal references.
-
-::
-
-  int
-  PyArray_XDECREF(PyArrayObject *mp)
-
-Decrement all internal references for object arrays.
-(or arrays with object fields)
-
-::
-
-  void
-  PyArray_SetStringFunction(PyObject *op, int repr)
-
-Set the array print function to be a Python function.
-
-::
-
-  PyArray_Descr *
-  PyArray_DescrFromType(int type)
-
-Get the PyArray_Descr structure for a type.
-
-::
-
-  PyObject *
-  PyArray_TypeObjectFromType(int type)
-
-Get a typeobject from a type-number -- can return NULL.
-
-New reference
-
-::
-
-  char *
-  PyArray_Zero(PyArrayObject *arr)
-
-Get pointer to zero of correct type for array.
-
-::
-
-  char *
-  PyArray_One(PyArrayObject *arr)
-
-Get pointer to one of correct type for array
-
-::
-
-  PyObject *
-  PyArray_CastToType(PyArrayObject *arr, PyArray_Descr *dtype, int
-                     is_f_order)
-
-For backward compatibility
-
-Cast an array using typecode structure.
-steals reference to dtype --- cannot be NULL
-
-This function always makes a copy of arr, even if the dtype
-doesn't change.
-
-::
-
-  int
-  PyArray_CastTo(PyArrayObject *out, PyArrayObject *mp)
-
-Cast to an already created array.
-
-::
-
-  int
-  PyArray_CastAnyTo(PyArrayObject *out, PyArrayObject *mp)
-
-Cast to an already created array.  Arrays don't have to be "broadcastable"
-Only requirement is they have the same number of elements.
-
-::
-
-  int
-  PyArray_CanCastSafely(int fromtype, int totype)
-
-Check the type coercion rules.
-
-::
-
-  npy_bool
-  PyArray_CanCastTo(PyArray_Descr *from, PyArray_Descr *to)
-
-leaves reference count alone --- cannot be NULL
-
-PyArray_CanCastTypeTo is equivalent to this, but adds a 'casting'
-parameter.
-
-::
-
-  int
-  PyArray_ObjectType(PyObject *op, int minimum_type)
-
-Return the typecode of the array a Python object would be converted to
-
-Returns the type number the result should have, or NPY_NOTYPE on error.
-
-::
-
-  PyArray_Descr *
-  PyArray_DescrFromObject(PyObject *op, PyArray_Descr *mintype)
-
-new reference -- accepts NULL for mintype
-
-::
-
-  PyArrayObject **
-  PyArray_ConvertToCommonType(PyObject *op, int *retn)
-
-
-This function is only used in one place within NumPy and should
-generally be avoided. It is provided mainly for backward compatibility.
-
-The user of the function has to free the returned array with PyDataMem_FREE.
-
-::
-
-  PyArray_Descr *
-  PyArray_DescrFromScalar(PyObject *sc)
-
-Return descr object from array scalar.
-
-New reference
-
-::
-
-  PyArray_Descr *
-  PyArray_DescrFromTypeObject(PyObject *type)
-
-
-::
-
-  npy_intp
-  PyArray_Size(PyObject *op)
-
-Compute the size of an array (in number of items)
-
-::
-
-  PyObject *
-  PyArray_Scalar(void *data, PyArray_Descr *descr, PyObject *base)
-
-Get scalar-equivalent to a region of memory described by a descriptor.
-
-::
-
-  PyObject *
-  PyArray_FromScalar(PyObject *scalar, PyArray_Descr *outcode)
-
-Get 0-dim array from scalar
-
-0-dim array from array-scalar object
-always contains a copy of the data
-unless outcode is NULL, it is of void type and the referrer does
-not own it either.
-
-steals reference to outcode
-
-::
-
-  void
-  PyArray_ScalarAsCtype(PyObject *scalar, void *ctypeptr)
-
-Convert to c-type
-
-no error checking is performed -- ctypeptr must be same type as scalar
-in case of flexible type, the data is not copied
-into ctypeptr which is expected to be a pointer to pointer
-
-::
-
-  int
-  PyArray_CastScalarToCtype(PyObject *scalar, void
-                            *ctypeptr, PyArray_Descr *outcode)
-
-Cast Scalar to c-type
-
-The output buffer must be large-enough to receive the value
-Even for flexible types which is different from ScalarAsCtype
-where only a reference for flexible types is returned
-
-This may not work right on narrow builds for NumPy unicode scalars.
-
-::
-
-  int
-  PyArray_CastScalarDirect(PyObject *scalar, PyArray_Descr
-                           *indescr, void *ctypeptr, int outtype)
-
-Cast Scalar to c-type
-
-::
-
-  PyObject *
-  PyArray_ScalarFromObject(PyObject *object)
-
-Get an Array Scalar From a Python Object
-
-Returns NULL if unsuccessful but error is only set if another error occurred.
-Currently only Numeric-like object supported.
-
-::
-
-  PyArray_VectorUnaryFunc *
-  PyArray_GetCastFunc(PyArray_Descr *descr, int type_num)
-
-Get a cast function to cast from the input descriptor to the
-output type_number (must be a registered data-type).
-Returns NULL if un-successful.
-
-::
-
-  PyObject *
-  PyArray_FromDims(int NPY_UNUSED(nd) , int *NPY_UNUSED(d) , int
-                   NPY_UNUSED(type) )
-
-Deprecated, use PyArray_SimpleNew instead.
-
-::
-
-  PyObject *
-  PyArray_FromDimsAndDataAndDescr(int NPY_UNUSED(nd) , int
-                                  *NPY_UNUSED(d) , PyArray_Descr
-                                  *descr, char *NPY_UNUSED(data) )
-
-Deprecated, use PyArray_NewFromDescr instead.
-
-::
-
-  PyObject *
-  PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int
-                  min_depth, int max_depth, int flags, PyObject
-                  *context)
-
-Does not check for NPY_ARRAY_ENSURECOPY and NPY_ARRAY_NOTSWAPPED in flags
-Steals a reference to newtype --- which can be NULL
-
-::
-
-  PyObject *
-  PyArray_EnsureArray(PyObject *op)
-
-This is a quick wrapper around
-PyArray_FromAny(op, NULL, 0, 0, NPY_ARRAY_ENSUREARRAY, NULL)
-that special cases Arrays and PyArray_Scalars up front
-It *steals a reference* to the object
-It also guarantees that the result is PyArray_Type
-Because it decrefs op if any conversion needs to take place
-so it can be used like PyArray_EnsureArray(some_function(...))
-
-::
-
-  PyObject *
-  PyArray_EnsureAnyArray(PyObject *op)
-
-
-::
-
-  PyObject *
-  PyArray_FromFile(FILE *fp, PyArray_Descr *dtype, npy_intp num, char
-                   *sep)
-
-
-Given a ``FILE *`` pointer ``fp``, and a ``PyArray_Descr``, return an
-array corresponding to the data encoded in that file.
-
-The reference to `dtype` is stolen (it is possible that the passed in
-dtype is not held on to).
-
-The number of elements to read is given as ``num``; if it is < 0, then
-then as many as possible are read.
-
-If ``sep`` is NULL or empty, then binary data is assumed, else
-text data, with ``sep`` as the separator between elements. Whitespace in
-the separator matches any length of whitespace in the text, and a match
-for whitespace around the separator is added.
-
-For memory-mapped files, use the buffer interface. No more data than
-necessary is read by this routine.
-
-::
-
-  PyObject *
-  PyArray_FromString(char *data, npy_intp slen, PyArray_Descr
-                     *dtype, npy_intp num, char *sep)
-
-
-Given a pointer to a string ``data``, a string length ``slen``, and
-a ``PyArray_Descr``, return an array corresponding to the data
-encoded in that string.
-
-If the dtype is NULL, the default array type is used (double).
-If non-null, the reference is stolen.
-
-If ``slen`` is < 0, then the end of string is used for text data.
-It is an error for ``slen`` to be < 0 for binary data (since embedded NULLs
-would be the norm).
-
-The number of elements to read is given as ``num``; if it is < 0, then
-then as many as possible are read.
-
-If ``sep`` is NULL or empty, then binary data is assumed, else
-text data, with ``sep`` as the separator between elements. Whitespace in
-the separator matches any length of whitespace in the text, and a match
-for whitespace around the separator is added.
-
-::
-
-  PyObject *
-  PyArray_FromBuffer(PyObject *buf, PyArray_Descr *type, npy_intp
-                     count, npy_intp offset)
-
-
-::
-
-  PyObject *
-  PyArray_FromIter(PyObject *obj, PyArray_Descr *dtype, npy_intp count)
-
-
-steals a reference to dtype (which cannot be NULL)
-
-::
-
-  PyObject *
-  PyArray_Return(PyArrayObject *mp)
-
-
-Return either an array or the appropriate Python object if the array
-is 0d and matches a Python type.
-steals reference to mp
-
-::
-
-  PyObject *
-  PyArray_GetField(PyArrayObject *self, PyArray_Descr *typed, int
-                   offset)
-
-Get a subset of bytes from each element of the array
-steals reference to typed, must not be NULL
-
-::
-
-  int
-  PyArray_SetField(PyArrayObject *self, PyArray_Descr *dtype, int
-                   offset, PyObject *val)
-
-Set a subset of bytes from each element of the array
-steals reference to dtype, must not be NULL
-
-::
-
-  PyObject *
-  PyArray_Byteswap(PyArrayObject *self, npy_bool inplace)
-
-
-::
-
-  PyObject *
-  PyArray_Resize(PyArrayObject *self, PyArray_Dims *newshape, int
-                 refcheck, NPY_ORDER NPY_UNUSED(order) )
-
-Resize (reallocate data).  Only works if nothing else is referencing this
-array and it is contiguous.  If refcheck is 0, then the reference count is
-not checked and assumed to be 1.  You still must own this data and have no
-weak-references and no base object.
-
-::
-
-  int
-  PyArray_MoveInto(PyArrayObject *dst, PyArrayObject *src)
-
-Move the memory of one array into another, allowing for overlapping data.
-
-Returns 0 on success, negative on failure.
-
-::
-
-  int
-  PyArray_CopyInto(PyArrayObject *dst, PyArrayObject *src)
-
-Copy an Array into another array.
-Broadcast to the destination shape if necessary.
-
-Returns 0 on success, -1 on failure.
-
-::
-
-  int
-  PyArray_CopyAnyInto(PyArrayObject *dst, PyArrayObject *src)
-
-Copy an Array into another array -- memory must not overlap
-Does not require src and dest to have "broadcastable" shapes
-(only the same number of elements).
-
-TODO: For NumPy 2.0, this could accept an order parameter which
-only allows NPY_CORDER and NPY_FORDER.  Could also rename
-this to CopyAsFlat to make the name more intuitive.
-
-Returns 0 on success, -1 on error.
-
-::
-
-  int
-  PyArray_CopyObject(PyArrayObject *dest, PyObject *src_object)
-
-
-::
-
-  PyObject *
-  PyArray_NewCopy(PyArrayObject *obj, NPY_ORDER order)
-
-Copy an array.
-
-::
-
-  PyObject *
-  PyArray_ToList(PyArrayObject *self)
-
-To List
-
-::
-
-  PyObject *
-  PyArray_ToString(PyArrayObject *self, NPY_ORDER order)
-
-
-::
-
-  int
-  PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format)
-
-To File
-
-::
-
-  int
-  PyArray_Dump(PyObject *self, PyObject *file, int protocol)
-
-
-::
-
-  PyObject *
-  PyArray_Dumps(PyObject *self, int protocol)
-
-
-::
-
-  int
-  PyArray_ValidType(int type)
-
-Is the typenum valid?
-
-::
-
-  void
-  PyArray_UpdateFlags(PyArrayObject *ret, int flagmask)
-
-Update Several Flags at once.
-
-::
-
-  PyObject *
-  PyArray_New(PyTypeObject *subtype, int nd, npy_intp const *dims, int
-              type_num, npy_intp const *strides, void *data, int
-              itemsize, int flags, PyObject *obj)
-
-Generic new array creation routine.
-
-::
-
-  PyObject *
-  PyArray_NewFromDescr(PyTypeObject *subtype, PyArray_Descr *descr, int
-                       nd, npy_intp const *dims, npy_intp const
-                       *strides, void *data, int flags, PyObject *obj)
-
-Generic new array creation routine.
-
-steals a reference to descr. On failure or when dtype->subarray is
-true, dtype will be decrefed.
-
-::
-
-  PyArray_Descr *
-  PyArray_DescrNew(PyArray_Descr *base)
-
-base cannot be NULL
-
-::
-
-  PyArray_Descr *
-  PyArray_DescrNewFromType(int type_num)
-
-
-::
-
-  double
-  PyArray_GetPriority(PyObject *obj, double default_)
-
-Get Priority from object
-
-::
-
-  PyObject *
-  PyArray_IterNew(PyObject *obj)
-
-Get Iterator.
-
-::
-
-  PyObject*
-  PyArray_MultiIterNew(int n, ... )
-
-Get MultiIterator,
-
-::
-
-  int
-  PyArray_PyIntAsInt(PyObject *o)
-
-
-::
-
-  npy_intp
-  PyArray_PyIntAsIntp(PyObject *o)
-
-
-::
-
-  int
-  PyArray_Broadcast(PyArrayMultiIterObject *mit)
-
-
-::
-
-  void
-  PyArray_FillObjectArray(PyArrayObject *arr, PyObject *obj)
-
-Assumes contiguous
-
-::
-
-  int
-  PyArray_FillWithScalar(PyArrayObject *arr, PyObject *obj)
-
-
-::
-
-  npy_bool
-  PyArray_CheckStrides(int elsize, int nd, npy_intp numbytes, npy_intp
-                       offset, npy_intp const *dims, npy_intp const
-                       *newstrides)
-
-
-::
-
-  PyArray_Descr *
-  PyArray_DescrNewByteorder(PyArray_Descr *self, char newendian)
-
-
-returns a copy of the PyArray_Descr structure with the byteorder
-altered:
-no arguments:  The byteorder is swapped (in all subfields as well)
-single argument:  The byteorder is forced to the given state
-(in all subfields as well)
-
-Valid states:  ('big', '>') or ('little' or '<')
-('native', or '=')
-
-If a descr structure with | is encountered it's own
-byte-order is not changed but any fields are:
-
-
-Deep bytorder change of a data-type descriptor
-Leaves reference count of self unchanged --- does not DECREF self ***
-
-::
-
-  PyObject *
-  PyArray_IterAllButAxis(PyObject *obj, int *inaxis)
-
-Get Iterator that iterates over all but one axis (don't use this with
-PyArray_ITER_GOTO1D).  The axis will be over-written if negative
-with the axis having the smallest stride.
-
-::
-
-  PyObject *
-  PyArray_CheckFromAny(PyObject *op, PyArray_Descr *descr, int
-                       min_depth, int max_depth, int requires, PyObject
-                       *context)
-
-steals a reference to descr -- accepts NULL
-
-::
-
-  PyObject *
-  PyArray_FromArray(PyArrayObject *arr, PyArray_Descr *newtype, int
-                    flags)
-
-steals reference to newtype --- acc. NULL
-
-::
-
-  PyObject *
-  PyArray_FromInterface(PyObject *origin)
-
-
-::
-
-  PyObject *
-  PyArray_FromStructInterface(PyObject *input)
-
-
-::
-
-  PyObject *
-  PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject
-                        *context)
-
-
-::
-
-  NPY_SCALARKIND
-  PyArray_ScalarKind(int typenum, PyArrayObject **arr)
-
-ScalarKind
-
-Returns the scalar kind of a type number, with an
-optional tweak based on the scalar value itself.
-If no scalar is provided, it returns INTPOS_SCALAR
-for both signed and unsigned integers, otherwise
-it checks the sign of any signed integer to choose
-INTNEG_SCALAR when appropriate.
-
-::
-
-  int
-  PyArray_CanCoerceScalar(int thistype, int neededtype, NPY_SCALARKIND
-                          scalar)
-
-
-Determines whether the data type 'thistype', with
-scalar kind 'scalar', can be coerced into 'neededtype'.
-
-::
-
-  PyObject *
-  PyArray_NewFlagsObject(PyObject *obj)
-
-
-Get New ArrayFlagsObject
-
-::
-
-  npy_bool
-  PyArray_CanCastScalar(PyTypeObject *from, PyTypeObject *to)
-
-See if array scalars can be cast.
-
-TODO: For NumPy 2.0, add a NPY_CASTING parameter.
-
-::
-
-  int
-  PyArray_CompareUCS4(npy_ucs4 const *s1, npy_ucs4 const *s2, size_t
-                      len)
-
-
-::
-
-  int
-  PyArray_RemoveSmallest(PyArrayMultiIterObject *multi)
-
-Adjusts previously broadcasted iterators so that the axis with
-the smallest sum of iterator strides is not iterated over.
-Returns dimension which is smallest in the range [0,multi->nd).
-A -1 is returned if multi->nd == 0.
-
-don't use with PyArray_ITER_GOTO1D because factors are not adjusted
-
-::
-
-  int
-  PyArray_ElementStrides(PyObject *obj)
-
-
-::
-
-  void
-  PyArray_Item_INCREF(char *data, PyArray_Descr *descr)
-
-XINCREF all objects in a single array item. This is complicated for
-structured datatypes where the position of objects needs to be extracted.
-The function is execute recursively for each nested field or subarrays dtype
-such as as `np.dtype([("field1", "O"), ("field2", "f,O", (3,2))])`
-
-::
-
-  void
-  PyArray_Item_XDECREF(char *data, PyArray_Descr *descr)
-
-
-XDECREF all objects in a single array item. This is complicated for
-structured datatypes where the position of objects needs to be extracted.
-The function is execute recursively for each nested field or subarrays dtype
-such as as `np.dtype([("field1", "O"), ("field2", "f,O", (3,2))])`
-
-::
-
-  PyObject *
-  PyArray_FieldNames(PyObject *fields)
-
-Return the tuple of ordered field names from a dictionary.
-
-::
-
-  PyObject *
-  PyArray_Transpose(PyArrayObject *ap, PyArray_Dims *permute)
-
-Return Transpose.
-
-::
-
-  PyObject *
-  PyArray_TakeFrom(PyArrayObject *self0, PyObject *indices0, int
-                   axis, PyArrayObject *out, NPY_CLIPMODE clipmode)
-
-Take
-
-::
-
-  PyObject *
-  PyArray_PutTo(PyArrayObject *self, PyObject*values0, PyObject
-                *indices0, NPY_CLIPMODE clipmode)
-
-Put values into an array
-
-::
-
-  PyObject *
-  PyArray_PutMask(PyArrayObject *self, PyObject*values0, PyObject*mask0)
-
-Put values into an array according to a mask.
-
-::
-
-  PyObject *
-  PyArray_Repeat(PyArrayObject *aop, PyObject *op, int axis)
-
-Repeat the array.
-
-::
-
-  PyObject *
-  PyArray_Choose(PyArrayObject *ip, PyObject *op, PyArrayObject
-                 *out, NPY_CLIPMODE clipmode)
-
-
-::
-
-  int
-  PyArray_Sort(PyArrayObject *op, int axis, NPY_SORTKIND which)
-
-Sort an array in-place
-
-::
-
-  PyObject *
-  PyArray_ArgSort(PyArrayObject *op, int axis, NPY_SORTKIND which)
-
-ArgSort an array
-
-::
-
-  PyObject *
-  PyArray_SearchSorted(PyArrayObject *op1, PyObject *op2, NPY_SEARCHSIDE
-                       side, PyObject *perm)
-
-
-Search the sorted array op1 for the location of the items in op2. The
-result is an array of indexes, one for each element in op2, such that if
-the item were to be inserted in op1 just before that index the array
-would still be in sorted order.
-
-Parameters
-----------
-op1 : PyArrayObject *
-Array to be searched, must be 1-D.
-op2 : PyObject *
-Array of items whose insertion indexes in op1 are wanted
-side : {NPY_SEARCHLEFT, NPY_SEARCHRIGHT}
-If NPY_SEARCHLEFT, return first valid insertion indexes
-If NPY_SEARCHRIGHT, return last valid insertion indexes
-perm : PyObject *
-Permutation array that sorts op1 (optional)
-
-Returns
--------
-ret : PyObject *
-New reference to npy_intp array containing indexes where items in op2
-could be validly inserted into op1. NULL on error.
-
-Notes
------
-Binary search is used to find the indexes.
-
-::
-
-  PyObject *
-  PyArray_ArgMax(PyArrayObject *op, int axis, PyArrayObject *out)
-
-ArgMax
-
-::
-
-  PyObject *
-  PyArray_ArgMin(PyArrayObject *op, int axis, PyArrayObject *out)
-
-ArgMin
-
-::
-
-  PyObject *
-  PyArray_Reshape(PyArrayObject *self, PyObject *shape)
-
-Reshape
-
-::
-
-  PyObject *
-  PyArray_Newshape(PyArrayObject *self, PyArray_Dims *newdims, NPY_ORDER
-                   order)
-
-New shape for an array
-
-::
-
-  PyObject *
-  PyArray_Squeeze(PyArrayObject *self)
-
-
-return a new view of the array object with all of its unit-length
-dimensions squeezed out if needed, otherwise
-return the same array.
-
-::
-
-  PyObject *
-  PyArray_View(PyArrayObject *self, PyArray_Descr *type, PyTypeObject
-               *pytype)
-
-View
-steals a reference to type -- accepts NULL
-
-::
-
-  PyObject *
-  PyArray_SwapAxes(PyArrayObject *ap, int a1, int a2)
-
-SwapAxes
-
-::
-
-  PyObject *
-  PyArray_Max(PyArrayObject *ap, int axis, PyArrayObject *out)
-
-Max
-
-::
-
-  PyObject *
-  PyArray_Min(PyArrayObject *ap, int axis, PyArrayObject *out)
-
-Min
-
-::
-
-  PyObject *
-  PyArray_Ptp(PyArrayObject *ap, int axis, PyArrayObject *out)
-
-Ptp
-
-::
-
-  PyObject *
-  PyArray_Mean(PyArrayObject *self, int axis, int rtype, PyArrayObject
-               *out)
-
-Mean
-
-::
-
-  PyObject *
-  PyArray_Trace(PyArrayObject *self, int offset, int axis1, int
-                axis2, int rtype, PyArrayObject *out)
-
-Trace
-
-::
-
-  PyObject *
-  PyArray_Diagonal(PyArrayObject *self, int offset, int axis1, int
-                   axis2)
-
-Diagonal
-
-In NumPy versions prior to 1.7,  this function always returned a copy of
-the diagonal array. In 1.7, the code has been updated to compute a view
-onto 'self', but it still copies this array before returning, as well as
-setting the internal WARN_ON_WRITE flag. In a future version, it will
-simply return a view onto self.
-
-::
-
-  PyObject *
-  PyArray_Clip(PyArrayObject *self, PyObject *min, PyObject
-               *max, PyArrayObject *out)
-
-Clip
-
-::
-
-  PyObject *
-  PyArray_Conjugate(PyArrayObject *self, PyArrayObject *out)
-
-Conjugate
-
-::
-
-  PyObject *
-  PyArray_Nonzero(PyArrayObject *self)
-
-Nonzero
-
-TODO: In NumPy 2.0, should make the iteration order a parameter.
-
-::
-
-  PyObject *
-  PyArray_Std(PyArrayObject *self, int axis, int rtype, PyArrayObject
-              *out, int variance)
-
-Set variance to 1 to by-pass square-root calculation and return variance
-Std
-
-::
-
-  PyObject *
-  PyArray_Sum(PyArrayObject *self, int axis, int rtype, PyArrayObject
-              *out)
-
-Sum
-
-::
-
-  PyObject *
-  PyArray_CumSum(PyArrayObject *self, int axis, int rtype, PyArrayObject
-                 *out)
-
-CumSum
-
-::
-
-  PyObject *
-  PyArray_Prod(PyArrayObject *self, int axis, int rtype, PyArrayObject
-               *out)
-
-Prod
-
-::
-
-  PyObject *
-  PyArray_CumProd(PyArrayObject *self, int axis, int
-                  rtype, PyArrayObject *out)
-
-CumProd
-
-::
-
-  PyObject *
-  PyArray_All(PyArrayObject *self, int axis, PyArrayObject *out)
-
-All
-
-::
-
-  PyObject *
-  PyArray_Any(PyArrayObject *self, int axis, PyArrayObject *out)
-
-Any
-
-::
-
-  PyObject *
-  PyArray_Compress(PyArrayObject *self, PyObject *condition, int
-                   axis, PyArrayObject *out)
-
-Compress
-
-::
-
-  PyObject *
-  PyArray_Flatten(PyArrayObject *a, NPY_ORDER order)
-
-Flatten
-
-::
-
-  PyObject *
-  PyArray_Ravel(PyArrayObject *arr, NPY_ORDER order)
-
-Ravel
-Returns a contiguous array
-
-::
-
-  npy_intp
-  PyArray_MultiplyList(npy_intp const *l1, int n)
-
-Multiply a List
-
-::
-
-  int
-  PyArray_MultiplyIntList(int const *l1, int n)
-
-Multiply a List of ints
-
-::
-
-  void *
-  PyArray_GetPtr(PyArrayObject *obj, npy_intp const*ind)
-
-Produce a pointer into array
-
-::
-
-  int
-  PyArray_CompareLists(npy_intp const *l1, npy_intp const *l2, int n)
-
-Compare Lists
-
-::
-
-  int
-  PyArray_AsCArray(PyObject **op, void *ptr, npy_intp *dims, int
-                   nd, PyArray_Descr*typedescr)
-
-Simulate a C-array
-steals a reference to typedescr -- can be NULL
-
-::
-
-  int
-  PyArray_As1D(PyObject **NPY_UNUSED(op) , char **NPY_UNUSED(ptr) , int
-               *NPY_UNUSED(d1) , int NPY_UNUSED(typecode) )
-
-Convert to a 1D C-array
-
-::
-
-  int
-  PyArray_As2D(PyObject **NPY_UNUSED(op) , char ***NPY_UNUSED(ptr) , int
-               *NPY_UNUSED(d1) , int *NPY_UNUSED(d2) , int
-               NPY_UNUSED(typecode) )
-
-Convert to a 2D C-array
-
-::
-
-  int
-  PyArray_Free(PyObject *op, void *ptr)
-
-Free pointers created if As2D is called
-
-::
-
-  int
-  PyArray_Converter(PyObject *object, PyObject **address)
-
-
-Useful to pass as converter function for O& processing in PyArgs_ParseTuple.
-
-This conversion function can be used with the "O&" argument for
-PyArg_ParseTuple.  It will immediately return an object of array type
-or will convert to a NPY_ARRAY_CARRAY any other object.
-
-If you use PyArray_Converter, you must DECREF the array when finished
-as you get a new reference to it.
-
-::
-
-  int
-  PyArray_IntpFromSequence(PyObject *seq, npy_intp *vals, int maxvals)
-
-PyArray_IntpFromSequence
-Returns the number of integers converted or -1 if an error occurred.
-vals must be large enough to hold maxvals
-
-::
-
-  PyObject *
-  PyArray_Concatenate(PyObject *op, int axis)
-
-Concatenate
-
-Concatenate an arbitrary Python sequence into an array.
-op is a python object supporting the sequence interface.
-Its elements will be concatenated together to form a single
-multidimensional array. If axis is NPY_MAXDIMS or bigger, then
-each sequence object will be flattened before concatenation
-
-::
-
-  PyObject *
-  PyArray_InnerProduct(PyObject *op1, PyObject *op2)
-
-Numeric.innerproduct(a,v)
-
-::
-
-  PyObject *
-  PyArray_MatrixProduct(PyObject *op1, PyObject *op2)
-
-Numeric.matrixproduct(a,v)
-just like inner product but does the swapaxes stuff on the fly
-
-::
-
-  PyObject *
-  PyArray_CopyAndTranspose(PyObject *op)
-
-Copy and Transpose
-
-Could deprecate this function, as there isn't a speed benefit over
-calling Transpose and then Copy.
-
-::
-
-  PyObject *
-  PyArray_Correlate(PyObject *op1, PyObject *op2, int mode)
-
-Numeric.correlate(a1,a2,mode)
-
-::
-
-  int
-  PyArray_TypestrConvert(int itemsize, int gentype)
-
-Typestr converter
-
-::
-
-  int
-  PyArray_DescrConverter(PyObject *obj, PyArray_Descr **at)
-
-Get typenum from an object -- None goes to NPY_DEFAULT_TYPE
-This function takes a Python object representing a type and converts it
-to a the correct PyArray_Descr * structure to describe the type.
-
-Many objects can be used to represent a data-type which in NumPy is
-quite a flexible concept.
-
-This is the central code that converts Python objects to
-Type-descriptor objects that are used throughout numpy.
-
-Returns a new reference in *at, but the returned should not be
-modified as it may be one of the canonical immutable objects or
-a reference to the input obj.
-
-::
-
-  int
-  PyArray_DescrConverter2(PyObject *obj, PyArray_Descr **at)
-
-Get typenum from an object -- None goes to NULL
-
-::
-
-  int
-  PyArray_IntpConverter(PyObject *obj, PyArray_Dims *seq)
-
-Get intp chunk from sequence
-
-This function takes a Python sequence object and allocates and
-fills in an intp array with the converted values.
-
-Remember to free the pointer seq.ptr when done using
-PyDimMem_FREE(seq.ptr)**
-
-::
-
-  int
-  PyArray_BufferConverter(PyObject *obj, PyArray_Chunk *buf)
-
-Get buffer chunk from object
-
-this function takes a Python object which exposes the (single-segment)
-buffer interface and returns a pointer to the data segment
-
-You should increment the reference count by one of buf->base
-if you will hang on to a reference
-
-You only get a borrowed reference to the object. Do not free the
-memory...
-
-::
-
-  int
-  PyArray_AxisConverter(PyObject *obj, int *axis)
-
-Get axis from an object (possibly None) -- a converter function,
-
-See also PyArray_ConvertMultiAxis, which also handles a tuple of axes.
-
-::
-
-  int
-  PyArray_BoolConverter(PyObject *object, npy_bool *val)
-
-Convert an object to true / false
-
-::
-
-  int
-  PyArray_ByteorderConverter(PyObject *obj, char *endian)
-
-Convert object to endian
-
-::
-
-  int
-  PyArray_OrderConverter(PyObject *object, NPY_ORDER *val)
-
-Convert an object to FORTRAN / C / ANY / KEEP
-
-::
-
-  unsigned char
-  PyArray_EquivTypes(PyArray_Descr *type1, PyArray_Descr *type2)
-
-
-This function returns true if the two typecodes are
-equivalent (same basic kind and same itemsize).
-
-::
-
-  PyObject *
-  PyArray_Zeros(int nd, npy_intp const *dims, PyArray_Descr *type, int
-                is_f_order)
-
-Zeros
-
-steals a reference to type. On failure or when dtype->subarray is
-true, dtype will be decrefed.
-accepts NULL type
-
-::
-
-  PyObject *
-  PyArray_Empty(int nd, npy_intp const *dims, PyArray_Descr *type, int
-                is_f_order)
-
-Empty
-
-accepts NULL type
-steals a reference to type
-
-::
-
-  PyObject *
-  PyArray_Where(PyObject *condition, PyObject *x, PyObject *y)
-
-Where
-
-::
-
-  PyObject *
-  PyArray_Arange(double start, double stop, double step, int type_num)
-
-Arange,
-
-::
-
-  PyObject *
-  PyArray_ArangeObj(PyObject *start, PyObject *stop, PyObject
-                    *step, PyArray_Descr *dtype)
-
-
-ArangeObj,
-
-this doesn't change the references
-
-::
-
-  int
-  PyArray_SortkindConverter(PyObject *obj, NPY_SORTKIND *sortkind)
-
-Convert object to sort kind
-
-::
-
-  PyObject *
-  PyArray_LexSort(PyObject *sort_keys, int axis)
-
-LexSort an array providing indices that will sort a collection of arrays
-lexicographically.  The first key is sorted on first, followed by the second key
--- requires that arg"merge"sort is available for each sort_key
-
-Returns an index array that shows the indexes for the lexicographic sort along
-the given axis.
-
-::
-
-  PyObject *
-  PyArray_Round(PyArrayObject *a, int decimals, PyArrayObject *out)
-
-Round
-
-::
-
-  unsigned char
-  PyArray_EquivTypenums(int typenum1, int typenum2)
-
-
-::
-
-  int
-  PyArray_RegisterDataType(PyArray_Descr *descr)
-
-Register Data type
-Does not change the reference count of descr
-
-::
-
-  int
-  PyArray_RegisterCastFunc(PyArray_Descr *descr, int
-                           totype, PyArray_VectorUnaryFunc *castfunc)
-
-Register Casting Function
-Replaces any function currently stored.
-
-::
-
-  int
-  PyArray_RegisterCanCast(PyArray_Descr *descr, int
-                          totype, NPY_SCALARKIND scalar)
-
-Register a type number indicating that a descriptor can be cast
-to it safely
-
-::
-
-  void
-  PyArray_InitArrFuncs(PyArray_ArrFuncs *f)
-
-Initialize arrfuncs to NULL
-
-::
-
-  PyObject *
-  PyArray_IntTupleFromIntp(int len, npy_intp const *vals)
-
-PyArray_IntTupleFromIntp
-
-::
-
-  int
-  PyArray_TypeNumFromName(char const *str)
-
-
-::
-
-  int
-  PyArray_ClipmodeConverter(PyObject *object, NPY_CLIPMODE *val)
-
-Convert an object to NPY_RAISE / NPY_CLIP / NPY_WRAP
-
-::
-
-  int
-  PyArray_OutputConverter(PyObject *object, PyArrayObject **address)
-
-Useful to pass as converter function for O& processing in
-PyArgs_ParseTuple for output arrays
-
-::
-
-  PyObject *
-  PyArray_BroadcastToShape(PyObject *obj, npy_intp *dims, int nd)
-
-Get Iterator broadcast to a particular shape
-
-::
-
-  void
-  _PyArray_SigintHandler(int signum)
-
-
-::
-
-  void*
-  _PyArray_GetSigintBuf(void )
-
-
-::
-
-  int
-  PyArray_DescrAlignConverter(PyObject *obj, PyArray_Descr **at)
-
-
-Get type-descriptor from an object forcing alignment if possible
-None goes to DEFAULT type.
-
-any object with the .fields attribute and/or .itemsize attribute (if the
-.fields attribute does not give the total size -- i.e. a partial record
-naming).  If itemsize is given it must be >= size computed from fields
-
-The .fields attribute must return a convertible dictionary if present.
-Result inherits from NPY_VOID.
-
-::
-
-  int
-  PyArray_DescrAlignConverter2(PyObject *obj, PyArray_Descr **at)
-
-
-Get type-descriptor from an object forcing alignment if possible
-None goes to NULL.
-
-::
-
-  int
-  PyArray_SearchsideConverter(PyObject *obj, void *addr)
-
-Convert object to searchsorted side
-
-::
-
-  PyObject *
-  PyArray_CheckAxis(PyArrayObject *arr, int *axis, int flags)
-
-PyArray_CheckAxis
-
-check that axis is valid
-convert 0-d arrays to 1-d arrays
-
-::
-
-  npy_intp
-  PyArray_OverflowMultiplyList(npy_intp const *l1, int n)
-
-Multiply a List of Non-negative numbers with over-flow detection.
-
-::
-
-  int
-  PyArray_CompareString(const char *s1, const char *s2, size_t len)
-
-
-::
-
-  PyObject*
-  PyArray_MultiIterFromObjects(PyObject **mps, int n, int nadd, ... )
-
-Get MultiIterator from array of Python objects and any additional
-
-PyObject **mps - array of PyObjects
-int n - number of PyObjects in the array
-int nadd - number of additional arrays to include in the iterator.
-
-Returns a multi-iterator object.
-
-::
-
-  int
-  PyArray_GetEndianness(void )
-
-
-::
-
-  unsigned int
-  PyArray_GetNDArrayCFeatureVersion(void )
-
-Returns the built-in (at compilation time) C API version
-
-::
-
-  PyObject *
-  PyArray_Correlate2(PyObject *op1, PyObject *op2, int mode)
-
-correlate(a1,a2,mode)
-
-This function computes the usual correlation (correlate(a1, a2) !=
-correlate(a2, a1), and conjugate the second argument for complex inputs
-
-::
-
-  PyObject*
-  PyArray_NeighborhoodIterNew(PyArrayIterObject *x, const npy_intp
-                              *bounds, int mode, PyArrayObject*fill)
-
-A Neighborhood Iterator object.
-
-::
-
-  void
-  PyArray_SetDatetimeParseFunction(PyObject *NPY_UNUSED(op) )
-
-This function is scheduled to be removed
-
-TO BE REMOVED - NOT USED INTERNALLY.
-
-::
-
-  void
-  PyArray_DatetimeToDatetimeStruct(npy_datetime NPY_UNUSED(val)
-                                   , NPY_DATETIMEUNIT NPY_UNUSED(fr)
-                                   , npy_datetimestruct *result)
-
-Fill the datetime struct from the value and resolution unit.
-
-TO BE REMOVED - NOT USED INTERNALLY.
-
-::
-
-  void
-  PyArray_TimedeltaToTimedeltaStruct(npy_timedelta NPY_UNUSED(val)
-                                     , NPY_DATETIMEUNIT NPY_UNUSED(fr)
-                                     , npy_timedeltastruct *result)
-
-Fill the timedelta struct from the timedelta value and resolution unit.
-
-TO BE REMOVED - NOT USED INTERNALLY.
-
-::
-
-  npy_datetime
-  PyArray_DatetimeStructToDatetime(NPY_DATETIMEUNIT NPY_UNUSED(fr)
-                                   , npy_datetimestruct *NPY_UNUSED(d) )
-
-Create a datetime value from a filled datetime struct and resolution unit.
-
-TO BE REMOVED - NOT USED INTERNALLY.
-
-::
-
-  npy_datetime
-  PyArray_TimedeltaStructToTimedelta(NPY_DATETIMEUNIT NPY_UNUSED(fr)
-                                     , npy_timedeltastruct
-                                     *NPY_UNUSED(d) )
-
-Create a timedelta value from a filled timedelta struct and resolution unit.
-
-TO BE REMOVED - NOT USED INTERNALLY.
-
-::
-
-  NpyIter *
-  NpyIter_New(PyArrayObject *op, npy_uint32 flags, NPY_ORDER
-              order, NPY_CASTING casting, PyArray_Descr*dtype)
-
-Allocate a new iterator for one array object.
-
-::
-
-  NpyIter *
-  NpyIter_MultiNew(int nop, PyArrayObject **op_in, npy_uint32
-                   flags, NPY_ORDER order, NPY_CASTING
-                   casting, npy_uint32 *op_flags, PyArray_Descr
-                   **op_request_dtypes)
-
-Allocate a new iterator for more than one array object, using
-standard NumPy broadcasting rules and the default buffer size.
-
-::
-
-  NpyIter *
-  NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32
-                      flags, NPY_ORDER order, NPY_CASTING
-                      casting, npy_uint32 *op_flags, PyArray_Descr
-                      **op_request_dtypes, int oa_ndim, int
-                      **op_axes, npy_intp *itershape, npy_intp
-                      buffersize)
-
-Allocate a new iterator for multiple array objects, and advanced
-options for controlling the broadcasting, shape, and buffer size.
-
-::
-
-  NpyIter *
-  NpyIter_Copy(NpyIter *iter)
-
-Makes a copy of the iterator
-
-::
-
-  int
-  NpyIter_Deallocate(NpyIter *iter)
-
-Deallocate an iterator.
-
-To correctly work when an error is in progress, we have to check
-`PyErr_Occurred()`. This is necessary when buffers are not finalized
-or WritebackIfCopy is used. We could avoid that check by exposing a new
-function which is passed in whether or not a Python error is already set.
-
-::
-
-  npy_bool
-  NpyIter_HasDelayedBufAlloc(NpyIter *iter)
-
-Whether the buffer allocation is being delayed
-
-::
-
-  npy_bool
-  NpyIter_HasExternalLoop(NpyIter *iter)
-
-Whether the iterator handles the inner loop
-
-::
-
-  int
-  NpyIter_EnableExternalLoop(NpyIter *iter)
-
-Removes the inner loop handling (so HasExternalLoop returns true)
-
-::
-
-  npy_intp *
-  NpyIter_GetInnerStrideArray(NpyIter *iter)
-
-Get the array of strides for the inner loop (when HasExternalLoop is true)
-
-This function may be safely called without holding the Python GIL.
-
-::
-
-  npy_intp *
-  NpyIter_GetInnerLoopSizePtr(NpyIter *iter)
-
-Get a pointer to the size of the inner loop  (when HasExternalLoop is true)
-
-This function may be safely called without holding the Python GIL.
-
-::
-
-  int
-  NpyIter_Reset(NpyIter *iter, char **errmsg)
-
-Resets the iterator to its initial state
-
-The use of errmsg is discouraged, it cannot be guaranteed that the GIL
-will not be grabbed on casting errors even when this is passed.
-
-If errmsg is non-NULL, it should point to a variable which will
-receive the error message, and no Python exception will be set.
-This is so that the function can be called from code not holding
-the GIL. Note that cast errors may still lead to the GIL being
-grabbed temporarily.
-
-::
-
-  int
-  NpyIter_ResetBasePointers(NpyIter *iter, char **baseptrs, char
-                            **errmsg)
-
-Resets the iterator to its initial state, with new base data pointers.
-This function requires great caution.
-
-If errmsg is non-NULL, it should point to a variable which will
-receive the error message, and no Python exception will be set.
-This is so that the function can be called from code not holding
-the GIL. Note that cast errors may still lead to the GIL being
-grabbed temporarily.
-
-::
-
-  int
-  NpyIter_ResetToIterIndexRange(NpyIter *iter, npy_intp istart, npy_intp
-                                iend, char **errmsg)
-
-Resets the iterator to a new iterator index range
-
-If errmsg is non-NULL, it should point to a variable which will
-receive the error message, and no Python exception will be set.
-This is so that the function can be called from code not holding
-the GIL. Note that cast errors may still lead to the GIL being
-grabbed temporarily.
-
-::
-
-  int
-  NpyIter_GetNDim(NpyIter *iter)
-
-Gets the number of dimensions being iterated
-
-::
-
-  int
-  NpyIter_GetNOp(NpyIter *iter)
-
-Gets the number of operands being iterated
-
-::
-
-  NpyIter_IterNextFunc *
-  NpyIter_GetIterNext(NpyIter *iter, char **errmsg)
-
-Compute the specialized iteration function for an iterator
-
-If errmsg is non-NULL, it should point to a variable which will
-receive the error message, and no Python exception will be set.
-This is so that the function can be called from code not holding
-the GIL.
-
-::
-
-  npy_intp
-  NpyIter_GetIterSize(NpyIter *iter)
-
-Gets the number of elements being iterated
-
-::
-
-  void
-  NpyIter_GetIterIndexRange(NpyIter *iter, npy_intp *istart, npy_intp
-                            *iend)
-
-Gets the range of iteration indices being iterated
-
-::
-
-  npy_intp
-  NpyIter_GetIterIndex(NpyIter *iter)
-
-Gets the current iteration index
-
-::
-
-  int
-  NpyIter_GotoIterIndex(NpyIter *iter, npy_intp iterindex)
-
-Sets the iterator position to the specified iterindex,
-which matches the iteration order of the iterator.
-
-Returns NPY_SUCCEED on success, NPY_FAIL on failure.
-
-::
-
-  npy_bool
-  NpyIter_HasMultiIndex(NpyIter *iter)
-
-Whether the iterator is tracking a multi-index
-
-::
-
-  int
-  NpyIter_GetShape(NpyIter *iter, npy_intp *outshape)
-
-Gets the broadcast shape if a multi-index is being tracked by the iterator,
-otherwise gets the shape of the iteration as Fortran-order
-(fastest-changing index first).
-
-The reason Fortran-order is returned when a multi-index
-is not enabled is that this is providing a direct view into how
-the iterator traverses the n-dimensional space. The iterator organizes
-its memory from fastest index to slowest index, and when
-a multi-index is enabled, it uses a permutation to recover the original
-order.
-
-Returns NPY_SUCCEED or NPY_FAIL.
-
-::
-
-  NpyIter_GetMultiIndexFunc *
-  NpyIter_GetGetMultiIndex(NpyIter *iter, char **errmsg)
-
-Compute a specialized get_multi_index function for the iterator
-
-If errmsg is non-NULL, it should point to a variable which will
-receive the error message, and no Python exception will be set.
-This is so that the function can be called from code not holding
-the GIL.
-
-::
-
-  int
-  NpyIter_GotoMultiIndex(NpyIter *iter, npy_intp const *multi_index)
-
-Sets the iterator to the specified multi-index, which must have the
-correct number of entries for 'ndim'.  It is only valid
-when NPY_ITER_MULTI_INDEX was passed to the constructor.  This operation
-fails if the multi-index is out of bounds.
-
-Returns NPY_SUCCEED on success, NPY_FAIL on failure.
-
-::
-
-  int
-  NpyIter_RemoveMultiIndex(NpyIter *iter)
-
-Removes multi-index support from an iterator.
-
-Returns NPY_SUCCEED or NPY_FAIL.
-
-::
-
-  npy_bool
-  NpyIter_HasIndex(NpyIter *iter)
-
-Whether the iterator is tracking an index
-
-::
-
-  npy_bool
-  NpyIter_IsBuffered(NpyIter *iter)
-
-Whether the iterator is buffered
-
-::
-
-  npy_bool
-  NpyIter_IsGrowInner(NpyIter *iter)
-
-Whether the inner loop can grow if buffering is unneeded
-
-::
-
-  npy_intp
-  NpyIter_GetBufferSize(NpyIter *iter)
-
-Gets the size of the buffer, or 0 if buffering is not enabled
-
-::
-
-  npy_intp *
-  NpyIter_GetIndexPtr(NpyIter *iter)
-
-Get a pointer to the index, if it is being tracked
-
-::
-
-  int
-  NpyIter_GotoIndex(NpyIter *iter, npy_intp flat_index)
-
-If the iterator is tracking an index, sets the iterator
-to the specified index.
-
-Returns NPY_SUCCEED on success, NPY_FAIL on failure.
-
-::
-
-  char **
-  NpyIter_GetDataPtrArray(NpyIter *iter)
-
-Get the array of data pointers (1 per object being iterated)
-
-This function may be safely called without holding the Python GIL.
-
-::
-
-  PyArray_Descr **
-  NpyIter_GetDescrArray(NpyIter *iter)
-
-Get the array of data type pointers (1 per object being iterated)
-
-::
-
-  PyArrayObject **
-  NpyIter_GetOperandArray(NpyIter *iter)
-
-Get the array of objects being iterated
-
-::
-
-  PyArrayObject *
-  NpyIter_GetIterView(NpyIter *iter, npy_intp i)
-
-Returns a view to the i-th object with the iterator's internal axes
-
-::
-
-  void
-  NpyIter_GetReadFlags(NpyIter *iter, char *outreadflags)
-
-Gets an array of read flags (1 per object being iterated)
-
-::
-
-  void
-  NpyIter_GetWriteFlags(NpyIter *iter, char *outwriteflags)
-
-Gets an array of write flags (1 per object being iterated)
-
-::
-
-  void
-  NpyIter_DebugPrint(NpyIter *iter)
-
-For debugging
-
-::
-
-  npy_bool
-  NpyIter_IterationNeedsAPI(NpyIter *iter)
-
-Whether the iteration loop, and in particular the iternext()
-function, needs API access.  If this is true, the GIL must
-be retained while iterating.
-
-NOTE: Internally (currently), `NpyIter_GetTransferFlags` will
-additionally provide information on whether floating point errors
-may be given during casts.  The flags only require the API use
-necessary for buffering though.  So an iterate which does not require
-buffering may indicate `NpyIter_IterationNeedsAPI`, but not include
-the flag in `NpyIter_GetTransferFlags`.
-
-::
-
-  void
-  NpyIter_GetInnerFixedStrideArray(NpyIter *iter, npy_intp *out_strides)
-
-Get an array of strides which are fixed.  Any strides which may
-change during iteration receive the value NPY_MAX_INTP.  Once
-the iterator is ready to iterate, call this to get the strides
-which will always be fixed in the inner loop, then choose optimized
-inner loop functions which take advantage of those fixed strides.
-
-This function may be safely called without holding the Python GIL.
-
-::
-
-  int
-  NpyIter_RemoveAxis(NpyIter *iter, int axis)
-
-Removes an axis from iteration. This requires that NPY_ITER_MULTI_INDEX
-was set for iterator creation, and does not work if buffering is
-enabled. This function also resets the iterator to its initial state.
-
-Returns NPY_SUCCEED or NPY_FAIL.
-
-::
-
-  npy_intp *
-  NpyIter_GetAxisStrideArray(NpyIter *iter, int axis)
-
-Gets the array of strides for the specified axis.
-If the iterator is tracking a multi-index, gets the strides
-for the axis specified, otherwise gets the strides for
-the iteration axis as Fortran order (fastest-changing axis first).
-
-Returns NULL if an error occurs.
-
-::
-
-  npy_bool
-  NpyIter_RequiresBuffering(NpyIter *iter)
-
-Whether the iteration could be done with no buffering.
-
-::
-
-  char **
-  NpyIter_GetInitialDataPtrArray(NpyIter *iter)
-
-Get the array of data pointers (1 per object being iterated),
-directly into the arrays (never pointing to a buffer), for starting
-unbuffered iteration. This always returns the addresses for the
-iterator position as reset to iterator index 0.
-
-These pointers are different from the pointers accepted by
-NpyIter_ResetBasePointers, because the direction along some
-axes may have been reversed, requiring base offsets.
-
-This function may be safely called without holding the Python GIL.
-
-::
-
-  int
-  NpyIter_CreateCompatibleStrides(NpyIter *iter, npy_intp
-                                  itemsize, npy_intp *outstrides)
-
-Builds a set of strides which are the same as the strides of an
-output array created using the NPY_ITER_ALLOCATE flag, where NULL
-was passed for op_axes.  This is for data packed contiguously,
-but not necessarily in C or Fortran order. This should be used
-together with NpyIter_GetShape and NpyIter_GetNDim.
-
-A use case for this function is to match the shape and layout of
-the iterator and tack on one or more dimensions.  For example,
-in order to generate a vector per input value for a numerical gradient,
-you pass in ndim*itemsize for itemsize, then add another dimension to
-the end with size ndim and stride itemsize.  To do the Hessian matrix,
-you do the same thing but add two dimensions, or take advantage of
-the symmetry and pack it into 1 dimension with a particular encoding.
-
-This function may only be called if the iterator is tracking a multi-index
-and if NPY_ITER_DONT_NEGATE_STRIDES was used to prevent an axis from
-being iterated in reverse order.
-
-If an array is created with this method, simply adding 'itemsize'
-for each iteration will traverse the new array matching the
-iterator.
-
-Returns NPY_SUCCEED or NPY_FAIL.
-
-::
-
-  int
-  PyArray_CastingConverter(PyObject *obj, NPY_CASTING *casting)
-
-Convert any Python object, *obj*, to an NPY_CASTING enum.
-
-::
-
-  npy_intp
-  PyArray_CountNonzero(PyArrayObject *self)
-
-Counts the number of non-zero elements in the array.
-
-Returns -1 on error.
-
-::
-
-  PyArray_Descr *
-  PyArray_PromoteTypes(PyArray_Descr *type1, PyArray_Descr *type2)
-
-Produces the smallest size and lowest kind type to which both
-input types can be cast.
-
-::
-
-  PyArray_Descr *
-  PyArray_MinScalarType(PyArrayObject *arr)
-
-If arr is a scalar (has 0 dimensions) with a built-in number data type,
-finds the smallest type size/kind which can still represent its data.
-Otherwise, returns the array's data type.
-
-
-::
-
-  PyArray_Descr *
-  PyArray_ResultType(npy_intp narrs, PyArrayObject *arrs[] , npy_intp
-                     ndtypes, PyArray_Descr *descrs[] )
-
-
-Produces the result type of a bunch of inputs, using the same rules
-as `np.result_type`.
-
-NOTE: This function is expected to through a transitional period or
-change behaviour.  DTypes should always be strictly enforced for
-0-D arrays, while "weak DTypes" will be used to represent Python
-integers, floats, and complex in all cases.
-(Within this function, these are currently flagged on the array
-object to work through `np.result_type`, this may change.)
-
-Until a time where this transition is complete, we probably cannot
-add new "weak DTypes" or allow users to create their own.
-
-::
-
-  npy_bool
-  PyArray_CanCastArrayTo(PyArrayObject *arr, PyArray_Descr
-                         *to, NPY_CASTING casting)
-
-Returns 1 if the array object may be cast to the given data type using
-the casting rule, 0 otherwise.  This differs from PyArray_CanCastTo in
-that it handles scalar arrays (0 dimensions) specially, by checking
-their value.
-
-::
-
-  npy_bool
-  PyArray_CanCastTypeTo(PyArray_Descr *from, PyArray_Descr
-                        *to, NPY_CASTING casting)
-
-Returns true if data of type 'from' may be cast to data of type
-'to' according to the rule 'casting'.
-
-::
-
-  PyArrayObject *
-  PyArray_EinsteinSum(char *subscripts, npy_intp nop, PyArrayObject
-                      **op_in, PyArray_Descr *dtype, NPY_ORDER
-                      order, NPY_CASTING casting, PyArrayObject *out)
-
-This function provides summation of array elements according to
-the Einstein summation convention.  For example:
-- trace(a)        -> einsum("ii", a)
-- transpose(a)    -> einsum("ji", a)
-- multiply(a,b)   -> einsum(",", a, b)
-- inner(a,b)      -> einsum("i,i", a, b)
-- outer(a,b)      -> einsum("i,j", a, b)
-- matvec(a,b)     -> einsum("ij,j", a, b)
-- matmat(a,b)     -> einsum("ij,jk", a, b)
-
-subscripts: The string of subscripts for einstein summation.
-nop:        The number of operands
-op_in:      The array of operands
-dtype:      Either NULL, or the data type to force the calculation as.
-order:      The order for the calculation/the output axes.
-casting:    What kind of casts should be permitted.
-out:        Either NULL, or an array into which the output should be placed.
-
-By default, the labels get placed in alphabetical order
-at the end of the output. So, if c = einsum("i,j", a, b)
-then c[i,j] == a[i]*b[j], but if c = einsum("j,i", a, b)
-then c[i,j] = a[j]*b[i].
-
-Alternatively, you can control the output order or prevent
-an axis from being summed/force an axis to be summed by providing
-indices for the output. This allows us to turn 'trace' into
-'diag', for example.
-- diag(a)         -> einsum("ii->i", a)
-- sum(a, axis=0)  -> einsum("i...->", a)
-
-Subscripts at the beginning and end may be specified by
-putting an ellipsis "..." in the middle.  For example,
-the function einsum("i...i", a) takes the diagonal of
-the first and last dimensions of the operand, and
-einsum("ij...,jk...->ik...") takes the matrix product using
-the first two indices of each operand instead of the last two.
-
-When there is only one operand, no axes being summed, and
-no output parameter, this function returns a view
-into the operand instead of making a copy.
-
-::
-
-  PyObject *
-  PyArray_NewLikeArray(PyArrayObject *prototype, NPY_ORDER
-                       order, PyArray_Descr *dtype, int subok)
-
-Creates a new array with the same shape as the provided one,
-with possible memory layout order and data type changes.
-
-prototype - The array the new one should be like.
-order     - NPY_CORDER - C-contiguous result.
-NPY_FORTRANORDER - Fortran-contiguous result.
-NPY_ANYORDER - Fortran if prototype is Fortran, C otherwise.
-NPY_KEEPORDER - Keeps the axis ordering of prototype.
-dtype     - If not NULL, overrides the data type of the result.
-subok     - If 1, use the prototype's array subtype, otherwise
-always create a base-class array.
-
-NOTE: If dtype is not NULL, steals the dtype reference.  On failure or when
-dtype->subarray is true, dtype will be decrefed.
-
-::
-
-  int
-  PyArray_GetArrayParamsFromObject(PyObject *NPY_UNUSED(op)
-                                   , PyArray_Descr
-                                   *NPY_UNUSED(requested_dtype)
-                                   , npy_bool NPY_UNUSED(writeable)
-                                   , PyArray_Descr
-                                   **NPY_UNUSED(out_dtype) , int
-                                   *NPY_UNUSED(out_ndim) , npy_intp
-                                   *NPY_UNUSED(out_dims) , PyArrayObject
-                                   **NPY_UNUSED(out_arr) , PyObject
-                                   *NPY_UNUSED(context) )
-
-
-::
-
-  int
-  PyArray_ConvertClipmodeSequence(PyObject *object, NPY_CLIPMODE
-                                  *modes, int n)
-
-Convert an object to an array of n NPY_CLIPMODE values.
-This is intended to be used in functions where a different mode
-could be applied to each axis, like in ravel_multi_index.
-
-::
-
-  PyObject *
-  PyArray_MatrixProduct2(PyObject *op1, PyObject
-                         *op2, PyArrayObject*out)
-
-Numeric.matrixproduct2(a,v,out)
-just like inner product but does the swapaxes stuff on the fly
-
-::
-
-  npy_bool
-  NpyIter_IsFirstVisit(NpyIter *iter, int iop)
-
-Checks to see whether this is the first time the elements
-of the specified reduction operand which the iterator points at are
-being seen for the first time. The function returns
-a reasonable answer for reduction operands and when buffering is
-disabled. The answer may be incorrect for buffered non-reduction
-operands.
-
-This function is intended to be used in EXTERNAL_LOOP mode only,
-and will produce some wrong answers when that mode is not enabled.
-
-If this function returns true, the caller should also
-check the inner loop stride of the operand, because if
-that stride is 0, then only the first element of the innermost
-external loop is being visited for the first time.
-
-WARNING: For performance reasons, 'iop' is not bounds-checked,
-it is not confirmed that 'iop' is actually a reduction
-operand, and it is not confirmed that EXTERNAL_LOOP
-mode is enabled. These checks are the responsibility of
-the caller, and should be done outside of any inner loops.
-
-::
-
-  int
-  PyArray_SetBaseObject(PyArrayObject *arr, PyObject *obj)
-
-Sets the 'base' attribute of the array. This steals a reference
-to 'obj'.
-
-Returns 0 on success, -1 on failure.
-
-::
-
-  void
-  PyArray_CreateSortedStridePerm(int ndim, npy_intp const
-                                 *strides, npy_stride_sort_item
-                                 *out_strideperm)
-
-
-This function populates the first ndim elements
-of strideperm with sorted descending by their absolute values.
-For example, the stride array (4, -2, 12) becomes
-[(2, 12), (0, 4), (1, -2)].
-
-::
-
-  void
-  PyArray_RemoveAxesInPlace(PyArrayObject *arr, const npy_bool *flags)
-
-
-Removes the axes flagged as True from the array,
-modifying it in place. If an axis flagged for removal
-has a shape entry bigger than one, this effectively selects
-index zero for that axis.
-
-WARNING: If an axis flagged for removal has a shape equal to zero,
-the array will point to invalid memory. The caller must
-validate this!
-If an axis flagged for removal has a shape larger than one,
-the aligned flag (and in the future the contiguous flags),
-may need explicit update.
-
-For example, this can be used to remove the reduction axes
-from a reduction result once its computation is complete.
-
-::
-
-  void
-  PyArray_DebugPrint(PyArrayObject *obj)
-
-Prints the raw data of the ndarray in a form useful for debugging
-low-level C issues.
-
-::
-
-  int
-  PyArray_FailUnlessWriteable(PyArrayObject *obj, const char *name)
-
-
-This function does nothing and returns 0 if *obj* is writeable.
-It raises an exception and returns -1 if *obj* is not writeable.
-It may also do other house-keeping, such as issuing warnings on
-arrays which are transitioning to become views. Always call this
-function at some point before writing to an array.
-
-name* is a name for the array, used to give better error messages.
-It can be something like "assignment destination", "output array",
-or even just "array".
-
-::
-
-  int
-  PyArray_SetUpdateIfCopyBase(PyArrayObject *arr, PyArrayObject *base)
-
-
-::
-
-  void *
-  PyDataMem_NEW(size_t size)
-
-Allocates memory for array data.
-
-::
-
-  void
-  PyDataMem_FREE(void *ptr)
-
-Free memory for array data.
-
-::
-
-  void *
-  PyDataMem_RENEW(void *ptr, size_t size)
-
-Reallocate/resize memory for array data.
-
-::
-
-  PyDataMem_EventHookFunc *
-  PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, void
-                         *user_data, void **old_data)
-
-Sets the allocation event hook for numpy array data.
-Takes a PyDataMem_EventHookFunc *, which has the signature:
-void hook(void *old, void *new, size_t size, void *user_data).
-Also takes a void *user_data, and void **old_data.
-
-Returns a pointer to the previous hook or NULL.  If old_data is
-non-NULL, the previous user_data pointer will be copied to it.
-
-If not NULL, hook will be called at the end of each PyDataMem_NEW/FREE/RENEW:
-result = PyDataMem_NEW(size)        -> (*hook)(NULL, result, size, user_data)
-PyDataMem_FREE(ptr)                 -> (*hook)(ptr, NULL, 0, user_data)
-result = PyDataMem_RENEW(ptr, size) -> (*hook)(ptr, result, size, user_data)
-
-When the hook is called, the GIL will be held by the calling
-thread.  The hook should be written to be reentrant, if it performs
-operations that might cause new allocation events (such as the
-creation/destruction numpy objects, or creating/destroying Python
-objects which might cause a gc)
-
-Deprecated in 1.23
-
-::
-
-  void
-  PyArray_MapIterSwapAxes(PyArrayMapIterObject *mit, PyArrayObject
-                          **ret, int getmap)
-
-
-Swap the axes to or from their inserted form. MapIter always puts the
-advanced (array) indices first in the iteration. But if they are
-consecutive, will insert/transpose them back before returning.
-This is stored as `mit->consec != 0` (the place where they are inserted)
-For assignments, the opposite happens: The values to be assigned are
-transposed (getmap=1 instead of getmap=0). `getmap=0` and `getmap=1`
-undo the other operation.
-
-::
-
-  PyObject *
-  PyArray_MapIterArray(PyArrayObject *a, PyObject *index)
-
-
-Use advanced indexing to iterate an array.
-
-::
-
-  void
-  PyArray_MapIterNext(PyArrayMapIterObject *mit)
-
-This function needs to update the state of the map iterator
-and point mit->dataptr to the memory-location of the next object
-
-Note that this function never handles an extra operand but provides
-compatibility for an old (exposed) API.
-
-::
-
-  int
-  PyArray_Partition(PyArrayObject *op, PyArrayObject *ktharray, int
-                    axis, NPY_SELECTKIND which)
-
-Partition an array in-place
-
-::
-
-  PyObject *
-  PyArray_ArgPartition(PyArrayObject *op, PyArrayObject *ktharray, int
-                       axis, NPY_SELECTKIND which)
-
-ArgPartition an array
-
-::
-
-  int
-  PyArray_SelectkindConverter(PyObject *obj, NPY_SELECTKIND *selectkind)
-
-Convert object to select kind
-
-::
-
-  void *
-  PyDataMem_NEW_ZEROED(size_t nmemb, size_t size)
-
-Allocates zeroed memory for array data.
-
-::
-
-  int
-  PyArray_CheckAnyScalarExact(PyObject *obj)
-
-return 1 if an object is exactly a numpy scalar
-
-::
-
-  PyObject *
-  PyArray_MapIterArrayCopyIfOverlap(PyArrayObject *a, PyObject
-                                    *index, int
-                                    copy_if_overlap, PyArrayObject
-                                    *extra_op)
-
-
-Same as PyArray_MapIterArray, but:
-
-If copy_if_overlap != 0, check if `a` has memory overlap with any of the
-arrays in `index` and with `extra_op`. If yes, make copies as appropriate
-to avoid problems if `a` is modified during the iteration.
-`iter->array` may contain a copied array (WRITEBACKIFCOPY set).
-
-::
-
-  int
-  PyArray_ResolveWritebackIfCopy(PyArrayObject *self)
-
-
-If WRITEBACKIFCOPY and self has data, reset the base WRITEABLE flag,
-copy the local data to base, release the local data, and set flags
-appropriately. Return 0 if not relevant, 1 if success, < 0 on failure
-
-::
-
-  int
-  PyArray_SetWritebackIfCopyBase(PyArrayObject *arr, PyArrayObject
-                                 *base)
-
-
-Precondition: 'arr' is a copy of 'base' (though possibly with different
-strides, ordering, etc.). This function sets the WRITEBACKIFCOPY flag and the
-->base pointer on 'arr', call PyArray_ResolveWritebackIfCopy to copy any
-changes back to 'base' before deallocating the array.
-
-Steals a reference to 'base'.
-
-Returns 0 on success, -1 on failure.
-
-::
-
-  PyObject *
-  PyDataMem_SetHandler(PyObject *handler)
-
-Set a new allocation policy. If the input value is NULL, will reset
-the policy to the default. Return the previous policy, or
-return NULL if an error has occurred. We wrap the user-provided
-functions so they will still call the python and numpy
-memory management callback hooks.
-
-::
-
-  PyObject *
-  PyDataMem_GetHandler()
-
-Return the policy that will be used to allocate data
-for the next PyArrayObject. On failure, return NULL.
-
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/ndarrayobject.h b/contrib/python/numpy/py3/numpy/core/include/numpy/ndarrayobject.h
index aaaefd7defb..36cfdd6f676 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/ndarrayobject.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/ndarrayobject.h
@@ -152,7 +152,7 @@ extern "C" {
                                             (k)*PyArray_STRIDES(obj)[2] + \
                                             (l)*PyArray_STRIDES(obj)[3]))
 
-static NPY_INLINE void
+static inline void
 PyArray_DiscardWritebackIfCopy(PyArrayObject *arr)
 {
     PyArrayObject_fields *fa = (PyArrayObject_fields *)arr;
@@ -214,7 +214,7 @@ PyArray_DiscardWritebackIfCopy(PyArrayObject *arr)
    dict).
 */
 
-static NPY_INLINE int
+static inline int
 NPY_TITLE_KEY_check(PyObject *key, PyObject *value)
 {
     PyObject *title;
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/ndarraytypes.h b/contrib/python/numpy/py3/numpy/core/include/numpy/ndarraytypes.h
index e894b08f692..742ba526122 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/ndarraytypes.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/ndarraytypes.h
@@ -44,23 +44,6 @@
 #define NPY_FAIL 0
 #define NPY_SUCCEED 1
 
-/*
- * Binary compatibility version number.  This number is increased
- * whenever the C-API is changed such that binary compatibility is
- * broken, i.e. whenever a recompile of extension modules is needed.
- */
-#define NPY_VERSION NPY_ABI_VERSION
-
-/*
- * Minor API version.  This number is increased whenever a change is
- * made to the C-API -- whether it breaks binary compatibility or not.
- * Some changes, such as adding a function pointer to the end of the
- * function table, can be made without breaking binary compatibility.
- * In this case, only the NPY_FEATURE_VERSION (*not* NPY_VERSION)
- * would be increased.  Whenever binary compatibility is broken, both
- * NPY_VERSION and NPY_FEATURE_VERSION should be increased.
- */
-#define NPY_FEATURE_VERSION NPY_API_VERSION
 
 enum NPY_TYPES {    NPY_BOOL=0,
                     NPY_BYTE, NPY_UBYTE,
@@ -729,11 +712,15 @@ typedef struct tagPyArrayObject_fields {
     int flags;
     /* For weak references */
     PyObject *weakreflist;
+#if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION
     void *_buffer_info;  /* private buffer info, tagged to allow warning */
+#endif
     /*
      * For malloc/calloc/realloc/free per object
      */
+#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
     PyObject *mem_handler;
+#endif
 } PyArrayObject_fields;
 
 /*
@@ -1463,12 +1450,12 @@ typedef struct {
  */
 
 /* General: those work for any mode */
-static NPY_INLINE int
+static inline int
 PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter);
-static NPY_INLINE int
+static inline int
 PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter);
 #if 0
-static NPY_INLINE int
+static inline int
 PyArrayNeighborhoodIter_Next2D(PyArrayNeighborhoodIterObject* iter);
 #endif
 
@@ -1514,85 +1501,85 @@ PyArrayNeighborhoodIter_Next2D(PyArrayNeighborhoodIterObject* iter);
  * ABI compatibility.
  */
 
-static NPY_INLINE int
+static inline int
 PyArray_NDIM(const PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->nd;
 }
 
-static NPY_INLINE void *
+static inline void *
 PyArray_DATA(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->data;
 }
 
-static NPY_INLINE char *
+static inline char *
 PyArray_BYTES(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->data;
 }
 
-static NPY_INLINE npy_intp *
+static inline npy_intp *
 PyArray_DIMS(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->dimensions;
 }
 
-static NPY_INLINE npy_intp *
+static inline npy_intp *
 PyArray_STRIDES(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->strides;
 }
 
-static NPY_INLINE npy_intp
+static inline npy_intp
 PyArray_DIM(const PyArrayObject *arr, int idim)
 {
     return ((PyArrayObject_fields *)arr)->dimensions[idim];
 }
 
-static NPY_INLINE npy_intp
+static inline npy_intp
 PyArray_STRIDE(const PyArrayObject *arr, int istride)
 {
     return ((PyArrayObject_fields *)arr)->strides[istride];
 }
 
-static NPY_INLINE NPY_RETURNS_BORROWED_REF PyObject *
+static inline NPY_RETURNS_BORROWED_REF PyObject *
 PyArray_BASE(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->base;
 }
 
-static NPY_INLINE NPY_RETURNS_BORROWED_REF PyArray_Descr *
+static inline NPY_RETURNS_BORROWED_REF PyArray_Descr *
 PyArray_DESCR(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->descr;
 }
 
-static NPY_INLINE int
+static inline int
 PyArray_FLAGS(const PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->flags;
 }
 
-static NPY_INLINE npy_intp
+static inline npy_intp
 PyArray_ITEMSIZE(const PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->descr->elsize;
 }
 
-static NPY_INLINE int
+static inline int
 PyArray_TYPE(const PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->descr->type_num;
 }
 
-static NPY_INLINE int
+static inline int
 PyArray_CHKFLAGS(const PyArrayObject *arr, int flags)
 {
     return (PyArray_FLAGS(arr) & flags) == flags;
 }
 
-static NPY_INLINE PyObject *
+static inline PyObject *
 PyArray_GETITEM(const PyArrayObject *arr, const char *itemptr)
 {
     return ((PyArrayObject_fields *)arr)->descr->f->getitem(
@@ -1604,7 +1591,7 @@ PyArray_GETITEM(const PyArrayObject *arr, const char *itemptr)
  * and of a type understood by the arrays dtype.
  * Use `PyArray_Pack` if the value may be of a different dtype.
  */
-static NPY_INLINE int
+static inline int
 PyArray_SETITEM(PyArrayObject *arr, char *itemptr, PyObject *v)
 {
     return ((PyArrayObject_fields *)arr)->descr->f->setitem(v, itemptr, arr);
@@ -1639,13 +1626,13 @@ PyArray_SETITEM(PyArrayObject *arr, char *itemptr, PyObject *v)
                                      (PyArrayObject *)(obj))
 #endif
 
-static NPY_INLINE PyArray_Descr *
+static inline PyArray_Descr *
 PyArray_DTYPE(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->descr;
 }
 
-static NPY_INLINE npy_intp *
+static inline npy_intp *
 PyArray_SHAPE(PyArrayObject *arr)
 {
     return ((PyArrayObject_fields *)arr)->dimensions;
@@ -1655,7 +1642,7 @@ PyArray_SHAPE(PyArrayObject *arr)
  * Enables the specified array flags. Does no checking,
  * assumes you know what you're doing.
  */
-static NPY_INLINE void
+static inline void
 PyArray_ENABLEFLAGS(PyArrayObject *arr, int flags)
 {
     ((PyArrayObject_fields *)arr)->flags |= flags;
@@ -1665,17 +1652,19 @@ PyArray_ENABLEFLAGS(PyArrayObject *arr, int flags)
  * Clears the specified array flags. Does no checking,
  * assumes you know what you're doing.
  */
-static NPY_INLINE void
+static inline void
 PyArray_CLEARFLAGS(PyArrayObject *arr, int flags)
 {
     ((PyArrayObject_fields *)arr)->flags &= ~flags;
 }
 
-static NPY_INLINE NPY_RETURNS_BORROWED_REF PyObject *
-PyArray_HANDLER(PyArrayObject *arr)
-{
-    return ((PyArrayObject_fields *)arr)->mem_handler;
-}
+#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
+    static inline NPY_RETURNS_BORROWED_REF PyObject *
+    PyArray_HANDLER(PyArrayObject *arr)
+    {
+        return ((PyArrayObject_fields *)arr)->mem_handler;
+    }
+#endif
 
 #define PyTypeNum_ISBOOL(type) ((type) == NPY_BOOL)
 
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_3kcompat.h b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_3kcompat.h
index 11cc477655a..62fde943aac 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_3kcompat.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_3kcompat.h
@@ -36,7 +36,7 @@ extern "C" {
  * included here because it is missing from the PyPy API. It completes the PyLong_As*
  * group of functions and can be useful in replacing PyInt_Check.
  */
-static NPY_INLINE int
+static inline int
 Npy__PyLong_AsInt(PyObject *obj)
 {
     int overflow;
@@ -56,7 +56,7 @@ Npy__PyLong_AsInt(PyObject *obj)
 
 #if defined(NPY_PY3K)
 /* Return True only if the long fits in a C long */
-static NPY_INLINE int PyInt_Check(PyObject *op) {
+static inline int PyInt_Check(PyObject *op) {
     int overflow = 0;
     if (!PyLong_Check(op)) {
         return 0;
@@ -99,32 +99,6 @@ static NPY_INLINE int PyInt_Check(PyObject *op) {
 
 #define Npy_EnterRecursiveCall(x) Py_EnterRecursiveCall(x)
 
-/* Py_SETREF was added in 3.5.2, and only if Py_LIMITED_API is absent */
-#if PY_VERSION_HEX < 0x03050200
-    #define Py_SETREF(op, op2)                      \
-        do {                                        \
-            PyObject *_py_tmp = (PyObject *)(op);   \
-            (op) = (op2);                           \
-            Py_DECREF(_py_tmp);                     \
-        } while (0)
-#endif
-
-/* introduced in https://github.com/python/cpython/commit/a24107b04c1277e3c1105f98aff5bfa3a98b33a0 */
-#if PY_VERSION_HEX < 0x030800A3
-    static NPY_INLINE PyObject *
-    _PyDict_GetItemStringWithError(PyObject *v, const char *key)
-    {
-        PyObject *kv, *rv;
-        kv = PyUnicode_FromString(key);
-        if (kv == NULL) {
-            return NULL;
-        }
-        rv = PyDict_GetItemWithError(v, kv);
-        Py_DECREF(kv);
-        return rv;
-    }
-#endif
-
 /*
  * PyString -> PyBytes
  */
@@ -193,15 +167,35 @@ static NPY_INLINE int PyInt_Check(PyObject *op) {
 
 #endif /* NPY_PY3K */
 
+/*
+ * Macros to protect CRT calls against instant termination when passed an
+ * invalid parameter (https://bugs.python.org/issue23524).
+ */
+#if defined _MSC_VER && _MSC_VER >= 1900
+
+#include <stdlib.h>
+
+extern _invalid_parameter_handler _Py_silent_invalid_parameter_handler;
+#define NPY_BEGIN_SUPPRESS_IPH { _invalid_parameter_handler _Py_old_handler = \
+    _set_thread_local_invalid_parameter_handler(_Py_silent_invalid_parameter_handler);
+#define NPY_END_SUPPRESS_IPH _set_thread_local_invalid_parameter_handler(_Py_old_handler); }
+
+#else
+
+#define NPY_BEGIN_SUPPRESS_IPH
+#define NPY_END_SUPPRESS_IPH
+
+#endif /* _MSC_VER >= 1900 */
+
 
-static NPY_INLINE void
+static inline void
 PyUnicode_ConcatAndDel(PyObject **left, PyObject *right)
 {
     Py_SETREF(*left, PyUnicode_Concat(*left, right));
     Py_DECREF(right);
 }
 
-static NPY_INLINE void
+static inline void
 PyUnicode_Concat2(PyObject **left, PyObject *right)
 {
     Py_SETREF(*left, PyUnicode_Concat(*left, right));
@@ -214,7 +208,7 @@ PyUnicode_Concat2(PyObject **left, PyObject *right)
 /*
  * Get a FILE* handle to the file represented by the Python object
  */
-static NPY_INLINE FILE*
+static inline FILE*
 npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos)
 {
     int fd, fd2, unbuf;
@@ -268,13 +262,17 @@ npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos)
 
     /* Convert to FILE* handle */
 #ifdef _WIN32
+    NPY_BEGIN_SUPPRESS_IPH
     handle = _fdopen(fd2, mode);
+    NPY_END_SUPPRESS_IPH
 #else
     handle = fdopen(fd2, mode);
 #endif
     if (handle == NULL) {
         PyErr_SetString(PyExc_IOError,
-                        "Getting a FILE* from a Python file object failed");
+                        "Getting a FILE* from a Python file object via "
+                        "_fdopen failed. If you built NumPy, you probably "
+                        "linked with the wrong debug/release runtime");
         return NULL;
     }
 
@@ -330,7 +328,7 @@ npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos)
 /*
  * Close the dup-ed file handle, and seek the Python one to the current position
  */
-static NPY_INLINE int
+static inline int
 npy_PyFile_DupClose2(PyObject *file, FILE* handle, npy_off_t orig_pos)
 {
     int fd, unbuf;
@@ -397,7 +395,7 @@ npy_PyFile_DupClose2(PyObject *file, FILE* handle, npy_off_t orig_pos)
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 npy_PyFile_Check(PyObject *file)
 {
     int fd;
@@ -415,7 +413,7 @@ npy_PyFile_Check(PyObject *file)
     return 1;
 }
 
-static NPY_INLINE PyObject*
+static inline PyObject*
 npy_PyFile_OpenFile(PyObject *filename, const char *mode)
 {
     PyObject *open;
@@ -426,7 +424,7 @@ npy_PyFile_OpenFile(PyObject *filename, const char *mode)
     return PyObject_CallFunction(open, "Os", filename, mode);
 }
 
-static NPY_INLINE int
+static inline int
 npy_PyFile_CloseFile(PyObject *file)
 {
     PyObject *ret;
@@ -442,7 +440,7 @@ npy_PyFile_CloseFile(PyObject *file)
 
 /* This is a copy of _PyErr_ChainExceptions
  */
-static NPY_INLINE void
+static inline void
 npy_PyErr_ChainExceptions(PyObject *exc, PyObject *val, PyObject *tb)
 {
     if (exc == NULL)
@@ -474,7 +472,7 @@ npy_PyErr_ChainExceptions(PyObject *exc, PyObject *val, PyObject *tb)
  *  - a minimal implementation for python 2
  *  - __cause__ used instead of __context__
  */
-static NPY_INLINE void
+static inline void
 npy_PyErr_ChainExceptionsCause(PyObject *exc, PyObject *val, PyObject *tb)
 {
     if (exc == NULL)
@@ -505,7 +503,7 @@ npy_PyErr_ChainExceptionsCause(PyObject *exc, PyObject *val, PyObject *tb)
  * PyObject_Cmp
  */
 #if defined(NPY_PY3K)
-static NPY_INLINE int
+static inline int
 PyObject_Cmp(PyObject *i1, PyObject *i2, int *cmp)
 {
     int v;
@@ -545,7 +543,7 @@ PyObject_Cmp(PyObject *i1, PyObject *i2, int *cmp)
  * The main job here is to get rid of the improved error handling
  * of PyCapsules. It's a shame...
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
 {
     PyObject *ret = PyCapsule_New(ptr, NULL, dtor);
@@ -555,7 +553,7 @@ NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
     return ret;
 }
 
-static NPY_INLINE PyObject *
+static inline PyObject *
 NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context, void (*dtor)(PyObject *))
 {
     PyObject *ret = NpyCapsule_FromVoidPtr(ptr, dtor);
@@ -567,7 +565,7 @@ NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context, void (*dtor)(PyObject *)
     return ret;
 }
 
-static NPY_INLINE void *
+static inline void *
 NpyCapsule_AsVoidPtr(PyObject *obj)
 {
     void *ret = PyCapsule_GetPointer(obj, NULL);
@@ -577,13 +575,13 @@ NpyCapsule_AsVoidPtr(PyObject *obj)
     return ret;
 }
 
-static NPY_INLINE void *
+static inline void *
 NpyCapsule_GetDesc(PyObject *obj)
 {
     return PyCapsule_GetContext(obj);
 }
 
-static NPY_INLINE int
+static inline int
 NpyCapsule_Check(PyObject *ptr)
 {
     return PyCapsule_CheckExact(ptr);
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_common.h b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_common.h
index 2bcc45e4f67..fb976aa6ae0 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_common.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_common.h
@@ -40,39 +40,6 @@
 #define NPY_GCC_OPT_3
 #endif
 
-/* compile target attributes */
-#if defined HAVE_ATTRIBUTE_TARGET_AVX && defined HAVE_LINK_AVX
-#define NPY_GCC_TARGET_AVX __attribute__((target("avx")))
-#else
-#define NPY_GCC_TARGET_AVX
-#endif
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
-#define HAVE_ATTRIBUTE_TARGET_FMA
-#define NPY_GCC_TARGET_FMA __attribute__((target("avx2,fma")))
-#endif
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX2 && defined HAVE_LINK_AVX2
-#define NPY_GCC_TARGET_AVX2 __attribute__((target("avx2")))
-#else
-#define NPY_GCC_TARGET_AVX2
-#endif
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F && defined HAVE_LINK_AVX512F
-#define NPY_GCC_TARGET_AVX512F __attribute__((target("avx512f")))
-#elif defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
-#define NPY_GCC_TARGET_AVX512F __attribute__((target("avx512f")))
-#else
-#define NPY_GCC_TARGET_AVX512F
-#endif
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX && defined HAVE_LINK_AVX512_SKX
-#define NPY_GCC_TARGET_AVX512_SKX __attribute__((target("avx512f,avx512dq,avx512vl,avx512bw,avx512cd")))
-#elif defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS
-#define NPY_GCC_TARGET_AVX512_SKX __attribute__((target("avx512f,avx512dq,avx512vl,avx512bw,avx512cd")))
-#else
-#define NPY_GCC_TARGET_AVX512_SKX
-#endif
 /*
  * mark an argument (starting from 1) that must not be NULL and is not checked
  * DO NOT USE IF FUNCTION CHECKS FOR NULL!! the compiler will remove the check
@@ -83,21 +50,6 @@
 #define NPY_GCC_NONNULL(n)
 #endif
 
-#if defined HAVE_XMMINTRIN_H && defined HAVE__MM_LOAD_PS
-#define NPY_HAVE_SSE_INTRINSICS
-#endif
-
-#if defined HAVE_EMMINTRIN_H && defined HAVE__MM_LOAD_PD
-#define NPY_HAVE_SSE2_INTRINSICS
-#endif
-
-#if defined HAVE_IMMINTRIN_H && defined HAVE_LINK_AVX2
-#define NPY_HAVE_AVX2_INTRINSICS
-#endif
-
-#if defined HAVE_IMMINTRIN_H && defined HAVE_LINK_AVX512F
-#define NPY_HAVE_AVX512F_INTRINSICS
-#endif
 /*
  * give a hint to the compiler which branch is more likely or unlikely
  * to occur, e.g. rare error cases:
@@ -120,7 +72,7 @@
 /* unlike _mm_prefetch also works on non-x86 */
 #define NPY_PREFETCH(x, rw, loc) __builtin_prefetch((x), (rw), (loc))
 #else
-#ifdef HAVE__MM_PREFETCH
+#ifdef NPY_HAVE_SSE
 /* _MM_HINT_ET[01] (rw = 1) unsupported, only available in gcc >= 4.9 */
 #define NPY_PREFETCH(x, rw, loc) _mm_prefetch((x), loc == 0 ? _MM_HINT_NTA : \
                                              (loc == 1 ? _MM_HINT_T2 : \
@@ -131,6 +83,7 @@
 #endif
 #endif
 
+/* `NPY_INLINE` kept for backwards compatibility; use `inline` instead */
 #if defined(_MSC_VER) && !defined(__clang__)
     #define NPY_INLINE __inline
 /* clang included here to handle clang-cl on Windows */
@@ -147,11 +100,19 @@
 #ifdef _MSC_VER
     #define NPY_FINLINE static __forceinline
 #elif defined(__GNUC__)
-    #define NPY_FINLINE static NPY_INLINE __attribute__((always_inline))
+    #define NPY_FINLINE static inline __attribute__((always_inline))
 #else
     #define NPY_FINLINE static
 #endif
 
+#if defined(_MSC_VER)
+    #define NPY_NOINLINE static __declspec(noinline)
+#elif defined(__GNUC__) || defined(__clang__)
+    #define NPY_NOINLINE static __attribute__((noinline))
+#else
+    #define NPY_NOINLINE static
+#endif
+
 #ifdef HAVE___THREAD
     #define NPY_TLS __thread
 #else
@@ -176,7 +137,7 @@
  #define NPY_STEALS_REF_TO_ARG(n)
 #endif
 
-/* 64 bit file position support, also on win-amd64. Ticket #1660 */
+/* 64 bit file position support, also on win-amd64. Issue gh-2256 */
 #if defined(_MSC_VER) && defined(_WIN64) && (_MSC_VER > 1400) || \
     defined(__MINGW32__) || defined(__MINGW64__)
     #include <io.h>
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_cpu.h b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_cpu.h
index 78d229e7dfc..a19f8e6bbdd 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_cpu.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_cpu.h
@@ -77,7 +77,7 @@
     #elif defined(__ARMEL__) || defined(__AARCH64EL__) || defined(_M_ARM64)
         #if defined(__ARM_32BIT_STATE)
             #define NPY_CPU_ARMEL_AARCH32
-        #elif defined(__ARM_64BIT_STATE) || defined(_M_ARM64)
+        #elif defined(__ARM_64BIT_STATE) || defined(_M_ARM64) || defined(__AARCH64EL__)
             #define NPY_CPU_ARMEL_AARCH64
         #else
             #define NPY_CPU_ARMEL
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_math.h b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_math.h
index 900508662e4..2fcd41eb0ba 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/npy_math.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/npy_math.h
@@ -8,7 +8,7 @@
 /* By adding static inline specifiers to npy_math function definitions when
    appropriate, compiler is given the opportunity to optimize */
 #if NPY_INLINE_MATH
-#define NPY_INPLACE NPY_INLINE static
+#define NPY_INPLACE static inline
 #else
 #define NPY_INPLACE
 #endif
@@ -24,25 +24,25 @@ extern "C" {
  *
  * XXX: I should test whether INFINITY and NAN are available on the platform
  */
-NPY_INLINE static float __npy_inff(void)
+static inline float __npy_inff(void)
 {
     const union { npy_uint32 __i; float __f;} __bint = {0x7f800000UL};
     return __bint.__f;
 }
 
-NPY_INLINE static float __npy_nanf(void)
+static inline float __npy_nanf(void)
 {
     const union { npy_uint32 __i; float __f;} __bint = {0x7fc00000UL};
     return __bint.__f;
 }
 
-NPY_INLINE static float __npy_pzerof(void)
+static inline float __npy_pzerof(void)
 {
     const union { npy_uint32 __i; float __f;} __bint = {0x00000000UL};
     return __bint.__f;
 }
 
-NPY_INLINE static float __npy_nzerof(void)
+static inline float __npy_nzerof(void)
 {
     const union { npy_uint32 __i; float __f;} __bint = {0x80000000UL};
     return __bint.__f;
@@ -198,13 +198,12 @@ NPY_INPLACE double npy_atan2(double x, double y);
 #define npy_sqrt sqrt
 #define npy_pow pow
 #define npy_modf modf
-
-double npy_nextafter(double x, double y);
+#define npy_nextafter nextafter
 
 double npy_spacing(double x);
 
 /*
- * IEEE 754 fpu handling. Those are guaranteed to be macros
+ * IEEE 754 fpu handling
  */
 
 /* use builtins to avoid function calls in tight loops
@@ -212,11 +211,7 @@ double npy_spacing(double x);
 #ifdef HAVE___BUILTIN_ISNAN
     #define npy_isnan(x) __builtin_isnan(x)
 #else
-    #ifndef NPY_HAVE_DECL_ISNAN
-        #define npy_isnan(x) ((x) != (x))
-    #else
-        #define npy_isnan(x) isnan(x)
-    #endif
+    #define npy_isnan(x) isnan(x)
 #endif
 
 
@@ -224,39 +219,17 @@ double npy_spacing(double x);
 #ifdef HAVE___BUILTIN_ISFINITE
     #define npy_isfinite(x) __builtin_isfinite(x)
 #else
-    #ifndef NPY_HAVE_DECL_ISFINITE
-        #ifdef _MSC_VER
-            #define npy_isfinite(x) _finite((x))
-        #else
-            #define npy_isfinite(x) !npy_isnan((x) + (-x))
-        #endif
-    #else
-        #define npy_isfinite(x) isfinite((x))
-    #endif
+    #define npy_isfinite(x) isfinite((x))
 #endif
 
 /* only available if npy_config.h is available (= numpys own build) */
 #ifdef HAVE___BUILTIN_ISINF
     #define npy_isinf(x) __builtin_isinf(x)
 #else
-    #ifndef NPY_HAVE_DECL_ISINF
-        #define npy_isinf(x) (!npy_isfinite(x) && !npy_isnan(x))
-    #else
-        #define npy_isinf(x) isinf((x))
-    #endif
+    #define npy_isinf(x) isinf((x))
 #endif
 
-#ifndef NPY_HAVE_DECL_SIGNBIT
-    int _npy_signbit_f(float x);
-    int _npy_signbit_d(double x);
-    int _npy_signbit_ld(long double x);
-    #define npy_signbit(x) \
-        (sizeof (x) == sizeof (long double) ? _npy_signbit_ld (x) \
-         : sizeof (x) == sizeof (double) ? _npy_signbit_d (x) \
-         : _npy_signbit_f (x))
-#else
-    #define npy_signbit(x) signbit((x))
-#endif
+#define npy_signbit(x) signbit((x))
 
 /*
  * float C99 math funcs that need fixups or are blocklist-able
@@ -299,8 +272,8 @@ NPY_INPLACE float npy_modff(float x, float* y);
 #define npy_frexpf frexpf
 #define npy_ldexpf ldexpf
 #define npy_copysignf copysignf
+#define npy_nextafterf nextafterf
 
-float npy_nextafterf(float x, float y);
 float npy_spacingf(float x);
 
 /*
@@ -343,8 +316,8 @@ NPY_INPLACE npy_longdouble npy_modfl(npy_longdouble x, npy_longdouble* y);
 #define npy_frexpl frexpl
 #define npy_ldexpl ldexpl
 #define npy_copysignl copysignl
+#define npy_nextafterl nextafterl
 
-npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y);
 npy_longdouble npy_spacingl(npy_longdouble x);
 
 /*
@@ -400,17 +373,17 @@ NPY_INPLACE npy_longdouble npy_heavisidel(npy_longdouble x, npy_longdouble h0);
                                              \
     return z1.z;
 
-static NPY_INLINE npy_cdouble npy_cpack(double x, double y)
+static inline npy_cdouble npy_cpack(double x, double y)
 {
     __NPY_CPACK_IMP(x, y, double, npy_cdouble);
 }
 
-static NPY_INLINE npy_cfloat npy_cpackf(float x, float y)
+static inline npy_cfloat npy_cpackf(float x, float y)
 {
     __NPY_CPACK_IMP(x, y, float, npy_cfloat);
 }
 
-static NPY_INLINE npy_clongdouble npy_cpackl(npy_longdouble x, npy_longdouble y)
+static inline npy_clongdouble npy_cpackl(npy_longdouble x, npy_longdouble y)
 {
     __NPY_CPACK_IMP(x, y, npy_longdouble, npy_clongdouble);
 }
@@ -432,32 +405,32 @@ static NPY_INLINE npy_clongdouble npy_cpackl(npy_longdouble x, npy_longdouble y)
                                                     \
     return __z_repr.a[index];
 
-static NPY_INLINE double npy_creal(npy_cdouble z)
+static inline double npy_creal(npy_cdouble z)
 {
     __NPY_CEXTRACT_IMP(z, 0, double, npy_cdouble);
 }
 
-static NPY_INLINE double npy_cimag(npy_cdouble z)
+static inline double npy_cimag(npy_cdouble z)
 {
     __NPY_CEXTRACT_IMP(z, 1, double, npy_cdouble);
 }
 
-static NPY_INLINE float npy_crealf(npy_cfloat z)
+static inline float npy_crealf(npy_cfloat z)
 {
     __NPY_CEXTRACT_IMP(z, 0, float, npy_cfloat);
 }
 
-static NPY_INLINE float npy_cimagf(npy_cfloat z)
+static inline float npy_cimagf(npy_cfloat z)
 {
     __NPY_CEXTRACT_IMP(z, 1, float, npy_cfloat);
 }
 
-static NPY_INLINE npy_longdouble npy_creall(npy_clongdouble z)
+static inline npy_longdouble npy_creall(npy_clongdouble z)
 {
     __NPY_CEXTRACT_IMP(z, 0, npy_longdouble, npy_clongdouble);
 }
 
-static NPY_INLINE npy_longdouble npy_cimagl(npy_clongdouble z)
+static inline npy_longdouble npy_cimagl(npy_clongdouble z)
 {
     __NPY_CEXTRACT_IMP(z, 1, npy_longdouble, npy_clongdouble);
 }
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/numpyconfig.h b/contrib/python/numpy/py3/numpy/core/include/numpy/numpyconfig.h
index 4b520983731..1c25aa5fc4d 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/numpyconfig.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/numpyconfig.h
@@ -56,29 +56,83 @@
 
 
 /**
- * To help with the NPY_NO_DEPRECATED_API macro, we include API version
- * numbers for specific versions of NumPy. To exclude all API that was
- * deprecated as of 1.7, add the following before #including any NumPy
- * headers:
+ * To help with both NPY_TARGET_VERSION and the NPY_NO_DEPRECATED_API macro,
+ * we include API version numbers for specific versions of NumPy.
+ * To exclude all API that was deprecated as of 1.7, add the following before
+ * #including any NumPy headers:
  *   #define NPY_NO_DEPRECATED_API  NPY_1_7_API_VERSION
+ * The same is true for NPY_TARGET_VERSION, although NumPy will default to
+ * a backwards compatible build anyway.
  */
 #define NPY_1_7_API_VERSION 0x00000007
 #define NPY_1_8_API_VERSION 0x00000008
-#define NPY_1_9_API_VERSION 0x00000008
-#define NPY_1_10_API_VERSION 0x00000008
-#define NPY_1_11_API_VERSION 0x00000008
-#define NPY_1_12_API_VERSION 0x00000008
-#define NPY_1_13_API_VERSION 0x00000008
-#define NPY_1_14_API_VERSION 0x00000008
-#define NPY_1_15_API_VERSION 0x00000008
-#define NPY_1_16_API_VERSION 0x00000008
-#define NPY_1_17_API_VERSION 0x00000008
-#define NPY_1_18_API_VERSION 0x00000008
-#define NPY_1_19_API_VERSION 0x00000008
+#define NPY_1_9_API_VERSION 0x00000009
+#define NPY_1_10_API_VERSION 0x0000000a
+#define NPY_1_11_API_VERSION 0x0000000a
+#define NPY_1_12_API_VERSION 0x0000000a
+#define NPY_1_13_API_VERSION 0x0000000b
+#define NPY_1_14_API_VERSION 0x0000000c
+#define NPY_1_15_API_VERSION 0x0000000c
+#define NPY_1_16_API_VERSION 0x0000000d
+#define NPY_1_17_API_VERSION 0x0000000d
+#define NPY_1_18_API_VERSION 0x0000000d
+#define NPY_1_19_API_VERSION 0x0000000d
 #define NPY_1_20_API_VERSION 0x0000000e
 #define NPY_1_21_API_VERSION 0x0000000e
 #define NPY_1_22_API_VERSION 0x0000000f
 #define NPY_1_23_API_VERSION 0x00000010
 #define NPY_1_24_API_VERSION 0x00000010
+#define NPY_1_25_API_VERSION 0x00000011
+
+
+/*
+ * Binary compatibility version number.  This number is increased
+ * whenever the C-API is changed such that binary compatibility is
+ * broken, i.e. whenever a recompile of extension modules is needed.
+ */
+#define NPY_VERSION NPY_ABI_VERSION
+
+/*
+ * Minor API version we are compiling to be compatible with.  The version
+ * Number is always increased when the API changes via: `NPY_API_VERSION`
+ * (and should maybe just track the NumPy version).
+ *
+ * If we have an internal build, we always target the current version of
+ * course.
+ *
+ * For downstream users, we default to an older version to provide them with
+ * maximum compatibility by default.  Downstream can choose to extend that
+ * default, or narrow it down if they wish to use newer API.  If you adjust
+ * this, consider the Python version support (example for 1.25.x):
+ *
+ * NumPy 1.25.x supports Python:                     3.9  3.10  3.11  (3.12)
+ * NumPy 1.19.x supports Python:      3.6  3.7  3.8  3.9
+ * NumPy 1.17.x supports Python: 3.5  3.6  3.7  3.8
+ * NumPy 1.15.x supports Python: ...  3.6  3.7
+ *
+ * Users of the stable ABI may wish to target the last Python that is not
+ * end of life.  This would be 3.8 at NumPy 1.25 release time.
+ * 1.17 as default was the choice of oldest-support-numpy at the time and
+ * has in practice no limit (comapared to 1.19).  Even earlier becomes legacy.
+ */
+#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD
+    /* NumPy internal build, always use current version. */
+    #define NPY_FEATURE_VERSION NPY_API_VERSION
+#elif defined(NPY_TARGET_VERSION) && NPY_TARGET_VERSION
+    /* user provided a target version, use it */
+    #define NPY_FEATURE_VERSION NPY_TARGET_VERSION
+#else
+    /* Use the default (increase when dropping Python 3.9 support) */
+    #define NPY_FEATURE_VERSION NPY_1_19_API_VERSION
+#endif
+
+/* Sanity check the (requested) feature version */
+#if NPY_FEATURE_VERSION > NPY_API_VERSION
+    #error "NPY_TARGET_VERSION higher than NumPy headers!"
+#elif NPY_FEATURE_VERSION < NPY_1_15_API_VERSION
+    /* No support for irrelevant old targets, no need for error, but warn. */
+    #warning "Requested NumPy target lower than supported NumPy 1.15."
+#endif
+
 
 #endif  /* NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ */
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/random/distributions.h b/contrib/python/numpy/py3/numpy/core/include/numpy/random/distributions.h
index 78bd06ff525..e7fa4bd00d4 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/random/distributions.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/random/distributions.h
@@ -198,7 +198,7 @@ RAND_INT_TYPE random_binomial_inversion(bitgen_t *bitgen_state,
                                         double p,
                                         binomial_t *binomial);
 double random_loggam(double x);
-static NPY_INLINE double next_double(bitgen_t *bitgen_state) {
+static inline double next_double(bitgen_t *bitgen_state) {
     return bitgen_state->next_double(bitgen_state->state);
 }
 
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/ufunc_api.txt b/contrib/python/numpy/py3/numpy/core/include/numpy/ufunc_api.txt
deleted file mode 100644
index 20a8a75f563..00000000000
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/ufunc_api.txt
+++ /dev/null
@@ -1,335 +0,0 @@
-
-=================
-NumPy Ufunc C-API
-=================
-::
-
-  PyObject *
-  PyUFunc_FromFuncAndData(PyUFuncGenericFunction *func, void
-                          **data, char *types, int ntypes, int nin, int
-                          nout, int identity, const char *name, const
-                          char *doc, int unused)
-
-
-::
-
-  int
-  PyUFunc_RegisterLoopForType(PyUFuncObject *ufunc, int
-                              usertype, PyUFuncGenericFunction
-                              function, const int *arg_types, void
-                              *data)
-
-
-::
-
-  int
-  PyUFunc_GenericFunction(PyUFuncObject *NPY_UNUSED(ufunc) , PyObject
-                          *NPY_UNUSED(args) , PyObject *NPY_UNUSED(kwds)
-                          , PyArrayObject **NPY_UNUSED(op) )
-
-
-::
-
-  void
-  PyUFunc_f_f_As_d_d(char **args, npy_intp const *dimensions, npy_intp
-                     const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_d_d(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_f_f(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_g_g(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_F_F_As_D_D(char **args, npy_intp const *dimensions, npy_intp
-                     const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_F_F(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_D_D(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_G_G(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_O_O(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_ff_f_As_dd_d(char **args, npy_intp const *dimensions, npy_intp
-                       const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_ff_f(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_dd_d(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_gg_g(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_FF_F_As_DD_D(char **args, npy_intp const *dimensions, npy_intp
-                       const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_DD_D(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_FF_F(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_GG_G(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_OO_O(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_O_O_method(char **args, npy_intp const *dimensions, npy_intp
-                     const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_OO_O_method(char **args, npy_intp const *dimensions, npy_intp
-                      const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_On_Om(char **args, npy_intp const *dimensions, npy_intp const
-                *steps, void *func)
-
-
-::
-
-  int
-  PyUFunc_GetPyValues(char *name, int *bufsize, int *errmask, PyObject
-                      **errobj)
-
-
-On return, if errobj is populated with a non-NULL value, the caller
-owns a new reference to errobj.
-
-::
-
-  int
-  PyUFunc_checkfperr(int errmask, PyObject *errobj, int *first)
-
-
-::
-
-  void
-  PyUFunc_clearfperr()
-
-
-::
-
-  int
-  PyUFunc_getfperr(void )
-
-
-::
-
-  int
-  PyUFunc_handlefperr(int errmask, PyObject *errobj, int retstatus, int
-                      *first)
-
-
-::
-
-  int
-  PyUFunc_ReplaceLoopBySignature(PyUFuncObject
-                                 *func, PyUFuncGenericFunction
-                                 newfunc, const int
-                                 *signature, PyUFuncGenericFunction
-                                 *oldfunc)
-
-
-::
-
-  PyObject *
-  PyUFunc_FromFuncAndDataAndSignature(PyUFuncGenericFunction *func, void
-                                      **data, char *types, int
-                                      ntypes, int nin, int nout, int
-                                      identity, const char *name, const
-                                      char *doc, int unused, const char
-                                      *signature)
-
-
-::
-
-  int
-  PyUFunc_SetUsesArraysAsData(void **NPY_UNUSED(data) , size_t
-                              NPY_UNUSED(i) )
-
-
-::
-
-  void
-  PyUFunc_e_e(char **args, npy_intp const *dimensions, npy_intp const
-              *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_e_e_As_f_f(char **args, npy_intp const *dimensions, npy_intp
-                     const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_e_e_As_d_d(char **args, npy_intp const *dimensions, npy_intp
-                     const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_ee_e(char **args, npy_intp const *dimensions, npy_intp const
-               *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_ee_e_As_ff_f(char **args, npy_intp const *dimensions, npy_intp
-                       const *steps, void *func)
-
-
-::
-
-  void
-  PyUFunc_ee_e_As_dd_d(char **args, npy_intp const *dimensions, npy_intp
-                       const *steps, void *func)
-
-
-::
-
-  int
-  PyUFunc_DefaultTypeResolver(PyUFuncObject *ufunc, NPY_CASTING
-                              casting, PyArrayObject
-                              **operands, PyObject
-                              *type_tup, PyArray_Descr **out_dtypes)
-
-
-This function applies the default type resolution rules
-for the provided ufunc.
-
-Returns 0 on success, -1 on error.
-
-::
-
-  int
-  PyUFunc_ValidateCasting(PyUFuncObject *ufunc, NPY_CASTING
-                          casting, PyArrayObject
-                          **operands, PyArray_Descr **dtypes)
-
-
-Validates that the input operands can be cast to
-the input types, and the output types can be cast to
-the output operands where provided.
-
-Returns 0 on success, -1 (with exception raised) on validation failure.
-
-::
-
-  int
-  PyUFunc_RegisterLoopForDescr(PyUFuncObject *ufunc, PyArray_Descr
-                               *user_dtype, PyUFuncGenericFunction
-                               function, PyArray_Descr
-                               **arg_dtypes, void *data)
-
-
-::
-
-  PyObject *
-  PyUFunc_FromFuncAndDataAndSignatureAndIdentity(PyUFuncGenericFunction
-                                                 *func, void
-                                                 **data, char
-                                                 *types, int ntypes, int
-                                                 nin, int nout, int
-                                                 identity, const char
-                                                 *name, const char
-                                                 *doc, const int
-                                                 unused, const char
-                                                 *signature, PyObject
-                                                 *identity_value)
-
-
diff --git a/contrib/python/numpy/py3/numpy/core/include/numpy/ufuncobject.h b/contrib/python/numpy/py3/numpy/core/include/numpy/ufuncobject.h
index bb063310079..9e00f2e56d2 100644
--- a/contrib/python/numpy/py3/numpy/core/include/numpy/ufuncobject.h
+++ b/contrib/python/numpy/py3/numpy/core/include/numpy/ufuncobject.h
@@ -197,7 +197,7 @@ typedef struct _tagPyUFuncObject {
         npy_uint32 iter_flags;
 
         /* New in NPY_API_VERSION 0x0000000D and above */
-
+    #if NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION
         /*
          * for each core_num_dim_ix distinct dimension names,
          * the possible "frozen" size (-1 if not frozen).
@@ -211,13 +211,15 @@ typedef struct _tagPyUFuncObject {
 
         /* Identity for reduction, when identity == PyUFunc_IdentityValue */
         PyObject *identity_value;
+    #endif  /* NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION */
 
         /* New in NPY_API_VERSION 0x0000000F and above */
-
+    #if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
         /* New private fields related to dispatching */
         void *_dispatch_cache;
         /* A PyListObject of `(tuple of DTypes, ArrayMethod/Promoter)` */
         PyObject *_loops;
+    #endif
 } PyUFuncObject;
 
 #include "arrayobject.h"
diff --git a/contrib/python/numpy/py3/numpy/core/memmap.py b/contrib/python/numpy/py3/numpy/core/memmap.py
index b0d9cb3af7b..79c695455d3 100644
--- a/contrib/python/numpy/py3/numpy/core/memmap.py
+++ b/contrib/python/numpy/py3/numpy/core/memmap.py
@@ -1,9 +1,9 @@
 from contextlib import nullcontext
 
 import numpy as np
+from .._utils import set_module
 from .numeric import uint8, ndarray, dtype
 from numpy.compat import os_fspath, is_pathlib_path
-from numpy.core.overrides import set_module
 
 __all__ = ['memmap']
 
@@ -59,6 +59,7 @@ class memmap(ndarray):
         | 'r+' | Open existing file for reading and writing.                 |
         +------+-------------------------------------------------------------+
         | 'w+' | Create or overwrite existing file for reading and writing.  |
+        |      | If ``mode == 'w+'`` then `shape` must also be specified.    |
         +------+-------------------------------------------------------------+
         | 'c'  | Copy-on-write: assignments affect data in memory, but       |
         |      | changes are not saved to disk.  The file on disk is         |
@@ -220,7 +221,7 @@ class memmap(ndarray):
                 ) from None
 
         if mode == 'w+' and shape is None:
-            raise ValueError("shape must be given")
+            raise ValueError("shape must be given if mode == 'w+'")
 
         if hasattr(filename, 'read'):
             f_ctx = nullcontext(filename)
diff --git a/contrib/python/numpy/py3/numpy/core/meson.build b/contrib/python/numpy/py3/numpy/core/meson.build
new file mode 100644
index 00000000000..ab773c56d73
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/meson.build
@@ -0,0 +1,1013 @@
+# This file should contain what setup.py + setup_common.py do (WIP)
+#
+# Potential issues to address or keep track of:
+#   - sincos detection incorrect on NetBSD: https://github.com/mesonbuild/meson/issues/10641
+
+# Versioning support
+#-------------------
+#
+# How to change C_API_VERSION ?
+#   - increase C_API_VERSION value
+#   - record the hash for the new C API with the cversions.py script
+#   and add the hash to cversions.txt
+# The hash values are used to remind developers when the C API number was not
+# updated - generates a MismatchCAPIWarning warning which is turned into an
+# exception for released version.
+
+# Binary compatibility version number. This number is increased whenever the
+# C-API is changed such that binary compatibility is broken, i.e. whenever a
+# recompile of extension modules is needed.
+C_ABI_VERSION = '0x01000009'
+
+# Minor API version.  This number is increased whenever a change is made to the
+# C-API -- whether it breaks binary compatibility or not.  Some changes, such
+# as adding a function pointer to the end of the function table, can be made
+# without breaking binary compatibility.  In this case, only the C_API_VERSION
+# (*not* C_ABI_VERSION) would be increased.  Whenever binary compatibility is
+# broken, both C_API_VERSION and C_ABI_VERSION should be increased.
+#
+# The version needs to be kept in sync with that in cversions.txt.
+#
+# 0x00000008 - 1.7.x
+# 0x00000009 - 1.8.x
+# 0x00000009 - 1.9.x
+# 0x0000000a - 1.10.x
+# 0x0000000a - 1.11.x
+# 0x0000000a - 1.12.x
+# 0x0000000b - 1.13.x
+# 0x0000000c - 1.14.x
+# 0x0000000c - 1.15.x
+# 0x0000000d - 1.16.x
+# 0x0000000d - 1.19.x
+# 0x0000000e - 1.20.x
+# 0x0000000e - 1.21.x
+# 0x0000000f - 1.22.x
+# 0x00000010 - 1.23.x
+# 0x00000010 - 1.24.x
+# 0x00000011 - 1.25.x
+# 0x00000011 - 1.26.x
+C_API_VERSION = '0x00000011'
+
+# Check whether we have a mismatch between the set C API VERSION and the
+# actual C API VERSION. Will raise a MismatchCAPIError if so.
+r = run_command('code_generators/verify_c_api_version.py', '--api-version', C_API_VERSION)
+
+if r.returncode() != 0
+  error('verify_c_api_version.py failed with output:\n' + r.stderr().strip())
+endif
+
+
+# Generate config.h and _numpyconfig.h
+# ------------------------------------
+#
+# There are two generated headers:
+#   - config.h: a private header, which is not installed and used only at
+#               build time, mostly to determine whether or not optional
+#               headers, compiler attributes, etc. are available
+#   - _numpyconfig.h: a header with public `NPY_` symbols which get made
+#                     available via numpyconfig.h
+#
+# Note that `HAVE_*` symbols indicate the presence or absence of a checked
+# property of the build environment. When available, these symbols get defined
+# to `1`; when not available they must not be defined at all. This is
+# important, because code in NumPy typically does not check the value but only
+# whether the symbol is defined. So `#define HAVE_SOMETHING 0` is wrong.
+
+cdata = configuration_data()
+
+cdata.set('NPY_ABI_VERSION', C_ABI_VERSION)
+cdata.set('NPY_API_VERSION', C_API_VERSION)
+
+use_svml = (
+  host_machine.system() == 'linux' and
+  host_machine.cpu_family() == 'x86_64' and
+  not get_option('disable-svml')
+)
+if use_svml
+  cdata.set10('NPY_CAN_LINK_SVML', true)
+  if not fs.exists('src/umath/svml')
+    error('Missing the `SVML` git submodule! Run `git submodule update --init` to fix this.')
+  endif
+endif
+if not fs.exists('src/npysort/x86-simd-sort/README.md')
+  error('Missing the `x86-simd-sort` git submodule! Run `git submodule update --init` to fix this.')
+endif
+
+# Check sizes of types. Note, some of these landed in config.h before, but were
+# unused. So clean that up and only define the NPY_SIZEOF flavors rather than
+# the SIZEOF ones
+types_to_check = [
+  ['NPY_SIZEOF_SHORT', 'short'],
+  ['NPY_SIZEOF_INT', 'int'],
+  ['NPY_SIZEOF_LONG', 'long'],
+  ['NPY_SIZEOF_LONGLONG', 'long long'],
+  ['NPY_SIZEOF_FLOAT', 'float'],
+  ['NPY_SIZEOF_DOUBLE', 'double'],
+  ['NPY_SIZEOF_LONGDOUBLE', 'long double'],
+]
+foreach symbol_type: types_to_check
+  cdata.set(symbol_type[0], cc.sizeof(symbol_type[1]))
+endforeach
+cdata.set('NPY_SIZEOF_OFF_T', cc.sizeof('off_t', prefix: '#include <sys/types.h>'))
+cdata.set('NPY_SIZEOF_PY_INTPTR_T',
+  cc.sizeof('Py_intptr_t', dependencies: py_dep, prefix: '#include <Python.h>'))
+cdata.set('NPY_SIZEOF_PY_LONG_LONG',
+  cc.sizeof('PY_LONG_LONG', dependencies: py_dep, prefix: '#include <Python.h>'))
+
+# Check for complex support
+if cc.has_header('complex.h')
+  cdata.set10('HAVE_COMPLEX_H', true)
+  cdata.set10('NPY_USE_C99_COMPLEX', true)
+  if cc.get_argument_syntax() == 'msvc'
+    complex_types_to_check = [
+      ['NPY_HAVE_COMPLEX_FLOAT', 'NPY_SIZEOF_COMPLEX_FLOAT', '_Fcomplex', 'float'],
+      ['NPY_HAVE_COMPLEX_DOUBLE', 'NPY_SIZEOF_COMPLEX_DOUBLE', '_Dcomplex', 'double'],
+      ['NPY_HAVE_COMPLEX_LONG_DOUBLE', 'NPY_SIZEOF_COMPLEX_LONGDOUBLE', '_Lcomplex', 'long double'],
+    ]
+  else
+    complex_types_to_check = [
+      ['NPY_HAVE_COMPLEX_FLOAT', 'NPY_SIZEOF_COMPLEX_FLOAT', 'complex float', 'float'],
+      ['NPY_HAVE_COMPLEX_DOUBLE', 'NPY_SIZEOF_COMPLEX_DOUBLE', 'complex double', 'double'],
+      ['NPY_HAVE_COMPLEX_LONG_DOUBLE', 'NPY_SIZEOF_COMPLEX_LONGDOUBLE', 'complex long double', 'long double'],
+    ]
+  endif
+  foreach symbol_type: complex_types_to_check
+    if cc.has_type(symbol_type[2], prefix: '#include <complex.h>')
+      cdata.set10(symbol_type[0], true)
+      # Determine size of NumPy's own, struct-based, complex types. Binary
+      # compatibility with C99 complex types is checked at build time in `npy_common.h`.
+      ftype = symbol_type[3]
+      cdata.set(symbol_type[1], cc.sizeof(f'struct {@ftype@ __x; @ftype@ __y;}'))
+    endif
+  endforeach
+endif
+
+# Mandatory functions: if not found, fail the build
+# Some of these can still be blocklisted if the C99 implementation
+# is buggy, see numpy/core/src/common/npy_config.h
+mandatory_funcs = [
+  'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'fabs',
+  'floor', 'ceil', 'sqrt', 'log10', 'log', 'exp', 'asin',
+  'acos', 'atan', 'fmod', 'modf', 'frexp', 'ldexp',
+  'expm1', 'log1p', 'acosh', 'asinh', 'atanh',
+  'rint', 'trunc', 'exp2',
+  'copysign', 'nextafter', 'strtoll', 'strtoull', 'cbrt',
+  'log2', 'pow', 'hypot', 'atan2',
+  'csin', 'csinh', 'ccos', 'ccosh', 'ctan', 'ctanh',
+  'creal', 'cimag', 'conj'
+]
+foreach func: mandatory_funcs
+  if not cc.has_function(func)
+    error('Function `{func}` not found')
+  endif
+endforeach
+
+c99_complex_funcs = [
+  'cabs', 'cacos', 'cacosh', 'carg', 'casin', 'casinh', 'catan',
+  'catanh', 'cexp', 'clog', 'cpow', 'csqrt',
+  # The long double variants (like csinl)  should be mandatory on C11,
+  # but are missing in FreeBSD. Issue gh-22850
+  'csin', 'csinh', 'ccos', 'ccosh', 'ctan', 'ctanh',
+
+]
+foreach func: c99_complex_funcs
+  func_single = func + 'f'
+  func_longdouble = func + 'l'
+  if cc.has_function(func)
+    cdata.set10('HAVE_' + func.to_upper(), true)
+  endif
+  if cc.has_function(func_single)
+    cdata.set10('HAVE_' + func_single.to_upper(), true)
+  endif
+  if cc.has_function(func_longdouble)
+    cdata.set10('HAVE_' + func_longdouble.to_upper(), true)
+  endif
+endforeach
+
+# We require C99 so these should always be found at build time. But for
+# libnpymath as a C99 compat layer, these may still be relevant.
+c99_macros = ['isfinite', 'isinf', 'isnan', 'signbit']
+foreach macro: c99_macros
+  if cc.has_function(macro)
+    cdata.set10('NPY_HAVE_DECL_' + macro.to_upper(), true)
+    if not cc.has_header_symbol('Python.h', macro, dependencies: py_dep)
+      # Add in config.h as well, except if it will clash with Python's config.h content
+      cdata.set10('HAVE_DECL_' + macro.to_upper(), true)
+    endif
+  endif
+endforeach
+
+# variable attributes tested via "int %s a" % attribute
+optional_variable_attributes = [
+  ['__thread', 'HAVE__THREAD'],
+  ['__declspec(thread)', 'HAVE___DECLSPEC_THREAD_']
+]
+foreach optional_attr: optional_variable_attributes
+  attr = optional_attr[0]
+  code = f'''
+    #pragma GCC diagnostic error "-Wattributes"
+    #pragma clang diagnostic error "-Wattributes"
+
+    int @attr@ foo;
+  '''
+  code += '''
+    int
+    main()
+    {
+      return 0;
+    }
+  '''
+  if cc.compiles(code)
+    cdata.set10(optional_attr[1], true)
+  endif
+endforeach
+
+inc_curdir = include_directories('.')
+optional_file_funcs = ['fallocate', 'ftello', 'fseeko']
+foreach filefunc_maybe: optional_file_funcs
+  config_value = 'HAVE_' + filefunc_maybe.to_upper()
+  # Some functions may already have HAVE_* defined by `Python.h`. Python puts
+  # its config.h in the public Python.h namespace, so we have a possible clash
+  # for the common functions we test. Hence we skip those checks.
+  if (filefunc_maybe == 'fallocate' or
+      not cc.has_header_symbol('Python.h', config_value, dependencies: py_dep)
+    )
+    if cc.has_function(filefunc_maybe,
+                       include_directories: inc_curdir,
+                       prefix: '#include "feature_detection_stdio.h"'
+      )
+      cdata.set10(config_value, true)
+    endif
+  endif
+endforeach
+
+# Optional locale function
+have_strtold_l = cc.has_function('strtold_l', include_directories: inc_curdir,
+  prefix:'''
+  #include <stdlib.h>
+  #include <xlocale.h>
+  #include "feature_detection_locale.h"
+''')
+if not have_strtold_l
+  # Retry with locale.h, seems to vary across Linux distros
+  have_strtold_l = cc.has_function('strtold_l', include_directories: inc_curdir,
+    prefix:'''
+    #include <stdlib.h>
+    #include <locale.h>
+    #include "feature_detection_locale.h"
+  ''')
+endif
+if have_strtold_l
+  cdata.set10('HAVE_STRTOLD_L', true)
+else
+  # FIXME: this is wrong! the HAVE_ define should not exist, or it'll be
+  # interpreted as the function being available (true/false does nothing, see
+  # note on HAVE_ defines higher up). This is necessary though in order to make
+  # the Linux CI job pass. So either the check is wrong somehow, or this
+  # function is not available in CI. For the latter there is a fallback path,
+  # but that is broken because we don't have the exact long double
+  # representation checks.
+  if cc.get_argument_syntax() != 'msvc'
+    cdata.set10('HAVE_STRTOLD_L', false)
+  endif
+endif
+
+# Other optional functions
+optional_misc_funcs = [
+  'backtrace',
+  'madvise',
+]
+foreach func: optional_misc_funcs
+  if cc.has_function(func,
+      include_directories: inc_curdir,
+      prefix: '#include "feature_detection_misc.h"'
+    )
+    cdata.set10('HAVE_' + func.to_upper(), true)
+  endif
+endforeach
+
+# SSE headers only enabled automatically on amd64/x32 builds
+optional_headers = [
+  'xmmintrin.h',  # SSE
+  'emmintrin.h',  # SSE2
+  'immintrin.h',  # AVX
+  'features.h',   # for glibc version linux
+  'xlocale.h',    # see GH#8367
+  'dlfcn.h',      # dladdr
+  'execinfo.h',   # backtrace
+  'libunwind.h',  # backtrace for LLVM/Clang using libunwind
+  'sys/mman.h',   # madvise
+]
+foreach header: optional_headers
+  if cc.has_header(header)
+    cdata.set10('HAVE_' + header.to_upper().replace('.', '_').replace('/', '_'), true)
+  endif
+endforeach
+
+# Optional compiler attributes
+# TODO: this doesn't work with cc.has_function_attribute, see
+#       https://github.com/mesonbuild/meson/issues/10732
+optional_function_attributes = [
+  ['optimize("unroll-loops")', 'OPTIMIZE_UNROLL_LOOPS'],
+  ['optimize("O3")', 'OPTIMIZE_OPT_3'],
+  ['optimize("O2")', 'OPTIMIZE_OPT_2'],
+  ['optimize("nonnull (1)")', 'NONNULL'],
+]
+#foreach attr: optional_function_attributes
+#  if cc.has_function_attribute(attr[0])
+#    cdata.set10('HAVE_ATTRIBUTE_' + attr[1], true)
+#  endif
+#endforeach
+
+# Optional GCC compiler builtins and their call arguments.
+# If given, a required header and definition name (HAVE_ prepended)
+# Call arguments are required as the compiler will do strict signature checking
+optional_intrinsics = [
+  ['__builtin_isnan', '5.', [], []],
+  ['__builtin_isinf', '5.', [], []],
+  ['__builtin_isfinite', '5.', [], []],
+  ['__builtin_bswap32', '5u', [], []],
+  ['__builtin_bswap64', '5u', [], []],
+  ['__builtin_expect', '5, 0', [], []],
+  # Test `long long` for arm+clang 13 (gh-22811, but we use all versions):
+  ['__builtin_mul_overflow', '(long long)5, 5, (int*)5', [], []],
+  ['__builtin_prefetch', '(float*)0, 0, 3', [], []],
+]
+foreach intrin: optional_intrinsics
+  func = intrin[0]
+  func_args = intrin[1]
+  header = intrin[2]
+  define = intrin[3]
+  code = ''
+  if header.length() == 1
+    header_name = header[0]
+    code += f'#include <@header_name@>'
+  endif
+  code += f'''
+    #ifdef _MSC_VER
+    #pragma function(@func@)
+    #endif
+    int main(void) {
+      @func@(@func_args@);
+      return 0;
+    };
+    '''
+  if define.length() == 1
+    define_name = define[0]
+  else
+    define_name = 'HAVE_' + func.to_upper()
+  endif
+  if cc.links(code)
+    cdata.set10(define_name, true)
+  endif
+endforeach
+
+# This is a port of the old python code for identifying the long double
+# representation to C.  The old Python code is in this range:
+# https://github.com/numpy/numpy/blob/eead09a3d02c09374942cdc787c0b5e4fe9e7472/numpy/core/setup_common.py#L264-L434
+# This port is in service of solving gh-23972
+# as well as https://github.com/mesonbuild/meson/issues/11068
+longdouble_format = meson.get_compiler('c').run(
+'''
+#include <stdio.h>
+#include <string.h>
+
+#define repcmp(z) (memcmp((const char *)&foo.x, z, sizeof(foo.x)) == 0)
+
+const struct {
+  char before[16];
+  long double x;
+  char after[8];
+} foo = {{'\0'}, -123456789.0, {'\0'}};
+
+int main(void) {
+  switch (sizeof(foo.x)) {
+  case 8: {
+    if (repcmp(
+            ((const char[]){0000, 0000, 0000, 0124, 0064, 0157, 0235, 0301}))) {
+      fprintf(stdout, "IEEE_DOUBLE_LE");
+      return 0;
+    }
+    if (repcmp(
+            ((const char[]){0301, 0235, 0157, 0064, 0124, 0000, 0000, 0000}))) {
+      fprintf(stdout, "IEEE_DOUBLE_BE");
+      return 0;
+    }
+    fprintf(stdout, "UNKNOWN");
+    return 1;
+  }
+  case 12: {
+    if (repcmp(((const char[]){0000, 0000, 0000, 0000, 0240, 0242, 0171, 0353,
+                               0031, 0300, 0000, 0000}))) {
+      fprintf(stdout, "INTEL_EXTENDED_12_BYTES_LE");
+      return 0;
+    }
+    if (repcmp(((const char[]){0300, 0031, 0000, 0000, 0353, 0171, 0242, 0240,
+                               0000, 0000, 0000, 0000}))) {
+      fprintf(stdout, "MOTOROLA_EXTENDED_12_BYTES_BE");
+      return 0;
+    }
+    fprintf(stdout, "UNKNOWN");
+    return 1;
+  }
+  case 16: {
+    if (repcmp(
+            ((const char[]){0000, 0000, 0000, 0000, 0240, 0242, 0171, 0353,
+                            0031, 0300, 0000, 0000, 0000, 0000, 0000, 0000}))) {
+      fprintf(stdout, "INTEL_EXTENDED_16_BYTES_LE");
+      return 0;
+    }
+    if (repcmp(
+            ((const char[]){0300, 0031, 0326, 0363, 0105, 0100, 0000, 0000,
+                            0000, 0000, 0000, 0000, 0000, 0000, 0000, 0000}))) {
+      fprintf(stdout, "IEEE_QUAD_BE");
+      return 0;
+    }
+    if (repcmp(
+            ((const char[]){0000, 0000, 0000, 0000, 0000, 0000, 0000, 0000,
+                            0000, 0000, 0100, 0105, 0363, 0326, 0031, 0300}))) {
+      fprintf(stdout, "IEEE_QUAD_LE");
+      return 0;
+    }
+    if (repcmp(
+            ((const char[]){0000, 0000, 0000, 0124, 0064, 0157, 0235, 0301,
+                            0000, 0000, 0000, 0000, 0000, 0000, 0000, 0000}))) {
+      fprintf(stdout, "IBM_DOUBLE_DOUBLE_LE");
+      return 0;
+    }
+    if (repcmp(
+            ((const char[]){0301, 0235, 0157, 0064, 0124, 0000, 0000, 0000,
+                            0000, 0000, 0000, 0000, 0000, 0000, 0000, 0000}))) {
+      fprintf(stdout, "IBM_DOUBLE_DOUBLE_BE");
+      return 0;
+    }
+    fprintf(stdout, "UNKNOWN");
+    return 1;
+  }
+  }
+}
+''').stdout()
+if longdouble_format == 'UNKNOWN' or longdouble_format == 'UNDEFINED'
+  error('Unknown long double format of size: ' + cc.sizeof('long double').to_string())
+endif
+cdata.set10('HAVE_LDOUBLE_' + longdouble_format, true)
+
+if cc.has_header('endian.h')
+  cdata.set10('NPY_HAVE_ENDIAN_H', true)
+endif
+if cc.has_header('sys/endian.h')
+  cdata.set10('NPY_HAVE_SYS_ENDIAN_H', true)
+endif
+if is_windows
+  cdata.set10('NPY_NO_SIGNAL', true)
+endif
+# Command-line switch; distutils build checked for `NPY_NOSMP` env var instead
+# TODO: document this (search for NPY_NOSMP in C API docs)
+cdata.set10('NPY_NO_SMP', get_option('disable-threading'))
+
+# Use bogus stride debug aid to flush out bugs where users use strides of
+# dimensions with length 1 to index a full contiguous array.
+cdata.set10('NPY_RELAXED_STRIDES_DEBUG', get_option('relaxed-strides-debug'))
+
+# Check whether we can use inttypes (C99) formats
+if cc.has_header_symbol('inttypes.h', 'PRIdPTR')
+  cdata.set10('NPY_USE_C99_FORMATS', true)
+endif
+
+visibility_hidden = ''
+if cc.has_function_attribute('visibility:hidden')
+  visibility_hidden = '__attribute__((visibility("hidden")))'
+endif
+cdata.set('NPY_VISIBILITY_HIDDEN', visibility_hidden)
+
+
+config_h = configure_file(
+  input: 'config.h.in',
+  output: 'config.h',
+  configuration: cdata,
+  install: false
+)
+
+_numpyconfig_h = configure_file(
+  input: 'include/numpy/_numpyconfig.h.in',
+  output: '_numpyconfig.h',
+  configuration: cdata,
+  install: true,
+  install_dir: np_dir / 'core/include/numpy'
+)
+
+# Build npymath static library
+# ----------------------------
+
+staticlib_cflags = []
+staticlib_cppflags = []
+if cc.get_id() == 'msvc'
+  # Disable voltbl section for vc142 to allow link using mingw-w64; see:
+  # https://github.com/matthew-brett/dll_investigation/issues/1#issuecomment-1100468171
+  # Needs to be added to static libraries that are shipped for reuse (i.e.,
+  # libnpymath and libnpyrandom)
+  if cc.has_argument('-d2VolatileMetadata-')
+     staticlib_cflags +=  '-d2VolatileMetadata-'
+  endif
+endif
+# TODO: change to "feature" option in meson_options.txt? See
+# https://mesonbuild.com/Build-options.html#build-options
+if get_option('disable-simd-optimizations')
+  staticlib_cflags += '-DNPY_DISABLE_OPTIMIZATION'
+  staticlib_cppflags += '-DNPY_DISABLE_OPTIMIZATION'
+endif
+
+npy_math_internal_h = custom_target(
+  output: 'npy_math_internal.h',
+  input: 'src/npymath/npy_math_internal.h.src',
+  command: [src_file_cli, '@INPUT@', '-o', '@OUTPUT@'],
+)
+
+npymath_sources = [
+  src_file.process('src/npymath/ieee754.c.src'),
+  src_file.process('src/npymath/npy_math_complex.c.src'),
+  npy_math_internal_h,
+  'src/npymath/halffloat.cpp',
+  'src/npymath/npy_math.c',
+]
+npymath_lib = static_library('npymath',
+  npymath_sources,
+  c_args: staticlib_cflags,
+  cpp_args: staticlib_cppflags,
+  include_directories: ['include', 'src/npymath', 'src/common'],
+  dependencies: py_dep,
+  install: true,
+  install_dir: np_dir / 'core/lib',
+  name_prefix: name_prefix_staticlib,
+  name_suffix: name_suffix_staticlib,
+)
+
+dir_separator = '/'
+if build_machine.system() == 'windows'
+  dir_separator = '\\'
+endif
+configure_file(
+  input: 'npymath.ini.in',
+  output: 'npymath.ini',
+  configuration: configuration_data({
+    'pkgname' : 'numpy.core',
+    'sep' : dir_separator,
+  }),
+  install: true,
+  install_dir: np_dir / 'core/lib/npy-pkg-config'
+)
+configure_file(
+  input: 'mlib.ini.in',
+  output: 'mlib.ini',
+  configuration: configuration_data({
+    'posix_mathlib' : mlib_linkflag,
+    'msvc_mathlib' : 'm.lib',
+  }),
+  install: true,
+  install_dir: np_dir / 'core/lib/npy-pkg-config'
+)
+
+if false
+  # This doesn't quite work (yet), it assumes we'll install headers under
+  # include/, and trying to add the correct path with `extra_cflags` runs into
+  # not being able to get a path relative to the prefix.
+  # Note that installing numpy headers under include/ would be a good idea, but
+  # that needs work (and may run into trouble with wheels perhaps, not quite
+  # clear if they allow this).
+  pkg = import('pkgconfig')
+  pkg.generate(npymath_lib,
+    name: 'npymath',
+    description: 'Portable, core math library implementing C99 standard',
+    url: 'https://github.com/numpy/numpy',
+    requires: 'numpy',
+    install_dir: np_dir / 'core/lib/npy-pkg-config',
+    extra_cflags: '-I${includedir}' + np_dir / 'core' / 'include',
+  )
+endif
+
+# Generate NumPy C API sources
+# ----------------------------
+
+# This is a single C file. It needs to be built before _multiarray_umath starts
+# building, but we can't add it to the sources of that extension (this C file
+# doesn't compile, it's only included in another r file. Hence use the slightly
+# hacky --ignore argument to the next custom_target().
+src_umath_api_c = custom_target('__umath_generated',
+  output : '__umath_generated.c',
+  input : 'code_generators/generate_umath.py',
+  command: [py, '@INPUT@', '-o', '@OUTPUT@'],
+)
+
+src_umath_doc_h = custom_target('_umath_doc_generated',
+  output : '_umath_doc_generated.h',
+  input : 'code_generators/generate_umath_doc.py',
+  command: [py, '@INPUT@', '-o', '@OUTPUT@'],
+)
+
+src_numpy_api = custom_target('__multiarray_api',
+  output : ['__multiarray_api.c', '__multiarray_api.h'],
+  input : 'code_generators/generate_numpy_api.py',
+  command: [py, '@INPUT@', '-o', '@OUTDIR@', '--ignore', src_umath_api_c],
+  install: true,  # NOTE: setup.py build installs all, but just need .h?
+  install_dir: np_dir / 'core/include/numpy'
+)
+
+src_ufunc_api = custom_target('__ufunc_api',
+  output : ['__ufunc_api.c', '__ufunc_api.h'],
+  input : 'code_generators/generate_ufunc_api.py',
+  command: [py, '@INPUT@', '-o', '@OUTDIR@'],
+  install: true,  # NOTE: setup.py build installs all, but just need .h?
+  install_dir: np_dir / 'core/include/numpy'
+)
+
+
+# Set common build flags for C and C++ code
+# -----------------------------------------
+
+# TODO: change to "feature" option in meson_options.txt? See
+# https://mesonbuild.com/Build-options.html#build-options
+disable_simd_optimizations = []
+if get_option('disable-simd-optimizations')
+  disable_simd_optimizations = '-DNPY_DISABLE_OPTIMIZATION'
+endif
+
+# Common build flags
+c_args_common = [
+  '-DNPY_INTERNAL_BUILD',
+  '-DHAVE_NPY_CONFIG_H',
+  disable_simd_optimizations,
+  cflags_large_file_support,
+]
+
+# Same as NPY_CXX_FLAGS (TODO: extend for what ccompiler_opt adds)
+cpp_args_common = c_args_common + [
+  '-D__STDC_VERSION__=0',  # for compatibility with C headers
+]
+if cc.get_argument_syntax() != 'msvc'
+  cpp_args_common += [
+    '-fno-exceptions',  # no exception support
+    '-fno-rtti',  # no runtime type information
+  ]
+endif
+
+# Other submodules depend on generated headers and include directories from
+# core, wrap those up into a reusable dependency. Also useful for some test
+# modules in this build file.
+np_core_dep = declare_dependency(
+  sources: [
+    _numpyconfig_h,
+    npy_math_internal_h,
+    src_numpy_api[1],  # __multiarray_api.h
+    src_ufunc_api[1],  # __ufunc_api.h
+  ],
+  include_directories: [
+    '.',
+    'include',
+    'src/common',
+  ],
+  compile_args: disable_simd_optimizations
+)
+
+
+# Build multiarray_tests module
+# -----------------------------
+py.extension_module('_multiarray_tests',
+  [
+    src_file.process('src/multiarray/_multiarray_tests.c.src'),
+    'src/common/mem_overlap.c',
+    'src/common/npy_argparse.c',
+    'src/common/npy_hashtable.c',
+    src_file.process('src/common/templ_common.h.src')
+  ],
+  c_args: c_args_common,
+  include_directories: ['src/multiarray', 'src/npymath'],
+  dependencies: np_core_dep,
+  link_with: npymath_lib,
+  gnu_symbol_visibility: 'default',
+  install: true,
+  subdir: 'numpy/core',
+)
+
+test_modules_src = [
+  ['_umath_tests', [
+      src_file.process('src/umath/_umath_tests.c.src'),
+      'src/umath/_umath_tests.dispatch.c',
+      'src/common/npy_cpu_features.c',
+    ]],
+  ['_rational_tests', 'src/umath/_rational_tests.c'],
+  ['_struct_ufunc_tests', 'src/umath/_struct_ufunc_tests.c'],
+  ['_operand_flag_tests', 'src/umath/_operand_flag_tests.c'],
+]
+foreach gen: test_modules_src
+  py.extension_module(gen[0],
+    gen[1],
+    c_args: c_args_common,
+    include_directories: ['src/multiarray', 'src/npymath'],
+    dependencies: np_core_dep,
+    install: true,
+    subdir: 'numpy/core',
+  )
+endforeach
+
+# Build _multiarray_umath module
+# ------------------------------
+src_multiarray_umath_common = [
+  'src/common/array_assign.c',
+  'src/common/mem_overlap.c',
+  'src/common/npy_argparse.c',
+  'src/common/npy_hashtable.c',
+  'src/common/npy_longdouble.c',
+  'src/common/ucsnarrow.c',
+  'src/common/ufunc_override.c',
+  'src/common/numpyos.c',
+  'src/common/npy_cpu_features.c',
+  src_file.process('src/common/templ_common.h.src')
+]
+if have_blas
+  src_multiarray_umath_common += [
+    'src/common/cblasfuncs.c',
+    'src/common/python_xerbla.c',
+  ]
+endif
+
+src_multiarray = [
+  'src/multiarray/abstractdtypes.c',
+  'src/multiarray/alloc.c',
+  src_file.process('src/multiarray/argfunc.dispatch.c.src'),
+  'src/multiarray/arrayobject.c',
+  src_file.process('src/multiarray/arraytypes.h.src'),
+  'src/multiarray/array_coercion.c',
+  'src/multiarray/array_method.c',
+  'src/multiarray/array_assign_scalar.c',
+  'src/multiarray/array_assign_array.c',
+  'src/multiarray/arrayfunction_override.c',
+  src_file.process('src/multiarray/arraytypes.c.src'),
+  'src/multiarray/buffer.c',
+  'src/multiarray/calculation.c',
+  'src/multiarray/compiled_base.c',
+  'src/multiarray/common.c',
+  'src/multiarray/common_dtype.c',
+  'src/multiarray/convert.c',
+  'src/multiarray/convert_datatype.c',
+  'src/multiarray/conversion_utils.c',
+  'src/multiarray/ctors.c',
+  'src/multiarray/datetime.c',
+  'src/multiarray/datetime_strings.c',
+  'src/multiarray/datetime_busday.c',
+  'src/multiarray/datetime_busdaycal.c',
+  'src/multiarray/descriptor.c',
+  'src/multiarray/dlpack.c',
+  'src/multiarray/dtypemeta.c',
+  'src/multiarray/dragon4.c',
+  'src/multiarray/dtype_transfer.c',
+  'src/multiarray/dtype_traversal.c',
+  src_file.process('src/multiarray/einsum.c.src'),
+  src_file.process('src/multiarray/einsum_sumprod.c.src'),
+  'src/multiarray/experimental_public_dtype_api.c',
+  'src/multiarray/flagsobject.c',
+  'src/multiarray/getset.c',
+  'src/multiarray/hashdescr.c',
+  'src/multiarray/item_selection.c',
+  'src/multiarray/iterators.c',
+  'src/multiarray/legacy_dtype_implementation.c',
+  src_file.process('src/multiarray/lowlevel_strided_loops.c.src'),
+  'src/multiarray/mapping.c',
+  'src/multiarray/methods.c',
+  'src/multiarray/multiarraymodule.c',
+  'src/multiarray/nditer_api.c',
+  'src/multiarray/nditer_constr.c',
+  'src/multiarray/nditer_pywrap.c',
+  src_file.process('src/multiarray/nditer_templ.c.src'),
+  'src/multiarray/number.c',
+  'src/multiarray/refcount.c',
+  src_file.process('src/multiarray/scalartypes.c.src'),
+  'src/multiarray/sequence.c',
+  'src/multiarray/shape.c',
+  'src/multiarray/scalarapi.c',
+  'src/multiarray/strfuncs.c',
+  'src/multiarray/temp_elide.c',
+  'src/multiarray/typeinfo.c',
+  'src/multiarray/usertypes.c',
+  'src/multiarray/vdot.c',
+  src_file.process('src/common/npy_sort.h.src'),
+  'src/npysort/simd_qsort.dispatch.cpp',
+  'src/npysort/simd_qsort_16bit.dispatch.cpp',
+  'src/npysort/quicksort.cpp',
+  'src/npysort/mergesort.cpp',
+  'src/npysort/timsort.cpp',
+  'src/npysort/heapsort.cpp',
+  'src/npysort/radixsort.cpp',
+  'src/common/npy_partition.h',
+  'src/npysort/selection.cpp',
+  'src/common/npy_binsearch.h',
+  'src/npysort/binsearch.cpp',
+  'src/multiarray/textreading/conversions.c',
+  'src/multiarray/textreading/field_types.c',
+  'src/multiarray/textreading/growth.c',
+  'src/multiarray/textreading/readtext.c',
+  'src/multiarray/textreading/rows.c',
+  'src/multiarray/textreading/stream_pyobject.c',
+  'src/multiarray/textreading/str_to_int.c',
+  'src/multiarray/textreading/tokenize.cpp',
+  # Remove this `arm64_exports.c` file once scipy macos arm64 build correctly
+  # links to the arm64 npymath library, see gh-22673
+  'src/npymath/arm64_exports.c',
+]
+
+src_umath = [
+  src_file.process('src/umath/funcs.inc.src'),
+  src_file.process('src/umath/loops.h.src'),
+  src_file.process('src/umath/loops_utils.h.src'),
+  src_file.process('src/umath/loops.c.src'),
+  src_file.process('src/umath/loops_arithm_fp.dispatch.c.src'),
+  src_file.process('src/umath/loops_arithmetic.dispatch.c.src'),
+  src_file.process('src/umath/loops_comparison.dispatch.c.src'),
+  src_file.process('src/umath/loops_exponent_log.dispatch.c.src'),
+  src_file.process('src/umath/loops_hyperbolic.dispatch.c.src'),
+  src_file.process('src/umath/loops_logical.dispatch.c.src'),
+  src_file.process('src/umath/loops_minmax.dispatch.c.src'),
+  src_file.process('src/umath/loops_modulo.dispatch.c.src'),
+  src_file.process('src/umath/loops_trigonometric.dispatch.c.src'),
+  src_file.process('src/umath/loops_umath_fp.dispatch.c.src'),
+  src_file.process('src/umath/loops_unary.dispatch.c.src'),
+  src_file.process('src/umath/loops_unary_fp.dispatch.c.src'),
+  src_file.process('src/umath/loops_unary_fp_le.dispatch.c.src'),
+  src_file.process('src/umath/loops_unary_complex.dispatch.c.src'),
+  src_file.process('src/umath/loops_autovec.dispatch.c.src'),
+  src_file.process('src/umath/matmul.c.src'),
+  src_file.process('src/umath/matmul.h.src'),
+  'src/umath/ufunc_type_resolution.c',
+  'src/umath/clip.cpp',
+  'src/umath/clip.h',
+  'src/umath/dispatching.c',
+  'src/umath/extobj.c',
+  'src/umath/legacy_array_method.c',
+  'src/umath/override.c',
+  'src/umath/reduction.c',
+  src_file.process('src/umath/scalarmath.c.src'),
+  'src/umath/ufunc_object.c',
+  'src/umath/umathmodule.c',
+  'src/umath/string_ufuncs.cpp',
+  'src/umath/wrapping_array_method.c',
+  # For testing. Eventually, should use public API and be separate:
+  'src/umath/_scaled_float_dtype.c',
+]
+
+# SVML object files. If functionality is migrated to universal intrinsics and
+# the object files are no longer needed, comment out the relevant object files
+# here. Note that this migration is desirable; we then get the performance
+# benefits for all platforms rather than only for AVX512 on 64-bit Linux, and
+# may be able to avoid the accuracy regressions in SVML.
+svml_objects = []
+if use_svml
+  svml_objects += [
+    'src/umath/svml/linux/avx512/svml_z0_acos_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_acos_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_acosh_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_acosh_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_asin_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_asin_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_asinh_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_asinh_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_atan2_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_atan2_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_atan_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_atan_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_atanh_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_atanh_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_cbrt_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_cbrt_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_cos_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_cos_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_cosh_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_cosh_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_exp2_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_exp2_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_exp_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_exp_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_expm1_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_expm1_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log10_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log10_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log1p_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log1p_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log2_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log2_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_log_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_pow_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_pow_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_sin_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_sin_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_sinh_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_sinh_s_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_tan_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_tan_s_la.s',
+    # 'src/umath/svml/linux/avx512/svml_z0_tanh_d_la.s',
+    'src/umath/svml/linux/avx512/svml_z0_tanh_s_la.s',
+  ]
+endif
+
+py.extension_module('_multiarray_umath',
+  [
+    config_h,
+    _numpyconfig_h,
+    src_multiarray,
+    src_multiarray_umath_common,
+    src_umath,
+    src_ufunc_api[1],  # __ufunc_api.h
+    src_numpy_api[1],  # __multiarray_api.h
+    src_umath_doc_h,
+    npy_math_internal_h,
+  ],
+  objects: svml_objects,
+  c_args: c_args_common,
+  cpp_args: cpp_args_common,
+  include_directories: [
+    'include',
+    'src/common',
+    'src/multiarray',
+    'src/npymath',
+    'src/umath',
+  ],
+  dependencies: blas_dep,
+  link_with: npymath_lib,
+  install: true,
+  subdir: 'numpy/core',
+)
+
+# Build SIMD module
+# -----------------
+
+py.extension_module('_simd',
+  [
+    'src/common/npy_cpu_features.c',
+    'src/_simd/_simd.c',
+    src_file.process('src/_simd/_simd_inc.h.src'),
+    src_file.process('src/_simd/_simd_data.inc.src'),
+    src_file.process('src/_simd/_simd.dispatch.c.src'),
+  ],
+  c_args: c_args_common,
+  include_directories: ['src/_simd', 'src/npymath'],
+  dependencies: np_core_dep,
+  link_with: npymath_lib,
+  install: true,
+  subdir: 'numpy/core',
+)
+
+python_sources = [
+  '__init__.py',
+  '__init__.pyi',
+  '_add_newdocs.py',
+  '_add_newdocs_scalars.py',
+  '_asarray.py',
+  '_asarray.pyi',
+  '_dtype.py',
+  '_dtype_ctypes.py',
+  '_exceptions.py',
+  '_internal.py',
+  '_internal.pyi',
+  '_machar.py',
+  '_methods.py',
+  '_string_helpers.py',
+  '_type_aliases.py',
+  '_type_aliases.pyi',
+  '_ufunc_config.py',
+  '_ufunc_config.pyi',
+  'arrayprint.py',
+  'arrayprint.pyi',
+  'cversions.py',
+  'defchararray.py',
+  'defchararray.pyi',
+  'einsumfunc.py',
+  'einsumfunc.pyi',
+  'fromnumeric.py',
+  'fromnumeric.pyi',
+  'function_base.py',
+  'function_base.pyi',
+  'getlimits.py',
+  'getlimits.pyi',
+  'memmap.py',
+  'memmap.pyi',
+  'multiarray.py',
+  'multiarray.pyi',
+  'numeric.py',
+  'numeric.pyi',
+  'numerictypes.py',
+  'numerictypes.pyi',
+  'overrides.py',
+  'records.py',
+  'records.pyi',
+  'shape_base.py',
+  'shape_base.pyi',
+  'umath.py',
+  'umath_tests.py',
+]
+
+py.install_sources(
+  python_sources,
+  subdir: 'numpy/core'
+)
+
+subdir('include')
+install_subdir('tests', install_dir: np_dir / 'core')
diff --git a/contrib/python/numpy/py3/numpy/core/multiarray.py b/contrib/python/numpy/py3/numpy/core/multiarray.py
index 31b77978392..d1128334595 100644
--- a/contrib/python/numpy/py3/numpy/core/multiarray.py
+++ b/contrib/python/numpy/py3/numpy/core/multiarray.py
@@ -14,10 +14,10 @@ from ._multiarray_umath import *  # noqa: F403
 # do not change them. issue gh-15518
 # _get_ndarray_c_version is semi-public, on purpose not added to __all__
 from ._multiarray_umath import (
-    fastCopyAndTranspose, _flagdict, from_dlpack, _insert, _reconstruct,
+    fastCopyAndTranspose, _flagdict, from_dlpack, _place, _reconstruct,
     _vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version,
     _get_madvise_hugepage, _set_madvise_hugepage,
-    _get_promotion_state, _set_promotion_state,
+    _get_promotion_state, _set_promotion_state, _using_numpy2_behavior
     )
 
 __all__ = [
@@ -25,7 +25,7 @@ __all__ = [
     'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS',
     'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI',
     'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP',
-    '_flagdict', 'from_dlpack', '_insert', '_reconstruct', '_vec_string',
+    '_flagdict', 'from_dlpack', '_place', '_reconstruct', '_vec_string',
     '_monotonicity', 'add_docstring', 'arange', 'array', 'asarray',
     'asanyarray', 'ascontiguousarray', 'asfortranarray', 'bincount',
     'broadcast', 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast',
@@ -42,7 +42,7 @@ __all__ = [
     'set_legacy_print_mode', 'set_numeric_ops', 'set_string_function',
     'set_typeDict', 'shares_memory', 'tracemalloc_domain', 'typeinfo',
     'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros',
-    '_get_promotion_state', '_set_promotion_state']
+    '_get_promotion_state', '_set_promotion_state', '_using_numpy2_behavior']
 
 # For backward compatibility, make sure pickle imports these functions from here
 _reconstruct.__module__ = 'numpy.core.multiarray'
@@ -72,6 +72,7 @@ seterrobj.__module__ = 'numpy'
 zeros.__module__ = 'numpy'
 _get_promotion_state.__module__ = 'numpy'
 _set_promotion_state.__module__ = 'numpy'
+_using_numpy2_behavior.__module__ = 'numpy'
 
 
 # We can't verify dispatcher signatures because NumPy's C functions don't
@@ -714,7 +715,7 @@ def result_type(*arrays_and_dtypes):
     the data types are combined with :func:`promote_types`
     to produce the return value.
 
-    Otherwise, `min_scalar_type` is called on each array, and
+    Otherwise, `min_scalar_type` is called on each scalar, and
     the resulting data types are all combined with :func:`promote_types`
     to produce the return value.
 
@@ -1122,13 +1123,13 @@ def copyto(dst, src, casting=None, where=None):
     >>> A
     array([[4, 5, 6],
            [7, 8, 9]])
-       
+
     """
     return (dst, src, where)
 
 
 @array_function_from_c_func_and_dispatcher(_multiarray_umath.putmask)
-def putmask(a, mask, values):
+def putmask(a, /, mask, values):
     """
     putmask(a, mask, values)
 
@@ -1345,7 +1346,7 @@ def shares_memory(a, b, max_work=None):
 
     Raises
     ------
-    numpy.TooHardError
+    numpy.exceptions.TooHardError
         Exceeded max_work.
 
     Returns
@@ -1379,7 +1380,7 @@ def shares_memory(a, b, max_work=None):
     >>> np.shares_memory(x1, x2, max_work=1000)
     Traceback (most recent call last):
     ...
-    numpy.TooHardError: Exceeded max_work
+    numpy.exceptions.TooHardError: Exceeded max_work
 
     Running ``np.shares_memory(x1, x2)`` without `max_work` set takes
     around 1 minute for this case. It is possible to find problems
diff --git a/contrib/python/numpy/py3/numpy/core/multiarray.pyi b/contrib/python/numpy/py3/numpy/core/multiarray.pyi
index 0701085b757..dc05f8126ba 100644
--- a/contrib/python/numpy/py3/numpy/core/multiarray.pyi
+++ b/contrib/python/numpy/py3/numpy/core/multiarray.pyi
@@ -428,6 +428,7 @@ def copyto(
 
 def putmask(
     a: NDArray[Any],
+    /,
     mask: _ArrayLikeBool_co,
     values: ArrayLike,
 ) -> None: ...
diff --git a/contrib/python/numpy/py3/numpy/core/numeric.py b/contrib/python/numpy/py3/numpy/core/numeric.py
index fdd81a20ebe..91ac3f8606f 100644
--- a/contrib/python/numpy/py3/numpy/core/numeric.py
+++ b/contrib/python/numpy/py3/numpy/core/numeric.py
@@ -4,6 +4,7 @@ import operator
 import sys
 import warnings
 import numbers
+import builtins
 
 import numpy as np
 from . import multiarray
@@ -17,7 +18,8 @@ from .multiarray import (
     fromstring, inner, lexsort, matmul, may_share_memory,
     min_scalar_type, ndarray, nditer, nested_iters, promote_types,
     putmask, result_type, set_numeric_ops, shares_memory, vdot, where,
-    zeros, normalize_axis_index, _get_promotion_state, _set_promotion_state)
+    zeros, normalize_axis_index, _get_promotion_state, _set_promotion_state,
+    _using_numpy2_behavior)
 
 from . import overrides
 from . import umath
@@ -26,7 +28,7 @@ from .overrides import set_array_function_like_doc, set_module
 from .umath import (multiply, invert, sin, PINF, NAN)
 from . import numerictypes
 from .numerictypes import longlong, intc, int_, float_, complex_, bool_
-from ._exceptions import TooHardError, AxisError
+from ..exceptions import ComplexWarning, TooHardError, AxisError
 from ._ufunc_config import errstate, _no_nep50_warning
 
 bitwise_not = invert
@@ -52,22 +54,10 @@ __all__ = [
     'identity', 'allclose', 'compare_chararrays', 'putmask',
     'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN',
     'False_', 'True_', 'bitwise_not', 'CLIP', 'RAISE', 'WRAP', 'MAXDIMS',
-    'BUFSIZE', 'ALLOW_THREADS', 'ComplexWarning', 'full', 'full_like',
+    'BUFSIZE', 'ALLOW_THREADS', 'full', 'full_like',
     'matmul', 'shares_memory', 'may_share_memory', 'MAY_SHARE_BOUNDS',
-    'MAY_SHARE_EXACT', 'TooHardError', 'AxisError',
-    '_get_promotion_state', '_set_promotion_state']
-
-
-@set_module('numpy')
-class ComplexWarning(RuntimeWarning):
-    """
-    The warning raised when casting a complex dtype to a real dtype.
-
-    As implemented, casting a complex number to a real discards its imaginary
-    part, but this behavior may not be what the user actually wants.
-
-    """
-    pass
+    'MAY_SHARE_EXACT', '_get_promotion_state', '_set_promotion_state',
+    '_using_numpy2_behavior']
 
 
 def _zeros_like_dispatcher(a, dtype=None, order=None, subok=None, shape=None):
@@ -143,10 +133,6 @@ def zeros_like(a, dtype=None, order='K', subok=True, shape=None):
     return res
 
 
-def _ones_dispatcher(shape, dtype=None, order=None, *, like=None):
-    return(like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def ones(shape, dtype=None, order='C', *, like=None):
@@ -200,16 +186,14 @@ def ones(shape, dtype=None, order='C', *, like=None):
 
     """
     if like is not None:
-        return _ones_with_like(shape, dtype=dtype, order=order, like=like)
+        return _ones_with_like(like, shape, dtype=dtype, order=order)
 
     a = empty(shape, dtype, order)
     multiarray.copyto(a, 1, casting='unsafe')
     return a
 
 
-_ones_with_like = array_function_dispatch(
-    _ones_dispatcher, use_like=True
-)(ones)
+_ones_with_like = array_function_dispatch()(ones)
 
 
 def _ones_like_dispatcher(a, dtype=None, order=None, subok=None, shape=None):
@@ -336,7 +320,8 @@ def full(shape, fill_value, dtype=None, order='C', *, like=None):
 
     """
     if like is not None:
-        return _full_with_like(shape, fill_value, dtype=dtype, order=order, like=like)
+        return _full_with_like(
+                like, shape, fill_value, dtype=dtype, order=order)
 
     if dtype is None:
         fill_value = asarray(fill_value)
@@ -346,9 +331,7 @@ def full(shape, fill_value, dtype=None, order='C', *, like=None):
     return a
 
 
-_full_with_like = array_function_dispatch(
-    _full_dispatcher, use_like=True
-)(full)
+_full_with_like = array_function_dispatch()(full)
 
 
 def _full_like_dispatcher(a, fill_value, dtype=None, order=None, subok=None, shape=None):
@@ -707,7 +690,7 @@ def correlate(a, v, mode='valid'):
     --------
     convolve : Discrete, linear convolution of two one-dimensional sequences.
     multiarray.correlate : Old, no conjugate, version of correlate.
-    scipy.signal.correlate : uses FFT which has superior performance on large arrays. 
+    scipy.signal.correlate : uses FFT which has superior performance on large arrays.
 
     Notes
     -----
@@ -721,7 +704,7 @@ def correlate(a, v, mode='valid'):
     `numpy.correlate` may perform slowly in large arrays (i.e. n = 1e5) because it does
     not use the FFT to compute the convolution; in that case, `scipy.signal.correlate` might
     be preferable.
-    
+
 
     Examples
     --------
@@ -738,7 +721,7 @@ def correlate(a, v, mode='valid'):
     array([ 0.5-0.5j,  1.0+0.j ,  1.5-1.5j,  3.0-1.j ,  0.0+0.j ])
 
     Note that you get the time reversed, complex conjugated result
-    (:math:`\overline{c_{-k}}`) when the two input sequences a and v change 
+    (:math:`\overline{c_{-k}}`) when the two input sequences a and v change
     places:
 
     >>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')
@@ -860,14 +843,13 @@ def outer(a, b, out=None):
     """
     Compute the outer product of two vectors.
 
-    Given two vectors, ``a = [a0, a1, ..., aM]`` and
-    ``b = [b0, b1, ..., bN]``,
+    Given two vectors `a` and `b` of length ``M`` and ``N``, repsectively,
     the outer product [1]_ is::
 
-      [[a0*b0  a0*b1 ... a0*bN ]
-       [a1*b0    .
+      [[a_0*b_0  a_0*b_1 ... a_0*b_{N-1} ]
+       [a_1*b_0    .
        [ ...          .
-       [aM*b0            aM*bN ]]
+       [a_{M-1}*b_0            a_{M-1}*b_{N-1} ]]
 
     Parameters
     ----------
@@ -899,9 +881,9 @@ def outer(a, b, out=None):
 
     References
     ----------
-    .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd
-             ed., Baltimore, MD, Johns Hopkins University Press, 1996,
-             pg. 8.
+    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd
+           ed., Baltimore, MD, Johns Hopkins University Press, 1996,
+           pg. 8.
 
     Examples
     --------
@@ -1300,7 +1282,7 @@ def rollaxis(a, axis, start=0):
            +-------------------+----------------------+
            | ``arr.ndim + 1``  | raise ``AxisError``  |
            +-------------------+----------------------+
-           
+
         .. |vdots|   unicode:: U+22EE .. Vertical Ellipsis
 
     Returns
@@ -1791,10 +1773,6 @@ def indices(dimensions, dtype=int, sparse=False):
     return res
 
 
-def _fromfunction_dispatcher(function, shape, *, dtype=None, like=None, **kwargs):
-    return (like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def fromfunction(function, shape, *, dtype=float, like=None, **kwargs):
@@ -1843,11 +1821,11 @@ def fromfunction(function, shape, *, dtype=float, like=None, **kwargs):
     >>> np.fromfunction(lambda i, j: i, (2, 2), dtype=float)
     array([[0., 0.],
            [1., 1.]])
-           
-    >>> np.fromfunction(lambda i, j: j, (2, 2), dtype=float)    
+
+    >>> np.fromfunction(lambda i, j: j, (2, 2), dtype=float)
     array([[0., 1.],
            [0., 1.]])
-           
+
     >>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
     array([[ True, False, False],
            [False,  True, False],
@@ -1860,15 +1838,14 @@ def fromfunction(function, shape, *, dtype=float, like=None, **kwargs):
 
     """
     if like is not None:
-        return _fromfunction_with_like(function, shape, dtype=dtype, like=like, **kwargs)
+        return _fromfunction_with_like(
+                like, function, shape, dtype=dtype, **kwargs)
 
     args = indices(shape, dtype=dtype)
     return function(*args, **kwargs)
 
 
-_fromfunction_with_like = array_function_dispatch(
-    _fromfunction_dispatcher, use_like=True
-)(fromfunction)
+_fromfunction_with_like = array_function_dispatch()(fromfunction)
 
 
 def _frombuffer(buf, dtype, shape, order):
@@ -2046,7 +2023,7 @@ def binary_repr(num, width=None):
         binary = bin(num)[2:]
         binwidth = len(binary)
         outwidth = (binwidth if width is None
-                    else max(binwidth, width))
+                    else builtins.max(binwidth, width))
         warn_if_insufficient(width, binwidth)
         return binary.zfill(outwidth)
 
@@ -2066,7 +2043,7 @@ def binary_repr(num, width=None):
             binary = bin(twocomp)[2:]
             binwidth = len(binary)
 
-            outwidth = max(binwidth, width)
+            outwidth = builtins.max(binwidth, width)
             warn_if_insufficient(width, binwidth)
             return '1' * (outwidth - binwidth) + binary
 
@@ -2143,10 +2120,6 @@ def _maketup(descr, val):
         return tuple(res)
 
 
-def _identity_dispatcher(n, dtype=None, *, like=None):
-    return (like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def identity(n, dtype=None, *, like=None):
@@ -2181,15 +2154,13 @@ def identity(n, dtype=None, *, like=None):
 
     """
     if like is not None:
-        return _identity_with_like(n, dtype=dtype, like=like)
+        return _identity_with_like(like, n, dtype=dtype)
 
     from numpy import eye
     return eye(n, dtype=dtype, like=like)
 
 
-_identity_with_like = array_function_dispatch(
-    _identity_dispatcher, use_like=True
-)(identity)
+_identity_with_like = array_function_dispatch()(identity)
 
 
 def _allclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None):
diff --git a/contrib/python/numpy/py3/numpy/core/numerictypes.py b/contrib/python/numpy/py3/numpy/core/numerictypes.py
index 21a00ac6fc6..aea41bc2eac 100644
--- a/contrib/python/numpy/py3/numpy/core/numerictypes.py
+++ b/contrib/python/numpy/py3/numpy/core/numerictypes.py
@@ -80,12 +80,13 @@ Exported symbols include:
 
 """
 import numbers
+import warnings
 
-from numpy.core.multiarray import (
+from .multiarray import (
         ndarray, array, dtype, datetime_data, datetime_as_string,
         busday_offset, busday_count, is_busday, busdaycalendar
         )
-from numpy.core.overrides import set_module
+from .._utils import set_module
 
 # we add more at the bottom
 __all__ = ['sctypeDict', 'sctypes',
@@ -599,6 +600,16 @@ def find_common_type(array_types, scalar_types):
     """
     Determine common type following standard coercion rules.
 
+    .. deprecated:: NumPy 1.25
+
+        This function is deprecated, use `numpy.promote_types` or
+        `numpy.result_type` instead.  To achieve semantics for the
+        `scalar_types` argument, use `numpy.result_type` and pass the Python
+        values `0`, `0.0`, or `0j`.
+        This will give the same results in almost all cases.
+        More information and rare exception can be found in the
+        `NumPy 1.25 release notes <https://numpy.org/devdocs/release/1.25.0-notes.html>`_.
+
     Parameters
     ----------
     array_types : sequence
@@ -646,6 +657,14 @@ def find_common_type(array_types, scalar_types):
     dtype('complex128')
 
     """
+    # Deprecated 2022-11-07, NumPy 1.25
+    warnings.warn(
+            "np.find_common_type is deprecated.  Please use `np.result_type` "
+            "or `np.promote_types`.\n"
+            "See https://numpy.org/devdocs/release/1.25.0-notes.html and the "
+            "docs for more information.  (Deprecated NumPy 1.25)",
+            DeprecationWarning, stacklevel=2)
+
     array_types = [dtype(x) for x in array_types]
     scalar_types = [dtype(x) for x in scalar_types]
 
diff --git a/contrib/python/numpy/py3/numpy/core/numerictypes.pyi b/contrib/python/numpy/py3/numpy/core/numerictypes.pyi
index d10e4822ad0..d05861b2eec 100644
--- a/contrib/python/numpy/py3/numpy/core/numerictypes.pyi
+++ b/contrib/python/numpy/py3/numpy/core/numerictypes.pyi
@@ -118,11 +118,6 @@ def issubdtype(arg1: DTypeLike, arg2: DTypeLike) -> bool: ...
 
 def sctype2char(sctype: DTypeLike) -> str: ...
 
-def find_common_type(
-    array_types: Iterable[DTypeLike],
-    scalar_types: Iterable[DTypeLike],
-) -> dtype[Any]: ...
-
 cast: _typedict[_CastFunc]
 nbytes: _typedict[int]
 typecodes: _TypeCodes
diff --git a/contrib/python/numpy/py3/numpy/core/overrides.py b/contrib/python/numpy/py3/numpy/core/overrides.py
index c567dfefda7..6403e65b02b 100644
--- a/contrib/python/numpy/py3/numpy/core/overrides.py
+++ b/contrib/python/numpy/py3/numpy/core/overrides.py
@@ -3,13 +3,13 @@ import collections
 import functools
 import os
 
+from .._utils import set_module
+from .._utils._inspect import getargspec
 from numpy.core._multiarray_umath import (
-    add_docstring, implement_array_function, _get_implementing_args)
-from numpy.compat._inspect import getargspec
+    add_docstring,  _get_implementing_args, _ArrayFunctionDispatcher)
 
 
-ARRAY_FUNCTION_ENABLED = bool(
-    int(os.environ.get('NUMPY_EXPERIMENTAL_ARRAY_FUNCTION', 1)))
+ARRAY_FUNCTIONS = set()
 
 array_function_like_doc = (
     """like : array_like, optional
@@ -30,40 +30,35 @@ def set_array_function_like_doc(public_api):
 
 
 add_docstring(
-    implement_array_function,
+    _ArrayFunctionDispatcher,
     """
-    Implement a function with checks for __array_function__ overrides.
+    Class to wrap functions with checks for __array_function__ overrides.
 
     All arguments are required, and can only be passed by position.
 
     Parameters
     ----------
+    dispatcher : function or None
+        The dispatcher function that returns a single sequence-like object
+        of all arguments relevant.  It must have the same signature (except
+        the default values) as the actual implementation.
+        If ``None``, this is a ``like=`` dispatcher and the
+        ``_ArrayFunctionDispatcher`` must be called with ``like`` as the
+        first (additional and positional) argument.
     implementation : function
-        Function that implements the operation on NumPy array without
-        overrides when called like ``implementation(*args, **kwargs)``.
-    public_api : function
-        Function exposed by NumPy's public API originally called like
-        ``public_api(*args, **kwargs)`` on which arguments are now being
-        checked.
-    relevant_args : iterable
-        Iterable of arguments to check for __array_function__ methods.
-    args : tuple
-        Arbitrary positional arguments originally passed into ``public_api``.
-    kwargs : dict
-        Arbitrary keyword arguments originally passed into ``public_api``.
+        Function that implements the operation on NumPy arrays without
+        overrides.  Arguments passed calling the ``_ArrayFunctionDispatcher``
+        will be forwarded to this (and the ``dispatcher``) as if using
+        ``*args, **kwargs``.
 
-    Returns
-    -------
-    Result from calling ``implementation()`` or an ``__array_function__``
-    method, as appropriate.
-
-    Raises
-    ------
-    TypeError : if no implementation is found.
+    Attributes
+    ----------
+    _implementation : function
+        The original implementation passed in.
     """)
 
 
-# exposed for testing purposes; used internally by implement_array_function
+# exposed for testing purposes; used internally by _ArrayFunctionDispatcher
 add_docstring(
     _get_implementing_args,
     """
@@ -107,36 +102,22 @@ def verify_matching_signatures(implementation, dispatcher):
                                'default argument values')
 
 
-def set_module(module):
-    """Decorator for overriding __module__ on a function or class.
-
-    Example usage::
-
-        @set_module('numpy')
-        def example():
-            pass
-
-        assert example.__module__ == 'numpy'
-    """
-    def decorator(func):
-        if module is not None:
-            func.__module__ = module
-        return func
-    return decorator
-
-
-def array_function_dispatch(dispatcher, module=None, verify=True,
-                            docs_from_dispatcher=False, use_like=False):
+def array_function_dispatch(dispatcher=None, module=None, verify=True,
+                            docs_from_dispatcher=False):
     """Decorator for adding dispatch with the __array_function__ protocol.
 
     See NEP-18 for example usage.
 
     Parameters
     ----------
-    dispatcher : callable
+    dispatcher : callable or None
         Function that when called like ``dispatcher(*args, **kwargs)`` with
         arguments from the NumPy function call returns an iterable of
         array-like arguments to check for ``__array_function__``.
+
+        If `None`, the first argument is used as the single `like=` argument
+        and not passed on.  A function implementing `like=` must call its
+        dispatcher with `like` as the first non-keyword argument.
     module : str, optional
         __module__ attribute to set on new function, e.g., ``module='numpy'``.
         By default, module is copied from the decorated function.
@@ -156,58 +137,33 @@ def array_function_dispatch(dispatcher, module=None, verify=True,
     Returns
     -------
     Function suitable for decorating the implementation of a NumPy function.
-    """
-
-    if not ARRAY_FUNCTION_ENABLED:
-        def decorator(implementation):
-            if docs_from_dispatcher:
-                add_docstring(implementation, dispatcher.__doc__)
-            if module is not None:
-                implementation.__module__ = module
-            return implementation
-        return decorator
 
+    """
     def decorator(implementation):
         if verify:
-            verify_matching_signatures(implementation, dispatcher)
+            if dispatcher is not None:
+                verify_matching_signatures(implementation, dispatcher)
+            else:
+                # Using __code__ directly similar to verify_matching_signature
+                co = implementation.__code__
+                last_arg = co.co_argcount + co.co_kwonlyargcount - 1
+                last_arg = co.co_varnames[last_arg]
+                if last_arg != "like" or co.co_kwonlyargcount == 0:
+                    raise RuntimeError(
+                        "__array_function__ expects `like=` to be the last "
+                        "argument and a keyword-only argument. "
+                        f"{implementation} does not seem to comply.")
 
         if docs_from_dispatcher:
             add_docstring(implementation, dispatcher.__doc__)
 
-        @functools.wraps(implementation)
-        def public_api(*args, **kwargs):
-            try:
-                relevant_args = dispatcher(*args, **kwargs)
-            except TypeError as exc:
-                # Try to clean up a signature related TypeError.  Such an
-                # error will be something like:
-                #     dispatcher.__name__() got an unexpected keyword argument
-                #
-                # So replace the dispatcher name in this case.  In principle
-                # TypeErrors may be raised from _within_ the dispatcher, so
-                # we check that the traceback contains a string that starts
-                # with the name.  (In principle we could also check the
-                # traceback length, as it would be deeper.)
-                msg = exc.args[0]
-                disp_name = dispatcher.__name__
-                if not isinstance(msg, str) or not msg.startswith(disp_name):
-                    raise
-
-                # Replace with the correct name and re-raise:
-                new_msg = msg.replace(disp_name, public_api.__name__)
-                raise TypeError(new_msg) from None
-
-            return implement_array_function(
-                implementation, public_api, relevant_args, args, kwargs,
-                use_like)
-
-        public_api.__code__ = public_api.__code__.replace(
-                co_name=implementation.__name__,
-                co_filename='<__array_function__ internals>')
+        public_api = _ArrayFunctionDispatcher(dispatcher, implementation)
+        public_api = functools.wraps(implementation)(public_api)
+
         if module is not None:
             public_api.__module__ = module
 
-        public_api._implementation = implementation
+        ARRAY_FUNCTIONS.add(public_api)
 
         return public_api
 
diff --git a/contrib/python/numpy/py3/numpy/core/records.py b/contrib/python/numpy/py3/numpy/core/records.py
index c014bc97cda..0fb49e8f70f 100644
--- a/contrib/python/numpy/py3/numpy/core/records.py
+++ b/contrib/python/numpy/py3/numpy/core/records.py
@@ -37,10 +37,10 @@ import warnings
 from collections import Counter
 from contextlib import nullcontext
 
+from .._utils import set_module
 from . import numeric as sb
 from . import numerictypes as nt
 from numpy.compat import os_fspath
-from numpy.core.overrides import set_module
 from .arrayprint import _get_legacy_print_mode
 
 # All of the functions allow formats to be a dtype
diff --git a/contrib/python/numpy/py3/numpy/core/setup.py b/contrib/python/numpy/py3/numpy/core/setup.py
index 927defa0625..c6cdd402596 100644
--- a/contrib/python/numpy/py3/numpy/core/setup.py
+++ b/contrib/python/numpy/py3/numpy/core/setup.py
@@ -20,7 +20,7 @@ from setup_common import *  # noqa: F403
 NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "1") != "0")
 if not NPY_RELAXED_STRIDES_CHECKING:
     raise SystemError(
-        "Support for NPY_RELAXED_STRIDES_CHECKING=0 has been remove as of "
+        "Support for NPY_RELAXED_STRIDES_CHECKING=0 has been removed as of "
         "NumPy 1.23.  This error will eventually be removed entirely.")
 
 # Put NPY_RELAXED_STRIDES_DEBUG=1 in the environment if you want numpy to use a
@@ -45,6 +45,8 @@ NPY_DISABLE_SVML = (os.environ.get('NPY_DISABLE_SVML', "0") == "1")
 # in time is only in build. -- Charles Harris, 2013-03-30
 
 class CallOnceOnly:
+    # NOTE: we don't need any of this in the Meson build,
+    # it takes care of caching
     def __init__(self):
         self._check_types = None
         self._check_ieee_macros = None
@@ -58,14 +60,6 @@ class CallOnceOnly:
             out = copy.deepcopy(pickle.loads(self._check_types))
         return out
 
-    def check_ieee_macros(self, *a, **kw):
-        if self._check_ieee_macros is None:
-            out = check_ieee_macros(*a, **kw)
-            self._check_ieee_macros = pickle.dumps(out)
-        else:
-            out = copy.deepcopy(pickle.loads(self._check_ieee_macros))
-        return out
-
     def check_complex(self, *a, **kw):
         if self._check_complex is None:
             out = check_complex(*a, **kw)
@@ -85,11 +79,25 @@ def can_link_svml():
             and "linux" in platform
             and sys.maxsize > 2**31)
 
-def check_svml_submodule(svmlpath):
-    if not os.path.exists(svmlpath + "/README.md"):
-        raise RuntimeError("Missing `SVML` submodule! Run `git submodule "
-                           "update --init` to fix this.")
-    return True
+def can_link_svml_fp16():
+    """SVML FP16 requires binutils >= 2.38 for an updated assembler
+    """
+    if can_link_svml():
+        import re
+        binutils_ver = os.popen("ld -v").readlines()[0].strip()
+        binutils_ver = re.search(r"\d\.\d\d", binutils_ver)
+        if binutils_ver is not None:
+            return float(binutils_ver.group()) >= 2.38
+        else:
+            return False
+
+def check_git_submodules():
+    out = os.popen("git submodule status")
+    modules = out.readlines()
+    for submodule in modules:
+        if (submodule.strip()[0] == "-"):
+            raise RuntimeError("git submodules are not initialized."
+                    "Please run `git submodule update --init` to fix this.")
 
 def pythonlib_dir():
     """return path where libpython* is."""
@@ -177,16 +185,6 @@ def check_math_capabilities(config, ext, moredefs, mathlibs):
         else:
             return 1
 
-    # GH-14787: Work around GCC<8.4 bug when compiling with AVX512
-    # support on Windows-based platforms
-    def check_gh14787(fn):
-        if fn == 'attribute_target_avx512f':
-            if (sys.platform in ('win32', 'cygwin') and
-                    config.check_compiler_gcc() and
-                    not config.check_gcc_version_at_least(8, 4)):
-                ext.extra_compile_args.extend(
-                        ['-ffixed-xmm%s' % n for n in range(16, 32)])
-
     #use_msvc = config.check_decl("_MSC_VER")
     if not check_funcs_once(MANDATORY_FUNCS, add_to_moredefs=False):
         raise SystemError("One of the required function to build numpy is not"
@@ -237,20 +235,8 @@ def check_math_capabilities(config, ext, moredefs, mathlibs):
     for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES:
         if config.check_gcc_function_attribute(dec, fn):
             moredefs.append((fname2def(fn), 1))
-            check_gh14787(fn)
 
     platform = sysconfig.get_platform()
-    if ("x86_64" in platform):
-        for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES_AVX:
-            if config.check_gcc_function_attribute(dec, fn):
-                moredefs.append((fname2def(fn), 1))
-                check_gh14787(fn)
-        for dec, fn, code, header in (
-        OPTIONAL_FUNCTION_ATTRIBUTES_WITH_INTRINSICS_AVX):
-            if config.check_gcc_function_attribute_with_intrinsics(
-                    dec, fn, code, header):
-                moredefs.append((fname2def(fn), 1))
-
     for fn in OPTIONAL_VARIABLE_ATTRIBUTES:
         if config.check_gcc_variable_attribute(fn):
             m = fn.replace("(", "_").replace(")", "_")
@@ -289,43 +275,6 @@ def check_complex(config, mathlibs):
 
     return priv, pub
 
-def check_ieee_macros(config):
-    priv = []
-    pub = []
-
-    macros = []
-
-    def _add_decl(f):
-        priv.append(fname2def("decl_%s" % f))
-        pub.append('NPY_%s' % fname2def("decl_%s" % f))
-
-    # XXX: hack to circumvent cpp pollution from python: python put its
-    # config.h in the public namespace, so we have a clash for the common
-    # functions we test. We remove every function tested by python's
-    # autoconf, hoping their own test are correct
-    _macros = ["isnan", "isinf", "signbit", "isfinite"]
-    for f in _macros:
-        py_symbol = fname2def("decl_%s" % f)
-        already_declared = config.check_decl(py_symbol,
-                headers=["Python.h", "math.h"])
-        if already_declared:
-            if config.check_macro_true(py_symbol,
-                    headers=["Python.h", "math.h"]):
-                pub.append('NPY_%s' % fname2def("decl_%s" % f))
-        else:
-            macros.append(f)
-    # Normally, isnan and isinf are macro (C99), but some platforms only have
-    # func, or both func and macro version. Check for macro only, and define
-    # replacement ones if not found.
-    # Note: including Python.h is necessary because it modifies some math.h
-    # definitions
-    for f in macros:
-        st = config.check_decl(f, headers=["Python.h", "math.h"])
-        if st:
-            _add_decl(f)
-
-    return priv, pub
-
 def check_types(config_cmd, ext, build_dir):
     private_defines = []
     public_defines = []
@@ -427,6 +376,8 @@ def check_types(config_cmd, ext, build_dir):
 
     return private_defines, public_defines
 
+# NOTE: this isn't needed in the Meson build,
+#       and we won't support a MATHLIB env var
 def check_mathlib(config_cmd):
     # Testing the C math library
     mathlibs = []
@@ -466,7 +417,6 @@ def configuration(parent_package='',top_path=None):
                                            exec_mod_from_location)
     from numpy.distutils.system_info import (get_info, blas_opt_info,
                                              lapack_opt_info)
-    from numpy.distutils.ccompiler_opt import NPY_CXX_FLAGS
     from numpy.version import release as is_released
 
     config = Configuration('core', parent_package, top_path)
@@ -477,6 +427,8 @@ def configuration(parent_package='',top_path=None):
     # actual C API VERSION. Will raise a MismatchCAPIError if so.
     check_api_version(C_API_VERSION, codegen_dir)
 
+    check_git_submodules()
+
     generate_umath_py = join(codegen_dir, 'generate_umath.py')
     n = dot_join(config.name, 'generate_umath')
     generate_umath = exec_mod_from_location('_'.join(n.split('.')),
@@ -504,12 +456,11 @@ def configuration(parent_package='',top_path=None):
             moredefs.append(('MATHLIB', ','.join(mathlibs)))
 
             check_math_capabilities(config_cmd, ext, moredefs, mathlibs)
-            moredefs.extend(cocache.check_ieee_macros(config_cmd)[0])
             moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[0])
 
             # Signal check
             if is_npy_no_signal():
-                moredefs.append('__NPY_PRIVATE_NO_SIGNAL')
+                moredefs.append('NPY_NO_SIGNAL')
 
             # Windows checks
             if sys.platform == 'win32' or os.name == 'nt':
@@ -623,7 +574,6 @@ def configuration(parent_package='',top_path=None):
                 moredefs.append(('NPY_NO_SMP', 0))
 
             mathlibs = check_mathlib(config_cmd)
-            moredefs.extend(cocache.check_ieee_macros(config_cmd)[1])
             moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[1])
 
             if NPY_RELAXED_STRIDES_DEBUG:
@@ -671,11 +621,11 @@ def configuration(parent_package='',top_path=None):
             try:
                 m = __import__(module_name)
                 log.info('executing %s', script)
-                h_file, c_file, doc_file = m.generate_api(os.path.join(build_dir, header_dir))
+                h_file, c_file = m.generate_api(os.path.join(build_dir, header_dir))
             finally:
                 del sys.path[0]
             config.add_data_files((header_dir, h_file),
-                                  (header_dir, doc_file))
+                                 )
             return (h_file,)
         return generate_api
 
@@ -704,8 +654,7 @@ def configuration(parent_package='',top_path=None):
 
     config.numpy_include_dirs.extend(config.paths('include'))
 
-    deps = [join('src', 'npymath', '_signbit.c'),
-            join('include', 'numpy', '*object.h'),
+    deps = [join('include', 'numpy', '*object.h'),
             join(codegen_dir, 'genapi.py'),
             ]
 
@@ -720,44 +669,6 @@ def configuration(parent_package='',top_path=None):
         # but we cannot use add_installed_pkg_config here either, so we only
         # update the substitution dictionary during npymath build
         config_cmd = config.get_config_cmd()
-        # Check that the toolchain works, to fail early if it doesn't
-        # (avoid late errors with MATHLIB which are confusing if the
-        # compiler does not work).
-        for lang, test_code, note in (
-            ('c', 'int main(void) { return 0;}', ''),
-            ('c++', (
-                    'int main(void)'
-                    '{ auto x = 0.0; return static_cast<int>(x); }'
-                ), (
-                    'note: A compiler with support for C++11 language '
-                    'features is required.'
-                )
-             ),
-        ):
-            is_cpp = lang == 'c++'
-            if is_cpp:
-                # this a workaround to get rid of invalid c++ flags
-                # without doing big changes to config.
-                # c tested first, compiler should be here
-                bk_c = config_cmd.compiler
-                config_cmd.compiler = bk_c.cxx_compiler()
-
-                # Check that Linux compiler actually support the default flags
-                if hasattr(config_cmd.compiler, 'compiler'):
-                    config_cmd.compiler.compiler.extend(NPY_CXX_FLAGS)
-                    config_cmd.compiler.compiler_so.extend(NPY_CXX_FLAGS)
-
-            st = config_cmd.try_link(test_code, lang=lang)
-            if not st:
-                # rerun the failing command in verbose mode
-                config_cmd.compiler.verbose = True
-                config_cmd.try_link(test_code, lang=lang)
-                raise RuntimeError(
-                    f"Broken toolchain: cannot link a simple {lang.upper()} "
-                    f"program. {note}"
-                )
-            if is_cpp:
-                config_cmd.compiler = bk_c
         mlibs = check_mathlib(config_cmd)
 
         posix_mlib = ' '.join(['-l%s' % l for l in mlibs])
@@ -770,7 +681,7 @@ def configuration(parent_package='',top_path=None):
                        # join('src', 'npymath', 'ieee754.cpp'),
                        join('src', 'npymath', 'ieee754.c.src'),
                        join('src', 'npymath', 'npy_math_complex.c.src'),
-                       join('src', 'npymath', 'halffloat.c'),
+                       join('src', 'npymath', 'halffloat.cpp'),
                        ]
 
     config.add_installed_library('npymath',
@@ -828,7 +739,8 @@ def configuration(parent_package='',top_path=None):
             join('src', 'common', 'numpyos.h'),
             join('src', 'common', 'npy_cpu_dispatch.h'),
             join('src', 'common', 'simd', 'simd.h'),
-            ]
+            join('src', 'common', 'common.hpp'),
+        ]
 
     common_src = [
             join('src', 'common', 'array_assign.c'),
@@ -882,6 +794,7 @@ def configuration(parent_package='',top_path=None):
             join('src', 'multiarray', 'descriptor.h'),
             join('src', 'multiarray', 'dtypemeta.h'),
             join('src', 'multiarray', 'dtype_transfer.h'),
+            join('src', 'multiarray', 'dtype_traversal.h'),
             join('src', 'multiarray', 'dragon4.h'),
             join('src', 'multiarray', 'einsum_debug.h'),
             join('src', 'multiarray', 'einsum_sumprod.h'),
@@ -955,6 +868,7 @@ def configuration(parent_package='',top_path=None):
             join('src', 'multiarray', 'dtypemeta.c'),
             join('src', 'multiarray', 'dragon4.c'),
             join('src', 'multiarray', 'dtype_transfer.c'),
+            join('src', 'multiarray', 'dtype_traversal.c'),
             join('src', 'multiarray', 'einsum.c.src'),
             join('src', 'multiarray', 'einsum_sumprod.c.src'),
             join('src', 'multiarray', 'experimental_public_dtype_api.c'),
@@ -984,7 +898,6 @@ def configuration(parent_package='',top_path=None):
             join('src', 'multiarray', 'usertypes.c'),
             join('src', 'multiarray', 'vdot.c'),
             join('src', 'common', 'npy_sort.h.src'),
-            join('src', 'npysort', 'x86-qsort.dispatch.cpp'),
             join('src', 'npysort', 'quicksort.cpp'),
             join('src', 'npysort', 'mergesort.cpp'),
             join('src', 'npysort', 'timsort.cpp'),
@@ -1006,6 +919,8 @@ def configuration(parent_package='',top_path=None):
             # links to the arm64 npymath library,
             # see gh-22673
             join('src', 'npymath', 'arm64_exports.c'),
+            join('src', 'npysort', 'simd_qsort.dispatch.cpp'),
+            join('src', 'npysort', 'simd_qsort_16bit.dispatch.cpp'),
             ]
 
     #######################################################################
@@ -1043,13 +958,15 @@ def configuration(parent_package='',top_path=None):
             join('src', 'umath', 'umathmodule.c'),
             join('src', 'umath', 'reduction.c'),
             join('src', 'umath', 'funcs.inc.src'),
-            join('src', 'umath', 'simd.inc.src'),
             join('src', 'umath', 'loops.h.src'),
             join('src', 'umath', 'loops_utils.h.src'),
             join('src', 'umath', 'loops.c.src'),
+            join('src', 'umath', 'loops_unary.dispatch.c.src'),
             join('src', 'umath', 'loops_unary_fp.dispatch.c.src'),
+            join('src', 'umath', 'loops_unary_fp_le.dispatch.c.src'),
             join('src', 'umath', 'loops_arithm_fp.dispatch.c.src'),
             join('src', 'umath', 'loops_arithmetic.dispatch.c.src'),
+            join('src', 'umath', 'loops_logical.dispatch.c.src'),
             join('src', 'umath', 'loops_minmax.dispatch.c.src'),
             join('src', 'umath', 'loops_trigonometric.dispatch.c.src'),
             join('src', 'umath', 'loops_umath_fp.dispatch.c.src'),
@@ -1057,6 +974,8 @@ def configuration(parent_package='',top_path=None):
             join('src', 'umath', 'loops_hyperbolic.dispatch.c.src'),
             join('src', 'umath', 'loops_modulo.dispatch.c.src'),
             join('src', 'umath', 'loops_comparison.dispatch.c.src'),
+            join('src', 'umath', 'loops_unary_complex.dispatch.c.src'),
+            join('src', 'umath', 'loops_autovec.dispatch.c.src'),
             join('src', 'umath', 'matmul.h.src'),
             join('src', 'umath', 'matmul.c.src'),
             join('src', 'umath', 'clip.h'),
@@ -1081,7 +1000,6 @@ def configuration(parent_package='',top_path=None):
             join('src', 'multiarray', 'common.h'),
             join('src', 'multiarray', 'number.h'),
             join('src', 'common', 'templ_common.h.src'),
-            join('src', 'umath', 'simd.inc.src'),
             join('src', 'umath', 'override.h'),
             join(codegen_dir, 'generate_ufunc_api.py'),
             join(codegen_dir, 'ufunc_docstrings.py'),
@@ -1096,18 +1014,17 @@ def configuration(parent_package='',top_path=None):
     # after all maintainable code.
     svml_filter = (
     )
-    if can_link_svml() and check_svml_submodule(svml_path):
+    if can_link_svml():
         svml_objs = glob.glob(svml_path + '/**/*.s', recursive=True)
         svml_objs = [o for o in svml_objs if not o.endswith(svml_filter)]
 
         # The ordering of names returned by glob is undefined, so we sort
         # to make builds reproducible.
         svml_objs.sort()
+        if not can_link_svml_fp16():
+            svml_objs = [o for o in svml_objs if not o.endswith('_h_la.s')]
 
     config.add_extension('_multiarray_umath',
-                         # Forcing C language even though we have C++ sources.
-                         # It forces the C linker and don't link C++ runtime.
-                         language = 'c',
                          sources=multiarray_src + umath_src +
                                  common_src +
                                  [generate_config_h,
@@ -1123,8 +1040,7 @@ def configuration(parent_package='',top_path=None):
                                 common_deps,
                          libraries=['npymath'],
                          extra_objects=svml_objs,
-                         extra_info=extra_info,
-                         extra_cxx_compile_args=NPY_CXX_FLAGS)
+                         extra_info=extra_info)
 
     #######################################################################
     #                        umath_tests module                           #
diff --git a/contrib/python/numpy/py3/numpy/core/setup_common.py b/contrib/python/numpy/py3/numpy/core/setup_common.py
index 0512457f486..b5bc0dec1f8 100644
--- a/contrib/python/numpy/py3/numpy/core/setup_common.py
+++ b/contrib/python/numpy/py3/numpy/core/setup_common.py
@@ -48,7 +48,13 @@ C_ABI_VERSION = 0x01000009
 # 0x0000000f - 1.22.x
 # 0x00000010 - 1.23.x
 # 0x00000010 - 1.24.x
-C_API_VERSION = 0x00000010
+# 0x00000011 - 1.25.x
+C_API_VERSION = 0x00000011
+
+# By default, when compiling downstream libraries against NumPy,```
+# pick an older feature version.  For example, for 1.25.x we default to the
+# 1.19 API and support going back all the way to 1.15.x (if so desired).
+# This is set up in `numpyconfig.h`.
 
 class MismatchCAPIError(ValueError):
     pass
@@ -91,6 +97,10 @@ def check_api_version(apiversion, codegen_dir):
                f"checksum in core/codegen_dir/cversions.txt is {api_hash}. If "
                "functions were added in the C API, you have to update "
                f"C_API_VERSION in {__file__}."
+               "\n"
+               "Please make sure that new additions are guarded with "
+               "MinVersion() to make them unavailable when wishing support "
+               "for older NumPy versions."
                )
         raise MismatchCAPIError(msg)
 
@@ -183,25 +193,7 @@ OPTIONAL_INTRINSICS = [("__builtin_isnan", '5.'),
                        # Test `long long` for arm+clang 13 (gh-22811,
                        # but we use all versions of __builtin_mul_overflow):
                        ("__builtin_mul_overflow", '(long long)5, 5, (int*)5'),
-                       # MMX only needed for icc, but some clangs don't have it
-                       ("_m_from_int64", '0', "emmintrin.h"),
-                       ("_mm_load_ps", '(float*)0', "xmmintrin.h"),  # SSE
-                       ("_mm_prefetch", '(float*)0, _MM_HINT_NTA',
-                        "xmmintrin.h"),  # SSE
-                       ("_mm_load_pd", '(double*)0', "emmintrin.h"),  # SSE2
                        ("__builtin_prefetch", "(float*)0, 0, 3"),
-                       # check that the linker can handle avx
-                       ("__asm__ volatile", '"vpand %xmm1, %xmm2, %xmm3"',
-                        "stdio.h", "LINK_AVX"),
-                       ("__asm__ volatile", '"vpand %ymm1, %ymm2, %ymm3"',
-                        "stdio.h", "LINK_AVX2"),
-                       ("__asm__ volatile", '"vpaddd %zmm1, %zmm2, %zmm3"',
-                        "stdio.h", "LINK_AVX512F"),
-                       ("__asm__ volatile", '"vfpclasspd $0x40, %zmm15, %k6\\n"\
-                                             "vmovdqu8 %xmm0, %xmm1\\n"\
-                                             "vpbroadcastmb2q %k0, %xmm0\\n"',
-                        "stdio.h", "LINK_AVX512_SKX"),
-                       ("__asm__ volatile", '"xgetbv"', "stdio.h", "XGETBV"),
                        ]
 
 # function attributes
@@ -216,44 +208,6 @@ OPTIONAL_FUNCTION_ATTRIBUTES = [('__attribute__((optimize("unroll-loops")))',
                                 ('__attribute__((nonnull (1)))',
                                  'attribute_nonnull'),
                                 ]
-
-OPTIONAL_FUNCTION_ATTRIBUTES_AVX = [('__attribute__((target ("avx")))',
-    'attribute_target_avx'),
-    ('__attribute__((target ("avx2")))',
-    'attribute_target_avx2'),
-    ('__attribute__((target ("avx512f")))',
-    'attribute_target_avx512f'),
-    ('__attribute__((target ("avx512f,avx512dq,avx512bw,avx512vl,avx512cd")))',
-    'attribute_target_avx512_skx'),
-    ]
-
-# function attributes with intrinsics
-# To ensure your compiler can compile avx intrinsics with just the attributes
-# gcc 4.8.4 support attributes but not with intrisics
-# tested via "#include<%s> int %s %s(void *){code; return 0;};" % (header, attribute, name, code)
-# function name will be converted to HAVE_<upper-case-name> preprocessor macro
-# The _mm512_castps_si512 instruction is specific check for AVX-512F support
-# in gcc-4.9 which is missing a subset of intrinsics. See
-# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=61878
-OPTIONAL_FUNCTION_ATTRIBUTES_WITH_INTRINSICS_AVX = [
-    ('__attribute__((target("avx2,fma")))',
-    'attribute_target_avx2_with_intrinsics',
-    '__m256 temp = _mm256_set1_ps(1.0); temp = \
-    _mm256_fmadd_ps(temp, temp, temp)',
-    'immintrin.h'),
-    ('__attribute__((target("avx512f")))',
-    'attribute_target_avx512f_with_intrinsics',
-    '__m512i temp = _mm512_castps_si512(_mm512_set1_ps(1.0))',
-    'immintrin.h'),
-    ('__attribute__((target ("avx512f,avx512dq,avx512bw,avx512vl,avx512cd")))',
-    'attribute_target_avx512_skx_with_intrinsics',
-    '__mmask8 temp = _mm512_fpclass_pd_mask(_mm512_set1_pd(1.0), 0x01);\
-    __m512i unused_temp = \
-        _mm512_castps_si512(_mm512_set1_ps(1.0));\
-    _mm_mask_storeu_epi8(NULL, 0xFF, _mm_broadcastmb_epi64(temp))',
-    'immintrin.h'),
-    ]
-
 def fname2def(name):
     return "HAVE_%s" % name.upper()
 
diff --git a/contrib/python/numpy/py3/numpy/core/shape_base.py b/contrib/python/numpy/py3/numpy/core/shape_base.py
index 84a6bd671de..250fffd424d 100644
--- a/contrib/python/numpy/py3/numpy/core/shape_base.py
+++ b/contrib/python/numpy/py3/numpy/core/shape_base.py
@@ -204,19 +204,15 @@ def atleast_3d(*arys):
         return res
 
 
-def _arrays_for_stack_dispatcher(arrays, stacklevel=4):
-    if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
-        warnings.warn('arrays to stack must be passed as a "sequence" type '
-                      'such as list or tuple. Support for non-sequence '
-                      'iterables such as generators is deprecated as of '
-                      'NumPy 1.16 and will raise an error in the future.',
-                      FutureWarning, stacklevel=stacklevel)
-        return ()
-    return arrays
+def _arrays_for_stack_dispatcher(arrays):
+    if not hasattr(arrays, "__getitem__"):
+        raise TypeError('arrays to stack must be passed as a "sequence" type '
+                        'such as list or tuple.')
+
+    return tuple(arrays)
 
 
-def _vhstack_dispatcher(tup, *, 
-                        dtype=None, casting=None):
+def _vhstack_dispatcher(tup, *, dtype=None, casting=None):
     return _arrays_for_stack_dispatcher(tup)
 
 
@@ -287,9 +283,6 @@ def vstack(tup, *, dtype=None, casting="same_kind"):
            [6]])
 
     """
-    if not overrides.ARRAY_FUNCTION_ENABLED:
-        # raise warning if necessary
-        _arrays_for_stack_dispatcher(tup, stacklevel=2)
     arrs = atleast_2d(*tup)
     if not isinstance(arrs, list):
         arrs = [arrs]
@@ -356,10 +349,6 @@ def hstack(tup, *, dtype=None, casting="same_kind"):
            [3, 6]])
 
     """
-    if not overrides.ARRAY_FUNCTION_ENABLED:
-        # raise warning if necessary
-        _arrays_for_stack_dispatcher(tup, stacklevel=2)
-
     arrs = atleast_1d(*tup)
     if not isinstance(arrs, list):
         arrs = [arrs]
@@ -372,7 +361,7 @@ def hstack(tup, *, dtype=None, casting="same_kind"):
 
 def _stack_dispatcher(arrays, axis=None, out=None, *,
                       dtype=None, casting=None):
-    arrays = _arrays_for_stack_dispatcher(arrays, stacklevel=6)
+    arrays = _arrays_for_stack_dispatcher(arrays)
     if out is not None:
         # optimize for the typical case where only arrays is provided
         arrays = list(arrays)
@@ -451,10 +440,6 @@ def stack(arrays, axis=0, out=None, *, dtype=None, casting="same_kind"):
            [3, 6]])
 
     """
-    if not overrides.ARRAY_FUNCTION_ENABLED:
-        # raise warning if necessary
-        _arrays_for_stack_dispatcher(arrays, stacklevel=2)
-
     arrays = [asanyarray(arr) for arr in arrays]
     if not arrays:
         raise ValueError('need at least one array to stack')
diff --git a/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c
index 614c97597d3..b930ac657a2 100644
--- a/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c
@@ -39,7 +39,9 @@ SIMD_IMPL_INTRIN_1(loads_u8, vu8, qu8)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_u8, vu8, qu8)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_u8x2, vu8x2, qu8)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_u8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -63,7 +65,7 @@ simd__intrin_store_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_u8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -87,7 +89,7 @@ simd__intrin_storea_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_u8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -111,7 +113,7 @@ simd__intrin_stores_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_u8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -135,7 +137,7 @@ simd__intrin_storel_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_u8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -159,6 +161,30 @@ simd__intrin_storeh_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_u8x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg vec_arg = {.dtype = simd_data_vu8x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_u8x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_u8x2(seq_arg.data.qu8, vec_arg.data.vu8x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu8, simd_data_qu8)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -167,8 +193,17 @@ simd__intrin_storeh_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_u8, vu8, qu8, u32, u8)
 SIMD_IMPL_INTRIN_2(load_tillz_u8, vu8, qu8, u32)
+#if 8 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_u8, vu8, qu8, u32, u8, u8)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u8, vu8, qu8, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_u8, vu8, qu8, u32, u8, u8)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u8, vu8, qu8, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_store_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -194,9 +229,72 @@ simd__intrin_store_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_store2_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu8};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u8(
+        seq_arg.data.qu8, nlane_arg.data.u32, vec_arg.data.vu8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu8, simd_data_qu8)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_store2_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu8};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u8(
+        seq_arg.data.qu8, nlane_arg.data.u32, vec_arg.data.vu8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu8, simd_data_qu8)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_loadn_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -208,6 +306,9 @@ simd__intrin_loadn_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u8};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_u8",
         simd_arg_converter, &seq_arg,
@@ -218,6 +319,9 @@ simd__intrin_loadn_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -226,7 +330,7 @@ simd__intrin_loadn_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -245,6 +349,80 @@ simd__intrin_loadn_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u8
     #endif
+    #if 0
+        , fill2_arg.data.u8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu8, .data = {.vu8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_loadn2_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u8 rvec = npyv_loadn2_u8(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u8
+    #endif
+    #if 0
+        , fill2_arg.data.u8
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu8, .data = {.vu8=rvec}
@@ -255,8 +433,81 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_loadn2_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u8 rvec = npyv_loadn2_u8(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u8
+    #endif
+    #if 0
+        , fill2_arg.data.u8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu8, .data = {.vu8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_loadn_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -268,6 +519,9 @@ simd__intrin_loadn_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_u8};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_u8",
         simd_arg_converter, &seq_arg,
@@ -278,6 +532,9 @@ simd__intrin_loadn_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -286,7 +543,7 @@ simd__intrin_loadn_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -305,6 +562,9 @@ simd__intrin_loadn_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.u8
     #endif
+    #if 0
+        , fill2_arg.data.u8
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu8, .data = {.vu8=rvec}
@@ -315,8 +575,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_loadn2_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u8};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u8 rvec = npyv_loadn2_till_u8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u8
+    #endif
+    #if 1
+        , fill2_arg.data.u8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu8, .data = {.vu8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_loadn2_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u8};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u8 rvec = npyv_loadn2_till_u8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u8
+    #endif
+    #if 1
+        , fill2_arg.data.u8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu8, .data = {.vu8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_loadn_tillz_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -328,6 +732,9 @@ simd__intrin_loadn_tillz_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u8};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_u8",
         simd_arg_converter, &seq_arg,
@@ -338,6 +745,9 @@ simd__intrin_loadn_tillz_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -346,7 +756,7 @@ simd__intrin_loadn_tillz_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -365,6 +775,9 @@ simd__intrin_loadn_tillz_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u8
     #endif
+    #if 0
+        , fill2_arg.data.u8
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu8, .data = {.vu8=rvec}
@@ -375,10 +788,154 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_loadn2_tillz_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u8 rvec = npyv_loadn2_tillz_u8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u8
+    #endif
+    #if 0
+        , fill2_arg.data.u8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu8, .data = {.vu8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_loadn2_tillz_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u8 rvec = npyv_loadn2_tillz_u8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u8
+    #endif
+    #if 0
+        , fill2_arg.data.u8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu8, .data = {.vu8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_storen_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -404,7 +961,7 @@ simd__intrin_storen_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -433,8 +990,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_storen2_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu8};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u8(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu8, simd_data_qu8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
-#line 180
+#line 216
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_storen2_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu8};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u8(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu8, simd_data_qu8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_storen_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -460,7 +1135,7 @@ simd__intrin_storen_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -489,6 +1164,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_storen2_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu8};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u8(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu8, simd_data_qu8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_storen2_till_u8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu8};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u8 *seq_ptr = seq_arg.data.qu8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u8(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu8, simd_data_qu8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 0
 
@@ -509,52 +1301,52 @@ SIMD_IMPL_INTRIN_1(extract0_u8, u8, vu8)
 SIMD_IMPL_INTRIN_1(setall_u8, vu8, u8)
 SIMD_IMPL_INTRIN_3(select_u8, vu8, vb8, vu8, vu8)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_u8, vu8, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_u8, vs8, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_u8, vu16, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_u8, vs16, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_u8, vu32, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_u8, vs32, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_u8, vu64, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_u8, vs64, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_u8, vf32, vu8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_u8, vf64, vu8)
 #endif // simd_sup2
@@ -564,7 +1356,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_u8, vf64, vu8)
  * special definition due to the nature of intrinsics
  * npyv_setf_u8 and npy_set_u8.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -587,7 +1379,7 @@ simd__intrin_setf_u8(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vu8);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -614,24 +1406,159 @@ simd__intrin_set_u8(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_u8, vu8, vu8, vu8)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_u8, vu8, vu8, vu8)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_u8, vu8x2, vu8, vu8)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_u8, vu8x2, vu8, vu8)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_u8, vu8x2, vu8, vu8)
+
 
 #if 1
 SIMD_IMPL_INTRIN_1(rev64_u8, vu8, vu8)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 8 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_u8
+npyv_permi128_u8_(npyv_u8 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_u8 ve0;
+    npyv_lanetype_u8 de0[npyv_nlanes_u8];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_u8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_u8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_u8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_u8(a, 0, 0, 0, 0);
+    }
+    npyv_store_u8(de0, ve0);
+    
+#line 360
+    npyv_u8 ve1;
+    npyv_lanetype_u8 de1[npyv_nlanes_u8];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_u8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_u8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_u8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_u8(a, 0, 0, 0, 0);
+    }
+    npyv_store_u8(de1, ve1);
+    
+#line 360
+    npyv_u8 ve2;
+    npyv_lanetype_u8 de2[npyv_nlanes_u8];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_u8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_u8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_u8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_u8(a, 0, 0, 0, 0);
+    }
+    npyv_store_u8(de2, ve2);
+    
+#line 360
+    npyv_u8 ve3;
+    npyv_lanetype_u8 de3[npyv_nlanes_u8];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_u8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_u8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_u8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_u8(a, 0, 0, 0, 0);
+    }
+    npyv_store_u8(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_u8; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_u8(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_u8_, vu8, vu8, u8, u8, u8, u8)
+#elif 8 == 64
+NPY_FINLINE npyv_u8
+npyv_permi128_u8_(npyv_u8 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_u8(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_u8(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_u8(a, 1, 1);
+    }
+    return npyv_permi128_u8(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_u8_, vu8, vu8, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -643,34 +1570,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_u8, vu8, vu8, 0)
 SIMD_IMPL_INTRIN_2IMM(shri_u8, vu8, vu8, 0)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_u8, vu8, vu8, vu8)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_u8, vu8, vu8, vu8)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_u8, vu8, vu8, vu8)
 
 
 SIMD_IMPL_INTRIN_1(not_u8, vu8, vu8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_u8, vb8, vu8, vu8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_u8, vb8, vu8, vu8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_u8, vb8, vu8, vu8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_u8, vb8, vu8, vu8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_u8, vb8, vu8, vu8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_u8, vb8, vu8, vu8)
 
 
@@ -682,10 +1609,10 @@ SIMD_IMPL_INTRIN_2(xnor_b8, vb8, vb8, vb8)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_u8, u8, vu8)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_u8, u8, vu8)
 
 /***************************
@@ -699,18 +1626,18 @@ SIMD_IMPL_INTRIN_1(expand_u16_u8, vu16x2, vu8)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_u8, vu8, vu8, vu8)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_u8, vu8, vu8, vu8)
 
 
 #if 1
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_u8, vu8, vu8, vu8)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_u8, vu8, vu8, vu8)
 
 #endif // sat_sup
@@ -729,18 +1656,21 @@ SIMD_IMPL_INTRIN_2(divc_u8, vu8, vu8, vu8x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_u8, vu8, vu8, vu8, vu8)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_u8, vu8, vu8, vu8, vu8)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_u8, vu8, vu8, vu8, vu8)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_u8, vu8, vu8, vu8, vu8)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_u8, vu8, vu8, vu8, vu8)
+
 #endif // fused_sup
 
 #if 0
@@ -755,55 +1685,55 @@ SIMD_IMPL_INTRIN_1(sumup_u8, u16, vu8)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_u8, vu8, vu8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_u8, vu8, vu8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_u8, vu8, vu8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_u8, vu8, vu8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_u8, vu8, vu8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_u8, vu8, vu8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_u8, vu8, vu8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_u8, vu8, vu8)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_u8, vu8, vu8, vu8)
 SIMD_IMPL_INTRIN_1(reduce_max_u8, u8, vu8)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_u8, vu8, vu8, vu8)
 SIMD_IMPL_INTRIN_1(reduce_min_u8, u8, vu8)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_u8, vu8, vu8, vu8)
 SIMD_IMPL_INTRIN_1(reduce_maxp_u8, u8, vu8)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_u8, vu8, vu8, vu8)
 SIMD_IMPL_INTRIN_1(reduce_minp_u8, u8, vu8)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_u8, vu8, vu8, vu8)
 SIMD_IMPL_INTRIN_1(reduce_maxn_u8, u8, vu8)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_u8, vu8, vu8, vu8)
 SIMD_IMPL_INTRIN_1(reduce_minn_u8, u8, vu8)
 
@@ -813,13 +1743,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_u8, u8, vu8)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_u8, vu8, vb8, vu8, vu8, vu8)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_u8, vu8, vb8, vu8, vu8, vu8)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_u8, vu8, vb8, vu8, vu8, vu8)
+SIMD_IMPL_INTRIN_3(ifdivz_u8, vu8, vb8, vu8, vu8)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -839,7 +1774,9 @@ SIMD_IMPL_INTRIN_1(loads_s8, vs8, qs8)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_s8, vs8, qs8)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_s8x2, vs8x2, qs8)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_s8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -863,7 +1800,7 @@ simd__intrin_store_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_s8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -887,7 +1824,7 @@ simd__intrin_storea_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_s8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -911,7 +1848,7 @@ simd__intrin_stores_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_s8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -935,7 +1872,7 @@ simd__intrin_storel_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_s8(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -959,6 +1896,30 @@ simd__intrin_storeh_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_s8x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg vec_arg = {.dtype = simd_data_vs8x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_s8x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_s8x2(seq_arg.data.qs8, vec_arg.data.vs8x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs8, simd_data_qs8)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -967,8 +1928,17 @@ simd__intrin_storeh_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_s8, vs8, qs8, u32, s8)
 SIMD_IMPL_INTRIN_2(load_tillz_s8, vs8, qs8, u32)
+#if 8 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_s8, vs8, qs8, u32, s8, s8)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s8, vs8, qs8, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_s8, vs8, qs8, u32, s8, s8)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s8, vs8, qs8, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_store_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -994,9 +1964,72 @@ simd__intrin_store_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_store2_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs8};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s8(
+        seq_arg.data.qs8, nlane_arg.data.u32, vec_arg.data.vs8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs8, simd_data_qs8)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_store2_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs8};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s8(
+        seq_arg.data.qs8, nlane_arg.data.u32, vec_arg.data.vs8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs8, simd_data_qs8)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_loadn_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1008,6 +2041,9 @@ simd__intrin_loadn_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s8};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_s8",
         simd_arg_converter, &seq_arg,
@@ -1018,6 +2054,9 @@ simd__intrin_loadn_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -1026,7 +2065,7 @@ simd__intrin_loadn_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -1045,6 +2084,9 @@ simd__intrin_loadn_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s8
     #endif
+    #if 0
+        , fill2_arg.data.s8
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs8, .data = {.vs8=rvec}
@@ -1055,8 +2097,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_loadn2_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s8 rvec = npyv_loadn2_s8(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s8
+    #endif
+    #if 0
+        , fill2_arg.data.s8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs8, .data = {.vs8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_loadn2_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s8 rvec = npyv_loadn2_s8(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s8
+    #endif
+    #if 0
+        , fill2_arg.data.s8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs8, .data = {.vs8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_loadn_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1068,6 +2254,9 @@ simd__intrin_loadn_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_s8};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_s8",
         simd_arg_converter, &seq_arg,
@@ -1078,6 +2267,9 @@ simd__intrin_loadn_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -1086,7 +2278,7 @@ simd__intrin_loadn_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -1105,6 +2297,80 @@ simd__intrin_loadn_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.s8
     #endif
+    #if 0
+        , fill2_arg.data.s8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs8, .data = {.vs8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_loadn2_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s8};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s8 rvec = npyv_loadn2_till_s8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s8
+    #endif
+    #if 1
+        , fill2_arg.data.s8
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs8, .data = {.vs8=rvec}
@@ -1115,8 +2381,81 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_loadn2_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s8};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s8 rvec = npyv_loadn2_till_s8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s8
+    #endif
+    #if 1
+        , fill2_arg.data.s8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs8, .data = {.vs8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_loadn_tillz_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1128,6 +2467,9 @@ simd__intrin_loadn_tillz_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s8};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_s8",
         simd_arg_converter, &seq_arg,
@@ -1138,6 +2480,9 @@ simd__intrin_loadn_tillz_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -1146,7 +2491,7 @@ simd__intrin_loadn_tillz_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -1165,6 +2510,80 @@ simd__intrin_loadn_tillz_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s8
     #endif
+    #if 0
+        , fill2_arg.data.s8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs8, .data = {.vs8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_loadn2_tillz_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s8 rvec = npyv_loadn2_tillz_s8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s8
+    #endif
+    #if 0
+        , fill2_arg.data.s8
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs8, .data = {.vs8=rvec}
@@ -1175,10 +2594,83 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_loadn2_tillz_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s8};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s8};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s8(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s8 rvec = npyv_loadn2_tillz_s8(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s8
+    #endif
+    #if 0
+        , fill2_arg.data.s8
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs8, .data = {.vs8=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_storen_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1204,7 +2696,7 @@ simd__intrin_storen_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -1233,8 +2725,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 180
+#line 216
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_storen2_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs8};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s8(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs8, simd_data_qs8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_storen2_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs8};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s8(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs8, simd_data_qs8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 8
 static PyObject *
 simd__intrin_storen_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1260,7 +2870,7 @@ simd__intrin_storen_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -1289,6 +2899,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 8
+static PyObject *
+simd__intrin_storen2_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs8};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s8(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs8, simd_data_qs8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 8
+static PyObject *
+simd__intrin_storen2_till_s8(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs8};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs8};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s8",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s8 *seq_ptr = seq_arg.data.qs8;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s8;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s8(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s8(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs8
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs8, simd_data_qs8)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 0
 
@@ -1309,52 +3036,52 @@ SIMD_IMPL_INTRIN_1(extract0_s8, s8, vs8)
 SIMD_IMPL_INTRIN_1(setall_s8, vs8, s8)
 SIMD_IMPL_INTRIN_3(select_s8, vs8, vb8, vs8, vs8)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_s8, vu8, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_s8, vs8, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_s8, vu16, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_s8, vs16, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_s8, vu32, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_s8, vs32, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_s8, vu64, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_s8, vs64, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_s8, vf32, vs8)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_s8, vf64, vs8)
 #endif // simd_sup2
@@ -1364,7 +3091,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_s8, vf64, vs8)
  * special definition due to the nature of intrinsics
  * npyv_setf_s8 and npy_set_s8.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1387,7 +3114,7 @@ simd__intrin_setf_s8(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vs8);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1414,24 +3141,159 @@ simd__intrin_set_s8(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_s8, vs8, vs8, vs8)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_s8, vs8, vs8, vs8)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_s8, vs8x2, vs8, vs8)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_s8, vs8x2, vs8, vs8)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_s8, vs8x2, vs8, vs8)
+
 
 #if 1
 SIMD_IMPL_INTRIN_1(rev64_s8, vs8, vs8)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 8 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_s8
+npyv_permi128_s8_(npyv_s8 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_s8 ve0;
+    npyv_lanetype_s8 de0[npyv_nlanes_s8];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_s8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_s8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_s8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_s8(a, 0, 0, 0, 0);
+    }
+    npyv_store_s8(de0, ve0);
+    
+#line 360
+    npyv_s8 ve1;
+    npyv_lanetype_s8 de1[npyv_nlanes_s8];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_s8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_s8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_s8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_s8(a, 0, 0, 0, 0);
+    }
+    npyv_store_s8(de1, ve1);
+    
+#line 360
+    npyv_s8 ve2;
+    npyv_lanetype_s8 de2[npyv_nlanes_s8];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_s8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_s8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_s8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_s8(a, 0, 0, 0, 0);
+    }
+    npyv_store_s8(de2, ve2);
+    
+#line 360
+    npyv_s8 ve3;
+    npyv_lanetype_s8 de3[npyv_nlanes_s8];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_s8(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_s8(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_s8(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_s8(a, 0, 0, 0, 0);
+    }
+    npyv_store_s8(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_s8; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_s8(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_s8_, vs8, vs8, u8, u8, u8, u8)
+#elif 8 == 64
+NPY_FINLINE npyv_s8
+npyv_permi128_s8_(npyv_s8 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_s8(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_s8(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_s8(a, 1, 1);
+    }
+    return npyv_permi128_s8(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_s8_, vs8, vs8, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -1443,34 +3305,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_s8, vs8, vs8, 0)
 SIMD_IMPL_INTRIN_2IMM(shri_s8, vs8, vs8, 0)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_s8, vs8, vs8, vs8)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_s8, vs8, vs8, vs8)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_s8, vs8, vs8, vs8)
 
 
 SIMD_IMPL_INTRIN_1(not_s8, vs8, vs8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_s8, vb8, vs8, vs8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_s8, vb8, vs8, vs8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_s8, vb8, vs8, vs8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_s8, vb8, vs8, vs8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_s8, vb8, vs8, vs8)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_s8, vb8, vs8, vs8)
 
 
@@ -1482,10 +3344,10 @@ SIMD_IMPL_INTRIN_2(xnor_b8, vb8, vb8, vb8)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_s8, u8, vs8)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_s8, u8, vs8)
 
 /***************************
@@ -1499,18 +3361,18 @@ SIMD_IMPL_INTRIN_1(expand_s8_s8, vs8x2, vs8)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_s8, vs8, vs8, vs8)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_s8, vs8, vs8, vs8)
 
 
 #if 1
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_s8, vs8, vs8, vs8)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_s8, vs8, vs8, vs8)
 
 #endif // sat_sup
@@ -1529,18 +3391,21 @@ SIMD_IMPL_INTRIN_2(divc_s8, vs8, vs8, vs8x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_s8, vs8, vs8, vs8, vs8)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_s8, vs8, vs8, vs8, vs8)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_s8, vs8, vs8, vs8, vs8)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_s8, vs8, vs8, vs8, vs8)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_s8, vs8, vs8, vs8, vs8)
+
 #endif // fused_sup
 
 #if 0
@@ -1555,55 +3420,55 @@ SIMD_IMPL_INTRIN_1(sumup_s8, s8, vs8)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_s8, vs8, vs8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_s8, vs8, vs8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_s8, vs8, vs8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_s8, vs8, vs8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_s8, vs8, vs8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_s8, vs8, vs8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_s8, vs8, vs8)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_s8, vs8, vs8)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_s8, vs8, vs8, vs8)
 SIMD_IMPL_INTRIN_1(reduce_max_s8, s8, vs8)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_s8, vs8, vs8, vs8)
 SIMD_IMPL_INTRIN_1(reduce_min_s8, s8, vs8)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_s8, vs8, vs8, vs8)
 SIMD_IMPL_INTRIN_1(reduce_maxp_s8, s8, vs8)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_s8, vs8, vs8, vs8)
 SIMD_IMPL_INTRIN_1(reduce_minp_s8, s8, vs8)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_s8, vs8, vs8, vs8)
 SIMD_IMPL_INTRIN_1(reduce_maxn_s8, s8, vs8)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_s8, vs8, vs8, vs8)
 SIMD_IMPL_INTRIN_1(reduce_minn_s8, s8, vs8)
 
@@ -1613,13 +3478,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_s8, s8, vs8)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_s8, vs8, vb8, vs8, vs8, vs8)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_s8, vs8, vb8, vs8, vs8, vs8)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_s8, vs8, vb8, vs8, vs8, vs8)
+SIMD_IMPL_INTRIN_3(ifdivz_s8, vs8, vb8, vs8, vs8)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -1639,7 +3509,9 @@ SIMD_IMPL_INTRIN_1(loads_u16, vu16, qu16)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_u16, vu16, qu16)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_u16x2, vu16x2, qu16)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_u16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -1663,7 +3535,7 @@ simd__intrin_store_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_u16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -1687,7 +3559,7 @@ simd__intrin_storea_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_u16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -1711,7 +3583,7 @@ simd__intrin_stores_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_u16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -1735,7 +3607,7 @@ simd__intrin_storel_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_u16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -1759,6 +3631,30 @@ simd__intrin_storeh_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_u16x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg vec_arg = {.dtype = simd_data_vu16x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_u16x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_u16x2(seq_arg.data.qu16, vec_arg.data.vu16x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu16, simd_data_qu16)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -1767,8 +3663,17 @@ simd__intrin_storeh_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_u16, vu16, qu16, u32, u16)
 SIMD_IMPL_INTRIN_2(load_tillz_u16, vu16, qu16, u32)
+#if 16 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_u16, vu16, qu16, u32, u16, u16)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u16, vu16, qu16, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_u16, vu16, qu16, u32, u16, u16)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u16, vu16, qu16, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_store_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1794,9 +3699,72 @@ simd__intrin_store_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_store2_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu16};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u16(
+        seq_arg.data.qu16, nlane_arg.data.u32, vec_arg.data.vu16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu16, simd_data_qu16)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_store2_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu16};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u16(
+        seq_arg.data.qu16, nlane_arg.data.u32, vec_arg.data.vu16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu16, simd_data_qu16)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_loadn_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1808,6 +3776,9 @@ simd__intrin_loadn_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u16};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_u16",
         simd_arg_converter, &seq_arg,
@@ -1818,6 +3789,9 @@ simd__intrin_loadn_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -1826,7 +3800,7 @@ simd__intrin_loadn_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -1845,6 +3819,80 @@ simd__intrin_loadn_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u16
     #endif
+    #if 0
+        , fill2_arg.data.u16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu16, .data = {.vu16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_loadn2_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u16 rvec = npyv_loadn2_u16(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u16
+    #endif
+    #if 0
+        , fill2_arg.data.u16
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu16, .data = {.vu16=rvec}
@@ -1855,8 +3903,81 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_loadn2_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u16 rvec = npyv_loadn2_u16(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u16
+    #endif
+    #if 0
+        , fill2_arg.data.u16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu16, .data = {.vu16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_loadn_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1868,6 +3989,9 @@ simd__intrin_loadn_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_u16};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_u16",
         simd_arg_converter, &seq_arg,
@@ -1878,6 +4002,9 @@ simd__intrin_loadn_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -1886,7 +4013,7 @@ simd__intrin_loadn_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -1905,6 +4032,9 @@ simd__intrin_loadn_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.u16
     #endif
+    #if 0
+        , fill2_arg.data.u16
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu16, .data = {.vu16=rvec}
@@ -1915,8 +4045,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_loadn2_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u16};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u16 rvec = npyv_loadn2_till_u16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u16
+    #endif
+    #if 1
+        , fill2_arg.data.u16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu16, .data = {.vu16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_loadn2_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u16};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u16 rvec = npyv_loadn2_till_u16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u16
+    #endif
+    #if 1
+        , fill2_arg.data.u16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu16, .data = {.vu16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_loadn_tillz_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -1928,6 +4202,9 @@ simd__intrin_loadn_tillz_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u16};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_u16",
         simd_arg_converter, &seq_arg,
@@ -1938,6 +4215,9 @@ simd__intrin_loadn_tillz_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -1946,7 +4226,7 @@ simd__intrin_loadn_tillz_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -1965,6 +4245,9 @@ simd__intrin_loadn_tillz_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u16
     #endif
+    #if 0
+        , fill2_arg.data.u16
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu16, .data = {.vu16=rvec}
@@ -1975,10 +4258,154 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_loadn2_tillz_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u16 rvec = npyv_loadn2_tillz_u16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u16
+    #endif
+    #if 0
+        , fill2_arg.data.u16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu16, .data = {.vu16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_loadn2_tillz_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u16 rvec = npyv_loadn2_tillz_u16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u16
+    #endif
+    #if 0
+        , fill2_arg.data.u16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu16, .data = {.vu16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_storen_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2004,7 +4431,7 @@ simd__intrin_storen_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -2033,8 +4460,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_storen2_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu16};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u16(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu16, simd_data_qu16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_storen2_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu16};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u16(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu16, simd_data_qu16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
-#line 180
+#line 216
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_storen_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2060,7 +4605,7 @@ simd__intrin_storen_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -2089,6 +4634,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_storen2_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu16};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u16(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu16, simd_data_qu16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_storen2_till_u16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu16};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u16 *seq_ptr = seq_arg.data.qu16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u16(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu16, simd_data_qu16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 0
 
@@ -2109,52 +4771,52 @@ SIMD_IMPL_INTRIN_1(extract0_u16, u16, vu16)
 SIMD_IMPL_INTRIN_1(setall_u16, vu16, u16)
 SIMD_IMPL_INTRIN_3(select_u16, vu16, vb16, vu16, vu16)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_u16, vu8, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_u16, vs8, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_u16, vu16, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_u16, vs16, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_u16, vu32, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_u16, vs32, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_u16, vu64, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_u16, vs64, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_u16, vf32, vu16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_u16, vf64, vu16)
 #endif // simd_sup2
@@ -2164,7 +4826,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_u16, vf64, vu16)
  * special definition due to the nature of intrinsics
  * npyv_setf_u16 and npy_set_u16.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2187,7 +4849,7 @@ simd__intrin_setf_u16(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vu16);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2214,24 +4876,159 @@ simd__intrin_set_u16(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_u16, vu16, vu16, vu16)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_u16, vu16, vu16, vu16)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_u16, vu16x2, vu16, vu16)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_u16, vu16x2, vu16, vu16)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_u16, vu16x2, vu16, vu16)
+
 
 #if 1
 SIMD_IMPL_INTRIN_1(rev64_u16, vu16, vu16)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 16 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_u16
+npyv_permi128_u16_(npyv_u16 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_u16 ve0;
+    npyv_lanetype_u16 de0[npyv_nlanes_u16];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_u16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_u16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_u16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_u16(a, 0, 0, 0, 0);
+    }
+    npyv_store_u16(de0, ve0);
+    
+#line 360
+    npyv_u16 ve1;
+    npyv_lanetype_u16 de1[npyv_nlanes_u16];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_u16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_u16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_u16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_u16(a, 0, 0, 0, 0);
+    }
+    npyv_store_u16(de1, ve1);
+    
+#line 360
+    npyv_u16 ve2;
+    npyv_lanetype_u16 de2[npyv_nlanes_u16];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_u16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_u16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_u16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_u16(a, 0, 0, 0, 0);
+    }
+    npyv_store_u16(de2, ve2);
+    
+#line 360
+    npyv_u16 ve3;
+    npyv_lanetype_u16 de3[npyv_nlanes_u16];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_u16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_u16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_u16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_u16(a, 0, 0, 0, 0);
+    }
+    npyv_store_u16(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_u16; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_u16(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_u16_, vu16, vu16, u8, u8, u8, u8)
+#elif 16 == 64
+NPY_FINLINE npyv_u16
+npyv_permi128_u16_(npyv_u16 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_u16(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_u16(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_u16(a, 1, 1);
+    }
+    return npyv_permi128_u16(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_u16_, vu16, vu16, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -2243,34 +5040,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_u16, vu16, vu16, 15)
 SIMD_IMPL_INTRIN_2IMM(shri_u16, vu16, vu16, 16)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_u16, vu16, vu16, vu16)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_u16, vu16, vu16, vu16)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_u16, vu16, vu16, vu16)
 
 
 SIMD_IMPL_INTRIN_1(not_u16, vu16, vu16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_u16, vb16, vu16, vu16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_u16, vb16, vu16, vu16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_u16, vb16, vu16, vu16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_u16, vb16, vu16, vu16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_u16, vb16, vu16, vu16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_u16, vb16, vu16, vu16)
 
 
@@ -2282,10 +5079,10 @@ SIMD_IMPL_INTRIN_2(xnor_b16, vb16, vb16, vb16)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_u16, u8, vu16)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_u16, u8, vu16)
 
 /***************************
@@ -2299,18 +5096,18 @@ SIMD_IMPL_INTRIN_1(expand_u32_u16, vu32x2, vu16)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_u16, vu16, vu16, vu16)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_u16, vu16, vu16, vu16)
 
 
 #if 1
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_u16, vu16, vu16, vu16)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_u16, vu16, vu16, vu16)
 
 #endif // sat_sup
@@ -2329,18 +5126,21 @@ SIMD_IMPL_INTRIN_2(divc_u16, vu16, vu16, vu16x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_u16, vu16, vu16, vu16, vu16)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_u16, vu16, vu16, vu16, vu16)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_u16, vu16, vu16, vu16, vu16)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_u16, vu16, vu16, vu16, vu16)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_u16, vu16, vu16, vu16, vu16)
+
 #endif // fused_sup
 
 #if 0
@@ -2355,55 +5155,55 @@ SIMD_IMPL_INTRIN_1(sumup_u16, u32, vu16)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_u16, vu16, vu16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_u16, vu16, vu16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_u16, vu16, vu16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_u16, vu16, vu16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_u16, vu16, vu16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_u16, vu16, vu16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_u16, vu16, vu16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_u16, vu16, vu16)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_u16, vu16, vu16, vu16)
 SIMD_IMPL_INTRIN_1(reduce_max_u16, u16, vu16)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_u16, vu16, vu16, vu16)
 SIMD_IMPL_INTRIN_1(reduce_min_u16, u16, vu16)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_u16, vu16, vu16, vu16)
 SIMD_IMPL_INTRIN_1(reduce_maxp_u16, u16, vu16)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_u16, vu16, vu16, vu16)
 SIMD_IMPL_INTRIN_1(reduce_minp_u16, u16, vu16)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_u16, vu16, vu16, vu16)
 SIMD_IMPL_INTRIN_1(reduce_maxn_u16, u16, vu16)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_u16, vu16, vu16, vu16)
 SIMD_IMPL_INTRIN_1(reduce_minn_u16, u16, vu16)
 
@@ -2413,13 +5213,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_u16, u16, vu16)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_u16, vu16, vb16, vu16, vu16, vu16)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_u16, vu16, vb16, vu16, vu16, vu16)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_u16, vu16, vb16, vu16, vu16, vu16)
+SIMD_IMPL_INTRIN_3(ifdivz_u16, vu16, vb16, vu16, vu16)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -2439,7 +5244,9 @@ SIMD_IMPL_INTRIN_1(loads_s16, vs16, qs16)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_s16, vs16, qs16)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_s16x2, vs16x2, qs16)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_s16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -2463,7 +5270,7 @@ simd__intrin_store_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_s16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -2487,7 +5294,7 @@ simd__intrin_storea_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_s16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -2511,7 +5318,7 @@ simd__intrin_stores_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_s16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -2535,7 +5342,7 @@ simd__intrin_storel_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_s16(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -2559,6 +5366,30 @@ simd__intrin_storeh_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_s16x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg vec_arg = {.dtype = simd_data_vs16x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_s16x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_s16x2(seq_arg.data.qs16, vec_arg.data.vs16x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs16, simd_data_qs16)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -2567,8 +5398,17 @@ simd__intrin_storeh_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_s16, vs16, qs16, u32, s16)
 SIMD_IMPL_INTRIN_2(load_tillz_s16, vs16, qs16, u32)
+#if 16 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_s16, vs16, qs16, u32, s16, s16)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s16, vs16, qs16, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_s16, vs16, qs16, u32, s16, s16)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s16, vs16, qs16, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_store_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2594,9 +5434,72 @@ simd__intrin_store_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_store2_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs16};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s16(
+        seq_arg.data.qs16, nlane_arg.data.u32, vec_arg.data.vs16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs16, simd_data_qs16)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_store2_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs16};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s16(
+        seq_arg.data.qs16, nlane_arg.data.u32, vec_arg.data.vs16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs16, simd_data_qs16)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_loadn_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2608,6 +5511,9 @@ simd__intrin_loadn_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s16};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_s16",
         simd_arg_converter, &seq_arg,
@@ -2618,6 +5524,9 @@ simd__intrin_loadn_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -2626,7 +5535,7 @@ simd__intrin_loadn_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -2645,6 +5554,151 @@ simd__intrin_loadn_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s16
     #endif
+    #if 0
+        , fill2_arg.data.s16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs16, .data = {.vs16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_loadn2_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s16 rvec = npyv_loadn2_s16(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s16
+    #endif
+    #if 0
+        , fill2_arg.data.s16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs16, .data = {.vs16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_loadn2_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s16 rvec = npyv_loadn2_s16(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s16
+    #endif
+    #if 0
+        , fill2_arg.data.s16
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs16, .data = {.vs16=rvec}
@@ -2655,8 +5709,10 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_loadn_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2668,6 +5724,9 @@ simd__intrin_loadn_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_s16};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_s16",
         simd_arg_converter, &seq_arg,
@@ -2678,6 +5737,9 @@ simd__intrin_loadn_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -2686,7 +5748,7 @@ simd__intrin_loadn_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -2705,6 +5767,151 @@ simd__intrin_loadn_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.s16
     #endif
+    #if 0
+        , fill2_arg.data.s16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs16, .data = {.vs16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_loadn2_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s16};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s16 rvec = npyv_loadn2_till_s16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s16
+    #endif
+    #if 1
+        , fill2_arg.data.s16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs16, .data = {.vs16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_loadn2_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s16};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s16 rvec = npyv_loadn2_till_s16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s16
+    #endif
+    #if 1
+        , fill2_arg.data.s16
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs16, .data = {.vs16=rvec}
@@ -2715,8 +5922,10 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_loadn_tillz_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2728,6 +5937,9 @@ simd__intrin_loadn_tillz_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s16};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_s16",
         simd_arg_converter, &seq_arg,
@@ -2738,6 +5950,9 @@ simd__intrin_loadn_tillz_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -2746,7 +5961,7 @@ simd__intrin_loadn_tillz_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -2765,6 +5980,9 @@ simd__intrin_loadn_tillz_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s16
     #endif
+    #if 0
+        , fill2_arg.data.s16
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs16, .data = {.vs16=rvec}
@@ -2775,10 +5993,154 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_loadn2_tillz_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s16 rvec = npyv_loadn2_tillz_s16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s16
+    #endif
+    #if 0
+        , fill2_arg.data.s16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs16, .data = {.vs16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_loadn2_tillz_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s16};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s16};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s16(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s16 rvec = npyv_loadn2_tillz_s16(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s16
+    #endif
+    #if 0
+        , fill2_arg.data.s16
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs16, .data = {.vs16=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_storen_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2804,7 +6166,7 @@ simd__intrin_storen_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -2833,8 +6195,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 180
+#line 216
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_storen2_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs16};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s16(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs16, simd_data_qs16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_storen2_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs16};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s16(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs16, simd_data_qs16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 16
 static PyObject *
 simd__intrin_storen_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2860,7 +6340,7 @@ simd__intrin_storen_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -2889,6 +6369,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 16
+static PyObject *
+simd__intrin_storen2_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs16};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s16(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs16, simd_data_qs16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 16
+static PyObject *
+simd__intrin_storen2_till_s16(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs16};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs16};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s16",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s16 *seq_ptr = seq_arg.data.qs16;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s16;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s16(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s16(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs16
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs16, simd_data_qs16)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 0
 
@@ -2909,52 +6506,52 @@ SIMD_IMPL_INTRIN_1(extract0_s16, s16, vs16)
 SIMD_IMPL_INTRIN_1(setall_s16, vs16, s16)
 SIMD_IMPL_INTRIN_3(select_s16, vs16, vb16, vs16, vs16)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_s16, vu8, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_s16, vs8, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_s16, vu16, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_s16, vs16, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_s16, vu32, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_s16, vs32, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_s16, vu64, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_s16, vs64, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_s16, vf32, vs16)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_s16, vf64, vs16)
 #endif // simd_sup2
@@ -2964,7 +6561,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_s16, vf64, vs16)
  * special definition due to the nature of intrinsics
  * npyv_setf_s16 and npy_set_s16.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -2987,7 +6584,7 @@ simd__intrin_setf_s16(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vs16);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3014,24 +6611,159 @@ simd__intrin_set_s16(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_s16, vs16, vs16, vs16)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_s16, vs16, vs16, vs16)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_s16, vs16x2, vs16, vs16)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_s16, vs16x2, vs16, vs16)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_s16, vs16x2, vs16, vs16)
+
 
 #if 1
 SIMD_IMPL_INTRIN_1(rev64_s16, vs16, vs16)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 16 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_s16
+npyv_permi128_s16_(npyv_s16 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_s16 ve0;
+    npyv_lanetype_s16 de0[npyv_nlanes_s16];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_s16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_s16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_s16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_s16(a, 0, 0, 0, 0);
+    }
+    npyv_store_s16(de0, ve0);
+    
+#line 360
+    npyv_s16 ve1;
+    npyv_lanetype_s16 de1[npyv_nlanes_s16];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_s16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_s16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_s16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_s16(a, 0, 0, 0, 0);
+    }
+    npyv_store_s16(de1, ve1);
+    
+#line 360
+    npyv_s16 ve2;
+    npyv_lanetype_s16 de2[npyv_nlanes_s16];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_s16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_s16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_s16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_s16(a, 0, 0, 0, 0);
+    }
+    npyv_store_s16(de2, ve2);
+    
+#line 360
+    npyv_s16 ve3;
+    npyv_lanetype_s16 de3[npyv_nlanes_s16];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_s16(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_s16(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_s16(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_s16(a, 0, 0, 0, 0);
+    }
+    npyv_store_s16(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_s16; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_s16(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_s16_, vs16, vs16, u8, u8, u8, u8)
+#elif 16 == 64
+NPY_FINLINE npyv_s16
+npyv_permi128_s16_(npyv_s16 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_s16(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_s16(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_s16(a, 1, 1);
+    }
+    return npyv_permi128_s16(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_s16_, vs16, vs16, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -3043,34 +6775,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_s16, vs16, vs16, 15)
 SIMD_IMPL_INTRIN_2IMM(shri_s16, vs16, vs16, 16)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_s16, vs16, vs16, vs16)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_s16, vs16, vs16, vs16)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_s16, vs16, vs16, vs16)
 
 
 SIMD_IMPL_INTRIN_1(not_s16, vs16, vs16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_s16, vb16, vs16, vs16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_s16, vb16, vs16, vs16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_s16, vb16, vs16, vs16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_s16, vb16, vs16, vs16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_s16, vb16, vs16, vs16)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_s16, vb16, vs16, vs16)
 
 
@@ -3082,10 +6814,10 @@ SIMD_IMPL_INTRIN_2(xnor_b16, vb16, vb16, vb16)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_s16, u8, vs16)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_s16, u8, vs16)
 
 /***************************
@@ -3099,18 +6831,18 @@ SIMD_IMPL_INTRIN_1(expand_s16_s16, vs16x2, vs16)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_s16, vs16, vs16, vs16)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_s16, vs16, vs16, vs16)
 
 
 #if 1
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_s16, vs16, vs16, vs16)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_s16, vs16, vs16, vs16)
 
 #endif // sat_sup
@@ -3129,18 +6861,21 @@ SIMD_IMPL_INTRIN_2(divc_s16, vs16, vs16, vs16x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_s16, vs16, vs16, vs16, vs16)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_s16, vs16, vs16, vs16, vs16)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_s16, vs16, vs16, vs16, vs16)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_s16, vs16, vs16, vs16, vs16)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_s16, vs16, vs16, vs16, vs16)
+
 #endif // fused_sup
 
 #if 0
@@ -3155,55 +6890,55 @@ SIMD_IMPL_INTRIN_1(sumup_s16, s16, vs16)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_s16, vs16, vs16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_s16, vs16, vs16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_s16, vs16, vs16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_s16, vs16, vs16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_s16, vs16, vs16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_s16, vs16, vs16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_s16, vs16, vs16)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_s16, vs16, vs16)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_s16, vs16, vs16, vs16)
 SIMD_IMPL_INTRIN_1(reduce_max_s16, s16, vs16)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_s16, vs16, vs16, vs16)
 SIMD_IMPL_INTRIN_1(reduce_min_s16, s16, vs16)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_s16, vs16, vs16, vs16)
 SIMD_IMPL_INTRIN_1(reduce_maxp_s16, s16, vs16)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_s16, vs16, vs16, vs16)
 SIMD_IMPL_INTRIN_1(reduce_minp_s16, s16, vs16)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_s16, vs16, vs16, vs16)
 SIMD_IMPL_INTRIN_1(reduce_maxn_s16, s16, vs16)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_s16, vs16, vs16, vs16)
 SIMD_IMPL_INTRIN_1(reduce_minn_s16, s16, vs16)
 
@@ -3213,13 +6948,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_s16, s16, vs16)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_s16, vs16, vb16, vs16, vs16, vs16)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_s16, vs16, vb16, vs16, vs16, vs16)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_s16, vs16, vb16, vs16, vs16, vs16)
+SIMD_IMPL_INTRIN_3(ifdivz_s16, vs16, vb16, vs16, vs16)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -3239,7 +6979,9 @@ SIMD_IMPL_INTRIN_1(loads_u32, vu32, qu32)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_u32, vu32, qu32)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_u32x2, vu32x2, qu32)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_u32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -3263,7 +7005,7 @@ simd__intrin_store_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_u32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -3287,7 +7029,7 @@ simd__intrin_storea_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_u32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -3311,7 +7053,7 @@ simd__intrin_stores_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_u32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -3335,7 +7077,7 @@ simd__intrin_storel_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_u32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -3359,6 +7101,30 @@ simd__intrin_storeh_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_u32x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg vec_arg = {.dtype = simd_data_vu32x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_u32x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_u32x2(seq_arg.data.qu32, vec_arg.data.vu32x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu32, simd_data_qu32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -3367,8 +7133,17 @@ simd__intrin_storeh_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_u32, vu32, qu32, u32, u32)
 SIMD_IMPL_INTRIN_2(load_tillz_u32, vu32, qu32, u32)
+#if 32 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_u32, vu32, qu32, u32, u32, u32)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u32, vu32, qu32, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_u32, vu32, qu32, u32, u32, u32)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u32, vu32, qu32, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_store_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3394,9 +7169,72 @@ simd__intrin_store_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_store2_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu32};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u32(
+        seq_arg.data.qu32, nlane_arg.data.u32, vec_arg.data.vu32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu32, simd_data_qu32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_store2_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu32};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u32(
+        seq_arg.data.qu32, nlane_arg.data.u32, vec_arg.data.vu32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu32, simd_data_qu32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3408,6 +7246,9 @@ simd__intrin_loadn_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_u32",
         simd_arg_converter, &seq_arg,
@@ -3418,6 +7259,9 @@ simd__intrin_loadn_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -3426,7 +7270,7 @@ simd__intrin_loadn_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -3445,6 +7289,9 @@ simd__intrin_loadn_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u32
     #endif
+    #if 0
+        , fill2_arg.data.u32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu32, .data = {.vu32=rvec}
@@ -3455,8 +7302,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u32 rvec = npyv_loadn2_u32(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u32
+    #endif
+    #if 0
+        , fill2_arg.data.u32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu32, .data = {.vu32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u32 rvec = npyv_loadn2_u32(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u32
+    #endif
+    #if 0
+        , fill2_arg.data.u32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu32, .data = {.vu32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3468,6 +7459,9 @@ simd__intrin_loadn_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_u32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_u32",
         simd_arg_converter, &seq_arg,
@@ -3478,6 +7472,9 @@ simd__intrin_loadn_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -3486,7 +7483,7 @@ simd__intrin_loadn_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -3505,6 +7502,9 @@ simd__intrin_loadn_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.u32
     #endif
+    #if 0
+        , fill2_arg.data.u32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu32, .data = {.vu32=rvec}
@@ -3515,8 +7515,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u32};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u32 rvec = npyv_loadn2_till_u32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u32
+    #endif
+    #if 1
+        , fill2_arg.data.u32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu32, .data = {.vu32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u32};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u32 rvec = npyv_loadn2_till_u32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u32
+    #endif
+    #if 1
+        , fill2_arg.data.u32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu32, .data = {.vu32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_tillz_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3528,6 +7672,9 @@ simd__intrin_loadn_tillz_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_u32",
         simd_arg_converter, &seq_arg,
@@ -3538,6 +7685,9 @@ simd__intrin_loadn_tillz_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -3546,7 +7696,7 @@ simd__intrin_loadn_tillz_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -3565,6 +7715,9 @@ simd__intrin_loadn_tillz_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u32
     #endif
+    #if 0
+        , fill2_arg.data.u32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu32, .data = {.vu32=rvec}
@@ -3575,10 +7728,154 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_tillz_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u32 rvec = npyv_loadn2_tillz_u32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u32
+    #endif
+    #if 0
+        , fill2_arg.data.u32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu32, .data = {.vu32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_tillz_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u32 rvec = npyv_loadn2_tillz_u32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u32
+    #endif
+    #if 0
+        , fill2_arg.data.u32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu32, .data = {.vu32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_storen_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3604,7 +7901,7 @@ simd__intrin_storen_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -3633,8 +7930,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 180
+#line 216
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_storen2_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu32};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u32(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu32, simd_data_qu32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_storen2_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu32};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u32(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu32, simd_data_qu32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_storen_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3660,7 +8075,7 @@ simd__intrin_storen_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -3689,6 +8104,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_storen2_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu32};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u32(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu32, simd_data_qu32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_storen2_till_u32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu32};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u32 *seq_ptr = seq_arg.data.qu32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u32(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu32, simd_data_qu32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 1
 
@@ -3709,52 +8241,52 @@ SIMD_IMPL_INTRIN_1(extract0_u32, u32, vu32)
 SIMD_IMPL_INTRIN_1(setall_u32, vu32, u32)
 SIMD_IMPL_INTRIN_3(select_u32, vu32, vb32, vu32, vu32)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_u32, vu8, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_u32, vs8, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_u32, vu16, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_u32, vs16, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_u32, vu32, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_u32, vs32, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_u32, vu64, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_u32, vs64, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_u32, vf32, vu32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_u32, vf64, vu32)
 #endif // simd_sup2
@@ -3764,7 +8296,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_u32, vf64, vu32)
  * special definition due to the nature of intrinsics
  * npyv_setf_u32 and npy_set_u32.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3787,7 +8319,7 @@ simd__intrin_setf_u32(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vu32);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -3814,24 +8346,159 @@ simd__intrin_set_u32(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_u32, vu32, vu32, vu32)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_u32, vu32, vu32, vu32)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_u32, vu32x2, vu32, vu32)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_u32, vu32x2, vu32, vu32)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_u32, vu32x2, vu32, vu32)
+
 
 #if 1
 SIMD_IMPL_INTRIN_1(rev64_u32, vu32, vu32)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 32 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_u32
+npyv_permi128_u32_(npyv_u32 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_u32 ve0;
+    npyv_lanetype_u32 de0[npyv_nlanes_u32];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_u32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_u32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_u32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_u32(a, 0, 0, 0, 0);
+    }
+    npyv_store_u32(de0, ve0);
+    
+#line 360
+    npyv_u32 ve1;
+    npyv_lanetype_u32 de1[npyv_nlanes_u32];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_u32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_u32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_u32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_u32(a, 0, 0, 0, 0);
+    }
+    npyv_store_u32(de1, ve1);
+    
+#line 360
+    npyv_u32 ve2;
+    npyv_lanetype_u32 de2[npyv_nlanes_u32];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_u32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_u32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_u32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_u32(a, 0, 0, 0, 0);
+    }
+    npyv_store_u32(de2, ve2);
+    
+#line 360
+    npyv_u32 ve3;
+    npyv_lanetype_u32 de3[npyv_nlanes_u32];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_u32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_u32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_u32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_u32(a, 0, 0, 0, 0);
+    }
+    npyv_store_u32(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_u32; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_u32(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_u32_, vu32, vu32, u8, u8, u8, u8)
+#elif 32 == 64
+NPY_FINLINE npyv_u32
+npyv_permi128_u32_(npyv_u32 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_u32(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_u32(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_u32(a, 1, 1);
+    }
+    return npyv_permi128_u32(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_u32_, vu32, vu32, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -3843,34 +8510,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_u32, vu32, vu32, 31)
 SIMD_IMPL_INTRIN_2IMM(shri_u32, vu32, vu32, 32)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_u32, vu32, vu32, vu32)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_u32, vu32, vu32, vu32)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_u32, vu32, vu32, vu32)
 
 
 SIMD_IMPL_INTRIN_1(not_u32, vu32, vu32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_u32, vb32, vu32, vu32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_u32, vb32, vu32, vu32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_u32, vb32, vu32, vu32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_u32, vb32, vu32, vu32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_u32, vb32, vu32, vu32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_u32, vb32, vu32, vu32)
 
 
@@ -3882,10 +8549,10 @@ SIMD_IMPL_INTRIN_2(xnor_b32, vb32, vb32, vb32)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_u32, u8, vu32)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_u32, u8, vu32)
 
 /***************************
@@ -3899,18 +8566,18 @@ SIMD_IMPL_INTRIN_1(expand_u32_u32, vu32x2, vu32)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_u32, vu32, vu32, vu32)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_u32, vu32, vu32, vu32)
 
 
 #if 0
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_u32, vu32, vu32, vu32)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_u32, vu32, vu32, vu32)
 
 #endif // sat_sup
@@ -3929,18 +8596,21 @@ SIMD_IMPL_INTRIN_2(divc_u32, vu32, vu32, vu32x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_u32, vu32, vu32, vu32, vu32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_u32, vu32, vu32, vu32, vu32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_u32, vu32, vu32, vu32, vu32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_u32, vu32, vu32, vu32, vu32)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_u32, vu32, vu32, vu32, vu32)
+
 #endif // fused_sup
 
 #if 1
@@ -3955,55 +8625,55 @@ SIMD_IMPL_INTRIN_1(sumup_u32, u32, vu32)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_u32, vu32, vu32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_u32, vu32, vu32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_u32, vu32, vu32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_u32, vu32, vu32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_u32, vu32, vu32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_u32, vu32, vu32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_u32, vu32, vu32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_u32, vu32, vu32)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_u32, vu32, vu32, vu32)
 SIMD_IMPL_INTRIN_1(reduce_max_u32, u32, vu32)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_u32, vu32, vu32, vu32)
 SIMD_IMPL_INTRIN_1(reduce_min_u32, u32, vu32)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_u32, vu32, vu32, vu32)
 SIMD_IMPL_INTRIN_1(reduce_maxp_u32, u32, vu32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_u32, vu32, vu32, vu32)
 SIMD_IMPL_INTRIN_1(reduce_minp_u32, u32, vu32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_u32, vu32, vu32, vu32)
 SIMD_IMPL_INTRIN_1(reduce_maxn_u32, u32, vu32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_u32, vu32, vu32, vu32)
 SIMD_IMPL_INTRIN_1(reduce_minn_u32, u32, vu32)
 
@@ -4013,13 +8683,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_u32, u32, vu32)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_u32, vu32, vb32, vu32, vu32, vu32)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_u32, vu32, vb32, vu32, vu32, vu32)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_u32, vu32, vb32, vu32, vu32, vu32)
+SIMD_IMPL_INTRIN_3(ifdivz_u32, vu32, vb32, vu32, vu32)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -4039,7 +8714,9 @@ SIMD_IMPL_INTRIN_1(loads_s32, vs32, qs32)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_s32, vs32, qs32)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_s32x2, vs32x2, qs32)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_s32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4063,7 +8740,7 @@ simd__intrin_store_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_s32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4087,7 +8764,7 @@ simd__intrin_storea_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_s32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4111,7 +8788,7 @@ simd__intrin_stores_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_s32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4135,7 +8812,7 @@ simd__intrin_storel_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_s32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4159,6 +8836,30 @@ simd__intrin_storeh_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_s32x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg vec_arg = {.dtype = simd_data_vs32x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_s32x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_s32x2(seq_arg.data.qs32, vec_arg.data.vs32x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs32, simd_data_qs32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -4167,8 +8868,17 @@ simd__intrin_storeh_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_s32, vs32, qs32, u32, s32)
 SIMD_IMPL_INTRIN_2(load_tillz_s32, vs32, qs32, u32)
+#if 32 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_s32, vs32, qs32, u32, s32, s32)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s32, vs32, qs32, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_s32, vs32, qs32, u32, s32, s32)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s32, vs32, qs32, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_store_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4194,9 +8904,72 @@ simd__intrin_store_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_store2_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs32};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s32(
+        seq_arg.data.qs32, nlane_arg.data.u32, vec_arg.data.vs32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs32, simd_data_qs32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_store2_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs32};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s32(
+        seq_arg.data.qs32, nlane_arg.data.u32, vec_arg.data.vs32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs32, simd_data_qs32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4208,6 +8981,9 @@ simd__intrin_loadn_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_s32",
         simd_arg_converter, &seq_arg,
@@ -4218,6 +8994,9 @@ simd__intrin_loadn_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -4226,7 +9005,7 @@ simd__intrin_loadn_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -4245,6 +9024,151 @@ simd__intrin_loadn_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s32
     #endif
+    #if 0
+        , fill2_arg.data.s32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs32, .data = {.vs32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s32 rvec = npyv_loadn2_s32(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s32
+    #endif
+    #if 0
+        , fill2_arg.data.s32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs32, .data = {.vs32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s32 rvec = npyv_loadn2_s32(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s32
+    #endif
+    #if 0
+        , fill2_arg.data.s32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs32, .data = {.vs32=rvec}
@@ -4255,8 +9179,10 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4268,6 +9194,9 @@ simd__intrin_loadn_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_s32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_s32",
         simd_arg_converter, &seq_arg,
@@ -4278,6 +9207,9 @@ simd__intrin_loadn_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -4286,7 +9218,7 @@ simd__intrin_loadn_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -4305,6 +9237,151 @@ simd__intrin_loadn_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.s32
     #endif
+    #if 0
+        , fill2_arg.data.s32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs32, .data = {.vs32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s32};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s32 rvec = npyv_loadn2_till_s32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s32
+    #endif
+    #if 1
+        , fill2_arg.data.s32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs32, .data = {.vs32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s32};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s32 rvec = npyv_loadn2_till_s32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s32
+    #endif
+    #if 1
+        , fill2_arg.data.s32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs32, .data = {.vs32=rvec}
@@ -4315,8 +9392,10 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_tillz_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4328,6 +9407,9 @@ simd__intrin_loadn_tillz_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_s32",
         simd_arg_converter, &seq_arg,
@@ -4338,6 +9420,9 @@ simd__intrin_loadn_tillz_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -4346,7 +9431,7 @@ simd__intrin_loadn_tillz_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -4365,6 +9450,9 @@ simd__intrin_loadn_tillz_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s32
     #endif
+    #if 0
+        , fill2_arg.data.s32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs32, .data = {.vs32=rvec}
@@ -4375,10 +9463,154 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_tillz_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s32 rvec = npyv_loadn2_tillz_s32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s32
+    #endif
+    #if 0
+        , fill2_arg.data.s32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs32, .data = {.vs32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_tillz_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s32 rvec = npyv_loadn2_tillz_s32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s32
+    #endif
+    #if 0
+        , fill2_arg.data.s32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs32, .data = {.vs32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_storen_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4404,7 +9636,7 @@ simd__intrin_storen_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -4433,8 +9665,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_storen2_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs32};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s32(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs32, simd_data_qs32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
-#line 180
+#line 216
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_storen2_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs32};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s32(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs32, simd_data_qs32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_storen_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4460,7 +9810,7 @@ simd__intrin_storen_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -4489,6 +9839,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_storen2_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs32};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s32(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs32, simd_data_qs32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_storen2_till_s32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs32};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s32 *seq_ptr = seq_arg.data.qs32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s32(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs32, simd_data_qs32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 1
 
@@ -4509,52 +9976,52 @@ SIMD_IMPL_INTRIN_1(extract0_s32, s32, vs32)
 SIMD_IMPL_INTRIN_1(setall_s32, vs32, s32)
 SIMD_IMPL_INTRIN_3(select_s32, vs32, vb32, vs32, vs32)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_s32, vu8, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_s32, vs8, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_s32, vu16, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_s32, vs16, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_s32, vu32, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_s32, vs32, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_s32, vu64, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_s32, vs64, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_s32, vf32, vs32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_s32, vf64, vs32)
 #endif // simd_sup2
@@ -4564,7 +10031,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_s32, vf64, vs32)
  * special definition due to the nature of intrinsics
  * npyv_setf_s32 and npy_set_s32.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4587,7 +10054,7 @@ simd__intrin_setf_s32(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vs32);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4614,24 +10081,159 @@ simd__intrin_set_s32(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_s32, vs32, vs32, vs32)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_s32, vs32, vs32, vs32)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_s32, vs32x2, vs32, vs32)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_s32, vs32x2, vs32, vs32)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_s32, vs32x2, vs32, vs32)
+
 
 #if 1
 SIMD_IMPL_INTRIN_1(rev64_s32, vs32, vs32)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 32 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_s32
+npyv_permi128_s32_(npyv_s32 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_s32 ve0;
+    npyv_lanetype_s32 de0[npyv_nlanes_s32];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_s32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_s32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_s32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_s32(a, 0, 0, 0, 0);
+    }
+    npyv_store_s32(de0, ve0);
+    
+#line 360
+    npyv_s32 ve1;
+    npyv_lanetype_s32 de1[npyv_nlanes_s32];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_s32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_s32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_s32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_s32(a, 0, 0, 0, 0);
+    }
+    npyv_store_s32(de1, ve1);
+    
+#line 360
+    npyv_s32 ve2;
+    npyv_lanetype_s32 de2[npyv_nlanes_s32];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_s32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_s32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_s32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_s32(a, 0, 0, 0, 0);
+    }
+    npyv_store_s32(de2, ve2);
+    
+#line 360
+    npyv_s32 ve3;
+    npyv_lanetype_s32 de3[npyv_nlanes_s32];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_s32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_s32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_s32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_s32(a, 0, 0, 0, 0);
+    }
+    npyv_store_s32(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_s32; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_s32(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_s32_, vs32, vs32, u8, u8, u8, u8)
+#elif 32 == 64
+NPY_FINLINE npyv_s32
+npyv_permi128_s32_(npyv_s32 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_s32(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_s32(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_s32(a, 1, 1);
+    }
+    return npyv_permi128_s32(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_s32_, vs32, vs32, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -4643,34 +10245,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_s32, vs32, vs32, 31)
 SIMD_IMPL_INTRIN_2IMM(shri_s32, vs32, vs32, 32)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_s32, vs32, vs32, vs32)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_s32, vs32, vs32, vs32)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_s32, vs32, vs32, vs32)
 
 
 SIMD_IMPL_INTRIN_1(not_s32, vs32, vs32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_s32, vb32, vs32, vs32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_s32, vb32, vs32, vs32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_s32, vb32, vs32, vs32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_s32, vb32, vs32, vs32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_s32, vb32, vs32, vs32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_s32, vb32, vs32, vs32)
 
 
@@ -4682,10 +10284,10 @@ SIMD_IMPL_INTRIN_2(xnor_b32, vb32, vb32, vb32)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_s32, u8, vs32)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_s32, u8, vs32)
 
 /***************************
@@ -4699,18 +10301,18 @@ SIMD_IMPL_INTRIN_1(expand_s32_s32, vs32x2, vs32)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_s32, vs32, vs32, vs32)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_s32, vs32, vs32, vs32)
 
 
 #if 0
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_s32, vs32, vs32, vs32)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_s32, vs32, vs32, vs32)
 
 #endif // sat_sup
@@ -4729,18 +10331,21 @@ SIMD_IMPL_INTRIN_2(divc_s32, vs32, vs32, vs32x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_s32, vs32, vs32, vs32, vs32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_s32, vs32, vs32, vs32, vs32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_s32, vs32, vs32, vs32, vs32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_s32, vs32, vs32, vs32, vs32)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_s32, vs32, vs32, vs32, vs32)
+
 #endif // fused_sup
 
 #if 0
@@ -4755,55 +10360,55 @@ SIMD_IMPL_INTRIN_1(sumup_s32, s32, vs32)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_s32, vs32, vs32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_s32, vs32, vs32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_s32, vs32, vs32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_s32, vs32, vs32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_s32, vs32, vs32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_s32, vs32, vs32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_s32, vs32, vs32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_s32, vs32, vs32)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_s32, vs32, vs32, vs32)
 SIMD_IMPL_INTRIN_1(reduce_max_s32, s32, vs32)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_s32, vs32, vs32, vs32)
 SIMD_IMPL_INTRIN_1(reduce_min_s32, s32, vs32)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_s32, vs32, vs32, vs32)
 SIMD_IMPL_INTRIN_1(reduce_maxp_s32, s32, vs32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_s32, vs32, vs32, vs32)
 SIMD_IMPL_INTRIN_1(reduce_minp_s32, s32, vs32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_s32, vs32, vs32, vs32)
 SIMD_IMPL_INTRIN_1(reduce_maxn_s32, s32, vs32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_s32, vs32, vs32, vs32)
 SIMD_IMPL_INTRIN_1(reduce_minn_s32, s32, vs32)
 
@@ -4813,13 +10418,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_s32, s32, vs32)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_s32, vs32, vb32, vs32, vs32, vs32)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_s32, vs32, vb32, vs32, vs32, vs32)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_s32, vs32, vb32, vs32, vs32, vs32)
+SIMD_IMPL_INTRIN_3(ifdivz_s32, vs32, vb32, vs32, vs32)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -4839,7 +10449,9 @@ SIMD_IMPL_INTRIN_1(loads_u64, vu64, qu64)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_u64, vu64, qu64)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_u64x2, vu64x2, qu64)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_u64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4863,7 +10475,7 @@ simd__intrin_store_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_u64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4887,7 +10499,7 @@ simd__intrin_storea_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_u64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4911,7 +10523,7 @@ simd__intrin_stores_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_u64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4935,7 +10547,7 @@ simd__intrin_storel_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_u64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -4959,6 +10571,30 @@ simd__intrin_storeh_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_u64x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg vec_arg = {.dtype = simd_data_vu64x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_u64x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_u64x2(seq_arg.data.qu64, vec_arg.data.vu64x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu64, simd_data_qu64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -4967,8 +10603,17 @@ simd__intrin_storeh_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_u64, vu64, qu64, u32, u64)
 SIMD_IMPL_INTRIN_2(load_tillz_u64, vu64, qu64, u32)
+#if 64 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_u64, vu64, qu64, u32, u64, u64)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u64, vu64, qu64, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_u64, vu64, qu64, u32, u64, u64)
+    SIMD_IMPL_INTRIN_2(load2_tillz_u64, vu64, qu64, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_store_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -4994,9 +10639,72 @@ simd__intrin_store_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_store2_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu64};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u64(
+        seq_arg.data.qu64, nlane_arg.data.u32, vec_arg.data.vu64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu64, simd_data_qu64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_store2_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vu64};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_u64(
+        seq_arg.data.qu64, nlane_arg.data.u32, vec_arg.data.vu64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu64, simd_data_qu64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5008,6 +10716,9 @@ simd__intrin_loadn_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_u64",
         simd_arg_converter, &seq_arg,
@@ -5018,6 +10729,9 @@ simd__intrin_loadn_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -5026,7 +10740,7 @@ simd__intrin_loadn_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -5045,6 +10759,9 @@ simd__intrin_loadn_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u64
     #endif
+    #if 0
+        , fill2_arg.data.u64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu64, .data = {.vu64=rvec}
@@ -5055,8 +10772,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u64 rvec = npyv_loadn2_u64(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u64
+    #endif
+    #if 0
+        , fill2_arg.data.u64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu64, .data = {.vu64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_u64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u64 rvec = npyv_loadn2_u64(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u64
+    #endif
+    #if 0
+        , fill2_arg.data.u64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu64, .data = {.vu64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5068,6 +10929,9 @@ simd__intrin_loadn_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_u64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_u64",
         simd_arg_converter, &seq_arg,
@@ -5078,6 +10942,9 @@ simd__intrin_loadn_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -5086,7 +10953,7 @@ simd__intrin_loadn_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -5105,6 +10972,9 @@ simd__intrin_loadn_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.u64
     #endif
+    #if 0
+        , fill2_arg.data.u64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu64, .data = {.vu64=rvec}
@@ -5115,8 +10985,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u64};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u64 rvec = npyv_loadn2_till_u64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u64
+    #endif
+    #if 1
+        , fill2_arg.data.u64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu64, .data = {.vu64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_u64};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_u64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u64 rvec = npyv_loadn2_till_u64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.u64
+    #endif
+    #if 1
+        , fill2_arg.data.u64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu64, .data = {.vu64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_tillz_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5128,6 +11142,9 @@ simd__intrin_loadn_tillz_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_u64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_u64",
         simd_arg_converter, &seq_arg,
@@ -5138,6 +11155,9 @@ simd__intrin_loadn_tillz_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -5146,7 +11166,7 @@ simd__intrin_loadn_tillz_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -5165,6 +11185,80 @@ simd__intrin_loadn_tillz_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.u64
     #endif
+    #if 0
+        , fill2_arg.data.u64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu64, .data = {.vu64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_tillz_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u64 rvec = npyv_loadn2_tillz_u64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u64
+    #endif
+    #if 0
+        , fill2_arg.data.u64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vu64, .data = {.vu64=rvec}
@@ -5175,10 +11269,83 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_tillz_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_u64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_u64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_u64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_u64 rvec = npyv_loadn2_tillz_u64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.u64
+    #endif
+    #if 0
+        , fill2_arg.data.u64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vu64, .data = {.vu64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_storen_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5204,7 +11371,7 @@ simd__intrin_storen_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -5233,8 +11400,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_storen2_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u64(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu64, simd_data_qu64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
-#line 180
+#line 216
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_storen2_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_u64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_u64(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu64, simd_data_qu64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_storen_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5260,7 +11545,7 @@ simd__intrin_storen_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -5289,6 +11574,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_storen2_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u64(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu64, simd_data_qu64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_storen2_till_u64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qu64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vu64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_u64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_u64 *seq_ptr = seq_arg.data.qu64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_u64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_u64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_u64(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vu64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qu64, simd_data_qu64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 1
 
@@ -5309,52 +11711,52 @@ SIMD_IMPL_INTRIN_1(extract0_u64, u64, vu64)
 SIMD_IMPL_INTRIN_1(setall_u64, vu64, u64)
 SIMD_IMPL_INTRIN_3(select_u64, vu64, vb64, vu64, vu64)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_u64, vu8, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_u64, vs8, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_u64, vu16, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_u64, vs16, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_u64, vu32, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_u64, vs32, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_u64, vu64, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_u64, vs64, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_u64, vf32, vu64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_u64, vf64, vu64)
 #endif // simd_sup2
@@ -5364,7 +11766,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_u64, vf64, vu64)
  * special definition due to the nature of intrinsics
  * npyv_setf_u64 and npy_set_u64.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5387,7 +11789,7 @@ simd__intrin_setf_u64(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vu64);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5414,24 +11816,159 @@ simd__intrin_set_u64(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_u64, vu64, vu64, vu64)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_u64, vu64, vu64, vu64)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_u64, vu64x2, vu64, vu64)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_u64, vu64x2, vu64, vu64)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_u64, vu64x2, vu64, vu64)
+
 
 #if 0
 SIMD_IMPL_INTRIN_1(rev64_u64, vu64, vu64)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 64 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_u64
+npyv_permi128_u64_(npyv_u64 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_u64 ve0;
+    npyv_lanetype_u64 de0[npyv_nlanes_u64];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_u64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_u64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_u64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_u64(a, 0, 0, 0, 0);
+    }
+    npyv_store_u64(de0, ve0);
+    
+#line 360
+    npyv_u64 ve1;
+    npyv_lanetype_u64 de1[npyv_nlanes_u64];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_u64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_u64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_u64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_u64(a, 0, 0, 0, 0);
+    }
+    npyv_store_u64(de1, ve1);
+    
+#line 360
+    npyv_u64 ve2;
+    npyv_lanetype_u64 de2[npyv_nlanes_u64];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_u64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_u64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_u64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_u64(a, 0, 0, 0, 0);
+    }
+    npyv_store_u64(de2, ve2);
+    
+#line 360
+    npyv_u64 ve3;
+    npyv_lanetype_u64 de3[npyv_nlanes_u64];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_u64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_u64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_u64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_u64(a, 0, 0, 0, 0);
+    }
+    npyv_store_u64(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_u64; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_u64(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_u64_, vu64, vu64, u8, u8, u8, u8)
+#elif 64 == 64
+NPY_FINLINE npyv_u64
+npyv_permi128_u64_(npyv_u64 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_u64(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_u64(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_u64(a, 1, 1);
+    }
+    return npyv_permi128_u64(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_u64_, vu64, vu64, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -5443,34 +11980,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_u64, vu64, vu64, 63)
 SIMD_IMPL_INTRIN_2IMM(shri_u64, vu64, vu64, 64)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_u64, vu64, vu64, vu64)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_u64, vu64, vu64, vu64)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_u64, vu64, vu64, vu64)
 
 
 SIMD_IMPL_INTRIN_1(not_u64, vu64, vu64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_u64, vb64, vu64, vu64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_u64, vb64, vu64, vu64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_u64, vb64, vu64, vu64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_u64, vb64, vu64, vu64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_u64, vb64, vu64, vu64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_u64, vb64, vu64, vu64)
 
 
@@ -5482,10 +12019,10 @@ SIMD_IMPL_INTRIN_2(xnor_b64, vb64, vb64, vb64)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_u64, u8, vu64)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_u64, u8, vu64)
 
 /***************************
@@ -5499,18 +12036,18 @@ SIMD_IMPL_INTRIN_1(expand_u64_u64, vu64x2, vu64)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_u64, vu64, vu64, vu64)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_u64, vu64, vu64, vu64)
 
 
 #if 0
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_u64, vu64, vu64, vu64)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_u64, vu64, vu64, vu64)
 
 #endif // sat_sup
@@ -5529,18 +12066,21 @@ SIMD_IMPL_INTRIN_2(divc_u64, vu64, vu64, vu64x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_u64, vu64, vu64, vu64, vu64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_u64, vu64, vu64, vu64, vu64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_u64, vu64, vu64, vu64, vu64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_u64, vu64, vu64, vu64, vu64)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_u64, vu64, vu64, vu64, vu64)
+
 #endif // fused_sup
 
 #if 1
@@ -5555,55 +12095,55 @@ SIMD_IMPL_INTRIN_1(sumup_u64, u64, vu64)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_u64, vu64, vu64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_u64, vu64, vu64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_u64, vu64, vu64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_u64, vu64, vu64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_u64, vu64, vu64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_u64, vu64, vu64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_u64, vu64, vu64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_u64, vu64, vu64)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_u64, vu64, vu64, vu64)
 SIMD_IMPL_INTRIN_1(reduce_max_u64, u64, vu64)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_u64, vu64, vu64, vu64)
 SIMD_IMPL_INTRIN_1(reduce_min_u64, u64, vu64)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_u64, vu64, vu64, vu64)
 SIMD_IMPL_INTRIN_1(reduce_maxp_u64, u64, vu64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_u64, vu64, vu64, vu64)
 SIMD_IMPL_INTRIN_1(reduce_minp_u64, u64, vu64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_u64, vu64, vu64, vu64)
 SIMD_IMPL_INTRIN_1(reduce_maxn_u64, u64, vu64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_u64, vu64, vu64, vu64)
 SIMD_IMPL_INTRIN_1(reduce_minn_u64, u64, vu64)
 
@@ -5613,13 +12153,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_u64, u64, vu64)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_u64, vu64, vb64, vu64, vu64, vu64)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_u64, vu64, vb64, vu64, vu64, vu64)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_u64, vu64, vb64, vu64, vu64, vu64)
+SIMD_IMPL_INTRIN_3(ifdivz_u64, vu64, vb64, vu64, vu64)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -5639,7 +12184,9 @@ SIMD_IMPL_INTRIN_1(loads_s64, vs64, qs64)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_s64, vs64, qs64)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_s64x2, vs64x2, qs64)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_s64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -5663,7 +12210,7 @@ simd__intrin_store_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_s64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -5687,7 +12234,7 @@ simd__intrin_storea_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_s64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -5711,7 +12258,7 @@ simd__intrin_stores_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_s64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -5735,7 +12282,7 @@ simd__intrin_storel_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_s64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -5759,6 +12306,30 @@ simd__intrin_storeh_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_s64x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg vec_arg = {.dtype = simd_data_vs64x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_s64x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_s64x2(seq_arg.data.qs64, vec_arg.data.vs64x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs64, simd_data_qs64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -5767,8 +12338,17 @@ simd__intrin_storeh_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_s64, vs64, qs64, u32, s64)
 SIMD_IMPL_INTRIN_2(load_tillz_s64, vs64, qs64, u32)
+#if 64 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_s64, vs64, qs64, u32, s64, s64)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s64, vs64, qs64, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_s64, vs64, qs64, u32, s64, s64)
+    SIMD_IMPL_INTRIN_2(load2_tillz_s64, vs64, qs64, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_store_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5794,9 +12374,72 @@ simd__intrin_store_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_store2_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs64};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s64(
+        seq_arg.data.qs64, nlane_arg.data.u32, vec_arg.data.vs64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs64, simd_data_qs64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_store2_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vs64};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_s64(
+        seq_arg.data.qs64, nlane_arg.data.u32, vec_arg.data.vs64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs64, simd_data_qs64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5808,6 +12451,9 @@ simd__intrin_loadn_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_s64",
         simd_arg_converter, &seq_arg,
@@ -5818,6 +12464,9 @@ simd__intrin_loadn_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -5826,7 +12475,7 @@ simd__intrin_loadn_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -5845,6 +12494,80 @@ simd__intrin_loadn_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s64
     #endif
+    #if 0
+        , fill2_arg.data.s64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs64, .data = {.vs64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s64 rvec = npyv_loadn2_s64(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s64
+    #endif
+    #if 0
+        , fill2_arg.data.s64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs64, .data = {.vs64=rvec}
@@ -5855,8 +12578,81 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_s64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s64 rvec = npyv_loadn2_s64(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s64
+    #endif
+    #if 0
+        , fill2_arg.data.s64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs64, .data = {.vs64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5868,6 +12664,9 @@ simd__intrin_loadn_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_s64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_s64",
         simd_arg_converter, &seq_arg,
@@ -5878,6 +12677,9 @@ simd__intrin_loadn_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -5886,7 +12688,7 @@ simd__intrin_loadn_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -5905,6 +12707,80 @@ simd__intrin_loadn_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.s64
     #endif
+    #if 0
+        , fill2_arg.data.s64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs64, .data = {.vs64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s64};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s64 rvec = npyv_loadn2_till_s64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s64
+    #endif
+    #if 1
+        , fill2_arg.data.s64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs64, .data = {.vs64=rvec}
@@ -5915,8 +12791,81 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_s64};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_s64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s64 rvec = npyv_loadn2_till_s64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.s64
+    #endif
+    #if 1
+        , fill2_arg.data.s64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs64, .data = {.vs64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_tillz_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -5928,6 +12877,9 @@ simd__intrin_loadn_tillz_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_s64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_s64",
         simd_arg_converter, &seq_arg,
@@ -5938,6 +12890,9 @@ simd__intrin_loadn_tillz_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -5946,7 +12901,7 @@ simd__intrin_loadn_tillz_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -5965,6 +12920,9 @@ simd__intrin_loadn_tillz_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.s64
     #endif
+    #if 0
+        , fill2_arg.data.s64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vs64, .data = {.vs64=rvec}
@@ -5975,10 +12933,154 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_tillz_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s64 rvec = npyv_loadn2_tillz_s64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s64
+    #endif
+    #if 0
+        , fill2_arg.data.s64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs64, .data = {.vs64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_tillz_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_s64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_s64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_s64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_s64 rvec = npyv_loadn2_tillz_s64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.s64
+    #endif
+    #if 0
+        , fill2_arg.data.s64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vs64, .data = {.vs64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_storen_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6004,7 +13106,7 @@ simd__intrin_storen_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -6033,8 +13135,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 180
+#line 216
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_storen2_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s64(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs64, simd_data_qs64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_storen2_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_s64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_s64(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs64, simd_data_qs64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_storen_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6060,7 +13280,7 @@ simd__intrin_storen_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -6089,6 +13309,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_storen2_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s64(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs64, simd_data_qs64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_storen2_till_s64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qs64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vs64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_s64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_s64 *seq_ptr = seq_arg.data.qs64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_s64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_s64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_s64(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vs64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qs64, simd_data_qs64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 1
 
@@ -6109,52 +13446,52 @@ SIMD_IMPL_INTRIN_1(extract0_s64, s64, vs64)
 SIMD_IMPL_INTRIN_1(setall_s64, vs64, s64)
 SIMD_IMPL_INTRIN_3(select_s64, vs64, vb64, vs64, vs64)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_s64, vu8, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_s64, vs8, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_s64, vu16, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_s64, vs16, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_s64, vu32, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_s64, vs32, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_s64, vu64, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_s64, vs64, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_s64, vf32, vs64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_s64, vf64, vs64)
 #endif // simd_sup2
@@ -6164,7 +13501,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_s64, vf64, vs64)
  * special definition due to the nature of intrinsics
  * npyv_setf_s64 and npy_set_s64.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6187,7 +13524,7 @@ simd__intrin_setf_s64(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vs64);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6214,24 +13551,159 @@ simd__intrin_set_s64(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_s64, vs64, vs64, vs64)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_s64, vs64, vs64, vs64)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_s64, vs64x2, vs64, vs64)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_s64, vs64x2, vs64, vs64)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_s64, vs64x2, vs64, vs64)
+
 
 #if 0
 SIMD_IMPL_INTRIN_1(rev64_s64, vs64, vs64)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 64 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_s64
+npyv_permi128_s64_(npyv_s64 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_s64 ve0;
+    npyv_lanetype_s64 de0[npyv_nlanes_s64];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_s64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_s64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_s64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_s64(a, 0, 0, 0, 0);
+    }
+    npyv_store_s64(de0, ve0);
+    
+#line 360
+    npyv_s64 ve1;
+    npyv_lanetype_s64 de1[npyv_nlanes_s64];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_s64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_s64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_s64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_s64(a, 0, 0, 0, 0);
+    }
+    npyv_store_s64(de1, ve1);
+    
+#line 360
+    npyv_s64 ve2;
+    npyv_lanetype_s64 de2[npyv_nlanes_s64];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_s64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_s64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_s64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_s64(a, 0, 0, 0, 0);
+    }
+    npyv_store_s64(de2, ve2);
+    
+#line 360
+    npyv_s64 ve3;
+    npyv_lanetype_s64 de3[npyv_nlanes_s64];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_s64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_s64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_s64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_s64(a, 0, 0, 0, 0);
+    }
+    npyv_store_s64(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_s64; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_s64(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_s64_, vs64, vs64, u8, u8, u8, u8)
+#elif 64 == 64
+NPY_FINLINE npyv_s64
+npyv_permi128_s64_(npyv_s64 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_s64(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_s64(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_s64(a, 1, 1);
+    }
+    return npyv_permi128_s64(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_s64_, vs64, vs64, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -6243,34 +13715,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_s64, vs64, vs64, 63)
 SIMD_IMPL_INTRIN_2IMM(shri_s64, vs64, vs64, 64)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_s64, vs64, vs64, vs64)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_s64, vs64, vs64, vs64)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_s64, vs64, vs64, vs64)
 
 
 SIMD_IMPL_INTRIN_1(not_s64, vs64, vs64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_s64, vb64, vs64, vs64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_s64, vb64, vs64, vs64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_s64, vb64, vs64, vs64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_s64, vb64, vs64, vs64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_s64, vb64, vs64, vs64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_s64, vb64, vs64, vs64)
 
 
@@ -6282,10 +13754,10 @@ SIMD_IMPL_INTRIN_2(xnor_b64, vb64, vb64, vb64)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_s64, u8, vs64)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_s64, u8, vs64)
 
 /***************************
@@ -6299,18 +13771,18 @@ SIMD_IMPL_INTRIN_1(expand_s64_s64, vs64x2, vs64)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_s64, vs64, vs64, vs64)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_s64, vs64, vs64, vs64)
 
 
 #if 0
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_s64, vs64, vs64, vs64)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_s64, vs64, vs64, vs64)
 
 #endif // sat_sup
@@ -6329,18 +13801,21 @@ SIMD_IMPL_INTRIN_2(divc_s64, vs64, vs64, vs64x3)
 #endif // intdiv_sup
 
 #if 0
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_s64, vs64, vs64, vs64, vs64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_s64, vs64, vs64, vs64, vs64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_s64, vs64, vs64, vs64, vs64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_s64, vs64, vs64, vs64, vs64)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_s64, vs64, vs64, vs64, vs64)
+
 #endif // fused_sup
 
 #if 0
@@ -6355,55 +13830,55 @@ SIMD_IMPL_INTRIN_1(sumup_s64, s64, vs64)
  * Math
  ***************************/
 #if 0
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_s64, vs64, vs64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_s64, vs64, vs64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_s64, vs64, vs64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_s64, vs64, vs64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_s64, vs64, vs64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_s64, vs64, vs64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_s64, vs64, vs64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_s64, vs64, vs64)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_s64, vs64, vs64, vs64)
 SIMD_IMPL_INTRIN_1(reduce_max_s64, s64, vs64)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_s64, vs64, vs64, vs64)
 SIMD_IMPL_INTRIN_1(reduce_min_s64, s64, vs64)
 
 
 #if 0
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_s64, vs64, vs64, vs64)
 SIMD_IMPL_INTRIN_1(reduce_maxp_s64, s64, vs64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_s64, vs64, vs64, vs64)
 SIMD_IMPL_INTRIN_1(reduce_minp_s64, s64, vs64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_s64, vs64, vs64, vs64)
 SIMD_IMPL_INTRIN_1(reduce_maxn_s64, s64, vs64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_s64, vs64, vs64, vs64)
 SIMD_IMPL_INTRIN_1(reduce_minn_s64, s64, vs64)
 
@@ -6413,13 +13888,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_s64, s64, vs64)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_s64, vs64, vb64, vs64, vs64, vs64)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_s64, vs64, vb64, vs64, vs64, vs64)
 
 
+#if 0
+SIMD_IMPL_INTRIN_4(ifdiv_s64, vs64, vb64, vs64, vs64, vs64)
+SIMD_IMPL_INTRIN_3(ifdivz_s64, vs64, vb64, vs64, vs64)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -6439,7 +13919,9 @@ SIMD_IMPL_INTRIN_1(loads_f32, vf32, qf32)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_f32, vf32, qf32)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_f32x2, vf32x2, qf32)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_f32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -6463,7 +13945,7 @@ simd__intrin_store_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_f32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -6487,7 +13969,7 @@ simd__intrin_storea_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_f32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -6511,7 +13993,7 @@ simd__intrin_stores_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_f32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -6535,7 +14017,7 @@ simd__intrin_storel_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_f32(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -6559,6 +14041,30 @@ simd__intrin_storeh_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_f32x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg vec_arg = {.dtype = simd_data_vf32x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_f32x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_f32x2(seq_arg.data.qf32, vec_arg.data.vf32x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf32, simd_data_qf32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -6567,8 +14073,17 @@ simd__intrin_storeh_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_f32, vf32, qf32, u32, f32)
 SIMD_IMPL_INTRIN_2(load_tillz_f32, vf32, qf32, u32)
+#if 32 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_f32, vf32, qf32, u32, f32, f32)
+    SIMD_IMPL_INTRIN_2(load2_tillz_f32, vf32, qf32, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_f32, vf32, qf32, u32, f32, f32)
+    SIMD_IMPL_INTRIN_2(load2_tillz_f32, vf32, qf32, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_store_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6594,9 +14109,72 @@ simd__intrin_store_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_store2_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vf32};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_f32(
+        seq_arg.data.qf32, nlane_arg.data.u32, vec_arg.data.vf32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf32, simd_data_qf32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_store2_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vf32};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_f32(
+        seq_arg.data.qf32, nlane_arg.data.u32, vec_arg.data.vf32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf32, simd_data_qf32)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6608,6 +14186,9 @@ simd__intrin_loadn_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_f32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_f32",
         simd_arg_converter, &seq_arg,
@@ -6618,6 +14199,9 @@ simd__intrin_loadn_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -6626,7 +14210,7 @@ simd__intrin_loadn_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -6645,6 +14229,9 @@ simd__intrin_loadn_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.f32
     #endif
+    #if 0
+        , fill2_arg.data.f32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vf32, .data = {.vf32=rvec}
@@ -6655,8 +14242,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_f32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f32 rvec = npyv_loadn2_f32(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f32
+    #endif
+    #if 0
+        , fill2_arg.data.f32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf32, .data = {.vf32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_f32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f32 rvec = npyv_loadn2_f32(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f32
+    #endif
+    #if 0
+        , fill2_arg.data.f32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf32, .data = {.vf32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6668,6 +14399,9 @@ simd__intrin_loadn_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_f32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_f32",
         simd_arg_converter, &seq_arg,
@@ -6678,6 +14412,9 @@ simd__intrin_loadn_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -6686,7 +14423,7 @@ simd__intrin_loadn_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -6705,6 +14442,9 @@ simd__intrin_loadn_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.f32
     #endif
+    #if 0
+        , fill2_arg.data.f32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vf32, .data = {.vf32=rvec}
@@ -6715,8 +14455,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_f32};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_f32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f32 rvec = npyv_loadn2_till_f32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.f32
+    #endif
+    #if 1
+        , fill2_arg.data.f32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf32, .data = {.vf32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_f32};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_f32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f32 rvec = npyv_loadn2_till_f32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.f32
+    #endif
+    #if 1
+        , fill2_arg.data.f32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf32, .data = {.vf32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_loadn_tillz_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6728,6 +14612,9 @@ simd__intrin_loadn_tillz_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_f32};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_f32",
         simd_arg_converter, &seq_arg,
@@ -6738,6 +14625,9 @@ simd__intrin_loadn_tillz_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -6746,7 +14636,7 @@ simd__intrin_loadn_tillz_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -6765,6 +14655,80 @@ simd__intrin_loadn_tillz_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.f32
     #endif
+    #if 0
+        , fill2_arg.data.f32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf32, .data = {.vf32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_loadn2_tillz_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_f32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f32 rvec = npyv_loadn2_tillz_f32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f32
+    #endif
+    #if 0
+        , fill2_arg.data.f32
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vf32, .data = {.vf32=rvec}
@@ -6775,10 +14739,83 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_loadn2_tillz_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f32};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_f32(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f32 rvec = npyv_loadn2_tillz_f32(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f32
+    #endif
+    #if 0
+        , fill2_arg.data.f32
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf32, .data = {.vf32=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_storen_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6804,7 +14841,7 @@ simd__intrin_storen_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -6833,8 +14870,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_storen2_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf32};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_f32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_f32(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf32, simd_data_qf32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
-#line 180
+#line 216
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_storen2_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf32};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_f32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_f32(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf32, simd_data_qf32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !0 || 0 == 32
 static PyObject *
 simd__intrin_storen_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6860,7 +15015,7 @@ simd__intrin_storen_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -6889,6 +15044,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 32
+static PyObject *
+simd__intrin_storen2_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf32};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_f32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_f32(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf32, simd_data_qf32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 32
+static PyObject *
+simd__intrin_storen2_till_f32(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf32};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf32};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_f32",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f32 *seq_ptr = seq_arg.data.qf32;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f32;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_f32(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_f32(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf32
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf32, simd_data_qf32)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 1
 
@@ -6909,52 +15181,52 @@ SIMD_IMPL_INTRIN_1(extract0_f32, f32, vf32)
 SIMD_IMPL_INTRIN_1(setall_f32, vf32, f32)
 SIMD_IMPL_INTRIN_3(select_f32, vf32, vb32, vf32, vf32)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_f32, vu8, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_f32, vs8, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_f32, vu16, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_f32, vs16, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_f32, vu32, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_f32, vs32, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_f32, vu64, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_f32, vs64, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_f32, vf32, vf32)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_f32, vf64, vf32)
 #endif // simd_sup2
@@ -6964,7 +15236,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_f32, vf64, vf32)
  * special definition due to the nature of intrinsics
  * npyv_setf_f32 and npy_set_f32.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -6987,7 +15259,7 @@ simd__intrin_setf_f32(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vf32);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7014,24 +15286,159 @@ simd__intrin_set_f32(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_f32, vf32, vf32, vf32)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_f32, vf32, vf32, vf32)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_f32, vf32x2, vf32, vf32)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_f32, vf32x2, vf32, vf32)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_f32, vf32x2, vf32, vf32)
+
 
 #if 1
 SIMD_IMPL_INTRIN_1(rev64_f32, vf32, vf32)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 32 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_f32
+npyv_permi128_f32_(npyv_f32 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_f32 ve0;
+    npyv_lanetype_f32 de0[npyv_nlanes_f32];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_f32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_f32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_f32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_f32(a, 0, 0, 0, 0);
+    }
+    npyv_store_f32(de0, ve0);
+    
+#line 360
+    npyv_f32 ve1;
+    npyv_lanetype_f32 de1[npyv_nlanes_f32];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_f32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_f32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_f32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_f32(a, 0, 0, 0, 0);
+    }
+    npyv_store_f32(de1, ve1);
+    
+#line 360
+    npyv_f32 ve2;
+    npyv_lanetype_f32 de2[npyv_nlanes_f32];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_f32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_f32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_f32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_f32(a, 0, 0, 0, 0);
+    }
+    npyv_store_f32(de2, ve2);
+    
+#line 360
+    npyv_f32 ve3;
+    npyv_lanetype_f32 de3[npyv_nlanes_f32];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_f32(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_f32(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_f32(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_f32(a, 0, 0, 0, 0);
+    }
+    npyv_store_f32(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_f32; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_f32(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_f32_, vf32, vf32, u8, u8, u8, u8)
+#elif 32 == 64
+NPY_FINLINE npyv_f32
+npyv_permi128_f32_(npyv_f32 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_f32(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_f32(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_f32(a, 1, 1);
+    }
+    return npyv_permi128_f32(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_f32_, vf32, vf32, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -7043,34 +15450,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_f32, vf32, vf32, 0)
 SIMD_IMPL_INTRIN_2IMM(shri_f32, vf32, vf32, 0)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_f32, vf32, vf32, vf32)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_f32, vf32, vf32, vf32)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_f32, vf32, vf32, vf32)
 
 
 SIMD_IMPL_INTRIN_1(not_f32, vf32, vf32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_f32, vb32, vf32, vf32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_f32, vb32, vf32, vf32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_f32, vb32, vf32, vf32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_f32, vb32, vf32, vf32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_f32, vb32, vf32, vf32)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_f32, vb32, vf32, vf32)
 
 
@@ -7082,10 +15489,10 @@ SIMD_IMPL_INTRIN_2(xnor_b32, vb32, vb32, vb32)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_f32, u8, vf32)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_f32, u8, vf32)
 
 /***************************
@@ -7099,18 +15506,18 @@ SIMD_IMPL_INTRIN_1(expand_f32_f32, vf32x2, vf32)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_f32, vf32, vf32, vf32)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_f32, vf32, vf32, vf32)
 
 
 #if 0
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_f32, vf32, vf32, vf32)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_f32, vf32, vf32, vf32)
 
 #endif // sat_sup
@@ -7129,18 +15536,21 @@ SIMD_IMPL_INTRIN_2(divc_f32, vf32, vf32, vf32x3)
 #endif // intdiv_sup
 
 #if 1
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_f32, vf32, vf32, vf32, vf32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_f32, vf32, vf32, vf32, vf32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_f32, vf32, vf32, vf32, vf32)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_f32, vf32, vf32, vf32, vf32)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_f32, vf32, vf32, vf32, vf32)
+
 #endif // fused_sup
 
 #if 1
@@ -7155,55 +15565,55 @@ SIMD_IMPL_INTRIN_1(sumup_f32, f32, vf32)
  * Math
  ***************************/
 #if 1
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_f32, vf32, vf32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_f32, vf32, vf32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_f32, vf32, vf32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_f32, vf32, vf32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_f32, vf32, vf32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_f32, vf32, vf32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_f32, vf32, vf32)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_f32, vf32, vf32)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_f32, vf32, vf32, vf32)
 SIMD_IMPL_INTRIN_1(reduce_max_f32, f32, vf32)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_f32, vf32, vf32, vf32)
 SIMD_IMPL_INTRIN_1(reduce_min_f32, f32, vf32)
 
 
 #if 1
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_f32, vf32, vf32, vf32)
 SIMD_IMPL_INTRIN_1(reduce_maxp_f32, f32, vf32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_f32, vf32, vf32, vf32)
 SIMD_IMPL_INTRIN_1(reduce_minp_f32, f32, vf32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_f32, vf32, vf32, vf32)
 SIMD_IMPL_INTRIN_1(reduce_maxn_f32, f32, vf32)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_f32, vf32, vf32, vf32)
 SIMD_IMPL_INTRIN_1(reduce_minn_f32, f32, vf32)
 
@@ -7213,13 +15623,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_f32, f32, vf32)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_f32, vf32, vb32, vf32, vf32, vf32)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_f32, vf32, vb32, vf32, vf32, vf32)
 
 
+#if 1
+SIMD_IMPL_INTRIN_4(ifdiv_f32, vf32, vb32, vf32, vf32, vf32)
+SIMD_IMPL_INTRIN_3(ifdivz_f32, vf32, vb32, vf32, vf32)
+#endif
+
 #endif // simd_sup
 
 #line 36
@@ -7239,7 +15654,9 @@ SIMD_IMPL_INTRIN_1(loads_f64, vf64, qf64)
 #line 43
 SIMD_IMPL_INTRIN_1(loadl_f64, vf64, qf64)
 
-#line 48
+SIMD_IMPL_INTRIN_1(load_f64x2, vf64x2, qf64)
+
+#line 51
 // special definition due to the nature of store
 static PyObject *
 simd__intrin_store_f64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -7263,7 +15680,7 @@ simd__intrin_store_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storea
 static PyObject *
 simd__intrin_storea_f64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -7287,7 +15704,7 @@ simd__intrin_storea_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of stores
 static PyObject *
 simd__intrin_stores_f64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -7311,7 +15728,7 @@ simd__intrin_stores_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storel
 static PyObject *
 simd__intrin_storel_f64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -7335,7 +15752,7 @@ simd__intrin_storel_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
-#line 48
+#line 51
 // special definition due to the nature of storeh
 static PyObject *
 simd__intrin_storeh_f64(PyObject* NPY_UNUSED(self), PyObject *args)
@@ -7359,6 +15776,30 @@ simd__intrin_storeh_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_RETURN_NONE;
 }
 
+#line 51
+// special definition due to the nature of store
+static PyObject *
+simd__intrin_store_f64x2(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg vec_arg = {.dtype = simd_data_vf64x2};
+    if (!PyArg_ParseTuple(
+        args, "O&O&:store_f64x2",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store_f64x2(seq_arg.data.qf64, vec_arg.data.vf64x2);
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf64, simd_data_qf64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+
 
 /****************************************
  * Non-contiguous/Partial Memory access
@@ -7367,8 +15808,17 @@ simd__intrin_storeh_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_f64, vf64, qf64, u32, f64)
 SIMD_IMPL_INTRIN_2(load_tillz_f64, vf64, qf64, u32)
+#if 64 == 32
+    SIMD_IMPL_INTRIN_4(load2_till_f64, vf64, qf64, u32, f64, f64)
+    SIMD_IMPL_INTRIN_2(load2_tillz_f64, vf64, qf64, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_f64, vf64, qf64, u32, f64, f64)
+    SIMD_IMPL_INTRIN_2(load2_tillz_f64, vf64, qf64, u32)
+#endif
 
 // Partial Store
+#line 95
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_store_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7394,9 +15844,72 @@ simd__intrin_store_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
+
+
+#line 95
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_store2_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vf64};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_f64(
+        seq_arg.data.qf64, nlane_arg.data.u32, vec_arg.data.vf64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf64, simd_data_qf64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
+
+#line 95
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_store2_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+    simd_arg vec_arg = {.dtype = simd_data_vf64};
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:store2_till_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &nlane_arg,
+        simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_store2_till_f64(
+        seq_arg.data.qf64, nlane_arg.data.u32, vec_arg.data.vf64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf64, simd_data_qf64)) {
+        simd_arg_free(&seq_arg);
+        return NULL;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+}
+#endif // chksize
+
 
 // Non-contiguous Load
-#line 114
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7408,6 +15921,9 @@ simd__intrin_loadn_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_f64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&:loadn_f64",
         simd_arg_converter, &seq_arg,
@@ -7418,6 +15934,9 @@ simd__intrin_loadn_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -7426,7 +15945,7 @@ simd__intrin_loadn_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -7445,6 +15964,9 @@ simd__intrin_loadn_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.f64
     #endif
+    #if 0
+        , fill2_arg.data.f64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vf64, .data = {.vf64=rvec}
@@ -7455,8 +15977,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_f64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f64 rvec = npyv_loadn2_f64(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f64
+    #endif
+    #if 0
+        , fill2_arg.data.f64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf64, .data = {.vf64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&:loadn2_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_f64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f64 rvec = npyv_loadn2_f64(
+        seq_ptr, stride
+    #if 0
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f64
+    #endif
+    #if 0
+        , fill2_arg.data.f64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf64, .data = {.vf64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7468,6 +16134,9 @@ simd__intrin_loadn_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
     simd_arg fill_arg = {.dtype = simd_data_f64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&O&:loadn_till_f64",
         simd_arg_converter, &seq_arg,
@@ -7478,6 +16147,9 @@ simd__intrin_loadn_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 1
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -7486,7 +16158,7 @@ simd__intrin_loadn_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -7505,6 +16177,9 @@ simd__intrin_loadn_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 1
         , fill_arg.data.f64
     #endif
+    #if 0
+        , fill2_arg.data.f64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vf64, .data = {.vf64=rvec}
@@ -7515,8 +16190,152 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 
-#line 114
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_f64};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_f64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f64 rvec = npyv_loadn2_till_f64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.f64
+    #endif
+    #if 1
+        , fill2_arg.data.f64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf64, .data = {.vf64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 1
+    simd_arg fill_arg = {.dtype = simd_data_f64};
+#endif
+#if 1
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&O&:loadn2_till_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 1
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_till_f64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f64 rvec = npyv_loadn2_till_f64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 1
+        , fill_arg.data.f64
+    #endif
+    #if 1
+        , fill2_arg.data.f64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf64, .data = {.vf64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_loadn_tillz_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7528,6 +16347,9 @@ simd__intrin_loadn_tillz_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
     simd_arg fill_arg = {.dtype = simd_data_f64};
 #endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
     if (!PyArg_ParseTuple(
         args, "O&O&O&:loadn_tillz_f64",
         simd_arg_converter, &seq_arg,
@@ -7538,6 +16360,9 @@ simd__intrin_loadn_tillz_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 #if 0
         ,simd_arg_converter, &fill_arg
 #endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -7546,7 +16371,7 @@ simd__intrin_loadn_tillz_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * 1;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -7565,6 +16390,9 @@ simd__intrin_loadn_tillz_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     #if 0
         , fill_arg.data.f64
     #endif
+    #if 0
+        , fill2_arg.data.f64
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_vf64, .data = {.vf64=rvec}
@@ -7575,10 +16403,154 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 136
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_loadn2_tillz_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_f64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f64 rvec = npyv_loadn2_tillz_f64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f64
+    #endif
+    #if 0
+        , fill2_arg.data.f64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf64, .data = {.vf64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 136
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_loadn2_tillz_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif // till
+#if 0
+    simd_arg fill_arg = {.dtype = simd_data_f64};
+#endif
+#if 0
+    simd_arg fill2_arg = {.dtype = simd_data_f64};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:loadn2_tillz_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill_arg
+#endif
+#if 0
+        ,simd_arg_converter, &fill2_arg
+#endif
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1 * 2;
+        min_seq_len = -min_seq_len;
+    }
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "loadn2_tillz_f64(), according to provided stride %d, the "
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_f64 rvec = npyv_loadn2_tillz_f64(
+        seq_ptr, stride
+    #if 1
+        , nlane_arg.data.u32
+    #endif
+    #if 0
+        , fill_arg.data.f64
+    #endif
+    #if 0
+        , fill2_arg.data.f64
+    #endif
+    );
+    simd_arg ret = {
+        .dtype = simd_data_vf64, .data = {.vf64=rvec}
+    };
+    simd_arg_free(&seq_arg);
+    return simd_arg_to_obj(&ret);
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 
 // Non-contiguous Store
-#line 180
+#line 216
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_storen_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7604,7 +16576,7 @@ simd__intrin_storen_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -7633,8 +16605,126 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_storen2_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_f64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_f64(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf64, simd_data_qf64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_storen2_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf64};
+#if 0
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&:storen_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 0
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_f64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_f64(
+        seq_ptr, stride
+    #if 0
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf64, simd_data_qf64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
-#line 180
+#line 216
+#if !0 || 0 == 64
 static PyObject *
 simd__intrin_storen_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7660,7 +16750,7 @@ simd__intrin_storen_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*1;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -7689,6 +16779,123 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
+
+#line 216
+#if !32 || 32 == 64
+static PyObject *
+simd__intrin_storen2_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_f64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_f64(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf64, simd_data_qf64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
+
+#line 216
+#if !64 || 64 == 64
+static PyObject *
+simd__intrin_storen2_till_f64(PyObject* NPY_UNUSED(self), PyObject *args)
+{
+    simd_arg seq_arg = {.dtype = simd_data_qf64};
+    simd_arg stride_arg = {.dtype = simd_data_s64};
+    simd_arg vec_arg = {.dtype = simd_data_vf64};
+#if 1
+    simd_arg nlane_arg = {.dtype = simd_data_u32};
+#endif
+    if (!PyArg_ParseTuple(
+        args, "O&O&O&O&:storen_f64",
+        simd_arg_converter, &seq_arg,
+        simd_arg_converter, &stride_arg
+#if 1
+        ,simd_arg_converter, &nlane_arg
+#endif
+        ,simd_arg_converter, &vec_arg
+    )) {
+        return NULL;
+    }
+    npyv_lanetype_f64 *seq_ptr = seq_arg.data.qf64;
+    npy_intp stride = (npy_intp)stride_arg.data.s64;
+    Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
+    Py_ssize_t min_seq_len = stride * npyv_nlanes_f64;
+    if (stride < 0) {
+        seq_ptr += cur_seq_len - 1*2;
+        min_seq_len = -min_seq_len;
+    }
+    // overflow guard
+    if (cur_seq_len < min_seq_len) {
+        PyErr_Format(PyExc_ValueError,
+            "storen2_till_f64(), according to provided stride %d, the"
+            "minimum acceptable size of the required sequence is %d, given(%d)",
+            stride, min_seq_len, cur_seq_len
+        );
+        goto err;
+    }
+    npyv_storen2_till_f64(
+        seq_ptr, stride
+    #if 1
+        ,nlane_arg.data.u32
+    #endif
+        ,vec_arg.data.vf64
+    );
+    // write-back
+    if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.qf64, simd_data_qf64)) {
+        goto err;
+    }
+    simd_arg_free(&seq_arg);
+    Py_RETURN_NONE;
+err:
+    simd_arg_free(&seq_arg);
+    return NULL;
+}
+#endif // chksize
 
 #endif // 1
 
@@ -7709,52 +16916,52 @@ SIMD_IMPL_INTRIN_1(extract0_f64, f64, vf64)
 SIMD_IMPL_INTRIN_1(setall_f64, vf64, f64)
 SIMD_IMPL_INTRIN_3(select_f64, vf64, vb64, vf64, vf64)
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u8_f64, vu8, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s8_f64, vs8, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u16_f64, vu16, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s16_f64, vs16, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u32_f64, vu32, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s32_f64, vs32, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_u64_f64, vu64, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if 1
 SIMD_IMPL_INTRIN_1(reinterpret_s64_f64, vs64, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F32
 SIMD_IMPL_INTRIN_1(reinterpret_f32_f64, vf32, vf64)
 #endif // simd_sup2
 
-#line 258
+#line 296
 #if NPY_SIMD_F64
 SIMD_IMPL_INTRIN_1(reinterpret_f64_f64, vf64, vf64)
 #endif // simd_sup2
@@ -7764,7 +16971,7 @@ SIMD_IMPL_INTRIN_1(reinterpret_f64_f64, vf64, vf64)
  * special definition due to the nature of intrinsics
  * npyv_setf_f64 and npy_set_f64.
 */
-#line 270
+#line 308
 static PyObject *
 simd__intrin_setf_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7787,7 +16994,7 @@ simd__intrin_setf_f64(PyObject* NPY_UNUSED(self), PyObject *args)
     return (PyObject*)PySIMDVector_FromData(r, simd_data_vf64);
 }
 
-#line 270
+#line 308
 static PyObject *
 simd__intrin_set_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -7814,24 +17021,159 @@ simd__intrin_set_f64(PyObject* NPY_UNUSED(self), PyObject *args)
 /***************************
  * Reorder
  ***************************/
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combinel_f64, vf64, vf64, vf64)
 
-#line 299
+#line 337
 SIMD_IMPL_INTRIN_2(combineh_f64, vf64, vf64, vf64)
 
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(combine_f64, vf64x2, vf64, vf64)
 
-#line 305
+#line 343
 SIMD_IMPL_INTRIN_2(zip_f64, vf64x2, vf64, vf64)
 
+#line 343
+SIMD_IMPL_INTRIN_2(unzip_f64, vf64x2, vf64, vf64)
+
 
 #if 0
 SIMD_IMPL_INTRIN_1(rev64_f64, vf64, vf64)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if 64 == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_f64
+npyv_permi128_f64_(npyv_f64 a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   #line 360
+    npyv_f64 ve0;
+    npyv_lanetype_f64 de0[npyv_nlanes_f64];
+    if (0) {}
+   #line 366
+    else if (e0 == 1) {
+        ve0 = npyv_permi128_f64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e0 == 2) {
+        ve0 = npyv_permi128_f64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e0 == 3) {
+        ve0 = npyv_permi128_f64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve0 = npyv_permi128_f64(a, 0, 0, 0, 0);
+    }
+    npyv_store_f64(de0, ve0);
+    
+#line 360
+    npyv_f64 ve1;
+    npyv_lanetype_f64 de1[npyv_nlanes_f64];
+    if (0) {}
+   #line 366
+    else if (e1 == 1) {
+        ve1 = npyv_permi128_f64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e1 == 2) {
+        ve1 = npyv_permi128_f64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e1 == 3) {
+        ve1 = npyv_permi128_f64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve1 = npyv_permi128_f64(a, 0, 0, 0, 0);
+    }
+    npyv_store_f64(de1, ve1);
+    
+#line 360
+    npyv_f64 ve2;
+    npyv_lanetype_f64 de2[npyv_nlanes_f64];
+    if (0) {}
+   #line 366
+    else if (e2 == 1) {
+        ve2 = npyv_permi128_f64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e2 == 2) {
+        ve2 = npyv_permi128_f64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e2 == 3) {
+        ve2 = npyv_permi128_f64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve2 = npyv_permi128_f64(a, 0, 0, 0, 0);
+    }
+    npyv_store_f64(de2, ve2);
+    
+#line 360
+    npyv_f64 ve3;
+    npyv_lanetype_f64 de3[npyv_nlanes_f64];
+    if (0) {}
+   #line 366
+    else if (e3 == 1) {
+        ve3 = npyv_permi128_f64(a, 1, 1, 1, 1);
+    }
+    
+#line 366
+    else if (e3 == 2) {
+        ve3 = npyv_permi128_f64(a, 2, 2, 2, 2);
+    }
+    
+#line 366
+    else if (e3 == 3) {
+        ve3 = npyv_permi128_f64(a, 3, 3, 3, 3);
+    }
+    
+    else {
+        ve3 = npyv_permi128_f64(a, 0, 0, 0, 0);
+    }
+    npyv_store_f64(de3, ve3);
+    
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_f64; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_f64(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_f64_, vf64, vf64, u8, u8, u8, u8)
+#elif 64 == 64
+NPY_FINLINE npyv_f64
+npyv_permi128_f64_(npyv_f64 a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_f64(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_f64(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_f64(a, 1, 1);
+    }
+    return npyv_permi128_f64(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_f64_, vf64, vf64, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -7843,34 +17185,34 @@ SIMD_IMPL_INTRIN_2IMM(shli_f64, vf64, vf64, 0)
 SIMD_IMPL_INTRIN_2IMM(shri_f64, vf64, vf64, 0)
 #endif // shl_imm
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(and_f64, vf64, vf64, vf64)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(or_f64, vf64, vf64, vf64)
 
-#line 326
+#line 418
 SIMD_IMPL_INTRIN_2(xor_f64, vf64, vf64, vf64)
 
 
 SIMD_IMPL_INTRIN_1(not_f64, vf64, vf64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpeq_f64, vb64, vf64, vf64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpneq_f64, vb64, vf64, vf64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpgt_f64, vb64, vf64, vf64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmpge_f64, vb64, vf64, vf64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmplt_f64, vb64, vf64, vf64)
 
-#line 334
+#line 426
 SIMD_IMPL_INTRIN_2(cmple_f64, vb64, vf64, vf64)
 
 
@@ -7882,10 +17224,10 @@ SIMD_IMPL_INTRIN_2(xnor_b64, vb64, vb64, vb64)
 #endif
 
 // test cross all vector lanes
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(any_f64, u8, vf64)
 
-#line 348
+#line 440
 SIMD_IMPL_INTRIN_1(all_f64, u8, vf64)
 
 /***************************
@@ -7899,18 +17241,18 @@ SIMD_IMPL_INTRIN_1(expand_f64_f64, vf64x2, vf64)
 /***************************
  * Arithmetic
  ***************************/
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(add_f64, vf64, vf64, vf64)
 
-#line 364
+#line 456
 SIMD_IMPL_INTRIN_2(sub_f64, vf64, vf64, vf64)
 
 
 #if 0
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(adds_f64, vf64, vf64, vf64)
 
-#line 371
+#line 463
 SIMD_IMPL_INTRIN_2(subs_f64, vf64, vf64, vf64)
 
 #endif // sat_sup
@@ -7929,18 +17271,21 @@ SIMD_IMPL_INTRIN_2(divc_f64, vf64, vf64, vf64x3)
 #endif // intdiv_sup
 
 #if 1
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(muladd_f64, vf64, vf64, vf64, vf64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(mulsub_f64, vf64, vf64, vf64, vf64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmuladd_f64, vf64, vf64, vf64, vf64)
 
-#line 392
+#line 484
 SIMD_IMPL_INTRIN_3(nmulsub_f64, vf64, vf64, vf64, vf64)
 
+#line 484
+SIMD_IMPL_INTRIN_3(muladdsub_f64, vf64, vf64, vf64, vf64)
+
 #endif // fused_sup
 
 #if 1
@@ -7955,55 +17300,55 @@ SIMD_IMPL_INTRIN_1(sumup_f64, f64, vf64)
  * Math
  ***************************/
 #if 1
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(sqrt_f64, vf64, vf64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(recip_f64, vf64, vf64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(abs_f64, vf64, vf64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(square_f64, vf64, vf64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(rint_f64, vf64, vf64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(ceil_f64, vf64, vf64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(trunc_f64, vf64, vf64)
 
-#line 411
+#line 503
 SIMD_IMPL_INTRIN_1(floor_f64, vf64, vf64)
 
 #endif
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(max_f64, vf64, vf64, vf64)
 SIMD_IMPL_INTRIN_1(reduce_max_f64, f64, vf64)
 
-#line 418
+#line 510
 SIMD_IMPL_INTRIN_2(min_f64, vf64, vf64, vf64)
 SIMD_IMPL_INTRIN_1(reduce_min_f64, f64, vf64)
 
 
 #if 1
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxp_f64, vf64, vf64, vf64)
 SIMD_IMPL_INTRIN_1(reduce_maxp_f64, f64, vf64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minp_f64, vf64, vf64, vf64)
 SIMD_IMPL_INTRIN_1(reduce_minp_f64, f64, vf64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(maxn_f64, vf64, vf64, vf64)
 SIMD_IMPL_INTRIN_1(reduce_maxn_f64, f64, vf64)
 
-#line 426
+#line 518
 SIMD_IMPL_INTRIN_2(minn_f64, vf64, vf64, vf64)
 SIMD_IMPL_INTRIN_1(reduce_minn_f64, f64, vf64)
 
@@ -8013,13 +17358,18 @@ SIMD_IMPL_INTRIN_1(reduce_minn_f64, f64, vf64)
 /***************************
  * Mask operations
  ***************************/
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifadd_f64, vf64, vb64, vf64, vf64, vf64)
 
-#line 438
+#line 530
  SIMD_IMPL_INTRIN_4(ifsub_f64, vf64, vb64, vf64, vf64, vf64)
 
 
+#if 1
+SIMD_IMPL_INTRIN_4(ifdiv_f64, vf64, vb64, vf64, vf64, vf64)
+SIMD_IMPL_INTRIN_3(ifdivz_f64, vf64, vb64, vf64, vf64)
+#endif
+
 #endif // simd_sup
 
 /*************************************************************************
@@ -8057,59 +17407,59 @@ SIMD_IMPL_INTRIN_0N(cleanup)
 /***************************
  * Operators
  ***************************/
-#line 481
+#line 578
 // Logical
 SIMD_IMPL_INTRIN_2(and_b8, vb8, vb8, vb8)
 SIMD_IMPL_INTRIN_2(or_b8,  vb8, vb8, vb8)
 SIMD_IMPL_INTRIN_2(xor_b8, vb8, vb8, vb8)
 SIMD_IMPL_INTRIN_1(not_b8, vb8, vb8)
 // test cross vector's lanes
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(any_b8, u8, vb8)
 
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(all_b8, u8, vb8)
 
 
-#line 481
+#line 578
 // Logical
 SIMD_IMPL_INTRIN_2(and_b16, vb16, vb16, vb16)
 SIMD_IMPL_INTRIN_2(or_b16,  vb16, vb16, vb16)
 SIMD_IMPL_INTRIN_2(xor_b16, vb16, vb16, vb16)
 SIMD_IMPL_INTRIN_1(not_b16, vb16, vb16)
 // test cross vector's lanes
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(any_b16, u8, vb16)
 
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(all_b16, u8, vb16)
 
 
-#line 481
+#line 578
 // Logical
 SIMD_IMPL_INTRIN_2(and_b32, vb32, vb32, vb32)
 SIMD_IMPL_INTRIN_2(or_b32,  vb32, vb32, vb32)
 SIMD_IMPL_INTRIN_2(xor_b32, vb32, vb32, vb32)
 SIMD_IMPL_INTRIN_1(not_b32, vb32, vb32)
 // test cross vector's lanes
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(any_b32, u8, vb32)
 
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(all_b32, u8, vb32)
 
 
-#line 481
+#line 578
 // Logical
 SIMD_IMPL_INTRIN_2(and_b64, vb64, vb64, vb64)
 SIMD_IMPL_INTRIN_2(or_b64,  vb64, vb64, vb64)
 SIMD_IMPL_INTRIN_2(xor_b64, vb64, vb64, vb64)
 SIMD_IMPL_INTRIN_1(not_b64, vb64, vb64)
 // test cross vector's lanes
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(any_b64, u8, vb64)
 
-#line 490
+#line 587
 SIMD_IMPL_INTRIN_1(all_b64, u8, vb64)
 
 
@@ -8117,16 +17467,16 @@ SIMD_IMPL_INTRIN_1(all_b64, u8, vb64)
  * Conversions
  ***************************/
 // Convert mask vector to integer bitfield
-#line 500
+#line 597
 SIMD_IMPL_INTRIN_1(tobits_b8, u64, vb8)
 
-#line 500
+#line 597
 SIMD_IMPL_INTRIN_1(tobits_b16, u64, vb16)
 
-#line 500
+#line 597
 SIMD_IMPL_INTRIN_1(tobits_b32, u64, vb32)
 
-#line 500
+#line 597
 SIMD_IMPL_INTRIN_1(tobits_b64, u64, vb64)
 
 
@@ -8139,68 +17489,126 @@ SIMD_IMPL_INTRIN_8(pack_b8_b64, vb8, vb64, vb64, vb64, vb64,
 //## Attach module functions
 //#########################################################################
 static PyMethodDef simd__intrinsics_methods[] = {
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_u8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_u8)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_u8x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_u8x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 0
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_u8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_u8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_u8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_u8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_u8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_u8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_u8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_u8)
 
+#if 8 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_u8)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_u8)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_u8)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_u8)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_u8)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_u8)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_u8)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_u8)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_u8)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_u8)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_u8)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_u8)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_u8)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_u8)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_u8)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_u8)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -8215,148 +17623,155 @@ SIMD_INTRIN_DEF(lut16_u8)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_u8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_u8)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_u8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_u8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_u8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_u8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_u8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_u8)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_u8)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_u8)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_u8)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_u8)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_u8)
+
 
 #if 1
 SIMD_INTRIN_DEF(rev64_u8)
 #endif
 
+#if 8 > 16
+{ "permi128_u8", simd__intrin_permi128_u8_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 0 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_u8)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_u8)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_u8)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_u8)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_u8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_u8)
 
 
@@ -8378,18 +17793,18 @@ SIMD_INTRIN_DEF(expand_u16_u8)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_u8)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_u8)
 
 
 #if 1
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_u8)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_u8)
 
 #endif // sat_sup
@@ -8408,18 +17823,21 @@ SIMD_INTRIN_DEF(divc_u8)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_u8)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_u8)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_u8)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_u8)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_u8)
+
 #endif // fused_sup
 
 #if 0
@@ -8433,55 +17851,55 @@ SIMD_INTRIN_DEF(sumup_u8)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_u8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_u8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_u8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_u8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_u8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_u8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_u8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_u8)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_u8)
 SIMD_INTRIN_DEF(reduce_max_u8)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_u8)
 SIMD_INTRIN_DEF(reduce_min_u8)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_u8)
 SIMD_INTRIN_DEF(reduce_maxp_u8)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_u8)
 SIMD_INTRIN_DEF(reduce_minp_u8)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_u8)
 SIMD_INTRIN_DEF(reduce_maxn_u8)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_u8)
 SIMD_INTRIN_DEF(reduce_minn_u8)
 
@@ -8491,77 +17909,144 @@ SIMD_INTRIN_DEF(reduce_minn_u8)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_u8)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_u8)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_u8)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_u8)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_s8)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_s8)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_s8x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_s8x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 0
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_s8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_s8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_s8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_s8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_s8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_s8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_s8)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_s8)
 
+#if 8 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_s8)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_s8)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_s8)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_s8)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_s8)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_s8)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_s8)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_s8)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_s8)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_s8)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_s8)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_s8)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_s8)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_s8)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_s8)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_s8)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -8576,148 +18061,155 @@ SIMD_INTRIN_DEF(lut16_s8)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_s8)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_s8)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_s8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_s8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_s8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_s8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_s8)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_s8)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_s8)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_s8)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_s8)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_s8)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_s8)
+
 
 #if 1
 SIMD_INTRIN_DEF(rev64_s8)
 #endif
 
+#if 8 > 16
+{ "permi128_s8", simd__intrin_permi128_s8_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 0 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_s8)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_s8)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_s8)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_s8)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_s8)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_s8)
 
 
@@ -8739,18 +18231,18 @@ SIMD_INTRIN_DEF(expand_s8_s8)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_s8)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_s8)
 
 
 #if 1
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_s8)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_s8)
 
 #endif // sat_sup
@@ -8769,18 +18261,21 @@ SIMD_INTRIN_DEF(divc_s8)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_s8)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_s8)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_s8)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_s8)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_s8)
+
 #endif // fused_sup
 
 #if 0
@@ -8794,55 +18289,55 @@ SIMD_INTRIN_DEF(sumup_s8)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_s8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_s8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_s8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_s8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_s8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_s8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_s8)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_s8)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_s8)
 SIMD_INTRIN_DEF(reduce_max_s8)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_s8)
 SIMD_INTRIN_DEF(reduce_min_s8)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_s8)
 SIMD_INTRIN_DEF(reduce_maxp_s8)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_s8)
 SIMD_INTRIN_DEF(reduce_minp_s8)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_s8)
 SIMD_INTRIN_DEF(reduce_maxn_s8)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_s8)
 SIMD_INTRIN_DEF(reduce_minn_s8)
 
@@ -8852,77 +18347,144 @@ SIMD_INTRIN_DEF(reduce_minn_s8)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_s8)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_s8)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_s8)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_s8)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_u16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_u16)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_u16x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_u16x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 0
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_u16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_u16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_u16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_u16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_u16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_u16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_u16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_u16)
 
+#if 16 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_u16)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_u16)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_u16)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_u16)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_u16)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_u16)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_u16)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_u16)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_u16)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_u16)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_u16)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_u16)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_u16)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_u16)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_u16)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_u16)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -8937,148 +18499,155 @@ SIMD_INTRIN_DEF(lut16_u16)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_u16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_u16)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_u16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_u16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_u16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_u16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_u16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_u16)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_u16)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_u16)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_u16)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_u16)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_u16)
+
 
 #if 1
 SIMD_INTRIN_DEF(rev64_u16)
 #endif
 
+#if 16 > 16
+{ "permi128_u16", simd__intrin_permi128_u16_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 15 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_u16)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_u16)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_u16)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_u16)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_u16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_u16)
 
 
@@ -9100,18 +18669,18 @@ SIMD_INTRIN_DEF(expand_u32_u16)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_u16)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_u16)
 
 
 #if 1
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_u16)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_u16)
 
 #endif // sat_sup
@@ -9130,18 +18699,21 @@ SIMD_INTRIN_DEF(divc_u16)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_u16)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_u16)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_u16)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_u16)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_u16)
+
 #endif // fused_sup
 
 #if 0
@@ -9155,55 +18727,55 @@ SIMD_INTRIN_DEF(sumup_u16)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_u16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_u16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_u16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_u16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_u16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_u16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_u16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_u16)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_u16)
 SIMD_INTRIN_DEF(reduce_max_u16)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_u16)
 SIMD_INTRIN_DEF(reduce_min_u16)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_u16)
 SIMD_INTRIN_DEF(reduce_maxp_u16)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_u16)
 SIMD_INTRIN_DEF(reduce_minp_u16)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_u16)
 SIMD_INTRIN_DEF(reduce_maxn_u16)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_u16)
 SIMD_INTRIN_DEF(reduce_minn_u16)
 
@@ -9213,77 +18785,144 @@ SIMD_INTRIN_DEF(reduce_minn_u16)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_u16)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_u16)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_u16)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_u16)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_s16)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_s16)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_s16x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_s16x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 0
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_s16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_s16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_s16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_s16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_s16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_s16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_s16)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_s16)
 
+#if 16 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_s16)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_s16)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_s16)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_s16)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_s16)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_s16)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_s16)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_s16)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_s16)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_s16)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_s16)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_s16)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_s16)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_s16)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_s16)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_s16)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -9298,148 +18937,155 @@ SIMD_INTRIN_DEF(lut16_s16)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_s16)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_s16)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_s16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_s16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_s16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_s16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_s16)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_s16)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_s16)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_s16)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_s16)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_s16)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_s16)
+
 
 #if 1
 SIMD_INTRIN_DEF(rev64_s16)
 #endif
 
+#if 16 > 16
+{ "permi128_s16", simd__intrin_permi128_s16_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 15 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_s16)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_s16)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_s16)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_s16)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_s16)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_s16)
 
 
@@ -9461,18 +19107,18 @@ SIMD_INTRIN_DEF(expand_s16_s16)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_s16)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_s16)
 
 
 #if 1
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_s16)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_s16)
 
 #endif // sat_sup
@@ -9491,18 +19137,21 @@ SIMD_INTRIN_DEF(divc_s16)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_s16)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_s16)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_s16)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_s16)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_s16)
+
 #endif // fused_sup
 
 #if 0
@@ -9516,55 +19165,55 @@ SIMD_INTRIN_DEF(sumup_s16)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_s16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_s16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_s16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_s16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_s16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_s16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_s16)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_s16)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_s16)
 SIMD_INTRIN_DEF(reduce_max_s16)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_s16)
 SIMD_INTRIN_DEF(reduce_min_s16)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_s16)
 SIMD_INTRIN_DEF(reduce_maxp_s16)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_s16)
 SIMD_INTRIN_DEF(reduce_minp_s16)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_s16)
 SIMD_INTRIN_DEF(reduce_maxn_s16)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_s16)
 SIMD_INTRIN_DEF(reduce_minn_s16)
 
@@ -9574,77 +19223,144 @@ SIMD_INTRIN_DEF(reduce_minn_s16)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_s16)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_s16)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_s16)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_s16)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_u32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_u32)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_u32x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_u32x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 1
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_u32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_u32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_u32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_u32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_u32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_u32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_u32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_u32)
 
+#if 32 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_u32)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_u32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_u32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_u32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_u32)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_u32)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_u32)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_u32)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_u32)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_u32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_u32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_u32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_u32)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_u32)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_u32)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_u32)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -9659,148 +19375,155 @@ SIMD_INTRIN_DEF(lut16_u32)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_u32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_u32)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_u32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_u32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_u32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_u32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_u32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_u32)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_u32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_u32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_u32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_u32)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_u32)
+
 
 #if 1
 SIMD_INTRIN_DEF(rev64_u32)
 #endif
 
+#if 32 > 16
+{ "permi128_u32", simd__intrin_permi128_u32_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 31 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_u32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_u32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_u32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_u32)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_u32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_u32)
 
 
@@ -9822,18 +19545,18 @@ SIMD_INTRIN_DEF(expand_u32_u32)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_u32)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_u32)
 
 
 #if 0
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_u32)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_u32)
 
 #endif // sat_sup
@@ -9852,18 +19575,21 @@ SIMD_INTRIN_DEF(divc_u32)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_u32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_u32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_u32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_u32)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_u32)
+
 #endif // fused_sup
 
 #if 1
@@ -9877,55 +19603,55 @@ SIMD_INTRIN_DEF(sumup_u32)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_u32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_u32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_u32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_u32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_u32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_u32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_u32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_u32)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_u32)
 SIMD_INTRIN_DEF(reduce_max_u32)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_u32)
 SIMD_INTRIN_DEF(reduce_min_u32)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_u32)
 SIMD_INTRIN_DEF(reduce_maxp_u32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_u32)
 SIMD_INTRIN_DEF(reduce_minp_u32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_u32)
 SIMD_INTRIN_DEF(reduce_maxn_u32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_u32)
 SIMD_INTRIN_DEF(reduce_minn_u32)
 
@@ -9935,77 +19661,144 @@ SIMD_INTRIN_DEF(reduce_minn_u32)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_u32)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_u32)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_u32)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_u32)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_s32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_s32)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_s32x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_s32x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 1
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_s32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_s32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_s32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_s32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_s32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_s32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_s32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_s32)
 
+#if 32 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_s32)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_s32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_s32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_s32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_s32)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_s32)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_s32)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_s32)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_s32)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_s32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_s32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_s32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_s32)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_s32)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_s32)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_s32)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -10020,148 +19813,155 @@ SIMD_INTRIN_DEF(lut16_s32)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_s32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_s32)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_s32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_s32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_s32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_s32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_s32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_s32)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_s32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_s32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_s32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_s32)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_s32)
+
 
 #if 1
 SIMD_INTRIN_DEF(rev64_s32)
 #endif
 
+#if 32 > 16
+{ "permi128_s32", simd__intrin_permi128_s32_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 31 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_s32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_s32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_s32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_s32)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_s32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_s32)
 
 
@@ -10183,18 +19983,18 @@ SIMD_INTRIN_DEF(expand_s32_s32)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_s32)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_s32)
 
 
 #if 0
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_s32)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_s32)
 
 #endif // sat_sup
@@ -10213,18 +20013,21 @@ SIMD_INTRIN_DEF(divc_s32)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_s32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_s32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_s32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_s32)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_s32)
+
 #endif // fused_sup
 
 #if 0
@@ -10238,55 +20041,55 @@ SIMD_INTRIN_DEF(sumup_s32)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_s32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_s32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_s32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_s32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_s32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_s32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_s32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_s32)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_s32)
 SIMD_INTRIN_DEF(reduce_max_s32)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_s32)
 SIMD_INTRIN_DEF(reduce_min_s32)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_s32)
 SIMD_INTRIN_DEF(reduce_maxp_s32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_s32)
 SIMD_INTRIN_DEF(reduce_minp_s32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_s32)
 SIMD_INTRIN_DEF(reduce_maxn_s32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_s32)
 SIMD_INTRIN_DEF(reduce_minn_s32)
 
@@ -10296,77 +20099,144 @@ SIMD_INTRIN_DEF(reduce_minn_s32)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_s32)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_s32)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_s32)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_s32)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_u64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_u64)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_u64x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_u64x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 1
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_u64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_u64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_u64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_u64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_u64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_u64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_u64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_u64)
 
+#if 64 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_u64)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_u64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_u64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_u64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_u64)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_u64)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_u64)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_u64)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_u64)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_u64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_u64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_u64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_u64)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_u64)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_u64)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_u64)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -10381,148 +20251,155 @@ SIMD_INTRIN_DEF(lut16_u64)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_u64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_u64)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_u64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_u64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_u64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_u64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_u64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_u64)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_u64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_u64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_u64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_u64)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_u64)
+
 
 #if 0
 SIMD_INTRIN_DEF(rev64_u64)
 #endif
 
+#if 64 > 16
+{ "permi128_u64", simd__intrin_permi128_u64_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 63 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_u64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_u64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_u64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_u64)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_u64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_u64)
 
 
@@ -10544,18 +20421,18 @@ SIMD_INTRIN_DEF(expand_u64_u64)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_u64)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_u64)
 
 
 #if 0
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_u64)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_u64)
 
 #endif // sat_sup
@@ -10574,18 +20451,21 @@ SIMD_INTRIN_DEF(divc_u64)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_u64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_u64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_u64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_u64)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_u64)
+
 #endif // fused_sup
 
 #if 1
@@ -10599,55 +20479,55 @@ SIMD_INTRIN_DEF(sumup_u64)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_u64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_u64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_u64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_u64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_u64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_u64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_u64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_u64)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_u64)
 SIMD_INTRIN_DEF(reduce_max_u64)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_u64)
 SIMD_INTRIN_DEF(reduce_min_u64)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_u64)
 SIMD_INTRIN_DEF(reduce_maxp_u64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_u64)
 SIMD_INTRIN_DEF(reduce_minp_u64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_u64)
 SIMD_INTRIN_DEF(reduce_maxn_u64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_u64)
 SIMD_INTRIN_DEF(reduce_minn_u64)
 
@@ -10657,77 +20537,144 @@ SIMD_INTRIN_DEF(reduce_minn_u64)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_u64)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_u64)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_u64)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_u64)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if 1
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_s64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_s64)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_s64x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_s64x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 1
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_s64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_s64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_s64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_s64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_s64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_s64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_s64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_s64)
 
+#if 64 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_s64)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_s64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_s64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_s64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_s64)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_s64)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_s64)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_s64)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_s64)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_s64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_s64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_s64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_s64)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_s64)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_s64)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_s64)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -10742,148 +20689,155 @@ SIMD_INTRIN_DEF(lut16_s64)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_s64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_s64)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_s64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_s64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_s64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_s64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_s64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_s64)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_s64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_s64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_s64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_s64)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_s64)
+
 
 #if 0
 SIMD_INTRIN_DEF(rev64_s64)
 #endif
 
+#if 64 > 16
+{ "permi128_s64", simd__intrin_permi128_s64_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 63 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_s64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_s64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_s64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_s64)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_s64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_s64)
 
 
@@ -10905,18 +20859,18 @@ SIMD_INTRIN_DEF(expand_s64_s64)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_s64)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_s64)
 
 
 #if 0
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_s64)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_s64)
 
 #endif // sat_sup
@@ -10935,18 +20889,21 @@ SIMD_INTRIN_DEF(divc_s64)
 #endif // intdiv_sup
 
 #if 0
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_s64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_s64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_s64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_s64)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_s64)
+
 #endif // fused_sup
 
 #if 0
@@ -10960,55 +20917,55 @@ SIMD_INTRIN_DEF(sumup_s64)
  * Math
  ***************************/
 #if 0
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_s64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_s64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_s64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_s64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_s64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_s64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_s64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_s64)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_s64)
 SIMD_INTRIN_DEF(reduce_max_s64)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_s64)
 SIMD_INTRIN_DEF(reduce_min_s64)
 
 
 #if 0
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_s64)
 SIMD_INTRIN_DEF(reduce_maxp_s64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_s64)
 SIMD_INTRIN_DEF(reduce_minp_s64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_s64)
 SIMD_INTRIN_DEF(reduce_maxn_s64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_s64)
 SIMD_INTRIN_DEF(reduce_minn_s64)
 
@@ -11018,77 +20975,144 @@ SIMD_INTRIN_DEF(reduce_minn_s64)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_s64)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_s64)
 
 
+#if 0
+#line 837
+SIMD_INTRIN_DEF(ifdiv_s64)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_s64)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if NPY_SIMD_F32
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_f32)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_f32)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_f32x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_f32x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 1
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_f32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_f32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_f32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_f32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_f32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_f32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_f32)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_f32)
 
+#if 32 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_f32)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_f32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_f32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_f32)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_f32)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_f32)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_f32)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_f32)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_f32)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_f32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_f32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_f32)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_f32)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_f32)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_f32)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_f32)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -11103,148 +21127,155 @@ SIMD_INTRIN_DEF(lut16_f32)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_f32)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_f32)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_f32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_f32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_f32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_f32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_f32)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_f32)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_f32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_f32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_f32)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_f32)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_f32)
+
 
 #if 1
 SIMD_INTRIN_DEF(rev64_f32)
 #endif
 
+#if 32 > 16
+{ "permi128_f32", simd__intrin_permi128_f32_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 0 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_f32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_f32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_f32)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_f32)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_f32)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_f32)
 
 
@@ -11266,18 +21297,18 @@ SIMD_INTRIN_DEF(expand_f32_f32)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_f32)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_f32)
 
 
 #if 0
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_f32)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_f32)
 
 #endif // sat_sup
@@ -11296,18 +21327,21 @@ SIMD_INTRIN_DEF(divc_f32)
 #endif // intdiv_sup
 
 #if 1
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_f32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_f32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_f32)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_f32)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_f32)
+
 #endif // fused_sup
 
 #if 1
@@ -11321,55 +21355,55 @@ SIMD_INTRIN_DEF(sumup_f32)
  * Math
  ***************************/
 #if 1
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_f32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_f32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_f32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_f32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_f32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_f32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_f32)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_f32)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_f32)
 SIMD_INTRIN_DEF(reduce_max_f32)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_f32)
 SIMD_INTRIN_DEF(reduce_min_f32)
 
 
 #if 1
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_f32)
 SIMD_INTRIN_DEF(reduce_maxp_f32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_f32)
 SIMD_INTRIN_DEF(reduce_minp_f32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_f32)
 SIMD_INTRIN_DEF(reduce_maxn_f32)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_f32)
 SIMD_INTRIN_DEF(reduce_minn_f32)
 
@@ -11379,77 +21413,144 @@ SIMD_INTRIN_DEF(reduce_minn_f32)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_f32)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_f32)
 
 
+#if 1
+#line 837
+SIMD_INTRIN_DEF(ifdiv_f32)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_f32)
+
+#endif
+
 #endif // simd_sup
 
-#line 533
+#line 630
 #if NPY_SIMD_F64
 
 /***************************
  * Memory
  ***************************/
-#line 541
+#line 638
 SIMD_INTRIN_DEF(load_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loada_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loads_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(loadl_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(store_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storea_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(stores_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storel_f64)
 
-#line 541
+#line 638
 SIMD_INTRIN_DEF(storeh_f64)
 
 
+#line 644
+SIMD_INTRIN_DEF(load_f64x2)
+
+#line 644
+SIMD_INTRIN_DEF(store_f64x2)
+
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
 #if 1
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_till_f64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(load_tillz_f64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_f64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_till_f64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(loadn_tillz_f64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(store_till_f64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_f64)
 
-#line 552
+#line 655
 SIMD_INTRIN_DEF(storen_till_f64)
 
+#if 64 == 32
+    #line 662
+    SIMD_INTRIN_DEF(load2_till_f64)
+    
+#line 662
+    SIMD_INTRIN_DEF(load2_tillz_f64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_f64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_till_f64)
+    
+#line 662
+    SIMD_INTRIN_DEF(loadn2_tillz_f64)
+    
+#line 662
+    SIMD_INTRIN_DEF(store2_till_f64)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_f64)
+    
+#line 662
+    SIMD_INTRIN_DEF(storen2_till_f64)
+    
+#else
+    #line 669
+    SIMD_INTRIN_DEF(load2_till_f64)
+    
+#line 669
+    SIMD_INTRIN_DEF(load2_tillz_f64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_f64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_till_f64)
+    
+#line 669
+    SIMD_INTRIN_DEF(loadn2_tillz_f64)
+    
+#line 669
+    SIMD_INTRIN_DEF(store2_till_f64)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_f64)
+    
+#line 669
+    SIMD_INTRIN_DEF(storen2_till_f64)
+    
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -11464,148 +21565,155 @@ SIMD_INTRIN_DEF(lut16_f64)
 /***************************
  * Misc
  ***************************/
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u8_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s8_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u16_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s16_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u32_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s32_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_u64_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if 1
 SIMD_INTRIN_DEF(reinterpret_s64_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F32
 SIMD_INTRIN_DEF(reinterpret_f32_f64)
 #endif // simd_sup2
 
-#line 572
+#line 690
 #if NPY_SIMD_F64
 SIMD_INTRIN_DEF(reinterpret_f64_f64)
 #endif // simd_sup2
 
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(set_f64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setf_f64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(setall_f64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(zero_f64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(select_f64)
 
-#line 580
+#line 698
 SIMD_INTRIN_DEF(extract0_f64)
 
 
 /***************************
  * Reorder
  ***************************/
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combinel_f64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combineh_f64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(combine_f64)
 
-#line 589
+#line 707
 SIMD_INTRIN_DEF(zip_f64)
 
+#line 707
+SIMD_INTRIN_DEF(unzip_f64)
+
 
 #if 0
 SIMD_INTRIN_DEF(rev64_f64)
 #endif
 
+#if 64 > 16
+{ "permi128_f64", simd__intrin_permi128_f64_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
 #if 0 > 0
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shl_f64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shr_f64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shli_f64)
 
-#line 603
+#line 725
 SIMD_INTRIN_DEF(shri_f64)
 
 #endif // shl_imm
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(and_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(or_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(xor_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(not_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpeq_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpneq_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpgt_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmpge_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmplt_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(cmple_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(any_f64)
 
-#line 611
+#line 733
 SIMD_INTRIN_DEF(all_f64)
 
 
@@ -11627,18 +21735,18 @@ SIMD_INTRIN_DEF(expand_f64_f64)
 /***************************
  * Arithmetic
  ***************************/
-#line 635
+#line 757
 SIMD_INTRIN_DEF(add_f64)
 
-#line 635
+#line 757
 SIMD_INTRIN_DEF(sub_f64)
 
 
 #if 0
-#line 642
+#line 764
 SIMD_INTRIN_DEF(adds_f64)
 
-#line 642
+#line 764
 SIMD_INTRIN_DEF(subs_f64)
 
 #endif // sat_sup
@@ -11657,18 +21765,21 @@ SIMD_INTRIN_DEF(divc_f64)
 #endif // intdiv_sup
 
 #if 1
-#line 663
+#line 785
 SIMD_INTRIN_DEF(muladd_f64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(mulsub_f64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmuladd_f64)
 
-#line 663
+#line 785
 SIMD_INTRIN_DEF(nmulsub_f64)
 
+#line 785
+SIMD_INTRIN_DEF(muladdsub_f64)
+
 #endif // fused_sup
 
 #if 1
@@ -11682,55 +21793,55 @@ SIMD_INTRIN_DEF(sumup_f64)
  * Math
  ***************************/
 #if 1
-#line 681
+#line 803
 SIMD_INTRIN_DEF(sqrt_f64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(recip_f64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(abs_f64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(square_f64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(rint_f64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(ceil_f64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(trunc_f64)
 
-#line 681
+#line 803
 SIMD_INTRIN_DEF(floor_f64)
 
 #endif
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(max_f64)
 SIMD_INTRIN_DEF(reduce_max_f64)
 
-#line 688
+#line 810
 SIMD_INTRIN_DEF(min_f64)
 SIMD_INTRIN_DEF(reduce_min_f64)
 
 
 #if 1
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxp_f64)
 SIMD_INTRIN_DEF(reduce_maxp_f64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minp_f64)
 SIMD_INTRIN_DEF(reduce_minp_f64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(maxn_f64)
 SIMD_INTRIN_DEF(reduce_maxn_f64)
 
-#line 696
+#line 818
 SIMD_INTRIN_DEF(minn_f64)
 SIMD_INTRIN_DEF(reduce_minn_f64)
 
@@ -11740,13 +21851,22 @@ SIMD_INTRIN_DEF(reduce_minn_f64)
 /***************************
  * Mask operations
  ***************************/
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifadd_f64)
 
-#line 708
+#line 830
  SIMD_INTRIN_DEF(ifsub_f64)
 
 
+#if 1
+#line 837
+SIMD_INTRIN_DEF(ifdiv_f64)
+
+#line 837
+SIMD_INTRIN_DEF(ifdivz_f64)
+
+#endif
+
 #endif // simd_sup
 
 /*************************************************************************
@@ -11784,59 +21904,59 @@ SIMD_INTRIN_DEF(cleanup)
 /***************************
  * Operators
  ***************************/
-#line 751
+#line 881
 // Logical
 SIMD_INTRIN_DEF(and_b8)
 SIMD_INTRIN_DEF(or_b8)
 SIMD_INTRIN_DEF(xor_b8)
 SIMD_INTRIN_DEF(not_b8)
 // test cross vector's lanes
-#line 760
+#line 890
 SIMD_INTRIN_DEF(any_b8)
 
-#line 760
+#line 890
 SIMD_INTRIN_DEF(all_b8)
 
 
-#line 751
+#line 881
 // Logical
 SIMD_INTRIN_DEF(and_b16)
 SIMD_INTRIN_DEF(or_b16)
 SIMD_INTRIN_DEF(xor_b16)
 SIMD_INTRIN_DEF(not_b16)
 // test cross vector's lanes
-#line 760
+#line 890
 SIMD_INTRIN_DEF(any_b16)
 
-#line 760
+#line 890
 SIMD_INTRIN_DEF(all_b16)
 
 
-#line 751
+#line 881
 // Logical
 SIMD_INTRIN_DEF(and_b32)
 SIMD_INTRIN_DEF(or_b32)
 SIMD_INTRIN_DEF(xor_b32)
 SIMD_INTRIN_DEF(not_b32)
 // test cross vector's lanes
-#line 760
+#line 890
 SIMD_INTRIN_DEF(any_b32)
 
-#line 760
+#line 890
 SIMD_INTRIN_DEF(all_b32)
 
 
-#line 751
+#line 881
 // Logical
 SIMD_INTRIN_DEF(and_b64)
 SIMD_INTRIN_DEF(or_b64)
 SIMD_INTRIN_DEF(xor_b64)
 SIMD_INTRIN_DEF(not_b64)
 // test cross vector's lanes
-#line 760
+#line 890
 SIMD_INTRIN_DEF(any_b64)
 
-#line 760
+#line 890
 SIMD_INTRIN_DEF(all_b64)
 
 
@@ -11844,16 +21964,16 @@ SIMD_INTRIN_DEF(all_b64)
  * Conversions
  ***************************/
 // Convert mask vector to integer bitfield
-#line 770
+#line 900
 SIMD_INTRIN_DEF(tobits_b8)
 
-#line 770
+#line 900
 SIMD_INTRIN_DEF(tobits_b16)
 
-#line 770
+#line 900
 SIMD_INTRIN_DEF(tobits_b32)
 
-#line 770
+#line 900
 SIMD_INTRIN_DEF(tobits_b64)
 
 
@@ -11914,52 +22034,52 @@ NPY_CPU_DISPATCH_CURFX(simd_create_module)(void)
     if (PySIMDVectorType_Init(m)) {
         goto err;
     }
-    #line 833
+    #line 963
     if (PyModule_AddIntConstant(m, "nlanes_u8", npyv_nlanes_u8)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_s8", npyv_nlanes_s8)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_u16", npyv_nlanes_u16)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_s16", npyv_nlanes_s16)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_u32", npyv_nlanes_u32)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_s32", npyv_nlanes_s32)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_u64", npyv_nlanes_u64)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_s64", npyv_nlanes_s64)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_f32", npyv_nlanes_f32)) {
         goto err;
     }
     
-#line 833
+#line 963
     if (PyModule_AddIntConstant(m, "nlanes_f64", npyv_nlanes_f64)) {
         goto err;
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c.src
index 48023af801d..f532c9e022f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/_simd/_simd.dispatch.c.src
@@ -42,23 +42,26 @@
  */
 SIMD_IMPL_INTRIN_1(@intrin@_@sfx@, v@sfx@, q@sfx@)
 /**end repeat1**/
+SIMD_IMPL_INTRIN_1(load_@sfx@x2, v@sfx@x2, q@sfx@)
+
 /**begin repeat1
- * # intrin = store, storea, stores, storel, storeh#
+ * # intrin = store, storea, stores, storel, storeh, store#
+ * # x = ,,,,, x2#
  */
 // special definition due to the nature of @intrin@
 static PyObject *
-simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
+simd__intrin_@intrin@_@sfx@@x@(PyObject* NPY_UNUSED(self), PyObject *args)
 {
     simd_arg seq_arg = {.dtype = simd_data_q@sfx@};
-    simd_arg vec_arg = {.dtype = simd_data_v@sfx@};
+    simd_arg vec_arg = {.dtype = simd_data_v@sfx@@x@};
     if (!PyArg_ParseTuple(
-        args, "O&O&:@intrin@_@sfx@",
+        args, "O&O&:@intrin@_@sfx@@x@",
         simd_arg_converter, &seq_arg,
         simd_arg_converter, &vec_arg
     )) {
         return NULL;
     }
-    npyv_@intrin@_@sfx@(seq_arg.data.q@sfx@, vec_arg.data.v@sfx@);
+    npyv_@intrin@_@sfx@@x@(seq_arg.data.q@sfx@, vec_arg.data.v@sfx@@x@);
     // write-back
     if (simd_sequence_fill_iterable(seq_arg.obj, seq_arg.data.q@sfx@, simd_data_q@sfx@)) {
         simd_arg_free(&seq_arg);
@@ -76,23 +79,35 @@ simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
 // Partial Load
 SIMD_IMPL_INTRIN_3(load_till_@sfx@, v@sfx@, q@sfx@, u32, @sfx@)
 SIMD_IMPL_INTRIN_2(load_tillz_@sfx@, v@sfx@, q@sfx@, u32)
+#if @size@ == 32
+    SIMD_IMPL_INTRIN_4(load2_till_@sfx@, v@sfx@, q@sfx@, u32, @sfx@, @sfx@)
+    SIMD_IMPL_INTRIN_2(load2_tillz_@sfx@, v@sfx@, q@sfx@, u32)
+#else
+    SIMD_IMPL_INTRIN_4(load2_till_@sfx@, v@sfx@, q@sfx@, u32, @sfx@, @sfx@)
+    SIMD_IMPL_INTRIN_2(load2_tillz_@sfx@, v@sfx@, q@sfx@, u32)
+#endif
 
 // Partial Store
+/**begin repeat1
+ * #intrin = store_till, store2_till, store2_till#
+ * #chksize= 0,          32,           64#
+ */
+#if !@chksize@ || @chksize@ == @size@
 static PyObject *
-simd__intrin_store_till_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
+simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
 {
     simd_arg seq_arg = {.dtype = simd_data_q@sfx@};
     simd_arg nlane_arg = {.dtype = simd_data_u32};
     simd_arg vec_arg = {.dtype = simd_data_v@sfx@};
     if (!PyArg_ParseTuple(
-        args, "O&O&O&:store_till_@sfx@",
+        args, "O&O&O&:@intrin@_@sfx@",
         simd_arg_converter, &seq_arg,
         simd_arg_converter, &nlane_arg,
         simd_arg_converter, &vec_arg
     )) {
         return NULL;
     }
-    npyv_store_till_@sfx@(
+    npyv_@intrin@_@sfx@(
         seq_arg.data.q@sfx@, nlane_arg.data.u32, vec_arg.data.v@sfx@
     );
     // write-back
@@ -103,14 +118,22 @@ simd__intrin_store_till_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
     simd_arg_free(&seq_arg);
     Py_RETURN_NONE;
 }
+#endif // chksize
 
+/**end repeat1**/
 // Non-contiguous Load
 /**begin repeat1
- * #intrin = loadn, loadn_till, loadn_tillz#
- * #till   = 0,     1,          1#
- * #fill   = 0,     1,          0#
- * #format = ,    O&O&,         O&#
- */
+ * #intrin = loadn,       loadn2,       loadn2,
+ *           loadn_till,  loadn2_till,  loadn2_till,
+ *           loadn_tillz, loadn2_tillz, loadn2_tillz#
+ * #scale  = 1,2,2,       1,2,2,         1,2,2#
+ * #till   = 0*3,         1*3,           1*3#
+ * #fill   = 0*3,         1*3,           0*3#
+ # #fill2  = 0*3,         0,1,1,         0*3#
+ * #format = ,,,          O&O&, O&O&O&*2,O&*3#
+ * #chksize= 0,32,64,     0,32,64,       0,32,64#
+ */
+#if !@chksize@ || @chksize@ == @size@
 static PyObject *
 simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -122,6 +145,9 @@ simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
 #if @fill@
     simd_arg fill_arg = {.dtype = simd_data_@sfx@};
 #endif
+#if @fill2@
+    simd_arg fill2_arg = {.dtype = simd_data_@sfx@};
+#endif
     if (!PyArg_ParseTuple(
         args, "@format@O&O&:@intrin@_@sfx@",
         simd_arg_converter, &seq_arg,
@@ -132,6 +158,9 @@ simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
 #if @fill@
         ,simd_arg_converter, &fill_arg
 #endif
+#if @fill2@
+        ,simd_arg_converter, &fill2_arg
+#endif
     )) {
         return NULL;
     }
@@ -140,7 +169,7 @@ simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_@sfx@;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1 * @scale@;
         min_seq_len = -min_seq_len;
     }
     if (cur_seq_len < min_seq_len) {
@@ -159,6 +188,9 @@ simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
     #if @fill@
         , fill_arg.data.@sfx@
     #endif
+    #if @fill2@
+        , fill2_arg.data.@sfx@
+    #endif
     );
     simd_arg ret = {
         .dtype = simd_data_v@sfx@, .data = {.v@sfx@=rvec}
@@ -169,14 +201,19 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 /**end repeat1**/
 
 // Non-contiguous Store
 /**begin repeat1
- * #intrin = storen, storen_till#
- * #till   = 0,      1#
- * #format = ,       O&#
+ * #intrin = storen,      storen2,      storen2,
+             storen_till, storen2_till, storen2_till#
+ * #scale  = 1,2,2,       1,2,2#
+ * #till   = 0*3,         1*3#
+ * #format = ,,,          O&*3#
+ * #chksize= 0,32,64,     0,32,64#
  */
+#if !@chksize@ || @chksize@ == @size@
 static PyObject *
 simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
 {
@@ -202,7 +239,7 @@ simd__intrin_@intrin@_@sfx@(PyObject* NPY_UNUSED(self), PyObject *args)
     Py_ssize_t cur_seq_len = simd_sequence_len(seq_ptr);
     Py_ssize_t min_seq_len = stride * npyv_nlanes_@sfx@;
     if (stride < 0) {
-        seq_ptr += cur_seq_len -1;
+        seq_ptr += cur_seq_len - 1*@scale@;
         min_seq_len = -min_seq_len;
     }
     // overflow guard
@@ -231,6 +268,7 @@ err:
     simd_arg_free(&seq_arg);
     return NULL;
 }
+#endif // chksize
 /**end repeat1**/
 #endif // @ncont_sup@
 
@@ -300,7 +338,7 @@ SIMD_IMPL_INTRIN_2(@intrin@_@sfx@, v@sfx@, v@sfx@, v@sfx@)
 /**end repeat1**/
 
 /**begin repeat1
- * # intrin = combine, zip#
+ * # intrin = combine, zip, unzip#
  */
 SIMD_IMPL_INTRIN_2(@intrin@_@sfx@, v@sfx@x2, v@sfx@, v@sfx@)
 /**end repeat1**/
@@ -309,6 +347,60 @@ SIMD_IMPL_INTRIN_2(@intrin@_@sfx@, v@sfx@x2, v@sfx@, v@sfx@)
 SIMD_IMPL_INTRIN_1(rev64_@sfx@, v@sfx@, v@sfx@)
 #endif
 
+// special implementation to convert runtime constants to immediate values
+#if @size@ == 32
+// one call for element index then gather them within one vector
+// instead of unroll the 255 possible cases.
+NPY_FINLINE npyv_@sfx@
+npyv_permi128_@sfx@_(npyv_@sfx@ a, unsigned e0, unsigned e1, unsigned e2, unsigned e3)
+{
+   /**begin repeat1
+    * # en = e0, e1, e2, e3#
+    */
+    npyv_@sfx@ v@en@;
+    npyv_lanetype_@sfx@ d@en@[npyv_nlanes_@sfx@];
+    if (0) {}
+   /**begin repeat2
+    * # imm = 1, 2, 3#
+    */
+    else if (@en@ == @imm@) {
+        v@en@ = npyv_permi128_@sfx@(a, @imm@, @imm@, @imm@, @imm@);
+    }
+    /**end repeat2**/
+    else {
+        v@en@ = npyv_permi128_@sfx@(a, 0, 0, 0, 0);
+    }
+    npyv_store_@sfx@(d@en@, v@en@);
+    /**end repeat1**/
+    if (e0 == e1 && e0 == e2 && e0 == e3) {
+        return ve0;
+    }
+    for (int i = 0; i < npyv_nlanes_@sfx@; i += 4) {
+        de0[i+1] = de1[i+1];
+        de0[i+2] = de2[i+2];
+        de0[i+3] = de3[i+3];
+    }
+    return npyv_load_@sfx@(de0);
+}
+SIMD_IMPL_INTRIN_5(permi128_@sfx@_, v@sfx@, v@sfx@, u8, u8, u8, u8)
+#elif @size@ == 64
+NPY_FINLINE npyv_@sfx@
+npyv_permi128_@sfx@_(npyv_@sfx@ a, unsigned e0, unsigned e1)
+{
+    if (e0 == 1 && e1 == 0) {
+        return npyv_permi128_@sfx@(a, 1, 0);
+    }
+    else if (e0 == 0 && e1 == 1) {
+        return npyv_permi128_@sfx@(a, 0, 1);
+    }
+    else if (e0 == 1 && e1 == 1) {
+        return npyv_permi128_@sfx@(a, 1, 1);
+    }
+    return npyv_permi128_@sfx@(a, 0, 0);
+}
+SIMD_IMPL_INTRIN_3(permi128_@sfx@_, v@sfx@, v@sfx@, u8, u8)
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -387,7 +479,7 @@ SIMD_IMPL_INTRIN_2(divc_@sfx@, v@sfx@, v@sfx@, v@sfx@x3)
 
 #if @fused_sup@
 /**begin repeat1
- * #intrin = muladd, mulsub, nmuladd, nmulsub#
+ * #intrin = muladd, mulsub, nmuladd, nmulsub, muladdsub#
  */
 SIMD_IMPL_INTRIN_3(@intrin@_@sfx@, v@sfx@, v@sfx@, v@sfx@, v@sfx@)
 /**end repeat1**/
@@ -438,6 +530,11 @@ SIMD_IMPL_INTRIN_1(reduce_@intrin@_@sfx@, @sfx@, v@sfx@)
  SIMD_IMPL_INTRIN_4(@intrin@_@sfx@, v@sfx@, v@bsfx@, v@sfx@, v@sfx@, v@sfx@)
 /**end repeat1**/
 
+#if @fp_only@
+SIMD_IMPL_INTRIN_4(ifdiv_@sfx@, v@sfx@, v@bsfx@, v@sfx@, v@sfx@, v@sfx@)
+SIMD_IMPL_INTRIN_3(ifdivz_@sfx@, v@sfx@, v@bsfx@, v@sfx@, v@sfx@)
+#endif
+
 #endif // simd_sup
 /**end repeat**/
 /*************************************************************************
@@ -541,6 +638,12 @@ static PyMethodDef simd__intrinsics_methods[] = {
 SIMD_INTRIN_DEF(@intrin@_@sfx@)
 /**end repeat1**/
 
+/**begin repeat1
+ * # intrin = load, store#
+ */
+SIMD_INTRIN_DEF(@intrin@_@sfx@x2)
+/**end repeat1**/
+
 /****************************************
  * Non-contiguous/Partial Memory access
  ****************************************/
@@ -551,6 +654,21 @@ SIMD_INTRIN_DEF(@intrin@_@sfx@)
  */
 SIMD_INTRIN_DEF(@intrin@_@sfx@)
 /**end repeat1**/
+#if @size@ == 32
+    /**begin repeat1
+     * #intrin = load2_till, load2_tillz, loadn2, loadn2_till, loadn2_tillz,
+     *           store2_till, storen2, storen2_till#
+     */
+    SIMD_INTRIN_DEF(@intrin@_@sfx@)
+    /**end repeat1**/
+#else
+    /**begin repeat1
+     * #intrin = load2_till, load2_tillz, loadn2, loadn2_till, loadn2_tillz,
+     *           store2_till, storen2, storen2_till#
+     */
+    SIMD_INTRIN_DEF(@intrin@_@sfx@)
+    /**end repeat1**/
+#endif
 #endif // ncont_sup
 
 /****************************
@@ -584,7 +702,7 @@ SIMD_INTRIN_DEF(@intrin@_@sfx@)
  * Reorder
  ***************************/
 /**begin repeat1
- * # intrin = combinel, combineh, combine, zip#
+ * # intrin = combinel, combineh, combine, zip, unzip#
  */
 SIMD_INTRIN_DEF(@intrin@_@sfx@)
 /**end repeat1**/
@@ -593,6 +711,10 @@ SIMD_INTRIN_DEF(@intrin@_@sfx@)
 SIMD_INTRIN_DEF(rev64_@sfx@)
 #endif
 
+#if @size@ > 16
+{ "permi128_@sfx@", simd__intrin_permi128_@sfx@_, METH_VARARGS, NULL },
+#endif
+
 /***************************
  * Operators
  ***************************/
@@ -658,7 +780,7 @@ SIMD_INTRIN_DEF(divc_@sfx@)
 
 #if @fused_sup@
 /**begin repeat1
- * #intrin = muladd, mulsub, nmuladd, nmulsub#
+ * #intrin = muladd, mulsub, nmuladd, nmulsub, muladdsub#
  */
 SIMD_INTRIN_DEF(@intrin@_@sfx@)
 /**end repeat1**/
@@ -708,6 +830,14 @@ SIMD_INTRIN_DEF(reduce_@intrin@_@sfx@)
  SIMD_INTRIN_DEF(@intrin@_@sfx@)
 /**end repeat1**/
 
+#if @fp_only@
+/**begin repeat1
+ * #intrin = ifdiv, ifdivz#
+ */
+SIMD_INTRIN_DEF(@intrin@_@sfx@)
+/**end repeat1**/
+#endif
+
 #endif // simd_sup
 /**end repeat**/
 /*************************************************************************
diff --git a/contrib/python/numpy/py3/numpy/core/src/_simd/_simd_easyintrin.inc b/contrib/python/numpy/py3/numpy/core/src/_simd/_simd_easyintrin.inc
index f2e0da26ece..e300e54843a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/_simd/_simd_easyintrin.inc
+++ b/contrib/python/numpy/py3/numpy/core/src/_simd/_simd_easyintrin.inc
@@ -197,6 +197,39 @@
         return simd_arg_to_obj(&ret);                     \
     }
 
+#define SIMD_IMPL_INTRIN_5(NAME, RET, IN0, IN1, IN2, IN3, IN4) \
+    static PyObject *simd__intrin_##NAME                  \
+    (PyObject* NPY_UNUSED(self), PyObject *args)          \
+    {                                                     \
+        simd_arg arg1 = {.dtype = simd_data_##IN0};       \
+        simd_arg arg2 = {.dtype = simd_data_##IN1};       \
+        simd_arg arg3 = {.dtype = simd_data_##IN2};       \
+        simd_arg arg4 = {.dtype = simd_data_##IN3};       \
+        simd_arg arg5 = {.dtype = simd_data_##IN4};       \
+        if (!PyArg_ParseTuple(                            \
+            args, "O&O&O&O&O&:"NPY_TOSTRING(NAME),        \
+            simd_arg_converter, &arg1,                    \
+            simd_arg_converter, &arg2,                    \
+            simd_arg_converter, &arg3,                    \
+            simd_arg_converter, &arg4,                    \
+            simd_arg_converter, &arg5                     \
+        )) return NULL;                                   \
+        simd_data data = {.RET = npyv_##NAME(             \
+            arg1.data.IN0, arg2.data.IN1,                 \
+            arg3.data.IN2, arg4.data.IN3,                 \
+            arg5.data.IN4                                 \
+        )};                                               \
+        simd_arg_free(&arg1);                             \
+        simd_arg_free(&arg2);                             \
+        simd_arg_free(&arg3);                             \
+        simd_arg_free(&arg4);                             \
+        simd_arg_free(&arg5);                             \
+        simd_arg ret = {                                  \
+            .data = data, .dtype = simd_data_##RET        \
+        };                                                \
+        return simd_arg_to_obj(&ret);                     \
+    }
+
 /**
  * Helper macros for repeating and expand a certain macro.
  * Mainly used for converting a scalar to an immediate constant.
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/.doxyfile b/contrib/python/numpy/py3/numpy/core/src/common/.doxyfile
new file mode 100644
index 00000000000..462cbbcfa18
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/.doxyfile
@@ -0,0 +1 @@
+INCLUDE_PATH += @CUR_DIR
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/common.hpp b/contrib/python/numpy/py3/numpy/core/src/common/common.hpp
new file mode 100644
index 00000000000..44ba449d8e0
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/common.hpp
@@ -0,0 +1,14 @@
+#ifndef NUMPY_CORE_SRC_COMMON_COMMON_HPP
+#define NUMPY_CORE_SRC_COMMON_COMMON_HPP
+/*
+ * The following C++ headers are safe to be used standalone, however,
+ * they are gathered to make it easy for us and for the future need to support PCH.
+ */
+#include "npdef.hpp"
+#include "utils.hpp"
+#include "npstd.hpp"
+#include "half.hpp"
+#include "meta.hpp"
+#include "float_status.hpp"
+
+#endif // NUMPY_CORE_SRC_COMMON_COMMON_HPP
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/dlpack/dlpack.h b/contrib/python/numpy/py3/numpy/core/src/common/dlpack/dlpack.h
index 29209aee12a..f0cbf61368c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/dlpack/dlpack.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/dlpack/dlpack.h
@@ -1,5 +1,5 @@
 // Taken from:
-// https://github.com/dmlc/dlpack/blob/9b6176fdecb55e9bf39b16f08b96913ed3f275b4/include/dlpack/dlpack.h
+// https://github.com/dmlc/dlpack/blob/ca4d00ad3e2e0f410eeab3264d21b8a39397f362/include/dlpack/dlpack.h
 /*!
  *  Copyright (c) 2017 by Contributors
  * \file dlpack.h
@@ -8,14 +8,20 @@
 #ifndef DLPACK_DLPACK_H_
 #define DLPACK_DLPACK_H_
 
+/**
+ * \brief Compatibility with C++
+ */
 #ifdef __cplusplus
 #define DLPACK_EXTERN_C extern "C"
 #else
 #define DLPACK_EXTERN_C
 #endif
 
-/*! \brief The current version of dlpack */
-#define DLPACK_VERSION 050
+/*! \brief The current major version of dlpack */
+#define DLPACK_MAJOR_VERSION 1
+
+/*! \brief The current minor version of dlpack */
+#define DLPACK_MINOR_VERSION 0
 
 /*! \brief DLPACK_DLL prefix for windows */
 #ifdef _WIN32
@@ -34,10 +40,41 @@
 #ifdef __cplusplus
 extern "C" {
 #endif
+
+/*!
+ * \brief The DLPack version.
+ *
+ * A change in major version indicates that we have changed the
+ * data layout of the ABI - DLManagedTensorVersioned.
+ *
+ * A change in minor version indicates that we have added new
+ * code, such as a new device type, but the ABI is kept the same.
+ *
+ * If an obtained DLPack tensor has a major version that disagrees
+ * with the version number specified in this header file
+ * (i.e. major != DLPACK_MAJOR_VERSION), the consumer must call the deleter
+ * (and it is safe to do so). It is not safe to access any other fields
+ * as the memory layout will have changed.
+ *
+ * In the case of a minor version mismatch, the tensor can be safely used as
+ * long as the consumer knows how to interpret all fields. Minor version
+ * updates indicate the addition of enumeration values.
+ */
+typedef struct {
+  /*! \brief DLPack major version. */
+  uint32_t major;
+  /*! \brief DLPack minor version. */
+  uint32_t minor;
+} DLPackVersion;
+
 /*!
  * \brief The device type in DLDevice.
  */
+#ifdef __cplusplus
+typedef enum : int32_t {
+#else
 typedef enum {
+#endif
   /*! \brief CPU device */
   kDLCPU = 1,
   /*! \brief CUDA GPU device */
@@ -70,6 +107,17 @@ typedef enum {
    * \brief CUDA managed/unified memory allocated by cudaMallocManaged
    */
   kDLCUDAManaged = 13,
+  /*!
+   * \brief Unified shared memory allocated on a oneAPI non-partititioned
+   * device. Call to oneAPI runtime is required to determine the device
+   * type, the USM allocation type and the sycl context it is bound to.
+   *
+   */
+  kDLOneAPI = 14,
+  /*! \brief GPU support for next generation WebGPU standard. */
+  kDLWebGPU = 15,
+  /*! \brief Qualcomm Hexagon DSP */
+  kDLHexagon = 16,
 } DLDeviceType;
 
 /*!
@@ -82,7 +130,7 @@ typedef struct {
    * \brief The device index.
    * For vanilla CPU memory, pinned memory, or managed memory, this is set to 0.
    */
-  int device_id;
+  int32_t device_id;
 } DLDevice;
 
 /*!
@@ -107,16 +155,21 @@ typedef enum {
    * (C/C++/Python layout: compact struct per complex number)
    */
   kDLComplex = 5U,
+  /*! \brief boolean */
+  kDLBool = 6U,
 } DLDataTypeCode;
 
 /*!
- * \brief The data type the tensor can hold.
+ * \brief The data type the tensor can hold. The data type is assumed to follow the
+ * native endian-ness. An explicit error message should be raised when attempting to
+ * export an array with non-native endianness
  *
  *  Examples
- *   - float: type_code = 2, bits = 32, lanes=1
- *   - float4(vectorized 4 float): type_code = 2, bits = 32, lanes=4
- *   - int8: type_code = 0, bits = 8, lanes=1
+ *   - float: type_code = 2, bits = 32, lanes = 1
+ *   - float4(vectorized 4 float): type_code = 2, bits = 32, lanes = 4
+ *   - int8: type_code = 0, bits = 8, lanes = 1
  *   - std::complex<float>: type_code = 5, bits = 64, lanes = 1
+ *   - bool: type_code = 6, bits = 8, lanes = 1 (as per common array library convention, the underlying storage size of bool is 8 bits)
  */
 typedef struct {
   /*!
@@ -138,9 +191,16 @@ typedef struct {
  */
 typedef struct {
   /*!
-   * \brief The opaque data pointer points to the allocated data. This will be
-   * CUDA device pointer or cl_mem handle in OpenCL. This pointer is always
-   * aligned to 256 bytes as in CUDA.
+   * \brief The data pointer points to the allocated data. This will be CUDA
+   * device pointer or cl_mem handle in OpenCL. It may be opaque on some device
+   * types. This pointer is always aligned to 256 bytes as in CUDA. The
+   * `byte_offset` field should be used to point to the beginning of the data.
+   *
+   * Note that as of Nov 2021, multiply libraries (CuPy, PyTorch, TensorFlow,
+   * TVM, perhaps others) do not adhere to this 256 byte alignment requirement
+   * on CPU/CUDA/ROCm, and always use `byte_offset=0`.  This must be fixed
+   * (after which this note will be updated); at the moment it is recommended
+   * to not rely on the data pointer being correctly aligned.
    *
    * For given DLTensor, the size of memory required to store the contents of
    * data is calculated as follows:
@@ -160,7 +220,7 @@ typedef struct {
   /*! \brief The device of the tensor */
   DLDevice device;
   /*! \brief Number of dimensions */
-  int ndim;
+  int32_t ndim;
   /*! \brief The data type of the pointer*/
   DLDataType dtype;
   /*! \brief The shape of the tensor */
@@ -180,6 +240,13 @@ typedef struct {
  *  not meant to transfer the tensor. When the borrowing framework doesn't need
  *  the tensor, it should call the deleter to notify the host that the resource
  *  is no longer needed.
+ *
+ * \note This data structure is used as Legacy DLManagedTensor
+ *       in DLPack exchange and is deprecated after DLPack v0.8
+ *       Use DLManagedTensorVersioned instead.
+ *       This data structure may get renamed or deleted in future versions.
+ *
+ * \sa DLManagedTensorVersioned
  */
 typedef struct DLManagedTensor {
   /*! \brief DLTensor which is being memory managed */
@@ -188,13 +255,65 @@ typedef struct DLManagedTensor {
    *   which DLManagedTensor is used in the framework. It can also be NULL.
    */
   void * manager_ctx;
-  /*! \brief Destructor signature void (*)(void*) - this should be called
-   *   to destruct manager_ctx which holds the DLManagedTensor. It can be NULL
-   *   if there is no way for the caller to provide a reasonable destructor.
-   *   The destructors deletes the argument self as well.
+  /*!
+   * \brief Destructor - this should be called
+   * to destruct the manager_ctx  which backs the DLManagedTensor. It can be
+   * NULL if there is no way for the caller to provide a reasonable destructor.
+   * The destructors deletes the argument self as well.
    */
   void (*deleter)(struct DLManagedTensor * self);
 } DLManagedTensor;
+
+// bit masks used in in the DLManagedTensorVersioned
+
+/*! \brief bit mask to indicate that the tensor is read only. */
+#define DLPACK_FLAG_BITMASK_READ_ONLY (1UL << 0UL)
+
+/*!
+ * \brief A versioned and managed C Tensor object, manage memory of DLTensor.
+ *
+ * This data structure is intended to facilitate the borrowing of DLTensor by
+ * another framework. It is not meant to transfer the tensor. When the borrowing
+ * framework doesn't need the tensor, it should call the deleter to notify the
+ * host that the resource is no longer needed.
+ *
+ * \note This is the current standard DLPack exchange data structure.
+ */
+struct DLManagedTensorVersioned {
+  /*!
+   * \brief The API and ABI version of the current managed Tensor
+   */
+  DLPackVersion version;
+  /*!
+   * \brief the context of the original host framework.
+   *
+   * Stores DLManagedTensorVersioned is used in the
+   * framework. It can also be NULL.
+   */
+  void *manager_ctx;
+  /*!
+   * \brief Destructor.
+   *
+   * This should be called to destruct manager_ctx which holds the DLManagedTensorVersioned.
+   * It can be NULL if there is no way for the caller to provide a reasonable
+   * destructor. The destructors deletes the argument self as well.
+   */
+  void (*deleter)(struct DLManagedTensorVersioned *self);
+  /*!
+   * \brief Additional bitmask flags information about the tensor.
+   *
+   * By default the flags should be set to 0.
+   *
+   * \note Future ABI changes should keep everything until this field
+   *       stable, to ensure that deleter can be correctly called.
+   *
+   * \sa DLPACK_FLAG_BITMASK_READ_ONLY
+   */
+  uint64_t flags;
+  /*! \brief DLTensor which is being memory managed */
+  DLTensor dl_tensor;
+};
+
 #ifdef __cplusplus
 }  // DLPACK_EXTERN_C
 #endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/float_status.hpp b/contrib/python/numpy/py3/numpy/core/src/common/float_status.hpp
new file mode 100644
index 00000000000..8e4d5e06a59
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/float_status.hpp
@@ -0,0 +1,134 @@
+#ifndef NUMPY_CORE_SRC_COMMON_FLOAT_STATUS_HPP
+#define NUMPY_CORE_SRC_COMMON_FLOAT_STATUS_HPP
+
+#include "npstd.hpp"
+
+#include <fenv.h>
+
+namespace np {
+
+/// @addtogroup cpp_core_utility
+/// @{
+/**
+ * Class wraps floating-point environment operations,
+ * provides lazy access to its functionality.
+ */
+class FloatStatus {
+ public:
+/*
+ * According to the C99 standard FE_DIVBYZERO, etc. may not be provided when
+ * unsupported.  In such cases NumPy will not report these correctly, but we
+ * should still allow compiling (whether tests pass or not).
+ * By defining them as 0 locally, we make them no-ops.  Unlike these defines,
+ * for example `musl` still defines all of the functions (as no-ops):
+ *     https://git.musl-libc.org/cgit/musl/tree/src/fenv/fenv.c
+ * and does similar replacement in its tests:
+ * http://nsz.repo.hu/git/?p=libc-test;a=blob;f=src/common/mtest.h;h=706c1ba23ea8989b17a2f72ed1a919e187c06b6a;hb=HEAD#l30
+ */
+#ifdef FE_DIVBYZERO
+    static constexpr int kDivideByZero = FE_DIVBYZERO;
+#else
+    static constexpr int kDivideByZero = 0;
+#endif
+#ifdef FE_INVALID
+    static constexpr int kInvalid = FE_INVALID;
+#else
+    static constexpr int kInvalid = 0;
+#endif
+#ifdef FE_INEXACT
+    static constexpr int kInexact = FE_INEXACT;
+#else
+    static constexpr int kInexact = 0;
+#endif
+#ifdef FE_OVERFLOW
+    static constexpr int kOverflow = FE_OVERFLOW;
+#else
+    static constexpr int kOverflow = 0;
+#endif
+#ifdef FE_UNDERFLOW
+    static constexpr int kUnderflow = FE_UNDERFLOW;
+#else
+    static constexpr int kUnderflow = 0;
+#endif
+    static constexpr int kAllExcept = (kDivideByZero | kInvalid | kInexact |
+                                       kOverflow | kUnderflow);
+
+    FloatStatus(bool clear_on_dst=true)
+        : clear_on_dst_(clear_on_dst)
+    {
+        if constexpr (kAllExcept != 0) {
+            fpstatus_ = fetestexcept(kAllExcept);
+        }
+        else {
+            fpstatus_ = 0;
+        }
+    }
+    ~FloatStatus()
+    {
+        if constexpr (kAllExcept != 0) {
+            if (fpstatus_ != 0 && clear_on_dst_) {
+                feclearexcept(kAllExcept);
+            }
+        }
+    }
+    constexpr bool IsDivideByZero() const
+    {
+        return (fpstatus_ & kDivideByZero) != 0;
+    }
+    constexpr bool IsInexact() const
+    {
+        return (fpstatus_ & kInexact) != 0;
+    }
+    constexpr bool IsInvalid() const
+    {
+        return (fpstatus_ & kInvalid) != 0;
+    }
+    constexpr bool IsOverFlow() const
+    {
+        return (fpstatus_ & kOverflow) != 0;
+    }
+    constexpr bool IsUnderFlow() const
+    {
+        return (fpstatus_ & kUnderflow) != 0;
+    }
+    static void RaiseDivideByZero()
+    {
+        if constexpr (kDivideByZero != 0) {
+            feraiseexcept(kDivideByZero);
+        }
+    }
+    static void RaiseInexact()
+    {
+        if constexpr (kInexact != 0) {
+            feraiseexcept(kInexact);
+        }
+    }
+    static void RaiseInvalid()
+    {
+        if constexpr (kInvalid != 0) {
+            feraiseexcept(kInvalid);
+        }
+    }
+    static void RaiseOverflow()
+    {
+        if constexpr (kOverflow != 0) {
+            feraiseexcept(kOverflow);
+        }
+    }
+    static void RaiseUnderflow()
+    {
+        if constexpr (kUnderflow != 0) {
+            feraiseexcept(kUnderflow);
+        }
+    }
+
+  private:
+    bool clear_on_dst_;
+    int fpstatus_;
+};
+
+/// @} cpp_core_utility
+} // namespace np
+
+#endif // NUMPY_CORE_SRC_COMMON_FLOAT_STATUS_HPP
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/get_attr_string.h b/contrib/python/numpy/py3/numpy/core/src/common/get_attr_string.h
index 90eca5ee65c..36d39189f9e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/get_attr_string.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/get_attr_string.h
@@ -4,7 +4,7 @@
 #include <Python.h>
 #include "ufunc_object.h"
 
-static NPY_INLINE npy_bool
+static inline npy_bool
 _is_basic_python_type(PyTypeObject *tp)
 {
     return (
@@ -46,7 +46,7 @@ _is_basic_python_type(PyTypeObject *tp)
  *
  * In future, could be made more like _Py_LookupSpecial
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 PyArray_LookupSpecial(PyObject *obj, PyObject *name_unicode)
 {
     PyTypeObject *tp = Py_TYPE(obj);
@@ -73,7 +73,7 @@ PyArray_LookupSpecial(PyObject *obj, PyObject *name_unicode)
  *
  * Kept for backwards compatibility. In future, we should deprecate this.
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 PyArray_LookupSpecial_OnInstance(PyObject *obj, PyObject *name_unicode)
 {
     PyTypeObject *tp = Py_TYPE(obj);
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/half.hpp b/contrib/python/numpy/py3/numpy/core/src/common/half.hpp
new file mode 100644
index 00000000000..4d16e3bcc1c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/half.hpp
@@ -0,0 +1,264 @@
+#ifndef NUMPY_CORE_SRC_COMMON_HALF_HPP
+#define NUMPY_CORE_SRC_COMMON_HALF_HPP
+
+#include "npstd.hpp"
+
+#include "npy_cpu_dispatch.h" // NPY_HAVE_CPU_FEATURES
+#include "half_private.hpp"
+
+// TODO(@seiko2plus):
+// - covers half-precision operations that being supported by numpy/halffloat.h
+// - add support for arithmetic operations
+// - enables __fp16 causes massive FP exceptions on aarch64,
+//   needs a deep investigation
+
+namespace np {
+
+/// @addtogroup cpp_core_types
+/// @{
+
+/// Provides a type that implements 16-bit floating point (half-precision).
+/// This type is ensured to be 16-bit size.
+#if 1 // ndef __ARM_FP16_FORMAT_IEEE
+class Half final {
+  public:
+    /// Whether `Half` has a full native HW support.
+    static constexpr bool kNative = false;
+    /// Whether `Half` has a native HW support for single/double conversion.
+    template<typename T>
+    static constexpr bool kNativeConversion = (
+        (
+            std::is_same_v<T, float> &&
+        #if defined(NPY_HAVE_FP16) || defined(NPY_HAVE_VSX3)
+            true
+        #else
+            false
+        #endif
+        ) || (
+            std::is_same_v<T, double> &&
+        #if defined(NPY_HAVE_AVX512FP16) || defined(NPY_HAVE_VSX3)
+            true
+        #else
+            false
+        #endif
+        )
+    );
+
+    /// Default constructor. initialize nothing.
+    Half() = default;
+
+    /// Constract from float
+    /// If there are no hardware optimization available, rounding will always
+    /// be set to ties to even.
+    explicit Half(float f)
+    {
+    #if defined(NPY_HAVE_FP16)
+        __m128 mf = _mm_load_ss(&f);
+        bits_ = static_cast<uint16_t>(_mm_cvtsi128_si32(_mm_cvtps_ph(mf, _MM_FROUND_TO_NEAREST_INT)));
+    #elif defined(NPY_HAVE_VSX3) && defined(NPY_HAVE_VSX_ASM)
+        __vector float vf32 = vec_splats(f);
+        __vector unsigned short vf16;
+        __asm__ __volatile__ ("xvcvsphp %x0,%x1" : "=wa" (vf16) : "wa" (vf32));
+        bits_ = vec_extract(vf16, 0);
+    #else
+        bits_ = half_private::FromFloatBits(BitCast<uint32_t>(f));
+    #endif
+    }
+
+    /// Construct from double.
+    /// If there are no hardware optimization available, rounding will always
+    /// be set to ties to even.
+    explicit Half(double f)
+    {
+    #if defined(NPY_HAVE_AVX512FP16)
+        __m128d md = _mm_load_sd(&f);
+        bits_ = static_cast<uint16_t>(_mm_cvtsi128_si32(_mm_castph_si128(_mm_cvtpd_ph(md))));
+    #elif defined(NPY_HAVE_VSX3) && defined(NPY_HAVE_VSX_ASM)
+        __vector double vf64 = vec_splats(f);
+        __vector unsigned short vf16;
+        __asm__ __volatile__ ("xvcvdphp %x0,%x1" : "=wa" (vf16) : "wa" (vf64));
+        bits_ = vec_extract(vf16, 0);
+    #else
+        bits_ = half_private::FromDoubleBits(BitCast<uint64_t>(f));
+    #endif
+    }
+
+    /// Cast to float
+    explicit operator float() const
+    {
+    #if defined(NPY_HAVE_FP16)
+        float ret;
+        _mm_store_ss(&ret, _mm_cvtph_ps(_mm_cvtsi32_si128(bits_)));
+        return ret;
+    #elif defined(NPY_HAVE_VSX3) && defined(vec_extract_fp_from_shorth)
+        return vec_extract(vec_extract_fp_from_shorth(vec_splats(bits_)), 0);
+    #elif defined(NPY_HAVE_VSX3) && defined(NPY_HAVE_VSX_ASM)
+        __vector float vf32;
+        __asm__ __volatile__("xvcvhpsp %x0,%x1"
+                             : "=wa"(vf32)
+                             : "wa"(vec_splats(bits_.u)));
+        return vec_extract(vf32, 0);
+    #else
+        return BitCast<float>(half_private::ToFloatBits(bits_));
+    #endif
+    }
+
+    /// Cast to double
+    explicit operator double() const
+    {
+    #if defined(NPY_HAVE_AVX512FP16)
+        double ret;
+        _mm_store_sd(&ret, _mm_cvtph_pd(_mm_castsi128_ph(_mm_cvtsi32_si128(bits_))));
+        return ret;
+    #elif defined(NPY_HAVE_VSX3) && defined(NPY_HAVE_VSX_ASM)
+        __vector float vf64;
+        __asm__ __volatile__("xvcvhpdp %x0,%x1"
+                             : "=wa"(vf32)
+                             : "wa"(vec_splats(bits_)));
+        return vec_extract(vf64, 0);
+    #else
+        return BitCast<double>(half_private::ToDoubleBits(bits_));
+    #endif
+    }
+
+    /// Returns a new Half constracted from the IEEE 754 binary16.
+    static constexpr Half FromBits(uint16_t bits)
+    {
+        Half h{};
+        h.bits_ = bits;
+        return h;
+    }
+    /// Returns the IEEE 754 binary16 representation.
+    constexpr uint16_t Bits() const
+    {
+        return bits_;
+    }
+
+    /// @name Comparison operators (orderd)
+    /// @{
+    constexpr bool operator==(Half r) const
+    {
+        return !(IsNaN() || r.IsNaN()) && Equal(r);
+    }
+    constexpr bool operator<(Half r) const
+    {
+        return !(IsNaN() || r.IsNaN()) && Less(r);
+    }
+    constexpr bool operator<=(Half r) const
+    {
+        return !(IsNaN() || r.IsNaN()) && LessEqual(r);
+    }
+    constexpr bool operator>(Half r) const
+    {
+        return r < *this;
+    }
+    constexpr bool operator>=(Half r) const
+    {
+        return r <= *this;
+    }
+    /// @}
+
+    /// @name Comparison operators (unorderd)
+    /// @{
+    constexpr bool operator!=(Half r) const
+    {
+        return !(*this == r);
+    }
+    /// @} Comparison operators
+
+    /// @name Comparison with no guarantee of NaN behavior
+    /// @{
+    constexpr bool Less(Half r) const
+    {
+        uint_fast16_t a = static_cast<uint_fast16_t>(bits_),
+                      b = static_cast<uint_fast16_t>(r.bits_);
+        bool sign_a = (a & 0x8000u) == 0x8000u;
+        bool sign_b = (b & 0x8000u) == 0x8000u;
+        // if both `a` and `b` have same sign
+        //   Test if `a` > `b` when `a` has the sign
+        //        or `a` < `b` when is not.
+        //   And make sure they are not equal to each other
+        //       in case of both are equal to +-0
+        // else
+        //   Test if  `a` has the sign.
+        //        and `a` != -0.0 and `b` != 0.0
+        return (sign_a == sign_b) ? (sign_a ^ (a < b)) && (a != b)
+                                  : sign_a && ((a | b) != 0x8000u);
+    }
+    constexpr bool LessEqual(Half r) const
+    {
+        uint_fast16_t a = static_cast<uint_fast16_t>(bits_),
+                      b = static_cast<uint_fast16_t>(r.bits_);
+        bool sign_a = (a & 0x8000u) == 0x8000u;
+        bool sign_b = (b & 0x8000u) == 0x8000u;
+        // if both `a` and `b` have same sign
+        //   Test if `a` > `b` when `a` has the sign
+        //        or `a` < `b` when is not.
+        //        or a == b (needed even if we used <= above instead
+        //                   since testing +-0 still required)
+        // else
+        //   Test if `a` has the sign
+        //        or `a` and `b` equal to +-0.0
+        return (sign_a == sign_b) ? (sign_a ^ (a < b)) || (a == b)
+                                  : sign_a || ((a | b) == 0x8000u);
+    }
+    constexpr bool Equal(Half r) const
+    {
+        // fast16 cast is not worth it, since unpack op should involved.
+        uint16_t a = bits_, b = r.bits_;
+        return a == b || ((a | b) == 0x8000u);
+    }
+    /// @} Comparison
+
+    /// @name Properties
+    // @{
+    constexpr bool IsNaN() const
+    {
+        return ((bits_ & 0x7c00u) == 0x7c00u) &&
+               ((bits_ & 0x03ffu) != 0);
+    }
+    /// @} Properties
+
+  private:
+    uint16_t bits_;
+};
+#else // __ARM_FP16_FORMAT_IEEE
+class Half final {
+  public:
+    static constexpr bool kNative = true;
+    template<typename T>
+    static constexpr bool kNativeConversion = (
+        std::is_same_v<T, float> || std::is_same_v<T, double>
+    );
+    Half() = default;
+    constexpr Half(__fp16 h) : half_(h)
+    {}
+    constexpr operator __fp16() const
+    { return half_; }
+    static Half FromBits(uint16_t bits)
+    {
+        Half h;
+        h.half_ = BitCast<__fp16>(bits);
+        return h;
+    }
+    uint16_t Bits() const
+    { return BitCast<uint16_t>(half_); }
+    constexpr bool Less(Half r) const
+    { return half_ < r.half_; }
+    constexpr bool LessEqual(Half r) const
+    { return half_ <= r.half_; }
+    constexpr bool Equal(Half r) const
+    { return half_ == r.half_; }
+    constexpr bool IsNaN() const
+    { return half_ != half_; }
+
+  private:
+    __fp16 half_;
+};
+#endif // __ARM_FP16_FORMAT_IEEE
+
+/// @} cpp_core_types
+
+} // namespace np
+
+#endif // NUMPY_CORE_SRC_COMMON_HALF_HPP
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/half_private.hpp b/contrib/python/numpy/py3/numpy/core/src/common/half_private.hpp
new file mode 100644
index 00000000000..7a64eb397be
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/half_private.hpp
@@ -0,0 +1,330 @@
+#ifndef NUMPY_CORE_SRC_COMMON_HALF_PRIVATE_HPP
+#define NUMPY_CORE_SRC_COMMON_HALF_PRIVATE_HPP
+
+#include "npstd.hpp"
+#include "float_status.hpp"
+
+/*
+ * The following functions that emulating float/double/half conversions
+ * are copied from npymath without any changes to its functionalty.
+ */
+namespace np { namespace half_private {
+
+template<bool gen_overflow=true, bool gen_underflow=true, bool round_even=true>
+inline uint16_t FromFloatBits(uint32_t f)
+{
+    uint32_t f_exp, f_sig;
+    uint16_t h_sgn, h_exp, h_sig;
+
+    h_sgn = (uint16_t) ((f&0x80000000u) >> 16);
+    f_exp = (f&0x7f800000u);
+
+    /* Exponent overflow/NaN converts to signed inf/NaN */
+    if (f_exp >= 0x47800000u) {
+        if (f_exp == 0x7f800000u) {
+            /* Inf or NaN */
+            f_sig = (f&0x007fffffu);
+            if (f_sig != 0) {
+                /* NaN - propagate the flag in the significand... */
+                uint16_t ret = (uint16_t) (0x7c00u + (f_sig >> 13));
+                /* ...but make sure it stays a NaN */
+                if (ret == 0x7c00u) {
+                    ret++;
+                }
+                return h_sgn + ret;
+            } else {
+                /* signed inf */
+                return (uint16_t) (h_sgn + 0x7c00u);
+            }
+        } else {
+            if constexpr (gen_overflow) {
+                /* overflow to signed inf */
+                FloatStatus::RaiseOverflow();
+            }
+            return (uint16_t) (h_sgn + 0x7c00u);
+        }
+    }
+
+    /* Exponent underflow converts to a subnormal half or signed zero */
+    if (f_exp <= 0x38000000u) {
+        /*
+         * Signed zeros, subnormal floats, and floats with small
+         * exponents all convert to signed zero half-floats.
+         */
+        if (f_exp < 0x33000000u) {
+            if constexpr (gen_underflow) {
+                /* If f != 0, it underflowed to 0 */
+                if ((f&0x7fffffff) != 0) {
+                    FloatStatus::RaiseUnderflow();
+                }
+            }
+            return h_sgn;
+        }
+        /* Make the subnormal significand */
+        f_exp >>= 23;
+        f_sig = (0x00800000u + (f&0x007fffffu));
+        if constexpr (gen_underflow) {
+            /* If it's not exactly represented, it underflowed */
+            if ((f_sig&(((uint32_t)1 << (126 - f_exp)) - 1)) != 0) {
+                FloatStatus::RaiseUnderflow();
+            }
+        }
+        /*
+         * Usually the significand is shifted by 13. For subnormals an
+         * additional shift needs to occur. This shift is one for the largest
+         * exponent giving a subnormal `f_exp = 0x38000000 >> 23 = 112`, which
+         * offsets the new first bit. At most the shift can be 1+10 bits.
+         */
+        f_sig >>= (113 - f_exp);
+        /* Handle rounding by adding 1 to the bit beyond half precision */
+        if constexpr (round_even) {
+            /*
+             * If the last bit in the half significand is 0 (already even), and
+             * the remaining bit pattern is 1000...0, then we do not add one
+             * to the bit after the half significand. However, the (113 - f_exp)
+             * shift can lose up to 11 bits, so the || checks them in the original.
+             * In all other cases, we can just add one.
+             */
+            if (((f_sig&0x00003fffu) != 0x00001000u) || (f&0x000007ffu)) {
+                f_sig += 0x00001000u;
+            }
+        }
+        else {
+            f_sig += 0x00001000u;
+        }
+        h_sig = (uint16_t) (f_sig >> 13);
+        /*
+         * If the rounding causes a bit to spill into h_exp, it will
+         * increment h_exp from zero to one and h_sig will be zero.
+         * This is the correct result.
+         */
+        return (uint16_t) (h_sgn + h_sig);
+    }
+
+    /* Regular case with no overflow or underflow */
+    h_exp = (uint16_t) ((f_exp - 0x38000000u) >> 13);
+    /* Handle rounding by adding 1 to the bit beyond half precision */
+    f_sig = (f&0x007fffffu);
+    if constexpr (round_even) {
+        /*
+         * If the last bit in the half significand is 0 (already even), and
+         * the remaining bit pattern is 1000...0, then we do not add one
+         * to the bit after the half significand.  In all other cases, we do.
+         */
+        if ((f_sig&0x00003fffu) != 0x00001000u) {
+            f_sig += 0x00001000u;
+        }
+    }
+    else {
+        f_sig += 0x00001000u;
+    }
+    h_sig = (uint16_t) (f_sig >> 13);
+    /*
+     * If the rounding causes a bit to spill into h_exp, it will
+     * increment h_exp by one and h_sig will be zero.  This is the
+     * correct result.  h_exp may increment to 15, at greatest, in
+     * which case the result overflows to a signed inf.
+     */
+    if constexpr (gen_overflow) {
+        h_sig += h_exp;
+        if (h_sig == 0x7c00u) {
+            FloatStatus::RaiseOverflow();
+        }
+        return h_sgn + h_sig;
+    }
+    else {
+        return h_sgn + h_exp + h_sig;
+    }
+}
+
+template<bool gen_overflow=true, bool gen_underflow=true, bool round_even=true>
+inline uint16_t FromDoubleBits(uint64_t d)
+{
+    uint64_t d_exp, d_sig;
+    uint16_t h_sgn, h_exp, h_sig;
+
+    h_sgn = (d&0x8000000000000000ULL) >> 48;
+    d_exp = (d&0x7ff0000000000000ULL);
+
+    /* Exponent overflow/NaN converts to signed inf/NaN */
+    if (d_exp >= 0x40f0000000000000ULL) {
+        if (d_exp == 0x7ff0000000000000ULL) {
+            /* Inf or NaN */
+            d_sig = (d&0x000fffffffffffffULL);
+            if (d_sig != 0) {
+                /* NaN - propagate the flag in the significand... */
+                uint16_t ret = (uint16_t) (0x7c00u + (d_sig >> 42));
+                /* ...but make sure it stays a NaN */
+                if (ret == 0x7c00u) {
+                    ret++;
+                }
+                return h_sgn + ret;
+            } else {
+                /* signed inf */
+                return h_sgn + 0x7c00u;
+            }
+        } else {
+            /* overflow to signed inf */
+            if constexpr (gen_overflow) {
+                FloatStatus::RaiseOverflow();
+            }
+            return h_sgn + 0x7c00u;
+        }
+    }
+
+    /* Exponent underflow converts to subnormal half or signed zero */
+    if (d_exp <= 0x3f00000000000000ULL) {
+        /*
+         * Signed zeros, subnormal floats, and floats with small
+         * exponents all convert to signed zero half-floats.
+         */
+        if (d_exp < 0x3e60000000000000ULL) {
+            if constexpr (gen_underflow) {
+                /* If d != 0, it underflowed to 0 */
+                if ((d&0x7fffffffffffffffULL) != 0) {
+                    FloatStatus::RaiseUnderflow();
+                }
+            }
+            return h_sgn;
+        }
+        /* Make the subnormal significand */
+        d_exp >>= 52;
+        d_sig = (0x0010000000000000ULL + (d&0x000fffffffffffffULL));
+        if constexpr (gen_underflow) {
+            /* If it's not exactly represented, it underflowed */
+            if ((d_sig&(((uint64_t)1 << (1051 - d_exp)) - 1)) != 0) {
+                FloatStatus::RaiseUnderflow();
+            }
+        }
+        /*
+         * Unlike floats, doubles have enough room to shift left to align
+         * the subnormal significand leading to no loss of the last bits.
+         * The smallest possible exponent giving a subnormal is:
+         * `d_exp = 0x3e60000000000000 >> 52 = 998`. All larger subnormals are
+         * shifted with respect to it. This adds a shift of 10+1 bits the final
+         * right shift when comparing it to the one in the normal branch.
+         */
+        assert(d_exp - 998 >= 0);
+        d_sig <<= (d_exp - 998);
+        /* Handle rounding by adding 1 to the bit beyond half precision */
+        if constexpr (round_even) {
+            /*
+             * If the last bit in the half significand is 0 (already even), and
+             * the remaining bit pattern is 1000...0, then we do not add one
+             * to the bit after the half significand.  In all other cases, we do.
+             */
+            if ((d_sig&0x003fffffffffffffULL) != 0x0010000000000000ULL) {
+                d_sig += 0x0010000000000000ULL;
+            }
+        }
+        else {
+            d_sig += 0x0010000000000000ULL;
+        }
+        h_sig = (uint16_t) (d_sig >> 53);
+        /*
+         * If the rounding causes a bit to spill into h_exp, it will
+         * increment h_exp from zero to one and h_sig will be zero.
+         * This is the correct result.
+         */
+        return h_sgn + h_sig;
+    }
+
+    /* Regular case with no overflow or underflow */
+    h_exp = (uint16_t) ((d_exp - 0x3f00000000000000ULL) >> 42);
+    /* Handle rounding by adding 1 to the bit beyond half precision */
+    d_sig = (d&0x000fffffffffffffULL);
+    if constexpr (round_even) {
+        /*
+         * If the last bit in the half significand is 0 (already even), and
+         * the remaining bit pattern is 1000...0, then we do not add one
+         * to the bit after the half significand.  In all other cases, we do.
+         */
+        if ((d_sig&0x000007ffffffffffULL) != 0x0000020000000000ULL) {
+            d_sig += 0x0000020000000000ULL;
+        }
+    }
+    else {
+        d_sig += 0x0000020000000000ULL;
+    }
+    h_sig = (uint16_t) (d_sig >> 42);
+
+    /*
+     * If the rounding causes a bit to spill into h_exp, it will
+     * increment h_exp by one and h_sig will be zero.  This is the
+     * correct result.  h_exp may increment to 15, at greatest, in
+     * which case the result overflows to a signed inf.
+     */
+    if constexpr (gen_overflow) {
+        h_sig += h_exp;
+        if (h_sig == 0x7c00u) {
+            FloatStatus::RaiseOverflow();
+        }
+        return h_sgn + h_sig;
+    }
+    else {
+        return h_sgn + h_exp + h_sig;
+    }
+}
+
+constexpr uint32_t ToFloatBits(uint16_t h)
+{
+    uint16_t h_exp = (h&0x7c00u);
+    uint32_t f_sgn = ((uint32_t)h&0x8000u) << 16;
+    switch (h_exp) {
+        case 0x0000u: { // 0 or subnormal
+            uint16_t h_sig = (h&0x03ffu);
+            // Signed zero
+            if (h_sig == 0) {
+                return f_sgn;
+            }
+            // Subnormal
+            h_sig <<= 1;
+            while ((h_sig&0x0400u) == 0) {
+                h_sig <<= 1;
+                h_exp++;
+            }
+            uint32_t f_exp = ((uint32_t)(127 - 15 - h_exp)) << 23;
+            uint32_t f_sig = ((uint32_t)(h_sig&0x03ffu)) << 13;
+            return f_sgn + f_exp + f_sig;
+        }
+        case 0x7c00u: // inf or NaN
+            // All-ones exponent and a copy of the significand
+            return f_sgn + 0x7f800000u + (((uint32_t)(h&0x03ffu)) << 13);
+        default: // normalized
+            // Just need to adjust the exponent and shift
+            return f_sgn + (((uint32_t)(h&0x7fffu) + 0x1c000u) << 13);
+    }
+}
+
+constexpr uint64_t ToDoubleBits(uint16_t h)
+{
+    uint16_t h_exp = (h&0x7c00u);
+    uint64_t d_sgn = ((uint64_t)h&0x8000u) << 48;
+    switch (h_exp) {
+        case 0x0000u: { // 0 or subnormal
+            uint16_t h_sig = (h&0x03ffu);
+            // Signed zero
+            if (h_sig == 0) {
+                return d_sgn;
+            }
+            // Subnormal
+            h_sig <<= 1;
+            while ((h_sig&0x0400u) == 0) {
+                h_sig <<= 1;
+                h_exp++;
+            }
+            uint64_t d_exp = ((uint64_t)(1023 - 15 - h_exp)) << 52;
+            uint64_t d_sig = ((uint64_t)(h_sig&0x03ffu)) << 42;
+            return d_sgn + d_exp + d_sig;
+        }
+        case 0x7c00u: // inf or NaN
+            // All-ones exponent and a copy of the significand
+            return d_sgn + 0x7ff0000000000000ULL + (((uint64_t)(h&0x03ffu)) << 42);
+        default: // normalized
+            // Just need to adjust the exponent and shift
+            return d_sgn + (((uint64_t)(h&0x7fffu) + 0xfc000u) << 42);
+    }
+}
+
+}} // namespace np::half_private
+#endif // NUMPY_CORE_SRC_COMMON_HALF_PRIVATE_HPP
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/lowlevel_strided_loops.h b/contrib/python/numpy/py3/numpy/core/src/common/lowlevel_strided_loops.h
index 924a34db5ee..9b987b0fa70 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/lowlevel_strided_loops.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/lowlevel_strided_loops.h
@@ -328,12 +328,14 @@ PyArray_TransferMaskedStridedToNDim(npy_intp ndim,
                 NPY_cast_info *cast_info);
 
 NPY_NO_EXPORT int
-mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
-                       PyArrayObject *result);
+mapiter_trivial_get(
+        PyArrayObject *self, PyArrayObject *ind, PyArrayObject *result,
+        int is_aligned, NPY_cast_info *cast_info);
 
 NPY_NO_EXPORT int
-mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
-                       PyArrayObject *result);
+mapiter_trivial_set(
+        PyArrayObject *self, PyArrayObject *ind, PyArrayObject *result,
+        int is_aligned, NPY_cast_info *cast_info);
 
 NPY_NO_EXPORT int
 mapiter_get(
@@ -425,7 +427,7 @@ PyArray_PrepareThreeRawArrayIter(int ndim, npy_intp const *shape,
  * blocking. See the 'npy_blocked_end' function documentation below for an
  * example of how this function is used.
  */
-static NPY_INLINE npy_intp
+static inline npy_intp
 npy_aligned_block_offset(const void * addr, const npy_uintp esize,
                          const npy_uintp alignment, const npy_uintp nvals)
 {
@@ -458,7 +460,7 @@ npy_aligned_block_offset(const void * addr, const npy_uintp esize,
  * for(; i < n; i++)
  *   <scalar-op>
  */
-static NPY_INLINE npy_intp
+static inline npy_intp
 npy_blocked_end(const npy_uintp peel, const npy_uintp esize,
                 const npy_uintp vsz, const npy_uintp nvals)
 {
@@ -472,7 +474,7 @@ npy_blocked_end(const npy_uintp peel, const npy_uintp esize,
 
 
 /* byte swapping functions */
-static NPY_INLINE npy_uint16
+static inline npy_uint16
 npy_bswap2(npy_uint16 x)
 {
     return ((x & 0xffu) << 8) | (x >> 8);
@@ -482,7 +484,7 @@ npy_bswap2(npy_uint16 x)
  * treat as int16 and byteswap unaligned memory,
  * some cpus don't support unaligned access
  */
-static NPY_INLINE void
+static inline void
 npy_bswap2_unaligned(char * x)
 {
     char a = x[0];
@@ -490,7 +492,7 @@ npy_bswap2_unaligned(char * x)
     x[1] = a;
 }
 
-static NPY_INLINE npy_uint32
+static inline npy_uint32
 npy_bswap4(npy_uint32 x)
 {
 #ifdef HAVE___BUILTIN_BSWAP32
@@ -501,7 +503,7 @@ npy_bswap4(npy_uint32 x)
 #endif
 }
 
-static NPY_INLINE void
+static inline void
 npy_bswap4_unaligned(char * x)
 {
     char a = x[0];
@@ -512,7 +514,7 @@ npy_bswap4_unaligned(char * x)
     x[2] = a;
 }
 
-static NPY_INLINE npy_uint64
+static inline npy_uint64
 npy_bswap8(npy_uint64 x)
 {
 #ifdef HAVE___BUILTIN_BSWAP64
@@ -529,7 +531,7 @@ npy_bswap8(npy_uint64 x)
 #endif
 }
 
-static NPY_INLINE void
+static inline void
 npy_bswap8_unaligned(char * x)
 {
     char a = x[0]; x[0] = x[7]; x[7] = a;
@@ -685,7 +687,7 @@ npy_bswap8_unaligned(char * x)
         size == 1 ? 0 : ((PyArray_NDIM(arr) == 1) ? \
                              PyArray_STRIDE(arr, 0) : PyArray_ITEMSIZE(arr)))
 
-static NPY_INLINE int
+static inline int
 PyArray_EQUIVALENTLY_ITERABLE_OVERLAP_OK(PyArrayObject *arr1, PyArrayObject *arr2,
                                          int arr1_read, int arr2_read)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/meta.hpp b/contrib/python/numpy/py3/numpy/core/src/common/meta.hpp
new file mode 100644
index 00000000000..27ea1857e33
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/meta.hpp
@@ -0,0 +1,54 @@
+#ifndef NUMPY_CORE_SRC_COMMON_META_HPP
+#define NUMPY_CORE_SRC_COMMON_META_HPP
+
+#include "npstd.hpp"
+
+namespace np { namespace meta {
+/// @addtogroup cpp_core_meta
+/// @{
+
+namespace details {
+template<int size, bool unsig>
+struct IntBySize;
+
+template<bool unsig>
+struct IntBySize<sizeof(uint8_t), unsig> {
+    using Type = typename std::conditional<
+        unsig, uint8_t, int8_t>::type;
+};
+template<bool unsig>
+struct IntBySize<sizeof(uint16_t), unsig> {
+    using Type = typename std::conditional<
+        unsig, uint16_t, int16_t>::type;
+};
+template<bool unsig>
+struct IntBySize<sizeof(uint32_t), unsig> {
+    using Type = typename std::conditional<
+        unsig, uint32_t, int32_t>::type;
+};
+template<bool unsig>
+struct IntBySize<sizeof(uint64_t), unsig> {
+    using Type = typename std::conditional<
+        unsig, uint64_t, int64_t>::type;
+};
+} // namespace details
+
+/// Provides safe conversion of any integer type synonyms
+/// to a fixed-width integer type.
+template<typename T>
+struct FixedWidth {
+    using TF_ = typename details::IntBySize<
+        sizeof(T), std::is_unsigned<T>::value
+    >::Type;
+
+    using Type = typename std::conditional<
+        std::is_integral<T>::value, TF_, T
+    >::type;
+};
+
+/// @} cpp_core_meta
+
+}} // namespace np::meta
+
+#endif // NUMPY_CORE_SRC_COMMON_META_HPP
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npdef.hpp b/contrib/python/numpy/py3/numpy/core/src/common/npdef.hpp
new file mode 100644
index 00000000000..56a0df52e5d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npdef.hpp
@@ -0,0 +1,28 @@
+#ifndef NUMPY_CORE_SRC_COMMON_NPDEF_HPP
+#define NUMPY_CORE_SRC_COMMON_NPDEF_HPP
+
+#if !defined(__cplusplus) || __cplusplus < 201703L
+    #error "NumPy requires a compiler with at least C++17 enabled"
+#endif
+
+/// @addtogroup cpp_core_defs
+/// @{
+
+/// Whether compiler supports C++20
+#if __cplusplus > 202002L
+    #define NP_HAS_CPP20 1
+#else
+    #define NP_HAS_CPP20 0
+#endif
+
+/// Wraps `__has_builtin`
+#if defined(__has_builtin)
+    #define NP_HAS_BUILTIN(INTRIN) __has_builtin(INTRIN)
+#else
+    #define NP_HAS_BUILTIN(INTRIN) 0
+#endif
+
+/// @} cpp_core_defs
+
+#endif // NUMPY_CORE_SRC_COMMON_NPDEF_HPP
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npstd.hpp b/contrib/python/numpy/py3/numpy/core/src/common/npstd.hpp
new file mode 100644
index 00000000000..92e3d5cc50f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npstd.hpp
@@ -0,0 +1,57 @@
+#ifndef NUMPY_CORE_SRC_COMMON_NPSTD_HPP
+#define NUMPY_CORE_SRC_COMMON_NPSTD_HPP
+
+#include <cstddef>
+#include <cstring>
+#include <cctype>
+#include <cstdint>
+
+#include <string>
+#include <algorithm>
+#include <utility>
+#include <cstdlib>
+#include <cmath>
+#include <complex>
+#include <type_traits>
+
+#include <numpy/npy_common.h>
+
+#include "npy_config.h"
+
+namespace np {
+/// @addtogroup cpp_core_types
+/// @{
+using std::uint8_t;
+using std::int8_t;
+using std::uint16_t;
+using std::int16_t;
+using std::uint32_t;
+using std::int32_t;
+using std::uint64_t;
+using std::int64_t;
+using std::uintptr_t;
+using std::intptr_t;
+using std::complex;
+using std::uint_fast16_t;
+using std::uint_fast32_t;
+
+/** Guard for long double.
+ *
+ * The C implementation defines long double as double
+ * on MinGW to provide compatibility with MSVC to unify
+ * one behavior under Windows OS, which makes npy_longdouble
+ * not fit to be used with template specialization or overloading.
+ *
+ * This type will be set to `void` when `npy_longdouble` is not defined
+ * as `long double`.
+ */
+using LongDouble = typename std::conditional<
+    !std::is_same<npy_longdouble, long double>::value,
+     void, npy_longdouble
+>::type;
+/// @} cpp_core_types
+
+} // namespace np
+
+#endif // NUMPY_CORE_SRC_COMMON_NPSTD_HPP
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_cblas.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_cblas.h
index 30fec1a653d..751854b6eb0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_cblas.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_cblas.h
@@ -66,7 +66,7 @@ enum CBLAS_SIDE {CblasLeft=141, CblasRight=142};
  * Convert NumPy stride to BLAS stride. Returns 0 if conversion cannot be done
  * (BLAS won't handle negative or zero strides the way we want).
  */
-static NPY_INLINE CBLAS_INT
+static inline CBLAS_INT
 blas_stride(npy_intp stride, unsigned itemsize)
 {
     /*
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_config.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_config.h
index d6886c5ea90..715b17777ae 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_config.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_config.h
@@ -2,6 +2,7 @@
 #define NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_
 
 #include "config.h"
+#include "npy_cpu_dispatch.h" // brings NPY_HAVE_[CPU features]
 #include "numpy/numpyconfig.h"
 #include "numpy/utils.h"
 #include "numpy/npy_os.h"
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.c b/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.c
index 5842471e47a..64a85f6fb20 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.c
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.c
@@ -1,6 +1,6 @@
 #include "npy_cpu_features.h"
 #include "npy_cpu_dispatch.h" // To guarantee the CPU baseline definitions are in scope.
-#include "numpy/npy_common.h" // for NPY_INLINE
+#include "numpy/npy_common.h"
 #include "numpy/npy_cpu.h" // To guarantee the CPU definitions are in scope.
 
 /******************** Private Definitions *********************/
@@ -14,13 +14,15 @@ static unsigned char npy__cpu_have[NPY_CPU_FEATURE_MAX];
 static void
 npy__cpu_init_features(void);
 /*
- * Disable CPU dispatched features at runtime if environment variable
- * 'NPY_DISABLE_CPU_FEATURES' is defined.
+ * Enable or disable CPU dispatched features at runtime if the environment variable
+ * `NPY_ENABLE_CPU_FEATURES`  or  `NPY_DISABLE_CPU_FEATURES`
+ * depends on the value of boolean parameter `disable`(toggle).
+ *
  * Multiple features can be present, and separated by space, comma, or tab.
- * Raises an error if parsing fails or if the feature was not enabled
+ * Raises an error if parsing fails or if the feature was not enabled or disabled
 */
 static int
-npy__cpu_try_disable_env(void);
+npy__cpu_check_env(int disable, const char *env);
 
 /* Ensure the build's CPU baseline features are supported at runtime */
 static int
@@ -43,9 +45,22 @@ npy_cpu_init(void)
     if (npy__cpu_validate_baseline() < 0) {
         return -1;
     }
-    if (npy__cpu_try_disable_env() < 0) {
+    char *enable_env = getenv("NPY_ENABLE_CPU_FEATURES");
+    char *disable_env = getenv("NPY_DISABLE_CPU_FEATURES");
+    int is_enable = enable_env && enable_env[0];
+    int is_disable = disable_env && disable_env[0];
+    if (is_enable & is_disable) {
+        PyErr_Format(PyExc_ImportError,
+            "Both NPY_DISABLE_CPU_FEATURES and NPY_ENABLE_CPU_FEATURES "
+            "environment variables cannot be set simultaneously."
+        );
         return -1;
     }
+    if (is_enable | is_disable) {
+        if (npy__cpu_check_env(is_disable, is_disable ? disable_env : enable_env) < 0) {
+            return -1;
+        }
+    }
     return 0;
 }
 
@@ -81,12 +96,14 @@ static struct {
                 {NPY_CPU_FEATURE_AVX512VBMI, "AVX512VBMI"},
                 {NPY_CPU_FEATURE_AVX512VBMI2, "AVX512VBMI2"},
                 {NPY_CPU_FEATURE_AVX512BITALG, "AVX512BITALG"},
+                {NPY_CPU_FEATURE_AVX512FP16 , "AVX512FP16"},
                 {NPY_CPU_FEATURE_AVX512_KNL, "AVX512_KNL"},
                 {NPY_CPU_FEATURE_AVX512_KNM, "AVX512_KNM"},
                 {NPY_CPU_FEATURE_AVX512_SKX, "AVX512_SKX"},
                 {NPY_CPU_FEATURE_AVX512_CLX, "AVX512_CLX"},
                 {NPY_CPU_FEATURE_AVX512_CNL, "AVX512_CNL"},
                 {NPY_CPU_FEATURE_AVX512_ICL, "AVX512_ICL"},
+                {NPY_CPU_FEATURE_AVX512_SPR, "AVX512_SPR"},
                 {NPY_CPU_FEATURE_VSX, "VSX"},
                 {NPY_CPU_FEATURE_VSX2, "VSX2"},
                 {NPY_CPU_FEATURE_VSX3, "VSX3"},
@@ -166,7 +183,7 @@ npy_cpu_dispatch_list(void)
  * features that had been configured via --cpu-baseline
  * otherwise it returns 0
 */
-static NPY_INLINE int
+static inline int
 npy__cpu_baseline_fid(const char *feature)
 {
 #if !defined(NPY_DISABLE_OPTIMIZATION) && NPY_WITH_CPU_BASELINE_N > 0
@@ -179,7 +196,7 @@ npy__cpu_baseline_fid(const char *feature)
  * features that had been configured via --cpu-dispatch
  * otherwise it returns 0
 */
-static NPY_INLINE int
+static inline int
 npy__cpu_dispatch_fid(const char *feature)
 {
 #if !defined(NPY_DISABLE_OPTIMIZATION) && NPY_WITH_CPU_DISPATCH_N > 0
@@ -208,7 +225,8 @@ npy__cpu_validate_baseline(void)
         *(fptr-1) = '\0'; // trim the last space
         PyErr_Format(PyExc_RuntimeError,
             "NumPy was built with baseline optimizations: \n"
-            "(" NPY_WITH_CPU_BASELINE ") but your machine doesn't support:\n(%s).",
+            "(" NPY_WITH_CPU_BASELINE ") but your machine "
+            "doesn't support:\n(%s).",
             baseline_failure
         );
         return -1;
@@ -218,27 +236,31 @@ npy__cpu_validate_baseline(void)
 }
 
 static int
-npy__cpu_try_disable_env(void)
-{
-    char *disenv = getenv("NPY_DISABLE_CPU_FEATURES");
-    if (disenv == NULL || disenv[0] == 0) {
-        return 0;
-    }
-    #define NPY__CPU_ENV_ERR_HEAD \
-        "During parsing environment variable 'NPY_DISABLE_CPU_FEATURES':\n"
+npy__cpu_check_env(int disable, const char *env) {
+
+    static const char *names[] = {
+        "enable", "disable",
+        "NPY_ENABLE_CPU_FEATURES", "NPY_DISABLE_CPU_FEATURES",
+        "During parsing environment variable: 'NPY_ENABLE_CPU_FEATURES':\n",
+        "During parsing environment variable: 'NPY_DISABLE_CPU_FEATURES':\n"
+    };
+    disable = disable ? 1 : 0;
+    const char *act_name = names[disable];
+    const char *env_name = names[disable + 2];
+    const char *err_head = names[disable + 4];
 
 #if !defined(NPY_DISABLE_OPTIMIZATION) && NPY_WITH_CPU_DISPATCH_N > 0
     #define NPY__MAX_VAR_LEN 1024 // More than enough for this era
-    size_t var_len = strlen(disenv) + 1;
+    size_t var_len = strlen(env) + 1;
     if (var_len > NPY__MAX_VAR_LEN) {
         PyErr_Format(PyExc_RuntimeError,
-            "Length of environment variable 'NPY_DISABLE_CPU_FEATURES' is %d, only %d accepted",
-            var_len, NPY__MAX_VAR_LEN - 1
+            "Length of environment variable '%s' is %d, only %d accepted",
+            env_name, var_len, NPY__MAX_VAR_LEN
         );
         return -1;
     }
-    char disable_features[NPY__MAX_VAR_LEN];
-    memcpy(disable_features, disenv, var_len);
+    char features[NPY__MAX_VAR_LEN];
+    memcpy(features, env, var_len);
 
     char nexist[NPY__MAX_VAR_LEN];
     char *nexist_cur = &nexist[0];
@@ -248,17 +270,19 @@ npy__cpu_try_disable_env(void)
 
     //comma and space including (htab, vtab, CR, LF, FF)
     const char *delim = ", \t\v\r\n\f";
-    char *feature = strtok(disable_features, delim);
+    char *feature = strtok(features, delim);
     while (feature) {
-        if (npy__cpu_baseline_fid(feature) > 0) {
-            PyErr_Format(PyExc_RuntimeError,
-                NPY__CPU_ENV_ERR_HEAD
-                "You cannot disable CPU feature '%s', since it is part of "
-                "the baseline optimizations:\n"
-                "(" NPY_WITH_CPU_BASELINE ").",
-                feature
-            );
-            return -1;
+        if (npy__cpu_baseline_fid(feature) > 0){
+            if (disable) {
+                PyErr_Format(PyExc_RuntimeError,
+                    "%s"
+                    "You cannot disable CPU feature '%s', since it is part of "
+                    "the baseline optimizations:\n"
+                    "(" NPY_WITH_CPU_BASELINE ").",
+                    err_head, feature
+                );
+                return -1;
+            } goto next;
         }
         // check if the feature is part of dispatched features
         int feature_id = npy__cpu_dispatch_fid(feature);
@@ -275,47 +299,58 @@ npy__cpu_try_disable_env(void)
             notsupp_cur[flen] = ' '; notsupp_cur += flen + 1;
             goto next;
         }
-        // Finally we can disable it
-        npy__cpu_have[feature_id] = 0;
+        // Finally we can disable or mark for enabling
+        npy__cpu_have[feature_id] = disable ? 0:2;
     next:
         feature = strtok(NULL, delim);
     }
+    if (!disable){
+        // Disables any unmarked dispatched feature.
+        #define NPY__CPU_DISABLE_DISPATCH_CB(FEATURE, DUMMY) \
+            if(npy__cpu_have[NPY_CAT(NPY_CPU_FEATURE_, FEATURE)] != 0)\
+            {npy__cpu_have[NPY_CAT(NPY_CPU_FEATURE_, FEATURE)]--;}\
+
+        NPY_WITH_CPU_DISPATCH_CALL(NPY__CPU_DISABLE_DISPATCH_CB, DUMMY) // extra arg for msvc
+    }
 
     *nexist_cur = '\0';
     if (nexist[0] != '\0') {
         *(nexist_cur-1) = '\0'; // trim the last space
-        if (PyErr_WarnFormat(PyExc_RuntimeWarning, 1,
-                NPY__CPU_ENV_ERR_HEAD
-                "You cannot disable CPU features (%s), since "
-                "they are not part of the dispatched optimizations\n"
-                "(" NPY_WITH_CPU_DISPATCH ").",
-                nexist
+        if (PyErr_WarnFormat(PyExc_ImportWarning, 1,
+            "%sYou cannot %s CPU features (%s), since "
+            "they are not part of the dispatched optimizations\n"
+            "(" NPY_WITH_CPU_DISPATCH ").",
+            err_head, act_name, nexist
         ) < 0) {
             return -1;
         }
+        return 0;
     }
 
+    #define NOTSUPP_BODY \
+                "%s" \
+                "You cannot %s CPU features (%s), since " \
+                "they are not supported by your machine.", \
+                err_head, act_name, notsupp
+
     *notsupp_cur = '\0';
     if (notsupp[0] != '\0') {
         *(notsupp_cur-1) = '\0'; // trim the last space
-        if (PyErr_WarnFormat(PyExc_RuntimeWarning, 1,
-                NPY__CPU_ENV_ERR_HEAD
-                "You cannot disable CPU features (%s), since "
-                "they are not supported by your machine.",
-                notsupp
-        ) < 0) {
+        if (!disable){
+            PyErr_Format(PyExc_RuntimeError, NOTSUPP_BODY);
             return -1;
         }
     }
 #else
-    if (PyErr_WarnFormat(PyExc_RuntimeWarning, 1,
-            NPY__CPU_ENV_ERR_HEAD
-            "You cannot use environment variable 'NPY_DISABLE_CPU_FEATURES', since "
+    if (PyErr_WarnFormat(PyExc_ImportWarning, 1,
+            "%s"
+            "You cannot use environment variable '%s', since "
         #ifdef NPY_DISABLE_OPTIMIZATION
-            "the NumPy library was compiled with optimization disabled."
+            "the NumPy library was compiled with optimization disabled.",
         #else
-            "the NumPy library was compiled without any dispatched optimizations."
+            "the NumPy library was compiled without any dispatched optimizations.",
         #endif
+        err_head, env_name, act_name
     ) < 0) {
         return -1;
     }
@@ -506,6 +541,11 @@ npy__cpu_init_features(void)
                                                          npy__cpu_have[NPY_CPU_FEATURE_AVX512VBMI2] &&
                                                          npy__cpu_have[NPY_CPU_FEATURE_AVX512BITALG] &&
                                                          npy__cpu_have[NPY_CPU_FEATURE_AVX512VPOPCNTDQ];
+        // Sapphire Rapids
+        npy__cpu_have[NPY_CPU_FEATURE_AVX512FP16]     = (reg[3] & (1 << 23))  != 0;
+        npy__cpu_have[NPY_CPU_FEATURE_AVX512_SPR]      = npy__cpu_have[NPY_CPU_FEATURE_AVX512_ICL] &&
+                                                         npy__cpu_have[NPY_CPU_FEATURE_AVX512FP16];
+
     }
 }
 
@@ -618,7 +658,7 @@ npy__cpu_init_features(void)
 
 #elif defined(__arm__) || defined(__aarch64__)
 
-static NPY_INLINE void
+static inline void
 npy__cpu_init_features_arm8(void)
 {
     npy__cpu_have[NPY_CPU_FEATURE_NEON]       =
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.h
index 96c543e7010..9180670c4a1 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_cpu_features.h
@@ -43,6 +43,7 @@ enum npy_cpu_features
     NPY_CPU_FEATURE_AVX512VNNI        = 42,
     NPY_CPU_FEATURE_AVX512VBMI2       = 43,
     NPY_CPU_FEATURE_AVX512BITALG      = 44,
+    NPY_CPU_FEATURE_AVX512FP16        = 45,
 
     // X86 CPU Groups
     // Knights Landing (F,CD,ER,PF)
@@ -57,6 +58,8 @@ enum npy_cpu_features
     NPY_CPU_FEATURE_AVX512_CNL        = 105,
     // Ice Lake        (F,CD,BW,DQ,VL,IFMA,VBMI,VNNI,VBMI2,BITALG,VPOPCNTDQ)
     NPY_CPU_FEATURE_AVX512_ICL        = 106,
+    // Sapphire Rapids (Ice Lake, AVX512FP16)
+    NPY_CPU_FEATURE_AVX512_SPR        = 107,
 
     // IBM/POWER VSX
     // POWER7
@@ -103,13 +106,25 @@ enum npy_cpu_features
  *  - detects runtime CPU features
  *  - check that baseline CPU features are present
  *  - uses 'NPY_DISABLE_CPU_FEATURES' to disable dispatchable features
+ *  - uses 'NPY_ENABLE_CPU_FEATURES' to enable dispatchable features
  *
  * It will set a RuntimeError when 
  *  - CPU baseline features from the build are not supported at runtime
  *  - 'NPY_DISABLE_CPU_FEATURES' tries to disable a baseline feature
- * and will warn if 'NPY_DISABLE_CPU_FEATURES' tries to disable a feature that
- * is not disabled (the machine or build does not support it, or the project was
- * not built with any feature optimization support)
+ *  - 'NPY_DISABLE_CPU_FEATURES' and 'NPY_ENABLE_CPU_FEATURES' are
+ *    simultaneously set
+ *  - 'NPY_ENABLE_CPU_FEATURES' tries to enable a feature that is not supported
+ *    by the machine or build
+ *  - 'NPY_ENABLE_CPU_FEATURES' tries to enable a feature when the project was
+ *    not built with any feature optimization support
+ *  
+ * It will set an ImportWarning when:
+ *  - 'NPY_DISABLE_CPU_FEATURES' tries to disable a feature that is not supported
+ *    by the machine or build
+ *  - 'NPY_DISABLE_CPU_FEATURES' or 'NPY_ENABLE_CPU_FEATURES' tries to
+ *    disable/enable a feature when the project was not built with any feature
+ *    optimization support
+ * 
  * return 0 on success otherwise return -1
  */
 NPY_VISIBILITY_HIDDEN int
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_ctypes.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_ctypes.h
index 05761cad3c1..4b56345744d 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_ctypes.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_ctypes.h
@@ -14,7 +14,7 @@
  * This entire function is just a wrapper around the Python function of the
  * same name.
  */
-NPY_INLINE static int
+static inline int
 npy_ctypes_check(PyTypeObject *obj)
 {
     static PyObject *py_func = NULL;
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_extint128.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_extint128.h
index d563c2ac858..776d71c772e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_extint128.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_extint128.h
@@ -9,7 +9,7 @@ typedef struct {
 
 
 /* Integer addition with overflow checking */
-static NPY_INLINE npy_int64
+static inline npy_int64
 safe_add(npy_int64 a, npy_int64 b, char *overflow_flag)
 {
     if (a > 0 && b > NPY_MAX_INT64 - a) {
@@ -23,7 +23,7 @@ safe_add(npy_int64 a, npy_int64 b, char *overflow_flag)
 
 
 /* Integer subtraction with overflow checking */
-static NPY_INLINE npy_int64
+static inline npy_int64
 safe_sub(npy_int64 a, npy_int64 b, char *overflow_flag)
 {
     if (a >= 0 && b < a - NPY_MAX_INT64) {
@@ -37,7 +37,7 @@ safe_sub(npy_int64 a, npy_int64 b, char *overflow_flag)
 
 
 /* Integer multiplication with overflow checking */
-static NPY_INLINE npy_int64
+static inline npy_int64
 safe_mul(npy_int64 a, npy_int64 b, char *overflow_flag)
 {
     if (a > 0) {
@@ -58,7 +58,7 @@ safe_mul(npy_int64 a, npy_int64 b, char *overflow_flag)
 
 
 /* Long integer init */
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 to_128(npy_int64 x)
 {
     npy_extint128_t result;
@@ -74,7 +74,7 @@ to_128(npy_int64 x)
 }
 
 
-static NPY_INLINE npy_int64
+static inline npy_int64
 to_64(npy_extint128_t x, char *overflow)
 {
     if (x.hi != 0 ||
@@ -87,7 +87,7 @@ to_64(npy_extint128_t x, char *overflow)
 
 
 /* Long integer multiply */
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 mul_64_64(npy_int64 a, npy_int64 b)
 {
     npy_extint128_t x, y, z;
@@ -127,7 +127,7 @@ mul_64_64(npy_int64 a, npy_int64 b)
 
 
 /* Long integer add */
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 add_128(npy_extint128_t x, npy_extint128_t y, char *overflow)
 {
     npy_extint128_t z;
@@ -170,7 +170,7 @@ add_128(npy_extint128_t x, npy_extint128_t y, char *overflow)
 
 
 /* Long integer negation */
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 neg_128(npy_extint128_t x)
 {
     npy_extint128_t z = x;
@@ -179,14 +179,14 @@ neg_128(npy_extint128_t x)
 }
 
 
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 sub_128(npy_extint128_t x, npy_extint128_t y, char *overflow)
 {
     return add_128(x, neg_128(y), overflow);
 }
 
 
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 shl_128(npy_extint128_t v)
 {
     npy_extint128_t z;
@@ -198,7 +198,7 @@ shl_128(npy_extint128_t v)
 }
 
 
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 shr_128(npy_extint128_t v)
 {
     npy_extint128_t z;
@@ -209,7 +209,7 @@ shr_128(npy_extint128_t v)
     return z;
 }
 
-static NPY_INLINE int
+static inline int
 gt_128(npy_extint128_t a, npy_extint128_t b)
 {
     if (a.sign > 0 && b.sign > 0) {
@@ -228,7 +228,7 @@ gt_128(npy_extint128_t a, npy_extint128_t b)
 
 
 /* Long integer divide */
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 divmod_128_64(npy_extint128_t x, npy_int64 b, npy_int64 *mod)
 {
     npy_extint128_t remainder, pointer, result, divisor;
@@ -284,7 +284,7 @@ divmod_128_64(npy_extint128_t x, npy_int64 b, npy_int64 *mod)
 
 
 /* Divide and round down (positive divisor; no overflows) */
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 floordiv_128_64(npy_extint128_t a, npy_int64 b)
 {
     npy_extint128_t result;
@@ -300,7 +300,7 @@ floordiv_128_64(npy_extint128_t a, npy_int64 b)
 
 
 /* Divide and round up (positive divisor; no overflows) */
-static NPY_INLINE npy_extint128_t
+static inline npy_extint128_t
 ceildiv_128_64(npy_extint128_t a, npy_int64 b)
 {
     npy_extint128_t result;
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_hashtable.c b/contrib/python/numpy/py3/numpy/core/src/common/npy_hashtable.c
index af82c0f85b2..14f6cca1b86 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_hashtable.c
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_hashtable.c
@@ -35,7 +35,7 @@
  *
  * Users cannot control pointers, so we do not have to worry about DoS attacks?
  */
-static NPY_INLINE Py_hash_t
+static inline Py_hash_t
 identity_list_hash(PyObject *const *v, int len)
 {
     Py_uhash_t acc = _NpyHASH_XXPRIME_5;
@@ -58,7 +58,7 @@ identity_list_hash(PyObject *const *v, int len)
 #undef _NpyHASH_XXROTATE
 
 
-static NPY_INLINE PyObject **
+static inline PyObject **
 find_item(PyArrayIdentityHash const *tb, PyObject *const *key)
 {
     Py_hash_t hash = identity_list_hash(key, tb->key_len);
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_import.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_import.h
index f36b6924a86..58b4ba0bc7e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_import.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_import.h
@@ -16,7 +16,7 @@
  * @param attr module attribute to cache.
  * @param cache Storage location for imported function.
  */
-NPY_INLINE static void
+static inline void
 npy_cache_import(const char *module, const char *attr, PyObject **cache)
 {
     if (NPY_UNLIKELY(*cache == NULL)) {
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_pycompat.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_pycompat.h
index 6641cd59109..ce6c34fa133 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_pycompat.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_pycompat.h
@@ -11,7 +11,7 @@
 #if PY_VERSION_HEX > 0x030a00a6
 #define Npy_HashDouble _Py_HashDouble
 #else
-static NPY_INLINE Py_hash_t
+static inline Py_hash_t
 Npy_HashDouble(PyObject *NPY_UNUSED(identity), double val)
 {
     return _Py_HashDouble(val);
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h b/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h
index 1f3c22af2fe..a2231b4b3d0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h
@@ -19,7 +19,7 @@
 #define NPY_ENOMEM 1
 #define NPY_ECOMP 2
 
-static NPY_INLINE int npy_get_msb(npy_uintp unum)
+static inline int npy_get_msb(npy_uintp unum)
 {
     int depth_limit = 0;
     while (unum >>= 1)  {
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h.src b/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h.src
index a3f556f56a9..d6e4357225a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h.src
+++ b/contrib/python/numpy/py3/numpy/core/src/common/npy_sort.h.src
@@ -9,7 +9,7 @@
 #define NPY_ENOMEM 1
 #define NPY_ECOMP 2
 
-static NPY_INLINE int npy_get_msb(npy_uintp unum)
+static inline int npy_get_msb(npy_uintp unum)
 {
     int depth_limit = 0;
     while (unum >>= 1)  {
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/numpyos.c b/contrib/python/numpy/py3/numpy/core/src/common/numpyos.c
index 5ee95191dab..14637305343 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/numpyos.c
+++ b/contrib/python/numpy/py3/numpy/core/src/common/numpyos.c
@@ -779,3 +779,25 @@ NumPyOS_strtoull(const char *str, char **endptr, int base)
     return strtoull(str, endptr, base);
 }
 
+#ifdef _MSC_VER
+
+#include <stdlib.h>
+
+#if _MSC_VER >= 1900 && 0
+/* npy3k_compat.h uses this function in the _Py_BEGIN/END_SUPPRESS_IPH
+ * macros. It does not need to be defined when building using MSVC
+ * earlier than 14.0 (_MSC_VER == 1900).
+ */
+
+static void __cdecl _silent_invalid_parameter_handler(
+    wchar_t const* expression,
+    wchar_t const* function,
+    wchar_t const* file,
+    unsigned int line,
+    uintptr_t pReserved) { }
+
+_invalid_parameter_handler _Py_silent_invalid_parameter_handler = _silent_invalid_parameter_handler;
+
+#endif
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/arithmetic.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/arithmetic.h
index ad968833877..58d842a6d3a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/arithmetic.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/arithmetic.h
@@ -247,6 +247,10 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
     // negate multiply and subtract, -(a*b) - c
     #define npyv_nmulsub_f32 _mm256_fnmsub_ps
     #define npyv_nmulsub_f64 _mm256_fnmsub_pd
+    // multiply, add for odd elements and subtract even elements.
+    // (a * b) -+ c
+    #define npyv_muladdsub_f32 _mm256_fmaddsub_ps
+    #define npyv_muladdsub_f64 _mm256_fmaddsub_pd
 #else
     // multiply and add, a*b + c
     NPY_FINLINE npyv_f32 npyv_muladd_f32(npyv_f32 a, npyv_f32 b, npyv_f32 c)
@@ -274,6 +278,13 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
         npyv_f64 neg_a = npyv_xor_f64(a, npyv_setall_f64(-0.0));
         return npyv_sub_f64(npyv_mul_f64(neg_a, b), c);
     }
+    // multiply, add for odd elements and subtract even elements.
+    // (a * b) -+ c
+    NPY_FINLINE npyv_f32 npyv_muladdsub_f32(npyv_f32 a, npyv_f32 b, npyv_f32 c)
+    { return _mm256_addsub_ps(npyv_mul_f32(a, b), c); }
+    NPY_FINLINE npyv_f64 npyv_muladdsub_f64(npyv_f64 a, npyv_f64 b, npyv_f64 c)
+    { return _mm256_addsub_pd(npyv_mul_f64(a, b), c); }
+
 #endif // !NPY_HAVE_FMA3
 
 /***************************
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/memory.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/memory.h
index 410c35dc887..993d3ba0d06 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/memory.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/memory.h
@@ -84,17 +84,6 @@ NPY_FINLINE npyv_s32 npyv_loadn_s32(const npy_int32 *ptr, npy_intp stride)
 NPY_FINLINE npyv_f32 npyv_loadn_f32(const float *ptr, npy_intp stride)
 { return _mm256_castsi256_ps(npyv_loadn_u32((const npy_uint32*)ptr, stride)); }
 //// 64
-#if 0 // slower
-NPY_FINLINE npyv_u64 npyv_loadn_u64(const npy_uint64 *ptr, npy_intp stride)
-{
-    const __m256i idx = npyv_set_s64(0, 1*stride, 2*stride, 3*stride);
-    return _mm256_i64gather_epi64((const void*)ptr, idx, 8);
-}
-NPY_FINLINE npyv_s64 npyv_loadn_s64(const npy_int64 *ptr, npy_intp stride)
-{ return npyv_loadn_u64((const npy_uint64*)ptr, stride); }
-NPY_FINLINE npyv_f64 npyv_loadn_f64(const double *ptr, npy_intp stride)
-{ return _mm256_castsi256_pd(npyv_loadn_u64((const npy_uint64*)ptr, stride)); }
-#endif
 NPY_FINLINE npyv_f64 npyv_loadn_f64(const double *ptr, npy_intp stride)
 {
     __m128d a0 = _mm_castsi128_pd(_mm_loadl_epi64((const __m128i*)ptr));
@@ -107,6 +96,32 @@ NPY_FINLINE npyv_u64 npyv_loadn_u64(const npy_uint64 *ptr, npy_intp stride)
 { return _mm256_castpd_si256(npyv_loadn_f64((const double*)ptr, stride)); }
 NPY_FINLINE npyv_s64 npyv_loadn_s64(const npy_int64 *ptr, npy_intp stride)
 { return _mm256_castpd_si256(npyv_loadn_f64((const double*)ptr, stride)); }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_f32 npyv_loadn2_f32(const float *ptr, npy_intp stride)
+{
+    __m128d a0 = _mm_castsi128_pd(_mm_loadl_epi64((const __m128i*)ptr));
+    __m128d a2 = _mm_castsi128_pd(_mm_loadl_epi64((const __m128i*)(ptr + stride*2)));
+    __m128d a01 = _mm_loadh_pd(a0, (const double*)(ptr + stride));
+    __m128d a23 = _mm_loadh_pd(a2, (const double*)(ptr + stride*3));
+    return _mm256_castpd_ps(_mm256_insertf128_pd(_mm256_castpd128_pd256(a01), a23, 1));
+}
+NPY_FINLINE npyv_u32 npyv_loadn2_u32(const npy_uint32 *ptr, npy_intp stride)
+{ return _mm256_castps_si256(npyv_loadn2_f32((const float*)ptr, stride)); }
+NPY_FINLINE npyv_s32 npyv_loadn2_s32(const npy_int32 *ptr, npy_intp stride)
+{ return _mm256_castps_si256(npyv_loadn2_f32((const float*)ptr, stride)); }
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_f64 npyv_loadn2_f64(const double *ptr, npy_intp stride)
+{
+    __m256d a = npyv_loadl_f64(ptr);
+    return _mm256_insertf128_pd(a, _mm_loadu_pd(ptr + stride), 1);
+}
+NPY_FINLINE npyv_u64 npyv_loadn2_u64(const npy_uint64 *ptr, npy_intp stride)
+{ return _mm256_castpd_si256(npyv_loadn2_f64((const double*)ptr, stride)); }
+NPY_FINLINE npyv_s64 npyv_loadn2_s64(const npy_int64 *ptr, npy_intp stride)
+{ return _mm256_castpd_si256(npyv_loadn2_f64((const double*)ptr, stride)); }
+
 /***************************
  * Non-contiguous Store
  ***************************/
@@ -143,6 +158,32 @@ NPY_FINLINE void npyv_storen_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a)
 NPY_FINLINE void npyv_storen_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
 { npyv_storen_f64((double*)ptr, stride, _mm256_castsi256_pd(a)); }
 
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_u32(npy_uint32 *ptr, npy_intp stride, npyv_u32 a)
+{
+    __m128d a0 = _mm256_castpd256_pd128(_mm256_castsi256_pd(a));
+    __m128d a1 = _mm256_extractf128_pd(_mm256_castsi256_pd(a), 1);
+    _mm_storel_pd((double*)ptr, a0);
+    _mm_storeh_pd((double*)(ptr + stride), a0);
+    _mm_storel_pd((double*)(ptr + stride*2), a1);
+    _mm_storeh_pd((double*)(ptr + stride*3), a1);
+}
+NPY_FINLINE void npyv_storen2_s32(npy_int32 *ptr, npy_intp stride, npyv_s32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, a); }
+NPY_FINLINE void npyv_storen2_f32(float *ptr, npy_intp stride, npyv_f32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, _mm256_castps_si256(a)); }
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a)
+{
+    npyv_storel_u64(ptr, a);
+    npyv_storeh_u64(ptr + stride, a);
+}
+NPY_FINLINE void npyv_storen2_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
+{ npyv_storen2_u64((npy_uint64*)ptr, stride, a); }
+NPY_FINLINE void npyv_storen2_f64(double *ptr, npy_intp stride, npyv_f64 a)
+{ npyv_storen2_u64((npy_uint64*)ptr, stride, _mm256_castpd_si256(a)); }
+
 /*********************************
  * Partial Load
  *********************************/
@@ -174,7 +215,7 @@ NPY_FINLINE npyv_s64 npyv_load_till_s64(const npy_int64 *ptr, npy_uintp nlane, n
     const __m256i steps = npyv_set_s64(0, 1, 2, 3);
     __m256i vnlane  = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane);
     __m256i mask    = _mm256_cmpgt_epi64(vnlane, steps);
-    __m256i payload = _mm256_maskload_epi64((const void*)ptr, mask);
+    __m256i payload = _mm256_maskload_epi64((const long long*)ptr, mask);
     return _mm256_blendv_epi8(vfill, payload, mask);
 }
 // fill zero to rest lanes
@@ -184,7 +225,45 @@ NPY_FINLINE npyv_s64 npyv_load_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
     const __m256i steps = npyv_set_s64(0, 1, 2, 3);
     __m256i vnlane  = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane);
     __m256i mask    = _mm256_cmpgt_epi64(vnlane, steps);
-    return _mm256_maskload_epi64((const void*)ptr, mask);
+    return _mm256_maskload_epi64((const long long*)ptr, mask);
+}
+
+//// 64-bit nlane
+NPY_FINLINE npyv_s32 npyv_load2_till_s32(const npy_int32 *ptr, npy_uintp nlane,
+                                          npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    const __m256i vfill = npyv_set_s32(
+        fill_lo, fill_hi, fill_lo, fill_hi,
+        fill_lo, fill_hi, fill_lo, fill_hi
+    );
+    const __m256i steps = npyv_set_s64(0, 1, 2, 3);
+    __m256i vnlane  = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane);
+    __m256i mask    = _mm256_cmpgt_epi64(vnlane, steps);
+    __m256i payload = _mm256_maskload_epi64((const long long*)ptr, mask);
+    return _mm256_blendv_epi8(vfill, payload, mask);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s32 npyv_load2_tillz_s32(const npy_int32 *ptr, npy_uintp nlane)
+{ return npyv_load_tillz_s64((const npy_int64*)ptr, nlane); }
+
+/// 128-bit nlane
+NPY_FINLINE npyv_u64 npyv_load2_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
+{
+    assert(nlane > 0);
+    npy_int64 m = -((npy_int64)(nlane > 1));
+    __m256i mask = npyv_set_s64(-1, -1, m, m);
+    return _mm256_maskload_epi64((const long long*)ptr, mask);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_u64 npyv_load2_till_s64(const npy_int64 *ptr, npy_uintp nlane,
+                                           npy_int64 fill_lo, npy_int64 fill_hi)
+{
+    const __m256i vfill = npyv_set_s64(0, 0, fill_lo, fill_hi);
+    npy_int64 m = -((npy_int64)(nlane > 1));
+    __m256i mask = npyv_set_s64(-1, -1, m, m);
+    __m256i payload = _mm256_maskload_epi64((const long long*)ptr, mask);
+    return _mm256_blendv_epi8(vfill, payload, mask);
 }
 /*********************************
  * Non-contiguous partial load
@@ -216,12 +295,53 @@ npyv_loadn_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npy_
     const __m256i steps = npyv_set_s64(0, 1, 2, 3);
     __m256i vnlane  = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane);
     __m256i mask    = _mm256_cmpgt_epi64(vnlane, steps);
-    return _mm256_mask_i64gather_epi64(vfill, (const void*)ptr, idx, mask, 8);
+    return _mm256_mask_i64gather_epi64(vfill, (const long long*)ptr, idx, mask, 8);
 }
 // fill zero to rest lanes
 NPY_FINLINE npyv_s64
 npyv_loadn_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
 { return npyv_loadn_till_s64(ptr, stride, nlane, 0); }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_s64 npyv_loadn2_till_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane,
+                                                 npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    const __m256i vfill = npyv_set_s32(
+        fill_lo, fill_hi, fill_lo, fill_hi,
+        fill_lo, fill_hi, fill_lo, fill_hi
+    );
+    const __m256i idx   = npyv_set_s64(0, 1*stride, 2*stride, 3*stride);
+    const __m256i steps = npyv_set_s64(0, 1, 2, 3);
+    __m256i vnlane  = npyv_setall_s64(nlane > 4 ? 4 : (int)nlane);
+    __m256i mask    = _mm256_cmpgt_epi64(vnlane, steps);
+    return _mm256_mask_i64gather_epi64(vfill, (const long long*)ptr, idx, mask, 4);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s32 npyv_loadn2_tillz_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane)
+{ return npyv_loadn2_till_s32(ptr, stride, nlane, 0, 0); }
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_s64 npyv_loadn2_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane,
+                                                  npy_int64 fill_lo, npy_int64 fill_hi)
+{
+    assert(nlane > 0);
+    __m256i a = npyv_loadl_s64(ptr);
+#if defined(_MSC_VER) && defined(_M_IX86)
+    __m128i fill =_mm_setr_epi32(
+        (int)fill_lo, (int)(fill_lo >> 32),
+        (int)fill_hi, (int)(fill_hi >> 32)
+    );
+#else
+    __m128i fill = _mm_set_epi64x(fill_hi, fill_lo);
+#endif
+    __m128i b = nlane > 1 ? _mm_loadu_si128((const __m128i*)(ptr + stride)) : fill;
+    return _mm256_inserti128_si256(a, b, 1);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s64 npyv_loadn2_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
+{ return npyv_loadn2_till_s64(ptr, stride, nlane, 0, 0); }
+
 /*********************************
  * Partial store
  *********************************/
@@ -241,7 +361,40 @@ NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a
     const __m256i steps = npyv_set_s64(0, 1, 2, 3);
     __m256i vnlane = npyv_setall_s64(nlane > 8 ? 8 : (int)nlane);
     __m256i mask   = _mm256_cmpgt_epi64(vnlane, steps);
-    _mm256_maskstore_epi64((void*)ptr, mask, a);
+    _mm256_maskstore_epi64((long long*)ptr, mask, a);
+}
+
+//// 64-bit nlane
+NPY_FINLINE void npyv_store2_till_s32(npy_int32 *ptr, npy_uintp nlane, npyv_s32 a)
+{ npyv_store_till_s64((npy_int64*)ptr, nlane, a); }
+
+//// 128-bit nlane
+NPY_FINLINE void npyv_store2_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a)
+{
+    assert(nlane > 0);
+#ifdef _MSC_VER
+   /*
+    * Although this version is compatible with all other compilers,
+    * there is no performance benefit in retaining the other branch.
+    * However, it serves as evidence of a newly emerging bug in MSVC
+    * that started to appear since v19.30.
+    * For some reason, the MSVC optimizer chooses to ignore the lower store (128-bit mov)
+    * and replace with full mov counting on ymmword pointer.
+    *
+    * For more details, please refer to the discussion on https://github.com/numpy/numpy/issues/23896.
+    */
+    if (nlane > 1) {
+        npyv_store_s64(ptr, a);
+    }
+    else {
+        npyv_storel_s64(ptr, a);
+    }
+#else
+    npyv_storel_s64(ptr, a);
+    if (nlane > 1) {
+        npyv_storeh_s64(ptr + 2, a);
+    }
+#endif
 }
 /*********************************
  * Non-contiguous partial store
@@ -252,23 +405,52 @@ NPY_FINLINE void npyv_storen_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp
     assert(nlane > 0);
     __m128i a0 = _mm256_castsi256_si128(a);
     __m128i a1 = _mm256_extracti128_si256(a, 1);
+
+    ptr[stride*0] = _mm_extract_epi32(a0, 0);
     switch(nlane) {
-    default:
-        ptr[stride*7] = _mm_extract_epi32(a1, 3);
-    case 7:
-        ptr[stride*6] = _mm_extract_epi32(a1, 2);
-    case 6:
-        ptr[stride*5] = _mm_extract_epi32(a1, 1);
+    case 1:
+        return;
+    case 2:
+        ptr[stride*1] = _mm_extract_epi32(a0, 1);
+        return;
+    case 3:
+        ptr[stride*1] = _mm_extract_epi32(a0, 1);
+        ptr[stride*2] = _mm_extract_epi32(a0, 2);
+        return;
+    case 4:
+        ptr[stride*1] = _mm_extract_epi32(a0, 1);
+        ptr[stride*2] = _mm_extract_epi32(a0, 2);
+        ptr[stride*3] = _mm_extract_epi32(a0, 3);
+        return;
     case 5:
+        ptr[stride*1] = _mm_extract_epi32(a0, 1);
+        ptr[stride*2] = _mm_extract_epi32(a0, 2);
+        ptr[stride*3] = _mm_extract_epi32(a0, 3);
         ptr[stride*4] = _mm_extract_epi32(a1, 0);
-    case 4:
+        return;
+    case 6:
+        ptr[stride*1] = _mm_extract_epi32(a0, 1);
+        ptr[stride*2] = _mm_extract_epi32(a0, 2);
         ptr[stride*3] = _mm_extract_epi32(a0, 3);
-    case 3:
+        ptr[stride*4] = _mm_extract_epi32(a1, 0);
+        ptr[stride*5] = _mm_extract_epi32(a1, 1);
+        return;
+    case 7:
+        ptr[stride*1] = _mm_extract_epi32(a0, 1);
         ptr[stride*2] = _mm_extract_epi32(a0, 2);
-    case 2:
+        ptr[stride*3] = _mm_extract_epi32(a0, 3);
+        ptr[stride*4] = _mm_extract_epi32(a1, 0);
+        ptr[stride*5] = _mm_extract_epi32(a1, 1);
+        ptr[stride*6] = _mm_extract_epi32(a1, 2);
+        return;
+    default:
         ptr[stride*1] = _mm_extract_epi32(a0, 1);
-    case 1:
-        ptr[stride*0] = _mm_extract_epi32(a0, 0);
+        ptr[stride*2] = _mm_extract_epi32(a0, 2);
+        ptr[stride*3] = _mm_extract_epi32(a0, 3);
+        ptr[stride*4] = _mm_extract_epi32(a1, 0);
+        ptr[stride*5] = _mm_extract_epi32(a1, 1);
+        ptr[stride*6] = _mm_extract_epi32(a1, 2);
+        ptr[stride*7] = _mm_extract_epi32(a1, 3);
     }
 }
 //// 64
@@ -277,19 +459,60 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
     assert(nlane > 0);
     __m128d a0 = _mm256_castpd256_pd128(_mm256_castsi256_pd(a));
     __m128d a1 = _mm256_extractf128_pd(_mm256_castsi256_pd(a), 1);
+
     double *dptr = (double*)ptr;
+    _mm_storel_pd(dptr, a0);
     switch(nlane) {
-    default:
-        _mm_storeh_pd(dptr + stride * 3, a1);
+    case 1:
+        return;
+    case 2:
+        _mm_storeh_pd(dptr + stride * 1, a0);
+        return;
     case 3:
+        _mm_storeh_pd(dptr + stride * 1, a0);
         _mm_storel_pd(dptr + stride * 2, a1);
-    case 2:
+        return;
+    default:
         _mm_storeh_pd(dptr + stride * 1, a0);
+        _mm_storel_pd(dptr + stride * 2, a1);
+        _mm_storeh_pd(dptr + stride * 3, a1);
+    }
+}
+
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
+{
+    assert(nlane > 0);
+    __m128d a0 = _mm256_castpd256_pd128(_mm256_castsi256_pd(a));
+    __m128d a1 = _mm256_extractf128_pd(_mm256_castsi256_pd(a), 1);
+
+    _mm_storel_pd((double*)ptr, a0);
+    switch(nlane) {
     case 1:
-        _mm_storel_pd(dptr + stride * 0, a0);
+        return;
+    case 2:
+        _mm_storeh_pd((double*)(ptr + stride * 1), a0);
+        return;
+    case 3:
+        _mm_storeh_pd((double*)(ptr + stride * 1), a0);
+        _mm_storel_pd((double*)(ptr + stride * 2), a1);
+        return;
+    default:
+        _mm_storeh_pd((double*)(ptr + stride * 1), a0);
+        _mm_storel_pd((double*)(ptr + stride * 2), a1);
+        _mm_storeh_pd((double*)(ptr + stride * 3), a1);
     }
 }
 
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npyv_s64 a)
+{
+    assert(nlane > 0);
+    npyv_storel_s64(ptr, a);
+    if (nlane > 1) {
+        npyv_storeh_s64(ptr + stride, a);
+    }
+}
 /*****************************************************************************
  * Implement partial load/store for u32/f32/u64/f64... via reinterpret cast
  *****************************************************************************/
@@ -300,7 +523,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load_till_##T_SFX(                   \
             (const npyv_lanetype_##T_SFX *)ptr, nlane, pun.to_##T_SFX                       \
         ));                                                                                 \
@@ -312,7 +536,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn_till_##T_SFX(                  \
             (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun.to_##T_SFX               \
         ));                                                                                 \
@@ -353,6 +578,110 @@ NPYV_IMPL_AVX2_REST_PARTIAL_TYPES(f32, s32)
 NPYV_IMPL_AVX2_REST_PARTIAL_TYPES(u64, s64)
 NPYV_IMPL_AVX2_REST_PARTIAL_TYPES(f64, s64)
 
+// 128-bit/64-bit stride (load/store pair)
+#define NPYV_IMPL_AVX2_REST_PARTIAL_TYPES_PAIR(F_SFX, T_SFX)                                \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_till_##F_SFX                                        \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane,                                     \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_till_##T_SFX(                  \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane, pun_lo.to_##T_SFX, pun_hi.to_##T_SFX \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_till_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane,                    \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_till_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun_lo.to_##T_SFX,           \
+            pun_hi.to_##T_SFX                                                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_tillz_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane)                                     \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_tillz_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane                                       \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_tillz_##F_SFX                                      \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane)                    \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_tillz_##T_SFX(                \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE void npyv_store2_till_##F_SFX                                               \
+    (npyv_lanetype_##F_SFX *ptr, npy_uintp nlane, npyv_##F_SFX a)                           \
+    {                                                                                       \
+        npyv_store2_till_##T_SFX(                                                           \
+            (npyv_lanetype_##T_SFX *)ptr, nlane,                                            \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }                                                                                       \
+    NPY_FINLINE void npyv_storen2_till_##F_SFX                                              \
+    (npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane, npyv_##F_SFX a)          \
+    {                                                                                       \
+        npyv_storen2_till_##T_SFX(                                                          \
+            (npyv_lanetype_##T_SFX *)ptr, stride, nlane,                                    \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }
+
+NPYV_IMPL_AVX2_REST_PARTIAL_TYPES_PAIR(u32, s32)
+NPYV_IMPL_AVX2_REST_PARTIAL_TYPES_PAIR(f32, s32)
+NPYV_IMPL_AVX2_REST_PARTIAL_TYPES_PAIR(u64, s64)
+NPYV_IMPL_AVX2_REST_PARTIAL_TYPES_PAIR(f64, s64)
+
+/************************************************************
+ *  de-interlave load / interleave contiguous store
+ ************************************************************/
+// two channels
+#define NPYV_IMPL_AVX2_MEM_INTERLEAVE(SFX, ZSFX)                             \
+    NPY_FINLINE npyv_##ZSFX##x2 npyv_zip_##ZSFX(npyv_##ZSFX, npyv_##ZSFX);   \
+    NPY_FINLINE npyv_##ZSFX##x2 npyv_unzip_##ZSFX(npyv_##ZSFX, npyv_##ZSFX); \
+    NPY_FINLINE npyv_##SFX##x2 npyv_load_##SFX##x2(                          \
+        const npyv_lanetype_##SFX *ptr                                       \
+    ) {                                                                      \
+        return npyv_unzip_##ZSFX(                                            \
+            npyv_load_##SFX(ptr), npyv_load_##SFX(ptr+npyv_nlanes_##SFX)     \
+        );                                                                   \
+    }                                                                        \
+    NPY_FINLINE void npyv_store_##SFX##x2(                                   \
+        npyv_lanetype_##SFX *ptr, npyv_##SFX##x2 v                           \
+    ) {                                                                      \
+        npyv_##SFX##x2 zip = npyv_zip_##ZSFX(v.val[0], v.val[1]);            \
+        npyv_store_##SFX(ptr, zip.val[0]);                                   \
+        npyv_store_##SFX(ptr + npyv_nlanes_##SFX, zip.val[1]);               \
+    }
+
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(u8, u8)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(s8, u8)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(u16, u16)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(s16, u16)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(u32, u32)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(s32, u32)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(u64, u64)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(s64, u64)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(f32, f32)
+NPYV_IMPL_AVX2_MEM_INTERLEAVE(f64, f64)
+
 /*********************************
  * Lookup tables
  *********************************/
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/operators.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/operators.h
index 86e0038d995..7b9b6a34413 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/operators.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/operators.h
@@ -205,7 +205,7 @@ NPY_FINLINE __m256i npyv_cmpge_u32(__m256i a, __m256i b)
 #define npyv_cmple_u64(A, B) npyv_cmpge_u64(B, A)
 #define npyv_cmple_s64(A, B) npyv_cmpge_s64(B, A)
 
-// precision comparison (orderd)
+// precision comparison (ordered)
 #define npyv_cmpeq_f32(A, B)  _mm256_castps_si256(_mm256_cmp_ps(A, B, _CMP_EQ_OQ))
 #define npyv_cmpeq_f64(A, B)  _mm256_castpd_si256(_mm256_cmp_pd(A, B, _CMP_EQ_OQ))
 #define npyv_cmpneq_f32(A, B) _mm256_castps_si256(_mm256_cmp_ps(A, B, _CMP_NEQ_UQ))
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/reorder.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/reorder.h
index 4d6ec8f759b..9ebe0e7f498 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/reorder.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx2/reorder.h
@@ -94,6 +94,75 @@ NPY_FINLINE npyv_f64x2 npyv_zip_f64(__m256d a, __m256d b)
     return npyv_combine_f64(ab0, ab1);
 }
 
+// deinterleave two vectors
+NPY_FINLINE npyv_u8x2 npyv_unzip_u8(npyv_u8 ab0, npyv_u8 ab1)
+{
+    const __m256i idx = _mm256_setr_epi8(
+        0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15,
+        0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15
+    );
+    __m256i ab_03 = _mm256_shuffle_epi8(ab0, idx);
+    __m256i ab_12 = _mm256_shuffle_epi8(ab1, idx);
+    npyv_u8x2 ab_lh = npyv_combine_u8(ab_03, ab_12);
+    npyv_u8x2 r;
+    r.val[0] = _mm256_unpacklo_epi64(ab_lh.val[0], ab_lh.val[1]);
+    r.val[1] = _mm256_unpackhi_epi64(ab_lh.val[0], ab_lh.val[1]);
+    return r;
+}
+#define npyv_unzip_s8 npyv_unzip_u8
+
+NPY_FINLINE npyv_u16x2 npyv_unzip_u16(npyv_u16 ab0, npyv_u16 ab1)
+{
+    const __m256i idx = _mm256_setr_epi8(
+        0,1, 4,5, 8,9, 12,13, 2,3, 6,7, 10,11, 14,15,
+        0,1, 4,5, 8,9, 12,13, 2,3, 6,7, 10,11, 14,15
+    );
+    __m256i ab_03 = _mm256_shuffle_epi8(ab0, idx);
+    __m256i ab_12 = _mm256_shuffle_epi8(ab1, idx);
+    npyv_u16x2 ab_lh = npyv_combine_u16(ab_03, ab_12);
+    npyv_u16x2 r;
+    r.val[0] = _mm256_unpacklo_epi64(ab_lh.val[0], ab_lh.val[1]);
+    r.val[1] = _mm256_unpackhi_epi64(ab_lh.val[0], ab_lh.val[1]);
+    return r;
+}
+#define npyv_unzip_s16 npyv_unzip_u16
+
+NPY_FINLINE npyv_u32x2 npyv_unzip_u32(npyv_u32 ab0, npyv_u32 ab1)
+{
+    const __m256i idx = npyv_set_u32(0, 2, 4, 6, 1, 3, 5, 7);
+    __m256i abl = _mm256_permutevar8x32_epi32(ab0, idx);
+    __m256i abh = _mm256_permutevar8x32_epi32(ab1, idx);
+    return npyv_combine_u32(abl, abh);
+}
+#define npyv_unzip_s32 npyv_unzip_u32
+
+NPY_FINLINE npyv_u64x2 npyv_unzip_u64(npyv_u64 ab0, npyv_u64 ab1)
+{
+    npyv_u64x2 ab_lh = npyv_combine_u64(ab0, ab1);
+    npyv_u64x2 r;
+    r.val[0] = _mm256_unpacklo_epi64(ab_lh.val[0], ab_lh.val[1]);
+    r.val[1] = _mm256_unpackhi_epi64(ab_lh.val[0], ab_lh.val[1]);
+    return r;
+}
+#define npyv_unzip_s64 npyv_unzip_u64
+
+NPY_FINLINE npyv_f32x2 npyv_unzip_f32(npyv_f32 ab0, npyv_f32 ab1)
+{
+    const __m256i idx = npyv_set_u32(0, 2, 4, 6, 1, 3, 5, 7);
+    __m256 abl = _mm256_permutevar8x32_ps(ab0, idx);
+    __m256 abh = _mm256_permutevar8x32_ps(ab1, idx);
+    return npyv_combine_f32(abl, abh);
+}
+
+NPY_FINLINE npyv_f64x2 npyv_unzip_f64(npyv_f64 ab0, npyv_f64 ab1)
+{
+    npyv_f64x2 ab_lh = npyv_combine_f64(ab0, ab1);
+    npyv_f64x2 r;
+    r.val[0] = _mm256_unpacklo_pd(ab_lh.val[0], ab_lh.val[1]);
+    r.val[1] = _mm256_unpackhi_pd(ab_lh.val[0], ab_lh.val[1]);
+    return r;
+}
+
 // Reverse elements of each 64-bit lane
 NPY_FINLINE npyv_u8 npyv_rev64_u8(npyv_u8 a)
 {
@@ -126,4 +195,22 @@ NPY_FINLINE npyv_f32 npyv_rev64_f32(npyv_f32 a)
     return _mm256_shuffle_ps(a, a, _MM_SHUFFLE(2, 3, 0, 1));
 }
 
+// Permuting the elements of each 128-bit lane by immediate index for
+// each element.
+#define npyv_permi128_u32(A, E0, E1, E2, E3) \
+    _mm256_shuffle_epi32(A, _MM_SHUFFLE(E3, E2, E1, E0))
+
+#define npyv_permi128_s32 npyv_permi128_u32
+
+#define npyv_permi128_u64(A, E0, E1) \
+    _mm256_shuffle_epi32(A, _MM_SHUFFLE(((E1)<<1)+1, ((E1)<<1), ((E0)<<1)+1, ((E0)<<1)))
+
+#define npyv_permi128_s64 npyv_permi128_u64
+
+#define npyv_permi128_f32(A, E0, E1, E2, E3) \
+    _mm256_permute_ps(A, _MM_SHUFFLE(E3, E2, E1, E0))
+
+#define npyv_permi128_f64(A, E0, E1) \
+    _mm256_permute_pd(A, ((E1)<<3) | ((E0)<<2) | ((E1)<<1) | (E0))
+
 #endif // _NPY_SIMD_AVX2_REORDER_H
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/arithmetic.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/arithmetic.h
index 1290dc0ade2..a63da87d0c4 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/arithmetic.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/arithmetic.h
@@ -349,6 +349,10 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
 // negate multiply and subtract, -(a*b) - c
 #define npyv_nmulsub_f32 _mm512_fnmsub_ps
 #define npyv_nmulsub_f64 _mm512_fnmsub_pd
+// multiply, add for odd elements and subtract even elements.
+// (a * b) -+ c
+#define npyv_muladdsub_f32 _mm512_fmaddsub_ps
+#define npyv_muladdsub_f64 _mm512_fmaddsub_pd
 
 /***************************
  * Summation: Calculates the sum of all vector elements.
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/maskop.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/maskop.h
index d1c188390a1..88fa4a68c38 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/maskop.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/maskop.h
@@ -51,4 +51,17 @@ NPYV_IMPL_AVX512_MASK_ADDSUB(s64, b64, epi64)
 NPYV_IMPL_AVX512_MASK_ADDSUB(f32, b32, ps)
 NPYV_IMPL_AVX512_MASK_ADDSUB(f64, b64, pd)
 
+// division, m ? a / b : c
+NPY_FINLINE npyv_f32 npyv_ifdiv_f32(npyv_b32 m, npyv_f32 a, npyv_f32 b, npyv_f32 c)
+{ return _mm512_mask_div_ps(c, m, a, b); }
+// conditional division, m ? a / b : 0
+NPY_FINLINE npyv_f32 npyv_ifdivz_f32(npyv_b32 m, npyv_f32 a, npyv_f32 b)
+{ return _mm512_maskz_div_ps(m, a, b); }
+// division, m ? a / b : c
+NPY_FINLINE npyv_f64 npyv_ifdiv_f64(npyv_b32 m, npyv_f64 a, npyv_f64 b, npyv_f64 c)
+{ return _mm512_mask_div_pd(c, m, a, b); }
+// conditional division, m ? a / b : 0
+NPY_FINLINE npyv_f64 npyv_ifdivz_f64(npyv_b32 m, npyv_f64 a, npyv_f64 b)
+{ return _mm512_maskz_div_pd(m, a, b); }
+
 #endif // _NPY_SIMD_AVX512_MASKOP_H
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/memory.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/memory.h
index 03fcb4630cd..fdf96a92c58 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/memory.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/memory.h
@@ -120,6 +120,52 @@ NPY_FINLINE npyv_s64 npyv_loadn_s64(const npy_int64 *ptr, npy_intp stride)
 { return npyv_loadn_u64((const npy_uint64*)ptr, stride); }
 NPY_FINLINE npyv_f64 npyv_loadn_f64(const double *ptr, npy_intp stride)
 { return _mm512_castsi512_pd(npyv_loadn_u64((const npy_uint64*)ptr, stride)); }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_u32 npyv_loadn2_u32(const npy_uint32 *ptr, npy_intp stride)
+{
+    __m128d a = _mm_loadh_pd(
+        _mm_castsi128_pd(_mm_loadl_epi64((const __m128i*)ptr)),
+        (const double*)(ptr + stride)
+    );
+    __m128d b = _mm_loadh_pd(
+        _mm_castsi128_pd(_mm_loadl_epi64((const __m128i*)(ptr + stride*2))),
+        (const double*)(ptr + stride*3)
+    );
+    __m128d c = _mm_loadh_pd(
+        _mm_castsi128_pd(_mm_loadl_epi64((const __m128i*)(ptr + stride*4))),
+        (const double*)(ptr + stride*5)
+    );
+    __m128d d = _mm_loadh_pd(
+        _mm_castsi128_pd(_mm_loadl_epi64((const __m128i*)(ptr + stride*6))),
+        (const double*)(ptr + stride*7)
+    );
+    return _mm512_castpd_si512(npyv512_combine_pd256(
+        _mm256_insertf128_pd(_mm256_castpd128_pd256(a), b, 1),
+        _mm256_insertf128_pd(_mm256_castpd128_pd256(c), d, 1)
+    ));
+}
+NPY_FINLINE npyv_s32 npyv_loadn2_s32(const npy_int32 *ptr, npy_intp stride)
+{ return npyv_loadn2_u32((const npy_uint32*)ptr, stride); }
+NPY_FINLINE npyv_f32 npyv_loadn2_f32(const float *ptr, npy_intp stride)
+{ return _mm512_castsi512_ps(npyv_loadn2_u32((const npy_uint32*)ptr, stride)); }
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_f64 npyv_loadn2_f64(const double *ptr, npy_intp stride)
+{
+    __m128d a = _mm_loadu_pd(ptr);
+    __m128d b = _mm_loadu_pd(ptr + stride);
+    __m128d c = _mm_loadu_pd(ptr + stride * 2);
+    __m128d d = _mm_loadu_pd(ptr + stride * 3);
+    return npyv512_combine_pd256(
+        _mm256_insertf128_pd(_mm256_castpd128_pd256(a), b, 1),
+        _mm256_insertf128_pd(_mm256_castpd128_pd256(c), d, 1)
+    );
+}
+NPY_FINLINE npyv_u64 npyv_loadn2_u64(const npy_uint64 *ptr, npy_intp stride)
+{ return npyv_reinterpret_u64_f64(npyv_loadn2_f64((const double*)ptr, stride)); }
+NPY_FINLINE npyv_s64 npyv_loadn2_s64(const npy_int64 *ptr, npy_intp stride)
+{ return npyv_loadn2_u64((const npy_uint64*)ptr, stride); }
 /***************************
  * Non-contiguous Store
  ***************************/
@@ -151,6 +197,48 @@ NPY_FINLINE void npyv_storen_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
 NPY_FINLINE void npyv_storen_f64(double *ptr, npy_intp stride, npyv_f64 a)
 { npyv_storen_u64((npy_uint64*)ptr, stride, _mm512_castpd_si512(a)); }
 
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_u32(npy_uint32 *ptr, npy_intp stride, npyv_u32 a)
+{
+    __m256d lo = _mm512_castpd512_pd256(_mm512_castsi512_pd(a));
+    __m256d hi = _mm512_extractf64x4_pd(_mm512_castsi512_pd(a), 1);
+    __m128d e0 = _mm256_castpd256_pd128(lo);
+    __m128d e1 = _mm256_extractf128_pd(lo, 1);
+    __m128d e2 = _mm256_castpd256_pd128(hi);
+    __m128d e3 = _mm256_extractf128_pd(hi, 1);
+    _mm_storel_pd((double*)(ptr + stride * 0), e0);
+    _mm_storeh_pd((double*)(ptr + stride * 1), e0);
+    _mm_storel_pd((double*)(ptr + stride * 2), e1);
+    _mm_storeh_pd((double*)(ptr + stride * 3), e1);
+    _mm_storel_pd((double*)(ptr + stride * 4), e2);
+    _mm_storeh_pd((double*)(ptr + stride * 5), e2);
+    _mm_storel_pd((double*)(ptr + stride * 6), e3);
+    _mm_storeh_pd((double*)(ptr + stride * 7), e3);
+}
+NPY_FINLINE void npyv_storen2_s32(npy_int32 *ptr, npy_intp stride, npyv_s32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, a); }
+NPY_FINLINE void npyv_storen2_f32(float *ptr, npy_intp stride, npyv_f32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, _mm512_castps_si512(a)); }
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a)
+{
+    __m256i lo = npyv512_lower_si256(a);
+    __m256i hi = npyv512_higher_si256(a);
+    __m128i e0 = _mm256_castsi256_si128(lo);
+    __m128i e1 = _mm256_extracti128_si256(lo, 1);
+    __m128i e2 = _mm256_castsi256_si128(hi);
+    __m128i e3 = _mm256_extracti128_si256(hi, 1);
+    _mm_storeu_si128((__m128i*)(ptr + stride * 0), e0);
+    _mm_storeu_si128((__m128i*)(ptr + stride * 1), e1);
+    _mm_storeu_si128((__m128i*)(ptr + stride * 2), e2);
+    _mm_storeu_si128((__m128i*)(ptr + stride * 3), e3);
+}
+NPY_FINLINE void npyv_storen2_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
+{ npyv_storen2_u64((npy_uint64*)ptr, stride, a); }
+NPY_FINLINE void npyv_storen2_f64(double *ptr, npy_intp stride, npyv_f64 a)
+{ npyv_storen2_u64((npy_uint64*)ptr, stride, _mm512_castpd_si512(a)); }
+
 /*********************************
  * Partial Load
  *********************************/
@@ -159,14 +247,14 @@ NPY_FINLINE npyv_s32 npyv_load_till_s32(const npy_int32 *ptr, npy_uintp nlane, n
 {
     assert(nlane > 0);
     const __m512i vfill = _mm512_set1_epi32(fill);
-    const __mmask16 mask = nlane > 31 ? -1 : (1 << nlane) - 1;
+    const __mmask16 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
     return _mm512_mask_loadu_epi32(vfill, mask, (const __m512i*)ptr);
 }
 // fill zero to rest lanes
 NPY_FINLINE npyv_s32 npyv_load_tillz_s32(const npy_int32 *ptr, npy_uintp nlane)
 {
     assert(nlane > 0);
-    const __mmask16 mask = nlane > 31 ? -1 : (1 << nlane) - 1;
+    const __mmask16 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
     return _mm512_maskz_loadu_epi32(mask, (const __m512i*)ptr);
 }
 //// 64
@@ -174,14 +262,44 @@ NPY_FINLINE npyv_s64 npyv_load_till_s64(const npy_int64 *ptr, npy_uintp nlane, n
 {
     assert(nlane > 0);
     const __m512i vfill = npyv_setall_s64(fill);
-    const __mmask8 mask = nlane > 31 ? -1 : (1 << nlane) - 1;
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
     return _mm512_mask_loadu_epi64(vfill, mask, (const __m512i*)ptr);
 }
 // fill zero to rest lanes
 NPY_FINLINE npyv_s64 npyv_load_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
 {
     assert(nlane > 0);
-    const __mmask8 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
+    return _mm512_maskz_loadu_epi64(mask, (const __m512i*)ptr);
+}
+
+//// 64-bit nlane
+NPY_FINLINE npyv_s32 npyv_load2_till_s32(const npy_int32 *ptr, npy_uintp nlane,
+                                          npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    const __m512i vfill = _mm512_set4_epi32(fill_hi, fill_lo, fill_hi, fill_lo);
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
+    return _mm512_mask_loadu_epi64(vfill, mask, (const __m512i*)ptr);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s32 npyv_load2_tillz_s32(const npy_int32 *ptr, npy_uintp nlane)
+{ return npyv_load_tillz_s64((const npy_int64*)ptr, nlane); }
+
+//// 128-bit nlane
+NPY_FINLINE npyv_u64 npyv_load2_till_s64(const npy_int64 *ptr, npy_uintp nlane,
+                                           npy_int64 fill_lo, npy_int64 fill_hi)
+{
+    assert(nlane > 0);
+    const __m512i vfill = _mm512_set4_epi64(fill_hi, fill_lo, fill_hi, fill_lo);
+    const __mmask8 mask = nlane > 3 ? -1 : (1 << (nlane*2)) - 1;
+    return _mm512_mask_loadu_epi64(vfill, mask, (const __m512i*)ptr);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s64 npyv_load2_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
+{
+    assert(nlane > 0);
+    const __mmask8 mask = nlane > 3 ? -1 : (1 << (nlane*2)) - 1;
     return _mm512_maskz_loadu_epi64(mask, (const __m512i*)ptr);
 }
 /*********************************
@@ -198,7 +316,7 @@ npyv_loadn_till_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npy_
     );
     const __m512i idx = _mm512_mullo_epi32(steps, _mm512_set1_epi32((int)stride));
     const __m512i vfill = _mm512_set1_epi32(fill);
-    const __mmask16 mask = nlane > 31 ? -1 : (1 << nlane) - 1;
+    const __mmask16 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
     return _mm512_mask_i32gather_epi32(vfill, mask, idx, (const __m512i*)ptr, 4);
 }
 // fill zero to rest lanes
@@ -215,13 +333,48 @@ npyv_loadn_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npy_
         4*stride, 5*stride, 6*stride, 7*stride
     );
     const __m512i vfill = npyv_setall_s64(fill);
-    const __mmask8 mask = nlane > 31 ? -1 : (1 << nlane) - 1;
+    const __mmask8 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
     return _mm512_mask_i64gather_epi64(vfill, mask, idx, (const __m512i*)ptr, 8);
 }
 // fill zero to rest lanes
 NPY_FINLINE npyv_s64
 npyv_loadn_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
 { return npyv_loadn_till_s64(ptr, stride, nlane, 0); }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_s64 npyv_loadn2_till_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane,
+                                                 npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    const __m512i idx = npyv_set_s64(
+        0*stride, 1*stride, 2*stride, 3*stride,
+        4*stride, 5*stride, 6*stride, 7*stride
+    );
+    const __m512i vfill = _mm512_set4_epi32(fill_hi, fill_lo, fill_hi, fill_lo);
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
+    return _mm512_mask_i64gather_epi64(vfill, mask, idx, (const __m512i*)ptr, 4);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s32 npyv_loadn2_tillz_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane)
+{ return npyv_loadn2_till_s32(ptr, stride, nlane, 0, 0); }
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_s64 npyv_loadn2_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane,
+                                                  npy_int64 fill_lo, npy_int64 fill_hi)
+{
+    assert(nlane > 0);
+    const __m512i idx = npyv_set_s64(
+       0,        1,          stride,   stride+1,
+       stride*2, stride*2+1, stride*3, stride*3+1
+    );
+    const __mmask8 mask = nlane > 3 ? -1 : (1 << (nlane*2)) - 1;
+    const __m512i vfill = _mm512_set4_epi64(fill_hi, fill_lo, fill_hi, fill_lo);
+    return _mm512_mask_i64gather_epi64(vfill, mask, idx, (const __m512i*)ptr, 8);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s64 npyv_loadn2_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
+{ return npyv_loadn2_till_s64(ptr, stride, nlane, 0, 0); }
+
 /*********************************
  * Partial store
  *********************************/
@@ -229,14 +382,30 @@ npyv_loadn_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
 NPY_FINLINE void npyv_store_till_s32(npy_int32 *ptr, npy_uintp nlane, npyv_s32 a)
 {
     assert(nlane > 0);
-    const __mmask16 mask = nlane > 31 ? -1 : (1 << nlane) - 1;
+    const __mmask16 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
     _mm512_mask_storeu_epi32((__m512i*)ptr, mask, a);
 }
 //// 64
 NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a)
 {
     assert(nlane > 0);
-    const __mmask8 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
+    _mm512_mask_storeu_epi64((__m512i*)ptr, mask, a);
+}
+
+//// 64-bit nlane
+NPY_FINLINE void npyv_store2_till_s32(npy_int32 *ptr, npy_uintp nlane, npyv_s32 a)
+{
+    assert(nlane > 0);
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
+    _mm512_mask_storeu_epi64((__m512i*)ptr, mask, a);
+}
+
+//// 128-bit nlane
+NPY_FINLINE void npyv_store2_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a)
+{
+    assert(nlane > 0);
+    const __mmask8 mask = nlane > 3 ? -1 : (1 << (nlane*2)) - 1;
     _mm512_mask_storeu_epi64((__m512i*)ptr, mask, a);
 }
 /*********************************
@@ -251,7 +420,7 @@ NPY_FINLINE void npyv_storen_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp
         0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
     );
     const __m512i idx = _mm512_mullo_epi32(steps, _mm512_set1_epi32((int)stride));
-    const __mmask16 mask = nlane > 31 ? -1 : (1 << nlane) - 1;
+    const __mmask16 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
     _mm512_mask_i32scatter_epi32((__m512i*)ptr, mask, idx, a, 4);
 }
 //// 64
@@ -262,7 +431,31 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         0*stride, 1*stride, 2*stride, 3*stride,
         4*stride, 5*stride, 6*stride, 7*stride
     );
-    const __mmask8 mask = nlane > 15 ? -1 : (1 << nlane) - 1;
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
+    _mm512_mask_i64scatter_epi64((__m512i*)ptr, mask, idx, a, 8);
+}
+
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
+{
+    assert(nlane > 0);
+    const __m512i idx = npyv_set_s64(
+        0*stride, 1*stride, 2*stride, 3*stride,
+        4*stride, 5*stride, 6*stride, 7*stride
+    );
+    const __mmask8 mask = nlane > 7 ? -1 : (1 << nlane) - 1;
+    _mm512_mask_i64scatter_epi64((__m512i*)ptr, mask, idx, a, 4);
+}
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npyv_s64 a)
+{
+    assert(nlane > 0);
+    const __m512i idx = npyv_set_s64(
+        0,        1,            stride,   stride+1,
+        2*stride, 2*stride+1, 3*stride, 3*stride+1
+    );
+    const __mmask8 mask = nlane > 3 ? -1 : (1 << (nlane*2)) - 1;
     _mm512_mask_i64scatter_epi64((__m512i*)ptr, mask, idx, a, 8);
 }
 
@@ -331,6 +524,110 @@ NPYV_IMPL_AVX512_REST_PARTIAL_TYPES(f32, s32)
 NPYV_IMPL_AVX512_REST_PARTIAL_TYPES(u64, s64)
 NPYV_IMPL_AVX512_REST_PARTIAL_TYPES(f64, s64)
 
+// 128-bit/64-bit stride (pair load/store)
+#define NPYV_IMPL_AVX512_REST_PARTIAL_TYPES_PAIR(F_SFX, T_SFX)                              \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_till_##F_SFX                                        \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane,                                     \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_till_##T_SFX(                  \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane, pun_lo.to_##T_SFX, pun_hi.to_##T_SFX \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_till_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane,                    \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_till_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun_lo.to_##T_SFX,           \
+            pun_hi.to_##T_SFX                                                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_tillz_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane)                                     \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_tillz_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane                                       \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_tillz_##F_SFX                                      \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane)                    \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_tillz_##T_SFX(                \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE void npyv_store2_till_##F_SFX                                               \
+    (npyv_lanetype_##F_SFX *ptr, npy_uintp nlane, npyv_##F_SFX a)                           \
+    {                                                                                       \
+        npyv_store2_till_##T_SFX(                                                           \
+            (npyv_lanetype_##T_SFX *)ptr, nlane,                                            \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }                                                                                       \
+    NPY_FINLINE void npyv_storen2_till_##F_SFX                                              \
+    (npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane, npyv_##F_SFX a)          \
+    {                                                                                       \
+        npyv_storen2_till_##T_SFX(                                                          \
+            (npyv_lanetype_##T_SFX *)ptr, stride, nlane,                                    \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }
+
+NPYV_IMPL_AVX512_REST_PARTIAL_TYPES_PAIR(u32, s32)
+NPYV_IMPL_AVX512_REST_PARTIAL_TYPES_PAIR(f32, s32)
+NPYV_IMPL_AVX512_REST_PARTIAL_TYPES_PAIR(u64, s64)
+NPYV_IMPL_AVX512_REST_PARTIAL_TYPES_PAIR(f64, s64)
+
+/************************************************************
+ *  de-interlave load / interleave contiguous store
+ ************************************************************/
+// two channels
+#define NPYV_IMPL_AVX512_MEM_INTERLEAVE(SFX, ZSFX)                           \
+    NPY_FINLINE npyv_##ZSFX##x2 npyv_zip_##ZSFX(npyv_##ZSFX, npyv_##ZSFX);   \
+    NPY_FINLINE npyv_##ZSFX##x2 npyv_unzip_##ZSFX(npyv_##ZSFX, npyv_##ZSFX); \
+    NPY_FINLINE npyv_##SFX##x2 npyv_load_##SFX##x2(                          \
+        const npyv_lanetype_##SFX *ptr                                       \
+    ) {                                                                      \
+        return npyv_unzip_##ZSFX(                                            \
+            npyv_load_##SFX(ptr), npyv_load_##SFX(ptr+npyv_nlanes_##SFX)     \
+        );                                                                   \
+    }                                                                        \
+    NPY_FINLINE void npyv_store_##SFX##x2(                                   \
+        npyv_lanetype_##SFX *ptr, npyv_##SFX##x2 v                           \
+    ) {                                                                      \
+        npyv_##SFX##x2 zip = npyv_zip_##ZSFX(v.val[0], v.val[1]);            \
+        npyv_store_##SFX(ptr, zip.val[0]);                                   \
+        npyv_store_##SFX(ptr + npyv_nlanes_##SFX, zip.val[1]);               \
+    }
+
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(u8, u8)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(s8, u8)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(u16, u16)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(s16, u16)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(u32, u32)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(s32, u32)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(u64, u64)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(s64, u64)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(f32, f32)
+NPYV_IMPL_AVX512_MEM_INTERLEAVE(f64, f64)
+
 /**************************************************
  * Lookup table
  *************************************************/
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/reorder.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/reorder.h
index c0b2477f38b..27e66b5e7ba 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/reorder.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/avx512/reorder.h
@@ -167,6 +167,140 @@ NPY_FINLINE npyv_f64x2 npyv_zip_f64(__m512d a, __m512d b)
     return r;
 }
 
+// deinterleave two vectors
+NPY_FINLINE npyv_u8x2 npyv_unzip_u8(npyv_u8 ab0, npyv_u8 ab1)
+{
+    npyv_u8x2 r;
+#ifdef NPY_HAVE_AVX512VBMI
+    const __m512i idx_a = npyv_set_u8(
+        0,  2,  4,   6,   8,   10,  12,  14,  16,  18,  20,  22,  24,  26,  28,  30,
+        32, 34, 36,  38,  40,  42,  44,  46,  48,  50,  52,  54,  56,  58,  60,  62,
+        64, 66, 68,  70,  72,  74,  76,  78,  80,  82,  84,  86,  88,  90,  92,  94,
+        96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126
+    );
+    const __m512i idx_b = npyv_set_u8(
+        1,  3,  5,   7,   9,   11,  13,  15,  17,  19,  21,  23,  25,  27,  29,  31,
+        33, 35, 37,  39,  41,  43,  45,  47,  49,  51,  53,  55,  57,  59,  61,  63,
+        65, 67, 69,  71,  73,  75,  77,  79,  81,  83,  85,  87,  89,  91,  93,  95,
+        97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127
+    );
+    r.val[0] = _mm512_permutex2var_epi8(ab0, idx_a, ab1);
+    r.val[1] = _mm512_permutex2var_epi8(ab0, idx_b, ab1);
+#else
+    #ifdef NPY_HAVE_AVX512BW
+        const __m512i idx = npyv_set_u8(
+            0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15,
+            0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15,
+            0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15,
+            0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15
+        );
+        __m512i abl = _mm512_shuffle_epi8(ab0, idx);
+        __m512i abh = _mm512_shuffle_epi8(ab1, idx);
+    #else
+        const __m256i idx = _mm256_setr_epi8(
+            0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15,
+            0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15
+        );
+        __m256i abl_lo = _mm256_shuffle_epi8(npyv512_lower_si256(ab0),  idx);
+        __m256i abl_hi = _mm256_shuffle_epi8(npyv512_higher_si256(ab0), idx);
+        __m256i abh_lo = _mm256_shuffle_epi8(npyv512_lower_si256(ab1),  idx);
+        __m256i abh_hi = _mm256_shuffle_epi8(npyv512_higher_si256(ab1), idx);
+        __m512i abl = npyv512_combine_si256(abl_lo, abl_hi);
+        __m512i abh = npyv512_combine_si256(abh_lo, abh_hi);
+    #endif
+    const __m512i idx_a = npyv_set_u64(0, 2, 4, 6, 8, 10, 12, 14);
+    const __m512i idx_b = npyv_set_u64(1, 3, 5, 7, 9, 11, 13, 15);
+    r.val[0] = _mm512_permutex2var_epi64(abl, idx_a, abh);
+    r.val[1] = _mm512_permutex2var_epi64(abl, idx_b, abh);
+#endif
+    return r;
+}
+#define npyv_unzip_s8 npyv_unzip_u8
+
+NPY_FINLINE npyv_u16x2 npyv_unzip_u16(npyv_u16 ab0, npyv_u16 ab1)
+{
+    npyv_u16x2 r;
+#ifdef NPY_HAVE_AVX512BW
+    const __m512i idx_a = npyv_set_u16(
+        0,  2,  4,  6,  8,  10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30,
+        32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62
+    );
+    const __m512i idx_b = npyv_set_u16(
+        1,  3,  5,  7,  9,  11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31,
+        33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63
+    );
+    r.val[0] = _mm512_permutex2var_epi16(ab0, idx_a, ab1);
+    r.val[1] = _mm512_permutex2var_epi16(ab0, idx_b, ab1);
+#else
+    const __m256i idx = _mm256_setr_epi8(
+        0,1, 4,5, 8,9, 12,13, 2,3, 6,7, 10,11, 14,15,
+        0,1, 4,5, 8,9, 12,13, 2,3, 6,7, 10,11, 14,15
+    );
+    __m256i abl_lo = _mm256_shuffle_epi8(npyv512_lower_si256(ab0),  idx);
+    __m256i abl_hi = _mm256_shuffle_epi8(npyv512_higher_si256(ab0), idx);
+    __m256i abh_lo = _mm256_shuffle_epi8(npyv512_lower_si256(ab1),  idx);
+    __m256i abh_hi = _mm256_shuffle_epi8(npyv512_higher_si256(ab1), idx);
+    __m512i abl = npyv512_combine_si256(abl_lo, abl_hi);
+    __m512i abh = npyv512_combine_si256(abh_lo, abh_hi);
+
+    const __m512i idx_a = npyv_set_u64(0, 2, 4, 6, 8, 10, 12, 14);
+    const __m512i idx_b = npyv_set_u64(1, 3, 5, 7, 9, 11, 13, 15);
+    r.val[0] = _mm512_permutex2var_epi64(abl, idx_a, abh);
+    r.val[1] = _mm512_permutex2var_epi64(abl, idx_b, abh);
+#endif
+    return r;
+}
+#define npyv_unzip_s16 npyv_unzip_u16
+
+NPY_FINLINE npyv_u32x2 npyv_unzip_u32(npyv_u32 ab0, npyv_u32 ab1)
+{
+    const __m512i idx_a = npyv_set_u32(
+        0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30
+    );
+    const __m512i idx_b = npyv_set_u32(
+        1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31
+    );
+    npyv_u32x2 r;
+    r.val[0] = _mm512_permutex2var_epi32(ab0, idx_a, ab1);
+    r.val[1] = _mm512_permutex2var_epi32(ab0, idx_b, ab1);
+    return r;
+}
+#define npyv_unzip_s32 npyv_unzip_u32
+
+NPY_FINLINE npyv_u64x2 npyv_unzip_u64(npyv_u64 ab0, npyv_u64 ab1)
+{
+    const __m512i idx_a = npyv_set_u64(0, 2, 4, 6, 8, 10, 12, 14);
+    const __m512i idx_b = npyv_set_u64(1, 3, 5, 7, 9, 11, 13, 15);
+    npyv_u64x2 r;
+    r.val[0] = _mm512_permutex2var_epi64(ab0, idx_a, ab1);
+    r.val[1] = _mm512_permutex2var_epi64(ab0, idx_b, ab1);
+    return r;
+}
+#define npyv_unzip_s64 npyv_unzip_u64
+
+NPY_FINLINE npyv_f32x2 npyv_unzip_f32(npyv_f32 ab0, npyv_f32 ab1)
+{
+    const __m512i idx_a = npyv_set_u32(
+        0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30
+    );
+    const __m512i idx_b = npyv_set_u32(
+        1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31
+    );
+    npyv_f32x2 r;
+    r.val[0] = _mm512_permutex2var_ps(ab0, idx_a, ab1);
+    r.val[1] = _mm512_permutex2var_ps(ab0, idx_b, ab1);
+    return r;
+}
+NPY_FINLINE npyv_f64x2 npyv_unzip_f64(npyv_f64 ab0, npyv_f64 ab1)
+{
+    const __m512i idx_a = npyv_set_u64(0, 2, 4, 6, 8, 10, 12, 14);
+    const __m512i idx_b = npyv_set_u64(1, 3, 5, 7, 9, 11, 13, 15);
+    npyv_f64x2 r;
+    r.val[0] = _mm512_permutex2var_pd(ab0, idx_a, ab1);
+    r.val[1] = _mm512_permutex2var_pd(ab0, idx_b, ab1);
+    return r;
+}
+
 // Reverse elements of each 64-bit lane
 NPY_FINLINE npyv_u8 npyv_rev64_u8(npyv_u8 a)
 {
@@ -223,4 +357,22 @@ NPY_FINLINE npyv_f32 npyv_rev64_f32(npyv_f32 a)
     return _mm512_shuffle_ps(a, a, (_MM_PERM_ENUM)_MM_SHUFFLE(2, 3, 0, 1));
 }
 
+// Permuting the elements of each 128-bit lane by immediate index for
+// each element.
+#define npyv_permi128_u32(A, E0, E1, E2, E3) \
+    _mm512_shuffle_epi32(A, _MM_SHUFFLE(E3, E2, E1, E0))
+
+#define npyv_permi128_s32 npyv_permi128_u32
+
+#define npyv_permi128_u64(A, E0, E1) \
+    _mm512_shuffle_epi32(A, _MM_SHUFFLE(((E1)<<1)+1, ((E1)<<1), ((E0)<<1)+1, ((E0)<<1)))
+
+#define npyv_permi128_s64 npyv_permi128_u64
+
+#define npyv_permi128_f32(A, E0, E1, E2, E3) \
+    _mm512_permute_ps(A, _MM_SHUFFLE(E3, E2, E1, E0))
+
+#define npyv_permi128_f64(A, E0, E1) \
+    _mm512_permute_pd(A, (((E1)<<7) | ((E0)<<6) | ((E1)<<5) | ((E0)<<4) | ((E1)<<3) | ((E0)<<2) | ((E1)<<1) | (E0)))
+
 #endif // _NPY_SIMD_AVX512_REORDER_H
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/emulate_maskop.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/emulate_maskop.h
index 2a808a153ed..6627e5010a8 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/emulate_maskop.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/emulate_maskop.h
@@ -42,5 +42,39 @@ NPYV_IMPL_EMULATE_MASK_ADDSUB(s64, b64)
 #if NPY_SIMD_F64
     NPYV_IMPL_EMULATE_MASK_ADDSUB(f64, b64)
 #endif
+#if NPY_SIMD_F32
+    // conditional division, m ? a / b : c
+    NPY_FINLINE npyv_f32
+    npyv_ifdiv_f32(npyv_b32 m, npyv_f32 a, npyv_f32 b, npyv_f32 c)
+    {
+        const npyv_f32 one = npyv_setall_f32(1.0f);
+        npyv_f32 div = npyv_div_f32(a, npyv_select_f32(m, b, one));
+        return npyv_select_f32(m, div, c);
+    }
+    // conditional division, m ? a / b : 0
+    NPY_FINLINE npyv_f32
+    npyv_ifdivz_f32(npyv_b32 m, npyv_f32 a, npyv_f32 b)
+    {
+        const npyv_f32 zero = npyv_zero_f32();
+        return npyv_ifdiv_f32(m, a, b, zero);
+    }
+#endif
+#if NPY_SIMD_F64
+    // conditional division, m ? a / b : c
+    NPY_FINLINE npyv_f64
+    npyv_ifdiv_f64(npyv_b64 m, npyv_f64 a, npyv_f64 b, npyv_f64 c)
+    {
+        const npyv_f64 one = npyv_setall_f64(1.0);
+        npyv_f64 div = npyv_div_f64(a, npyv_select_f64(m, b, one));
+        return npyv_select_f64(m, div, c);
+    }
+    // conditional division, m ? a / b : 0
+    NPY_FINLINE npyv_f64
+    npyv_ifdivz_f64(npyv_b64 m, npyv_f64 a, npyv_f64 b)
+    {
+        const npyv_f64 zero = npyv_zero_f64();
+        return npyv_ifdiv_f64(m, a, b, zero);
+    }
+#endif
 
 #endif // _NPY_SIMD_EMULATE_MASKOP_H
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/arithmetic.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/arithmetic.h
index 00994806df6..683620111a3 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/arithmetic.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/arithmetic.h
@@ -265,6 +265,14 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
     NPY_FINLINE npyv_f32 npyv_nmulsub_f32(npyv_f32 a, npyv_f32 b, npyv_f32 c)
     { return vmlsq_f32(vnegq_f32(c), a, b); }
 #endif
+// multiply, add for odd elements and subtract even elements.
+// (a * b) -+ c
+NPY_FINLINE npyv_f32 npyv_muladdsub_f32(npyv_f32 a, npyv_f32 b, npyv_f32 c)
+{
+    const npyv_f32 msign = npyv_set_f32(-0.0f, 0.0f, -0.0f, 0.0f);
+    return npyv_muladd_f32(a, b, npyv_xor_f32(msign, c));
+}
+
 /***************************
  * FUSED F64
  ***************************/
@@ -277,6 +285,11 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
     { return vfmsq_f64(c, a, b); }
     NPY_FINLINE npyv_f64 npyv_nmulsub_f64(npyv_f64 a, npyv_f64 b, npyv_f64 c)
     { return vfmsq_f64(vnegq_f64(c), a, b); }
+    NPY_FINLINE npyv_f64 npyv_muladdsub_f64(npyv_f64 a, npyv_f64 b, npyv_f64 c)
+    {
+        const npyv_f64 msign = npyv_set_f64(-0.0, 0.0);
+        return npyv_muladd_f64(a, b, npyv_xor_f64(msign, c));
+    }
 #endif // NPY_SIMD_F64
 
 /***************************
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/math.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/math.h
index 01344a41fe2..58d14809fbf 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/math.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/math.h
@@ -352,7 +352,7 @@ NPY_FINLINE npyv_f32 npyv_rint_f32(npyv_f32 a)
         npyv_u32 nfinite_mask = vshlq_n_u32(vreinterpretq_u32_f32(a), 1);
                  nfinite_mask = vandq_u32(nfinite_mask, exp_mask);
                  nfinite_mask = vceqq_u32(nfinite_mask, exp_mask);
-        // elminate nans/inf to avoid invalid fp errors
+        // eliminate nans/inf to avoid invalid fp errors
         npyv_f32 x = vreinterpretq_f32_u32(
             veorq_u32(nfinite_mask, vreinterpretq_u32_f32(a)));
         /**
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/memory.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/memory.h
index 7060ea62830..6163440c39c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/memory.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/memory.h
@@ -102,6 +102,29 @@ NPY_FINLINE npyv_f64 npyv_loadn_f64(const double *ptr, npy_intp stride)
     );
 }
 #endif
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_u32 npyv_loadn2_u32(const npy_uint32 *ptr, npy_intp stride)
+{
+    return vcombine_u32(
+        vld1_u32((const uint32_t*)ptr), vld1_u32((const uint32_t*)ptr + stride)
+    );
+}
+NPY_FINLINE npyv_s32 npyv_loadn2_s32(const npy_int32 *ptr, npy_intp stride)
+{ return npyv_reinterpret_s32_u32(npyv_loadn2_u32((const npy_uint32*)ptr, stride)); }
+NPY_FINLINE npyv_f32 npyv_loadn2_f32(const float *ptr, npy_intp stride)
+{ return npyv_reinterpret_f32_u32(npyv_loadn2_u32((const npy_uint32*)ptr, stride)); }
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_u64 npyv_loadn2_u64(const npy_uint64 *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_u64(ptr); }
+NPY_FINLINE npyv_s64 npyv_loadn2_s64(const npy_int64 *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_s64(ptr); }
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_f64 npyv_loadn2_f64(const double *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_f64(ptr); }
+#endif
+
 /***************************
  * Non-contiguous Store
  ***************************/
@@ -131,6 +154,32 @@ NPY_FINLINE void npyv_storen_f64(double *ptr, npy_intp stride, npyv_f64 a)
 { npyv_storen_s64((npy_int64*)ptr, stride, npyv_reinterpret_s64_f64(a)); }
 #endif
 
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_u32(npy_uint32 *ptr, npy_intp stride, npyv_u32 a)
+{
+#if NPY_SIMD_F64
+    vst1q_lane_u64((uint64_t*)ptr, npyv_reinterpret_u64_u32(a), 0);
+    vst1q_lane_u64((uint64_t*)(ptr + stride), npyv_reinterpret_u64_u32(a), 1);
+#else
+    // armhf strict to alignment
+    vst1_u32((uint32_t*)ptr, vget_low_u32(a));
+    vst1_u32((uint32_t*)ptr + stride, vget_high_u32(a));
+#endif
+}
+NPY_FINLINE void npyv_storen2_s32(npy_int32 *ptr, npy_intp stride, npyv_s32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, npyv_reinterpret_u32_s32(a)); }
+NPY_FINLINE void npyv_storen2_f32(float *ptr, npy_intp stride, npyv_f32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, npyv_reinterpret_u32_f32(a)); }
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a)
+{ (void)stride; npyv_store_u64(ptr, a); }
+NPY_FINLINE void npyv_storen2_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
+{ (void)stride; npyv_store_s64(ptr, a); }
+#if NPY_SIMD_F64
+NPY_FINLINE void npyv_storen2_f64(double *ptr, npy_intp stride, npyv_f64 a)
+{ (void)stride; npyv_store_f64(ptr, a); }
+#endif
 /*********************************
  * Partial Load
  *********************************/
@@ -168,6 +217,29 @@ NPY_FINLINE npyv_s64 npyv_load_till_s64(const npy_int64 *ptr, npy_uintp nlane, n
 NPY_FINLINE npyv_s64 npyv_load_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
 { return npyv_load_till_s64(ptr, nlane, 0); }
 
+//// 64-bit nlane
+NPY_FINLINE npyv_s32 npyv_load2_till_s32(const npy_int32 *ptr, npy_uintp nlane,
+                                          npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        const int32_t NPY_DECL_ALIGNED(16) fill[2] = {fill_lo, fill_hi};
+        return vcombine_s32(vld1_s32((const int32_t*)ptr), vld1_s32(fill));
+    }
+    return npyv_load_s32(ptr);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s32 npyv_load2_tillz_s32(const npy_int32 *ptr, npy_uintp nlane)
+{ return vreinterpretq_s32_s64(npyv_load_tillz_s64((const npy_int64*)ptr, nlane)); }
+
+//// 128-bit nlane
+NPY_FINLINE npyv_s64 npyv_load2_till_s64(const npy_int64 *ptr, npy_uintp nlane,
+                                           npy_int64 fill_lo, npy_int64 fill_hi)
+{ (void)nlane; (void)fill_lo; (void)fill_hi; return npyv_load_s64(ptr); }
+
+NPY_FINLINE npyv_s64 npyv_load2_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
+{ (void)nlane; return npyv_load_s64(ptr); }
+
 /*********************************
  * Non-contiguous partial load
  *********************************/
@@ -206,6 +278,34 @@ npyv_loadn_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npy_
 NPY_FINLINE npyv_s64 npyv_loadn_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
 { return npyv_loadn_till_s64(ptr, stride, nlane, 0); }
 
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_s32 npyv_loadn2_till_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane,
+                                                 npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        const int32_t NPY_DECL_ALIGNED(16) fill[2] = {fill_lo, fill_hi};
+        return vcombine_s32(vld1_s32((const int32_t*)ptr), vld1_s32(fill));
+    }
+    return npyv_loadn2_s32(ptr, stride);
+}
+NPY_FINLINE npyv_s32 npyv_loadn2_tillz_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        return vcombine_s32(vld1_s32((const int32_t*)ptr), vdup_n_s32(0));
+    }
+    return npyv_loadn2_s32(ptr, stride);
+}
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_s64 npyv_loadn2_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane,
+                                          npy_int64 fill_lo, npy_int64 fill_hi)
+{ assert(nlane > 0); (void)stride; (void)nlane; (void)fill_lo; (void)fill_hi; return npyv_load_s64(ptr); }
+
+NPY_FINLINE npyv_s64 npyv_loadn2_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
+{ assert(nlane > 0); (void)stride; (void)nlane; return npyv_load_s64(ptr); }
+
 /*********************************
  * Partial store
  *********************************/
@@ -238,6 +338,30 @@ NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a
     }
     npyv_store_s64(ptr, a);
 }
+
+//// 64-bit nlane
+NPY_FINLINE void npyv_store2_till_s32(npy_int32 *ptr, npy_uintp nlane, npyv_s32 a)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        // armhf strict to alignment, may cause bus error
+    #if NPY_SIMD_F64
+        vst1q_lane_s64((int64_t*)ptr, npyv_reinterpret_s64_s32(a), 0);
+    #else
+        npyv_storel_s32(ptr, a);
+    #endif
+        return;
+    }
+    npyv_store_s32(ptr, a);
+}
+
+//// 128-bit nlane
+NPY_FINLINE void npyv_store2_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a)
+{
+    assert(nlane > 0); (void)nlane;
+    npyv_store_s64(ptr, a);
+}
+
 /*********************************
  * Non-contiguous partial store
  *********************************/
@@ -245,16 +369,21 @@ NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a
 NPY_FINLINE void npyv_storen_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
 {
     assert(nlane > 0);
+    vst1q_lane_s32((int32_t*)ptr, a, 0);
     switch(nlane) {
-    default:
-        vst1q_lane_s32((int32_t*)ptr + stride*3, a, 3);
+    case 1:
+        return;
+    case 2:
+        vst1q_lane_s32((int32_t*)ptr + stride, a, 1);
+        return;
     case 3:
+        vst1q_lane_s32((int32_t*)ptr + stride, a, 1);
         vst1q_lane_s32((int32_t*)ptr + stride*2, a, 2);
-    case 2:
+        return;
+    default:
         vst1q_lane_s32((int32_t*)ptr + stride, a, 1);
-    case 1:
-        vst1q_lane_s32((int32_t*)ptr, a, 0);
-        break;
+        vst1q_lane_s32((int32_t*)ptr + stride*2, a, 2);
+        vst1q_lane_s32((int32_t*)ptr + stride*3, a, 3);
     }
 }
 //// 64
@@ -268,6 +397,27 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
     npyv_storen_s64(ptr, stride, a);
 }
 
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
+{
+    assert(nlane > 0);
+#if NPY_SIMD_F64
+    vst1q_lane_s64((int64_t*)ptr, npyv_reinterpret_s64_s32(a), 0);
+    if (nlane > 1) {
+        vst1q_lane_s64((int64_t*)(ptr + stride), npyv_reinterpret_s64_s32(a), 1);
+    }
+#else
+    npyv_storel_s32(ptr, a);
+    if (nlane > 1) {
+        npyv_storeh_s32(ptr + stride, a);
+    }
+#endif
+}
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npyv_s64 a)
+{ assert(nlane > 0); (void)stride; (void)nlane; npyv_store_s64(ptr, a); }
+
 /*****************************************************************
  * Implement partial load/store for u32/f32/u64/f64... via casting
  *****************************************************************/
@@ -278,7 +428,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load_till_##T_SFX(                   \
             (const npyv_lanetype_##T_SFX *)ptr, nlane, pun.to_##T_SFX                       \
         ));                                                                                 \
@@ -290,7 +441,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn_till_##T_SFX(                  \
             (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun.to_##T_SFX               \
         ));                                                                                 \
@@ -332,6 +484,131 @@ NPYV_IMPL_NEON_REST_PARTIAL_TYPES(u64, s64)
 #if NPY_SIMD_F64
 NPYV_IMPL_NEON_REST_PARTIAL_TYPES(f64, s64)
 #endif
+
+// 128-bit/64-bit stride
+#define NPYV_IMPL_NEON_REST_PARTIAL_TYPES_PAIR(F_SFX, T_SFX)                                \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_till_##F_SFX                                        \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane,                                     \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_till_##T_SFX(                  \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane, pun_lo.to_##T_SFX, pun_hi.to_##T_SFX \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_till_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane,                    \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_till_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun_lo.to_##T_SFX,           \
+            pun_hi.to_##T_SFX                                                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_tillz_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane)                                     \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_tillz_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane                                       \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_tillz_##F_SFX                                      \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane)                    \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_tillz_##T_SFX(                \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE void npyv_store2_till_##F_SFX                                               \
+    (npyv_lanetype_##F_SFX *ptr, npy_uintp nlane, npyv_##F_SFX a)                           \
+    {                                                                                       \
+        npyv_store2_till_##T_SFX(                                                           \
+            (npyv_lanetype_##T_SFX *)ptr, nlane,                                            \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }                                                                                       \
+    NPY_FINLINE void npyv_storen2_till_##F_SFX                                              \
+    (npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane, npyv_##F_SFX a)          \
+    {                                                                                       \
+        npyv_storen2_till_##T_SFX(                                                          \
+            (npyv_lanetype_##T_SFX *)ptr, stride, nlane,                                    \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }
+
+NPYV_IMPL_NEON_REST_PARTIAL_TYPES_PAIR(u32, s32)
+NPYV_IMPL_NEON_REST_PARTIAL_TYPES_PAIR(f32, s32)
+NPYV_IMPL_NEON_REST_PARTIAL_TYPES_PAIR(u64, s64)
+#if NPY_SIMD_F64
+NPYV_IMPL_NEON_REST_PARTIAL_TYPES_PAIR(f64, s64)
+#endif
+
+/************************************************************
+ *  de-interlave load / interleave contiguous store
+ ************************************************************/
+// two channels
+#define NPYV_IMPL_NEON_MEM_INTERLEAVE(SFX, T_PTR)                        \
+    NPY_FINLINE npyv_##SFX##x2 npyv_load_##SFX##x2(                      \
+        const npyv_lanetype_##SFX *ptr                                   \
+    ) {                                                                  \
+        return vld2q_##SFX((const T_PTR*)ptr);                           \
+    }                                                                    \
+    NPY_FINLINE void npyv_store_##SFX##x2(                               \
+        npyv_lanetype_##SFX *ptr, npyv_##SFX##x2 v                       \
+    ) {                                                                  \
+        vst2q_##SFX((T_PTR*)ptr, v);                                     \
+    }
+
+NPYV_IMPL_NEON_MEM_INTERLEAVE(u8,  uint8_t)
+NPYV_IMPL_NEON_MEM_INTERLEAVE(s8,  int8_t)
+NPYV_IMPL_NEON_MEM_INTERLEAVE(u16, uint16_t)
+NPYV_IMPL_NEON_MEM_INTERLEAVE(s16, int16_t)
+NPYV_IMPL_NEON_MEM_INTERLEAVE(u32, uint32_t)
+NPYV_IMPL_NEON_MEM_INTERLEAVE(s32, int32_t)
+NPYV_IMPL_NEON_MEM_INTERLEAVE(f32, float)
+
+#if NPY_SIMD_F64
+    NPYV_IMPL_NEON_MEM_INTERLEAVE(f64, double)
+    NPYV_IMPL_NEON_MEM_INTERLEAVE(u64, uint64_t)
+    NPYV_IMPL_NEON_MEM_INTERLEAVE(s64, int64_t)
+#else
+    #define NPYV_IMPL_NEON_MEM_INTERLEAVE_64(SFX)                               \
+        NPY_FINLINE npyv_##SFX##x2 npyv_load_##SFX##x2(                         \
+            const npyv_lanetype_##SFX *ptr)                                     \
+        {                                                                       \
+            npyv_##SFX a = npyv_load_##SFX(ptr);                                \
+            npyv_##SFX b = npyv_load_##SFX(ptr + 2);                            \
+            npyv_##SFX##x2 r;                                                   \
+            r.val[0] = vcombine_##SFX(vget_low_##SFX(a),  vget_low_##SFX(b));   \
+            r.val[1] = vcombine_##SFX(vget_high_##SFX(a), vget_high_##SFX(b));  \
+            return r;                                                           \
+        }                                                                       \
+        NPY_FINLINE void npyv_store_##SFX##x2(                                  \
+            npyv_lanetype_##SFX *ptr, npyv_##SFX##x2 v)                         \
+        {                                                                       \
+            npyv_store_##SFX(ptr, vcombine_##SFX(                               \
+                vget_low_##SFX(v.val[0]),  vget_low_##SFX(v.val[1])));          \
+            npyv_store_##SFX(ptr + 2, vcombine_##SFX(                           \
+                vget_high_##SFX(v.val[0]),  vget_high_##SFX(v.val[1])));        \
+        }
+        NPYV_IMPL_NEON_MEM_INTERLEAVE_64(u64)
+        NPYV_IMPL_NEON_MEM_INTERLEAVE_64(s64)
+#endif
 /*********************************
  * Lookup table
  *********************************/
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/operators.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/operators.h
index ef05d4463c4..e18ea94b80f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/operators.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/operators.h
@@ -249,9 +249,9 @@ NPY_FINLINE npyv_b32 npyv_notnan_f32(npyv_f32 a)
  */
     npyv_b32 ret;
     #if NPY_SIMD_F64
-        __asm ("fcmeq %0.4s, %1.4s, %1.4s" : "=w" (ret) : "w" (a));
+        __asm("fcmeq %0.4s, %1.4s, %1.4s" : "=w" (ret) : "w" (a));
     #else
-        __asm ("vceq.f32 %q0, %q1, %q1" : "=w" (ret) : "w" (a));
+        __asm("vceq.f32 %q0, %q1, %q1" : "=w" (ret) : "w" (a));
     #endif
     return ret;
 #else
@@ -263,7 +263,7 @@ NPY_FINLINE npyv_b32 npyv_notnan_f32(npyv_f32 a)
     {
     #if defined(__clang__)
         npyv_b64 ret;
-        __asm ("fcmeq %0.2d, %1.2d, %1.2d" : "=w" (ret) : "w" (a));
+        __asm("fcmeq %0.2d, %1.2d, %1.2d" : "=w" (ret) : "w" (a));
         return ret;
     #else
         return vceqq_f64(a, a);
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/reorder.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/reorder.h
index 50b06ed11c0..8bf68f5be0d 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/reorder.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/neon/reorder.h
@@ -76,36 +76,45 @@ NPYV_IMPL_NEON_COMBINE(npyv_f32, f32)
 NPYV_IMPL_NEON_COMBINE(npyv_f64, f64)
 #endif
 
-// interleave two vectors
-#define NPYV_IMPL_NEON_ZIP(T_VEC, SFX)                       \
-    NPY_FINLINE T_VEC##x2 npyv_zip_##SFX(T_VEC a, T_VEC b)   \
-    {                                                        \
-        T_VEC##x2 r;                                         \
-        r.val[0] = vzip1q_##SFX(a, b);                       \
-        r.val[1] = vzip2q_##SFX(a, b);                       \
-        return r;                                            \
-    }
-
+// interleave & deinterleave two vectors
 #ifdef __aarch64__
-    NPYV_IMPL_NEON_ZIP(npyv_u8,  u8)
-    NPYV_IMPL_NEON_ZIP(npyv_s8,  s8)
-    NPYV_IMPL_NEON_ZIP(npyv_u16, u16)
-    NPYV_IMPL_NEON_ZIP(npyv_s16, s16)
-    NPYV_IMPL_NEON_ZIP(npyv_u32, u32)
-    NPYV_IMPL_NEON_ZIP(npyv_s32, s32)
-    NPYV_IMPL_NEON_ZIP(npyv_f32, f32)
-    NPYV_IMPL_NEON_ZIP(npyv_f64, f64)
+    #define NPYV_IMPL_NEON_ZIP(T_VEC, SFX)                       \
+        NPY_FINLINE T_VEC##x2 npyv_zip_##SFX(T_VEC a, T_VEC b)   \
+        {                                                        \
+            T_VEC##x2 r;                                         \
+            r.val[0] = vzip1q_##SFX(a, b);                       \
+            r.val[1] = vzip2q_##SFX(a, b);                       \
+            return r;                                            \
+        }                                                        \
+        NPY_FINLINE T_VEC##x2 npyv_unzip_##SFX(T_VEC a, T_VEC b) \
+        {                                                        \
+            T_VEC##x2 r;                                         \
+            r.val[0] = vuzp1q_##SFX(a, b);                       \
+            r.val[1] = vuzp2q_##SFX(a, b);                       \
+            return r;                                            \
+        }
 #else
-    #define npyv_zip_u8  vzipq_u8
-    #define npyv_zip_s8  vzipq_s8
-    #define npyv_zip_u16 vzipq_u16
-    #define npyv_zip_s16 vzipq_s16
-    #define npyv_zip_u32 vzipq_u32
-    #define npyv_zip_s32 vzipq_s32
-    #define npyv_zip_f32 vzipq_f32
+    #define NPYV_IMPL_NEON_ZIP(T_VEC, SFX)                       \
+        NPY_FINLINE T_VEC##x2 npyv_zip_##SFX(T_VEC a, T_VEC b)   \
+        { return vzipq_##SFX(a, b); }                            \
+        NPY_FINLINE T_VEC##x2 npyv_unzip_##SFX(T_VEC a, T_VEC b) \
+        { return vuzpq_##SFX(a, b); }
 #endif
+
+NPYV_IMPL_NEON_ZIP(npyv_u8,  u8)
+NPYV_IMPL_NEON_ZIP(npyv_s8,  s8)
+NPYV_IMPL_NEON_ZIP(npyv_u16, u16)
+NPYV_IMPL_NEON_ZIP(npyv_s16, s16)
+NPYV_IMPL_NEON_ZIP(npyv_u32, u32)
+NPYV_IMPL_NEON_ZIP(npyv_s32, s32)
+NPYV_IMPL_NEON_ZIP(npyv_f32, f32)
+
 #define npyv_zip_u64 npyv_combine_u64
 #define npyv_zip_s64 npyv_combine_s64
+#define npyv_zip_f64 npyv_combine_f64
+#define npyv_unzip_u64 npyv_combine_u64
+#define npyv_unzip_s64 npyv_combine_s64
+#define npyv_unzip_f64 npyv_combine_f64
 
 // Reverse elements of each 64-bit lane
 #define npyv_rev64_u8  vrev64q_u8
@@ -116,4 +125,65 @@ NPYV_IMPL_NEON_COMBINE(npyv_f64, f64)
 #define npyv_rev64_s32 vrev64q_s32
 #define npyv_rev64_f32 vrev64q_f32
 
+// Permuting the elements of each 128-bit lane by immediate index for
+// each element.
+#ifdef __clang__
+    #define npyv_permi128_u32(A, E0, E1, E2, E3) \
+        __builtin_shufflevector(A, A, E0, E1, E2, E3)
+#elif defined(__GNUC__)
+    #define npyv_permi128_u32(A, E0, E1, E2, E3) \
+        __builtin_shuffle(A, npyv_set_u32(E0, E1, E2, E3))
+#else
+    #define npyv_permi128_u32(A, E0, E1, E2, E3)          \
+        npyv_set_u32(                                     \
+            vgetq_lane_u32(A, E0), vgetq_lane_u32(A, E1), \
+            vgetq_lane_u32(A, E2), vgetq_lane_u32(A, E3)  \
+        )
+    #define npyv_permi128_s32(A, E0, E1, E2, E3)          \
+        npyv_set_s32(                                     \
+            vgetq_lane_s32(A, E0), vgetq_lane_s32(A, E1), \
+            vgetq_lane_s32(A, E2), vgetq_lane_s32(A, E3)  \
+        )
+    #define npyv_permi128_f32(A, E0, E1, E2, E3)          \
+        npyv_set_f32(                                     \
+            vgetq_lane_f32(A, E0), vgetq_lane_f32(A, E1), \
+            vgetq_lane_f32(A, E2), vgetq_lane_f32(A, E3)  \
+        )
+#endif
+
+#if defined(__clang__) || defined(__GNUC__)
+    #define npyv_permi128_s32 npyv_permi128_u32
+    #define npyv_permi128_f32 npyv_permi128_u32
+#endif
+
+#ifdef __clang__
+    #define npyv_permi128_u64(A, E0, E1) \
+        __builtin_shufflevector(A, A, E0, E1)
+#elif defined(__GNUC__)
+    #define npyv_permi128_u64(A, E0, E1) \
+        __builtin_shuffle(A, npyv_set_u64(E0, E1))
+#else
+    #define npyv_permi128_u64(A, E0, E1)                  \
+        npyv_set_u64(                                     \
+            vgetq_lane_u64(A, E0), vgetq_lane_u64(A, E1)  \
+        )
+    #define npyv_permi128_s64(A, E0, E1)                  \
+        npyv_set_s64(                                     \
+            vgetq_lane_s64(A, E0), vgetq_lane_s64(A, E1)  \
+        )
+    #define npyv_permi128_f64(A, E0, E1)                  \
+        npyv_set_f64(                                     \
+            vgetq_lane_f64(A, E0), vgetq_lane_f64(A, E1)  \
+        )
+#endif
+
+#if defined(__clang__) || defined(__GNUC__)
+    #define npyv_permi128_s64 npyv_permi128_u64
+    #define npyv_permi128_f64 npyv_permi128_u64
+#endif
+
+#if !NPY_SIMD_F64
+    #undef npyv_permi128_f64
+#endif
+
 #endif // _NPY_SIMD_NEON_REORDER_H
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/arithmetic.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/arithmetic.h
index bced3510811..72a87eac171 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/arithmetic.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/arithmetic.h
@@ -282,6 +282,10 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
     // negate multiply and subtract, -(a*b) - c
     #define npyv_nmulsub_f32 _mm_fnmsub_ps
     #define npyv_nmulsub_f64 _mm_fnmsub_pd
+    // multiply, add for odd elements and subtract even elements.
+    // (a * b) -+ c
+    #define npyv_muladdsub_f32 _mm_fmaddsub_ps
+    #define npyv_muladdsub_f64 _mm_fmaddsub_pd
 #elif defined(NPY_HAVE_FMA4)
     // multiply and add, a*b + c
     #define npyv_muladd_f32 _mm_macc_ps
@@ -292,6 +296,10 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
     // negate multiply and add, -(a*b) + c
     #define npyv_nmuladd_f32 _mm_nmacc_ps
     #define npyv_nmuladd_f64 _mm_nmacc_pd
+    // multiply, add for odd elements and subtract even elements.
+    // (a * b) -+ c
+    #define npyv_muladdsub_f32 _mm_maddsub_ps
+    #define npyv_muladdsub_f64 _mm_maddsub_pd
 #else
     // multiply and add, a*b + c
     NPY_FINLINE npyv_f32 npyv_muladd_f32(npyv_f32 a, npyv_f32 b, npyv_f32 c)
@@ -308,6 +316,28 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
     { return npyv_sub_f32(c, npyv_mul_f32(a, b)); }
     NPY_FINLINE npyv_f64 npyv_nmuladd_f64(npyv_f64 a, npyv_f64 b, npyv_f64 c)
     { return npyv_sub_f64(c, npyv_mul_f64(a, b)); }
+    // multiply, add for odd elements and subtract even elements.
+    // (a * b) -+ c
+    NPY_FINLINE npyv_f32 npyv_muladdsub_f32(npyv_f32 a, npyv_f32 b, npyv_f32 c)
+    {
+        npyv_f32 m = npyv_mul_f32(a, b);
+    #if NPY_HAVE_SSE3
+        return _mm_addsub_ps(m, c);
+    #else
+        const npyv_f32 msign = npyv_set_f32(-0.0f, 0.0f, -0.0f, 0.0f);
+        return npyv_add_f32(m, npyv_xor_f32(msign, c));
+    #endif
+    }
+    NPY_FINLINE npyv_f64 npyv_muladdsub_f64(npyv_f64 a, npyv_f64 b, npyv_f64 c)
+    {
+        npyv_f64 m = npyv_mul_f64(a, b);
+    #if NPY_HAVE_SSE3
+        return _mm_addsub_pd(m, c);
+    #else
+        const npyv_f64 msign = npyv_set_f64(-0.0, 0.0);
+        return npyv_add_f64(m, npyv_xor_f64(msign, c));
+    #endif
+    }
 #endif // NPY_HAVE_FMA3
 #ifndef NPY_HAVE_FMA3 // for FMA4 and NON-FMA3
     // negate multiply and subtract, -(a*b) - c
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/math.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/math.h
index 6b42d8ba381..b51c935afeb 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/math.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/math.h
@@ -298,7 +298,7 @@ NPY_FINLINE npyv_f64 npyv_rint_f64(npyv_f64 a)
     const __m128d szero = _mm_set1_pd(-0.0);
     const __m128d two_power_52 = _mm_set1_pd(0x10000000000000);
     __m128d nan_mask = _mm_cmpunord_pd(a, a);
-    // elminate nans to avoid invalid fp errors within cmpge
+    // eliminate nans to avoid invalid fp errors within cmpge
     __m128d abs_x = npyv_abs_f64(_mm_xor_pd(nan_mask, a));
     // round by add magic number 2^52
     // assuming that MXCSR register is set to rounding
@@ -344,7 +344,7 @@ NPY_FINLINE npyv_f64 npyv_rint_f64(npyv_f64 a)
         const __m128d szero = _mm_set1_pd(-0.0);
         const __m128d two_power_52 = _mm_set1_pd(0x10000000000000);
         __m128d nan_mask = _mm_cmpunord_pd(a, a);
-        // elminate nans to avoid invalid fp errors within cmpge
+        // eliminate nans to avoid invalid fp errors within cmpge
         __m128d x = _mm_xor_pd(nan_mask, a);
         __m128d abs_x = npyv_abs_f64(x);
         __m128d sign_x = _mm_and_pd(x, szero);
@@ -377,7 +377,7 @@ NPY_FINLINE npyv_f64 npyv_rint_f64(npyv_f64 a)
                 nfinite_mask = _mm_and_si128(nfinite_mask, exp_mask);
                 nfinite_mask = _mm_cmpeq_epi32(nfinite_mask, exp_mask);
 
-        // elminate nans/inf to avoid invalid fp errors
+        // eliminate nans/inf to avoid invalid fp errors
         __m128 x = _mm_xor_ps(a, _mm_castsi128_ps(nfinite_mask));
         __m128i trunci = _mm_cvttps_epi32(x);
         __m128 trunc = _mm_cvtepi32_ps(trunci);
@@ -394,7 +394,7 @@ NPY_FINLINE npyv_f64 npyv_rint_f64(npyv_f64 a)
         const __m128d szero = _mm_set1_pd(-0.0);
         const __m128d two_power_52 = _mm_set1_pd(0x10000000000000);
         __m128d nan_mask = _mm_cmpunord_pd(a, a);
-        // elminate nans to avoid invalid fp errors within cmpge
+        // eliminate nans to avoid invalid fp errors within cmpge
         __m128d abs_x = npyv_abs_f64(_mm_xor_pd(nan_mask, a));
         // round by add magic number 2^52
         // assuming that MXCSR register is set to rounding
@@ -443,7 +443,7 @@ NPY_FINLINE npyv_f64 npyv_rint_f64(npyv_f64 a)
         const __m128d szero = _mm_set1_pd(-0.0f);
         const __m128d two_power_52 = _mm_set1_pd(0x10000000000000);
         __m128d nan_mask = _mm_cmpunord_pd(a, a);
-        // elminate nans to avoid invalid fp errors within cmpge
+        // eliminate nans to avoid invalid fp errors within cmpge
         __m128d x = _mm_xor_pd(nan_mask, a);
         __m128d abs_x = npyv_abs_f64(x);
         __m128d sign_x = _mm_and_pd(x, szero);
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/memory.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/memory.h
index 3ff64848d28..4c8e86a6f9d 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/memory.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/memory.h
@@ -103,6 +103,28 @@ NPY_FINLINE npyv_u64 npyv_loadn_u64(const npy_uint64 *ptr, npy_intp stride)
 { return _mm_castpd_si128(npyv_loadn_f64((const double*)ptr, stride)); }
 NPY_FINLINE npyv_s64 npyv_loadn_s64(const npy_int64 *ptr, npy_intp stride)
 { return _mm_castpd_si128(npyv_loadn_f64((const double*)ptr, stride)); }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_f32 npyv_loadn2_f32(const float *ptr, npy_intp stride)
+{
+    __m128d r = _mm_loadh_pd(
+        npyv_loadl_f64((const double*)ptr), (const double*)(ptr + stride)
+    );
+    return _mm_castpd_ps(r);
+}
+NPY_FINLINE npyv_u32 npyv_loadn2_u32(const npy_uint32 *ptr, npy_intp stride)
+{ return _mm_castps_si128(npyv_loadn2_f32((const float*)ptr, stride)); }
+NPY_FINLINE npyv_s32 npyv_loadn2_s32(const npy_int32 *ptr, npy_intp stride)
+{ return _mm_castps_si128(npyv_loadn2_f32((const float*)ptr, stride)); }
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_f64 npyv_loadn2_f64(const double *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_f64(ptr); }
+NPY_FINLINE npyv_u64 npyv_loadn2_u64(const npy_uint64 *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_u64(ptr); }
+NPY_FINLINE npyv_s64 npyv_loadn2_s64(const npy_int64 *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_s64(ptr); }
+
 /***************************
  * Non-contiguous Store
  ***************************/
@@ -135,6 +157,24 @@ NPY_FINLINE void npyv_storen_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a)
 NPY_FINLINE void npyv_storen_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
 { npyv_storen_f64((double*)ptr, stride, _mm_castsi128_pd(a)); }
 
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_u32(npy_uint32 *ptr, npy_intp stride, npyv_u32 a)
+{
+    _mm_storel_pd((double*)ptr, _mm_castsi128_pd(a));
+    _mm_storeh_pd((double*)(ptr + stride), _mm_castsi128_pd(a));
+}
+NPY_FINLINE void npyv_storen2_s32(npy_int32 *ptr, npy_intp stride, npyv_s32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, a); }
+NPY_FINLINE void npyv_storen2_f32(float *ptr, npy_intp stride, npyv_f32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, _mm_castps_si128(a)); }
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a)
+{ (void)stride; npyv_store_u64(ptr, a); }
+NPY_FINLINE void npyv_storen2_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
+{ (void)stride; npyv_store_s64(ptr, a); }
+NPY_FINLINE void npyv_storen2_f64(double *ptr, npy_intp stride, npyv_f64 a)
+{ (void)stride; npyv_store_f64(ptr, a); }
 /*********************************
  * Partial Load
  *********************************/
@@ -204,13 +244,14 @@ NPY_FINLINE npyv_s32 npyv_load_tillz_s32(const npy_int32 *ptr, npy_uintp nlane)
         return _mm_cvtsi32_si128(*ptr);
     case 2:
         return _mm_loadl_epi64((const __m128i*)ptr);
-    case 3:;
-        npyv_s32 a = _mm_loadl_epi64((const __m128i*)ptr);
-    #ifdef NPY_HAVE_SSE41
-        return _mm_insert_epi32(a, ptr[2], 2);
-    #else
-        return _mm_unpacklo_epi64(a, _mm_cvtsi32_si128(ptr[2]));
-    #endif
+    case 3: {
+            npyv_s32 a = _mm_loadl_epi64((const __m128i*)ptr);
+        #ifdef NPY_HAVE_SSE41
+            return _mm_insert_epi32(a, ptr[2], 2);
+        #else
+            return _mm_unpacklo_epi64(a, _mm_cvtsi32_si128(ptr[2]));
+        #endif
+        }
     default:
         return npyv_load_s32(ptr);
     }
@@ -246,6 +287,32 @@ NPY_FINLINE npyv_s64 npyv_load_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
     }
     return npyv_load_s64(ptr);
 }
+
+//// 64-bit nlane
+NPY_FINLINE npyv_s32 npyv_load2_till_s32(const npy_int32 *ptr, npy_uintp nlane,
+                                          npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        const __m128i vfill = npyv_set_s32(fill_lo, fill_hi, fill_lo, fill_hi);
+        return _mm_castpd_si128(
+            _mm_loadl_pd(_mm_castsi128_pd(vfill), (double*)ptr)
+        );
+    }
+    return npyv_load_s32(ptr);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s32 npyv_load2_tillz_s32(const npy_int32 *ptr, npy_uintp nlane)
+{ return npyv_load_tillz_s64((const npy_int64*)ptr, nlane); }
+
+//// 128-bit nlane
+NPY_FINLINE npyv_s64 npyv_load2_till_s64(const npy_int64 *ptr, npy_uintp nlane,
+                                           npy_int64 fill_lo, npy_int64 fill_hi)
+{ (void)nlane; (void)fill_lo; (void)fill_hi; return npyv_load_s64(ptr); }
+
+NPY_FINLINE npyv_s64 npyv_load2_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
+{ (void)nlane; return npyv_load_s64(ptr); }
+
 /*********************************
  * Non-contiguous partial load
  *********************************/
@@ -305,23 +372,27 @@ npyv_loadn_tillz_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane)
     case 1:
         return _mm_cvtsi32_si128(ptr[0]);
     case 2:;
-        npyv_s32 a = _mm_cvtsi32_si128(ptr[0]);
-#ifdef NPY_HAVE_SSE41
-        return _mm_insert_epi32(a, ptr[stride], 1);
-#else
-        return _mm_unpacklo_epi32(a, _mm_cvtsi32_si128(ptr[stride]));
-#endif // NPY_HAVE_SSE41
-    case 3:;
-        a = _mm_cvtsi32_si128(ptr[0]);
-#ifdef NPY_HAVE_SSE41
-        a = _mm_insert_epi32(a, ptr[stride], 1);
-        a = _mm_insert_epi32(a, ptr[stride*2], 2);
-        return a;
-#else
-        a = _mm_unpacklo_epi32(a, _mm_cvtsi32_si128(ptr[stride]));
-        a = _mm_unpacklo_epi64(a, _mm_cvtsi32_si128(ptr[stride*2]));
-        return a;
-#endif // NPY_HAVE_SSE41
+        {
+            npyv_s32 a = _mm_cvtsi32_si128(ptr[0]);
+    #ifdef NPY_HAVE_SSE41
+            return _mm_insert_epi32(a, ptr[stride], 1);
+    #else
+            return _mm_unpacklo_epi32(a, _mm_cvtsi32_si128(ptr[stride]));
+    #endif // NPY_HAVE_SSE41
+        }
+    case 3:
+        {
+            npyv_s32 a = _mm_cvtsi32_si128(ptr[0]);
+    #ifdef NPY_HAVE_SSE41
+            a = _mm_insert_epi32(a, ptr[stride], 1);
+            a = _mm_insert_epi32(a, ptr[stride*2], 2);
+            return a;
+    #else
+            a = _mm_unpacklo_epi32(a, _mm_cvtsi32_si128(ptr[stride]));
+            a = _mm_unpacklo_epi64(a, _mm_cvtsi32_si128(ptr[stride*2]));
+            return a;
+    #endif // NPY_HAVE_SSE41
+        }
     default:
         return npyv_loadn_s32(ptr, stride);
     }
@@ -358,6 +429,37 @@ NPY_FINLINE npyv_s64 npyv_loadn_tillz_s64(const npy_int64 *ptr, npy_intp stride,
     }
     return npyv_loadn_s64(ptr, stride);
 }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_s32 npyv_loadn2_till_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane,
+                                                 npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        const __m128i vfill = npyv_set_s32(0, 0, fill_lo, fill_hi);
+        return _mm_castpd_si128(
+            _mm_loadl_pd(_mm_castsi128_pd(vfill), (double*)ptr)
+        );
+    }
+    return npyv_loadn2_s32(ptr, stride);
+}
+NPY_FINLINE npyv_s32 npyv_loadn2_tillz_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        return _mm_loadl_epi64((const __m128i*)ptr);
+    }
+    return npyv_loadn2_s32(ptr, stride);
+}
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_s64 npyv_loadn2_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane,
+                                                  npy_int64 fill_lo, npy_int64 fill_hi)
+{ assert(nlane > 0); (void)stride; (void)nlane; (void)fill_lo; (void)fill_hi; return npyv_load_s64(ptr); }
+
+NPY_FINLINE npyv_s64 npyv_loadn2_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
+{ assert(nlane > 0); (void)stride; (void)nlane; return npyv_load_s64(ptr); }
+
 /*********************************
  * Partial store
  *********************************/
@@ -394,6 +496,17 @@ NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a
     }
     npyv_store_s64(ptr, a);
 }
+//// 64-bit nlane
+NPY_FINLINE void npyv_store2_till_s32(npy_int32 *ptr, npy_uintp nlane, npyv_s32 a)
+{ npyv_store_till_s64((npy_int64*)ptr, nlane, a); }
+
+//// 128-bit nlane
+NPY_FINLINE void npyv_store2_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a)
+{
+    assert(nlane > 0); (void)nlane;
+    npyv_store_s64(ptr, a);
+}
+
 /*********************************
  * Non-contiguous partial store
  *********************************/
@@ -401,25 +514,35 @@ NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a
 NPY_FINLINE void npyv_storen_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
 {
     assert(nlane > 0);
+    ptr[stride*0] = _mm_cvtsi128_si32(a);
     switch(nlane) {
+    case 1:
+        return;
 #ifdef NPY_HAVE_SSE41
-    default:
-        ptr[stride*3] = _mm_extract_epi32(a, 3);
+    case 2:
+        ptr[stride*1] = _mm_extract_epi32(a, 1);
+        return;
     case 3:
+        ptr[stride*1] = _mm_extract_epi32(a, 1);
         ptr[stride*2] = _mm_extract_epi32(a, 2);
-    case 2:
+        return;
+    default:
         ptr[stride*1] = _mm_extract_epi32(a, 1);
+        ptr[stride*2] = _mm_extract_epi32(a, 2);
+        ptr[stride*3] = _mm_extract_epi32(a, 3);
 #else
-    default:
-        ptr[stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(a, _MM_SHUFFLE(0, 0, 0, 3)));
+    case 2:
+        ptr[stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(a, _MM_SHUFFLE(0, 0, 0, 1)));
+        return;
     case 3:
+        ptr[stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(a, _MM_SHUFFLE(0, 0, 0, 1)));
         ptr[stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(a, _MM_SHUFFLE(0, 0, 0, 2)));
-    case 2:
+        return;
+    default:
         ptr[stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(a, _MM_SHUFFLE(0, 0, 0, 1)));
+        ptr[stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(a, _MM_SHUFFLE(0, 0, 0, 2)));
+        ptr[stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(a, _MM_SHUFFLE(0, 0, 0, 3)));
 #endif
-    case 1:
-        ptr[stride*0] = _mm_cvtsi128_si32(a);
-        break;
     }
 }
 //// 64
@@ -432,6 +555,21 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
     }
     npyv_storen_s64(ptr, stride, a);
 }
+
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
+{
+    assert(nlane > 0);
+    npyv_storel_s32(ptr, a);
+    if (nlane > 1) {
+        npyv_storeh_s32(ptr + stride, a);
+    }
+}
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npyv_s64 a)
+{ assert(nlane > 0); (void)stride; (void)nlane; npyv_store_s64(ptr, a); }
+
 /*****************************************************************
  * Implement partial load/store for u32/f32/u64/f64... via casting
  *****************************************************************/
@@ -442,7 +580,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load_till_##T_SFX(                   \
             (const npyv_lanetype_##T_SFX *)ptr, nlane, pun.to_##T_SFX                       \
         ));                                                                                 \
@@ -454,7 +593,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn_till_##T_SFX(                  \
             (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun.to_##T_SFX               \
         ));                                                                                 \
@@ -495,6 +635,110 @@ NPYV_IMPL_SSE_REST_PARTIAL_TYPES(f32, s32)
 NPYV_IMPL_SSE_REST_PARTIAL_TYPES(u64, s64)
 NPYV_IMPL_SSE_REST_PARTIAL_TYPES(f64, s64)
 
+// 128-bit/64-bit stride
+#define NPYV_IMPL_SSE_REST_PARTIAL_TYPES_PAIR(F_SFX, T_SFX)                                 \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_till_##F_SFX                                        \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane,                                     \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_till_##T_SFX(                  \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane, pun_lo.to_##T_SFX, pun_hi.to_##T_SFX \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_till_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane,                    \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_till_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun_lo.to_##T_SFX,           \
+            pun_hi.to_##T_SFX                                                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_tillz_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane)                                     \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_tillz_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane                                       \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_tillz_##F_SFX                                      \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane)                    \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_tillz_##T_SFX(                \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE void npyv_store2_till_##F_SFX                                               \
+    (npyv_lanetype_##F_SFX *ptr, npy_uintp nlane, npyv_##F_SFX a)                           \
+    {                                                                                       \
+        npyv_store2_till_##T_SFX(                                                           \
+            (npyv_lanetype_##T_SFX *)ptr, nlane,                                            \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }                                                                                       \
+    NPY_FINLINE void npyv_storen2_till_##F_SFX                                              \
+    (npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane, npyv_##F_SFX a)          \
+    {                                                                                       \
+        npyv_storen2_till_##T_SFX(                                                          \
+            (npyv_lanetype_##T_SFX *)ptr, stride, nlane,                                    \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }
+
+NPYV_IMPL_SSE_REST_PARTIAL_TYPES_PAIR(u32, s32)
+NPYV_IMPL_SSE_REST_PARTIAL_TYPES_PAIR(f32, s32)
+NPYV_IMPL_SSE_REST_PARTIAL_TYPES_PAIR(u64, s64)
+NPYV_IMPL_SSE_REST_PARTIAL_TYPES_PAIR(f64, s64)
+
+/************************************************************
+ *  de-interlave load / interleave contiguous store
+ ************************************************************/
+// two channels
+#define NPYV_IMPL_SSE_MEM_INTERLEAVE(SFX, ZSFX)                              \
+    NPY_FINLINE npyv_##ZSFX##x2 npyv_zip_##ZSFX(npyv_##ZSFX, npyv_##ZSFX);   \
+    NPY_FINLINE npyv_##ZSFX##x2 npyv_unzip_##ZSFX(npyv_##ZSFX, npyv_##ZSFX); \
+    NPY_FINLINE npyv_##SFX##x2 npyv_load_##SFX##x2(                          \
+        const npyv_lanetype_##SFX *ptr                                       \
+    ) {                                                                      \
+        return npyv_unzip_##ZSFX(                                            \
+            npyv_load_##SFX(ptr), npyv_load_##SFX(ptr+npyv_nlanes_##SFX)     \
+        );                                                                   \
+    }                                                                        \
+    NPY_FINLINE void npyv_store_##SFX##x2(                                   \
+        npyv_lanetype_##SFX *ptr, npyv_##SFX##x2 v                           \
+    ) {                                                                      \
+        npyv_##SFX##x2 zip = npyv_zip_##ZSFX(v.val[0], v.val[1]);            \
+        npyv_store_##SFX(ptr, zip.val[0]);                                   \
+        npyv_store_##SFX(ptr + npyv_nlanes_##SFX, zip.val[1]);               \
+    }
+
+NPYV_IMPL_SSE_MEM_INTERLEAVE(u8, u8)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(s8, u8)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(u16, u16)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(s16, u16)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(u32, u32)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(s32, u32)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(u64, u64)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(s64, u64)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(f32, f32)
+NPYV_IMPL_SSE_MEM_INTERLEAVE(f64, f64)
+
 /*********************************
  * Lookup table
  *********************************/
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/reorder.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/reorder.h
index d96ab9c5688..9a57f648972 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/reorder.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/sse/reorder.h
@@ -81,6 +81,75 @@ NPYV_IMPL_SSE_ZIP(npyv_s64, s64, epi64)
 NPYV_IMPL_SSE_ZIP(npyv_f32, f32, ps)
 NPYV_IMPL_SSE_ZIP(npyv_f64, f64, pd)
 
+// deinterleave two vectors
+NPY_FINLINE npyv_u8x2 npyv_unzip_u8(npyv_u8 ab0, npyv_u8 ab1)
+{
+#ifdef NPY_HAVE_SSSE3
+    const __m128i idx = _mm_setr_epi8(
+        0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15
+    );
+    __m128i abl = _mm_shuffle_epi8(ab0, idx);
+    __m128i abh = _mm_shuffle_epi8(ab1, idx);
+    return npyv_combine_u8(abl, abh);
+#else
+    __m128i ab_083b = _mm_unpacklo_epi8(ab0, ab1);
+    __m128i ab_4c6e = _mm_unpackhi_epi8(ab0, ab1);
+    __m128i ab_048c = _mm_unpacklo_epi8(ab_083b, ab_4c6e);
+    __m128i ab_36be = _mm_unpackhi_epi8(ab_083b, ab_4c6e);
+    __m128i ab_0346 = _mm_unpacklo_epi8(ab_048c, ab_36be);
+    __m128i ab_8bc8 = _mm_unpackhi_epi8(ab_048c, ab_36be);
+    npyv_u8x2 r;
+    r.val[0] = _mm_unpacklo_epi8(ab_0346, ab_8bc8);
+    r.val[1] = _mm_unpackhi_epi8(ab_0346, ab_8bc8);
+    return r;
+#endif
+}
+#define npyv_unzip_s8 npyv_unzip_u8
+
+NPY_FINLINE npyv_u16x2 npyv_unzip_u16(npyv_u16 ab0, npyv_u16 ab1)
+{
+#ifdef NPY_HAVE_SSSE3
+    const __m128i idx = _mm_setr_epi8(
+        0,1, 4,5, 8,9, 12,13, 2,3, 6,7, 10,11, 14,15
+    );
+    __m128i abl = _mm_shuffle_epi8(ab0, idx);
+    __m128i abh = _mm_shuffle_epi8(ab1, idx);
+    return npyv_combine_u16(abl, abh);
+#else
+    __m128i ab_0415 = _mm_unpacklo_epi16(ab0, ab1);
+    __m128i ab_263f = _mm_unpackhi_epi16(ab0, ab1);
+    __m128i ab_0246 = _mm_unpacklo_epi16(ab_0415, ab_263f);
+    __m128i ab_135f = _mm_unpackhi_epi16(ab_0415, ab_263f);
+    npyv_u16x2 r;
+    r.val[0] = _mm_unpacklo_epi16(ab_0246, ab_135f);
+    r.val[1] = _mm_unpackhi_epi16(ab_0246, ab_135f);
+    return r;
+#endif
+}
+#define npyv_unzip_s16 npyv_unzip_u16
+
+NPY_FINLINE npyv_u32x2 npyv_unzip_u32(npyv_u32 ab0, npyv_u32 ab1)
+{
+    __m128i abl = _mm_shuffle_epi32(ab0, _MM_SHUFFLE(3, 1, 2, 0));
+    __m128i abh = _mm_shuffle_epi32(ab1, _MM_SHUFFLE(3, 1, 2, 0));
+    return npyv_combine_u32(abl, abh);
+}
+#define npyv_unzip_s32 npyv_unzip_u32
+
+NPY_FINLINE npyv_u64x2 npyv_unzip_u64(npyv_u64 ab0, npyv_u64 ab1)
+{ return npyv_combine_u64(ab0, ab1); }
+#define npyv_unzip_s64 npyv_unzip_u64
+
+NPY_FINLINE npyv_f32x2 npyv_unzip_f32(npyv_f32 ab0, npyv_f32 ab1)
+{
+    npyv_f32x2 r;
+    r.val[0] = _mm_shuffle_ps(ab0, ab1, _MM_SHUFFLE(2, 0, 2, 0));
+    r.val[1] = _mm_shuffle_ps(ab0, ab1, _MM_SHUFFLE(3, 1, 3, 1));
+    return r;
+}
+NPY_FINLINE npyv_f64x2 npyv_unzip_f64(npyv_f64 ab0, npyv_f64 ab1)
+{ return npyv_combine_f64(ab0, ab1); }
+
 // Reverse elements of each 64-bit lane
 NPY_FINLINE npyv_u16 npyv_rev64_u16(npyv_u16 a)
 {
@@ -122,4 +191,22 @@ NPY_FINLINE npyv_f32 npyv_rev64_f32(npyv_f32 a)
     return _mm_shuffle_ps(a, a, _MM_SHUFFLE(2, 3, 0, 1));
 }
 
+// Permuting the elements of each 128-bit lane by immediate index for
+// each element.
+#define npyv_permi128_u32(A, E0, E1, E2, E3) \
+    _mm_shuffle_epi32(A, _MM_SHUFFLE(E3, E2, E1, E0))
+
+#define npyv_permi128_s32 npyv_permi128_u32
+
+#define npyv_permi128_u64(A, E0, E1) \
+    _mm_shuffle_epi32(A, _MM_SHUFFLE(((E1)<<1)+1, ((E1)<<1), ((E0)<<1)+1, ((E0)<<1)))
+
+#define npyv_permi128_s64 npyv_permi128_u64
+
+#define npyv_permi128_f32(A, E0, E1, E2, E3) \
+    _mm_shuffle_ps(A, A, _MM_SHUFFLE(E3, E2, E1, E0))
+
+#define npyv_permi128_f64(A, E0, E1) \
+    _mm_shuffle_pd(A, A, _MM_SHUFFLE2(E1, E0))
+
 #endif // _NPY_SIMD_SSE_REORDER_H
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/arithmetic.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/arithmetic.h
index a2e9d07eb6a..85f4d6b26d6 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/arithmetic.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/arithmetic.h
@@ -322,6 +322,20 @@ NPY_FINLINE npyv_s64 npyv_divc_s64(npyv_s64 a, const npyv_s64x3 divisor)
     NPY_FINLINE npyv_f64 npyv_nmulsub_f64(npyv_f64 a, npyv_f64 b, npyv_f64 c)
     { return vec_neg(vec_madd(a, b, c)); }
 #endif
+// multiply, add for odd elements and subtract even elements.
+// (a * b) -+ c
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_f32 npyv_muladdsub_f32(npyv_f32 a, npyv_f32 b, npyv_f32 c)
+{
+    const npyv_f32 msign = npyv_set_f32(-0.0f, 0.0f, -0.0f, 0.0f);
+    return npyv_muladd_f32(a, b, npyv_xor_f32(msign, c));
+}
+#endif
+NPY_FINLINE npyv_f64 npyv_muladdsub_f64(npyv_f64 a, npyv_f64 b, npyv_f64 c)
+{
+    const npyv_f64 msign = npyv_set_f64(-0.0, 0.0);
+    return npyv_muladd_f64(a, b, npyv_xor_f64(msign, c));
+}
 /***************************
  * Summation
  ***************************/
@@ -352,6 +366,7 @@ NPY_FINLINE float npyv_sum_f32(npyv_f32 a)
 {
     npyv_f32 sum = vec_add(a, npyv_combineh_f32(a, a));
     return vec_extract(sum, 0) + vec_extract(sum, 1);
+    (void)sum;
 }
 #endif
 
@@ -372,6 +387,7 @@ NPY_FINLINE npy_uint16 npyv_sumup_u8(npyv_u8 a)
     npyv_u32 four = vec_sum4s(a, zero);
     npyv_s32 one  = vec_sums((npyv_s32)four, (npyv_s32)zero);
     return (npy_uint16)vec_extract(one, 3);
+    (void)one;
 #endif
 }
 
@@ -386,6 +402,7 @@ NPY_FINLINE npy_uint32 npyv_sumup_u16(npyv_u16 a)
     npyv_u32   four  = vec_add(eight.val[0], eight.val[1]);
     npyv_s32   one   = vec_sums((npyv_s32)four, zero);
     return (npy_uint32)vec_extract(one, 3);
+    (void)one;
 #endif
 }
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/conversion.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/conversion.h
index 0c0d5b3ac50..922109f7b74 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/conversion.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/conversion.h
@@ -96,6 +96,8 @@ npyv_pack_b8_b64(npyv_b64 a, npyv_b64 b, npyv_b64 c, npyv_b64 d,
     #else
         return vec_extract(r, 4);
     #endif
+        // to suppress ambiguous warning: variable `r` but not used [-Wunused-but-set-variable]
+	(void)r;
     }
     NPY_FINLINE npy_uint64 npyv_tobits_b16(npyv_b16 a)
     {
@@ -106,6 +108,8 @@ npyv_pack_b8_b64(npyv_b64 a, npyv_b64 b, npyv_b64 c, npyv_b64 d,
     #else
         return vec_extract(r, 8);
     #endif
+	// to suppress ambiguous warning: variable `r` but not used [-Wunused-but-set-variable]
+        (void)r;
     }
     NPY_FINLINE npy_uint64 npyv_tobits_b32(npyv_b32 a)
     {
@@ -120,6 +124,8 @@ npyv_pack_b8_b64(npyv_b64 a, npyv_b64 b, npyv_b64 c, npyv_b64 d,
     #else
         return vec_extract(r, 8);
     #endif
+	// to suppress ambiguous warning: variable `r` but not used [-Wunused-but-set-variable]
+        (void)r;
     }
     NPY_FINLINE npy_uint64 npyv_tobits_b64(npyv_b64 a)
     {
@@ -134,6 +140,8 @@ npyv_pack_b8_b64(npyv_b64 a, npyv_b64 b, npyv_b64 c, npyv_b64 d,
     #else
         return vec_extract(r, 8);
     #endif
+	// to suppress ambiguous warning: variable `r` but not used [-Wunused-but-set-variable]
+        (void)r;
     }
 #else
     NPY_FINLINE npy_uint64 npyv_tobits_b8(npyv_b8 a)
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/math.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/math.h
index 95b16fdf76e..85690f76c68 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/math.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/math.h
@@ -186,6 +186,7 @@ NPY_IMPL_VEC_REDUCE_MINMAX(max, int32, s32)
     {                                                                   \
         npyv_##SFX r = vec_##INTRIN(a, vec_sld(a, a, 8));               \
         return (npy_##STYPE)vec_extract(r, 0);                          \
+    	(void)r;					 	                                \
     }
 NPY_IMPL_VEC_REDUCE_MINMAX(min, uint64, u64)
 NPY_IMPL_VEC_REDUCE_MINMAX(max, uint64, u64)
@@ -225,6 +226,7 @@ NPY_IMPL_VEC_REDUCE_MINMAX(max, int64, s64)
     {                                                             \
         npyv_f64 r = vec_##INTRIN(a, vec_sld(a, a, 8));           \
         return vec_extract(r, 0);                                 \
+        (void)r;                                                  \
     }                                                             \
     NPY_FINLINE double npyv_reduce_##INTRIN##n_f64(npyv_f64 a)    \
     {                                                             \
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/memory.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/memory.h
index 495f698bf4d..4545e53e909 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/memory.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/memory.h
@@ -46,14 +46,8 @@
 #endif
 
 // avoid aliasing rules
-#ifdef __cplusplus
-    template<typename T_PTR>
-    NPY_FINLINE npy_uint64 *npyv__ptr2u64(const T_PTR *ptr)
-    { npy_uint64 *ptr64 = (npy_uint64*)ptr; return ptr64; }
-#else
-    NPY_FINLINE npy_uint64 *npyv__ptr2u64(const void *ptr)
-    { npy_uint64 *ptr64 = (npy_uint64*)ptr; return ptr64; }
-#endif // __cplusplus
+NPY_FINLINE npy_uint64 *npyv__ptr2u64(const void *ptr)
+{ npy_uint64 *ptr64 = (npy_uint64*)ptr; return ptr64; }
 
 // load lower part
 NPY_FINLINE npyv_u64 npyv__loadl(const void *ptr)
@@ -91,7 +85,7 @@ NPY_FINLINE npyv_u64 npyv__loadl(const void *ptr)
     NPY_FINLINE void npyv_store_##SFX(npyv_lanetype_##SFX *ptr, npyv_##SFX vec)         \
     { npyv__store((npyv_lanetype_##DW_CAST*)ptr, (npyv_##DW_CAST)vec); }                \
     NPY_FINLINE void npyv_storea_##SFX(npyv_lanetype_##SFX *ptr, npyv_##SFX vec)        \
-    { npyv__storea((npyv_lanetype_##DW_CAST*)ptr, (npyv_##DW_CAST)vec); }               \
+    { npyv__storea((npyv_lanetype_u32*)ptr, (npyv_u32)vec); }                           \
     NPY_FINLINE void npyv_stores_##SFX(npyv_lanetype_##SFX *ptr, npyv_##SFX vec)        \
     { npyv_storea_##SFX(ptr, vec); }                                                    \
     NPY_FINLINE void npyv_storel_##SFX(npyv_lanetype_##SFX *ptr, npyv_##SFX vec)        \
@@ -136,6 +130,24 @@ NPY_FINLINE npyv_s64 npyv_loadn_s64(const npy_int64 *ptr, npy_intp stride)
 { return npyv_set_s64(ptr[0], ptr[stride]); }
 NPY_FINLINE npyv_f64 npyv_loadn_f64(const double *ptr, npy_intp stride)
 { return npyv_set_f64(ptr[0], ptr[stride]); }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_u32 npyv_loadn2_u32(const npy_uint32 *ptr, npy_intp stride)
+{ return (npyv_u32)npyv_set_u64(*(npy_uint64*)ptr, *(npy_uint64*)(ptr + stride)); }
+NPY_FINLINE npyv_s32 npyv_loadn2_s32(const npy_int32 *ptr, npy_intp stride)
+{ return (npyv_s32)npyv_set_u64(*(npy_uint64*)ptr, *(npy_uint64*)(ptr + stride)); }
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_f32 npyv_loadn2_f32(const float *ptr, npy_intp stride)
+{ return (npyv_f32)npyv_set_u64(*(npy_uint64*)ptr, *(npy_uint64*)(ptr + stride)); }
+#endif
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_u64 npyv_loadn2_u64(const npy_uint64 *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_u64(ptr); }
+NPY_FINLINE npyv_s64 npyv_loadn2_s64(const npy_int64 *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_s64(ptr); }
+NPY_FINLINE npyv_f64 npyv_loadn2_f64(const double *ptr, npy_intp stride)
+{ (void)stride; return npyv_load_f64(ptr); }
+
 /***************************
  * Non-contiguous Store
  ***************************/
@@ -164,6 +176,26 @@ NPY_FINLINE void npyv_storen_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
 NPY_FINLINE void npyv_storen_f64(double *ptr, npy_intp stride, npyv_f64 a)
 { npyv_storen_u64((npy_uint64*)ptr, stride, (npyv_u64)a); }
 
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_u32(npy_uint32 *ptr, npy_intp stride, npyv_u32 a)
+{
+    *(npy_uint64*)ptr = vec_extract((npyv_u64)a, 0);
+    *(npy_uint64*)(ptr + stride) = vec_extract((npyv_u64)a, 1);
+}
+NPY_FINLINE void npyv_storen2_s32(npy_int32 *ptr, npy_intp stride, npyv_s32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, (npyv_u32)a); }
+#if NPY_SIMD_F32
+NPY_FINLINE void npyv_storen2_f32(float *ptr, npy_intp stride, npyv_f32 a)
+{ npyv_storen2_u32((npy_uint32*)ptr, stride, (npyv_u32)a); }
+#endif
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_u64(npy_uint64 *ptr, npy_intp stride, npyv_u64 a)
+{ (void)stride; npyv_store_u64(ptr, a); }
+NPY_FINLINE void npyv_storen2_s64(npy_int64 *ptr, npy_intp stride, npyv_s64 a)
+{ (void)stride; npyv_store_s64(ptr, a); }
+NPY_FINLINE void npyv_storen2_f64(double *ptr, npy_intp stride, npyv_f64 a)
+{ (void)stride; npyv_store_f64(ptr, a); }
+
 /*********************************
  * Partial Load
  *********************************/
@@ -226,6 +258,27 @@ NPY_FINLINE npyv_s64 npyv_load_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
     return npyv_load_till_s64(ptr, nlane, 0);
 #endif
 }
+//// 64-bit nlane
+NPY_FINLINE npyv_s32 npyv_load2_till_s32(const npy_int32 *ptr, npy_uintp nlane,
+                                          npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        return npyv_set_s32(ptr[0], ptr[1], fill_lo, fill_hi);
+    }
+    return npyv_load_s32(ptr);
+}
+// fill zero to rest lanes
+NPY_FINLINE npyv_s32 npyv_load2_tillz_s32(const npy_int32 *ptr, npy_uintp nlane)
+{ return (npyv_s32)npyv_load_tillz_s64((const npy_int64*)ptr, nlane); }
+
+//// 128-bit nlane
+NPY_FINLINE npyv_s64 npyv_load2_till_s64(const npy_int64 *ptr, npy_uintp nlane,
+                                           npy_int64 fill_lo, npy_int64 fill_hi)
+{ (void)nlane; (void)fill_lo; (void)fill_hi; return npyv_load_s64(ptr); }
+
+NPY_FINLINE npyv_s64 npyv_load2_tillz_s64(const npy_int64 *ptr, npy_uintp nlane)
+{ (void)nlane; return npyv_load_s64(ptr); }
 /*********************************
  * Non-contiguous partial load
  *********************************/
@@ -265,6 +318,34 @@ npyv_loadn_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npy_
 // fill zero to rest lanes
 NPY_FINLINE npyv_s64 npyv_loadn_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
 { return npyv_loadn_till_s64(ptr, stride, nlane, 0); }
+
+//// 64-bit load over 32-bit stride
+NPY_FINLINE npyv_s32 npyv_loadn2_till_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane,
+                                                 npy_int32 fill_lo, npy_int32 fill_hi)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        return npyv_set_s32(ptr[0], ptr[1], fill_lo, fill_hi);
+    }
+    return npyv_loadn2_s32(ptr, stride);
+}
+NPY_FINLINE npyv_s32 npyv_loadn2_tillz_s32(const npy_int32 *ptr, npy_intp stride, npy_uintp nlane)
+{
+    assert(nlane > 0);
+    if (nlane == 1) {
+        return (npyv_s32)npyv_set_s64(*(npy_int64*)ptr, 0);
+    }
+    return npyv_loadn2_s32(ptr, stride);
+}
+
+//// 128-bit load over 64-bit stride
+NPY_FINLINE npyv_s64 npyv_loadn2_till_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane,
+                                                  npy_int64 fill_lo, npy_int64 fill_hi)
+{ assert(nlane > 0); (void)stride; (void)nlane; (void)fill_lo; (void)fill_hi; return npyv_load_s64(ptr); }
+
+NPY_FINLINE npyv_s64 npyv_loadn2_tillz_s64(const npy_int64 *ptr, npy_intp stride, npy_uintp nlane)
+{ assert(nlane > 0); (void)stride; (void)nlane; return npyv_load_s64(ptr); }
+
 /*********************************
  * Partial store
  *********************************/
@@ -307,6 +388,18 @@ NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a
     npyv_store_s64(ptr, a);
 #endif
 }
+
+//// 64-bit nlane
+NPY_FINLINE void npyv_store2_till_s32(npy_int32 *ptr, npy_uintp nlane, npyv_s32 a)
+{ npyv_store_till_s64((npy_int64*)ptr, nlane, (npyv_s64)a); }
+
+//// 128-bit nlane
+NPY_FINLINE void npyv_store2_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a)
+{
+    assert(nlane > 0); (void)nlane;
+    npyv_store_s64(ptr, a);
+}
+
 /*********************************
  * Non-contiguous partial store
  *********************************/
@@ -314,16 +407,21 @@ NPY_FINLINE void npyv_store_till_s64(npy_int64 *ptr, npy_uintp nlane, npyv_s64 a
 NPY_FINLINE void npyv_storen_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
 {
     assert(nlane > 0);
+    ptr[stride*0] = vec_extract(a, 0);
     switch(nlane) {
-    default:
-        ptr[stride*3] = vec_extract(a, 3);
-    case 3:
-        ptr[stride*2] = vec_extract(a, 2);
+    case 1:
+        return;
     case 2:
         ptr[stride*1] = vec_extract(a, 1);
-    case 1:
-        ptr[stride*0] = vec_extract(a, 0);
-        break;
+        return;
+    case 3:
+        ptr[stride*1] = vec_extract(a, 1);
+        ptr[stride*2] = vec_extract(a, 2);
+        return;
+    default:
+         ptr[stride*1] = vec_extract(a, 1);
+         ptr[stride*2] = vec_extract(a, 2);
+         ptr[stride*3] = vec_extract(a, 3);
     }
 }
 //// 64
@@ -336,6 +434,21 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
     }
     npyv_storen_s64(ptr, stride, a);
 }
+
+//// 64-bit store over 32-bit stride
+NPY_FINLINE void npyv_storen2_till_s32(npy_int32 *ptr, npy_intp stride, npy_uintp nlane, npyv_s32 a)
+{
+    assert(nlane > 0);
+    npyv_storel_s32(ptr, a);
+    if (nlane > 1) {
+        npyv_storeh_s32(ptr + stride, a);
+    }
+}
+
+//// 128-bit store over 64-bit stride
+NPY_FINLINE void npyv_storen2_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp nlane, npyv_s64 a)
+{ assert(nlane > 0); (void)stride; (void)nlane; npyv_store_s64(ptr, a); }
+
 /*****************************************************************
  * Implement partial load/store for u32/f32/u64/f64... via casting
  *****************************************************************/
@@ -346,7 +459,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load_till_##T_SFX(                   \
             (const npyv_lanetype_##T_SFX *)ptr, nlane, pun.to_##T_SFX                       \
         ));                                                                                 \
@@ -358,7 +472,8 @@ NPY_FINLINE void npyv_storen_till_s64(npy_int64 *ptr, npy_intp stride, npy_uintp
         union {                                                                             \
             npyv_lanetype_##F_SFX from_##F_SFX;                                             \
             npyv_lanetype_##T_SFX to_##T_SFX;                                               \
-        } pun = {.from_##F_SFX = fill};                                                     \
+        } pun;                                                                              \
+        pun.from_##F_SFX = fill;                                                            \
         return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn_till_##T_SFX(                  \
             (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun.to_##T_SFX               \
         ));                                                                                 \
@@ -401,6 +516,114 @@ NPYV_IMPL_VEC_REST_PARTIAL_TYPES(f32, s32)
 NPYV_IMPL_VEC_REST_PARTIAL_TYPES(u64, s64)
 NPYV_IMPL_VEC_REST_PARTIAL_TYPES(f64, s64)
 
+// 128-bit/64-bit stride
+#define NPYV_IMPL_VEC_REST_PARTIAL_TYPES_PAIR(F_SFX, T_SFX)                                 \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_till_##F_SFX                                        \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane,                                     \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_till_##T_SFX(                  \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane, pun_lo.to_##T_SFX, pun_hi.to_##T_SFX \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_till_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane,                    \
+     npyv_lanetype_##F_SFX fill_lo, npyv_lanetype_##F_SFX fill_hi)                          \
+    {                                                                                       \
+        union pun {                                                                         \
+            npyv_lanetype_##F_SFX from_##F_SFX;                                             \
+            npyv_lanetype_##T_SFX to_##T_SFX;                                               \
+        };                                                                                  \
+        union pun pun_lo;                                                                   \
+        union pun pun_hi;                                                                   \
+        pun_lo.from_##F_SFX = fill_lo;                                                      \
+        pun_hi.from_##F_SFX = fill_hi;                                                      \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_till_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane, pun_lo.to_##T_SFX,           \
+            pun_hi.to_##T_SFX                                                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_load2_tillz_##F_SFX                                       \
+    (const npyv_lanetype_##F_SFX *ptr, npy_uintp nlane)                                     \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_load2_tillz_##T_SFX(                 \
+            (const npyv_lanetype_##T_SFX *)ptr, nlane                                       \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE npyv_##F_SFX npyv_loadn2_tillz_##F_SFX                                      \
+    (const npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane)                    \
+    {                                                                                       \
+        return npyv_reinterpret_##F_SFX##_##T_SFX(npyv_loadn2_tillz_##T_SFX(                \
+            (const npyv_lanetype_##T_SFX *)ptr, stride, nlane                               \
+        ));                                                                                 \
+    }                                                                                       \
+    NPY_FINLINE void npyv_store2_till_##F_SFX                                               \
+    (npyv_lanetype_##F_SFX *ptr, npy_uintp nlane, npyv_##F_SFX a)                           \
+    {                                                                                       \
+        npyv_store2_till_##T_SFX(                                                           \
+            (npyv_lanetype_##T_SFX *)ptr, nlane,                                            \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }                                                                                       \
+    NPY_FINLINE void npyv_storen2_till_##F_SFX                                              \
+    (npyv_lanetype_##F_SFX *ptr, npy_intp stride, npy_uintp nlane, npyv_##F_SFX a)          \
+    {                                                                                       \
+        npyv_storen2_till_##T_SFX(                                                          \
+            (npyv_lanetype_##T_SFX *)ptr, stride, nlane,                                    \
+            npyv_reinterpret_##T_SFX##_##F_SFX(a)                                           \
+        );                                                                                  \
+    }
+
+NPYV_IMPL_VEC_REST_PARTIAL_TYPES_PAIR(u32, s32)
+#if NPY_SIMD_F32
+NPYV_IMPL_VEC_REST_PARTIAL_TYPES_PAIR(f32, s32)
+#endif
+NPYV_IMPL_VEC_REST_PARTIAL_TYPES_PAIR(u64, s64)
+NPYV_IMPL_VEC_REST_PARTIAL_TYPES_PAIR(f64, s64)
+
+/************************************************************
+ *  de-interlave load / interleave contiguous store
+ ************************************************************/
+// two channels
+#define NPYV_IMPL_VEC_MEM_INTERLEAVE(SFX)                                \
+    NPY_FINLINE npyv_##SFX##x2 npyv_zip_##SFX(npyv_##SFX, npyv_##SFX);   \
+    NPY_FINLINE npyv_##SFX##x2 npyv_unzip_##SFX(npyv_##SFX, npyv_##SFX); \
+    NPY_FINLINE npyv_##SFX##x2 npyv_load_##SFX##x2(                      \
+        const npyv_lanetype_##SFX *ptr                                   \
+    ) {                                                                  \
+        return npyv_unzip_##SFX(                                         \
+            npyv_load_##SFX(ptr), npyv_load_##SFX(ptr+npyv_nlanes_##SFX) \
+        );                                                               \
+    }                                                                    \
+    NPY_FINLINE void npyv_store_##SFX##x2(                               \
+        npyv_lanetype_##SFX *ptr, npyv_##SFX##x2 v                       \
+    ) {                                                                  \
+        npyv_##SFX##x2 zip = npyv_zip_##SFX(v.val[0], v.val[1]);         \
+        npyv_store_##SFX(ptr, zip.val[0]);                               \
+        npyv_store_##SFX(ptr + npyv_nlanes_##SFX, zip.val[1]);           \
+    }
+
+NPYV_IMPL_VEC_MEM_INTERLEAVE(u8)
+NPYV_IMPL_VEC_MEM_INTERLEAVE(s8)
+NPYV_IMPL_VEC_MEM_INTERLEAVE(u16)
+NPYV_IMPL_VEC_MEM_INTERLEAVE(s16)
+NPYV_IMPL_VEC_MEM_INTERLEAVE(u32)
+NPYV_IMPL_VEC_MEM_INTERLEAVE(s32)
+NPYV_IMPL_VEC_MEM_INTERLEAVE(u64)
+NPYV_IMPL_VEC_MEM_INTERLEAVE(s64)
+#if NPY_SIMD_F32
+NPYV_IMPL_VEC_MEM_INTERLEAVE(f32)
+#endif
+NPYV_IMPL_VEC_MEM_INTERLEAVE(f64)
+
 /*********************************
  * Lookup table
  *********************************/
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/misc.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/misc.h
index 7ea0f21f65e..79c194d90ff 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/misc.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/misc.h
@@ -26,28 +26,28 @@
 #define NPYV_IMPL_VEC_SPLTW(T_VEC, V) ((T_VEC){V, V, V, V})
 #define NPYV_IMPL_VEC_SPLTD(T_VEC, V) ((T_VEC){V, V})
 
-#define npyv_setall_u8(VAL)  NPYV_IMPL_VEC_SPLTB(npyv_u8,  (unsigned char)VAL)
-#define npyv_setall_s8(VAL)  NPYV_IMPL_VEC_SPLTB(npyv_s8,  (signed char)VAL)
-#define npyv_setall_u16(VAL) NPYV_IMPL_VEC_SPLTH(npyv_u16, (unsigned short)VAL)
-#define npyv_setall_s16(VAL) NPYV_IMPL_VEC_SPLTH(npyv_s16, (short)VAL)
-#define npyv_setall_u32(VAL) NPYV_IMPL_VEC_SPLTW(npyv_u32, (unsigned int)VAL)
-#define npyv_setall_s32(VAL) NPYV_IMPL_VEC_SPLTW(npyv_s32, (int)VAL)
+#define npyv_setall_u8(VAL)  NPYV_IMPL_VEC_SPLTB(npyv_u8,  (unsigned char)(VAL))
+#define npyv_setall_s8(VAL)  NPYV_IMPL_VEC_SPLTB(npyv_s8,  (signed char)(VAL))
+#define npyv_setall_u16(VAL) NPYV_IMPL_VEC_SPLTH(npyv_u16, (unsigned short)(VAL))
+#define npyv_setall_s16(VAL) NPYV_IMPL_VEC_SPLTH(npyv_s16, (short)(VAL))
+#define npyv_setall_u32(VAL) NPYV_IMPL_VEC_SPLTW(npyv_u32, (unsigned int)(VAL))
+#define npyv_setall_s32(VAL) NPYV_IMPL_VEC_SPLTW(npyv_s32, (int)(VAL))
 #if NPY_SIMD_F32
-    #define npyv_setall_f32(VAL) NPYV_IMPL_VEC_SPLTW(npyv_f32, VAL)
+    #define npyv_setall_f32(VAL) NPYV_IMPL_VEC_SPLTW(npyv_f32, (VAL))
 #endif
-#define npyv_setall_u64(VAL) NPYV_IMPL_VEC_SPLTD(npyv_u64, (npy_uint64)VAL)
-#define npyv_setall_s64(VAL) NPYV_IMPL_VEC_SPLTD(npyv_s64, (npy_int64)VAL)
+#define npyv_setall_u64(VAL) NPYV_IMPL_VEC_SPLTD(npyv_u64, (npy_uint64)(VAL))
+#define npyv_setall_s64(VAL) NPYV_IMPL_VEC_SPLTD(npyv_s64, (npy_int64)(VAL))
 #define npyv_setall_f64(VAL) NPYV_IMPL_VEC_SPLTD(npyv_f64, VAL)
 
 // vector with specific values set to each lane and
 // set a specific value to all remained lanes
-#define npyv_setf_u8(FILL, ...)  ((npyv_u8){NPYV__SET_FILL_16(char, FILL, __VA_ARGS__)})
-#define npyv_setf_s8(FILL, ...)  ((npyv_s8){NPYV__SET_FILL_16(char, FILL, __VA_ARGS__)})
-#define npyv_setf_u16(FILL, ...) ((npyv_u16){NPYV__SET_FILL_8(short, FILL, __VA_ARGS__)})
+#define npyv_setf_u8(FILL, ...)  ((npyv_u8){NPYV__SET_FILL_16(unsigned char, FILL, __VA_ARGS__)})
+#define npyv_setf_s8(FILL, ...)  ((npyv_s8){NPYV__SET_FILL_16(signed char, FILL, __VA_ARGS__)})
+#define npyv_setf_u16(FILL, ...) ((npyv_u16){NPYV__SET_FILL_8(unsigned short, FILL, __VA_ARGS__)})
 #define npyv_setf_s16(FILL, ...) ((npyv_s16){NPYV__SET_FILL_8(short, FILL, __VA_ARGS__)})
-#define npyv_setf_u32(FILL, ...) ((npyv_u32){NPYV__SET_FILL_4(int, FILL, __VA_ARGS__)})
+#define npyv_setf_u32(FILL, ...) ((npyv_u32){NPYV__SET_FILL_4(unsigned int, FILL, __VA_ARGS__)})
 #define npyv_setf_s32(FILL, ...) ((npyv_s32){NPYV__SET_FILL_4(int, FILL, __VA_ARGS__)})
-#define npyv_setf_u64(FILL, ...) ((npyv_u64){NPYV__SET_FILL_2(npy_int64, FILL, __VA_ARGS__)})
+#define npyv_setf_u64(FILL, ...) ((npyv_u64){NPYV__SET_FILL_2(npy_uint64, FILL, __VA_ARGS__)})
 #define npyv_setf_s64(FILL, ...) ((npyv_s64){NPYV__SET_FILL_2(npy_int64, FILL, __VA_ARGS__)})
 #if NPY_SIMD_F32
     #define npyv_setf_f32(FILL, ...) ((npyv_f32){NPYV__SET_FILL_4(float, FILL, __VA_ARGS__)})
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/reorder.h b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/reorder.h
index b60b9287dcd..3910980a23b 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/reorder.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/simd/vec/reorder.h
@@ -68,6 +68,85 @@ NPYV_IMPL_VEC_COMBINE_ZIP(npyv_s64, s64)
 #endif
 NPYV_IMPL_VEC_COMBINE_ZIP(npyv_f64, f64)
 
+// deinterleave two vectors
+NPY_FINLINE npyv_u8x2 npyv_unzip_u8(npyv_u8 ab0, npyv_u8 ab1)
+{
+    const npyv_u8 idx_even = npyv_set_u8(
+        0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30
+    );
+    const npyv_u8 idx_odd = npyv_set_u8(
+        1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31
+    );
+    npyv_u8x2 r;
+    r.val[0] = vec_perm(ab0, ab1, idx_even);
+    r.val[1] = vec_perm(ab0, ab1, idx_odd);
+    return r;
+}
+NPY_FINLINE npyv_s8x2 npyv_unzip_s8(npyv_s8 ab0, npyv_s8 ab1)
+{
+    npyv_u8x2 ru = npyv_unzip_u8((npyv_u8)ab0, (npyv_u8)ab1);
+    npyv_s8x2 r;
+    r.val[0] = (npyv_s8)ru.val[0];
+    r.val[1] = (npyv_s8)ru.val[1];
+    return r;
+}
+NPY_FINLINE npyv_u16x2 npyv_unzip_u16(npyv_u16 ab0, npyv_u16 ab1)
+{
+    const npyv_u8 idx_even = npyv_set_u8(
+        0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29
+    );
+    const npyv_u8 idx_odd = npyv_set_u8(
+        2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31
+    );
+    npyv_u16x2 r;
+    r.val[0] = vec_perm(ab0, ab1, idx_even);
+    r.val[1] = vec_perm(ab0, ab1, idx_odd);
+    return r;
+}
+NPY_FINLINE npyv_s16x2 npyv_unzip_s16(npyv_s16 ab0, npyv_s16 ab1)
+{
+    npyv_u16x2 ru = npyv_unzip_u16((npyv_u16)ab0, (npyv_u16)ab1);
+    npyv_s16x2 r;
+    r.val[0] = (npyv_s16)ru.val[0];
+    r.val[1] = (npyv_s16)ru.val[1];
+    return r;
+}
+NPY_FINLINE npyv_u32x2 npyv_unzip_u32(npyv_u32 ab0, npyv_u32 ab1)
+{
+    npyv_u32 m0 = vec_mergeh(ab0, ab1);
+    npyv_u32 m1 = vec_mergel(ab0, ab1);
+    npyv_u32 r0 = vec_mergeh(m0, m1);
+    npyv_u32 r1 = vec_mergel(m0, m1);
+    npyv_u32x2 r;
+    r.val[0] = r0;
+    r.val[1] = r1;
+    return r;
+}
+NPY_FINLINE npyv_s32x2 npyv_unzip_s32(npyv_s32 ab0, npyv_s32 ab1)
+{
+    npyv_u32x2 ru = npyv_unzip_u32((npyv_u32)ab0, (npyv_u32)ab1);
+    npyv_s32x2 r;
+    r.val[0] = (npyv_s32)ru.val[0];
+    r.val[1] = (npyv_s32)ru.val[1];
+    return r;
+}
+#if NPY_SIMD_F32
+    NPY_FINLINE npyv_f32x2 npyv_unzip_f32(npyv_f32 ab0, npyv_f32 ab1)
+    {
+        npyv_u32x2 ru = npyv_unzip_u32((npyv_u32)ab0, (npyv_u32)ab1);
+        npyv_f32x2 r;
+        r.val[0] = (npyv_f32)ru.val[0];
+        r.val[1] = (npyv_f32)ru.val[1];
+        return r;
+    }
+#endif
+NPY_FINLINE npyv_u64x2 npyv_unzip_u64(npyv_u64 ab0, npyv_u64 ab1)
+{ return npyv_combine_u64(ab0, ab1); }
+NPY_FINLINE npyv_s64x2 npyv_unzip_s64(npyv_s64 ab0, npyv_s64 ab1)
+{ return npyv_combine_s64(ab0, ab1); }
+NPY_FINLINE npyv_f64x2 npyv_unzip_f64(npyv_f64 ab0, npyv_f64 ab1)
+{ return npyv_combine_f64(ab0, ab1); }
+
 // Reverse elements of each 64-bit lane
 NPY_FINLINE npyv_u8 npyv_rev64_u8(npyv_u8 a)
 {
@@ -111,4 +190,24 @@ NPY_FINLINE npyv_s32 npyv_rev64_s32(npyv_s32 a)
     { return (npyv_f32)npyv_rev64_u32((npyv_u32)a); }
 #endif
 
+// Permuting the elements of each 128-bit lane by immediate index for
+// each element.
+#define npyv_permi128_u32(A, E0, E1, E2, E3)      \
+    vec_perm(A, A, npyv_set_u8(                   \
+        (E0<<2), (E0<<2)+1, (E0<<2)+2, (E0<<2)+3, \
+        (E1<<2), (E1<<2)+1, (E1<<2)+2, (E1<<2)+3, \
+        (E2<<2), (E2<<2)+1, (E2<<2)+2, (E2<<2)+3, \
+        (E3<<2), (E3<<2)+1, (E3<<2)+2, (E3<<2)+3  \
+    ))
+#define npyv_permi128_s32 npyv_permi128_u32
+#define npyv_permi128_f32 npyv_permi128_u32
+
+#if defined(__IBMC__) || defined(vec_permi)
+    #define npyv_permi128_u64(A, E0, E1) vec_permi(A, A, ((E0)<<1) | (E1))
+#else
+    #define npyv_permi128_u64(A, E0, E1) vec_xxpermdi(A, A, ((E0)<<1) | (E1))
+#endif
+#define npyv_permi128_s64 npyv_permi128_u64
+#define npyv_permi128_f64 npyv_permi128_u64
+
 #endif // _NPY_SIMD_VEC_REORDER_H
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h b/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h
index ee6c7bdcadb..c62cae0868e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h
@@ -25,7 +25,7 @@
  * that is not checked. Could use absolute values and adjust the sign if that
  * functionality was desired.
  */
-static NPY_INLINE int
+static inline int
 npy_mul_with_overflow_int(npy_int * r, npy_int a, npy_int b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
@@ -60,7 +60,7 @@ npy_mul_with_overflow_int(npy_int * r, npy_int a, npy_int b)
  * that is not checked. Could use absolute values and adjust the sign if that
  * functionality was desired.
  */
-static NPY_INLINE int
+static inline int
 npy_mul_with_overflow_uint(npy_uint * r, npy_uint a, npy_uint b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
@@ -95,7 +95,7 @@ npy_mul_with_overflow_uint(npy_uint * r, npy_uint a, npy_uint b)
  * that is not checked. Could use absolute values and adjust the sign if that
  * functionality was desired.
  */
-static NPY_INLINE int
+static inline int
 npy_mul_with_overflow_long(npy_long * r, npy_long a, npy_long b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
@@ -130,7 +130,7 @@ npy_mul_with_overflow_long(npy_long * r, npy_long a, npy_long b)
  * that is not checked. Could use absolute values and adjust the sign if that
  * functionality was desired.
  */
-static NPY_INLINE int
+static inline int
 npy_mul_with_overflow_ulong(npy_ulong * r, npy_ulong a, npy_ulong b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
@@ -165,7 +165,7 @@ npy_mul_with_overflow_ulong(npy_ulong * r, npy_ulong a, npy_ulong b)
  * that is not checked. Could use absolute values and adjust the sign if that
  * functionality was desired.
  */
-static NPY_INLINE int
+static inline int
 npy_mul_with_overflow_longlong(npy_longlong * r, npy_longlong a, npy_longlong b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
@@ -200,7 +200,7 @@ npy_mul_with_overflow_longlong(npy_longlong * r, npy_longlong a, npy_longlong b)
  * that is not checked. Could use absolute values and adjust the sign if that
  * functionality was desired.
  */
-static NPY_INLINE int
+static inline int
 npy_mul_with_overflow_ulonglong(npy_ulonglong * r, npy_ulonglong a, npy_ulonglong b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
@@ -227,7 +227,7 @@ npy_mul_with_overflow_ulonglong(npy_ulonglong * r, npy_ulonglong a, npy_ulonglon
 }
 
 
-static NPY_INLINE int
+static inline int
 npy_mul_sizes_with_overflow (npy_intp * r, npy_intp a, npy_intp b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h.src b/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h.src
index 84e13481be8..d0d1917fec7 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h.src
+++ b/contrib/python/numpy/py3/numpy/core/src/common/templ_common.h.src
@@ -25,7 +25,7 @@
  * that is not checked. Could use absolute values and adjust the sign if that
  * functionality was desired.
  */
-static NPY_INLINE int
+static inline int
 npy_mul_with_overflow_@name@(@type@ * r, @type@ a, @type@ b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
@@ -52,7 +52,7 @@ npy_mul_with_overflow_@name@(@type@ * r, @type@ a, @type@ b)
 }
 /**end repeat**/
 
-static NPY_INLINE int
+static inline int
 npy_mul_sizes_with_overflow (npy_intp * r, npy_intp a, npy_intp b)
 {
 #ifdef HAVE___BUILTIN_MUL_OVERFLOW
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/ucsnarrow.h b/contrib/python/numpy/py3/numpy/core/src/common/ucsnarrow.h
index 6fe15719987..4b17a2809c1 100644
--- a/contrib/python/numpy/py3/numpy/core/src/common/ucsnarrow.h
+++ b/contrib/python/numpy/py3/numpy/core/src/common/ucsnarrow.h
@@ -2,6 +2,6 @@
 #define NUMPY_CORE_SRC_COMMON_NPY_UCSNARROW_H_
 
 NPY_NO_EXPORT PyUnicodeObject *
-PyUnicode_FromUCS4(char *src, Py_ssize_t size, int swap, int align);
+PyUnicode_FromUCS4(char const *src, Py_ssize_t size, int swap, int align);
 
 #endif  /* NUMPY_CORE_SRC_COMMON_NPY_UCSNARROW_H_ */
diff --git a/contrib/python/numpy/py3/numpy/core/src/common/utils.hpp b/contrib/python/numpy/py3/numpy/core/src/common/utils.hpp
new file mode 100644
index 00000000000..f847cab4483
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/common/utils.hpp
@@ -0,0 +1,51 @@
+#ifndef NUMPY_CORE_SRC_COMMON_UTILS_HPP
+#define NUMPY_CORE_SRC_COMMON_UTILS_HPP
+
+#include "npdef.hpp"
+
+#if NP_HAS_CPP20
+    #include <bit>
+#endif
+
+#include <type_traits>
+#include <string.h>
+
+namespace np {
+
+/** Create a value of type `To` from the bits of `from`.
+ *
+ * similar to `std::bit_cast` but compatible with C++17,
+ * should perform similar to `*reinterpret_cast<To*>(&from)`
+ * or through punning without expecting any undefined behaviors.
+ */
+template<typename To, typename From>
+#if NP_HAS_BUILTIN(__builtin_bit_cast) || NP_HAS_CPP20
+[[nodiscard]] constexpr
+#else
+inline
+#endif
+To BitCast(const From &from) noexcept
+{
+    static_assert(
+        sizeof(To) == sizeof(From),
+        "both data types must have the same size");
+
+    static_assert(
+        std::is_trivially_copyable_v<To> &&
+        std::is_trivially_copyable_v<From>,
+        "both data types must be trivially copyable");
+
+#if NP_HAS_CPP20
+    return std::bit_cast<To>(from);
+#elif NP_HAS_BUILTIN(__builtin_bit_cast)
+    return __builtin_bit_cast(To, from);
+#else
+    To to;
+    memcpy(&to, &from, sizeof(from));
+    return to;
+#endif
+}
+
+} // namespace np
+#endif // NUMPY_CORE_SRC_COMMON_UTILS_HPP
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c
index 791690edffc..8a934416a03 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c
@@ -2124,13 +2124,13 @@ get_fpu_mode(PyObject *NPY_UNUSED(self), PyObject *args)
         return NULL;
     }
 
-#if defined(_MSC_VER)
+#if defined(_MSC_VER) && !defined(__clang__)
     {
         unsigned int result = 0;
         result = _controlfp(0, 0);
         return PyLong_FromLongLong(result);
     }
-#elif defined(__GNUC__) && (defined(__x86_64__) || defined(__i386__))
+#elif (defined(__GNUC__) && (defined(__x86_64__) || defined(__i386__))) || (defined(_MSC_VER) && defined(__clang__))
     {
         unsigned short cw = 0;
         __asm__("fstcw %w0" : "=m" (cw));
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c.src
index 1fd28e72192..482596f9765 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/_multiarray_tests.c.src
@@ -2054,13 +2054,13 @@ get_fpu_mode(PyObject *NPY_UNUSED(self), PyObject *args)
         return NULL;
     }
 
-#if defined(_MSC_VER)
+#if defined(_MSC_VER) && !defined(__clang__)
     {
         unsigned int result = 0;
         result = _controlfp(0, 0);
         return PyLong_FromLongLong(result);
     }
-#elif defined(__GNUC__) && (defined(__x86_64__) || defined(__i386__))
+#elif (defined(__GNUC__) && (defined(__x86_64__) || defined(__i386__))) || (defined(_MSC_VER) && defined(__clang__))
     {
         unsigned short cw = 0;
         __asm__("fstcw %w0" : "=m" (cw));
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.c
index 8dc595738e2..da44fd01f11 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.c
@@ -13,7 +13,7 @@
 #include "common.h"
 
 
-static NPY_INLINE PyArray_Descr *
+static inline PyArray_Descr *
 int_default_descriptor(PyArray_DTypeMeta* NPY_UNUSED(cls))
 {
     return PyArray_DescrFromType(NPY_LONG);
@@ -51,7 +51,7 @@ discover_descriptor_from_pyint(
 }
 
 
-static NPY_INLINE PyArray_Descr *
+static inline PyArray_Descr *
 float_default_descriptor(PyArray_DTypeMeta* NPY_UNUSED(cls))
 {
     return PyArray_DescrFromType(NPY_DOUBLE);
@@ -66,7 +66,7 @@ discover_descriptor_from_pyfloat(
     return PyArray_DescrFromType(NPY_DOUBLE);
 }
 
-static NPY_INLINE PyArray_Descr *
+static inline PyArray_Descr *
 complex_default_descriptor(PyArray_DTypeMeta* NPY_UNUSED(cls))
 {
     return PyArray_DescrFromType(NPY_CDOUBLE);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.h
index 6901ec213a9..a3f6ceb056e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/abstractdtypes.h
@@ -30,7 +30,7 @@ initialize_and_map_pytypes_to_dtypes(void);
  *        replaced with the abstract DType.
  * @return 0 if the `obj` was not a python scalar, and 1 if it was.
  */
-static NPY_INLINE int
+static inline int
 npy_mark_tmp_array_if_pyscalar(
         PyObject *obj, PyArrayObject *arr, PyArray_DTypeMeta **dtype)
 {
@@ -58,7 +58,8 @@ npy_mark_tmp_array_if_pyscalar(
         }
         return 1;
     }
-    else if (PyComplex_Check(obj) && PyArray_TYPE(arr) == NPY_CDOUBLE) {
+    else if (PyComplex_Check(obj) && !PyArray_IsScalar(obj, CDouble)
+             && PyArray_TYPE(arr) == NPY_CDOUBLE) {
         ((PyArrayObject_fields *)arr)->flags |= NPY_ARRAY_WAS_PYTHON_COMPLEX;
         if (dtype != NULL) {
             Py_INCREF(&PyArray_PyComplexAbstractDType);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.c
index 8b765aa954d..d3b8612f709 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.c
@@ -88,7 +88,7 @@ _set_madvise_hugepage(PyObject *NPY_UNUSED(self), PyObject *enabled_obj)
  * base function for data cache with 1 byte buckets and dimension cache with
  * sizeof(npy_intp) byte buckets
  */
-static NPY_INLINE void *
+static inline void *
 _npy_alloc_cache(npy_uintp nelem, npy_uintp esz, npy_uint msz,
                  cache_bucket * cache, void * (*alloc)(size_t))
 {
@@ -126,7 +126,7 @@ _npy_alloc_cache(npy_uintp nelem, npy_uintp esz, npy_uint msz,
  * return pointer p to cache, nelem is number of elements of the cache bucket
  * size (1 or sizeof(npy_intp)) of the block pointed too
  */
-static NPY_INLINE void
+static inline void
 _npy_free_cache(void * p, npy_uintp nelem, npy_uint msz,
                 cache_bucket * cache, void (*dealloc)(void *))
 {
@@ -206,7 +206,7 @@ npy_free_cache_dim(void * p, npy_uintp sz)
 }
 
 /* Similar to array_dealloc in arrayobject.c */
-static NPY_INLINE void
+static inline void
 WARN_NO_RETURN(PyObject* warning, const char * msg) {
     if (PyErr_WarnEx(warning, msg, 1) < 0) {
         PyObject * s;
@@ -364,7 +364,7 @@ PyDataMem_RENEW(void *ptr, size_t size)
 // The default data mem allocator malloc routine does not make use of a ctx.
 // It should be called only through PyDataMem_UserNEW
 // since itself does not handle eventhook and tracemalloc logic.
-static NPY_INLINE void *
+static inline void *
 default_malloc(void *NPY_UNUSED(ctx), size_t size)
 {
     return _npy_alloc_cache(size, 1, NBUCKETS, datacache, &malloc);
@@ -373,7 +373,7 @@ default_malloc(void *NPY_UNUSED(ctx), size_t size)
 // The default data mem allocator calloc routine does not make use of a ctx.
 // It should be called only through PyDataMem_UserNEW_ZEROED
 // since itself does not handle eventhook and tracemalloc logic.
-static NPY_INLINE void *
+static inline void *
 default_calloc(void *NPY_UNUSED(ctx), size_t nelem, size_t elsize)
 {
     void * p;
@@ -395,7 +395,7 @@ default_calloc(void *NPY_UNUSED(ctx), size_t nelem, size_t elsize)
 // The default data mem allocator realloc routine does not make use of a ctx.
 // It should be called only through PyDataMem_UserRENEW
 // since itself does not handle eventhook and tracemalloc logic.
-static NPY_INLINE void *
+static inline void *
 default_realloc(void *NPY_UNUSED(ctx), void *ptr, size_t new_size)
 {
     return realloc(ptr, new_size);
@@ -404,7 +404,7 @@ default_realloc(void *NPY_UNUSED(ctx), void *ptr, size_t new_size)
 // The default data mem allocator free routine does not make use of a ctx.
 // It should be called only through PyDataMem_UserFREE
 // since itself does not handle eventhook and tracemalloc logic.
-static NPY_INLINE void
+static inline void
 default_free(void *NPY_UNUSED(ctx), void *ptr, size_t size)
 {
     _npy_free_cache(ptr, size, NBUCKETS, datacache, &free);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.h
index 0f5a7524cd8..186eb54870a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/alloc.h
@@ -31,13 +31,13 @@ npy_alloc_cache_dim(npy_uintp sz);
 NPY_NO_EXPORT void
 npy_free_cache_dim(void * p, npy_uintp sd);
 
-static NPY_INLINE void
+static inline void
 npy_free_cache_dim_obj(PyArray_Dims dims)
 {
     npy_free_cache_dim(dims.ptr, dims.len);
 }
 
-static NPY_INLINE void
+static inline void
 npy_free_cache_dim_array(PyArrayObject * arr)
 {
     npy_free_cache_dim(PyArray_DIMS(arr), PyArray_NDIM(arr));
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c
index 7cbf6f55eb0..973edd913f0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c
@@ -763,7 +763,7 @@ simd_argmax_u32(npyv_lanetype_u32 *ip, npy_intp len)
         npyv_b32 nnan_ab = npyv_and_b32(nnan_a, nnan_b);
         npyv_b32 nnan_cd = npyv_and_b32(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b32(npyv_and_b32(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b32(nnan_a);
             nnan_4[1] = npyv_tobits_b32(nnan_b);
@@ -788,7 +788,7 @@ simd_argmax_u32(npyv_lanetype_u32 *ip, npy_intp len)
     #if 0
         npyv_b32 nnan_a = npyv_notnan_u32(a);
         npy_uint64 nnan = npyv_tobits_b32(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -901,7 +901,7 @@ simd_argmin_u32(npyv_lanetype_u32 *ip, npy_intp len)
         npyv_b32 nnan_ab = npyv_and_b32(nnan_a, nnan_b);
         npyv_b32 nnan_cd = npyv_and_b32(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b32(npyv_and_b32(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b32(nnan_a);
             nnan_4[1] = npyv_tobits_b32(nnan_b);
@@ -926,7 +926,7 @@ simd_argmin_u32(npyv_lanetype_u32 *ip, npy_intp len)
     #if 0
         npyv_b32 nnan_a = npyv_notnan_u32(a);
         npy_uint64 nnan = npyv_tobits_b32(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -1043,7 +1043,7 @@ simd_argmax_s32(npyv_lanetype_s32 *ip, npy_intp len)
         npyv_b32 nnan_ab = npyv_and_b32(nnan_a, nnan_b);
         npyv_b32 nnan_cd = npyv_and_b32(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b32(npyv_and_b32(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b32(nnan_a);
             nnan_4[1] = npyv_tobits_b32(nnan_b);
@@ -1068,7 +1068,7 @@ simd_argmax_s32(npyv_lanetype_s32 *ip, npy_intp len)
     #if 0
         npyv_b32 nnan_a = npyv_notnan_s32(a);
         npy_uint64 nnan = npyv_tobits_b32(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -1181,7 +1181,7 @@ simd_argmin_s32(npyv_lanetype_s32 *ip, npy_intp len)
         npyv_b32 nnan_ab = npyv_and_b32(nnan_a, nnan_b);
         npyv_b32 nnan_cd = npyv_and_b32(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b32(npyv_and_b32(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b32(nnan_a);
             nnan_4[1] = npyv_tobits_b32(nnan_b);
@@ -1206,7 +1206,7 @@ simd_argmin_s32(npyv_lanetype_s32 *ip, npy_intp len)
     #if 0
         npyv_b32 nnan_a = npyv_notnan_s32(a);
         npy_uint64 nnan = npyv_tobits_b32(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -1323,7 +1323,7 @@ simd_argmax_u64(npyv_lanetype_u64 *ip, npy_intp len)
         npyv_b64 nnan_ab = npyv_and_b64(nnan_a, nnan_b);
         npyv_b64 nnan_cd = npyv_and_b64(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b64(npyv_and_b64(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b64(nnan_a);
             nnan_4[1] = npyv_tobits_b64(nnan_b);
@@ -1348,7 +1348,7 @@ simd_argmax_u64(npyv_lanetype_u64 *ip, npy_intp len)
     #if 0
         npyv_b64 nnan_a = npyv_notnan_u64(a);
         npy_uint64 nnan = npyv_tobits_b64(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -1461,7 +1461,7 @@ simd_argmin_u64(npyv_lanetype_u64 *ip, npy_intp len)
         npyv_b64 nnan_ab = npyv_and_b64(nnan_a, nnan_b);
         npyv_b64 nnan_cd = npyv_and_b64(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b64(npyv_and_b64(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b64(nnan_a);
             nnan_4[1] = npyv_tobits_b64(nnan_b);
@@ -1486,7 +1486,7 @@ simd_argmin_u64(npyv_lanetype_u64 *ip, npy_intp len)
     #if 0
         npyv_b64 nnan_a = npyv_notnan_u64(a);
         npy_uint64 nnan = npyv_tobits_b64(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -1603,7 +1603,7 @@ simd_argmax_s64(npyv_lanetype_s64 *ip, npy_intp len)
         npyv_b64 nnan_ab = npyv_and_b64(nnan_a, nnan_b);
         npyv_b64 nnan_cd = npyv_and_b64(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b64(npyv_and_b64(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b64(nnan_a);
             nnan_4[1] = npyv_tobits_b64(nnan_b);
@@ -1628,7 +1628,7 @@ simd_argmax_s64(npyv_lanetype_s64 *ip, npy_intp len)
     #if 0
         npyv_b64 nnan_a = npyv_notnan_s64(a);
         npy_uint64 nnan = npyv_tobits_b64(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -1741,7 +1741,7 @@ simd_argmin_s64(npyv_lanetype_s64 *ip, npy_intp len)
         npyv_b64 nnan_ab = npyv_and_b64(nnan_a, nnan_b);
         npyv_b64 nnan_cd = npyv_and_b64(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b64(npyv_and_b64(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b64(nnan_a);
             nnan_4[1] = npyv_tobits_b64(nnan_b);
@@ -1766,7 +1766,7 @@ simd_argmin_s64(npyv_lanetype_s64 *ip, npy_intp len)
     #if 0
         npyv_b64 nnan_a = npyv_notnan_s64(a);
         npy_uint64 nnan = npyv_tobits_b64(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -1883,7 +1883,7 @@ simd_argmax_f32(npyv_lanetype_f32 *ip, npy_intp len)
         npyv_b32 nnan_ab = npyv_and_b32(nnan_a, nnan_b);
         npyv_b32 nnan_cd = npyv_and_b32(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b32(npyv_and_b32(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b32(nnan_a);
             nnan_4[1] = npyv_tobits_b32(nnan_b);
@@ -1908,7 +1908,7 @@ simd_argmax_f32(npyv_lanetype_f32 *ip, npy_intp len)
     #if 1
         npyv_b32 nnan_a = npyv_notnan_f32(a);
         npy_uint64 nnan = npyv_tobits_b32(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -2021,7 +2021,7 @@ simd_argmin_f32(npyv_lanetype_f32 *ip, npy_intp len)
         npyv_b32 nnan_ab = npyv_and_b32(nnan_a, nnan_b);
         npyv_b32 nnan_cd = npyv_and_b32(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b32(npyv_and_b32(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b32(nnan_a);
             nnan_4[1] = npyv_tobits_b32(nnan_b);
@@ -2046,7 +2046,7 @@ simd_argmin_f32(npyv_lanetype_f32 *ip, npy_intp len)
     #if 1
         npyv_b32 nnan_a = npyv_notnan_f32(a);
         npy_uint64 nnan = npyv_tobits_b32(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -2163,7 +2163,7 @@ simd_argmax_f64(npyv_lanetype_f64 *ip, npy_intp len)
         npyv_b64 nnan_ab = npyv_and_b64(nnan_a, nnan_b);
         npyv_b64 nnan_cd = npyv_and_b64(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b64(npyv_and_b64(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b64(nnan_a);
             nnan_4[1] = npyv_tobits_b64(nnan_b);
@@ -2188,7 +2188,7 @@ simd_argmax_f64(npyv_lanetype_f64 *ip, npy_intp len)
     #if 1
         npyv_b64 nnan_a = npyv_notnan_f64(a);
         npy_uint64 nnan = npyv_tobits_b64(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
@@ -2301,7 +2301,7 @@ simd_argmin_f64(npyv_lanetype_f64 *ip, npy_intp len)
         npyv_b64 nnan_ab = npyv_and_b64(nnan_a, nnan_b);
         npyv_b64 nnan_cd = npyv_and_b64(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_b64(npyv_and_b64(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_b64(nnan_a);
             nnan_4[1] = npyv_tobits_b64(nnan_b);
@@ -2326,7 +2326,7 @@ simd_argmin_f64(npyv_lanetype_f64 *ip, npy_intp len)
     #if 1
         npyv_b64 nnan_a = npyv_notnan_f64(a);
         npy_uint64 nnan = npyv_tobits_b64(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c.src
index 1d7753275d7..d79be1df503 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/argfunc.dispatch.c.src
@@ -194,7 +194,7 @@ simd_@func@_@sfx@(npyv_lanetype_@sfx@ *ip, npy_intp len)
         npyv_@bsfx@ nnan_ab = npyv_and_@bsfx@(nnan_a, nnan_b);
         npyv_@bsfx@ nnan_cd = npyv_and_@bsfx@(nnan_c, nnan_d);
         npy_uint64 nnan = npyv_tobits_@bsfx@(npyv_and_@bsfx@(nnan_ab, nnan_cd));
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             npy_uint64 nnan_4[4];
             nnan_4[0] = npyv_tobits_@bsfx@(nnan_a);
             nnan_4[1] = npyv_tobits_@bsfx@(nnan_b);
@@ -219,7 +219,7 @@ simd_@func@_@sfx@(npyv_lanetype_@sfx@ *ip, npy_intp len)
     #if @is_fp@
         npyv_@bsfx@ nnan_a = npyv_notnan_@sfx@(a);
         npy_uint64 nnan = npyv_tobits_@bsfx@(nnan_a);
-        if (nnan != ((1LL << vstep) - 1)) {
+        if ((unsigned long long int)nnan != ((1ULL << vstep) - 1)) {
             for (int vi = 0; vi < vstep; ++vi) {
                 if (!((nnan >> vi) & 1)) {
                     return i + vi;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.c
index 0241fb82cc8..ba911805298 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.c
@@ -16,6 +16,7 @@
 #include "common_dtype.h"
 #include "dtypemeta.h"
 
+#include "npy_argparse.h"
 #include "abstractdtypes.h"
 #include "array_coercion.h"
 #include "ctors.h"
@@ -209,7 +210,7 @@ _PyArray_MapPyTypeToDType(
  * @param pytype Python Type to look up
  * @return DType, None if it is a known non-scalar, or NULL if an unknown object.
  */
-static NPY_INLINE PyArray_DTypeMeta *
+static inline PyArray_DTypeMeta *
 npy_discover_dtype_from_pytype(PyTypeObject *pytype)
 {
     PyObject *DType;
@@ -262,7 +263,7 @@ PyArray_DiscoverDTypeFromScalarType(PyTypeObject *pytype)
  *        it can/wants to handle the (possible) scalar value.
  * @return New reference to either a DType class, Py_None, or NULL on error.
  */
-static NPY_INLINE PyArray_DTypeMeta *
+static inline PyArray_DTypeMeta *
 discover_dtype_from_pyobject(
         PyObject *obj, enum _dtype_discovery_flags *flags,
         PyArray_DTypeMeta *fixed_DType)
@@ -346,7 +347,7 @@ discover_dtype_from_pyobject(
  * @param obj The Python scalar object. At the time of calling this function
  *        it must be known that `obj` should represent a scalar.
  */
-static NPY_INLINE PyArray_Descr *
+static inline PyArray_Descr *
 find_scalar_descriptor(
         PyArray_DTypeMeta *fixed_DType, PyArray_DTypeMeta *DType,
         PyObject *obj)
@@ -611,7 +612,7 @@ static coercion_cache_obj *_coercion_cache_cache[COERCION_CACHE_CACHE_SIZE];
 /*
  * Steals a reference to the object.
  */
-static NPY_INLINE int
+static inline int
 npy_new_coercion_cache(
         PyObject *converted_obj, PyObject *arr_or_sequence, npy_bool sequence,
         coercion_cache_obj ***next_ptr, int ndim)
@@ -681,7 +682,7 @@ npy_free_coercion_cache(coercion_cache_obj *next) {
  * @param flags dtype discover flags to signal failed promotion.
  * @return -1 on error, 0 on success.
  */
-static NPY_INLINE int
+static inline int
 handle_promotion(PyArray_Descr **out_descr, PyArray_Descr *descr,
         PyArray_DTypeMeta *fixed_DType, enum _dtype_discovery_flags *flags)
 {
@@ -726,7 +727,7 @@ handle_promotion(PyArray_Descr **out_descr, PyArray_Descr *descr,
  *
  * @return 0 on success -1 on error
  */
-static NPY_INLINE int
+static inline int
 handle_scalar(
         PyObject *obj, int curr_dims, int *max_dims,
         PyArray_Descr **out_descr, npy_intp *out_shape,
@@ -873,42 +874,62 @@ find_descriptor_from_array(
 
 /**
  * Given a dtype or DType object, find the correct descriptor to cast the
- * array to.
+ * array to.  In some places, this function is used with dtype=NULL which
+ * means that legacy behavior is used: The dtype instances "S0", "U0", and
+ * "V0" are converted to mean the DType classes instead.
+ * When dtype != NULL, this path is ignored, and the function does nothing
+ * unless descr == NULL. If both descr and dtype are null, it returns the
+ * descriptor for the array.
  *
  * This function is identical to normal casting using only the dtype, however,
  * it supports inspecting the elements when the array has object dtype
  * (and the given datatype describes a parametric DType class).
  *
  * @param arr
- * @param dtype A dtype instance or class.
+ * @param dtype NULL or a dtype class
+ * @param descr A dtype instance, if the dtype is NULL the dtype class is
+ *              found and e.g. "S0" is converted to denote only String.
  * @return A concrete dtype instance or NULL
  */
 NPY_NO_EXPORT PyArray_Descr *
-PyArray_AdaptDescriptorToArray(PyArrayObject *arr, PyObject *dtype)
+PyArray_AdaptDescriptorToArray(
+        PyArrayObject *arr, PyArray_DTypeMeta *dtype, PyArray_Descr *descr)
 {
     /* If the requested dtype is flexible, adapt it */
-    PyArray_Descr *new_dtype;
-    PyArray_DTypeMeta *new_DType;
+    PyArray_Descr *new_descr;
     int res;
 
-    res = PyArray_ExtractDTypeAndDescriptor((PyObject *)dtype,
-            &new_dtype, &new_DType);
-    if (res < 0) {
-        return NULL;
+    if (dtype != NULL && descr != NULL) {
+        /* descr was given and no special logic, return (call not necessary) */
+        Py_INCREF(descr);
+        return descr;
     }
-    if (new_dtype == NULL) {
-        res = find_descriptor_from_array(arr, new_DType, &new_dtype);
+    if (dtype == NULL) {
+        res = PyArray_ExtractDTypeAndDescriptor(descr, &new_descr, &dtype);
         if (res < 0) {
-            Py_DECREF(new_DType);
             return NULL;
         }
-        if (new_dtype == NULL) {
-            /* This is an object array but contained no elements, use default */
-            new_dtype = NPY_DT_CALL_default_descr(new_DType);
+        if (new_descr != NULL) {
+            Py_DECREF(dtype);
+            return new_descr;
         }
     }
-    Py_DECREF(new_DType);
-    return new_dtype;
+    else {
+        assert(descr == NULL);  /* gueranteed above */
+        Py_INCREF(dtype);
+    }
+
+    res = find_descriptor_from_array(arr, dtype, &new_descr);
+    if (res < 0) {
+        Py_DECREF(dtype);
+        return NULL;
+    }
+    if (new_descr == NULL) {
+        /* This is an object array but contained no elements, use default */
+        new_descr = NPY_DT_CALL_default_descr(dtype);
+    }
+    Py_XDECREF(dtype);
+    return new_descr;
 }
 
 
@@ -1004,53 +1025,6 @@ PyArray_DiscoverDTypeAndShape_Recursive(
             Py_DECREF(arr);
             arr = NULL;
         }
-        else if (curr_dims > 0 && curr_dims != max_dims) {
-            /*
-             * Deprecated 2020-12-09, NumPy 1.20
-             *
-             * See https://github.com/numpy/numpy/issues/17965
-             * Shapely had objects which are not sequences but did export
-             * the array-interface (and so are arguably array-like).
-             * Previously numpy would not use array-like information during
-             * shape discovery, so that it ended up acting as if this was
-             * an (unknown) scalar but with the specified dtype.
-             * Thus we ignore "scalars" here, as the value stored in the
-             * array should be acceptable.
-             */
-            if (PyArray_NDIM(arr) > 0 && NPY_UNLIKELY(!PySequence_Check(obj))) {
-                if (PyErr_WarnFormat(PyExc_FutureWarning, 1,
-                        "The input object of type '%s' is an array-like "
-                        "implementing one of the corresponding protocols "
-                        "(`__array__`, `__array_interface__` or "
-                        "`__array_struct__`); but not a sequence (or 0-D). "
-                        "In the future, this object will be coerced as if it "
-                        "was first converted using `np.array(obj)`. "
-                        "To retain the old behaviour, you have to either "
-                        "modify the type '%s', or assign to an empty array "
-                        "created with `np.empty(correct_shape, dtype=object)`.",
-                        Py_TYPE(obj)->tp_name, Py_TYPE(obj)->tp_name) < 0) {
-                    Py_DECREF(arr);
-                    return -1;
-                }
-                /*
-                 * Strangely enough, even though we threw away the result here,
-                 * we did use it during descriptor discovery, so promote it:
-                 */
-                if (update_shape(curr_dims, &max_dims, out_shape,
-                        0, NULL, NPY_FALSE, flags) < 0) {
-                    *flags |= FOUND_RAGGED_ARRAY;
-                    Py_DECREF(arr);
-                    return max_dims;
-                }
-                if (!(*flags & DESCRIPTOR_WAS_SET) && handle_promotion(
-                        out_descr, PyArray_DESCR(arr), fixed_DType, flags) < 0) {
-                    Py_DECREF(arr);
-                    return -1;
-                }
-                Py_DECREF(arr);
-                return max_dims;
-            }
-        }
     }
     if (arr != NULL) {
         /*
@@ -1260,7 +1234,9 @@ PyArray_DiscoverDTypeAndShape(
     }
 
     if (requested_descr != NULL) {
-        assert(fixed_DType == NPY_DTYPE(requested_descr));
+        if (fixed_DType != NULL) {
+            assert(fixed_DType == NPY_DTYPE(requested_descr));
+        }
         /* The output descriptor must be the input. */
         Py_INCREF(requested_descr);
         *out_descr = requested_descr;
@@ -1380,105 +1356,25 @@ PyArray_DiscoverDTypeAndShape(
 }
 
 
-
-/**
- * Check the descriptor is a legacy "flexible" DType instance, this is
- * an instance which is (normally) not attached to an array, such as a string
- * of length 0 or a datetime with no unit.
- * These should be largely deprecated, and represent only the DType class
- * for most `dtype` parameters.
- *
- * TODO: This function should eventually receive a deprecation warning and
- *       be removed.
- *
- * @param descr
- * @return 1 if this is not a concrete dtype instance 0 otherwise
- */
-static int
-descr_is_legacy_parametric_instance(PyArray_Descr *descr)
-{
-    if (PyDataType_ISUNSIZED(descr)) {
-        return 1;
-    }
-    /* Flexible descr with generic time unit (which can be adapted) */
-    if (PyDataType_ISDATETIME(descr)) {
-        PyArray_DatetimeMetaData *meta;
-        meta = get_datetime_metadata_from_dtype(descr);
-        if (meta->base == NPY_FR_GENERIC) {
-            return 1;
-        }
-    }
-    return 0;
-}
-
-
-/**
- * Given either a DType instance or class, (or legacy flexible instance),
- * ands sets output dtype instance and DType class. Both results may be
- * NULL, but if `out_descr` is set `out_DType` will always be the
- * corresponding class.
- *
- * @param dtype
- * @param out_descr
- * @param out_DType
- * @return 0 on success -1 on failure
- */
-NPY_NO_EXPORT int
-PyArray_ExtractDTypeAndDescriptor(PyObject *dtype,
-        PyArray_Descr **out_descr, PyArray_DTypeMeta **out_DType)
-{
-    *out_DType = NULL;
-    *out_descr = NULL;
-
-    if (dtype != NULL) {
-        if (PyObject_TypeCheck(dtype, (PyTypeObject *)&PyArrayDTypeMeta_Type)) {
-            assert(dtype != (PyObject * )&PyArrayDescr_Type);  /* not np.dtype */
-            *out_DType = (PyArray_DTypeMeta *)dtype;
-            Py_INCREF(*out_DType);
-        }
-        else if (PyObject_TypeCheck((PyObject *)Py_TYPE(dtype),
-                    (PyTypeObject *)&PyArrayDTypeMeta_Type)) {
-            *out_DType = NPY_DTYPE(dtype);
-            Py_INCREF(*out_DType);
-            if (!descr_is_legacy_parametric_instance((PyArray_Descr *)dtype)) {
-                *out_descr = (PyArray_Descr *)dtype;
-                Py_INCREF(*out_descr);
-            }
-        }
-        else {
-            PyErr_SetString(PyExc_TypeError,
-                    "dtype parameter must be a DType instance or class.");
-            return -1;
-        }
-    }
-    return 0;
-}
-
-
 /*
  * Python API function to expose the dtype+shape discovery functionality
  * directly.
  */
 NPY_NO_EXPORT PyObject *
 _discover_array_parameters(PyObject *NPY_UNUSED(self),
-                           PyObject *args, PyObject *kwargs)
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
-    static char *kwlist[] = {"obj", "dtype", NULL};
-
     PyObject *obj;
-    PyObject *dtype = NULL;
-    PyArray_Descr *fixed_descriptor = NULL;
-    PyArray_DTypeMeta *fixed_DType = NULL;
+    npy_dtype_info dt_info = {NULL, NULL};
     npy_intp shape[NPY_MAXDIMS];
 
-    if (!PyArg_ParseTupleAndKeywords(
-            args, kwargs, "O|O:_discover_array_parameters", kwlist,
-            &obj, &dtype)) {
-        return NULL;
-    }
-
-    if (PyArray_ExtractDTypeAndDescriptor(dtype,
-            &fixed_descriptor, &fixed_DType) < 0) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments(
+            "_discover_array_parameters", args, len_args, kwnames,
+            "", NULL, &obj,
+            "|dtype", &PyArray_DTypeOrDescrConverterOptional, &dt_info,
+            NULL, NULL, NULL) < 0) {
+        /* fixed is last to parse, so never necessary to clean up */
         return NULL;
     }
 
@@ -1487,9 +1383,9 @@ _discover_array_parameters(PyObject *NPY_UNUSED(self),
     int ndim = PyArray_DiscoverDTypeAndShape(
             obj, NPY_MAXDIMS, shape,
             &coercion_cache,
-            fixed_DType, fixed_descriptor, (PyArray_Descr **)&out_dtype, 0);
-    Py_XDECREF(fixed_DType);
-    Py_XDECREF(fixed_descriptor);
+            dt_info.dtype, dt_info.descr, (PyArray_Descr **)&out_dtype, 0);
+    Py_XDECREF(dt_info.dtype);
+    Py_XDECREF(dt_info.descr);
     if (ndim < 0) {
         return NULL;
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.h
index 63d543cf750..0757c1cb801 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_coercion.h
@@ -26,7 +26,8 @@ NPY_NO_EXPORT int
 PyArray_Pack(PyArray_Descr *descr, char *item, PyObject *value);
 
 NPY_NO_EXPORT PyArray_Descr *
-PyArray_AdaptDescriptorToArray(PyArrayObject *arr, PyObject *dtype);
+PyArray_AdaptDescriptorToArray(
+        PyArrayObject *arr, PyArray_DTypeMeta *dtype, PyArray_Descr *descr);
 
 NPY_NO_EXPORT int
 PyArray_DiscoverDTypeAndShape(
@@ -36,14 +37,9 @@ PyArray_DiscoverDTypeAndShape(
         PyArray_DTypeMeta *fixed_DType, PyArray_Descr *requested_descr,
         PyArray_Descr **out_descr, int never_copy);
 
-NPY_NO_EXPORT int
-PyArray_ExtractDTypeAndDescriptor(PyObject *dtype,
-        PyArray_Descr **out_descr, PyArray_DTypeMeta **out_DType);
-
 NPY_NO_EXPORT PyObject *
 _discover_array_parameters(PyObject *NPY_UNUSED(self),
-                           PyObject *args, PyObject *kwargs);
-
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames);
 
 /* Would make sense to inline the freeing functions everywhere */
 /* Frees the coercion cache object recursively. */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.c
index ff74a453069..87515bb03aa 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.c
@@ -27,15 +27,18 @@
  *    weak reference to the input DTypes.
  */
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#define _UMATHMODULE
 #define _MULTIARRAYMODULE
 
 #include <npy_pycompat.h>
 #include "arrayobject.h"
+#include "array_coercion.h"
 #include "array_method.h"
 #include "dtypemeta.h"
 #include "common_dtype.h"
 #include "convert_datatype.h"
 #include "common.h"
+#include "numpy/ufuncobject.h"
 
 
 /*
@@ -99,7 +102,7 @@ default_resolve_descriptors(
 }
 
 
-NPY_INLINE static int
+static inline int
 is_contiguous(
         npy_intp const *strides, PyArray_Descr *const *descriptors, int nargs)
 {
@@ -248,6 +251,7 @@ fill_arraymethod_from_slots(
     /* Set the defaults */
     meth->get_strided_loop = &npy_default_get_strided_loop;
     meth->resolve_descriptors = &default_resolve_descriptors;
+    meth->get_reduction_initial = NULL;  /* no initial/identity by default */
 
     /* Fill in the slots passed by the user */
     /*
@@ -260,7 +264,7 @@ fill_arraymethod_from_slots(
             case NPY_METH_resolve_descriptors:
                 meth->resolve_descriptors = slot->pfunc;
                 continue;
-            case NPY_METH_get_loop:
+            case _NPY_METH_get_loop:
                 /*
                  * NOTE: get_loop is considered "unstable" in the public API,
                  *       I do not like the signature, and the `move_references`
@@ -284,6 +288,12 @@ fill_arraymethod_from_slots(
             case NPY_METH_unaligned_contiguous_loop:
                 meth->unaligned_contiguous_loop = slot->pfunc;
                 continue;
+            case NPY_METH_get_reduction_initial:
+                meth->get_reduction_initial = slot->pfunc;
+                continue;
+            case NPY_METH_contiguous_indexed_loop:
+                meth->contiguous_indexed_loop = slot->pfunc;
+                continue;
             default:
                 break;
         }
@@ -334,27 +344,39 @@ fill_arraymethod_from_slots(
     }
 
     /* Check whether the provided loops make sense. */
+    if (meth->flags & NPY_METH_SUPPORTS_UNALIGNED) {
+        if (meth->unaligned_strided_loop == NULL) {
+            PyErr_Format(PyExc_TypeError,
+                    "Must provide unaligned strided inner loop when using "
+                    "NPY_METH_SUPPORTS_UNALIGNED flag (in method: %s)",
+                    spec->name);
+            return -1;
+        }
+    }
+    else {
+        if (meth->unaligned_strided_loop != NULL) {
+            PyErr_Format(PyExc_TypeError,
+                    "Must not provide unaligned strided inner loop when not "
+                    "using NPY_METH_SUPPORTS_UNALIGNED flag (in method: %s)",
+                    spec->name);
+            return -1;
+        }
+    }
+    /* Fill in the blanks: */
+    if (meth->unaligned_contiguous_loop == NULL) {
+        meth->unaligned_contiguous_loop = meth->unaligned_strided_loop;
+    }
     if (meth->strided_loop == NULL) {
-        PyErr_Format(PyExc_TypeError,
-                "Must provide a strided inner loop function. (method: %s)",
-                spec->name);
-        return -1;
+        meth->strided_loop = meth->unaligned_strided_loop;
     }
     if (meth->contiguous_loop == NULL) {
         meth->contiguous_loop = meth->strided_loop;
     }
-    if (meth->unaligned_contiguous_loop != NULL &&
-            meth->unaligned_strided_loop == NULL) {
-        PyErr_Format(PyExc_TypeError,
-                "Must provide unaligned strided inner loop when providing "
-                "a contiguous version. (method: %s)", spec->name);
-        return -1;
-    }
-    if ((meth->unaligned_strided_loop == NULL) !=
-            !(meth->flags & NPY_METH_SUPPORTS_UNALIGNED)) {
+
+    if (meth->strided_loop == NULL) {
         PyErr_Format(PyExc_TypeError,
-                "Must provide unaligned strided inner loop when providing "
-                "a contiguous version. (method: %s)", spec->name);
+                "Must provide a strided inner loop function. (method: %s)",
+                spec->name);
         return -1;
     }
 
@@ -883,7 +905,7 @@ generic_masked_strided_loop(PyArrayMethod_Context *context,
 /*
  * Fetches a strided-loop function that supports a boolean mask as additional
  * (last) operand to the strided-loop.  It is otherwise largely identical to
- * the `get_loop` method which it wraps.
+ * the `get_strided_loop` method which it wraps.
  * This is the core implementation for the ufunc `where=...` keyword argument.
  *
  * NOTE: This function does not support `move_references` or inner dimensions.
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.h
index c9ec8903d1b..c82a968cd13 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/array_method.h
@@ -11,40 +11,7 @@
 extern "C" {
 #endif
 
-typedef enum {
-    /* Flag for whether the GIL is required */
-    NPY_METH_REQUIRES_PYAPI = 1 << 1,
-    /*
-     * Some functions cannot set floating point error flags, this flag
-     * gives us the option (not requirement) to skip floating point error
-     * setup/check. No function should set error flags and ignore them
-     * since it would interfere with chaining operations (e.g. casting).
-     */
-    /*
-     * TODO: Change this into a positive flag?  That would make "combing"
-     *       multiple methods easier. OTOH, if we add more flags, the default
-     *       would be 0 just like it is here.
-     */
-    NPY_METH_NO_FLOATINGPOINT_ERRORS = 1 << 2,
-    /* Whether the method supports unaligned access (not runtime) */
-    NPY_METH_SUPPORTS_UNALIGNED = 1 << 3,
-    /*
-     * Private flag for now for *logic* functions.  The logical functions
-     * `logical_or` and `logical_and` can always cast the inputs to booleans
-     * "safely" (because that is how the cast to bool is defined).
-     * @seberg: I am not sure this is the best way to handle this, so its
-     * private for now (also it is very limited anyway).
-     * There is one "exception". NA aware dtypes cannot cast to bool
-     * (hopefully), so the `??->?` loop should error even with this flag.
-     * But a second NA fallback loop will be necessary.
-     */
-    _NPY_METH_FORCE_CAST_INPUTS = 1 << 17,
-
-    /* All flags which can change at runtime */
-    NPY_METH_RUNTIME_FLAGS = (
-            NPY_METH_REQUIRES_PYAPI |
-            NPY_METH_NO_FLOATINGPOINT_ERRORS),
-} NPY_ARRAYMETHOD_FLAGS;
+#include "numpy/_dtype_api.h"
 
 
 /*
@@ -60,119 +27,6 @@ typedef enum {
             ((flags1 | flags2) & ~PyArrayMethod_MINIMAL_FLAGS)  \
             | (flags1 & flags2)))
 
-
-struct PyArrayMethodObject_tag;
-
-/*
- * This struct is specific to an individual (possibly repeated) call of
- * the ArrayMethods strided operator, and as such is passed into the various
- * methods of the ArrayMethod object (the resolve_descriptors function,
- * the get_loop function and the individual lowlevel strided operator calls).
- * It thus has to be persistent for one end-user call, and then be discarded.
- *
- * TODO: Before making this public, we should review which information should
- *       be stored on the Context/BoundArrayMethod vs. the ArrayMethod.
- */
-typedef struct {
-    PyObject *caller;  /* E.g. the original ufunc, may be NULL */
-    struct PyArrayMethodObject_tag *method;
-
-    /* Operand descriptors, filled in by resolve_descriptors */
-    PyArray_Descr **descriptors;
-} PyArrayMethod_Context;
-
-
-typedef int (PyArrayMethod_StridedLoop)(PyArrayMethod_Context *context,
-        char *const *data, const npy_intp *dimensions, const npy_intp *strides,
-        NpyAuxData *transferdata);
-
-
-typedef NPY_CASTING (resolve_descriptors_function)(
-        struct PyArrayMethodObject_tag *method,
-        PyArray_DTypeMeta **dtypes,
-        PyArray_Descr **given_descrs,
-        PyArray_Descr **loop_descrs,
-        npy_intp *view_offset);
-
-
-typedef int (get_loop_function)(
-        PyArrayMethod_Context *context,
-        int aligned, int move_references,
-        const npy_intp *strides,
-        PyArrayMethod_StridedLoop **out_loop,
-        NpyAuxData **out_transferdata,
-        NPY_ARRAYMETHOD_FLAGS *flags);
-
-
-/*
- * The following functions are only used be the wrapping array method defined
- * in umath/wrapping_array_method.c
- */
-
-/*
- * The function to convert the given descriptors (passed in to
- * `resolve_descriptors`) and translates them for the wrapped loop.
- * The new descriptors MUST be viewable with the old ones, `NULL` must be
- * supported (for outputs) and should normally be forwarded.
- *
- * The function must clean up on error.
- *
- * NOTE: We currently assume that this translation gives "viewable" results.
- *       I.e. there is no additional casting related to the wrapping process.
- *       In principle that could be supported, but not sure it is useful.
- *       This currently also means that e.g. alignment must apply identically
- *       to the new dtypes.
- *
- * TODO: Due to the fact that `resolve_descriptors` is also used for `can_cast`
- *       there is no way to "pass out" the result of this function.  This means
- *       it will be called twice for every ufunc call.
- *       (I am considering including `auxdata` as an "optional" parameter to
- *       `resolve_descriptors`, so that it can be filled there if not NULL.)
- */
-typedef int translate_given_descrs_func(int nin, int nout,
-        PyArray_DTypeMeta *wrapped_dtypes[],
-        PyArray_Descr *given_descrs[], PyArray_Descr *new_descrs[]);
-
-/**
- * The function to convert the actual loop descriptors (as returned by the
- * original `resolve_descriptors` function) to the ones the output array
- * should use.
- * This function must return "viewable" types, it must not mutate them in any
- * form that would break the inner-loop logic.  Does not need to support NULL.
- *
- * The function must clean up on error.
- *
- * @param nargs Number of arguments
- * @param new_dtypes The DTypes of the output (usually probably not needed)
- * @param given_descrs Original given_descrs to the resolver, necessary to
- *        fetch any information related to the new dtypes from the original.
- * @param original_descrs The `loop_descrs` returned by the wrapped loop.
- * @param loop_descrs The output descriptors, compatible to `original_descrs`.
- *
- * @returns 0 on success, -1 on failure.
- */
-typedef int translate_loop_descrs_func(int nin, int nout,
-        PyArray_DTypeMeta *new_dtypes[], PyArray_Descr *given_descrs[],
-        PyArray_Descr *original_descrs[], PyArray_Descr *loop_descrs[]);
-
-
-/*
- * This struct will be public and necessary for creating a new ArrayMethod
- * object (casting and ufuncs).
- * We could version the struct, although since we allow passing arbitrary
- * data using the slots, and have flags, that may be enough?
- * (See also PyBoundArrayMethodObject.)
- */
-typedef struct {
-    const char *name;
-    int nin, nout;
-    NPY_CASTING casting;
-    NPY_ARRAYMETHOD_FLAGS flags;
-    PyArray_DTypeMeta **dtypes;
-    PyType_Slot *slots;
-} PyArrayMethod_Spec;
-
-
 /*
  * Structure of the ArrayMethod. This structure should probably not be made
  * public. If necessary, we can make certain operations on it public
@@ -193,16 +47,20 @@ typedef struct PyArrayMethodObject_tag {
     NPY_ARRAYMETHOD_FLAGS flags;
     resolve_descriptors_function *resolve_descriptors;
     get_loop_function *get_strided_loop;
+    get_reduction_initial_function  *get_reduction_initial;
     /* Typical loop functions (contiguous ones are used in current casts) */
     PyArrayMethod_StridedLoop *strided_loop;
     PyArrayMethod_StridedLoop *contiguous_loop;
     PyArrayMethod_StridedLoop *unaligned_strided_loop;
     PyArrayMethod_StridedLoop *unaligned_contiguous_loop;
+    PyArrayMethod_StridedLoop *contiguous_indexed_loop;
     /* Chunk only used for wrapping array method defined in umath */
     struct PyArrayMethodObject_tag *wrapped_meth;
     PyArray_DTypeMeta **wrapped_dtypes;
     translate_given_descrs_func *translate_given_descrs;
     translate_loop_descrs_func *translate_loop_descrs;
+    /* Chunk reserved for use by the legacy fallback arraymethod */
+    char legacy_initial[sizeof(npy_clongdouble)];  /* initial value storage */
 } PyArrayMethodObject;
 
 
@@ -226,18 +84,6 @@ typedef struct {
 extern NPY_NO_EXPORT PyTypeObject PyArrayMethod_Type;
 extern NPY_NO_EXPORT PyTypeObject PyBoundArrayMethod_Type;
 
-/*
- * SLOTS IDs For the ArrayMethod creation, one public, the IDs are fixed.
- * TODO: Before making it public, consider adding a large constant to private
- *       slots.
- */
-#define NPY_METH_resolve_descriptors 1
-#define NPY_METH_get_loop 2
-#define NPY_METH_strided_loop 3
-#define NPY_METH_contiguous_loop 4
-#define NPY_METH_unaligned_strided_loop 5
-#define NPY_METH_unaligned_contiguous_loop 6
-
 
 /*
  * Used internally (initially) for real to complex loops only
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.c
index a3d55bdc22d..3c55e216484 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.c
@@ -1,11 +1,15 @@
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
 #define _MULTIARRAYMODULE
 
+#include <Python.h>
+#include "structmember.h"
+
 #include "npy_pycompat.h"
 #include "get_attr_string.h"
 #include "npy_import.h"
 #include "multiarraymodule.h"
 
+#include "arrayfunction_override.h"
 
 /* Return the ndarray.__array_function__ method. */
 static PyObject *
@@ -200,190 +204,67 @@ call_array_function(PyObject* argument, PyObject* method,
 }
 
 
-/**
- * Internal handler for the array-function dispatching. The helper returns
- * either the result, or NotImplemented (as a borrowed reference).
- *
- * @param public_api The public API symbol used for dispatching
- * @param relevant_args Arguments which may implement __array_function__
- * @param args Original arguments
- * @param kwargs Original keyword arguments
- *
- * @returns The result of the dispatched version, or a borrowed reference
- *          to NotImplemented to indicate the default implementation should
- *          be used.
+
+/*
+ * Helper to convert from vectorcall convention, since the protocol requires
+ * args and kwargs to be passed as tuple and dict explicitly.
+ * We always pass a dict, so always returns it.
  */
-static PyObject *
-array_implement_array_function_internal(
-    PyObject *public_api, PyObject *relevant_args,
-    PyObject *args, PyObject *kwargs)
+static int
+get_args_and_kwargs(
+        PyObject *const *fast_args, Py_ssize_t len_args, PyObject *kwnames,
+        PyObject **out_args, PyObject **out_kwargs)
 {
-    PyObject *implementing_args[NPY_MAXARGS];
-    PyObject *array_function_methods[NPY_MAXARGS];
-    PyObject *types = NULL;
+    len_args = PyVectorcall_NARGS(len_args);
+    PyObject *args = PyTuple_New(len_args);
+    PyObject *kwargs = NULL;
 
-    PyObject *result = NULL;
-
-    static PyObject *errmsg_formatter = NULL;
-
-    relevant_args = PySequence_Fast(
-        relevant_args,
-        "dispatcher for __array_function__ did not return an iterable");
-    if (relevant_args == NULL) {
-        return NULL;
+    if (args == NULL) {
+        return -1;
     }
-
-    /* Collect __array_function__ implementations */
-    int num_implementing_args = get_implementing_args_and_methods(
-        relevant_args, implementing_args, array_function_methods);
-    if (num_implementing_args == -1) {
-        goto cleanup;
+    for (Py_ssize_t i = 0; i < len_args; i++) {
+        Py_INCREF(fast_args[i]);
+        PyTuple_SET_ITEM(args, i, fast_args[i]);
     }
-
-    /*
-     * Handle the typical case of no overrides. This is merely an optimization
-     * if some arguments are ndarray objects, but is also necessary if no
-     * arguments implement __array_function__ at all (e.g., if they are all
-     * built-in types).
-     */
-    int any_overrides = 0;
-    for (int j = 0; j < num_implementing_args; j++) {
-        if (!is_default_array_function(array_function_methods[j])) {
-            any_overrides = 1;
-            break;
-        }
+    kwargs = PyDict_New();
+    if (kwargs == NULL) {
+        Py_DECREF(args);
+        return -1;
     }
-    if (!any_overrides) {
-        /*
-         * When the default implementation should be called, return
-         * `Py_NotImplemented` to indicate this.
-         */
-        result = Py_NotImplemented;
-        goto cleanup;
-    }
-
-    /*
-     * Create a Python object for types.
-     * We use a tuple, because it's the fastest Python collection to create
-     * and has the bonus of being immutable.
-     */
-    types = PyTuple_New(num_implementing_args);
-    if (types == NULL) {
-        goto cleanup;
-    }
-    for (int j = 0; j < num_implementing_args; j++) {
-        PyObject *arg_type = (PyObject *)Py_TYPE(implementing_args[j]);
-        Py_INCREF(arg_type);
-        PyTuple_SET_ITEM(types, j, arg_type);
-    }
-
-    /* Call __array_function__ methods */
-    for (int j = 0; j < num_implementing_args; j++) {
-        PyObject *argument = implementing_args[j];
-        PyObject *method = array_function_methods[j];
-
-        /*
-         * We use `public_api` instead of `implementation` here so
-         * __array_function__ implementations can do equality/identity
-         * comparisons.
-         */
-        result = call_array_function(
-            argument, method, public_api, types, args, kwargs);
-
-        if (result == Py_NotImplemented) {
-            /* Try the next one */
-            Py_DECREF(result);
-            result = NULL;
-        }
-        else {
-            /* Either a good result, or an exception was raised. */
-            goto cleanup;
+    if (kwnames != NULL) {
+        Py_ssize_t nkwargs = PyTuple_GET_SIZE(kwnames);
+        for (Py_ssize_t i = 0; i < nkwargs; i++) {
+            PyObject *key = PyTuple_GET_ITEM(kwnames, i);
+            PyObject *value = fast_args[i+len_args];
+            if (PyDict_SetItem(kwargs, key, value) < 0) {
+                Py_DECREF(args);
+                Py_DECREF(kwargs);
+                return -1;
+            }
         }
     }
+    *out_args = args;
+    *out_kwargs = kwargs;
+    return 0;
+}
+
 
+static void
+set_no_matching_types_error(PyObject *public_api, PyObject *types)
+{
+    static PyObject *errmsg_formatter = NULL;
     /* No acceptable override found, raise TypeError. */
     npy_cache_import("numpy.core._internal",
                      "array_function_errmsg_formatter",
                      &errmsg_formatter);
     if (errmsg_formatter != NULL) {
         PyObject *errmsg = PyObject_CallFunctionObjArgs(
-            errmsg_formatter, public_api, types, NULL);
+                errmsg_formatter, public_api, types, NULL);
         if (errmsg != NULL) {
             PyErr_SetObject(PyExc_TypeError, errmsg);
             Py_DECREF(errmsg);
         }
     }
-
-cleanup:
-    for (int j = 0; j < num_implementing_args; j++) {
-        Py_DECREF(implementing_args[j]);
-        Py_DECREF(array_function_methods[j]);
-    }
-    Py_XDECREF(types);
-    Py_DECREF(relevant_args);
-    return result;
-}
-
-
-/*
- * Implements the __array_function__ protocol for a Python function, as described in
- * in NEP-18. See numpy.core.overrides for a full docstring.
- */
-NPY_NO_EXPORT PyObject *
-array_implement_array_function(
-    PyObject *NPY_UNUSED(dummy), PyObject *positional_args)
-{
-    PyObject *res, *implementation, *public_api, *relevant_args, *args, *kwargs;
-    /*
-     * Very few functions use **kwargs, only check for like then (note that
-     * this is a backport only change, 1.25.x has been refactored)
-     */
-    PyObject *uses_like;
-
-    if (!PyArg_UnpackTuple(
-            positional_args, "implement_array_function", 6, 6,
-            &implementation, &public_api, &relevant_args, &args, &kwargs,
-            &uses_like)) {
-        return NULL;
-    }
-
-    /*
-     * Remove `like=` kwarg, which is NumPy-exclusive and thus not present
-     * in downstream libraries. If `like=` is specified but doesn't
-     * implement `__array_function__`, raise a `TypeError`.
-     */
-    if (uses_like == Py_True
-            && kwargs != NULL && PyDict_Contains(kwargs, npy_ma_str_like)) {
-        PyObject *like_arg = PyDict_GetItem(kwargs, npy_ma_str_like);
-        if (like_arg != NULL) {
-            PyObject *tmp_has_override = get_array_function(like_arg);
-            if (tmp_has_override == NULL) {
-                return PyErr_Format(PyExc_TypeError,
-                        "The `like` argument must be an array-like that "
-                        "implements the `__array_function__` protocol.");
-            }
-            Py_DECREF(tmp_has_override);
-            PyDict_DelItem(kwargs, npy_ma_str_like);
-
-            /*
-             * If `like=` kwarg was removed, `implementation` points to the NumPy
-             * public API, as `public_api` is in that case the wrapper dispatcher
-             * function. For example, in the `np.full` case, `implementation` is
-             * `np.full`, whereas `public_api` is `_full_with_like`. This is done
-             * to ensure `__array_function__` implementations can do
-             * equality/identity comparisons when `like=` is present.
-             */
-            public_api = implementation;
-        }
-    }
-
-    res = array_implement_array_function_internal(
-        public_api, relevant_args, args, kwargs);
-
-    if (res == Py_NotImplemented) {
-        return PyObject_Call(implementation, args, kwargs);
-    }
-    return res;
 }
 
 /*
@@ -399,64 +280,52 @@ array_implement_c_array_function_creation(
     PyObject *args, PyObject *kwargs,
     PyObject *const *fast_args, Py_ssize_t len_args, PyObject *kwnames)
 {
-    PyObject *relevant_args = NULL;
+    PyObject *dispatch_types = NULL;
     PyObject *numpy_module = NULL;
     PyObject *public_api = NULL;
     PyObject *result = NULL;
 
     /* If `like` doesn't implement `__array_function__`, raise a `TypeError` */
-    PyObject *tmp_has_override = get_array_function(like);
-    if (tmp_has_override == NULL) {
+    PyObject *method = get_array_function(like);
+    if (method == NULL) {
         return PyErr_Format(PyExc_TypeError,
                 "The `like` argument must be an array-like that "
                 "implements the `__array_function__` protocol.");
     }
-    Py_DECREF(tmp_has_override);
-
-    if (fast_args != NULL) {
+    if (is_default_array_function(method)) {
         /*
-         * Convert from vectorcall convention, since the protocol requires
-         * the normal convention.  We have to do this late to ensure the
-         * normal path where NotImplemented is returned is fast.
+         * Return a borrowed reference of Py_NotImplemented to defer back to
+         * the original function.
          */
+        Py_DECREF(method);
+        return Py_NotImplemented;
+    }
+
+    /* We needs args and kwargs for __array_function__ (when not using it). */
+    if (fast_args != NULL) {
         assert(args == NULL);
         assert(kwargs == NULL);
-        args = PyTuple_New(len_args);
-        if (args == NULL) {
-            return NULL;
-        }
-        for (Py_ssize_t i = 0; i < len_args; i++) {
-            Py_INCREF(fast_args[i]);
-            PyTuple_SET_ITEM(args, i, fast_args[i]);
-        }
-        if (kwnames != NULL) {
-            kwargs = PyDict_New();
-            if (kwargs == NULL) {
-                Py_DECREF(args);
-                return NULL;
-            }
-            Py_ssize_t nkwargs = PyTuple_GET_SIZE(kwnames);
-            for (Py_ssize_t i = 0; i < nkwargs; i++) {
-                PyObject *key = PyTuple_GET_ITEM(kwnames, i);
-                PyObject *value = fast_args[i+len_args];
-                if (PyDict_SetItem(kwargs, key, value) < 0) {
-                    Py_DECREF(args);
-                    Py_DECREF(kwargs);
-                    return NULL;
-                }
-            }
+        if (get_args_and_kwargs(
+                fast_args, len_args, kwnames, &args, &kwargs) < 0) {
+            goto finish;
         }
     }
+    else {
+        Py_INCREF(args);
+        Py_INCREF(kwargs);
+    }
 
-    relevant_args = PyTuple_Pack(1, like);
-    if (relevant_args == NULL) {
+    dispatch_types = PyTuple_Pack(1, Py_TYPE(like));
+    if (dispatch_types == NULL) {
         goto finish;
     }
+
     /* The like argument must be present in the keyword arguments, remove it */
     if (PyDict_DelItem(kwargs, npy_ma_str_like) < 0) {
         goto finish;
     }
 
+    /* Fetch the actual symbol (the long way right now) */
     numpy_module = PyImport_Import(npy_ma_str_numpy);
     if (numpy_module == NULL) {
         goto finish;
@@ -473,16 +342,21 @@ array_implement_c_array_function_creation(
         goto finish;
     }
 
-    result = array_implement_array_function_internal(
-            public_api, relevant_args, args, kwargs);
+    result = call_array_function(like, method,
+            public_api, dispatch_types, args, kwargs);
 
-  finish:
-    if (kwnames != NULL) {
-        /* args and kwargs were converted from vectorcall convention */
-        Py_XDECREF(args);
-        Py_XDECREF(kwargs);
+    if (result == Py_NotImplemented) {
+        /* This shouldn't really happen as there is only one type, but... */
+        Py_DECREF(result);
+        result = NULL;
+        set_no_matching_types_error(public_api, dispatch_types);
     }
-    Py_XDECREF(relevant_args);
+
+  finish:
+    Py_DECREF(method);
+    Py_XDECREF(args);
+    Py_XDECREF(kwargs);
+    Py_XDECREF(dispatch_types);
     Py_XDECREF(public_api);
     return result;
 }
@@ -537,3 +411,373 @@ cleanup:
     Py_DECREF(relevant_args);
     return result;
 }
+
+
+typedef struct {
+    PyObject_HEAD
+    vectorcallfunc vectorcall;
+    PyObject *dict;
+    PyObject *relevant_arg_func;
+    PyObject *default_impl;
+    /* The following fields are used to clean up TypeError messages only: */
+    PyObject *dispatcher_name;
+    PyObject *public_name;
+} PyArray_ArrayFunctionDispatcherObject;
+
+
+static void
+dispatcher_dealloc(PyArray_ArrayFunctionDispatcherObject *self)
+{
+    Py_CLEAR(self->relevant_arg_func);
+    Py_CLEAR(self->default_impl);
+    Py_CLEAR(self->dict);
+    Py_CLEAR(self->dispatcher_name);
+    Py_CLEAR(self->public_name);
+    PyObject_FREE(self);
+}
+
+
+static void
+fix_name_if_typeerror(PyArray_ArrayFunctionDispatcherObject *self)
+{
+    if (!PyErr_ExceptionMatches(PyExc_TypeError)) {
+        return;
+    }
+
+    PyObject *exc, *val, *tb, *message;
+    PyErr_Fetch(&exc, &val, &tb);
+
+    if (!PyUnicode_CheckExact(val)) {
+        /*
+         * We expect the error to be unnormalized, but maybe it isn't always
+         * the case, so normalize and fetch args[0] if it isn't a string.
+         */
+        PyErr_NormalizeException(&exc, &val, &tb);
+
+        PyObject *args = PyObject_GetAttrString(val, "args");
+        if (args == NULL || !PyTuple_CheckExact(args)
+                || PyTuple_GET_SIZE(args) != 1) {
+            Py_XDECREF(args);
+            goto restore_error;
+        }
+        message = PyTuple_GET_ITEM(args, 0);
+        Py_INCREF(message);
+        Py_DECREF(args);
+        if (!PyUnicode_CheckExact(message)) {
+            Py_DECREF(message);
+            goto restore_error;
+        }
+    }
+    else {
+        Py_INCREF(val);
+        message = val;
+    }
+
+    Py_ssize_t cmp = PyUnicode_Tailmatch(
+            message, self->dispatcher_name, 0, -1, -1);
+    if (cmp <= 0) {
+        Py_DECREF(message);
+        goto restore_error;
+    }
+    Py_SETREF(message, PyUnicode_Replace(
+            message, self->dispatcher_name, self->public_name, 1));
+    if (message == NULL) {
+        goto restore_error;
+    }
+    PyErr_SetObject(PyExc_TypeError, message);
+    Py_DECREF(exc);
+    Py_XDECREF(val);
+    Py_XDECREF(tb);
+    Py_DECREF(message);
+    return;
+
+  restore_error:
+    /* replacement not successful, so restore original error */
+    PyErr_Restore(exc, val, tb);
+}
+
+
+static PyObject *
+dispatcher_vectorcall(PyArray_ArrayFunctionDispatcherObject *self,
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+    PyObject *result = NULL;
+    PyObject *types = NULL;
+    PyObject *relevant_args = NULL;
+
+    PyObject *public_api;
+
+    /* __array_function__ passes args, kwargs.  These may be filled: */
+    PyObject *packed_args = NULL;
+    PyObject *packed_kwargs = NULL;
+
+    PyObject *implementing_args[NPY_MAXARGS];
+    PyObject *array_function_methods[NPY_MAXARGS];
+
+    int num_implementing_args;
+
+    if (self->relevant_arg_func != NULL) {
+        public_api = (PyObject *)self;
+
+        /* Typical path, need to call the relevant_arg_func and unpack them */
+        relevant_args = PyObject_Vectorcall(
+                self->relevant_arg_func, args, len_args, kwnames);
+        if (relevant_args == NULL) {
+            fix_name_if_typeerror(self);
+            return NULL;
+        }
+        Py_SETREF(relevant_args, PySequence_Fast(relevant_args,
+                "dispatcher for __array_function__ did not return an iterable"));
+        if (relevant_args == NULL) {
+            return NULL;
+        }
+
+        num_implementing_args = get_implementing_args_and_methods(
+                relevant_args, implementing_args, array_function_methods);
+        if (num_implementing_args < 0) {
+            Py_DECREF(relevant_args);
+            return NULL;
+        }
+    }
+    else {
+        /* For like= dispatching from Python, the public_symbol is the impl */
+        public_api = self->default_impl;
+
+        /*
+         * We are dealing with `like=` from Python.  For simplicity, the
+         * Python code passes it on as the first argument.
+         */
+        if (PyVectorcall_NARGS(len_args) == 0) {
+            PyErr_Format(PyExc_TypeError,
+                    "`like` argument dispatching, but first argument is not "
+                    "positional in call to %S.", self->default_impl);
+            return NULL;
+        }
+
+        array_function_methods[0] = get_array_function(args[0]);
+        if (array_function_methods[0] == NULL) {
+            return PyErr_Format(PyExc_TypeError,
+                    "The `like` argument must be an array-like that "
+                    "implements the `__array_function__` protocol.");
+        }
+        num_implementing_args = 1;
+        implementing_args[0] = args[0];
+        Py_INCREF(implementing_args[0]);
+
+        /* do not pass the like argument */
+        len_args = PyVectorcall_NARGS(len_args) - 1;
+        len_args |= PY_VECTORCALL_ARGUMENTS_OFFSET;
+        args++;
+    }
+
+    /*
+     * Handle the typical case of no overrides. This is merely an optimization
+     * if some arguments are ndarray objects, but is also necessary if no
+     * arguments implement __array_function__ at all (e.g., if they are all
+     * built-in types).
+     */
+    int any_overrides = 0;
+    for (int j = 0; j < num_implementing_args; j++) {
+        if (!is_default_array_function(array_function_methods[j])) {
+            any_overrides = 1;
+            break;
+        }
+    }
+    if (!any_overrides) {
+        /* Directly call the actual implementation. */
+        result = PyObject_Vectorcall(self->default_impl, args, len_args, kwnames);
+        goto cleanup;
+    }
+
+    /* Find args and kwargs as tuple and dict, as we pass them out: */
+    if (get_args_and_kwargs(
+            args, len_args, kwnames, &packed_args, &packed_kwargs) < 0) {
+        goto cleanup;
+    }
+
+    /*
+     * Create a Python object for types.
+     * We use a tuple, because it's the fastest Python collection to create
+     * and has the bonus of being immutable.
+     */
+    types = PyTuple_New(num_implementing_args);
+    if (types == NULL) {
+        goto cleanup;
+    }
+    for (int j = 0; j < num_implementing_args; j++) {
+        PyObject *arg_type = (PyObject *)Py_TYPE(implementing_args[j]);
+        Py_INCREF(arg_type);
+        PyTuple_SET_ITEM(types, j, arg_type);
+    }
+
+    /* Call __array_function__ methods */
+    for (int j = 0; j < num_implementing_args; j++) {
+        PyObject *argument = implementing_args[j];
+        PyObject *method = array_function_methods[j];
+
+        result = call_array_function(
+                argument, method, public_api, types,
+                packed_args, packed_kwargs);
+
+        if (result == Py_NotImplemented) {
+            /* Try the next one */
+            Py_DECREF(result);
+            result = NULL;
+        }
+        else {
+            /* Either a good result, or an exception was raised. */
+            goto cleanup;
+        }
+    }
+
+    set_no_matching_types_error(public_api, types);
+
+cleanup:
+    for (int j = 0; j < num_implementing_args; j++) {
+        Py_DECREF(implementing_args[j]);
+        Py_DECREF(array_function_methods[j]);
+    }
+    Py_XDECREF(packed_args);
+    Py_XDECREF(packed_kwargs);
+    Py_XDECREF(types);
+    Py_XDECREF(relevant_args);
+    return result;
+}
+
+
+static PyObject *
+dispatcher_new(PyTypeObject *NPY_UNUSED(cls), PyObject *args, PyObject *kwargs)
+{
+    PyArray_ArrayFunctionDispatcherObject *self;
+
+    self = PyObject_New(
+            PyArray_ArrayFunctionDispatcherObject,
+            &PyArrayFunctionDispatcher_Type);
+    if (self == NULL) {
+        return PyErr_NoMemory();
+    }
+
+    char *kwlist[] = {"", "", NULL};
+    if (!PyArg_ParseTupleAndKeywords(
+            args, kwargs, "OO:_ArrayFunctionDispatcher", kwlist,
+            &self->relevant_arg_func, &self->default_impl)) {
+        Py_DECREF(self);
+        return NULL;
+    }
+
+    self->vectorcall = (vectorcallfunc)dispatcher_vectorcall;
+    Py_INCREF(self->default_impl);
+    self->dict = NULL;
+    self->dispatcher_name = NULL;
+    self->public_name = NULL;
+
+    if (self->relevant_arg_func == Py_None) {
+        /* NULL in the relevant arg function means we use `like=` */
+        Py_CLEAR(self->relevant_arg_func);
+    }
+    else {
+        /* Fetch names to clean up TypeErrors (show actual name) */
+        Py_INCREF(self->relevant_arg_func);
+        self->dispatcher_name = PyObject_GetAttrString(
+            self->relevant_arg_func, "__qualname__");
+        if (self->dispatcher_name == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->public_name = PyObject_GetAttrString(
+            self->default_impl, "__qualname__");
+        if (self->public_name == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+    }
+
+    /* Need to be like a Python function that has arbitrary attributes */
+    self->dict = PyDict_New();
+    if (self->dict == NULL) {
+        Py_DECREF(self);
+        return NULL;
+    }
+    return (PyObject *)self;
+}
+
+
+static PyObject *
+dispatcher_str(PyArray_ArrayFunctionDispatcherObject *self)
+{
+    return PyObject_Str(self->default_impl);
+}
+
+
+static PyObject *
+dispatcher_repr(PyObject *self)
+{
+    PyObject *name = PyObject_GetAttrString(self, "__name__");
+    if (name == NULL) {
+        return NULL;
+    }
+    /* Print like a normal function */
+    return PyUnicode_FromFormat("<function %S at %p>", name, self);
+}
+
+
+static PyObject *
+func_dispatcher___get__(PyObject *self, PyObject *obj, PyObject *cls)
+{
+    if (obj == NULL) {
+        /* Act like a static method, no need to bind */
+        Py_INCREF(self);
+        return self;
+    }
+    return PyMethod_New(self, obj);
+}
+
+
+static PyObject *
+dispatcher_get_implementation(
+        PyArray_ArrayFunctionDispatcherObject *self, void *NPY_UNUSED(closure))
+{
+    Py_INCREF(self->default_impl);
+    return self->default_impl;
+}
+
+
+static PyObject *
+dispatcher_reduce(PyObject *self, PyObject *NPY_UNUSED(args))
+{
+    return PyObject_GetAttrString(self, "__qualname__");
+}
+
+
+static struct PyMethodDef func_dispatcher_methods[] = {
+    {"__reduce__",
+        (PyCFunction)dispatcher_reduce, METH_NOARGS, NULL},
+    {NULL, NULL, 0, NULL}
+};
+
+
+static struct PyGetSetDef func_dispatcher_getset[] = {
+    {"__dict__", &PyObject_GenericGetDict, 0, NULL, 0},
+    {"_implementation", (getter)&dispatcher_get_implementation, 0, NULL, 0},
+    {0, 0, 0, 0, 0}
+};
+
+
+NPY_NO_EXPORT PyTypeObject PyArrayFunctionDispatcher_Type = {
+     PyVarObject_HEAD_INIT(NULL, 0)
+     .tp_name = "numpy._ArrayFunctionDispatcher",
+     .tp_basicsize = sizeof(PyArray_ArrayFunctionDispatcherObject),
+     /* We have a dict, so in theory could traverse, but in practice... */
+     .tp_dictoffset = offsetof(PyArray_ArrayFunctionDispatcherObject, dict),
+     .tp_dealloc = (destructor)dispatcher_dealloc,
+     .tp_new = (newfunc)dispatcher_new,
+     .tp_str = (reprfunc)dispatcher_str,
+     .tp_repr = (reprfunc)dispatcher_repr,
+     .tp_flags = (Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_VECTORCALL
+                  | Py_TPFLAGS_METHOD_DESCRIPTOR),
+     .tp_methods = func_dispatcher_methods,
+     .tp_getset = func_dispatcher_getset,
+     .tp_descr_get = func_dispatcher___get__,
+     .tp_call = &PyVectorcall_Call,
+     .tp_vectorcall_offset = offsetof(PyArray_ArrayFunctionDispatcherObject, vectorcall),
+};
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.h
index 09f7ee5480e..3b8b88bacf3 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayfunction_override.h
@@ -1,9 +1,7 @@
 #ifndef NUMPY_CORE_SRC_MULTIARRAY_ARRAYFUNCTION_OVERRIDE_H_
 #define NUMPY_CORE_SRC_MULTIARRAY_ARRAYFUNCTION_OVERRIDE_H_
 
-NPY_NO_EXPORT PyObject *
-array_implement_array_function(
-    PyObject *NPY_UNUSED(dummy), PyObject *positional_args);
+extern NPY_NO_EXPORT PyTypeObject PyArrayFunctionDispatcher_Type;
 
 NPY_NO_EXPORT PyObject *
 array__get_implementing_args(
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.c
index b1302738d98..4298ca38343 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.c
@@ -56,11 +56,15 @@ maintainer email:  oliphant.travis@ieee.org
 #include "alloc.h"
 #include "mem_overlap.h"
 #include "numpyos.h"
+#include "refcount.h"
 #include "strfuncs.h"
 
 #include "binop_override.h"
 #include "array_coercion.h"
 
+
+NPY_NO_EXPORT npy_bool numpy_warn_if_no_mem_policy = 0;
+
 /*NUMPY_API
   Compute the size of an array (in number of items)
 */
@@ -394,7 +398,7 @@ PyArray_ResolveWritebackIfCopy(PyArrayObject * self)
    to stderr and clear the error
 */
 
-static NPY_INLINE void
+static inline void
 WARN_IN_DEALLOC(PyObject* warning, const char * msg) {
     if (PyErr_WarnEx(warning, msg, 1) < 0) {
         PyObject * s;
@@ -452,14 +456,15 @@ array_dealloc(PyArrayObject *self)
     }
 
     if ((fa->flags & NPY_ARRAY_OWNDATA) && fa->data) {
-        /* Free internal references if an Object array */
-        if (PyDataType_FLAGCHK(fa->descr, NPY_ITEM_REFCOUNT)) {
-            PyArray_XDECREF(self);
+        /* Free any internal references */
+        if (PyDataType_REFCHK(fa->descr)) {
+            if (PyArray_ClearArray(self) < 0) {
+                PyErr_WriteUnraisable(NULL);
+            }
         }
         if (fa->mem_handler == NULL) {
-            char *env = getenv("NUMPY_WARN_IF_NO_MEM_POLICY");
-            if ((env != NULL) && (strncmp(env, "1", 1) == 0)) {
-                char const * msg = "Trying to dealloc data, but a memory policy "
+            if (numpy_warn_if_no_mem_policy) {
+                char const *msg = "Trying to dealloc data, but a memory policy "
                     "is not set. If you take ownership of the data, you must "
                     "set a base owning the data (e.g. a PyCapsule).";
                 WARN_IN_DEALLOC(PyExc_RuntimeWarning, msg);
@@ -875,108 +880,6 @@ DEPRECATE_silence_error(const char *msg) {
     return 0;
 }
 
-/*
- * Comparisons can fail, but we do not always want to pass on the exception
- * (see comment in array_richcompare below), but rather return NotImplemented.
- * Here, an exception should be set on entrance.
- * Returns either NotImplemented with the exception cleared, or NULL
- * with the exception set.
- * Raises deprecation warnings for cases where behaviour is meant to change
- * (2015-05-14, 1.10)
- */
-
-NPY_NO_EXPORT PyObject *
-_failed_comparison_workaround(PyArrayObject *self, PyObject *other, int cmp_op)
-{
-    PyObject *exc, *val, *tb;
-    PyArrayObject *array_other;
-    int other_is_flexible, ndim_other;
-    int self_is_flexible = PyTypeNum_ISFLEXIBLE(PyArray_DESCR(self)->type_num);
-
-    PyErr_Fetch(&exc, &val, &tb);
-    /*
-     * Determine whether other has a flexible dtype; here, inconvertible
-     * is counted as inflexible.  (This repeats work done in the ufunc,
-     * but OK to waste some time in an unlikely path.)
-     */
-    array_other = (PyArrayObject *)PyArray_FROM_O(other);
-    if (array_other) {
-        other_is_flexible = PyTypeNum_ISFLEXIBLE(
-            PyArray_DESCR(array_other)->type_num);
-        ndim_other = PyArray_NDIM(array_other);
-        Py_DECREF(array_other);
-    }
-    else {
-        PyErr_Clear(); /* we restore the original error if needed */
-        other_is_flexible = 0;
-        ndim_other = 0;
-    }
-    if (cmp_op == Py_EQ || cmp_op == Py_NE) {
-        /*
-         * note: for == and !=, a structured dtype self cannot get here,
-         * but a string can. Other can be string or structured.
-         */
-        if (other_is_flexible || self_is_flexible) {
-            /*
-             * For scalars, returning NotImplemented is correct.
-             * For arrays, we emit a future deprecation warning.
-             * When this warning is removed, a correctly shaped
-             * array of bool should be returned.
-             */
-            if (ndim_other != 0 || PyArray_NDIM(self) != 0) {
-                /* 2015-05-14, 1.10 */
-                if (DEPRECATE_FUTUREWARNING(
-                        "elementwise comparison failed; returning scalar "
-                        "instead, but in the future will perform "
-                        "elementwise comparison") < 0) {
-                    goto fail;
-                }
-            }
-        }
-        else {
-            /*
-             * If neither self nor other had a flexible dtype, the error cannot
-             * have been caused by a lack of implementation in the ufunc.
-             *
-             * 2015-05-14, 1.10
-             */
-            if (DEPRECATE(
-                    "elementwise comparison failed; "
-                    "this will raise an error in the future.") < 0) {
-                goto fail;
-            }
-        }
-        Py_XDECREF(exc);
-        Py_XDECREF(val);
-        Py_XDECREF(tb);
-        Py_INCREF(Py_NotImplemented);
-        return Py_NotImplemented;
-    }
-    else if (other_is_flexible || self_is_flexible) {
-        /*
-         * For LE, LT, GT, GE and a flexible self or other, we return
-         * NotImplemented, which is the correct answer since the ufuncs do
-         * not in fact implement loops for those.  This will get us the
-         * desired TypeError.
-         */
-        Py_XDECREF(exc);
-        Py_XDECREF(val);
-        Py_XDECREF(tb);
-        Py_INCREF(Py_NotImplemented);
-        return Py_NotImplemented;
-    }
-    else {
-        /* LE, LT, GT, or GE with non-flexible other; just pass on error */
-        goto fail;
-    }
-
-fail:
-    /*
-     * Reraise the original exception, possibly chaining with a new one.
-     */
-    npy_PyErr_ChainExceptionsCause(exc, val, tb);
-    return NULL;
-}
 
 NPY_NO_EXPORT PyObject *
 array_richcompare(PyArrayObject *self, PyObject *other, int cmp_op)
@@ -1074,33 +977,99 @@ array_richcompare(PyArrayObject *self, PyObject *other, int cmp_op)
         Py_INCREF(Py_NotImplemented);
         return Py_NotImplemented;
     }
-    if (result == NULL) {
+
+    /*
+     * At this point `self` can take control of the operation by converting
+     * `other` to an array (it would have a chance to take control).
+     * If we are not in `==` and `!=`, this is an error and we hope that
+     * the existing error makes sense and derives from `TypeError` (which
+     * python would raise for `NotImplemented`) when it should.
+     *
+     * However, if the issue is no matching loop for the given dtypes and
+     * we are inside == and !=, then returning an array of True or False
+     * makes sense (following Python behavior for `==` and `!=`).
+     * Effectively: Both *dtypes* told us that they cannot be compared.
+     *
+     * In theory, the error could be raised from within an object loop, the
+     * solution to that could be pushing this into the ufunc (where we can
+     * distinguish the two easily).  In practice, it seems like it should not
+     * but a huge problem:  The ufunc loop will itself call `==` which should
+     * probably never raise a UFuncNoLoopError.
+     *
+     * TODO: If/once we correctly push structured comparisons into the ufunc
+     *       we could consider pushing this path into the ufunc itself as a
+     *       fallback loop (which ignores the input arrays).
+     *       This would have the advantage that subclasses implementing
+     *       `__array_ufunc__` do not explicitly need `__eq__` and `__ne__`.
+     */
+    if (result == NULL
+            && (cmp_op == Py_EQ || cmp_op == Py_NE)
+            && PyErr_ExceptionMatches(npy_UFuncNoLoopError)) {
+        PyErr_Clear();
+
+        PyArrayObject *array_other = (PyArrayObject *)PyArray_FROM_O(other);
+        if (PyArray_TYPE(array_other) == NPY_VOID) {
+            /*
+            * Void arrays are currently not handled by ufuncs, so if the other
+            * is a void array, we defer to it (will raise a TypeError).
+            */
+            Py_DECREF(array_other);
+            Py_RETURN_NOTIMPLEMENTED;
+        }
+
+        if (PyArray_NDIM(self) == 0 && PyArray_NDIM(array_other) == 0) {
+            /*
+             * (seberg) not sure that this is best, but we preserve Python
+             * bool result for "scalar" inputs for now by returning
+             * `NotImplemented`.
+             */
+            Py_DECREF(array_other);
+            Py_RETURN_NOTIMPLEMENTED;
+        }
+
+        /* Hack warning: using NpyIter to allocate broadcasted result. */
+        PyArrayObject *ops[3] = {self, array_other, NULL};
+        npy_uint32 flags = NPY_ITER_ZEROSIZE_OK | NPY_ITER_REFS_OK;
+        npy_uint32 op_flags[3] = {
+            NPY_ITER_READONLY, NPY_ITER_READONLY,
+            NPY_ITER_ALLOCATE | NPY_ITER_WRITEONLY};
+
+        PyArray_Descr *bool_descr = PyArray_DescrFromType(NPY_BOOL);
+        PyArray_Descr *op_descrs[3] = {
+            PyArray_DESCR(self), PyArray_DESCR(array_other), bool_descr};
+
+        NpyIter *iter = NpyIter_MultiNew(
+                    3, ops, flags, NPY_KEEPORDER, NPY_NO_CASTING,
+                    op_flags, op_descrs);
+
+        Py_CLEAR(bool_descr);
+        Py_CLEAR(array_other);
+        if (iter == NULL) {
+            return NULL;
+        }
+        PyArrayObject *res = NpyIter_GetOperandArray(iter)[2];
+        Py_INCREF(res);
+        if (NpyIter_Deallocate(iter) != NPY_SUCCEED) {
+            Py_DECREF(res);
+            return NULL;
+        }
+
         /*
-         * 2015-05-14, 1.10; updated 2018-06-18, 1.16.
-         *
-         * Comparisons can raise errors when element-wise comparison is not
-         * possible. Some of these, though, should not be passed on.
-         * In particular, the ufuncs do not have loops for flexible dtype,
-         * so those should be treated separately.  Furthermore, for EQ and NE,
-         * we should never fail.
-         *
-         * Our ideal behaviour would be:
-         *
-         * 1. For EQ and NE:
-         *   - If self and other are scalars, return NotImplemented,
-         *     so that python can assign True of False as appropriate.
-         *   - If either is an array, return an array of False or True.
-         *
-         * 2. For LT, LE, GE, GT:
-         *   - If self or other was flexible, return NotImplemented
-         *     (as is in fact the case), so python can raise a TypeError.
-         *   - If other is not convertible to an array, pass on the error
-         *     (MHvK, 2018-06-18: not sure about this, but it's what we have).
-         *
-         * However, for backwards compatibility, we cannot yet return arrays,
-         * so we raise warnings instead.
+         * The array is guaranteed to be newly allocated and thus contiguous,
+         * so simply fill it with 0 or 1.
          */
-        result = _failed_comparison_workaround(self, other, cmp_op);
+        memset(PyArray_BYTES(res), cmp_op == Py_EQ ? 0 : 1, PyArray_NBYTES(res));
+
+        /* Ensure basic subclass support by wrapping: */
+        if (!PyArray_CheckExact(self)) {
+            /*
+             * If other is also a subclass (with higher priority) we would
+             * already have deferred.  So use `self` for wrapping.  If users
+             * need more, they need to override `==` and `!=`.
+             */
+            Py_SETREF(res, PyArray_SubclassWrap(self, res));
+        }
+        return (PyObject *)res;
     }
     return result;
 }
@@ -1256,7 +1225,7 @@ array_new(PyTypeObject *subtype, PyObject *args, PyObject *kwds)
                                      (int)dims.len,
                                      dims.ptr,
                                      strides.ptr, NULL, is_f_order, NULL, NULL,
-                                     0, 1);
+                                     _NPY_ARRAY_ALLOW_EMPTY_STRING);
         if (ret == NULL) {
             descr = NULL;
             goto fail;
@@ -1293,7 +1262,7 @@ array_new(PyTypeObject *subtype, PyObject *args, PyObject *kwds)
                 subtype, descr,
                 dims.len, dims.ptr, strides.ptr, offset + (char *)buffer.ptr,
                 buffer.flags, NULL, buffer.base,
-                0, 1);
+                _NPY_ARRAY_ALLOW_EMPTY_STRING);
         if (ret == NULL) {
             descr = NULL;
             goto fail;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.h
index f7d0734dbad..b71354a5e4d 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arrayobject.h
@@ -5,6 +5,8 @@
 #ifndef NUMPY_CORE_SRC_MULTIARRAY_ARRAYOBJECT_H_
 #define NUMPY_CORE_SRC_MULTIARRAY_ARRAYOBJECT_H_
 
+extern NPY_NO_EXPORT npy_bool numpy_warn_if_no_mem_policy;
+
 NPY_NO_EXPORT PyObject *
 _strings_richcompare(PyArrayObject *self, PyArrayObject *other, int cmp_op,
                      int rstrip);
@@ -40,6 +42,13 @@ static const int NPY_ARRAY_WARN_ON_WRITE = (1 << 31);
 static const int NPY_ARRAY_WAS_PYTHON_INT = (1 << 30);
 static const int NPY_ARRAY_WAS_PYTHON_FLOAT = (1 << 29);
 static const int NPY_ARRAY_WAS_PYTHON_COMPLEX = (1 << 28);
+/*
+ * Mark that this was a huge int which was turned into an object array (or
+ * unsigned/non-default integer array), but then replaced by a temporary
+ * array for further processing. This flag is only used in the ufunc machinery
+ * where it is tricky to cover correctly all type resolution paths.
+ */
+static const int NPY_ARRAY_WAS_INT_AND_REPLACED = (1 << 27);
 static const int NPY_ARRAY_WAS_PYTHON_LITERAL = (1 << 30 | 1 << 29 | 1 << 28);
 
 #endif  /* NUMPY_CORE_SRC_MULTIARRAY_ARRAYOBJECT_H_ */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c
index b91b1c9b65f..e3060541751 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c
@@ -65,7 +65,7 @@
  * This is especially important for nonzero and copyswap, which may run with
  * the GIL released.
  */
-static NPY_INLINE PyArrayObject_fields
+static inline PyArrayObject_fields
 get_dummy_stack_array(PyArrayObject *orig)
 {
     PyArrayObject_fields new_fields;
@@ -78,7 +78,7 @@ get_dummy_stack_array(PyArrayObject *orig)
 
 
 /* check for sequences, but ignore the types numpy considers scalars */
-static NPY_INLINE npy_bool
+static inline npy_bool
 PySequence_NoString_Check(PyObject *op) {
     return
         PySequence_Check(op) &&
@@ -333,7 +333,7 @@ BYTE_safe_pyint_setitem(PyObject *obj, npy_byte *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -400,7 +400,7 @@ SHORT_safe_pyint_setitem(PyObject *obj, npy_short *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -467,7 +467,7 @@ INT_safe_pyint_setitem(PyObject *obj, npy_int *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -534,7 +534,7 @@ LONG_safe_pyint_setitem(PyObject *obj, npy_long *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -601,7 +601,7 @@ LONGLONG_safe_pyint_setitem(PyObject *obj, npy_longlong *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -668,7 +668,7 @@ UBYTE_safe_pyint_setitem(PyObject *obj, npy_ubyte *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -735,7 +735,7 @@ USHORT_safe_pyint_setitem(PyObject *obj, npy_ushort *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -802,7 +802,7 @@ UINT_safe_pyint_setitem(PyObject *obj, npy_uint *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -869,7 +869,7 @@ ULONG_safe_pyint_setitem(PyObject *obj, npy_ulong *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -936,7 +936,7 @@ ULONGLONG_safe_pyint_setitem(PyObject *obj, npy_ulonglong *result)
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -2221,7 +2221,7 @@ CLONGDOUBLE_setitem(PyObject *op, void *ov, void *vap)
 
 
 
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 string_to_long_double(PyObject*op)
 {
     char *s;
@@ -19003,7 +19003,7 @@ CDOUBLE_fromstr(char *str, void *ip, char **endptr,
  */
 
 
-static NPY_INLINE void
+static inline void
 _basic_copyn(void *dst, npy_intp dstride, void *src, npy_intp sstride,
              npy_intp n, int elsize) {
     if (src == NULL) {
@@ -19018,7 +19018,7 @@ _basic_copyn(void *dst, npy_intp dstride, void *src, npy_intp sstride,
     }
 }
 
-static NPY_INLINE void
+static inline void
 _basic_copy(void *dst, void *src, int elsize) {
     if (src == NULL) {
         return;
@@ -30246,19 +30246,350 @@ set_typeinfo(PyObject *dict)
      * should be defined on the class and inherited to the scalar.
      * (NPY_HALF is the largest builtin one.)
      */
-    for (i = 0; i <= NPY_HALF; i++) {
-        if (dtypemeta_wrap_legacy_descriptor(_builtin_descrs[i]) < 0) {
-            return -1;
-        }
+    #line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_BOOL],
+            "numpy.dtypes." NPY_BOOL_Name "DType",
+#ifdef NPY_BOOL_alias
+            "numpy.dtypes." NPY_BOOL_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
     }
 
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_BYTE],
+            "numpy.dtypes." NPY_BYTE_Name "DType",
+#ifdef NPY_BYTE_alias
+            "numpy.dtypes." NPY_BYTE_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_UBYTE],
+            "numpy.dtypes." NPY_UBYTE_Name "DType",
+#ifdef NPY_UBYTE_alias
+            "numpy.dtypes." NPY_UBYTE_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_SHORT],
+            "numpy.dtypes." NPY_SHORT_Name "DType",
+#ifdef NPY_SHORT_alias
+            "numpy.dtypes." NPY_SHORT_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_USHORT],
+            "numpy.dtypes." NPY_USHORT_Name "DType",
+#ifdef NPY_USHORT_alias
+            "numpy.dtypes." NPY_USHORT_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_INT],
+            "numpy.dtypes." NPY_INT_Name "DType",
+#ifdef NPY_INT_alias
+            "numpy.dtypes." NPY_INT_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_UINT],
+            "numpy.dtypes." NPY_UINT_Name "DType",
+#ifdef NPY_UINT_alias
+            "numpy.dtypes." NPY_UINT_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_LONG],
+            "numpy.dtypes." NPY_LONG_Name "DType",
+#ifdef NPY_LONG_alias
+            "numpy.dtypes." NPY_LONG_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_ULONG],
+            "numpy.dtypes." NPY_ULONG_Name "DType",
+#ifdef NPY_ULONG_alias
+            "numpy.dtypes." NPY_ULONG_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_LONGLONG],
+            "numpy.dtypes." NPY_LONGLONG_Name "DType",
+#ifdef NPY_LONGLONG_alias
+            "numpy.dtypes." NPY_LONGLONG_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_ULONGLONG],
+            "numpy.dtypes." NPY_ULONGLONG_Name "DType",
+#ifdef NPY_ULONGLONG_alias
+            "numpy.dtypes." NPY_ULONGLONG_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_HALF],
+            "numpy.dtypes." NPY_HALF_Name "DType",
+#ifdef NPY_HALF_alias
+            "numpy.dtypes." NPY_HALF_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_FLOAT],
+            "numpy.dtypes." NPY_FLOAT_Name "DType",
+#ifdef NPY_FLOAT_alias
+            "numpy.dtypes." NPY_FLOAT_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_DOUBLE],
+            "numpy.dtypes." NPY_DOUBLE_Name "DType",
+#ifdef NPY_DOUBLE_alias
+            "numpy.dtypes." NPY_DOUBLE_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_LONGDOUBLE],
+            "numpy.dtypes." NPY_LONGDOUBLE_Name "DType",
+#ifdef NPY_LONGDOUBLE_alias
+            "numpy.dtypes." NPY_LONGDOUBLE_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_CFLOAT],
+            "numpy.dtypes." NPY_CFLOAT_Name "DType",
+#ifdef NPY_CFLOAT_alias
+            "numpy.dtypes." NPY_CFLOAT_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_CDOUBLE],
+            "numpy.dtypes." NPY_CDOUBLE_Name "DType",
+#ifdef NPY_CDOUBLE_alias
+            "numpy.dtypes." NPY_CDOUBLE_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_CLONGDOUBLE],
+            "numpy.dtypes." NPY_CLONGDOUBLE_Name "DType",
+#ifdef NPY_CLONGDOUBLE_alias
+            "numpy.dtypes." NPY_CLONGDOUBLE_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_OBJECT],
+            "numpy.dtypes." NPY_OBJECT_Name "DType",
+#ifdef NPY_OBJECT_alias
+            "numpy.dtypes." NPY_OBJECT_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_STRING],
+            "numpy.dtypes." NPY_STRING_Name "DType",
+#ifdef NPY_STRING_alias
+            "numpy.dtypes." NPY_STRING_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_UNICODE],
+            "numpy.dtypes." NPY_UNICODE_Name "DType",
+#ifdef NPY_UNICODE_alias
+            "numpy.dtypes." NPY_UNICODE_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_VOID],
+            "numpy.dtypes." NPY_VOID_Name "DType",
+#ifdef NPY_VOID_alias
+            "numpy.dtypes." NPY_VOID_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_DATETIME],
+            "numpy.dtypes." NPY_DATETIME_Name "DType",
+#ifdef NPY_DATETIME_alias
+            "numpy.dtypes." NPY_DATETIME_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+#line 4659
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_TIMEDELTA],
+            "numpy.dtypes." NPY_TIMEDELTA_Name "DType",
+#ifdef NPY_TIMEDELTA_alias
+            "numpy.dtypes." NPY_TIMEDELTA_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
+    }
+
+    
+
     /*
      * Add cast functions for the new types
      */
 
-    #line 4669
+    #line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_BOOL];
     if (dtype->f->castdict == NULL) {
@@ -30285,7 +30616,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_BOOL];
     if (dtype->f->castdict == NULL) {
@@ -30312,7 +30643,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_BOOL];
     if (dtype->f->castdict == NULL) {
@@ -30341,9 +30672,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_BYTE];
     if (dtype->f->castdict == NULL) {
@@ -30370,7 +30701,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_BYTE];
     if (dtype->f->castdict == NULL) {
@@ -30397,7 +30728,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_BYTE];
     if (dtype->f->castdict == NULL) {
@@ -30426,9 +30757,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_UBYTE];
     if (dtype->f->castdict == NULL) {
@@ -30455,7 +30786,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_UBYTE];
     if (dtype->f->castdict == NULL) {
@@ -30482,7 +30813,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_UBYTE];
     if (dtype->f->castdict == NULL) {
@@ -30511,9 +30842,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_SHORT];
     if (dtype->f->castdict == NULL) {
@@ -30540,7 +30871,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_SHORT];
     if (dtype->f->castdict == NULL) {
@@ -30567,7 +30898,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_SHORT];
     if (dtype->f->castdict == NULL) {
@@ -30596,9 +30927,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_USHORT];
     if (dtype->f->castdict == NULL) {
@@ -30625,7 +30956,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_USHORT];
     if (dtype->f->castdict == NULL) {
@@ -30652,7 +30983,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_USHORT];
     if (dtype->f->castdict == NULL) {
@@ -30681,9 +31012,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_INT];
     if (dtype->f->castdict == NULL) {
@@ -30710,7 +31041,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_INT];
     if (dtype->f->castdict == NULL) {
@@ -30737,7 +31068,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_INT];
     if (dtype->f->castdict == NULL) {
@@ -30766,9 +31097,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_UINT];
     if (dtype->f->castdict == NULL) {
@@ -30795,7 +31126,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_UINT];
     if (dtype->f->castdict == NULL) {
@@ -30822,7 +31153,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_UINT];
     if (dtype->f->castdict == NULL) {
@@ -30851,9 +31182,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_LONG];
     if (dtype->f->castdict == NULL) {
@@ -30880,7 +31211,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_LONG];
     if (dtype->f->castdict == NULL) {
@@ -30907,7 +31238,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_LONG];
     if (dtype->f->castdict == NULL) {
@@ -30936,9 +31267,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_ULONG];
     if (dtype->f->castdict == NULL) {
@@ -30965,7 +31296,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_ULONG];
     if (dtype->f->castdict == NULL) {
@@ -30992,7 +31323,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_ULONG];
     if (dtype->f->castdict == NULL) {
@@ -31021,9 +31352,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_LONGLONG];
     if (dtype->f->castdict == NULL) {
@@ -31050,7 +31381,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_LONGLONG];
     if (dtype->f->castdict == NULL) {
@@ -31077,7 +31408,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_LONGLONG];
     if (dtype->f->castdict == NULL) {
@@ -31106,9 +31437,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_ULONGLONG];
     if (dtype->f->castdict == NULL) {
@@ -31135,7 +31466,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_ULONGLONG];
     if (dtype->f->castdict == NULL) {
@@ -31162,7 +31493,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_ULONGLONG];
     if (dtype->f->castdict == NULL) {
@@ -31191,9 +31522,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_HALF];
     if (dtype->f->castdict == NULL) {
@@ -31220,7 +31551,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_HALF];
     if (dtype->f->castdict == NULL) {
@@ -31247,7 +31578,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_HALF];
     if (dtype->f->castdict == NULL) {
@@ -31276,9 +31607,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_FLOAT];
     if (dtype->f->castdict == NULL) {
@@ -31305,7 +31636,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_FLOAT];
     if (dtype->f->castdict == NULL) {
@@ -31332,7 +31663,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_FLOAT];
     if (dtype->f->castdict == NULL) {
@@ -31361,9 +31692,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_DOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31390,7 +31721,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_DOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31417,7 +31748,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_DOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31446,9 +31777,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_LONGDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31475,7 +31806,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_LONGDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31502,7 +31833,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_LONGDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31531,9 +31862,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_CFLOAT];
     if (dtype->f->castdict == NULL) {
@@ -31560,7 +31891,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_CFLOAT];
     if (dtype->f->castdict == NULL) {
@@ -31587,7 +31918,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_CFLOAT];
     if (dtype->f->castdict == NULL) {
@@ -31616,9 +31947,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_CDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31645,7 +31976,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_CDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31672,7 +32003,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_CDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31701,9 +32032,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_CLONGDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31730,7 +32061,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_CLONGDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31757,7 +32088,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_CLONGDOUBLE];
     if (dtype->f->castdict == NULL) {
@@ -31786,9 +32117,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_OBJECT];
     if (dtype->f->castdict == NULL) {
@@ -31815,7 +32146,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_OBJECT];
     if (dtype->f->castdict == NULL) {
@@ -31842,7 +32173,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_OBJECT];
     if (dtype->f->castdict == NULL) {
@@ -31871,9 +32202,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_STRING];
     if (dtype->f->castdict == NULL) {
@@ -31900,7 +32231,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_STRING];
     if (dtype->f->castdict == NULL) {
@@ -31927,7 +32258,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_STRING];
     if (dtype->f->castdict == NULL) {
@@ -31956,9 +32287,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_UNICODE];
     if (dtype->f->castdict == NULL) {
@@ -31985,7 +32316,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_UNICODE];
     if (dtype->f->castdict == NULL) {
@@ -32012,7 +32343,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_UNICODE];
     if (dtype->f->castdict == NULL) {
@@ -32041,9 +32372,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_VOID];
     if (dtype->f->castdict == NULL) {
@@ -32070,7 +32401,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_VOID];
     if (dtype->f->castdict == NULL) {
@@ -32097,7 +32428,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_VOID];
     if (dtype->f->castdict == NULL) {
@@ -32126,9 +32457,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_DATETIME];
     if (dtype->f->castdict == NULL) {
@@ -32155,7 +32486,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_DATETIME];
     if (dtype->f->castdict == NULL) {
@@ -32182,7 +32513,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_DATETIME];
     if (dtype->f->castdict == NULL) {
@@ -32211,9 +32542,9 @@ set_typeinfo(PyObject *dict)
     
 
     
-#line 4669
+#line 4687
 
-    #line 4674
+    #line 4692
 
     dtype = _builtin_descrs[NPY_TIMEDELTA];
     if (dtype->f->castdict == NULL) {
@@ -32240,7 +32571,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_TIMEDELTA];
     if (dtype->f->castdict == NULL) {
@@ -32267,7 +32598,7 @@ set_typeinfo(PyObject *dict)
     Py_DECREF(cobj);
 
     
-#line 4674
+#line 4692
 
     dtype = _builtin_descrs[NPY_TIMEDELTA];
     if (dtype->f->castdict == NULL) {
@@ -32312,132 +32643,132 @@ set_typeinfo(PyObject *dict)
         _letter_to_num[i] = NPY_NTYPES;
     }
 
-    #line 4729
+    #line 4747
 
     _letter_to_num[NPY_BOOLLTR] = NPY_BOOL;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_BYTELTR] = NPY_BYTE;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_UBYTELTR] = NPY_UBYTE;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_SHORTLTR] = NPY_SHORT;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_USHORTLTR] = NPY_USHORT;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_INTLTR] = NPY_INT;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_UINTLTR] = NPY_UINT;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_INTPLTR] = NPY_INTP;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_UINTPLTR] = NPY_UINTP;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_LONGLTR] = NPY_LONG;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_ULONGLTR] = NPY_ULONG;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_LONGLONGLTR] = NPY_LONGLONG;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_ULONGLONGLTR] = NPY_ULONGLONG;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_HALFLTR] = NPY_HALF;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_FLOATLTR] = NPY_FLOAT;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_DOUBLELTR] = NPY_DOUBLE;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_LONGDOUBLELTR] = NPY_LONGDOUBLE;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_CFLOATLTR] = NPY_CFLOAT;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_CDOUBLELTR] = NPY_CDOUBLE;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_CLONGDOUBLELTR] = NPY_CLONGDOUBLE;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_OBJECTLTR] = NPY_OBJECT;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_STRINGLTR] = NPY_STRING;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_UNICODELTR] = NPY_UNICODE;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_VOIDLTR] = NPY_VOID;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_DATETIMELTR] = NPY_DATETIME;
 
     
-#line 4729
+#line 4747
 
     _letter_to_num[NPY_TIMEDELTALTR] = NPY_TIMEDELTA;
 
@@ -32445,139 +32776,139 @@ set_typeinfo(PyObject *dict)
 
     _letter_to_num[NPY_STRINGLTR2] = NPY_STRING;
 
-    #line 4745
+    #line 4763
 
     BOOL_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     BYTE_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     UBYTE_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     SHORT_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     USHORT_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     INT_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     UINT_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     LONG_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     ULONG_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     LONGLONG_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     ULONGLONG_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     HALF_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     FLOAT_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     DOUBLE_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     LONGDOUBLE_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     CFLOAT_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     CDOUBLE_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     CLONGDOUBLE_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     OBJECT_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     STRING_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     UNICODE_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     VOID_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     DATETIME_Descr.fields = Py_None;
 
     
-#line 4745
+#line 4763
 
     TIMEDELTA_Descr.fields = Py_None;
 
     
 
 
-    #line 4754
+    #line 4772
 
     PyDataType_MAKEUNSIZED(&STRING_Descr);
 
     
-#line 4754
+#line 4772
 
     PyDataType_MAKEUNSIZED(&UNICODE_Descr);
 
     
-#line 4754
+#line 4772
 
     PyDataType_MAKEUNSIZED(&VOID_Descr);
 
@@ -32588,7 +32919,7 @@ set_typeinfo(PyObject *dict)
     if (infodict == NULL) return -1;
 
     int ret;
-    #line 4798
+    #line 4816
 
     s = PyArray_typeinforanged(
         NPY_BOOLLTR, NPY_BOOL, NPY_BITSOF_BOOL, _ALIGN(npy_bool),
@@ -32609,7 +32940,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_BYTELTR, NPY_BYTE, NPY_BITSOF_BYTE, _ALIGN(npy_byte),
@@ -32630,7 +32961,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_UBYTELTR, NPY_UBYTE, NPY_BITSOF_BYTE, _ALIGN(npy_ubyte),
@@ -32651,7 +32982,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_SHORTLTR, NPY_SHORT, NPY_BITSOF_SHORT, _ALIGN(npy_short),
@@ -32672,7 +33003,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_USHORTLTR, NPY_USHORT, NPY_BITSOF_SHORT, _ALIGN(npy_ushort),
@@ -32693,7 +33024,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_INTLTR, NPY_INT, NPY_BITSOF_INT, _ALIGN(npy_int),
@@ -32714,7 +33045,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_UINTLTR, NPY_UINT, NPY_BITSOF_INT, _ALIGN(npy_uint),
@@ -32735,7 +33066,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_INTPLTR, NPY_INTP, NPY_BITSOF_INTP, _ALIGN(npy_intp),
@@ -32756,7 +33087,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_UINTPLTR, NPY_UINTP, NPY_BITSOF_INTP, _ALIGN(npy_uintp),
@@ -32777,7 +33108,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_LONGLTR, NPY_LONG, NPY_BITSOF_LONG, _ALIGN(npy_long),
@@ -32798,7 +33129,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_ULONGLTR, NPY_ULONG, NPY_BITSOF_LONG, _ALIGN(npy_ulong),
@@ -32819,7 +33150,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_LONGLONGLTR, NPY_LONGLONG, NPY_BITSOF_LONGLONG, _ALIGN(npy_longlong),
@@ -32840,7 +33171,7 @@ set_typeinfo(PyObject *dict)
 
 
     
-#line 4798
+#line 4816
 
     s = PyArray_typeinforanged(
         NPY_ULONGLONGLTR, NPY_ULONGLONG, NPY_BITSOF_LONGLONG, _ALIGN(npy_ulonglong),
@@ -32863,7 +33194,7 @@ set_typeinfo(PyObject *dict)
     
 
 
-    #line 4829
+    #line 4847
     s = PyArray_typeinfo(
         NPY_HALFLTR, NPY_HALF, NPY_BITSOF_HALF,
         _ALIGN(npy_half), &PyHalfArrType_Type
@@ -32880,7 +33211,7 @@ set_typeinfo(PyObject *dict)
     }
 
     
-#line 4829
+#line 4847
     s = PyArray_typeinfo(
         NPY_FLOATLTR, NPY_FLOAT, NPY_BITSOF_FLOAT,
         _ALIGN(npy_float), &PyFloatArrType_Type
@@ -32897,7 +33228,7 @@ set_typeinfo(PyObject *dict)
     }
 
     
-#line 4829
+#line 4847
     s = PyArray_typeinfo(
         NPY_DOUBLELTR, NPY_DOUBLE, NPY_BITSOF_DOUBLE,
         _ALIGN(npy_double), &PyDoubleArrType_Type
@@ -32914,7 +33245,7 @@ set_typeinfo(PyObject *dict)
     }
 
     
-#line 4829
+#line 4847
     s = PyArray_typeinfo(
         NPY_LONGDOUBLELTR, NPY_LONGDOUBLE, NPY_BITSOF_LONGDOUBLE,
         _ALIGN(npy_longdouble), &PyLongDoubleArrType_Type
@@ -32931,7 +33262,7 @@ set_typeinfo(PyObject *dict)
     }
 
     
-#line 4829
+#line 4847
     s = PyArray_typeinfo(
         NPY_CFLOATLTR, NPY_CFLOAT, NPY_BITSOF_CFLOAT,
         _ALIGN(npy_cfloat), &PyCFloatArrType_Type
@@ -32948,7 +33279,7 @@ set_typeinfo(PyObject *dict)
     }
 
     
-#line 4829
+#line 4847
     s = PyArray_typeinfo(
         NPY_CDOUBLELTR, NPY_CDOUBLE, NPY_BITSOF_CDOUBLE,
         _ALIGN(npy_cdouble), &PyCDoubleArrType_Type
@@ -32965,7 +33296,7 @@ set_typeinfo(PyObject *dict)
     }
 
     
-#line 4829
+#line 4847
     s = PyArray_typeinfo(
         NPY_CLONGDOUBLELTR, NPY_CLONGDOUBLE, NPY_BITSOF_CLONGDOUBLE,
         _ALIGN(npy_clongdouble), &PyCLongDoubleArrType_Type
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c.src
index 56f78984381..b84625c776c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.c.src
@@ -55,7 +55,7 @@
  * This is especially important for nonzero and copyswap, which may run with
  * the GIL released.
  */
-static NPY_INLINE PyArrayObject_fields
+static inline PyArrayObject_fields
 get_dummy_stack_array(PyArrayObject *orig)
 {
     PyArrayObject_fields new_fields;
@@ -68,7 +68,7 @@ get_dummy_stack_array(PyArrayObject *orig)
 
 
 /* check for sequences, but ignore the types numpy considers scalars */
-static NPY_INLINE npy_bool
+static inline npy_bool
 PySequence_NoString_Check(PyObject *op) {
     return
         PySequence_Check(op) &&
@@ -291,7 +291,7 @@ static int
                     "Python integers to integer arrays.  The conversion "
                     "of %.100R to %S will fail in the future.\n"
                     "For the old behavior, usually:\n"
-                    "    np.array(value).astype(dtype)`\n"
+                    "    np.array(value).astype(dtype)\n"
                     "will give the desired result (the cast overflows).",
                     obj, descr) < 0) {
                 Py_DECREF(descr);
@@ -523,7 +523,7 @@ NPY_NO_EXPORT int
 
 /**end repeat**/
 
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 string_to_long_double(PyObject*op)
 {
     char *s;
@@ -2150,7 +2150,7 @@ static int
  */
 
 
-static NPY_INLINE void
+static inline void
 _basic_copyn(void *dst, npy_intp dstride, void *src, npy_intp sstride,
              npy_intp n, int elsize) {
     if (src == NULL) {
@@ -2165,7 +2165,7 @@ _basic_copyn(void *dst, npy_intp dstride, void *src, npy_intp sstride,
     }
 }
 
-static NPY_INLINE void
+static inline void
 _basic_copy(void *dst, void *src, int elsize) {
     if (src == NULL) {
         return;
@@ -4646,12 +4646,30 @@ set_typeinfo(PyObject *dict)
      * should be defined on the class and inherited to the scalar.
      * (NPY_HALF is the largest builtin one.)
      */
-    for (i = 0; i <= NPY_HALF; i++) {
-        if (dtypemeta_wrap_legacy_descriptor(_builtin_descrs[i]) < 0) {
-            return -1;
-        }
+    /**begin repeat
+     *
+     * #NAME = BOOL,
+     *         BYTE, UBYTE, SHORT, USHORT, INT, UINT,
+     *         LONG, ULONG, LONGLONG, ULONGLONG,
+     *         HALF, FLOAT, DOUBLE, LONGDOUBLE,
+     *         CFLOAT, CDOUBLE, CLONGDOUBLE,
+     *         OBJECT, STRING, UNICODE, VOID,
+     *         DATETIME, TIMEDELTA#
+     */
+    if (dtypemeta_wrap_legacy_descriptor(
+            _builtin_descrs[NPY_@NAME@],
+            "numpy.dtypes." NPY_@NAME@_Name "DType",
+#ifdef NPY_@NAME@_alias
+            "numpy.dtypes." NPY_@NAME@_Alias "DType"
+#else
+            NULL
+#endif
+            ) < 0) {
+        return -1;
     }
 
+    /**end repeat**/
+
     /*
      * Add cast functions for the new types
      */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h
index 119a8f3ef82..322ac3dc7b3 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h
@@ -346,5 +346,403 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT int LONGDOUBLE_argmin,
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT int BOOL_argmax,
     (npy_bool *ip, npy_intp n, npy_intp *max_ind, PyArrayObject *aip))
 
+
+/*
+ * Define DType and scalar type names and aliases as used in Python.
+ */
+
+#line 91
+#define NPY_BOOL_name "bool_"
+#define NPY_BOOL_Name "Bool"
+
+
+#line 91
+#define NPY_HALF_name "float16"
+#define NPY_HALF_Name "Float16"
+
+
+#line 91
+#define NPY_FLOAT_name "float32"
+#define NPY_FLOAT_Name "Float32"
+
+
+#line 91
+#define NPY_DOUBLE_name "float64"
+#define NPY_DOUBLE_Name "Float64"
+
+
+#line 91
+#define NPY_LONGDOUBLE_name "longdouble"
+#define NPY_LONGDOUBLE_Name "LongDouble"
+
+
+#line 91
+#define NPY_CFLOAT_name "complex64"
+#define NPY_CFLOAT_Name "Complex64"
+
+
+#line 91
+#define NPY_CDOUBLE_name "complex128"
+#define NPY_CDOUBLE_Name "Complex128"
+
+
+#line 91
+#define NPY_CLONGDOUBLE_name "clongdouble"
+#define NPY_CLONGDOUBLE_Name "CLongDouble"
+
+
+#line 91
+#define NPY_STRING_name "bytes_"
+#define NPY_STRING_Name "Bytes"
+
+
+#line 91
+#define NPY_UNICODE_name "str_"
+#define NPY_UNICODE_Name "Str"
+
+
+#line 91
+#define NPY_VOID_name "void"
+#define NPY_VOID_Name "Void"
+
+
+#line 91
+#define NPY_OBJECT_name "object_"
+#define NPY_OBJECT_Name "Object"
+
+
+#line 91
+#define NPY_DATETIME_name "datetime64"
+#define NPY_DATETIME_Name "DateTime64"
+
+
+#line 91
+#define NPY_TIMEDELTA_name "timedelta64"
+#define NPY_TIMEDELTA_Name "TimeDelta64"
+
+
+
+
+/*
+ * Give integers different names when they are the same size (gh-9799).
+ * `intX` always refers to the first int of that size in the sequence
+ * `['LONG', 'LONGLONG', 'INT', 'SHORT', 'BYTE']`.
+ * Unfortunately, since the bitsize names are not strictly fixed, we add
+ * the C name for all integer types (as aliases).
+ *
+ * Right now, we do not define the C aliases for floats (which are always
+ * the same).
+ */
+
+#if NPY_SIZEOF_BYTE == NPY_SIZEOF_SHORT
+    #define BYTE_not_size_named
+#endif
+#if NPY_SIZEOF_SHORT == NPY_SIZEOF_INT
+    #define SHORT_not_size_named
+#endif
+#if NPY_SIZEOF_INT == NPY_SIZEOF_LONG
+    #define INT_not_size_named
+#endif
+#if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONG
+    #define LONGLONG_not_size_named
+#endif
+
+
+#line 133
+
+#ifdef BYTE_not_size_named
+    #define NPY_BYTE_name "byte"
+    #define NPY_BYTE_Name "Byte"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_BYTE_alias "byte"
+    #define NPY_BYTE_Alias "Byte"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_BYTE == 8
+        #define NPY_BYTE_name "int8"
+        #define NPY_BYTE_Name "Int8"
+    #elif NPY_BITSOF_BYTE == 16
+        #define NPY_BYTE_name "int16"
+        #define NPY_BYTE_Name "Int16"
+    #elif NPY_BITSOF_BYTE == 32
+        #define NPY_BYTE_name "int32"
+        #define NPY_BYTE_Name "Int32"
+    #elif NPY_BITSOF_BYTE == 64
+        #define NPY_BYTE_name "int64"
+        #define NPY_BYTE_Name "Int64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef SHORT_not_size_named
+    #define NPY_SHORT_name "short"
+    #define NPY_SHORT_Name "Short"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_SHORT_alias "short"
+    #define NPY_SHORT_Alias "Short"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_SHORT == 8
+        #define NPY_SHORT_name "int8"
+        #define NPY_SHORT_Name "Int8"
+    #elif NPY_BITSOF_SHORT == 16
+        #define NPY_SHORT_name "int16"
+        #define NPY_SHORT_Name "Int16"
+    #elif NPY_BITSOF_SHORT == 32
+        #define NPY_SHORT_name "int32"
+        #define NPY_SHORT_Name "Int32"
+    #elif NPY_BITSOF_SHORT == 64
+        #define NPY_SHORT_name "int64"
+        #define NPY_SHORT_Name "Int64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef INT_not_size_named
+    #define NPY_INT_name "intc"
+    #define NPY_INT_Name "Int"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_INT_alias "intc"
+    #define NPY_INT_Alias "Int"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_INT == 8
+        #define NPY_INT_name "int8"
+        #define NPY_INT_Name "Int8"
+    #elif NPY_BITSOF_INT == 16
+        #define NPY_INT_name "int16"
+        #define NPY_INT_Name "Int16"
+    #elif NPY_BITSOF_INT == 32
+        #define NPY_INT_name "int32"
+        #define NPY_INT_Name "Int32"
+    #elif NPY_BITSOF_INT == 64
+        #define NPY_INT_name "int64"
+        #define NPY_INT_Name "Int64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef LONG_not_size_named
+    #define NPY_LONG_name "int_"
+    #define NPY_LONG_Name "Long"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_LONG_alias "int_"
+    #define NPY_LONG_Alias "Long"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_LONG == 8
+        #define NPY_LONG_name "int8"
+        #define NPY_LONG_Name "Int8"
+    #elif NPY_BITSOF_LONG == 16
+        #define NPY_LONG_name "int16"
+        #define NPY_LONG_Name "Int16"
+    #elif NPY_BITSOF_LONG == 32
+        #define NPY_LONG_name "int32"
+        #define NPY_LONG_Name "Int32"
+    #elif NPY_BITSOF_LONG == 64
+        #define NPY_LONG_name "int64"
+        #define NPY_LONG_Name "Int64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef LONGLONG_not_size_named
+    #define NPY_LONGLONG_name "longlong"
+    #define NPY_LONGLONG_Name "LongLong"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_LONGLONG_alias "longlong"
+    #define NPY_LONGLONG_Alias "LongLong"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_LONGLONG == 8
+        #define NPY_LONGLONG_name "int8"
+        #define NPY_LONGLONG_Name "Int8"
+    #elif NPY_BITSOF_LONGLONG == 16
+        #define NPY_LONGLONG_name "int16"
+        #define NPY_LONGLONG_Name "Int16"
+    #elif NPY_BITSOF_LONGLONG == 32
+        #define NPY_LONGLONG_name "int32"
+        #define NPY_LONGLONG_Name "Int32"
+    #elif NPY_BITSOF_LONGLONG == 64
+        #define NPY_LONGLONG_name "int64"
+        #define NPY_LONGLONG_Name "Int64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef BYTE_not_size_named
+    #define NPY_UBYTE_name "ubyte"
+    #define NPY_UBYTE_Name "UByte"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_UBYTE_alias "ubyte"
+    #define NPY_UBYTE_Alias "UByte"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_BYTE == 8
+        #define NPY_UBYTE_name "uint8"
+        #define NPY_UBYTE_Name "UInt8"
+    #elif NPY_BITSOF_BYTE == 16
+        #define NPY_UBYTE_name "uint16"
+        #define NPY_UBYTE_Name "UInt16"
+    #elif NPY_BITSOF_BYTE == 32
+        #define NPY_UBYTE_name "uint32"
+        #define NPY_UBYTE_Name "UInt32"
+    #elif NPY_BITSOF_BYTE == 64
+        #define NPY_UBYTE_name "uint64"
+        #define NPY_UBYTE_Name "UInt64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef SHORT_not_size_named
+    #define NPY_USHORT_name "ushort"
+    #define NPY_USHORT_Name "UShort"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_USHORT_alias "ushort"
+    #define NPY_USHORT_Alias "UShort"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_SHORT == 8
+        #define NPY_USHORT_name "uint8"
+        #define NPY_USHORT_Name "UInt8"
+    #elif NPY_BITSOF_SHORT == 16
+        #define NPY_USHORT_name "uint16"
+        #define NPY_USHORT_Name "UInt16"
+    #elif NPY_BITSOF_SHORT == 32
+        #define NPY_USHORT_name "uint32"
+        #define NPY_USHORT_Name "UInt32"
+    #elif NPY_BITSOF_SHORT == 64
+        #define NPY_USHORT_name "uint64"
+        #define NPY_USHORT_Name "UInt64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef INT_not_size_named
+    #define NPY_UINT_name "uintc"
+    #define NPY_UINT_Name "UInt"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_UINT_alias "uintc"
+    #define NPY_UINT_Alias "UInt"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_INT == 8
+        #define NPY_UINT_name "uint8"
+        #define NPY_UINT_Name "UInt8"
+    #elif NPY_BITSOF_INT == 16
+        #define NPY_UINT_name "uint16"
+        #define NPY_UINT_Name "UInt16"
+    #elif NPY_BITSOF_INT == 32
+        #define NPY_UINT_name "uint32"
+        #define NPY_UINT_Name "UInt32"
+    #elif NPY_BITSOF_INT == 64
+        #define NPY_UINT_name "uint64"
+        #define NPY_UINT_Name "UInt64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef LONG_not_size_named
+    #define NPY_ULONG_name "uint"
+    #define NPY_ULONG_Name "ULong"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_ULONG_alias "uint"
+    #define NPY_ULONG_Alias "ULong"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_LONG == 8
+        #define NPY_ULONG_name "uint8"
+        #define NPY_ULONG_Name "UInt8"
+    #elif NPY_BITSOF_LONG == 16
+        #define NPY_ULONG_name "uint16"
+        #define NPY_ULONG_Name "UInt16"
+    #elif NPY_BITSOF_LONG == 32
+        #define NPY_ULONG_name "uint32"
+        #define NPY_ULONG_Name "UInt32"
+    #elif NPY_BITSOF_LONG == 64
+        #define NPY_ULONG_name "uint64"
+        #define NPY_ULONG_Name "UInt64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+#line 133
+
+#ifdef LONGLONG_not_size_named
+    #define NPY_ULONGLONG_name "ulonglong"
+    #define NPY_ULONGLONG_Name "ULongLong"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_ULONGLONG_alias "ulonglong"
+    #define NPY_ULONGLONG_Alias "ULongLong"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_LONGLONG == 8
+        #define NPY_ULONGLONG_name "uint8"
+        #define NPY_ULONGLONG_Name "UInt8"
+    #elif NPY_BITSOF_LONGLONG == 16
+        #define NPY_ULONGLONG_name "uint16"
+        #define NPY_ULONGLONG_Name "UInt16"
+    #elif NPY_BITSOF_LONGLONG == 32
+        #define NPY_ULONGLONG_name "uint32"
+        #define NPY_ULONGLONG_Name "UInt32"
+    #elif NPY_BITSOF_LONGLONG == 64
+        #define NPY_ULONGLONG_name "uint64"
+        #define NPY_ULONGLONG_Name "UInt64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+
+
+#undef BYTE_not_size_named
+#undef SHORT_not_size_named
+#undef INT_not_size_named
+#undef LONGLONG_not_size_named
+
 #endif  /* NUMPY_CORE_SRC_MULTIARRAY_ARRAYTYPES_H_ */
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h.src
index aad464ccf95..90a075dad4f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/arraytypes.h.src
@@ -66,4 +66,102 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT int @TYPE@_@func@,
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT int BOOL_argmax,
     (npy_bool *ip, npy_intp n, npy_intp *max_ind, PyArrayObject *aip))
 
+
+/*
+ * Define DType and scalar type names and aliases as used in Python.
+ */
+
+/**begin repeat
+ * #NAME = BOOL,
+ *         HALF, FLOAT, DOUBLE, LONGDOUBLE,
+ *         CFLOAT, CDOUBLE, CLONGDOUBLE,
+ *         STRING, UNICODE, VOID, OBJECT,
+ *         DATETIME, TIMEDELTA#
+ * #name = bool_,
+ *         float16, float32, float64, longdouble,
+ *         complex64, complex128, clongdouble,
+ *         bytes_, str_, void, object_,
+ *         datetime64, timedelta64#
+ * #Name = Bool,
+ *         Float16, Float32, Float64, LongDouble,
+ *         Complex64, Complex128, CLongDouble,
+ *         Bytes, Str, Void, Object,
+ *         DateTime64, TimeDelta64#
+ */
+#define NPY_@NAME@_name "@name@"
+#define NPY_@NAME@_Name "@Name@"
+
+/**end repeat**/
+
+
+/*
+ * Give integers different names when they are the same size (gh-9799).
+ * `intX` always refers to the first int of that size in the sequence
+ * `['LONG', 'LONGLONG', 'INT', 'SHORT', 'BYTE']`.
+ * Unfortunately, since the bitsize names are not strictly fixed, we add
+ * the C name for all integer types (as aliases).
+ *
+ * Right now, we do not define the C aliases for floats (which are always
+ * the same).
+ */
+
+#if NPY_SIZEOF_BYTE == NPY_SIZEOF_SHORT
+    #define BYTE_not_size_named
+#endif
+#if NPY_SIZEOF_SHORT == NPY_SIZEOF_INT
+    #define SHORT_not_size_named
+#endif
+#if NPY_SIZEOF_INT == NPY_SIZEOF_LONG
+    #define INT_not_size_named
+#endif
+#if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONG
+    #define LONGLONG_not_size_named
+#endif
+
+
+/**begin repeat
+ * #NAME = BYTE, SHORT, INT, LONG, LONGLONG,
+ *         UBYTE, USHORT, UINT, ULONG, ULONGLONG#
+ * #CNAME = (BYTE, SHORT, INT, LONG, LONGLONG)*2#
+ * #cname = byte, short, intc, int_, longlong,
+ *          ubyte, ushort, uintc, uint, ulonglong#
+ * #CName = Byte, Short, Int, Long, LongLong,
+ *          UByte, UShort, UInt, ULong, ULongLong#
+ * #bitname = int*5, uint*5#
+ * #BitName = Int*5, UInt*5#
+ */
+
+#ifdef @CNAME@_not_size_named
+    #define NPY_@NAME@_name "@cname@"
+    #define NPY_@NAME@_Name "@CName@"
+#else
+    /* The C-name is considered just an alias for these: */
+    #define NPY_@NAME@_alias "@cname@"
+    #define NPY_@NAME@_Alias "@CName@"
+
+    /* The bitsof macro includes math, so cannot be stringified */
+    #if NPY_BITSOF_@CNAME@ == 8
+        #define NPY_@NAME@_name "@bitname@8"
+        #define NPY_@NAME@_Name "@BitName@8"
+    #elif NPY_BITSOF_@CNAME@ == 16
+        #define NPY_@NAME@_name "@bitname@16"
+        #define NPY_@NAME@_Name "@BitName@16"
+    #elif NPY_BITSOF_@CNAME@ == 32
+        #define NPY_@NAME@_name "@bitname@32"
+        #define NPY_@NAME@_Name "@BitName@32"
+    #elif NPY_BITSOF_@CNAME@ == 64
+        #define NPY_@NAME@_name "@bitname@64"
+        #define NPY_@NAME@_Name "@BitName@64"
+    #else
+        #error "need to fix integer bit-length name code"
+    #endif
+#endif
+
+/**end repeat**/
+
+#undef BYTE_not_size_named
+#undef SHORT_not_size_named
+#undef INT_not_size_named
+#undef LONGLONG_not_size_named
+
 #endif  /* NUMPY_CORE_SRC_MULTIARRAY_ARRAYTYPES_H_ */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/buffer.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/buffer.c
index 0307d41a8b3..c9d881e164f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/buffer.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/buffer.c
@@ -17,6 +17,7 @@
 #include "numpyos.h"
 #include "arrayobject.h"
 #include "scalartypes.h"
+#include "dtypemeta.h"
 
 /*************************************************************************
  ****************   Implement Buffer Protocol ****************************
@@ -136,7 +137,7 @@ fail:
  * AND, the descr element size is a multiple of the alignment,
  * AND, the array data is positioned to alignment granularity.
  */
-static NPY_INLINE int
+static inline int
 _is_natively_aligned_at(PyArray_Descr *descr,
                         PyArrayObject *arr, Py_ssize_t offset)
 {
@@ -417,9 +418,16 @@ _buffer_format_string(PyArray_Descr *descr, _tmp_string_t *str,
             break;
         }
         default:
-            PyErr_Format(PyExc_ValueError,
-                         "cannot include dtype '%c' in a buffer",
-                         descr->type);
+            if (NPY_DT_is_legacy(NPY_DTYPE(descr))) {
+                PyErr_Format(PyExc_ValueError,
+                             "cannot include dtype '%c' in a buffer",
+                             descr->type);
+            }
+            else {
+                PyErr_Format(PyExc_ValueError,
+                             "cannot include dtype '%s' in a buffer",
+                             ((PyTypeObject*)NPY_DTYPE(descr))->tp_name);
+            }
             return -1;
         }
     }
@@ -591,7 +599,7 @@ _buffer_info_cmp(_buffer_info_t *a, _buffer_info_t *b)
  * a useful error message instead of crashing hard if a C-subclass uses
  * the same field.
  */
-static NPY_INLINE void *
+static inline void *
 buffer_info_tag(void *buffer_info)
 {
     if (buffer_info == NULL) {
@@ -603,7 +611,7 @@ buffer_info_tag(void *buffer_info)
 }
 
 
-static NPY_INLINE int
+static inline int
 _buffer_info_untag(
         void *tagged_buffer_info, _buffer_info_t **buffer_info, PyObject *obj)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/calculation.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/calculation.c
index a985a2308bf..62a994c64ae 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/calculation.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/calculation.c
@@ -7,6 +7,7 @@
 
 #include "numpy/arrayobject.h"
 #include "lowlevel_strided_loops.h"
+#include "dtypemeta.h"
 
 #include "npy_config.h"
 
@@ -50,7 +51,7 @@ _PyArray_ArgMinMaxCommon(PyArrayObject *op,
     int axis_copy = axis;
     npy_intp _shape_buf[NPY_MAXDIMS];
     npy_intp *out_shape;
-    // Keep the number of dimensions and shape of 
+    // Keep the number of dimensions and shape of
     // original array. Helps when `keepdims` is True.
     npy_intp* original_op_shape = PyArray_DIMS(op);
     int out_ndim = PyArray_NDIM(op);
@@ -87,9 +88,13 @@ _PyArray_ArgMinMaxCommon(PyArrayObject *op,
         op = ap;
     }
 
-    /* Will get native-byte order contiguous copy. */
-    ap = (PyArrayObject *)PyArray_ContiguousFromAny((PyObject *)op,
-                                  PyArray_DESCR(op)->type_num, 1, 0);
+    // Will get native-byte order contiguous copy.
+    PyArray_Descr *descr = NPY_DT_CALL_ensure_canonical(PyArray_DESCR(op));
+    if (descr == NULL) {
+        return NULL;
+    }
+    ap = (PyArrayObject *)PyArray_FromArray(op, descr, NPY_ARRAY_DEFAULT);
+
     Py_DECREF(op);
     if (ap == NULL) {
         return NULL;
@@ -106,9 +111,9 @@ _PyArray_ArgMinMaxCommon(PyArrayObject *op,
             for (int i = 0; i < out_ndim; i++) {
                 out_shape[i] = 1;
             }
-        } 
+        }
         else {
-            /* 
+            /*
              * While `ap` may be transposed, we can ignore this for `out` because the
              * transpose only reorders the size 1 `axis` (not changing memory layout).
              */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/common.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/common.c
index da8d23a262a..573d0d6063c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/common.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/common.c
@@ -128,24 +128,6 @@ PyArray_DTypeFromObject(PyObject *obj, int maxdims, PyArray_Descr **out_dtype)
 }
 
 
-NPY_NO_EXPORT int
-_zerofill(PyArrayObject *ret)
-{
-    if (PyDataType_REFCHK(PyArray_DESCR(ret))) {
-        PyObject *zero = PyLong_FromLong(0);
-        PyArray_FillObjectArray(ret, zero);
-        Py_DECREF(zero);
-        if (PyErr_Occurred()) {
-            return -1;
-        }
-    }
-    else {
-        npy_intp n = PyArray_NBYTES(ret);
-        memset(PyArray_DATA(ret), 0, n);
-    }
-    return 0;
-}
-
 NPY_NO_EXPORT npy_bool
 _IsWriteable(PyArrayObject *ap)
 {
@@ -459,3 +441,32 @@ new_array_for_sum(PyArrayObject *ap1, PyArrayObject *ap2, PyArrayObject* out,
     }
 }
 
+NPY_NO_EXPORT int
+check_is_convertible_to_scalar(PyArrayObject *v)
+{
+    if (PyArray_NDIM(v) == 0) {
+        return 0;
+    }
+
+    /* Remove this if-else block when the deprecation expires */
+    if (PyArray_SIZE(v) == 1) {
+        /* Numpy 1.25.0, 2023-01-02 */
+        if (DEPRECATE(
+                "Conversion of an array with ndim > 0 to a scalar "
+                "is deprecated, and will error in future. "
+                "Ensure you extract a single element from your array "
+                "before performing this operation. "
+                "(Deprecated NumPy 1.25.)") < 0) {
+            return -1;
+        }
+        return 0;
+    } else {
+        PyErr_SetString(PyExc_TypeError,
+            "only length-1 arrays can be converted to Python scalars");
+        return -1;
+    }
+
+    PyErr_SetString(PyExc_TypeError,
+            "only 0-dimensional arrays can be converted to Python scalars");
+    return -1;
+}
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/common.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/common.h
index 4ea9bcf455f..cb9fadc4e9f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/common.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/common.h
@@ -55,9 +55,6 @@ PyArray_DTypeFromObject(PyObject *obj, int maxdims,
 NPY_NO_EXPORT PyArray_Descr *
 _array_find_python_scalar_type(PyObject *op);
 
-NPY_NO_EXPORT int
-_zerofill(PyArrayObject *ret);
-
 NPY_NO_EXPORT npy_bool
 _IsWriteable(PyArrayObject *ap);
 
@@ -103,7 +100,7 @@ _may_have_objects(PyArray_Descr *dtype);
  * If _save is not NULL it is assumed the GIL is not taken and it
  * is acquired in the case of an error
  */
-static NPY_INLINE int
+static inline int
 check_and_adjust_index(npy_intp *index, npy_intp max_item, int axis,
                        PyThreadState * _save)
 {
@@ -137,7 +134,7 @@ check_and_adjust_index(npy_intp *index, npy_intp max_item, int axis,
  *
  * msg_prefix: borrowed reference, a string to prepend to the message
  */
-static NPY_INLINE int
+static inline int
 check_and_adjust_axis_msg(int *axis, int ndim, PyObject *msg_prefix)
 {
     /* Check that index is valid, taking into account negative indices */
@@ -149,7 +146,7 @@ check_and_adjust_axis_msg(int *axis, int ndim, PyObject *msg_prefix)
         static PyObject *AxisError_cls = NULL;
         PyObject *exc;
 
-        npy_cache_import("numpy.core._exceptions", "AxisError", &AxisError_cls);
+        npy_cache_import("numpy.exceptions", "AxisError", &AxisError_cls);
         if (AxisError_cls == NULL) {
             return -1;
         }
@@ -171,7 +168,7 @@ check_and_adjust_axis_msg(int *axis, int ndim, PyObject *msg_prefix)
     }
     return 0;
 }
-static NPY_INLINE int
+static inline int
 check_and_adjust_axis(int *axis, int ndim)
 {
     return check_and_adjust_axis_msg(axis, ndim, Py_None);
@@ -203,7 +200,7 @@ check_and_adjust_axis(int *axis, int ndim)
 /*
  * return true if pointer is aligned to 'alignment'
  */
-static NPY_INLINE int
+static inline int
 npy_is_aligned(const void * p, const npy_uintp alignment)
 {
     /*
@@ -217,7 +214,7 @@ npy_is_aligned(const void * p, const npy_uintp alignment)
 }
 
 /* Get equivalent "uint" alignment given an itemsize, for use in copy code */
-static NPY_INLINE npy_uintp
+static inline npy_uintp
 npy_uint_alignment(int itemsize)
 {
     npy_uintp alignment = 0; /* return value of 0 means unaligned */
@@ -264,7 +261,7 @@ npy_uint_alignment(int itemsize)
      */
     __attribute__((no_sanitize("alignment")))
 #endif
-static NPY_INLINE char *
+static inline char *
 npy_memchr(char * haystack, char needle,
            npy_intp stride, npy_intp size, npy_intp * psubloopsize, int invert)
 {
@@ -314,7 +311,7 @@ npy_memchr(char * haystack, char needle,
  * flag means that NULL entries are replaced with None, which is occasionally
  * useful.
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 PyArray_TupleFromItems(int n, PyObject *const *items, int make_null_none)
 {
     PyObject *tuple = PyTuple_New(n);
@@ -335,6 +332,13 @@ PyArray_TupleFromItems(int n, PyObject *const *items, int make_null_none)
     return tuple;
 }
 
+/*
+ * Returns 0 if the array has rank 0, -1 otherwise. Prints a deprecation
+ * warning for arrays of _size_ 1.
+ */
+NPY_NO_EXPORT int
+check_is_convertible_to_scalar(PyArrayObject *v);
+
 
 #include "ucsnarrow.h"
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/common_dtype.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/common_dtype.c
index 3561a905aa7..38a13022140 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/common_dtype.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/common_dtype.c
@@ -8,6 +8,7 @@
 #include "numpy/arrayobject.h"
 
 #include "common_dtype.h"
+#include "convert_datatype.h"
 #include "dtypemeta.h"
 #include "abstractdtypes.h"
 
@@ -61,7 +62,7 @@ PyArray_CommonDType(PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtype2)
     }
     if (common_dtype == (PyArray_DTypeMeta *)Py_NotImplemented) {
         Py_DECREF(Py_NotImplemented);
-        PyErr_Format(PyExc_TypeError,
+        PyErr_Format(npy_DTypePromotionError,
                 "The DTypes %S and %S do not have a common DType. "
                 "For example they cannot be stored in a single array unless "
                 "the dtype is `object`.", dtype1, dtype2);
@@ -288,7 +289,7 @@ PyArray_PromoteDTypeSequence(
                 Py_INCREF(dtypes_in[l]);
                 PyTuple_SET_ITEM(dtypes_in_tuple, l, (PyObject *)dtypes_in[l]);
             }
-            PyErr_Format(PyExc_TypeError,
+            PyErr_Format(npy_DTypePromotionError,
                     "The DType %S could not be promoted by %S. This means that "
                     "no common DType exists for the given inputs. "
                     "For example they cannot be stored in a single array unless "
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.c
index a013a0841e9..d2395c5bf41 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.c
@@ -8,6 +8,7 @@
 #include "numpy/arrayobject.h"
 #include "numpy/npy_3kcompat.h"
 #include "numpy/npy_math.h"
+#include "npy_argparse.h"
 #include "npy_config.h"
 #include "templ_common.h" /* for npy_mul_sizes_with_overflow */
 #include "lowlevel_strided_loops.h" /* for npy_bswap8 */
@@ -109,7 +110,8 @@ minmax(const npy_intp *data, npy_intp data_len, npy_intp *mn, npy_intp *mx)
  * output array.
  */
 NPY_NO_EXPORT PyObject *
-arr_bincount(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds)
+arr_bincount(PyObject *NPY_UNUSED(self), PyObject *const *args,
+                            Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *list = NULL, *weight = Py_None, *mlength = NULL;
     PyArrayObject *lst = NULL, *ans = NULL, *wts = NULL;
@@ -117,11 +119,14 @@ arr_bincount(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds)
     npy_intp minlength = 0;
     npy_intp i;
     double *weights , *dans;
-    static char *kwlist[] = {"list", "weights", "minlength", NULL};
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|OO:bincount",
-                kwlist, &list, &weight, &mlength)) {
-            goto fail;
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("bincount", args, len_args, kwnames,
+                "list", NULL, &list,
+                "|weights", NULL, &weight,
+                "|minlength", NULL, &mlength,
+                NULL, NULL, NULL) < 0) {
+        return NULL;
     }
 
     lst = (PyArrayObject *)PyArray_ContiguousFromAny(list, NPY_INTP, 1, 1);
@@ -265,7 +270,7 @@ arr__monotonicity(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds)
  * indicated by the mask
  */
 NPY_NO_EXPORT PyObject *
-arr_insert(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
+arr_place(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
 {
     char *src, *dest;
     npy_bool *mask_data;
@@ -378,7 +383,7 @@ arr_insert(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
 #ifdef __INTEL_COMPILER
 #pragma intel optimization_level 0
 #endif
-static NPY_INLINE npy_intp
+static inline npy_intp
 _linear_search(const npy_double key, const npy_double *arr, const npy_intp len, const npy_intp i0)
 {
     npy_intp i;
@@ -489,7 +494,8 @@ binary_search_with_guess(const npy_double key, const npy_double *arr,
 #undef LIKELY_IN_CACHE_SIZE
 
 NPY_NO_EXPORT PyObject *
-arr_interp(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
+arr_interp(PyObject *NPY_UNUSED(self), PyObject *const *args, Py_ssize_t len_args,
+                             PyObject *kwnames)
 {
 
     PyObject *fp, *xp, *x;
@@ -500,12 +506,16 @@ arr_interp(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
     const npy_double *dy, *dx, *dz;
     npy_double *dres, *slopes = NULL;
 
-    static char *kwlist[] = {"x", "xp", "fp", "left", "right", NULL};
-
     NPY_BEGIN_THREADS_DEF;
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwdict, "OOO|OO:interp", kwlist,
-                                     &x, &xp, &fp, &left, &right)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("interp", args, len_args, kwnames,
+                "x", NULL, &x,
+                "xp", NULL, &xp,
+                "fp", NULL, &fp,
+                "|left", NULL, &left,
+                "|right", NULL, &right,
+                NULL, NULL, NULL) < 0) {
         return NULL;
     }
 
@@ -654,7 +664,8 @@ fail:
 
 /* As for arr_interp but for complex fp values */
 NPY_NO_EXPORT PyObject *
-arr_interp_complex(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
+arr_interp_complex(PyObject *NPY_UNUSED(self), PyObject *const *args, Py_ssize_t len_args,
+                             PyObject *kwnames)
 {
 
     PyObject *fp, *xp, *x;
@@ -667,12 +678,16 @@ arr_interp_complex(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
     npy_cdouble lval, rval;
     npy_cdouble *dres, *slopes = NULL;
 
-    static char *kwlist[] = {"x", "xp", "fp", "left", "right", NULL};
-
     NPY_BEGIN_THREADS_DEF;
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwdict, "OOO|OO:interp_complex",
-                                     kwlist, &x, &xp, &fp, &left, &right)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("interp_complex", args, len_args, kwnames,
+                "x", NULL, &x,
+                "xp", NULL, &xp,
+                "fp", NULL, &fp,
+                "|left", NULL, &left,
+                "|right", NULL, &right,
+                NULL, NULL, NULL) < 0) {
         return NULL;
     }
 
@@ -1389,7 +1404,7 @@ fail:
 
 /* Can only be called if doc is currently NULL */
 NPY_NO_EXPORT PyObject *
-arr_add_docstring(PyObject *NPY_UNUSED(dummy), PyObject *args)
+arr_add_docstring(PyObject *NPY_UNUSED(dummy), PyObject *const *args, Py_ssize_t len_args)
 {
     PyObject *obj;
     PyObject *str;
@@ -1401,7 +1416,16 @@ arr_add_docstring(PyObject *NPY_UNUSED(dummy), PyObject *args)
         Py_RETURN_NONE;
     }
 
-    if (!PyArg_ParseTuple(args, "OO!:add_docstring", &obj, &PyUnicode_Type, &str)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("add_docstring", args, len_args, NULL,
+            "", NULL, &obj,
+            "", NULL, &str,
+            NULL, NULL, NULL) < 0) {
+        return NULL;
+    }
+    if (!PyUnicode_Check(str)) {
+        PyErr_SetString(PyExc_TypeError,
+                "argument docstring of add_docstring should be a str");
         return NULL;
     }
 
@@ -1491,7 +1515,7 @@ arr_add_docstring(PyObject *NPY_UNUSED(dummy), PyObject *args)
  * byte array. Truth values are determined as usual: 0 is false, everything
  * else is true.
  */
-static NPY_GCC_OPT_3 NPY_INLINE void
+static NPY_GCC_OPT_3 inline void
 pack_inner(const char *inptr,
            npy_intp element_size,   /* in bytes */
            npy_intp n_in,
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.h
index d3bc08cb233..e0e73ac798b 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/compiled_base.h
@@ -4,21 +4,21 @@
 #include "numpy/ndarraytypes.h"
 
 NPY_NO_EXPORT PyObject *
-arr_insert(PyObject *, PyObject *, PyObject *);
+arr_place(PyObject *, PyObject *, PyObject *);
 NPY_NO_EXPORT PyObject *
-arr_bincount(PyObject *, PyObject *, PyObject *);
+arr_bincount(PyObject *, PyObject *const *, Py_ssize_t, PyObject *);
 NPY_NO_EXPORT PyObject *
 arr__monotonicity(PyObject *, PyObject *, PyObject *kwds);
 NPY_NO_EXPORT PyObject *
-arr_interp(PyObject *, PyObject *, PyObject *);
+arr_interp(PyObject *, PyObject *const *, Py_ssize_t, PyObject *, PyObject *);
 NPY_NO_EXPORT PyObject *
-arr_interp_complex(PyObject *, PyObject *, PyObject *);
+arr_interp_complex(PyObject *, PyObject *const *, Py_ssize_t, PyObject *, PyObject *);
 NPY_NO_EXPORT PyObject *
 arr_ravel_multi_index(PyObject *, PyObject *, PyObject *);
 NPY_NO_EXPORT PyObject *
 arr_unravel_index(PyObject *, PyObject *, PyObject *);
 NPY_NO_EXPORT PyObject *
-arr_add_docstring(PyObject *, PyObject *);
+arr_add_docstring(PyObject *, PyObject *const *, Py_ssize_t);
 NPY_NO_EXPORT PyObject *
 io_pack(PyObject *, PyObject *, PyObject *);
 NPY_NO_EXPORT PyObject *
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.c
index 96afb6c00ce..84bf08206f5 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.c
@@ -84,7 +84,7 @@ PyArray_OutputConverter(PyObject *object, PyArrayObject **address)
  * to `PyArray_PyIntAsIntp`.  Exists mainly to retain old behaviour of
  * `PyArray_IntpConverter` and `PyArray_IntpFromSequence`
  */
-static NPY_INLINE npy_intp
+static inline npy_intp
 dimension_from_scalar(PyObject *ob)
 {
     npy_intp value = PyArray_PyIntAsIntp(ob);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.h
index 4d0fbb8941b..62f54749ef0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/conversion_utils.h
@@ -52,7 +52,7 @@ NPY_NO_EXPORT int
 PyArray_TypestrConvert(int itemsize, int gentype);
 
 
-static NPY_INLINE PyObject *
+static inline PyObject *
 PyArray_PyIntFromIntp(npy_intp const value)
 {
 #if NPY_SIZEOF_INTP <= NPY_SIZEOF_LONG
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/convert.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/convert.c
index ef231587d22..60c1a1b9b01 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/convert.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/convert.c
@@ -21,6 +21,7 @@
 
 #include "convert.h"
 #include "array_coercion.h"
+#include "refcount.h"
 
 int
 fallocate(int fd, int mode, off_t offset, off_t len);
@@ -156,11 +157,35 @@ PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format)
             size = PyArray_SIZE(self);
             NPY_BEGIN_ALLOW_THREADS;
 
-#if defined (_MSC_VER) && defined(_WIN64)
-            /* Workaround Win64 fwrite() bug. Ticket #1660 */
+#if defined(_WIN64)
+            /*
+             * Workaround Win64 fwrite() bug. Issue gh-2256
+             * The native 64 windows runtime has this issue, the above will
+             * also trigger UCRT (which doesn't), so it could be more precise.
+             *
+             * If you touch this code, please run this test which is so slow
+             * it was removed from the test suite. Note that the original
+             * failure mode involves an infinite loop during tofile()
+             *
+             * import tempfile, numpy as np
+             * from numpy.testing import (assert_)
+             * fourgbplus = 2**32 + 2**16
+             * testbytes = np.arange(8, dtype=np.int8)
+             * n = len(testbytes)
+             * flike = tempfile.NamedTemporaryFile()
+             * f = flike.file
+             * np.tile(testbytes, fourgbplus // testbytes.nbytes).tofile(f)
+             * flike.seek(0)
+             * a = np.fromfile(f, dtype=np.int8)
+             * flike.close()
+             * assert_(len(a) == fourgbplus)
+             * # check only start and end for speed:
+             * assert_((a[:n] == testbytes).all())
+             * assert_((a[-n:] == testbytes).all())
+             */
             {
-                npy_intp maxsize = 2147483648 / PyArray_DESCR(self)->elsize;
-                npy_intp chunksize;
+                size_t maxsize = 2147483648 / (size_t)PyArray_DESCR(self)->elsize;
+                size_t chunksize;
 
                 n = 0;
                 while (size > 0) {
@@ -168,7 +193,7 @@ PyArray_ToFile(PyArrayObject *self, FILE *fp, char *sep, char *format)
                     n2 = fwrite((const void *)
                              ((char *)PyArray_DATA(self) + (n * PyArray_DESCR(self)->elsize)),
                              (size_t) PyArray_DESCR(self)->elsize,
-                             (size_t) chunksize, fp);
+                             chunksize, fp);
                     if (n2 < chunksize) {
                         break;
                     }
@@ -375,7 +400,7 @@ PyArray_FillWithScalar(PyArrayObject *arr, PyObject *obj)
     char *value = (char *)value_buffer_stack;
     PyArray_Descr *descr = PyArray_DESCR(arr);
 
-    if (descr->elsize > sizeof(value_buffer_stack)) {
+    if ((size_t)descr->elsize > sizeof(value_buffer_stack)) {
         /* We need a large temporary buffer... */
         value_buffer_heap = PyObject_Calloc(1, descr->elsize);
         if (value_buffer_heap == NULL) {
@@ -399,14 +424,16 @@ PyArray_FillWithScalar(PyArrayObject *arr, PyObject *obj)
             descr, value);
 
     if (PyDataType_REFCHK(descr)) {
-        PyArray_Item_XDECREF(value, descr);
+        PyArray_ClearBuffer(descr, value, 0, 1, 1);
     }
     PyMem_FREE(value_buffer_heap);
     return retcode;
 }
 
 /*
- * Fills an array with zeros.
+ * Internal function to fill an array with zeros.
+ * Used in einsum and dot, which ensures the dtype is, in some sense, numerical
+ * and not a str or struct
  *
  * dst: The destination array.
  * wheremask: If non-NULL, a boolean mask specifying where to set the values.
@@ -417,21 +444,26 @@ NPY_NO_EXPORT int
 PyArray_AssignZero(PyArrayObject *dst,
                    PyArrayObject *wheremask)
 {
-    npy_bool value;
-    PyArray_Descr *bool_dtype;
-    int retcode;
-
-    /* Create a raw bool scalar with the value False */
-    bool_dtype = PyArray_DescrFromType(NPY_BOOL);
-    if (bool_dtype == NULL) {
-        return -1;
+    int retcode = 0;
+    if (PyArray_ISOBJECT(dst)) {
+        PyObject * pZero = PyLong_FromLong(0);
+        retcode = PyArray_AssignRawScalar(dst, PyArray_DESCR(dst),
+                                     (char *)&pZero, wheremask, NPY_SAFE_CASTING);
+        Py_DECREF(pZero);
     }
-    value = 0;
+    else {
+        /* Create a raw bool scalar with the value False */
+        PyArray_Descr *bool_dtype = PyArray_DescrFromType(NPY_BOOL);
+        if (bool_dtype == NULL) {
+            return -1;
+        }
+        npy_bool value = 0;
 
-    retcode = PyArray_AssignRawScalar(dst, bool_dtype, (char *)&value,
-                                      wheremask, NPY_SAFE_CASTING);
+        retcode = PyArray_AssignRawScalar(dst, bool_dtype, (char *)&value,
+                                          wheremask, NPY_SAFE_CASTING);
 
-    Py_DECREF(bool_dtype);
+        Py_DECREF(bool_dtype);
+    }
     return retcode;
 }
 
@@ -491,7 +523,7 @@ PyArray_View(PyArrayObject *self, PyArray_Descr *type, PyTypeObject *pytype)
             PyArray_NDIM(self), PyArray_DIMS(self), PyArray_STRIDES(self),
             PyArray_DATA(self),
             flags, (PyObject *)self, (PyObject *)self,
-            0, 1);
+            _NPY_ARRAY_ENSURE_DTYPE_IDENTITY);
     if (ret == NULL) {
         Py_XDECREF(type);
         return NULL;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.c
index 627784db3e2..36307fd4d3f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.c
@@ -18,6 +18,7 @@
 #include "can_cast_table.h"
 #include "common.h"
 #include "ctors.h"
+#include "descriptor.h"
 #include "dtypemeta.h"
 #include "common_dtype.h"
 #include "scalartypes.h"
@@ -31,6 +32,7 @@
 #include "array_method.h"
 #include "usertypes.h"
 #include "dtype_transfer.h"
+#include "dtype_traversal.h"
 #include "arrayobject.h"
 
 
@@ -50,6 +52,9 @@ NPY_NO_EXPORT npy_intp REQUIRED_STR_LEN[] = {0, 3, 5, 10, 10, 20, 20, 20, 20};
  */
 NPY_NO_EXPORT int npy_promotion_state = NPY_USE_LEGACY_PROMOTION;
 NPY_NO_EXPORT PyObject *NO_NEP50_WARNING_CTX = NULL;
+NPY_NO_EXPORT PyObject *npy_DTypePromotionError = NULL;
+NPY_NO_EXPORT PyObject *npy_UFuncNoLoopError = NULL;
+
 
 static PyObject *
 PyArray_GetGenericToVoidCastingImpl(void);
@@ -149,7 +154,7 @@ PyArray_GetCastingImpl(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to)
 {
     PyObject *res;
     if (from == to) {
-        res = NPY_DT_SLOTS(from)->within_dtype_castingimpl;
+        res = (PyObject *)NPY_DT_SLOTS(from)->within_dtype_castingimpl;
     }
     else {
         res = PyDict_GetItemWithError(NPY_DT_SLOTS(from)->castingimpls, (PyObject *)to);
@@ -326,7 +331,7 @@ PyArray_CastToType(PyArrayObject *arr, PyArray_Descr *dtype, int is_f_order)
         return NULL;
     }
 
-    Py_SETREF(dtype, PyArray_AdaptDescriptorToArray(arr, (PyObject *)dtype));
+    Py_SETREF(dtype, PyArray_AdaptDescriptorToArray(arr, NULL, dtype));
     if (dtype == NULL) {
         return NULL;
     }
@@ -384,18 +389,14 @@ PyArray_GetCastFunc(PyArray_Descr *descr, int type_num)
             !PyTypeNum_ISCOMPLEX(type_num) &&
             PyTypeNum_ISNUMBER(type_num) &&
             !PyTypeNum_ISBOOL(type_num)) {
-        PyObject *cls = NULL, *obj = NULL;
-        int ret;
-        obj = PyImport_ImportModule("numpy.core");
-
-        if (obj) {
-            cls = PyObject_GetAttrString(obj, "ComplexWarning");
-            Py_DECREF(obj);
+        static PyObject *cls = NULL;
+        npy_cache_import("numpy.exceptions", "ComplexWarning", &cls);
+        if (cls == NULL) {
+            return NULL;
         }
-        ret = PyErr_WarnEx(cls,
+        int ret = PyErr_WarnEx(cls,
                 "Casting complex values to real discards "
                 "the imaginary part", 1);
-        Py_XDECREF(cls);
         if (ret < 0) {
             return NULL;
         }
@@ -1149,7 +1150,7 @@ PyArray_CastDescrToDType(PyArray_Descr *descr, PyArray_DTypeMeta *given_DType)
  */
 NPY_NO_EXPORT PyArray_Descr *
 PyArray_FindConcatenationDescriptor(
-        npy_intp n, PyArrayObject **arrays, PyObject *requested_dtype)
+        npy_intp n, PyArrayObject **arrays, PyArray_Descr *requested_dtype)
 {
     if (requested_dtype == NULL) {
         return PyArray_ResultType(n, arrays, 0, NULL);
@@ -2343,10 +2344,12 @@ PyArray_ConvertToCommonType(PyObject *op, int *retn)
         }
 
         mps[i] = (PyArrayObject *)PyArray_FROM_O(tmp);
-        Py_DECREF(tmp);
         if (mps[i] == NULL) {
+            Py_DECREF(tmp);
             goto fail;
         }
+        npy_mark_tmp_array_if_pyscalar(tmp, mps[i], NULL);
+        Py_DECREF(tmp);
     }
 
     common_descr = PyArray_ResultType(n, mps, 0, NULL);
@@ -2415,8 +2418,7 @@ PyArray_AddCastingImplementation(PyBoundArrayMethodObject *meth)
             return -1;
         }
         Py_INCREF(meth->method);
-        NPY_DT_SLOTS(meth->dtypes[0])->within_dtype_castingimpl = (
-                (PyObject *)meth->method);
+        NPY_DT_SLOTS(meth->dtypes[0])->within_dtype_castingimpl = meth->method;
 
         return 0;
     }
@@ -2613,7 +2615,7 @@ complex_to_noncomplex_get_loop(
 {
     static PyObject *cls = NULL;
     int ret;
-    npy_cache_import("numpy.core", "ComplexWarning", &cls);
+    npy_cache_import("numpy.exceptions", "ComplexWarning", &cls);
     if (cls == NULL) {
         return -1;
     }
@@ -2669,7 +2671,7 @@ add_numeric_cast(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to)
          * consider moving this warning into the inner-loop at some point
          * for simplicity (this requires ensuring it is only emitted once).
          */
-        slots[5].slot = NPY_METH_get_loop;
+        slots[5].slot = _NPY_METH_get_loop;
         slots[5].pfunc = &complex_to_noncomplex_get_loop;
         slots[6].slot = 0;
         slots[6].pfunc = NULL;
@@ -2690,7 +2692,7 @@ add_numeric_cast(PyArray_DTypeMeta *from, PyArray_DTypeMeta *to)
         /* When there is no casting (equivalent C-types) use byteswap loops */
         slots[0].slot = NPY_METH_resolve_descriptors;
         slots[0].pfunc = &legacy_same_dtype_resolve_descriptors;
-        slots[1].slot = NPY_METH_get_loop;
+        slots[1].slot = _NPY_METH_get_loop;
         slots[1].pfunc = &get_byteswap_loop;
         slots[2].slot = 0;
         slots[2].pfunc = NULL;
@@ -2874,7 +2876,7 @@ add_other_to_and_from_string_cast(
      */
     PyArray_DTypeMeta *dtypes[2] = {other, string};
     PyType_Slot slots[] = {
-            {NPY_METH_get_loop, &legacy_cast_get_strided_loop},
+            {_NPY_METH_get_loop, &legacy_cast_get_strided_loop},
             {NPY_METH_resolve_descriptors, &cast_to_string_resolve_descriptors},
             {0, NULL}};
     PyArrayMethod_Spec spec = {
@@ -3013,7 +3015,7 @@ PyArray_InitializeStringCasts(void)
     /* string<->string and unicode<->unicode have their own specialized casts */
     PyArray_DTypeMeta *dtypes[2];
     PyType_Slot slots[] = {
-            {NPY_METH_get_loop, &string_to_string_get_loop},
+            {_NPY_METH_get_loop, &string_to_string_get_loop},
             {NPY_METH_resolve_descriptors, &string_to_string_resolve_descriptors},
             {0, NULL}};
     PyArrayMethod_Spec spec = {
@@ -3712,7 +3714,7 @@ PyArray_InitializeVoidToVoidCast(void)
     PyArray_DTypeMeta *Void = PyArray_DTypeFromTypeNum(NPY_VOID);
     PyArray_DTypeMeta *dtypes[2] = {Void, Void};
     PyType_Slot slots[] = {
-            {NPY_METH_get_loop, &void_to_void_get_loop},
+            {_NPY_METH_get_loop, &void_to_void_get_loop},
             {NPY_METH_resolve_descriptors, &void_to_void_resolve_descriptors},
             {0, NULL}};
     PyArrayMethod_Spec spec = {
@@ -3895,7 +3897,7 @@ PyArray_InitializeObjectToObjectCast(void)
     PyArray_DTypeMeta *Object = PyArray_DTypeFromTypeNum(NPY_OBJECT);
     PyArray_DTypeMeta *dtypes[2] = {Object, Object};
     PyType_Slot slots[] = {
-            {NPY_METH_get_loop, &object_to_object_get_loop},
+            {_NPY_METH_get_loop, &object_to_object_get_loop},
             {0, NULL}};
     PyArrayMethod_Spec spec = {
             .name = "object_to_object_cast",
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.h
index b6bc7d8a74b..a68c669ee14 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/convert_datatype.h
@@ -14,6 +14,8 @@ extern NPY_NO_EXPORT npy_intp REQUIRED_STR_LEN[];
 #define NPY_USE_WEAK_PROMOTION_AND_WARN 2
 extern NPY_NO_EXPORT int npy_promotion_state;
 extern NPY_NO_EXPORT PyObject *NO_NEP50_WARNING_CTX;
+extern NPY_NO_EXPORT PyObject *npy_DTypePromotionError;
+extern NPY_NO_EXPORT PyObject *npy_UFuncNoLoopError;
 
 NPY_NO_EXPORT int
 npy_give_promotion_warnings(void);
@@ -80,7 +82,7 @@ PyArray_CastDescrToDType(PyArray_Descr *descr, PyArray_DTypeMeta *given_DType);
 
 NPY_NO_EXPORT PyArray_Descr *
 PyArray_FindConcatenationDescriptor(
-        npy_intp n, PyArrayObject **arrays, PyObject *requested_dtype);
+        npy_intp n, PyArrayObject **arrays, PyArray_Descr *requested_dtype);
 
 NPY_NO_EXPORT int
 PyArray_AddCastingImplementation(PyBoundArrayMethodObject *meth);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.c
index 43fff6786c6..2153efd5320 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.c
@@ -20,6 +20,8 @@
 #include "common.h"
 #include "ctors.h"
 #include "convert_datatype.h"
+#include "descriptor.h"
+#include "dtypemeta.h"
 #include "shape.h"
 #include "npy_buffer.h"
 #include "lowlevel_strided_loops.h"
@@ -626,8 +628,7 @@ NPY_NO_EXPORT PyObject *
 PyArray_NewFromDescr_int(
         PyTypeObject *subtype, PyArray_Descr *descr, int nd,
         npy_intp const *dims, npy_intp const *strides, void *data,
-        int flags, PyObject *obj, PyObject *base, int zeroed,
-        int allow_emptystring)
+        int flags, PyObject *obj, PyObject *base, _NPY_CREATION_FLAGS cflags)
 {
     PyArrayObject_fields *fa;
     npy_intp nbytes;
@@ -642,44 +643,54 @@ PyArray_NewFromDescr_int(
         return NULL;
     }
 
-    if (descr->subarray) {
-        PyObject *ret;
-        npy_intp newdims[2*NPY_MAXDIMS];
-        npy_intp *newstrides = NULL;
-        memcpy(newdims, dims, nd*sizeof(npy_intp));
-        if (strides) {
-            newstrides = newdims + NPY_MAXDIMS;
-            memcpy(newstrides, strides, nd*sizeof(npy_intp));
-        }
-        nd =_update_descr_and_dimensions(&descr, newdims,
-                                         newstrides, nd);
-        ret = PyArray_NewFromDescr_int(
-                subtype, descr,
-                nd, newdims, newstrides, data,
-                flags, obj, base,
-                zeroed, allow_emptystring);
-        return ret;
-    }
-
-    /* Check datatype element size */
     nbytes = descr->elsize;
-    if (PyDataType_ISUNSIZED(descr)) {
-        if (!PyDataType_ISFLEXIBLE(descr)) {
-            PyErr_SetString(PyExc_TypeError, "Empty data-type");
-            Py_DECREF(descr);
-            return NULL;
-        }
-        else if (PyDataType_ISSTRING(descr) && !allow_emptystring &&
-                 data == NULL) {
-            PyArray_DESCR_REPLACE(descr);
-            if (descr == NULL) {
+    /*
+     * Unless explicitly asked not to, we do replace dtypes in some cases.
+     * This mainly means that we never create arrays with a subarray dtype
+     * (unless for internal use when requested).  And neither do we create
+     * S0/U0 arrays in most cases (unless data == NULL so this is probably
+     * a view where growing the dtype would be presumable wrong).
+     */
+    if (!(cflags & _NPY_ARRAY_ENSURE_DTYPE_IDENTITY)) {
+        if (descr->subarray) {
+            PyObject *ret;
+            npy_intp newdims[2*NPY_MAXDIMS];
+            npy_intp *newstrides = NULL;
+            memcpy(newdims, dims, nd*sizeof(npy_intp));
+            if (strides) {
+                newstrides = newdims + NPY_MAXDIMS;
+                memcpy(newstrides, strides, nd*sizeof(npy_intp));
+            }
+            nd =_update_descr_and_dimensions(&descr, newdims,
+                                            newstrides, nd);
+            ret = PyArray_NewFromDescr_int(
+                    subtype, descr,
+                    nd, newdims, newstrides, data,
+                    flags, obj, base, cflags);
+            return ret;
+        }
+
+        /* Check datatype element size */
+        if (PyDataType_ISUNSIZED(descr)) {
+            if (!PyDataType_ISFLEXIBLE(descr) &&
+                NPY_DT_is_legacy(NPY_DTYPE(descr))) {
+                PyErr_SetString(PyExc_TypeError, "Empty data-type");
+                Py_DECREF(descr);
                 return NULL;
             }
-            if (descr->type_num == NPY_STRING) {
-                nbytes = descr->elsize = 1;
-            }
-            else {
-                nbytes = descr->elsize = sizeof(npy_ucs4);
+            else if (PyDataType_ISSTRING(descr)
+                        && !(cflags & _NPY_ARRAY_ALLOW_EMPTY_STRING)
+                        && data == NULL) {
+                PyArray_DESCR_REPLACE(descr);
+                if (descr == NULL) {
+                    return NULL;
+                }
+                if (descr->type_num == NPY_STRING) {
+                    nbytes = descr->elsize = 1;
+                }
+                else {
+                    nbytes = descr->elsize = sizeof(npy_ucs4);
+                }
             }
         }
     }
@@ -712,6 +723,11 @@ PyArray_NewFromDescr_int(
     fa->base = (PyObject *)NULL;
     fa->weakreflist = (PyObject *)NULL;
 
+    /* needed for zero-filling logic below, defined and initialized up here
+       so cleanup logic can go in the fail block */
+    NPY_traverse_info fill_zero_info;
+    NPY_traverse_info_init(&fill_zero_info);
+
     if (nd > 0) {
         fa->dimensions = npy_alloc_cache_dim(2 * nd);
         if (fa->dimensions == NULL) {
@@ -781,6 +797,32 @@ PyArray_NewFromDescr_int(
 
 
     if (data == NULL) {
+        /* float errors do not matter and we do not release GIL */
+        NPY_ARRAYMETHOD_FLAGS zero_flags;
+        get_traverse_loop_function *get_fill_zero_loop =
+            NPY_DT_SLOTS(NPY_DTYPE(descr))->get_fill_zero_loop;
+        if (get_fill_zero_loop != NULL) {
+            if (get_fill_zero_loop(
+                    NULL, descr, 1, descr->elsize, &(fill_zero_info.func),
+                    &(fill_zero_info.auxdata), &zero_flags) < 0) {
+                goto fail;
+            }
+        }
+
+        /*
+         * We always want a zero-filled array allocated with calloc if
+         * NPY_NEEDS_INIT is set on the dtype, for safety.  We also want a
+         * zero-filled array if zeroed is set and the zero-filling loop isn't
+         * defined, for better performance.
+         *
+         * If the zero-filling loop is defined and zeroed is set, allocate
+         * with malloc and let the zero-filling loop fill the array buffer
+         * with valid zero values for the dtype.
+         */
+        int use_calloc = (
+                PyDataType_FLAGCHK(descr, NPY_NEEDS_INIT) ||
+                ((cflags & _NPY_ARRAY_ZEROED) && (fill_zero_info.func == NULL)));
+
         /* Store the handler in case the default is modified */
         fa->mem_handler = PyDataMem_GetHandler();
         if (fa->mem_handler == NULL) {
@@ -798,11 +840,8 @@ PyArray_NewFromDescr_int(
                 fa->strides[i] = 0;
             }
         }
-        /*
-         * It is bad to have uninitialized OBJECT pointers
-         * which could also be sub-fields of a VOID array
-         */
-        if (zeroed || PyDataType_FLAGCHK(descr, NPY_NEEDS_INIT)) {
+
+        if (use_calloc) {
             data = PyDataMem_UserNEW_ZEROED(nbytes, 1, fa->mem_handler);
         }
         else {
@@ -813,6 +852,19 @@ PyArray_NewFromDescr_int(
             goto fail;
         }
 
+        /*
+         * If the array needs special dtype-specific zero-filling logic, do that
+         */
+        if (NPY_UNLIKELY((cflags & _NPY_ARRAY_ZEROED)
+                         && (fill_zero_info.func != NULL))) {
+            npy_intp size = PyArray_MultiplyList(fa->dimensions, fa->nd);
+            if (fill_zero_info.func(
+                    NULL, descr, data, size, descr->elsize,
+                    fill_zero_info.auxdata) < 0) {
+                goto fail;
+            }
+        }
+
         fa->flags |= NPY_ARRAY_OWNDATA;
     }
     else {
@@ -907,9 +959,11 @@ PyArray_NewFromDescr_int(
             }
         }
     }
+    NPY_traverse_info_xfree(&fill_zero_info);
     return (PyObject *)fa;
 
  fail:
+    NPY_traverse_info_xfree(&fill_zero_info);
     Py_XDECREF(fa->mem_handler);
     Py_DECREF(fa);
     return NULL;
@@ -957,7 +1011,7 @@ PyArray_NewFromDescrAndBase(
 {
     return PyArray_NewFromDescr_int(subtype, descr, nd,
                                     dims, strides, data,
-                                    flags, obj, base, 0, 0);
+                                    flags, obj, base, 0);
 }
 
 /*
@@ -1440,167 +1494,44 @@ PyArray_GetArrayParamsFromObject(PyObject *NPY_UNUSED(op),
 }
 
 
-/*
- * This function is a legacy implementation to retain subarray dtype
- * behaviour in array coercion. The behaviour here makes sense if tuples
- * of matching dimensionality are being coerced. Due to the difficulty
- * that the result is ill-defined for lists of array-likes, this is deprecated.
- *
- * WARNING: Do not use this function, it exists purely to support a deprecated
- *          code path.
+/*NUMPY_API
+ * Does not check for NPY_ARRAY_ENSURECOPY and NPY_ARRAY_NOTSWAPPED in flags
+ * Steals a reference to newtype --- which can be NULL
  */
-static int
-setArrayFromSequence(PyArrayObject *a, PyObject *s,
-                        int dim, PyArrayObject * dst)
+NPY_NO_EXPORT PyObject *
+PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
+                int max_depth, int flags, PyObject *context)
 {
-    Py_ssize_t i, slen;
-    int res = -1;
-
-    /* first recursion, view equal destination */
-    if (dst == NULL)
-        dst = a;
-
-    /*
-     * This code is to ensure that the sequence access below will
-     * return a lower-dimensional sequence.
-     */
-
-    /* INCREF on entry DECREF on exit */
-    Py_INCREF(s);
-
-    PyObject *seq = NULL;
-
-    if (PyArray_Check(s)) {
-        if (!(PyArray_CheckExact(s))) {
-            /*
-             * make sure a base-class array is used so that the dimensionality
-             * reduction assumption is correct.
-             */
-            /* This will DECREF(s) if replaced */
-            s = PyArray_EnsureArray(s);
-            if (s == NULL) {
-                goto fail;
-            }
-        }
-
-        /* dst points to correct array subsection */
-        if (PyArray_CopyInto(dst, (PyArrayObject *)s) < 0) {
-            goto fail;
-        }
+    npy_dtype_info dt_info = {NULL, NULL};
 
-        Py_DECREF(s);
-        return 0;
-    }
+    int res = PyArray_ExtractDTypeAndDescriptor(
+        newtype, &dt_info.descr, &dt_info.dtype);
 
-    if (dim > PyArray_NDIM(a)) {
-        PyErr_Format(PyExc_ValueError,
-                 "setArrayFromSequence: sequence/array dimensions mismatch.");
-        goto fail;
-    }
-
-    /* Try __array__ before using s as a sequence */
-    PyObject *tmp = _array_from_array_like(s, NULL, 0, NULL, 0);
-    if (tmp == NULL) {
-        goto fail;
-    }
-    else if (tmp == Py_NotImplemented) {
-        Py_DECREF(tmp);
-    }
-    else {
-        int r = PyArray_CopyInto(dst, (PyArrayObject *)tmp);
-        Py_DECREF(tmp);
-        if (r < 0) {
-            goto fail;
-        }
-        Py_DECREF(s);
-        return 0;
-    }
-
-    seq = PySequence_Fast(s, "Could not convert object to sequence");
-    if (seq == NULL) {
-        goto fail;
-    }
-    slen = PySequence_Fast_GET_SIZE(seq);
-
-    /*
-     * Either the dimensions match, or the sequence has length 1 and can
-     * be broadcast to the destination.
-     */
-    if (slen != PyArray_DIMS(a)[dim] && slen != 1) {
-        PyErr_Format(PyExc_ValueError,
-                 "cannot copy sequence with size %zd to array axis "
-                 "with dimension %" NPY_INTP_FMT, slen, PyArray_DIMS(a)[dim]);
-        goto fail;
-    }
-
-    /* Broadcast the one element from the sequence to all the outputs */
-    if (slen == 1) {
-        PyObject *o = PySequence_Fast_GET_ITEM(seq, 0);
-        npy_intp alen = PyArray_DIM(a, dim);
-
-        for (i = 0; i < alen; i++) {
-            if ((PyArray_NDIM(a) - dim) > 1) {
-                PyArrayObject * tmp =
-                    (PyArrayObject *)array_item_asarray(dst, i);
-                if (tmp == NULL) {
-                    goto fail;
-                }
-
-                res = setArrayFromSequence(a, o, dim+1, tmp);
-                Py_DECREF(tmp);
-            }
-            else {
-                char * b = (PyArray_BYTES(dst) + i * PyArray_STRIDES(dst)[0]);
-                res = PyArray_SETITEM(dst, b, o);
-            }
-            if (res < 0) {
-                goto fail;
-            }
-        }
-    }
-    /* Copy element by element */
-    else {
-        for (i = 0; i < slen; i++) {
-            PyObject * o = PySequence_Fast_GET_ITEM(seq, i);
-            if ((PyArray_NDIM(a) - dim) > 1) {
-                PyArrayObject * tmp =
-                    (PyArrayObject *)array_item_asarray(dst, i);
-                if (tmp == NULL) {
-                    goto fail;
-                }
+    Py_XDECREF(newtype);
 
-                res = setArrayFromSequence(a, o, dim+1, tmp);
-                Py_DECREF(tmp);
-            }
-            else {
-                char * b = (PyArray_BYTES(dst) + i * PyArray_STRIDES(dst)[0]);
-                res = PyArray_SETITEM(dst, b, o);
-            }
-            if (res < 0) {
-                goto fail;
-            }
-        }
+    if (res < 0) {
+        Py_XDECREF(dt_info.descr);
+        Py_XDECREF(dt_info.dtype);
+        return NULL;
     }
 
-    Py_DECREF(seq);
-    Py_DECREF(s);
-    return 0;
+    PyObject* ret =  PyArray_FromAny_int(op, dt_info.descr, dt_info.dtype,
+                                         min_depth, max_depth, flags, context);
 
- fail:
-    Py_XDECREF(seq);
-    Py_DECREF(s);
-    return res;
+    Py_XDECREF(dt_info.descr);
+    Py_XDECREF(dt_info.dtype);
+    return ret;
 }
 
-
-
-/*NUMPY_API
- * Does not check for NPY_ARRAY_ENSURECOPY and NPY_ARRAY_NOTSWAPPED in flags
- * Steals a reference to newtype --- which can be NULL
+/*
+ * Internal version of PyArray_FromAny that accepts a dtypemeta. Borrows
+ * references to the descriptor and dtype.
  */
+
 NPY_NO_EXPORT PyObject *
-PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
-                int max_depth, int flags, PyObject *context)
+PyArray_FromAny_int(PyObject *op, PyArray_Descr *in_descr,
+                    PyArray_DTypeMeta *in_DType, int min_depth, int max_depth,
+                    int flags, PyObject *context)
 {
     /*
      * This is the main code to make a NumPy array from a Python
@@ -1617,89 +1548,14 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
         return NULL;
     }
 
-    PyArray_Descr *fixed_descriptor;
-    PyArray_DTypeMeta *fixed_DType;
-    if (PyArray_ExtractDTypeAndDescriptor((PyObject *)newtype,
-            &fixed_descriptor, &fixed_DType) < 0) {
-        Py_XDECREF(newtype);
-        return NULL;
-    }
-    Py_XDECREF(newtype);
-
     ndim = PyArray_DiscoverDTypeAndShape(op,
-            NPY_MAXDIMS, dims, &cache, fixed_DType, fixed_descriptor, &dtype,
+            NPY_MAXDIMS, dims, &cache, in_DType, in_descr, &dtype,
             flags & NPY_ARRAY_ENSURENOCOPY);
 
-    Py_XDECREF(fixed_descriptor);
-    Py_XDECREF(fixed_DType);
     if (ndim < 0) {
         return NULL;
     }
 
-    if (NPY_UNLIKELY(fixed_descriptor != NULL && PyDataType_HASSUBARRAY(dtype))) {
-        /*
-         * When a subarray dtype was passed in, its dimensions are appended
-         * to the array dimension (causing a dimension mismatch).
-         * There is a problem with that, because if we coerce from non-arrays
-         * we do this correctly by element (as defined by tuples), but for
-         * arrays we first append the dimensions and then assign to the base
-         * dtype and then assign which causes the problem.
-         *
-         * Thus, we check if there is an array included, in that case we
-         * give a FutureWarning.
-         * When the warning is removed, PyArray_Pack will have to ensure
-         * that it does not append the dimensions when creating the subarrays
-         * to assign `arr[0] = obj[0]`.
-         */
-        int includes_array = 0;
-        if (cache != NULL) {
-            /* This is not ideal, but it is a pretty special case */
-            coercion_cache_obj *next = cache;
-            while (next != NULL) {
-                if (!next->sequence) {
-                    includes_array = 1;
-                    break;
-                }
-                next = next->next;
-            }
-        }
-        if (includes_array) {
-            npy_free_coercion_cache(cache);
-
-            ret = (PyArrayObject *) PyArray_NewFromDescr(
-                    &PyArray_Type, dtype, ndim, dims, NULL, NULL,
-                    flags & NPY_ARRAY_F_CONTIGUOUS, NULL);
-            if (ret == NULL) {
-                return NULL;
-            }
-            assert(PyArray_NDIM(ret) != ndim);
-
-            /* NumPy 1.20, 2020-10-01 */
-            if (DEPRECATE_FUTUREWARNING(
-                    "creating an array with a subarray dtype will behave "
-                    "differently when the `np.array()` (or `asarray`, etc.) "
-                    "call includes an array or array object.\n"
-                    "If you are converting a single array or a list of arrays,"
-                    "you can opt-in to the future behaviour using:\n"
-                    "    np.array(arr, dtype=np.dtype(['f', dtype]))['f']\n"
-                    "    np.array([arr1, arr2], dtype=np.dtype(['f', dtype]))['f']\n"
-                    "\n"
-                    "By including a new field and indexing it after the "
-                    "conversion.\n"
-                    "This may lead to a different result or to current failures "
-                    "succeeding.  (FutureWarning since NumPy 1.20)") < 0) {
-                Py_DECREF(ret);
-                return NULL;
-            }
-
-            if (setArrayFromSequence(ret, op, 0, NULL) < 0) {
-                Py_DECREF(ret);
-                return NULL;
-            }
-            return (PyObject *)ret;
-        }
-    }
-
     if (dtype == NULL) {
         dtype = PyArray_DescrFromType(NPY_DEFAULT_TYPE);
     }
@@ -1734,7 +1590,7 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
     }
     else if (cache == NULL && PyArray_IsScalar(op, Void) &&
             !(((PyVoidScalarObject *)op)->flags & NPY_ARRAY_OWNDATA) &&
-            newtype == NULL) {
+             ((in_descr == NULL) && (in_DType == NULL))) {
         /*
          * Special case, we return a *view* into void scalars, mainly to
          * allow things similar to the "reversed" assignment:
@@ -1765,7 +1621,7 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
         return NULL;
     }
 
-    if (cache == NULL && newtype != NULL &&
+    if (cache == NULL && in_descr != NULL &&
             PyDataType_ISSIGNED(dtype) &&
             PyArray_IsScalar(op, Generic)) {
         assert(ndim == 0);
@@ -1839,8 +1695,10 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
     /* Decrease the number of dimensions to the detected ones */
     int out_ndim = PyArray_NDIM(ret);
     PyArray_Descr *out_descr = PyArray_DESCR(ret);
-    ((PyArrayObject_fields *)ret)->nd = ndim;
-    ((PyArrayObject_fields *)ret)->descr = dtype;
+    if (out_ndim != ndim) {
+        ((PyArrayObject_fields *)ret)->nd = ndim;
+        ((PyArrayObject_fields *)ret)->descr = dtype;
+    }
 
     int success = PyArray_AssignFromCache(ret, cache);
 
@@ -1896,7 +1754,6 @@ PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
  * NPY_ARRAY_FORCECAST will cause a cast to occur regardless of whether or not
  * it is safe.
  *
- * context is passed through to PyArray_GetArrayParamsFromObject
  */
 
 /*NUMPY_API
@@ -1906,24 +1763,57 @@ NPY_NO_EXPORT PyObject *
 PyArray_CheckFromAny(PyObject *op, PyArray_Descr *descr, int min_depth,
                      int max_depth, int requires, PyObject *context)
 {
+    npy_dtype_info dt_info = {NULL, NULL};
+
+    int res = PyArray_ExtractDTypeAndDescriptor(
+        descr, &dt_info.descr, &dt_info.dtype);
+
+    Py_XDECREF(descr);
+
+    if (res < 0) {
+        Py_XDECREF(dt_info.descr);
+        Py_XDECREF(dt_info.dtype);
+        return NULL;
+    }
+
+    PyObject* ret =  PyArray_CheckFromAny_int(
+        op, dt_info.descr, dt_info.dtype, min_depth, max_depth, requires,
+        context);
+
+    Py_XDECREF(dt_info.descr);
+    Py_XDECREF(dt_info.dtype);
+    return ret;
+}
+
+/*
+ * Internal version of PyArray_CheckFromAny that accepts a dtypemeta. Borrows
+ * references to the descriptor and dtype.
+ */
+
+NPY_NO_EXPORT PyObject *
+PyArray_CheckFromAny_int(PyObject *op, PyArray_Descr *in_descr,
+                         PyArray_DTypeMeta *in_DType, int min_depth,
+                         int max_depth, int requires, PyObject *context)
+{
     PyObject *obj;
     if (requires & NPY_ARRAY_NOTSWAPPED) {
-        if (!descr && PyArray_Check(op) &&
+        if (!in_descr && PyArray_Check(op) &&
                 PyArray_ISBYTESWAPPED((PyArrayObject* )op)) {
-            descr = PyArray_DescrNew(PyArray_DESCR((PyArrayObject *)op));
-            if (descr == NULL) {
+            in_descr = PyArray_DescrNew(PyArray_DESCR((PyArrayObject *)op));
+            if (in_descr == NULL) {
                 return NULL;
             }
         }
-        else if (descr && !PyArray_ISNBO(descr->byteorder)) {
-            PyArray_DESCR_REPLACE(descr);
+        else if (in_descr && !PyArray_ISNBO(in_descr->byteorder)) {
+            PyArray_DESCR_REPLACE(in_descr);
         }
-        if (descr && descr->byteorder != NPY_IGNORE) {
-            descr->byteorder = NPY_NATIVE;
+        if (in_descr && in_descr->byteorder != NPY_IGNORE) {
+            in_descr->byteorder = NPY_NATIVE;
         }
     }
 
-    obj = PyArray_FromAny(op, descr, min_depth, max_depth, requires, context);
+    obj = PyArray_FromAny_int(op, in_descr, in_DType, min_depth,
+                              max_depth, requires, context);
     if (obj == NULL) {
         return NULL;
     }
@@ -2031,13 +1921,28 @@ PyArray_FromArray(PyArrayObject *arr, PyArray_Descr *newtype, int flags)
         if ((flags & NPY_ARRAY_ENSUREARRAY)) {
             subok = 0;
         }
+        Py_INCREF(newtype);
         ret = (PyArrayObject *)PyArray_NewLikeArray(arr, order,
                                                     newtype, subok);
         if (ret == NULL) {
+            Py_DECREF(newtype);
             return NULL;
         }
 
-        if (PyArray_CopyInto(ret, arr) < 0) {
+        int actual_ndim = PyArray_NDIM(ret);
+        PyArray_Descr *actual_dtype = PyArray_DESCR(ret);
+        if (actual_ndim != PyArray_NDIM(arr)) {
+            ((PyArrayObject_fields *)ret)->nd = PyArray_NDIM(arr);
+            ((PyArrayObject_fields *)ret)->descr = newtype;
+        }
+
+        int success = PyArray_CopyInto(ret, arr);
+
+        Py_DECREF(newtype);
+        ((PyArrayObject_fields *)ret)->nd = actual_ndim;
+        ((PyArrayObject_fields *)ret)->descr = actual_dtype;
+
+        if (success < 0) {
             Py_DECREF(ret);
             return NULL;
         }
@@ -2956,23 +2861,13 @@ PyArray_Zeros(int nd, npy_intp const *dims, PyArray_Descr *type, int is_f_order)
             &PyArray_Type, type,
             nd, dims, NULL, NULL,
             is_f_order, NULL, NULL,
-            1, 0);
+            _NPY_ARRAY_ZEROED);
 
     if (ret == NULL) {
         return NULL;
     }
 
-    /* handle objects */
-    if (PyDataType_REFCHK(PyArray_DESCR(ret))) {
-        if (_zerofill(ret) < 0) {
-            Py_DECREF(ret);
-            return NULL;
-        }
-    }
-
-
     return (PyObject *)ret;
-
 }
 
 /*NUMPY_API
@@ -3620,7 +3515,7 @@ PyArray_FromFile(FILE *fp, PyArray_Descr *dtype, npy_intp num, char *sep)
                 &PyArray_Type, dtype,
                 1, &num, NULL, NULL,
                 0, NULL, NULL,
-                0, 1);
+                _NPY_ARRAY_ALLOW_EMPTY_STRING);
     }
     if ((sep == NULL) || (strlen(sep) == 0)) {
         ret = array_fromfile_binary(fp, dtype, num, &nread);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.h
index 98160b1cc48..a876ab2c062 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/ctors.h
@@ -13,12 +13,29 @@ PyArray_NewFromDescrAndBase(
         npy_intp const *dims, npy_intp const *strides, void *data,
         int flags, PyObject *obj, PyObject *base);
 
+
+/* Private options for NewFromDescriptor */
+typedef enum {
+    /*
+     * Indicate the the array should be zeroed, we may use calloc to do so
+     * (otherwise much like ).
+     */
+    _NPY_ARRAY_ZEROED = 1 << 0,
+    /* Whether to allow empty strings (implied by ensure dtype identity) */
+    _NPY_ARRAY_ALLOW_EMPTY_STRING = 1 << 1,
+    /*
+     * If we take a view into an existing array and use its dtype, then that
+     * dtype must be preserved (for example for subarray and S0, but also
+     * possibly for future dtypes that store more metadata).
+     */
+    _NPY_ARRAY_ENSURE_DTYPE_IDENTITY = 1 << 2,
+} _NPY_CREATION_FLAGS;
+
 NPY_NO_EXPORT PyObject *
 PyArray_NewFromDescr_int(
         PyTypeObject *subtype, PyArray_Descr *descr, int nd,
         npy_intp const *dims, npy_intp const *strides, void *data,
-        int flags, PyObject *obj, PyObject *base, int zeroed,
-        int allow_emptystring);
+        int flags, PyObject *obj, PyObject *base, _NPY_CREATION_FLAGS cflags);
 
 NPY_NO_EXPORT PyObject *
 PyArray_NewLikeArrayWithShape(
@@ -36,10 +53,20 @@ _array_from_array_like(PyObject *op,
         int never_copy);
 
 NPY_NO_EXPORT PyObject *
+PyArray_FromAny_int(PyObject *op, PyArray_Descr *in_descr,
+                    PyArray_DTypeMeta *in_DType, int min_depth, int max_depth,
+                    int flags, PyObject *context);
+
+NPY_NO_EXPORT PyObject *
 PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
                 int max_depth, int flags, PyObject *context);
 
 NPY_NO_EXPORT PyObject *
+PyArray_CheckFromAny_int(PyObject *op, PyArray_Descr *in_descr,
+                         PyArray_DTypeMeta *in_DType, int min_depth,
+                         int max_depth, int requires, PyObject *context);
+
+NPY_NO_EXPORT PyObject *
 PyArray_CheckFromAny(PyObject *op, PyArray_Descr *descr, int min_depth,
                      int max_depth, int requires, PyObject *context);
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime.c
index 2abd68ca23f..820f40fd885 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime.c
@@ -1622,6 +1622,12 @@ compute_datetime_metadata_greatest_common_divisor(
 
     return 0;
 
+    /*
+     * We do not use `DTypePromotionError` below.  The reason this is that a
+     * `DTypePromotionError` indicates that `arr_dt1 != arr_dt2` for
+     * all values, but this is wrong for "0".  This could be changed but
+     * for now we consider them errors that occur _while_ promoting.
+     */
 incompatible_units: {
         PyObject *umeta1 = metastr_to_unicode(meta1, 0);
         if (umeta1 == NULL) {
@@ -4094,7 +4100,7 @@ PyArray_InitializeDatetimeCasts()
     };
     slots[0].slot = NPY_METH_resolve_descriptors;
     slots[0].pfunc = &time_to_time_resolve_descriptors;
-    slots[1].slot = NPY_METH_get_loop;
+    slots[1].slot = _NPY_METH_get_loop;
     slots[1].pfunc = &time_to_time_get_loop;
     slots[2].slot = 0;
     slots[2].pfunc = NULL;
@@ -4124,7 +4130,7 @@ PyArray_InitializeDatetimeCasts()
 
     slots[0].slot = NPY_METH_resolve_descriptors;
     slots[0].pfunc = &datetime_to_timedelta_resolve_descriptors;
-    slots[1].slot = NPY_METH_get_loop;
+    slots[1].slot = _NPY_METH_get_loop;
     slots[1].pfunc = &legacy_cast_get_strided_loop;
     slots[2].slot = 0;
     slots[2].pfunc = NULL;
@@ -4197,7 +4203,7 @@ PyArray_InitializeDatetimeCasts()
     slots[0].slot = NPY_METH_resolve_descriptors;
     slots[0].pfunc = &time_to_string_resolve_descriptors;
     /* Strided loop differs for the two */
-    slots[1].slot = NPY_METH_get_loop;
+    slots[1].slot = _NPY_METH_get_loop;
     slots[2].slot = 0;
     slots[2].pfunc = NULL;
 
@@ -4246,7 +4252,7 @@ PyArray_InitializeDatetimeCasts()
     /* The default type resolution should work fine. */
     slots[0].slot = NPY_METH_resolve_descriptors;
     slots[0].pfunc = &string_to_datetime_cast_resolve_descriptors;
-    slots[1].slot = NPY_METH_get_loop;
+    slots[1].slot = _NPY_METH_get_loop;
     slots[1].pfunc = &string_to_datetime_cast_get_loop;
     slots[2].slot = 0;
     slots[2].pfunc = NULL;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime_busday.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime_busday.c
index d3e9e145130..93ed0972ec9 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime_busday.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/datetime_busday.c
@@ -365,6 +365,7 @@ apply_business_day_count(npy_datetime date_begin, npy_datetime date_end,
                     npy_datetime *holidays_begin, npy_datetime *holidays_end)
 {
     npy_int64 count, whole_weeks;
+
     int day_of_week = 0;
     int swapped = 0;
 
@@ -386,6 +387,10 @@ apply_business_day_count(npy_datetime date_begin, npy_datetime date_end,
         date_begin = date_end;
         date_end = tmp;
         swapped = 1;
+        // we swapped date_begin and date_end, so we need to correct for the
+        // original date_end that should not be included. gh-23197
+        date_begin++;
+        date_end++;
     }
 
     /* Remove any earlier holidays */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.c
index d1973442a68..68d398d6480 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.c
@@ -74,9 +74,6 @@ _try_convert_from_ctypes_type(PyTypeObject *type)
     return (PyArray_Descr *)res;
 }
 
-static PyArray_Descr *
-_convert_from_any(PyObject *obj, int align);
-
 /*
  * This function creates a dtype object when the object has a "dtype" attribute,
  * and it can be converted to a dtype object.
@@ -1393,6 +1390,141 @@ PyArray_DescrConverter2(PyObject *obj, PyArray_Descr **at)
     }
 }
 
+
+/**
+ * Check the descriptor is a legacy "flexible" DType instance, this is
+ * an instance which is (normally) not attached to an array, such as a string
+ * of length 0 or a datetime with no unit.
+ * These should be largely deprecated, and represent only the DType class
+ * for most `dtype` parameters.
+ *
+ * TODO: This function should eventually receive a deprecation warning and
+ *       be removed.
+ *
+ * @param descr
+ * @return 1 if this is not a concrete dtype instance 0 otherwise
+ */
+static int
+descr_is_legacy_parametric_instance(PyArray_Descr *descr,
+                                    PyArray_DTypeMeta *DType)
+{
+    if (!NPY_DT_is_legacy(DType)) {
+        return 0;
+    }
+
+    if (PyDataType_ISUNSIZED(descr)) {
+        return 1;
+    }
+    /* Flexible descr with generic time unit (which can be adapted) */
+    if (PyDataType_ISDATETIME(descr)) {
+        PyArray_DatetimeMetaData *meta;
+        meta = get_datetime_metadata_from_dtype(descr);
+        if (meta->base == NPY_FR_GENERIC) {
+            return 1;
+        }
+    }
+    return 0;
+}
+
+
+/**
+ * Given a descriptor (dtype instance), handles conversion of legacy flexible
+ * "unsized" descriptors to their DType.  It returns the DType and descriptor
+ * both results can be NULL (if the input is).  But it always sets the DType
+ * when a descriptor is set.
+ *
+ * @param dtype
+ * @param out_descr
+ * @param out_DType
+ * @return 0 on success -1 on failure
+ */
+NPY_NO_EXPORT int
+PyArray_ExtractDTypeAndDescriptor(PyArray_Descr *dtype,
+        PyArray_Descr **out_descr, PyArray_DTypeMeta **out_DType)
+{
+    *out_DType = NULL;
+    *out_descr = NULL;
+
+    if (dtype != NULL) {
+        *out_DType = NPY_DTYPE(dtype);
+        Py_INCREF(*out_DType);
+        if (!descr_is_legacy_parametric_instance((PyArray_Descr *)dtype,
+                                                    *out_DType)) {
+            *out_descr = (PyArray_Descr *)dtype;
+            Py_INCREF(*out_descr);
+        }
+    }
+    return 0;
+}
+
+
+/**
+ * Converter function filling in an npy_dtype_info struct on success.
+ *
+ * @param obj representing a dtype instance (descriptor) or DType class.
+ * @param[out] npy_dtype_info filled with the DType class and dtype/descriptor
+ *         instance.  The class is always set while the instance may be NULL.
+ *         On error, both will be NULL.
+ * @return 0 on failure and 1 on success (as a converter)
+ */
+NPY_NO_EXPORT int
+PyArray_DTypeOrDescrConverterRequired(PyObject *obj, npy_dtype_info *dt_info)
+{
+    /*
+     * Allow dtype classes pass, this could also be generalized to at least
+     * some scalar types (right now most of these give instances or)
+     */
+    dt_info->dtype = NULL;
+    dt_info->descr = NULL;
+
+    if (PyObject_TypeCheck(obj, &PyArrayDTypeMeta_Type)) {
+        Py_INCREF(obj);
+        dt_info->dtype = (PyArray_DTypeMeta *)obj;
+        dt_info->descr = NULL;
+        return NPY_SUCCEED;
+    }
+    PyArray_Descr *descr;
+    if (PyArray_DescrConverter(obj, &descr) != NPY_SUCCEED) {
+        return NPY_FAIL;
+    }
+    /*
+     * The above converts e.g. "S" or "S0" to the prototype instance, we make
+     * it behave the same as the DType.  This is not fully correct, "S0" should
+     * be considered an instance with actual 0 length.
+     * TODO: It would be nice to fix that eventually.
+     */
+    int res = PyArray_ExtractDTypeAndDescriptor(
+                descr, &dt_info->descr, &dt_info->dtype);
+    Py_DECREF(descr);
+    if (res < 0) {
+        return NPY_FAIL;
+    }
+    return NPY_SUCCEED;
+}
+
+
+/**
+ * Converter function filling in an npy_dtype_info struct on success.  It
+ * accepts `None` and does nothing in that case (user must initialize to
+ * NULL anyway).
+ *
+ * @param obj None or obj representing a dtype instance (descr) or DType class.
+ * @param[out] npy_dtype_info filled with the DType class and dtype/descriptor
+ *         instance.  If `obj` is None, is not modified.  Otherwise the class
+ *         is always set while the instance may be NULL.
+ *         On error, both will be NULL.
+ * @return 0 on failure and 1 on success (as a converter)
+ */
+NPY_NO_EXPORT int
+PyArray_DTypeOrDescrConverterOptional(PyObject *obj, npy_dtype_info *dt_info)
+{
+    if (obj == Py_None) {
+        /* caller must have initialized for the optional version */
+        return NPY_SUCCEED;
+    }
+    return PyArray_DTypeOrDescrConverterRequired(obj, dt_info);
+}
+
 /**
  * Get a dtype instance from a python type
  */
@@ -2315,7 +2447,6 @@ arraydescr_new(PyTypeObject *subtype,
                 PyErr_NoMemory();
                 return NULL;
             }
-            PyObject_Init((PyObject *)descr, subtype);
             descr->f = &NPY_DT_SLOTS(DType)->f;
             Py_XINCREF(DType->scalar_type);
             descr->typeobj = DType->scalar_type;
@@ -3141,25 +3272,15 @@ arraydescr_newbyteorder(PyArray_Descr *self, PyObject *args)
 static PyObject *
 arraydescr_class_getitem(PyObject *cls, PyObject *args)
 {
-    PyObject *generic_alias;
+    const Py_ssize_t args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
 
-#ifdef Py_GENERICALIASOBJECT_H
-    Py_ssize_t args_len;
-
-    args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
     if (args_len != 1) {
         return PyErr_Format(PyExc_TypeError,
                             "Too %s arguments for %s",
                             args_len > 1 ? "many" : "few",
                             ((PyTypeObject *)cls)->tp_name);
     }
-    generic_alias = Py_GenericAlias(cls, args);
-#else
-    PyErr_SetString(PyExc_TypeError,
-                    "Type subscription requires python >= 3.9");
-    generic_alias = NULL;
-#endif
-    return generic_alias;
+    return Py_GenericAlias(cls, args);
 }
 
 static PyMethodDef arraydescr_methods[] = {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.h
index 7e6f212f287..b14edc3aa45 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/descriptor.h
@@ -1,6 +1,29 @@
 #ifndef NUMPY_CORE_SRC_MULTIARRAY_DESCRIPTOR_H_
 #define NUMPY_CORE_SRC_MULTIARRAY_DESCRIPTOR_H_
 
+
+/*
+ * In some API calls we wish to allow users to pass a DType class or a
+ * dtype instances with different meanings.
+ * This struct is mainly used for the argument parsing in
+ * `PyArray_DTypeOrDescrConverter`.
+ */
+typedef struct {
+    PyArray_DTypeMeta *dtype;
+    PyArray_Descr *descr;
+} npy_dtype_info;
+
+
+NPY_NO_EXPORT int
+PyArray_DTypeOrDescrConverterOptional(PyObject *, npy_dtype_info *dt_info);
+
+NPY_NO_EXPORT int
+PyArray_DTypeOrDescrConverterRequired(PyObject *, npy_dtype_info *dt_info);
+
+NPY_NO_EXPORT int
+PyArray_ExtractDTypeAndDescriptor(PyArray_Descr *dtype,
+        PyArray_Descr **out_descr, PyArray_DTypeMeta **out_DType);
+
 NPY_NO_EXPORT PyObject *arraydescr_protocol_typestr_get(
         PyArray_Descr *, void *);
 NPY_NO_EXPORT PyObject *arraydescr_protocol_descr_get(
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/dlpack.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/dlpack.c
index 1c6eb58f258..d26701df8c5 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/dlpack.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/dlpack.c
@@ -171,7 +171,10 @@ array_dlpack(PyArrayObject *self,
     managed_dtype.bits = 8 * itemsize;
     managed_dtype.lanes = 1;
 
-    if (PyDataType_ISSIGNED(dtype)) {
+    if (PyDataType_ISBOOL(dtype)) {
+        managed_dtype.code = kDLBool;
+    }
+    else if (PyDataType_ISSIGNED(dtype)) {
         managed_dtype.code = kDLInt;
     }
     else if (PyDataType_ISUNSIGNED(dtype)) {
@@ -331,6 +334,11 @@ from_dlpack(PyObject *NPY_UNUSED(self), PyObject *obj) {
     const uint8_t bits = managed->dl_tensor.dtype.bits;
     const npy_intp itemsize = bits / 8;
     switch (managed->dl_tensor.dtype.code) {
+    case kDLBool:
+        if (bits == 8) {
+            typenum = NPY_BOOL;
+        }
+        break;
     case kDLInt:
         switch (bits)
         {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.c
index c07670488a3..f7966204051 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.c
@@ -32,6 +32,7 @@
 
 #include "shape.h"
 #include "dtype_transfer.h"
+#include "dtype_traversal.h"
 #include "alloc.h"
 #include "dtypemeta.h"
 #include "array_method.h"
@@ -73,18 +74,6 @@ _safe_print(PyObject *obj)
 }
 #endif
 
-/*
- * Returns a transfer function which DECREFs any references in src_type.
- *
- * Returns NPY_SUCCEED or NPY_FAIL.
- */
-static int
-get_decref_transfer_function(int aligned,
-                            npy_intp src_stride,
-                            PyArray_Descr *src_dtype,
-                            NPY_cast_info *cast_info,
-                            int *out_needs_api);
-
 
 /*************************** COPY REFERENCES *******************************/
 
@@ -159,7 +148,7 @@ typedef struct {
     NpyAuxData base;
     PyArray_GetItemFunc *getitem;
     PyArrayObject_fields arr_fields;
-    NPY_cast_info decref_src;
+    NPY_traverse_info decref_src;
 } _any_to_object_auxdata;
 
 
@@ -169,7 +158,7 @@ _any_to_object_auxdata_free(NpyAuxData *auxdata)
     _any_to_object_auxdata *data = (_any_to_object_auxdata *)auxdata;
 
     Py_DECREF(data->arr_fields.descr);
-    NPY_cast_info_xfree(&data->decref_src);
+    NPY_traverse_info_xfree(&data->decref_src);
     PyMem_Free(data);
 }
 
@@ -187,7 +176,7 @@ _any_to_object_auxdata_clone(NpyAuxData *auxdata)
     Py_INCREF(res->arr_fields.descr);
 
     if (data->decref_src.func != NULL) {
-        if (NPY_cast_info_copy(&res->decref_src, &data->decref_src) < 0) {
+        if (NPY_traverse_info_copy(&res->decref_src, &data->decref_src) < 0) {
             NPY_AUXDATA_FREE((NpyAuxData *)res);
             return NULL;
         }
@@ -228,8 +217,8 @@ _strided_to_strided_any_to_object(
     }
     if (data->decref_src.func != NULL) {
         /* If necessary, clear the input buffer (`move_references`) */
-        if (data->decref_src.func(&data->decref_src.context,
-                &orig_src, &N, &src_stride, data->decref_src.auxdata) < 0) {
+        if (data->decref_src.func(NULL, data->decref_src.descr,
+                orig_src, N, src_stride, data->decref_src.auxdata) < 0) {
             return -1;
         }
     }
@@ -265,18 +254,18 @@ any_to_object_get_loop(
     data->arr_fields.nd = 0;
 
     data->getitem = context->descriptors[0]->f->getitem;
-    NPY_cast_info_init(&data->decref_src);
+    NPY_traverse_info_init(&data->decref_src);
 
     if (move_references && PyDataType_REFCHK(context->descriptors[0])) {
-        int needs_api;
-        if (get_decref_transfer_function(
+        NPY_ARRAYMETHOD_FLAGS clear_flags;
+        if (PyArray_GetClearFunction(
                 aligned, strides[0], context->descriptors[0],
-                &data->decref_src,
-                &needs_api) == NPY_FAIL)  {
+                &data->decref_src, &clear_flags) < 0)  {
             NPY_AUXDATA_FREE(*out_transferdata);
             *out_transferdata = NULL;
             return -1;
         }
+        *flags = PyArrayMethod_COMBINED_FLAGS(*flags, clear_flags);
     }
     return 0;
 }
@@ -578,7 +567,7 @@ wrap_copy_swap_function(
             &PyArray_Type, dtype,
             1, &shape, NULL, NULL,
             0, NULL, NULL,
-            0, 1);
+            _NPY_ARRAY_ENSURE_DTYPE_IDENTITY);
     if (data->arr == NULL) {
         PyMem_Free(data);
         return NPY_FAIL;
@@ -1327,7 +1316,7 @@ get_legacy_dtype_cast_function(
             &PyArray_Type, tmp_dtype,
             1, &shape, NULL, NULL,
             0, NULL, NULL,
-            0, 1);
+            _NPY_ARRAY_ENSURE_DTYPE_IDENTITY);
     if (data->aip == NULL) {
         PyMem_Free(data);
         return NPY_FAIL;
@@ -1354,7 +1343,7 @@ get_legacy_dtype_cast_function(
             &PyArray_Type, tmp_dtype,
             1, &shape, NULL, NULL,
             0, NULL, NULL,
-            0, 1);
+            _NPY_ARRAY_ENSURE_DTYPE_IDENTITY);
     if (data->aop == NULL) {
         Py_DECREF(data->aip);
         PyMem_Free(data);
@@ -1393,7 +1382,7 @@ typedef struct {
     npy_intp N;
     NPY_cast_info wrapped;
     /* If finish->func is non-NULL the source needs a decref */
-    NPY_cast_info decref_src;
+    NPY_traverse_info decref_src;
 } _one_to_n_data;
 
 /* transfer data free function */
@@ -1401,7 +1390,7 @@ static void _one_to_n_data_free(NpyAuxData *data)
 {
     _one_to_n_data *d = (_one_to_n_data *)data;
     NPY_cast_info_xfree(&d->wrapped);
-    NPY_cast_info_xfree(&d->decref_src);
+    NPY_traverse_info_xfree(&d->decref_src);
     PyMem_Free(data);
 }
 
@@ -1420,7 +1409,7 @@ static NpyAuxData *_one_to_n_data_clone(NpyAuxData *data)
     newdata->base.clone = &_one_to_n_data_clone;
     newdata->N = d->N;
     /* Initialize in case of error, or if it is unused */
-    NPY_cast_info_init(&newdata->decref_src);
+    NPY_traverse_info_init(&newdata->decref_src);
 
     if (NPY_cast_info_copy(&newdata->wrapped, &d->wrapped) < 0) {
         _one_to_n_data_free((NpyAuxData *)newdata);
@@ -1430,7 +1419,7 @@ static NpyAuxData *_one_to_n_data_clone(NpyAuxData *data)
         return (NpyAuxData *)newdata;
     }
 
-    if (NPY_cast_info_copy(&newdata->decref_src, &d->decref_src) < 0) {
+    if (NPY_traverse_info_copy(&newdata->decref_src, &d->decref_src) < 0) {
         _one_to_n_data_free((NpyAuxData *)newdata);
         return NULL;
     }
@@ -1490,8 +1479,8 @@ _strided_to_strided_one_to_n_with_finish(
             return -1;
         }
 
-        if (d->decref_src.func(&d->decref_src.context,
-                &src, &one_item, &zero_stride, d->decref_src.auxdata) < 0) {
+        if (d->decref_src.func(NULL, d->decref_src.descr,
+                src, one_item, zero_stride, d->decref_src.auxdata) < 0) {
             return -1;
         }
 
@@ -1522,7 +1511,7 @@ get_one_to_n_transfer_function(int aligned,
     data->base.free = &_one_to_n_data_free;
     data->base.clone = &_one_to_n_data_clone;
     data->N = N;
-    NPY_cast_info_init(&data->decref_src);  /* In case of error */
+    NPY_traverse_info_init(&data->decref_src);  /* In case of error */
 
     /*
      * move_references is set to 0, handled in the wrapping transfer fn,
@@ -1542,15 +1531,14 @@ get_one_to_n_transfer_function(int aligned,
 
     /* If the src object will need a DECREF, set src_dtype */
     if (move_references && PyDataType_REFCHK(src_dtype)) {
-        *out_flags |= NPY_METH_REQUIRES_PYAPI;
-        if (get_decref_transfer_function(aligned,
-                            src_stride,
-                            src_dtype,
-                            &data->decref_src,
-                            NULL) != NPY_SUCCEED) {
+        NPY_ARRAYMETHOD_FLAGS clear_flags;
+        if (PyArray_GetClearFunction(
+                aligned, src_stride, src_dtype,
+                &data->decref_src, &clear_flags) < 0) {
             NPY_AUXDATA_FREE((NpyAuxData *)data);
             return NPY_FAIL;
         }
+        *out_flags = PyArrayMethod_COMBINED_FLAGS(*out_flags, clear_flags);
     }
 
     if (data->decref_src.func == NULL) {
@@ -1741,8 +1729,8 @@ typedef struct {
 typedef struct {
     NpyAuxData base;
     NPY_cast_info wrapped;
-    NPY_cast_info decref_src;
-    NPY_cast_info decref_dst;  /* The use-case should probably be deprecated */
+    NPY_traverse_info decref_src;
+    NPY_traverse_info decref_dst;  /* The use-case should probably be deprecated */
     npy_intp src_N, dst_N;
     /* This gets a run-length encoded representation of the transfer */
     npy_intp run_count;
@@ -1755,8 +1743,8 @@ static void _subarray_broadcast_data_free(NpyAuxData *data)
 {
     _subarray_broadcast_data *d = (_subarray_broadcast_data *)data;
     NPY_cast_info_xfree(&d->wrapped);
-    NPY_cast_info_xfree(&d->decref_src);
-    NPY_cast_info_xfree(&d->decref_dst);
+    NPY_traverse_info_xfree(&d->decref_src);
+    NPY_traverse_info_xfree(&d->decref_dst);
     PyMem_Free(data);
 }
 
@@ -1780,21 +1768,21 @@ static NpyAuxData *_subarray_broadcast_data_clone(NpyAuxData *data)
     newdata->run_count = d->run_count;
     memcpy(newdata->offsetruns, d->offsetruns, offsetruns_size);
 
-    NPY_cast_info_init(&newdata->decref_src);
-    NPY_cast_info_init(&newdata->decref_dst);
+    NPY_traverse_info_init(&newdata->decref_src);
+    NPY_traverse_info_init(&newdata->decref_dst);
 
     if (NPY_cast_info_copy(&newdata->wrapped, &d->wrapped) < 0) {
         _subarray_broadcast_data_free((NpyAuxData *)newdata);
         return NULL;
     }
     if (d->decref_src.func != NULL) {
-        if (NPY_cast_info_copy(&newdata->decref_src, &d->decref_src) < 0) {
+        if (NPY_traverse_info_copy(&newdata->decref_src, &d->decref_src) < 0) {
             _subarray_broadcast_data_free((NpyAuxData *) newdata);
             return NULL;
         }
     }
     if (d->decref_dst.func != NULL) {
-        if (NPY_cast_info_copy(&newdata->decref_dst, &d->decref_dst) < 0) {
+        if (NPY_traverse_info_copy(&newdata->decref_dst, &d->decref_dst) < 0) {
             _subarray_broadcast_data_free((NpyAuxData *) newdata);
             return NULL;
         }
@@ -1883,8 +1871,8 @@ _strided_to_strided_subarray_broadcast_withrefs(
             }
             else {
                 if (d->decref_dst.func != NULL) {
-                    if (d->decref_dst.func(&d->decref_dst.context,
-                            &dst_ptr, &count, &dst_subitemsize,
+                    if (d->decref_dst.func(NULL, d->decref_dst.descr,
+                            dst_ptr, count, dst_subitemsize,
                             d->decref_dst.auxdata) < 0) {
                         return -1;
                     }
@@ -1895,8 +1883,8 @@ _strided_to_strided_subarray_broadcast_withrefs(
         }
 
         if (d->decref_src.func != NULL) {
-            if (d->decref_src.func(&d->decref_src.context,
-                    &src, &d->src_N, &src_subitemsize,
+            if (d->decref_src.func(NULL, d->decref_src.descr,
+                    src, d->src_N, src_subitemsize,
                     d->decref_src.auxdata) < 0) {
                 return -1;
             }
@@ -1939,8 +1927,8 @@ get_subarray_broadcast_transfer_function(int aligned,
     data->src_N = src_size;
     data->dst_N = dst_size;
 
-    NPY_cast_info_init(&data->decref_src);
-    NPY_cast_info_init(&data->decref_dst);
+    NPY_traverse_info_init(&data->decref_src);
+    NPY_traverse_info_init(&data->decref_dst);
 
     /*
      * move_references is set to 0, handled in the wrapping transfer fn,
@@ -1959,12 +1947,9 @@ get_subarray_broadcast_transfer_function(int aligned,
 
     /* If the src object will need a DECREF */
     if (move_references && PyDataType_REFCHK(src_dtype)) {
-        if (PyArray_GetDTypeTransferFunction(aligned,
-                        src_dtype->elsize, 0,
-                        src_dtype, NULL,
-                        1,
-                        &data->decref_src,
-                        out_flags) != NPY_SUCCEED) {
+        if (PyArray_GetClearFunction(aligned,
+                        src_dtype->elsize, src_dtype,
+                        &data->decref_src, out_flags) < 0) {
             NPY_AUXDATA_FREE((NpyAuxData *)data);
             return NPY_FAIL;
         }
@@ -1972,12 +1957,9 @@ get_subarray_broadcast_transfer_function(int aligned,
 
     /* If the dst object needs a DECREF to set it to NULL */
     if (PyDataType_REFCHK(dst_dtype)) {
-        if (PyArray_GetDTypeTransferFunction(aligned,
-                        dst_dtype->elsize, 0,
-                        dst_dtype, NULL,
-                        1,
-                        &data->decref_dst,
-                        out_flags) != NPY_SUCCEED) {
+        if (PyArray_GetClearFunction(aligned,
+                        dst_dtype->elsize, dst_dtype,
+                        &data->decref_dst, out_flags) < 0) {
             NPY_AUXDATA_FREE((NpyAuxData *)data);
             return NPY_FAIL;
         }
@@ -2182,6 +2164,7 @@ typedef struct {
 typedef struct {
     NpyAuxData base;
     npy_intp field_count;
+    NPY_traverse_info decref_src;
     _single_field_transfer fields[];
 } _field_transfer_data;
 
@@ -2190,6 +2173,7 @@ typedef struct {
 static void _field_transfer_data_free(NpyAuxData *data)
 {
     _field_transfer_data *d = (_field_transfer_data *)data;
+    NPY_traverse_info_xfree(&d->decref_src);
 
     for (npy_intp i = 0; i < d->field_count; ++i) {
         NPY_cast_info_xfree(&d->fields[i].info);
@@ -2213,6 +2197,10 @@ static NpyAuxData *_field_transfer_data_clone(NpyAuxData *data)
     }
     newdata->base = d->base;
     newdata->field_count = 0;
+    if (NPY_traverse_info_copy(&newdata->decref_src, &d->decref_src) < 0) {
+        PyMem_Free(newdata);
+        return NULL;
+    }
 
     /* Copy all the fields transfer data */
     for (npy_intp i = 0; i < field_count; ++i) {
@@ -2254,6 +2242,11 @@ _strided_to_strided_field_transfer(
                     return -1;
                 }
             }
+            if (d->decref_src.func != NULL && d->decref_src.func(
+                    NULL, d->decref_src.descr, src, blocksize, src_stride,
+                    d->decref_src.auxdata) < 0) {
+                return -1;
+            }
             N -= NPY_LOWLEVEL_BUFFER_BLOCKSIZE;
             src += NPY_LOWLEVEL_BUFFER_BLOCKSIZE*src_stride;
             dst += NPY_LOWLEVEL_BUFFER_BLOCKSIZE*dst_stride;
@@ -2267,6 +2260,11 @@ _strided_to_strided_field_transfer(
                     return -1;
                 }
             }
+            if (d->decref_src.func != NULL && d->decref_src.func(
+                    NULL, d->decref_src.descr, src, blocksize, src_stride,
+                    d->decref_src.auxdata) < 0) {
+                return -1;
+            }
             return 0;
         }
     }
@@ -2313,6 +2311,7 @@ get_fields_transfer_function(int NPY_UNUSED(aligned),
         data->base.free = &_field_transfer_data_free;
         data->base.clone = &_field_transfer_data_clone;
         data->field_count = 0;
+        NPY_traverse_info_init(&data->decref_src);
 
         *out_flags = PyArrayMethod_MINIMAL_FLAGS;
         for (i = 0; i < field_count; ++i) {
@@ -2340,23 +2339,17 @@ get_fields_transfer_function(int NPY_UNUSED(aligned),
         }
 
         /*
-         * If references should be decrefd in src, add another transfer
-         * function to do that. Since a decref function only uses a single
-         * input, the second one (normally output) just does not matter here.
+         * If references should be decrefd in src, add a clear function.
          */
         if (move_references && PyDataType_REFCHK(src_dtype)) {
-            *out_flags |= NPY_METH_REQUIRES_PYAPI;
-            if (get_decref_transfer_function(0,
-                                    src_stride,
-                                    src_dtype,
-                                    &data->fields[field_count].info,
-                                    NULL) != NPY_SUCCEED) {
+            NPY_ARRAYMETHOD_FLAGS clear_flags;
+            if (PyArray_GetClearFunction(
+                    0, src_stride, src_dtype, &data->decref_src,
+                    &clear_flags) < 0) {
                 NPY_AUXDATA_FREE((NpyAuxData *)data);
                 return NPY_FAIL;
             }
-            data->fields[field_count].src_offset = 0;
-            data->fields[field_count].dst_offset = 0;
-            data->field_count = field_count;
+            *out_flags = PyArrayMethod_COMBINED_FLAGS(*out_flags, clear_flags);
         }
 
         *out_stransfer = &_strided_to_strided_field_transfer;
@@ -2384,6 +2377,7 @@ get_fields_transfer_function(int NPY_UNUSED(aligned),
         }
         data->base.free = &_field_transfer_data_free;
         data->base.clone = &_field_transfer_data_clone;
+        NPY_traverse_info_init(&data->decref_src);
 
         key = PyTuple_GET_ITEM(src_dtype->names, 0);
         tup = PyDict_GetItem(src_dtype->fields, key);
@@ -2432,6 +2426,7 @@ get_fields_transfer_function(int NPY_UNUSED(aligned),
     data->base.free = &_field_transfer_data_free;
     data->base.clone = &_field_transfer_data_clone;
     data->field_count = 0;
+    NPY_traverse_info_init(&data->decref_src);
 
     *out_flags = PyArrayMethod_MINIMAL_FLAGS;
     /* set up the transfer function for each field */
@@ -2473,69 +2468,6 @@ get_fields_transfer_function(int NPY_UNUSED(aligned),
     return NPY_SUCCEED;
 }
 
-static int
-get_decref_fields_transfer_function(int NPY_UNUSED(aligned),
-                            npy_intp src_stride,
-                            PyArray_Descr *src_dtype,
-                            PyArrayMethod_StridedLoop **out_stransfer,
-                            NpyAuxData **out_transferdata,
-                            int *out_needs_api)
-{
-    PyObject *names, *key, *tup, *title;
-    PyArray_Descr *src_fld_dtype;
-    npy_int i, structsize;
-    Py_ssize_t field_count;
-    int src_offset;
-
-    names = src_dtype->names;
-    field_count = PyTuple_GET_SIZE(src_dtype->names);
-
-    /* Over-allocating here: less fields may be used */
-    structsize = sizeof(_field_transfer_data) +
-                    field_count * sizeof(_single_field_transfer);
-    /* Allocate the data and populate it */
-    _field_transfer_data *data = PyMem_Malloc(structsize);
-    if (data == NULL) {
-        PyErr_NoMemory();
-        return NPY_FAIL;
-    }
-    data->base.free = &_field_transfer_data_free;
-    data->base.clone = &_field_transfer_data_clone;
-    data->field_count = 0;
-
-    _single_field_transfer *field = data->fields;
-    for (i = 0; i < field_count; ++i) {
-        key = PyTuple_GET_ITEM(names, i);
-        tup = PyDict_GetItem(src_dtype->fields, key);
-        if (!PyArg_ParseTuple(tup, "Oi|O", &src_fld_dtype,
-                                                &src_offset, &title)) {
-            NPY_AUXDATA_FREE((NpyAuxData *)data);
-            return NPY_FAIL;
-        }
-        if (PyDataType_REFCHK(src_fld_dtype)) {
-            if (out_needs_api) {
-                *out_needs_api = 1;
-            }
-            if (get_decref_transfer_function(0,
-                                    src_stride,
-                                    src_fld_dtype,
-                                    &field->info,
-                                    out_needs_api) != NPY_SUCCEED) {
-                NPY_AUXDATA_FREE((NpyAuxData *)data);
-                return NPY_FAIL;
-            }
-            field->src_offset = src_offset;
-            data->field_count++;
-            field++;
-        }
-    }
-
-    *out_stransfer = &_strided_to_strided_field_transfer;
-    *out_transferdata = (NpyAuxData *)data;
-
-    return NPY_SUCCEED;
-}
-
 
 /************************* MASKED TRANSFER WRAPPER *************************/
 
@@ -2544,7 +2476,7 @@ typedef struct {
     /* The transfer function being wrapped (could likely be stored directly) */
     NPY_cast_info wrapped;
     /* The src decref function if necessary */
-    NPY_cast_info decref_src;
+    NPY_traverse_info decref_src;
 } _masked_wrapper_transfer_data;
 
 /* transfer data free function */
@@ -2553,7 +2485,7 @@ _masked_wrapper_transfer_data_free(NpyAuxData *data)
 {
     _masked_wrapper_transfer_data *d = (_masked_wrapper_transfer_data *)data;
     NPY_cast_info_xfree(&d->wrapped);
-    NPY_cast_info_xfree(&d->decref_src);
+    NPY_traverse_info_xfree(&d->decref_src);
     PyMem_Free(data);
 }
 
@@ -2576,7 +2508,7 @@ _masked_wrapper_transfer_data_clone(NpyAuxData *data)
         return NULL;
     }
     if (d->decref_src.func != NULL) {
-        if (NPY_cast_info_copy(&newdata->decref_src, &d->decref_src) < 0) {
+        if (NPY_traverse_info_copy(&newdata->decref_src, &d->decref_src) < 0) {
             NPY_AUXDATA_FREE((NpyAuxData *)newdata);
             return NULL;
         }
@@ -2586,7 +2518,7 @@ _masked_wrapper_transfer_data_clone(NpyAuxData *data)
 }
 
 static int
-_strided_masked_wrapper_decref_transfer_function(
+_strided_masked_wrapper_clear_function(
         PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
         const npy_intp *dimensions, const npy_intp *strides,
         npy_bool *mask, npy_intp mask_stride,
@@ -2603,8 +2535,8 @@ _strided_masked_wrapper_decref_transfer_function(
         /* Skip masked values, still calling decref for move_references */
         mask = (npy_bool*)npy_memchr((char *)mask, 0, mask_stride, N,
                                      &subloopsize, 1);
-        if (d->decref_src.func(&d->decref_src.context,
-                &src, &subloopsize, &src_stride, d->decref_src.auxdata) < 0) {
+        if (d->decref_src.func(NULL, d->decref_src.descr,
+                src, subloopsize, src_stride, d->decref_src.auxdata) < 0) {
             return -1;
         }
         dst += subloopsize * dst_stride;
@@ -2670,139 +2602,18 @@ _strided_masked_wrapper_transfer_function(
 }
 
 
-/*************************** CLEAR SRC *******************************/
 
+/* A no-op function (currently only used for cleanup purposes really) */
 static int
-_dec_src_ref_nop(
+_cast_no_op(
         PyArrayMethod_Context *NPY_UNUSED(context),
         char *const *NPY_UNUSED(args), const npy_intp *NPY_UNUSED(dimensions),
         const npy_intp *NPY_UNUSED(strides), NpyAuxData *NPY_UNUSED(auxdata))
 {
-    /* NOP */
+    /* Do nothing */
     return 0;
 }
 
-static int
-_strided_to_null_dec_src_ref_reference(
-        PyArrayMethod_Context *NPY_UNUSED(context),
-        char *const *args, const npy_intp *dimensions,
-        const npy_intp *strides, NpyAuxData *NPY_UNUSED(auxdata))
-{
-    char *src = args[0];
-    npy_intp N = dimensions[0];
-    npy_intp stride = strides[0];
-
-    PyObject *src_ref = NULL;
-    while (N > 0) {
-        /* Release the reference in src and set it to NULL */
-        NPY_DT_DBG_REFTRACE("dec src ref (null dst)", src_ref);
-        memcpy(&src_ref, src, sizeof(src_ref));
-        Py_XDECREF(src_ref);
-        memset(src, 0, sizeof(PyObject *));
-
-        src += stride;
-        --N;
-    }
-    return 0;
-}
-
-
-/*
- * Get a function to decref.  Currently, this uses a cast info slot, which
- * means that the second (destination) descriptor is always set to NULL
- * and generally does not have to be passed.
- * Since we do not currently have an `ArrayMethod` representing this, the
- * method is also set to NULL.
- *
- * TODO: this function should probably be moved onto the DType eventually,
- *       which would allow for user DTypes to include dynamic allocated
- *       memory or Python objects.
- */
-static int
-get_decref_transfer_function(int aligned,
-                            npy_intp src_stride,
-                            PyArray_Descr *src_dtype,
-                            NPY_cast_info *cast_info,
-                            int *out_needs_api)
-{
-    NPY_cast_info_init(cast_info);
-
-    /* If there are no references, it's a nop */
-    if (!PyDataType_REFCHK(src_dtype)) {
-        cast_info->func = &_dec_src_ref_nop;
-        cast_info->auxdata = NULL;
-        goto finalize;
-    }
-    /* If it's a single reference, it's one decref */
-    else if (src_dtype->type_num == NPY_OBJECT) {
-        if (out_needs_api) {
-            *out_needs_api = 1;
-        }
-
-        cast_info->func = &_strided_to_null_dec_src_ref_reference;
-        cast_info->auxdata = NULL;
-        goto finalize;
-    }
-    /* If there are subarrays, need to wrap it */
-    else if (PyDataType_HASSUBARRAY(src_dtype)) {
-        PyArray_Dims src_shape = {NULL, -1};
-        npy_intp src_size;
-
-        if (out_needs_api) {
-            *out_needs_api = 1;
-        }
-
-        if (!(PyArray_IntpConverter(src_dtype->subarray->shape,
-                                            &src_shape))) {
-            PyErr_SetString(PyExc_ValueError,
-                    "invalid subarray shape");
-            return NPY_FAIL;
-        }
-        src_size = PyArray_MultiplyList(src_shape.ptr, src_shape.len);
-        npy_free_cache_dim_obj(src_shape);
-
-        NPY_ARRAYMETHOD_FLAGS ignored_flags;
-        if (get_n_to_n_transfer_function(aligned,
-                src_stride, 0,
-                src_dtype->subarray->base, NULL, 1, src_size,
-                &cast_info->func, &cast_info->auxdata,
-                &ignored_flags) != NPY_SUCCEED) {
-            return NPY_FAIL;
-        }
-
-        goto finalize;
-    }
-    /* If there are fields, need to do each field */
-    else if (PyDataType_HASFIELDS(src_dtype)) {
-        if (out_needs_api) {
-            *out_needs_api = 1;
-        }
-
-        if (get_decref_fields_transfer_function(aligned,
-                            src_stride, src_dtype,
-                            &cast_info->func, &cast_info->auxdata,
-                            out_needs_api) < 0) {
-            return NPY_FAIL;
-        }
-        goto finalize;
-    }
-    else {
-        PyErr_Format(PyExc_RuntimeError,
-                "Internal error, tried to fetch decref function for the "
-                "unsupported DType '%S'.", src_dtype);
-        return NPY_FAIL;
-    }
-
-  finalize:
-    /* Make sure all important fields are either set or cleared */
-    Py_INCREF(src_dtype);
-    cast_info->descriptors[0] = src_dtype;
-    cast_info->descriptors[1] = NULL;
-    cast_info->context.method = NULL;
-    cast_info->context.caller = NULL;
-    return NPY_SUCCEED;
-}
-
 
 /*
  * ********************* Generalized Multistep Cast ************************
@@ -3010,7 +2821,7 @@ _strided_to_strided_multistep_cast(
  * Initialize most of a cast-info structure, this step does not fetch the
  * transferfunction and transferdata.
  */
-static NPY_INLINE int
+static inline int
 init_cast_info(
         NPY_cast_info *cast_info, NPY_CASTING *casting, npy_intp *view_offset,
         PyArray_Descr *src_dtype, PyArray_Descr *dst_dtype, int main_step)
@@ -3083,14 +2894,14 @@ _clear_cast_info_after_get_loop_failure(NPY_cast_info *cast_info)
     /* As public API we could choose to clear auxdata != NULL */
     assert(cast_info->auxdata == NULL);
     /* Set func to be non-null so that `NPY_cats_info_xfree` does not skip */
-    cast_info->func = &_dec_src_ref_nop;
+    cast_info->func = &_cast_no_op;
     NPY_cast_info_xfree(cast_info);
 }
 
 
 /*
  * Helper for PyArray_GetDTypeTransferFunction, which fetches a single
- * transfer function from the each casting implementation (ArrayMethod).
+ * transfer function from the each casting implementation (ArrayMethod)
  * May set the transfer function to NULL when the cast can be achieved using
  * a view.
  * TODO: Expand the view functionality for general offsets, not just 0:
@@ -3120,6 +2931,8 @@ define_cast_for_descrs(
         int move_references,
         NPY_cast_info *cast_info, NPY_ARRAYMETHOD_FLAGS *out_flags)
 {
+    assert(dst_dtype != NULL);  /* Was previously used for decref */
+
     /* Storage for all cast info in case multi-step casting is necessary */
     _multistep_castdata castdata;
     /* Initialize funcs to NULL to simplify cleanup on error. */
@@ -3164,7 +2977,7 @@ define_cast_for_descrs(
         /* Prepare the actual cast (if necessary): */
         if (from_view_offset == 0 && !must_wrap) {
             /* This step is not necessary and can be skipped */
-            castdata.from.func = &_dec_src_ref_nop;  /* avoid NULL */
+            castdata.from.func = &_cast_no_op;  /* avoid NULL */
             NPY_cast_info_xfree(&castdata.from);
         }
         else {
@@ -3203,7 +3016,7 @@ define_cast_for_descrs(
         /* Prepare the actual cast (if necessary): */
         if (to_view_offset == 0 && !must_wrap) {
             /* This step is not necessary and can be skipped. */
-            castdata.to.func = &_dec_src_ref_nop;  /* avoid NULL */
+            castdata.to.func = &_cast_no_op;  /* avoid NULL */
             NPY_cast_info_xfree(&castdata.to);
         }
         else {
@@ -3279,33 +3092,6 @@ PyArray_GetDTypeTransferFunction(int aligned,
                             NPY_cast_info *cast_info,
                             NPY_ARRAYMETHOD_FLAGS *out_flags)
 {
-    assert(src_dtype != NULL);
-
-    /*
-     * If one of the dtypes is NULL, we give back either a src decref
-     * function or a dst setzero function
-     *
-     * TODO: Eventually, we may wish to support user dtype with references
-     *       (including and beyond bare `PyObject *` this may require extending
-     *       the ArrayMethod API and those paths should likely be split out
-     *       from this function.)
-     */
-    if (dst_dtype == NULL) {
-        assert(move_references);
-        int needs_api = 0;
-        int res = get_decref_transfer_function(aligned,
-                                src_dtype->elsize,
-                                src_dtype,
-                                cast_info,
-                                &needs_api);
-        /* decref'ing never creates floating point errors, so just ignore it */
-        *out_flags = PyArrayMethod_MINIMAL_FLAGS;
-        if (needs_api) {
-            *out_flags |= NPY_METH_REQUIRES_PYAPI;
-        }
-        return res;
-    }
-
     if (define_cast_for_descrs(aligned,
             src_stride, dst_stride,
             src_dtype, dst_dtype, move_references,
@@ -3563,20 +3349,19 @@ PyArray_GetMaskedDTypeTransferFunction(int aligned,
 
     /* If the src object will need a DECREF, get a function to handle that */
     if (move_references && PyDataType_REFCHK(src_dtype)) {
-        *out_flags |= NPY_METH_REQUIRES_PYAPI;
-        if (get_decref_transfer_function(aligned,
-                            src_stride,
-                            src_dtype,
-                            &data->decref_src,
-                            NULL) != NPY_SUCCEED) {
+        NPY_ARRAYMETHOD_FLAGS clear_flags;
+        if (PyArray_GetClearFunction(
+                aligned, src_stride, src_dtype,
+                &data->decref_src, &clear_flags) < 0) {
             NPY_AUXDATA_FREE((NpyAuxData *)data);
             return NPY_FAIL;
         }
+        *out_flags = PyArrayMethod_COMBINED_FLAGS(*out_flags, clear_flags);
         cast_info->func = (PyArrayMethod_StridedLoop *)
-                &_strided_masked_wrapper_decref_transfer_function;
+                &_strided_masked_wrapper_clear_function;
     }
     else {
-        NPY_cast_info_init(&data->decref_src);
+        NPY_traverse_info_init(&data->decref_src);
         cast_info->func = (PyArrayMethod_StridedLoop *)
                 &_strided_masked_wrapper_transfer_function;
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.h
index 9ae332e385b..04df5cb64c2 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_transfer.h
@@ -31,7 +31,7 @@ typedef struct {
  * copied.
  * If set up otherwise, func must be NULL'ed to indicate no-cleanup necessary.
  */
-static NPY_INLINE void
+static inline void
 NPY_cast_info_init(NPY_cast_info *cast_info)
 {
     cast_info->func = NULL;  /* mark as uninitialized. */
@@ -52,7 +52,7 @@ NPY_cast_info_init(NPY_cast_info *cast_info)
  * First checks whether `cast_info.func == NULL`, and assume it is
  * uninitialized in that case.
  */
-static NPY_INLINE void
+static inline void
 NPY_cast_info_xfree(NPY_cast_info *cast_info)
 {
     if (cast_info->func == NULL) {
@@ -71,7 +71,7 @@ NPY_cast_info_xfree(NPY_cast_info *cast_info)
  * Move the data from `original` to `cast_info`. Original is cleared
  * (its func set to NULL).
  */
-static NPY_INLINE void
+static inline void
 NPY_cast_info_move(NPY_cast_info *cast_info, NPY_cast_info *original)
 {
     *cast_info = *original;
@@ -87,7 +87,7 @@ NPY_cast_info_move(NPY_cast_info *cast_info, NPY_cast_info *original)
  * NOTE: It is acceptable to call this with the same struct if the struct
  *       has been filled by a valid memcpy from an initialized one.
  */
-static NPY_INLINE int
+static inline int
 NPY_cast_info_copy(NPY_cast_info *cast_info, NPY_cast_info *original)
 {
     cast_info->context.descriptors = cast_info->descriptors;
@@ -146,7 +146,6 @@ object_to_any_get_loop(
         NpyAuxData **out_transferdata,
         NPY_ARRAYMETHOD_FLAGS *flags);
 
-
 NPY_NO_EXPORT int
 wrap_aligned_transferfunction(
         int aligned, int must_wrap,
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_traversal.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_traversal.c
new file mode 100644
index 00000000000..f76119f947f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_traversal.c
@@ -0,0 +1,651 @@
+/*
+ * This file is simlar to the low-level loops for data type transfer
+ * in `dtype_transfer.c` but for those which only require visiting
+ * a single array (and mutating it in-place).
+ *
+ * As of writing, it is only used for CLEARing, which means mainly
+ * Python object DECREF/dealloc followed by NULL'ing the data
+ * (to support double clearing and ensure data is again in a usable state).
+ * However, memory initialization and traverse follows similar
+ * protocols (although traversal needs additional arguments).
+ */
+
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#define _MULTIARRAYMODULE
+#define _UMATHMODULE
+
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <structmember.h>
+
+#include "numpy/ndarraytypes.h"
+#include "numpy/arrayobject.h"
+
+#include "alloc.h"
+#include "array_method.h"
+#include "dtypemeta.h"
+#include "dtype_traversal.h"
+
+
+/* Buffer size with the same use case as the one in dtype_transfer.c */
+#define NPY_LOWLEVEL_BUFFER_BLOCKSIZE  128
+
+
+typedef int get_traverse_func_function(
+        void *traverse_context, PyArray_Descr *dtype, int aligned,
+        npy_intp stride, NPY_traverse_info *clear_info,
+        NPY_ARRAYMETHOD_FLAGS *flags);
+
+/*
+ * Generic Clear function helpers:
+ */
+
+static int
+get_clear_function(
+        void *traverse_context, PyArray_Descr *dtype, int aligned,
+        npy_intp stride, NPY_traverse_info *clear_info,
+        NPY_ARRAYMETHOD_FLAGS *flags)
+{
+    NPY_traverse_info_init(clear_info);
+    /* not that cleanup code bothers to check e.g. for floating point flags */
+    *flags = PyArrayMethod_MINIMAL_FLAGS;
+
+    get_traverse_loop_function *get_clear = NPY_DT_SLOTS(NPY_DTYPE(dtype))->get_clear_loop;
+    if (get_clear == NULL) {
+        PyErr_Format(PyExc_RuntimeError,
+                "Internal error, `get_clear_loop` not set for the DType '%S'",
+                dtype);
+        return -1;
+    }
+
+    if (get_clear(traverse_context, dtype, aligned, stride,
+                  &clear_info->func, &clear_info->auxdata, flags) <  0) {
+        /* callee should clean up, but make sure outside debug mode */
+        assert(clear_info->func == NULL);
+        clear_info->func = NULL;
+        return -1;
+    }
+    Py_INCREF(dtype);
+    clear_info->descr = dtype;
+
+    return 0;
+}
+
+/*
+ * Helper to set up a strided loop used for clearing.  Clearing means
+ * deallocating any references (e.g. via Py_DECREF) and resetting the data
+ * back into a usable/initialized state (e.g. by NULLing any references).
+ *
+ * The function will error when called on a dtype which does not have
+ * references (and thus the get_clear_loop slot NULL).
+ * Note that old-style user-dtypes use the "void" version.
+ *
+ * NOTE: This function may have a use for a `traverse_context` at some point
+ *       but right now, it is always NULL and only exists to allow adding it
+ *       in the future without changing the strided-loop signature.
+ */
+NPY_NO_EXPORT int
+PyArray_GetClearFunction(
+        int aligned, npy_intp stride, PyArray_Descr *dtype,
+        NPY_traverse_info *clear_info, NPY_ARRAYMETHOD_FLAGS *flags)
+{
+    return get_clear_function(NULL, dtype, aligned, stride, clear_info, flags);
+}
+
+
+/*
+ * Generic zerofill/fill function helper:
+ */
+
+static int
+get_zerofill_function(
+        void *traverse_context, PyArray_Descr *dtype, int aligned,
+        npy_intp stride, NPY_traverse_info *zerofill_info,
+        NPY_ARRAYMETHOD_FLAGS *flags)
+{
+    NPY_traverse_info_init(zerofill_info);
+    /* not that filling code bothers to check e.g. for floating point flags */
+    *flags = PyArrayMethod_MINIMAL_FLAGS;
+
+    get_traverse_loop_function *get_zerofill = NPY_DT_SLOTS(NPY_DTYPE(dtype))->get_fill_zero_loop;
+    if (get_zerofill == NULL) {
+        /* Allowed to be NULL (and accept it here) */
+        return 0;
+    }
+
+    if (get_zerofill(traverse_context, dtype, aligned, stride,
+                     &zerofill_info->func, &zerofill_info->auxdata, flags) <  0) {
+        /* callee should clean up, but make sure outside debug mode */
+        assert(zerofill_info->func == NULL);
+        zerofill_info->func = NULL;
+        return -1;
+    }
+    if (zerofill_info->func == NULL) {
+        /* Zerofill also may return func=NULL without an error. */
+        return 0;
+    }
+
+    Py_INCREF(dtype);
+    zerofill_info->descr = dtype;
+
+    return 0;
+}
+
+
+/****************** Python Object clear ***********************/
+
+static int
+clear_object_strided_loop(
+        void *NPY_UNUSED(traverse_context), PyArray_Descr *NPY_UNUSED(descr),
+        char *data, npy_intp size, npy_intp stride,
+        NpyAuxData *NPY_UNUSED(auxdata))
+{
+    PyObject *aligned_copy = NULL;
+    while (size > 0) {
+        /* Release the reference in src and set it to NULL */
+        memcpy(&aligned_copy, data, sizeof(PyObject *));
+        Py_XDECREF(aligned_copy);
+        memset(data, 0, sizeof(PyObject *));
+
+        data += stride;
+        --size;
+    }
+    return 0;
+}
+
+
+NPY_NO_EXPORT int
+npy_get_clear_object_strided_loop(
+        void *NPY_UNUSED(traverse_context), PyArray_Descr *NPY_UNUSED(descr),
+        int NPY_UNUSED(aligned), npy_intp NPY_UNUSED(fixed_stride),
+        traverse_loop_function **out_loop, NpyAuxData **out_auxdata,
+        NPY_ARRAYMETHOD_FLAGS *flags)
+{
+    *flags = NPY_METH_REQUIRES_PYAPI|NPY_METH_NO_FLOATINGPOINT_ERRORS;
+    *out_loop = &clear_object_strided_loop;
+    return 0;
+}
+
+
+/**************** Python Object zero fill *********************/
+
+static int
+fill_zero_object_strided_loop(
+        void *NPY_UNUSED(traverse_context), PyArray_Descr *NPY_UNUSED(descr),
+        char *data, npy_intp size, npy_intp stride,
+        NpyAuxData *NPY_UNUSED(auxdata))
+{
+    PyObject *zero = PyLong_FromLong(0);
+    while (size--) {
+        Py_INCREF(zero);
+        // assumes `data` doesn't have a pre-existing object inside it
+        memcpy(data, &zero, sizeof(zero));
+        data += stride;
+    }
+    Py_DECREF(zero);
+    return 0;
+}
+
+NPY_NO_EXPORT int
+npy_object_get_fill_zero_loop(void *NPY_UNUSED(traverse_context),
+                              PyArray_Descr *NPY_UNUSED(descr),
+                              int NPY_UNUSED(aligned),
+                              npy_intp NPY_UNUSED(fixed_stride),
+                              traverse_loop_function **out_loop,
+                              NpyAuxData **NPY_UNUSED(out_auxdata),
+                              NPY_ARRAYMETHOD_FLAGS *flags)
+{
+    *flags = NPY_METH_REQUIRES_PYAPI | NPY_METH_NO_FLOATINGPOINT_ERRORS;
+    *out_loop = &fill_zero_object_strided_loop;
+    return 0;
+}
+
+/**************** Structured DType generic funcationality ***************/
+
+/*
+ * Note that legacy user dtypes also make use of this.  Someone managed to
+ * hack objects into them by adding a field that contains objects and this
+ * remains (somewhat) valid.
+ * (Unlike our voids, those fields must be hardcoded probably, but...)
+ *
+ * The below functionality mirrors the casting functionality relatively
+ * closely.
+ */
+
+typedef struct {
+    npy_intp src_offset;
+    NPY_traverse_info info;
+} single_field_traverse_data;
+
+typedef struct {
+    NpyAuxData base;
+    npy_intp field_count;
+    single_field_traverse_data fields[];
+} fields_traverse_data;
+
+
+/* traverse data free function */
+static void
+fields_traverse_data_free(NpyAuxData *data)
+{
+    fields_traverse_data *d = (fields_traverse_data *)data;
+
+    for (npy_intp i = 0; i < d->field_count; ++i) {
+        NPY_traverse_info_xfree(&d->fields[i].info);
+    }
+    PyMem_Free(d);
+}
+
+
+/* traverse data copy function (untested due to no direct use currently) */
+static NpyAuxData *
+fields_traverse_data_clone(NpyAuxData *data)
+{
+    fields_traverse_data *d = (fields_traverse_data *)data;
+
+    npy_intp field_count = d->field_count;
+    npy_intp structsize = sizeof(fields_traverse_data) +
+                    field_count * sizeof(single_field_traverse_data);
+
+    /* Allocate the data and populate it */
+    fields_traverse_data *newdata = PyMem_Malloc(structsize);
+    if (newdata == NULL) {
+        return NULL;
+    }
+    newdata->base = d->base;
+    newdata->field_count = 0;
+
+    /* Copy all the fields transfer data */
+    single_field_traverse_data *in_field = d->fields;
+    single_field_traverse_data *new_field = newdata->fields;
+
+    for (; newdata->field_count < field_count;
+                newdata->field_count++, in_field++, new_field++) {
+        new_field->src_offset = in_field->src_offset;
+
+        if (NPY_traverse_info_copy(&new_field->info, &in_field->info) < 0) {
+            fields_traverse_data_free((NpyAuxData *)newdata);
+            return NULL;
+        }
+    }
+
+    return (NpyAuxData *)newdata;
+}
+
+
+static int
+traverse_fields_function(
+        void *traverse_context, PyArray_Descr *NPY_UNUSED(descr),
+        char *data, npy_intp N, npy_intp stride,
+        NpyAuxData *auxdata)
+{
+    fields_traverse_data *d = (fields_traverse_data *)auxdata;
+    npy_intp i, field_count = d->field_count;
+
+    /* Do the traversing a block at a time for better memory caching */
+    const npy_intp blocksize = NPY_LOWLEVEL_BUFFER_BLOCKSIZE;
+
+    for (;;) {
+        if (N > blocksize) {
+            for (i = 0; i < field_count; ++i) {
+                single_field_traverse_data field = d->fields[i];
+                if (field.info.func(traverse_context,
+                        field.info.descr, data + field.src_offset,
+                        blocksize, stride, field.info.auxdata) < 0) {
+                    return -1;
+                }
+            }
+            N -= blocksize;
+            data += blocksize * stride;
+        }
+        else {
+            for (i = 0; i < field_count; ++i) {
+                single_field_traverse_data field = d->fields[i];
+                if (field.info.func(traverse_context,
+                        field.info.descr, data + field.src_offset,
+                        N, stride, field.info.auxdata) < 0) {
+                    return -1;
+                }
+            }
+            return 0;
+        }
+    }
+}
+
+
+static int
+get_fields_traverse_function(
+        void *traverse_context, PyArray_Descr *dtype, int NPY_UNUSED(aligned),
+        npy_intp stride, traverse_loop_function **out_func,
+        NpyAuxData **out_auxdata, NPY_ARRAYMETHOD_FLAGS *flags,
+        get_traverse_func_function *get_traverse_func)
+{
+    PyObject *names, *key, *tup, *title;
+    PyArray_Descr *fld_dtype;
+    npy_int i, structsize;
+    Py_ssize_t field_count;
+
+    names = dtype->names;
+    field_count = PyTuple_GET_SIZE(dtype->names);
+
+    /* Over-allocating here: less fields may be used */
+    structsize = (sizeof(fields_traverse_data) +
+                    field_count * sizeof(single_field_traverse_data));
+    /* Allocate the data and populate it */
+    fields_traverse_data *data = PyMem_Malloc(structsize);
+    if (data == NULL) {
+        PyErr_NoMemory();
+        return -1;
+    }
+    data->base.free = &fields_traverse_data_free;
+    data->base.clone = &fields_traverse_data_clone;
+    data->field_count = 0;
+
+    single_field_traverse_data *field = data->fields;
+    for (i = 0; i < field_count; ++i) {
+        int offset;
+
+        key = PyTuple_GET_ITEM(names, i);
+        tup = PyDict_GetItem(dtype->fields, key);
+        if (!PyArg_ParseTuple(tup, "Oi|O", &fld_dtype, &offset, &title)) {
+            NPY_AUXDATA_FREE((NpyAuxData *)data);
+            return -1;
+        }
+        if (get_traverse_func == &get_clear_function
+                && !PyDataType_REFCHK(fld_dtype)) {
+            /* No need to do clearing (could change to use NULL return) */
+            continue;
+        }
+        NPY_ARRAYMETHOD_FLAGS clear_flags;
+        if (get_traverse_func(
+                traverse_context, fld_dtype, 0,
+                stride, &field->info, &clear_flags) < 0) {
+            NPY_AUXDATA_FREE((NpyAuxData *)data);
+            return -1;
+        }
+        if (field->info.func == NULL) {
+            /* zerofill allows NULL func as "default" memset to zero */
+            continue;
+        }
+        *flags = PyArrayMethod_COMBINED_FLAGS(*flags, clear_flags);
+        field->src_offset = offset;
+        data->field_count++;
+        field++;
+    }
+
+    *out_func = &traverse_fields_function;
+    *out_auxdata = (NpyAuxData *)data;
+
+    return 0;
+}
+
+
+typedef struct {
+    NpyAuxData base;
+    npy_intp count;
+    NPY_traverse_info info;
+} subarray_traverse_data;
+
+
+/* traverse data free function */
+static void
+subarray_traverse_data_free(NpyAuxData *data)
+{
+    subarray_traverse_data *d = (subarray_traverse_data *)data;
+
+    NPY_traverse_info_xfree(&d->info);
+    PyMem_Free(d);
+}
+
+
+/*
+ * We seem to be neither using nor exposing this right now, so leave it NULL.
+ * (The implementation below should be functional.)
+ */
+#define subarray_traverse_data_clone NULL
+
+#ifndef subarray_traverse_data_clone
+/* traverse data copy function */
+static NpyAuxData *
+subarray_traverse_data_clone(NpyAuxData *data)
+{
+    subarray_traverse_data *d = (subarray_traverse_data *)data;
+
+    /* Allocate the data and populate it */
+    subarray_traverse_data *newdata = PyMem_Malloc(sizeof(subarray_traverse_data));
+    if (newdata == NULL) {
+        return NULL;
+    }
+    newdata->base = d->base;
+    newdata->count = d->count;
+
+    if (NPY_traverse_info_copy(&newdata->info, &d->info) < 0) {
+        PyMem_Free(newdata);
+        return NULL;
+    }
+
+    return (NpyAuxData *)newdata;
+}
+#endif
+
+
+static int
+traverse_subarray_func(
+        void *traverse_context, PyArray_Descr *NPY_UNUSED(descr),
+        char *data, npy_intp N, npy_intp stride,
+        NpyAuxData *auxdata)
+{
+    subarray_traverse_data *subarr_data = (subarray_traverse_data *)auxdata;
+
+    traverse_loop_function *func = subarr_data->info.func;
+    PyArray_Descr *sub_descr = subarr_data->info.descr;
+    npy_intp sub_N = subarr_data->count;
+    NpyAuxData *sub_auxdata = subarr_data->info.auxdata;
+    npy_intp sub_stride = sub_descr->elsize;
+
+    while (N--) {
+        if (func(traverse_context, sub_descr, data,
+                 sub_N, sub_stride, sub_auxdata) < 0) {
+            return -1;
+        }
+        data += stride;
+    }
+    return 0;
+}
+
+
+static int
+get_subarray_traverse_func(
+        void *traverse_context, PyArray_Descr *dtype, int aligned,
+        npy_intp size, npy_intp stride, traverse_loop_function **out_func,
+        NpyAuxData **out_auxdata, NPY_ARRAYMETHOD_FLAGS *flags,
+        get_traverse_func_function *get_traverse_func)
+{
+    subarray_traverse_data *auxdata = PyMem_Malloc(sizeof(subarray_traverse_data));
+    if (auxdata == NULL) {
+        PyErr_NoMemory();
+        return -1;
+    }
+
+    auxdata->count = size;
+    auxdata->base.free = &subarray_traverse_data_free;
+    auxdata->base.clone = subarray_traverse_data_clone;
+
+    if (get_traverse_func(
+            traverse_context, dtype, aligned,
+            dtype->elsize, &auxdata->info, flags) < 0) {
+        PyMem_Free(auxdata);
+        return -1;
+    }
+    if (auxdata->info.func == NULL) {
+        /* zerofill allows func to be NULL, in which we need not do anything */
+        PyMem_Free(auxdata);
+        *out_func = NULL;
+        *out_auxdata = NULL;
+        return 0;
+    }
+    *out_func = &traverse_subarray_func;
+    *out_auxdata = (NpyAuxData *)auxdata;
+
+    return 0;
+}
+
+
+static int
+clear_no_op(
+        void *NPY_UNUSED(traverse_context), PyArray_Descr *NPY_UNUSED(descr),
+        char *NPY_UNUSED(data), npy_intp NPY_UNUSED(size),
+        npy_intp NPY_UNUSED(stride), NpyAuxData *NPY_UNUSED(auxdata))
+{
+    return 0;
+}
+
+NPY_NO_EXPORT int
+npy_get_clear_void_and_legacy_user_dtype_loop(
+        void *traverse_context, PyArray_Descr *dtype, int aligned,
+        npy_intp stride, traverse_loop_function **out_func,
+        NpyAuxData **out_auxdata, NPY_ARRAYMETHOD_FLAGS *flags)
+{
+    /*
+     * If there are no references, it's a nop.  This path should not be hit
+     * but structured dtypes are tricky when a dtype which included references
+     * was sliced to not include any.
+     */
+    if (!PyDataType_REFCHK(dtype)) {
+        *out_func = &clear_no_op;
+        return 0;
+    }
+
+    if (PyDataType_HASSUBARRAY(dtype)) {
+        PyArray_Dims shape = {NULL, -1};
+        npy_intp size;
+
+        if (!(PyArray_IntpConverter(dtype->subarray->shape, &shape))) {
+            PyErr_SetString(PyExc_ValueError,
+                    "invalid subarray shape");
+            return -1;
+        }
+        size = PyArray_MultiplyList(shape.ptr, shape.len);
+        npy_free_cache_dim_obj(shape);
+
+        if (get_subarray_traverse_func(
+                traverse_context, dtype->subarray->base, aligned, size, stride,
+                out_func, out_auxdata, flags, &get_clear_function) < 0) {
+            return -1;
+        }
+
+        return 0;
+    }
+    /* If there are fields, need to do each field */
+    else if (PyDataType_HASFIELDS(dtype)) {
+        if (get_fields_traverse_function(
+                traverse_context, dtype, aligned, stride,
+                out_func, out_auxdata, flags, &get_clear_function) < 0) {
+            return -1;
+        }
+        return 0;
+    }
+    else if (dtype->type_num == NPY_VOID) {
+        /* 
+         * Void dtypes can have "ghosts" of objects marking the dtype because
+         * holes (or the raw bytes if fields are gone) may include objects.
+         * Paths that need those flags should probably be considered incorrect.
+         * But as long as this can happen (a V8 that indicates references)
+         * we need to make it a no-op here.
+         */
+        *out_func = &clear_no_op;
+        return 0;
+    }
+
+    PyErr_Format(PyExc_RuntimeError,
+            "Internal error, tried to fetch clear function for the "
+            "user dtype '%S' without fields or subarray (legacy support).",
+            dtype);
+    return -1;
+}
+
+/**************** Structured DType zero fill ***************/
+
+
+static int
+zerofill_fields_function(
+        void *traverse_context, PyArray_Descr *descr,
+        char *data, npy_intp N, npy_intp stride,
+        NpyAuxData *auxdata)
+{
+    npy_intp itemsize = descr->elsize;
+
+    /*
+     * TODO: We could optimize this by chunking, but since we currently memset
+     *       each element always, just loop manually.
+     */
+    while (N--) {
+        memset(data, 0, itemsize);
+        if (traverse_fields_function(
+                traverse_context, descr, data, 1, stride, auxdata) < 0) {
+            return -1;
+        }
+        data +=stride;
+    }
+    return 0;
+}
+
+/*
+ * Similar to other (e.g. clear) traversal loop getter, but unlike it, we
+ * do need to take care of zeroing out everything (in principle not gaps).
+ * So we add a memset before calling the actual traverse function for the
+ * structured path.
+ */
+NPY_NO_EXPORT int
+npy_get_zerofill_void_and_legacy_user_dtype_loop(
+        void *traverse_context, PyArray_Descr *dtype, int aligned,
+        npy_intp stride, traverse_loop_function **out_func,
+        NpyAuxData **out_auxdata, NPY_ARRAYMETHOD_FLAGS *flags)
+{
+    if (PyDataType_HASSUBARRAY(dtype)) {
+        PyArray_Dims shape = {NULL, -1};
+        npy_intp size;
+
+        if (!(PyArray_IntpConverter(dtype->subarray->shape, &shape))) {
+            PyErr_SetString(PyExc_ValueError,
+                    "invalid subarray shape");
+            return -1;
+        }
+        size = PyArray_MultiplyList(shape.ptr, shape.len);
+        npy_free_cache_dim_obj(shape);
+
+        if (get_subarray_traverse_func(
+                traverse_context, dtype->subarray->base, aligned, size, stride,
+                out_func, out_auxdata, flags, &get_zerofill_function) < 0) {
+            return -1;
+        }
+
+        return 0;
+    }
+    /* If there are fields, need to do each field */
+    else if (PyDataType_HASFIELDS(dtype)) {
+        if (get_fields_traverse_function(
+                traverse_context, dtype, aligned, stride,
+                out_func, out_auxdata, flags, &get_zerofill_function) < 0) {
+            return -1;
+        }
+        if (((fields_traverse_data *)*out_auxdata)->field_count == 0) {
+            /* If there are no fields, just return NULL for zerofill */
+            NPY_AUXDATA_FREE(*out_auxdata);
+            *out_auxdata = NULL;
+            *out_func = NULL;
+            return 0;
+        }
+        /* 
+         * Traversal skips fields that have no custom zeroing, so we need
+         * to take care of it.
+         */
+        *out_func = &zerofill_fields_function;
+        return 0;
+    }
+
+    /* Otherwise, assume there is nothing to do (user dtypes reach here) */
+    *out_auxdata = NULL;
+    *out_func = NULL;
+    return 0;
+}
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_traversal.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_traversal.h
new file mode 100644
index 00000000000..fc12c0f7ba2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtype_traversal.h
@@ -0,0 +1,98 @@
+#ifndef NUMPY_CORE_SRC_MULTIARRAY_DTYPE_TRAVERSAL_H_
+#define NUMPY_CORE_SRC_MULTIARRAY_DTYPE_TRAVERSAL_H_
+
+#include "array_method.h"
+
+/* NumPy DType clear (object DECREF + NULLing) implementations */
+
+NPY_NO_EXPORT int
+npy_get_clear_object_strided_loop(
+        void *traverse_context, PyArray_Descr *descr, int aligned,
+        npy_intp fixed_stride,
+        traverse_loop_function **out_loop, NpyAuxData **out_traversedata,
+        NPY_ARRAYMETHOD_FLAGS *flags);
+
+NPY_NO_EXPORT int
+npy_get_clear_void_and_legacy_user_dtype_loop(
+        void *traverse_context, PyArray_Descr *descr, int aligned,
+        npy_intp fixed_stride,
+        traverse_loop_function **out_loop, NpyAuxData **out_traversedata,
+        NPY_ARRAYMETHOD_FLAGS *flags);
+
+/* NumPy DType zero-filling implementations */
+
+NPY_NO_EXPORT int
+npy_object_get_fill_zero_loop(
+        void *NPY_UNUSED(traverse_context), PyArray_Descr *NPY_UNUSED(descr),
+        int NPY_UNUSED(aligned), npy_intp NPY_UNUSED(fixed_stride),
+        traverse_loop_function **out_loop, NpyAuxData **NPY_UNUSED(out_auxdata),
+        NPY_ARRAYMETHOD_FLAGS *flags);
+
+NPY_NO_EXPORT int
+npy_get_zerofill_void_and_legacy_user_dtype_loop(
+        void *traverse_context, PyArray_Descr *dtype, int aligned,
+        npy_intp stride, traverse_loop_function **out_func,
+        NpyAuxData **out_auxdata, NPY_ARRAYMETHOD_FLAGS *flags);
+
+
+/* Helper to deal with calling or nesting simple strided loops */
+
+typedef struct {
+    traverse_loop_function *func;
+    NpyAuxData *auxdata;
+    PyArray_Descr *descr;
+} NPY_traverse_info;
+
+
+static inline void
+NPY_traverse_info_init(NPY_traverse_info *cast_info)
+{
+    cast_info->func = NULL;  /* mark as uninitialized. */
+    cast_info->auxdata = NULL;  /* allow leaving auxdata untouched */
+    cast_info->descr = NULL;  /* mark as uninitialized. */
+}
+
+
+static inline void
+NPY_traverse_info_xfree(NPY_traverse_info *traverse_info)
+{
+    if (traverse_info->func == NULL) {
+        return;
+    }
+    traverse_info->func = NULL;
+    NPY_AUXDATA_FREE(traverse_info->auxdata);
+    Py_XDECREF(traverse_info->descr);
+}
+
+
+static inline int
+NPY_traverse_info_copy(
+        NPY_traverse_info *traverse_info, NPY_traverse_info *original)
+{
+    traverse_info->func = NULL;
+    if (original->func == NULL) {
+        /* Allow copying also of unused clear info */
+        return 0;
+    }
+    traverse_info->auxdata = NULL;
+    if (original->auxdata != NULL) {
+        traverse_info->auxdata = NPY_AUXDATA_CLONE(original->auxdata);
+        if (traverse_info->auxdata == NULL) {
+            return -1;
+        }
+    }
+    Py_INCREF(original->descr);
+    traverse_info->descr = original->descr;
+    traverse_info->func = original->func;
+
+    return 0;
+}
+
+
+NPY_NO_EXPORT int
+PyArray_GetClearFunction(
+        int aligned, npy_intp stride, PyArray_Descr *dtype,
+        NPY_traverse_info *clear_info, NPY_ARRAYMETHOD_FLAGS *flags);
+
+
+#endif  /* NUMPY_CORE_SRC_MULTIARRAY_DTYPE_TRAVERSAL_H_ */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.c
index b5226729150..9c1f384d817 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.c
@@ -9,7 +9,9 @@
 #include <numpy/ndarraytypes.h>
 #include <numpy/arrayscalars.h>
 #include "npy_pycompat.h"
+#include "npy_import.h"
 
+#include "arraytypes.h"
 #include "common.h"
 #include "dtypemeta.h"
 #include "descriptor.h"
@@ -18,6 +20,10 @@
 #include "scalartypes.h"
 #include "convert_datatype.h"
 #include "usertypes.h"
+#include "conversion_utils.h"
+#include "templ_common.h"
+#include "refcount.h"
+#include "dtype_traversal.h"
 
 #include <assert.h>
 
@@ -73,7 +79,7 @@ dtypemeta_init(PyTypeObject *NPY_UNUSED(type),
  * @param self
  * @return nonzero if the object is garbage collected
  */
-static NPY_INLINE int
+static inline int
 dtypemeta_is_gc(PyObject *dtype_class)
 {
     return PyType_Type.tp_is_gc(dtype_class);
@@ -122,6 +128,50 @@ legacy_dtype_default_new(PyArray_DTypeMeta *self,
     return (PyObject *)self->singleton;
 }
 
+static PyObject *
+string_unicode_new(PyArray_DTypeMeta *self, PyObject *args, PyObject *kwargs)
+{
+    npy_intp size;
+
+    static char *kwlist[] = {"", NULL};
+
+    if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist,
+                                     PyArray_IntpFromPyIntConverter, &size)) {
+        return NULL;
+    }
+
+    if (size < 0) {
+        PyErr_Format(PyExc_ValueError,
+                     "Strings cannot have a negative size but a size of "
+                     "%"NPY_INTP_FMT" was given", size);
+        return NULL;
+    }
+
+    PyArray_Descr *res = PyArray_DescrNewFromType(self->type_num);
+
+    if (res == NULL) {
+        return NULL;
+    }
+
+    if (self->type_num == NPY_UNICODE) {
+        // unicode strings are 4 bytes per character
+        if (npy_mul_sizes_with_overflow(&size, size, 4)) {
+            PyErr_SetString(
+                PyExc_TypeError,
+                "Strings too large to store inside array.");
+            return NULL;
+        }
+    }
+
+    if (size > NPY_MAX_INT) {
+        PyErr_SetString(PyExc_TypeError,
+                        "Strings too large to store inside array.");
+        return NULL;
+    }
+
+    res->elsize = (int)size;
+    return (PyObject *)res;
+}
 
 static PyArray_Descr *
 nonparametric_discover_descr_from_pyobject(
@@ -151,7 +201,7 @@ string_discover_descr_from_pyobject(
         }
         if (itemsize > NPY_MAX_INT) {
             PyErr_SetString(PyExc_TypeError,
-                    "string to large to store inside array.");
+                    "string too large to store inside array.");
         }
         PyArray_Descr *res = PyArray_DescrNewFromType(cls->type_num);
         if (res == NULL) {
@@ -404,7 +454,7 @@ void_common_instance(PyArray_Descr *descr1, PyArray_Descr *descr2)
     if (descr1->subarray == NULL && descr1->names == NULL &&
             descr2->subarray == NULL && descr2->names == NULL) {
         if (descr1->elsize != descr2->elsize) {
-            PyErr_SetString(PyExc_TypeError,
+            PyErr_SetString(npy_DTypePromotionError,
                     "Invalid type promotion with void datatypes of different "
                     "lengths. Use the `np.bytes_` datatype instead to pad the "
                     "shorter value with trailing zero bytes.");
@@ -443,7 +493,7 @@ void_common_instance(PyArray_Descr *descr1, PyArray_Descr *descr2)
             return NULL;
         }
         if (!cmp) {
-            PyErr_SetString(PyExc_TypeError,
+            PyErr_SetString(npy_DTypePromotionError,
                     "invalid type promotion with subarray datatypes "
                     "(shape mismatch).");
             return NULL;
@@ -473,11 +523,12 @@ void_common_instance(PyArray_Descr *descr1, PyArray_Descr *descr2)
         return new_descr;
     }
 
-    PyErr_SetString(PyExc_TypeError,
+    PyErr_SetString(npy_DTypePromotionError,
             "invalid type promotion with structured datatype(s).");
     return NULL;
 }
 
+
 NPY_NO_EXPORT int
 python_builtins_are_known_scalar_types(
         PyArray_DTypeMeta *NPY_UNUSED(cls), PyTypeObject *pytype)
@@ -617,6 +668,12 @@ string_unicode_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other)
 static PyArray_DTypeMeta *
 datetime_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other)
 {
+    /*
+     * Timedelta/datetime shouldn't actually promote at all.  That they
+     * currently do means that we need additional hacks in the comparison
+     * type resolver.  For comparisons we have to make sure we reject it
+     * nicely in order to return an array of True/False values.
+     */
     if (cls->type_num == NPY_DATETIME && other->type_num == NPY_TIMEDELTA) {
         /*
          * TODO: We actually currently do allow promotion here. This is
@@ -671,12 +728,17 @@ object_common_dtype(
  * be a HeapType and its instances should be exact PyArray_Descr structs.
  *
  * @param descr The descriptor that should be wrapped.
- * @param name The name for the DType, if NULL the type character is used.
+ * @param name The name for the DType.
+ * @param alias A second name which is also set to the new class for builtins
+ *              (i.e. `np.types.LongDType` for `np.types.Int64DType`).
+ *              Some may have more aliases, as `intp` is not its own thing,
+ *              as of writing this, these are not added here.
  *
  * @returns 0 on success, -1 on failure.
  */
 NPY_NO_EXPORT int
-dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
+dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr,
+        const char *name, const char *alias)
 {
     int has_type_set = Py_TYPE(descr) == &PyArrayDescr_Type;
 
@@ -703,47 +765,14 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
         return -1;
     }
 
-    /*
-     * Note: we have no intention of freeing the memory again since this
-     * behaves identically to static type definition (see comment above).
-     * This is seems cleaner for the legacy API, in the new API both static
-     * and heap types are possible (some difficulty arises from the fact that
-     * these are instances of DTypeMeta and not type).
-     * In particular our own DTypes can be true static declarations.
-     * However, this function remains necessary for legacy user dtypes.
-     */
-
-    const char *scalar_name = descr->typeobj->tp_name;
-    /*
-     * We have to take only the name, and ignore the module to get
-     * a reasonable __name__, since static types are limited in this regard
-     * (this is not ideal, but not a big issue in practice).
-     * This is what Python does to print __name__ for static types.
-     */
-    const char *dot = strrchr(scalar_name, '.');
-    if (dot) {
-        scalar_name = dot + 1;
-    }
-    Py_ssize_t name_length = strlen(scalar_name) + 14;
-
-    char *tp_name = PyMem_Malloc(name_length);
-    if (tp_name == NULL) {
-        PyErr_NoMemory();
-        return -1;
-    }
-
-    snprintf(tp_name, name_length, "numpy.dtype[%s]", scalar_name);
-
     NPY_DType_Slots *dt_slots = PyMem_Malloc(sizeof(NPY_DType_Slots));
     if (dt_slots == NULL) {
-        PyMem_Free(tp_name);
         return -1;
     }
     memset(dt_slots, '\0', sizeof(NPY_DType_Slots));
 
     PyArray_DTypeMeta *dtype_class = PyMem_Malloc(sizeof(PyArray_DTypeMeta));
     if (dtype_class == NULL) {
-        PyMem_Free(tp_name);
         PyMem_Free(dt_slots);
         return -1;
     }
@@ -765,13 +794,19 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
             .tp_flags = Py_TPFLAGS_DEFAULT,
             .tp_base = &PyArrayDescr_Type,
             .tp_new = (newfunc)legacy_dtype_default_new,
+            .tp_doc = (
+                "DType class corresponding to the scalar type and dtype of "
+                "the same name.\n\n"
+                "Please see `numpy.dtype` for the typical way to create\n"
+                "dtype instances and :ref:`arrays.dtypes` for additional\n"
+                "information."),
         },},
         .flags = NPY_DT_LEGACY,
         /* Further fields are not common between DTypes */
     };
     memcpy(dtype_class, &prototype, sizeof(PyArray_DTypeMeta));
     /* Fix name of the Type*/
-    ((PyTypeObject *)dtype_class)->tp_name = tp_name;
+    ((PyTypeObject *)dtype_class)->tp_name = name;
     dtype_class->dt_slots = dt_slots;
 
     /* Let python finish the initialization (probably unnecessary) */
@@ -803,6 +838,7 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
     dt_slots->common_dtype = default_builtin_common_dtype;
     dt_slots->common_instance = NULL;
     dt_slots->ensure_canonical = ensure_native_byteorder;
+    dt_slots->get_fill_zero_loop = NULL;
 
     if (PyTypeNum_ISSIGNED(dtype_class->type_num)) {
         /* Convert our scalars (raise on too large unsigned and NaN, etc.) */
@@ -814,6 +850,8 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
     }
     else if (descr->type_num == NPY_OBJECT) {
         dt_slots->common_dtype = object_common_dtype;
+        dt_slots->get_fill_zero_loop = npy_object_get_fill_zero_loop;
+        dt_slots->get_clear_loop = npy_get_clear_object_strided_loop;
     }
     else if (PyTypeNum_ISDATETIME(descr->type_num)) {
         /* Datetimes are flexible, but were not considered previously */
@@ -835,6 +873,10 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
                     void_discover_descr_from_pyobject);
             dt_slots->common_instance = void_common_instance;
             dt_slots->ensure_canonical = void_ensure_canonical;
+            dt_slots->get_fill_zero_loop = 
+                    npy_get_zerofill_void_and_legacy_user_dtype_loop;
+            dt_slots->get_clear_loop =
+                    npy_get_clear_void_and_legacy_user_dtype_loop;
         }
         else {
             dt_slots->default_descr = string_and_unicode_default_descr;
@@ -843,9 +885,14 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
                     string_discover_descr_from_pyobject);
             dt_slots->common_dtype = string_unicode_common_dtype;
             dt_slots->common_instance = string_unicode_common_instance;
+            ((PyTypeObject*)dtype_class)->tp_new = (newfunc)string_unicode_new;
         }
     }
 
+    if (PyTypeNum_ISNUMBER(descr->type_num)) {
+        dtype_class->flags |= NPY_DT_NUMERIC;
+    }
+
     if (_PyArray_MapPyTypeToDType(dtype_class, descr->typeobj,
             PyTypeNum_ISUSERDEF(dtype_class->type_num)) < 0) {
         Py_DECREF(dtype_class);
@@ -855,6 +902,21 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
     /* Finally, replace the current class of the descr */
     Py_SET_TYPE(descr, (PyTypeObject *)dtype_class);
 
+    /* And it to the types submodule if it is a builtin dtype */
+    if (!PyTypeNum_ISUSERDEF(descr->type_num)) {
+        static PyObject *add_dtype_helper = NULL;
+        npy_cache_import("numpy.dtypes", "_add_dtype_helper", &add_dtype_helper);
+        if (add_dtype_helper == NULL) {
+            return -1;
+        }
+
+        if (PyObject_CallFunction(
+                add_dtype_helper,
+                "Os", (PyObject *)dtype_class, alias) == NULL) {
+            return -1;
+        }
+    }
+
     return 0;
 }
 
@@ -865,22 +927,35 @@ dtypemeta_get_abstract(PyArray_DTypeMeta *self) {
 }
 
 static PyObject *
+dtypemeta_get_legacy(PyArray_DTypeMeta *self) {
+    return PyBool_FromLong(NPY_DT_is_legacy(self));
+}
+
+static PyObject *
 dtypemeta_get_parametric(PyArray_DTypeMeta *self) {
     return PyBool_FromLong(NPY_DT_is_parametric(self));
 }
 
+static PyObject *
+dtypemeta_get_is_numeric(PyArray_DTypeMeta *self) {
+    return PyBool_FromLong(NPY_DT_is_numeric(self));
+}
+
 /*
  * Simple exposed information, defined for each DType (class).
  */
 static PyGetSetDef dtypemeta_getset[] = {
         {"_abstract", (getter)dtypemeta_get_abstract, NULL, NULL, NULL},
+        {"_legacy", (getter)dtypemeta_get_legacy, NULL, NULL, NULL},
         {"_parametric", (getter)dtypemeta_get_parametric, NULL, NULL, NULL},
+        {"_is_numeric", (getter)dtypemeta_get_is_numeric, NULL, NULL, NULL},
         {NULL, NULL, NULL, NULL, NULL}
 };
 
 static PyMemberDef dtypemeta_members[] = {
     {"type",
-        T_OBJECT, offsetof(PyArray_DTypeMeta, scalar_type), READONLY, NULL},
+        T_OBJECT, offsetof(PyArray_DTypeMeta, scalar_type), READONLY,
+        "scalar type corresponding to the DType."},
     {NULL, 0, 0, 0, NULL},
 };
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.h
index 15318f04004..32212228caa 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/dtypemeta.h
@@ -1,44 +1,18 @@
 #ifndef NUMPY_CORE_SRC_MULTIARRAY_DTYPEMETA_H_
 #define NUMPY_CORE_SRC_MULTIARRAY_DTYPEMETA_H_
 
+#include "array_method.h"
+#include "dtype_traversal.h"
+
 #ifdef __cplusplus
 extern "C" {
 #endif
 
-/* DType flags, currently private, since we may just expose functions */
-#define NPY_DT_LEGACY 1 << 0
-#define NPY_DT_ABSTRACT 1 << 1
-#define NPY_DT_PARAMETRIC 1 << 2
-
-
-typedef PyArray_Descr *(discover_descr_from_pyobject_function)(
-        PyArray_DTypeMeta *cls, PyObject *obj);
-
-/*
- * Before making this public, we should decide whether it should pass
- * the type, or allow looking at the object. A possible use-case:
- * `np.array(np.array([0]), dtype=np.ndarray)`
- * Could consider arrays that are not `dtype=ndarray` "scalars".
- */
-typedef int (is_known_scalar_type_function)(
-        PyArray_DTypeMeta *cls, PyTypeObject *obj);
-
-typedef PyArray_Descr *(default_descr_function)(PyArray_DTypeMeta *cls);
-typedef PyArray_DTypeMeta *(common_dtype_function)(
-        PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtype2);
-typedef PyArray_Descr *(common_instance_function)(
-        PyArray_Descr *dtype1, PyArray_Descr *dtype2);
-typedef PyArray_Descr *(ensure_canonical_function)(PyArray_Descr *dtype);
+#include "numpy/_dtype_api.h"
 
-/*
- * TODO: These two functions are currently only used for experimental DType
- *       API support.  Their relation should be "reversed": NumPy should
- *       always use them internally.
- *       There are open points about "casting safety" though, e.g. setting
- *       elements is currently always unsafe.
- */
-typedef int(setitemfunction)(PyArray_Descr *, PyObject *, char *);
-typedef PyObject *(getitemfunction)(PyArray_Descr *, char *);
+/* DType flags, currently private, since we may just expose functions 
+   Other publicly visible flags are in _dtype_api.h                   */
+#define NPY_DT_LEGACY 1 << 0
 
 
 typedef struct {
@@ -55,10 +29,40 @@ typedef struct {
     setitemfunction *setitem;
     getitemfunction *getitem;
     /*
+     * Either NULL or fetches a clearing function.  Clearing means deallocating
+     * any referenced data and setting it to a safe state.  For Python objects
+     * this means using `Py_CLEAR` which is equivalent to `Py_DECREF` and
+     * setting the `PyObject *` to NULL.
+     * After the clear, the data must be fillable via cast/copy and calling
+     * clear a second time must be safe.
+     * If the DType class does not implement `get_clear_loop` setting
+     * NPY_ITEM_REFCOUNT on its dtype instances is invalid.  Note that it is
+     * acceptable for  NPY_ITEM_REFCOUNT to inidicate references that are not
+     * Python objects.
+     */
+    get_traverse_loop_function *get_clear_loop;
+    /*
+       Either NULL or a function that sets a function pointer to a traversal
+       loop that fills an array with zero values appropriate for the dtype. If
+       get_fill_zero_loop is undefined or the function pointer set by it is
+       NULL, the array buffer is allocated with calloc. If this function is
+       defined and it sets a non-NULL function pointer, the array buffer is
+       allocated with malloc and the zero-filling loop function pointer is
+       called to fill the buffer. For the best performance, avoid using this
+       function if a zero-filled array buffer allocated with calloc makes sense
+       for the dtype.
+
+       Note that this is currently used only for zero-filling a newly allocated
+       array. While it can be used to zero-fill an already-filled buffer, that
+       will not work correctly for arrays holding references. If you need to do
+       that, clear the array first.
+    */
+    get_traverse_loop_function *get_fill_zero_loop;
+    /*
      * The casting implementation (ArrayMethod) to convert between two
      * instances of this DType, stored explicitly for fast access:
      */
-    PyObject *within_dtype_castingimpl;
+    PyArrayMethodObject *within_dtype_castingimpl;
     /*
      * Dictionary of ArrayMethods representing most possible casts
      * (structured and object are exceptions).
@@ -74,6 +78,13 @@ typedef struct {
     PyArray_ArrFuncs f;
 } NPY_DType_Slots;
 
+// This must be updated if new slots before within_dtype_castingimpl
+// are added
+#define NPY_NUM_DTYPE_SLOTS 10
+#define NPY_NUM_DTYPE_PYARRAY_ARRFUNCS_SLOTS 22
+#define NPY_DT_MAX_ARRFUNCS_SLOT \
+  NPY_NUM_DTYPE_PYARRAY_ARRFUNCS_SLOTS + _NPY_DT_ARRFUNCS_OFFSET
+
 
 #define NPY_DTYPE(descr) ((PyArray_DTypeMeta *)Py_TYPE(descr))
 #define NPY_DT_SLOTS(dtype) ((NPY_DType_Slots *)(dtype)->dt_slots)
@@ -81,6 +92,8 @@ typedef struct {
 #define NPY_DT_is_legacy(dtype) (((dtype)->flags & NPY_DT_LEGACY) != 0)
 #define NPY_DT_is_abstract(dtype) (((dtype)->flags & NPY_DT_ABSTRACT) != 0)
 #define NPY_DT_is_parametric(dtype) (((dtype)->flags & NPY_DT_PARAMETRIC) != 0)
+#define NPY_DT_is_numeric(dtype) (((dtype)->flags & NPY_DT_NUMERIC) != 0)
+#define NPY_DT_is_user_defined(dtype) (((dtype)->type_num == -1))
 
 /*
  * Macros for convenient classmethod calls, since these require
@@ -111,7 +124,7 @@ typedef struct {
  * (Error checking is not required for DescrFromType, assuming that the
  * type is valid.)
  */
-static NPY_INLINE PyArray_DTypeMeta *
+static inline PyArray_DTypeMeta *
 PyArray_DTypeFromTypeNum(int typenum)
 {
     PyArray_Descr *descr = PyArray_DescrFromType(typenum);
@@ -127,7 +140,8 @@ python_builtins_are_known_scalar_types(
         PyArray_DTypeMeta *cls, PyTypeObject *pytype);
 
 NPY_NO_EXPORT int
-dtypemeta_wrap_legacy_descriptor(PyArray_Descr *dtypem);
+dtypemeta_wrap_legacy_descriptor(
+        PyArray_Descr *dtypem, const char *name, const char *alias);
 
 #ifdef __cplusplus
 }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c
index 0f91c08ebeb..a88344d885c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c
@@ -27,6 +27,7 @@
 #include <numpy/npy_common.h>
 #include <numpy/arrayobject.h>
 #include <npy_pycompat.h>
+#include <array_assign.h>   //PyArray_AssignRawScalar
 
 #include <ctype.h>
 
@@ -331,8 +332,7 @@ get_single_op_view(PyArrayObject *op, char *labels,
                 Py_TYPE(op), PyArray_DESCR(op),
                 ndim_output, new_dims, new_strides, PyArray_DATA(op),
                 PyArray_ISWRITEABLE(op) ? NPY_ARRAY_WRITEABLE : 0,
-                (PyObject *)op, (PyObject *)op,
-                0, 0);
+                (PyObject *)op, (PyObject *)op, 0);
 
         if (*ret == NULL) {
             return -1;
@@ -542,10 +542,17 @@ unbuffered_loop_nop1_ndim2(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    }
     for (coord = shape[1]; coord > 0; --coord) {
         sop(1, ptrs[0], strides[0], shape[0]);
 
+        if (needs_api && PyErr_Occurred()){
+            return -1;
+        }
+
         ptr = ptrs[1][0] + strides[1][0];
         ptrs[0][0] = ptrs[1][0] = ptr;
         ptr = ptrs[1][1] + strides[1][1];
@@ -596,11 +603,18 @@ unbuffered_loop_nop1_ndim3(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    }
     for (coords[1] = shape[2]; coords[1] > 0; --coords[1]) {
         for (coords[0] = shape[1]; coords[0] > 0; --coords[0]) {
             sop(1, ptrs[0], strides[0], shape[0]);
 
+            if (needs_api && PyErr_Occurred()){
+                return -1;
+            }
+
             ptr = ptrs[1][0] + strides[1][0];
             ptrs[0][0] = ptrs[1][0] = ptr;
             ptr = ptrs[1][1] + strides[1][1];
@@ -653,10 +667,17 @@ unbuffered_loop_nop2_ndim2(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    }
     for (coord = shape[1]; coord > 0; --coord) {
         sop(2, ptrs[0], strides[0], shape[0]);
 
+        if(needs_api && PyErr_Occurred()){
+            return -1;
+        }
+
         ptr = ptrs[1][0] + strides[1][0];
         ptrs[0][0] = ptrs[1][0] = ptr;
         ptr = ptrs[1][1] + strides[1][1];
@@ -709,11 +730,18 @@ unbuffered_loop_nop2_ndim3(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    }
     for (coords[1] = shape[2]; coords[1] > 0; --coords[1]) {
         for (coords[0] = shape[1]; coords[0] > 0; --coords[0]) {
             sop(2, ptrs[0], strides[0], shape[0]);
 
+            if(needs_api && PyErr_Occurred()){
+                return -1;
+            }
+
             ptr = ptrs[1][0] + strides[1][0];
             ptrs[0][0] = ptrs[1][0] = ptr;
             ptr = ptrs[1][1] + strides[1][1];
@@ -796,7 +824,7 @@ PyArray_EinsteinSum(char *subscripts, npy_intp nop,
     int *op_axes[NPY_MAXARGS];
     npy_uint32 iter_flags, op_flags[NPY_MAXARGS];
 
-    NpyIter *iter;
+    NpyIter *iter = NULL;
     sum_of_products_fn sop;
     npy_intp fixed_strides[NPY_MAXARGS];
 
@@ -1035,7 +1063,7 @@ PyArray_EinsteinSum(char *subscripts, npy_intp nop,
         goto fail;
     }
 
-    /* Initialize the output to all zeros */
+    /* Initialize the output to all zeros or None*/
     ret = NpyIter_GetOperandArray(iter)[nop];
     if (PyArray_AssignZero(ret, NULL) < 0) {
         goto fail;
@@ -1115,7 +1143,6 @@ PyArray_EinsteinSum(char *subscripts, npy_intp nop,
 
         iternext = NpyIter_GetIterNext(iter, NULL);
         if (iternext == NULL) {
-            NpyIter_Deallocate(iter);
             goto fail;
         }
         dataptr = NpyIter_GetDataPtrArray(iter);
@@ -1150,6 +1177,7 @@ finish:
     return ret;
 
 fail:
+    NpyIter_Deallocate(iter);
     for (iop = 0; iop < nop; ++iop) {
         Py_XDECREF(op[iop]);
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c.src
index cd1a5898269..ddcf6f7bd74 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum.c.src
@@ -17,6 +17,7 @@
 #include <numpy/npy_common.h>
 #include <numpy/arrayobject.h>
 #include <npy_pycompat.h>
+#include <array_assign.h>   //PyArray_AssignRawScalar
 
 #include <ctype.h>
 
@@ -321,8 +322,7 @@ get_single_op_view(PyArrayObject *op, char *labels,
                 Py_TYPE(op), PyArray_DESCR(op),
                 ndim_output, new_dims, new_strides, PyArray_DATA(op),
                 PyArray_ISWRITEABLE(op) ? NPY_ARRAY_WRITEABLE : 0,
-                (PyObject *)op, (PyObject *)op,
-                0, 0);
+                (PyObject *)op, (PyObject *)op, 0);
 
         if (*ret == NULL) {
             return -1;
@@ -532,10 +532,17 @@ unbuffered_loop_nop1_ndim2(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    }
     for (coord = shape[1]; coord > 0; --coord) {
         sop(1, ptrs[0], strides[0], shape[0]);
 
+        if (needs_api && PyErr_Occurred()){
+            return -1;
+        }
+
         ptr = ptrs[1][0] + strides[1][0];
         ptrs[0][0] = ptrs[1][0] = ptr;
         ptr = ptrs[1][1] + strides[1][1];
@@ -586,11 +593,18 @@ unbuffered_loop_nop1_ndim3(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    }
     for (coords[1] = shape[2]; coords[1] > 0; --coords[1]) {
         for (coords[0] = shape[1]; coords[0] > 0; --coords[0]) {
             sop(1, ptrs[0], strides[0], shape[0]);
 
+            if (needs_api && PyErr_Occurred()){
+                return -1;
+            }
+
             ptr = ptrs[1][0] + strides[1][0];
             ptrs[0][0] = ptrs[1][0] = ptr;
             ptr = ptrs[1][1] + strides[1][1];
@@ -643,10 +657,17 @@ unbuffered_loop_nop2_ndim2(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[1] * shape[0]);
+    }
     for (coord = shape[1]; coord > 0; --coord) {
         sop(2, ptrs[0], strides[0], shape[0]);
 
+        if(needs_api && PyErr_Occurred()){
+            return -1;
+        }
+
         ptr = ptrs[1][0] + strides[1][0];
         ptrs[0][0] = ptrs[1][0] = ptr;
         ptr = ptrs[1][1] + strides[1][1];
@@ -699,11 +720,18 @@ unbuffered_loop_nop2_ndim3(NpyIter *iter)
      * Since the iterator wasn't tracking coordinates, the
      * loop provided by the iterator is in Fortran-order.
      */
-    NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    int needs_api = NpyIter_IterationNeedsAPI(iter);
+    if (!needs_api) {
+        NPY_BEGIN_THREADS_THRESHOLDED(shape[2] * shape[1] * shape[0]);
+    }
     for (coords[1] = shape[2]; coords[1] > 0; --coords[1]) {
         for (coords[0] = shape[1]; coords[0] > 0; --coords[0]) {
             sop(2, ptrs[0], strides[0], shape[0]);
 
+            if(needs_api && PyErr_Occurred()){
+                return -1;
+            }
+
             ptr = ptrs[1][0] + strides[1][0];
             ptrs[0][0] = ptrs[1][0] = ptr;
             ptr = ptrs[1][1] + strides[1][1];
@@ -786,7 +814,7 @@ PyArray_EinsteinSum(char *subscripts, npy_intp nop,
     int *op_axes[NPY_MAXARGS];
     npy_uint32 iter_flags, op_flags[NPY_MAXARGS];
 
-    NpyIter *iter;
+    NpyIter *iter = NULL;
     sum_of_products_fn sop;
     npy_intp fixed_strides[NPY_MAXARGS];
 
@@ -1025,7 +1053,7 @@ PyArray_EinsteinSum(char *subscripts, npy_intp nop,
         goto fail;
     }
 
-    /* Initialize the output to all zeros */
+    /* Initialize the output to all zeros or None*/
     ret = NpyIter_GetOperandArray(iter)[nop];
     if (PyArray_AssignZero(ret, NULL) < 0) {
         goto fail;
@@ -1105,7 +1133,6 @@ PyArray_EinsteinSum(char *subscripts, npy_intp nop,
 
         iternext = NpyIter_GetIterNext(iter, NULL);
         if (iternext == NULL) {
-            NpyIter_Deallocate(iter);
             goto fail;
         }
         dataptr = NpyIter_GetDataPtrArray(iter);
@@ -1140,6 +1167,7 @@ finish:
     return ret;
 
 fail:
+    NpyIter_Deallocate(iter);
     for (iop = 0; iop < nop; ++iop) {
         Py_XDECREF(op[iop]);
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c
index b915e36d592..a3a1a0c669b 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c
@@ -82813,171 +82813,604 @@ bool_sum_of_products_outstride0_any(int nop, char **dataptr,
 
 
 
+#line 1044
+static void
+object_sum_of_products_any(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_one(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_two(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_three(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_contig_any(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_contig_one(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_contig_two(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_contig_three(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_outstride0_any(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_outstride0_one(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_outstride0_two(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+#line 1044
+static void
+object_sum_of_products_outstride0_three(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+
+
 /* These tables need to match up with the type enum */
 static sum_of_products_fn
 _contig_outstride0_unary_specialization_table[NPY_NTYPES] = {
-#line 1054
+#line 1105
 #if 0
     &bool_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &byte_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &ubyte_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &short_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &ushort_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &int_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &uint_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &long_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &ulong_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &longlong_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &ulonglong_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &float_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &double_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &longdouble_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &cfloat_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &cdouble_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &clongdouble_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 0
     &object_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 0
     &string_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 0
     &unicode_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 0
     &void_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 0
     &datetime_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 0
     &timedelta_sum_of_products_contig_outstride0_one,
 #else
     NULL,
 #endif
 
-#line 1054
+#line 1105
 #if 1
     &half_sum_of_products_contig_outstride0_one,
 #else
@@ -82987,7 +83420,7 @@ _contig_outstride0_unary_specialization_table[NPY_NTYPES] = {
 }; /* End of _contig_outstride0_unary_specialization_table */
 
 static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
-#line 1085
+#line 1136
 #if 0
 {
     &bool_sum_of_products_stride0_contig_outstride0_two,
@@ -83000,7 +83433,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &byte_sum_of_products_stride0_contig_outstride0_two,
@@ -83013,7 +83446,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &ubyte_sum_of_products_stride0_contig_outstride0_two,
@@ -83026,7 +83459,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &short_sum_of_products_stride0_contig_outstride0_two,
@@ -83039,7 +83472,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &ushort_sum_of_products_stride0_contig_outstride0_two,
@@ -83052,7 +83485,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &int_sum_of_products_stride0_contig_outstride0_two,
@@ -83065,7 +83498,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &uint_sum_of_products_stride0_contig_outstride0_two,
@@ -83078,7 +83511,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &long_sum_of_products_stride0_contig_outstride0_two,
@@ -83091,7 +83524,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &ulong_sum_of_products_stride0_contig_outstride0_two,
@@ -83104,7 +83537,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &longlong_sum_of_products_stride0_contig_outstride0_two,
@@ -83117,7 +83550,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &ulonglong_sum_of_products_stride0_contig_outstride0_two,
@@ -83130,7 +83563,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &float_sum_of_products_stride0_contig_outstride0_two,
@@ -83143,7 +83576,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &double_sum_of_products_stride0_contig_outstride0_two,
@@ -83156,7 +83589,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &longdouble_sum_of_products_stride0_contig_outstride0_two,
@@ -83169,7 +83602,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &cfloat_sum_of_products_stride0_contig_outstride0_two,
@@ -83182,7 +83615,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &cdouble_sum_of_products_stride0_contig_outstride0_two,
@@ -83195,7 +83628,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &clongdouble_sum_of_products_stride0_contig_outstride0_two,
@@ -83208,7 +83641,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &object_sum_of_products_stride0_contig_outstride0_two,
@@ -83221,7 +83654,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &string_sum_of_products_stride0_contig_outstride0_two,
@@ -83234,7 +83667,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &unicode_sum_of_products_stride0_contig_outstride0_two,
@@ -83247,7 +83680,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &void_sum_of_products_stride0_contig_outstride0_two,
@@ -83260,7 +83693,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &datetime_sum_of_products_stride0_contig_outstride0_two,
@@ -83273,7 +83706,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 0
 {
     &timedelta_sum_of_products_stride0_contig_outstride0_two,
@@ -83286,7 +83719,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
     {NULL, NULL, NULL, NULL, NULL},
 #endif
 
-#line 1085
+#line 1136
 #if 1
 {
     &half_sum_of_products_stride0_contig_outstride0_two,
@@ -83302,7 +83735,7 @@ static sum_of_products_fn _binary_specialization_table[NPY_NTYPES][5] = {
 }; /* End of _binary_specialization_table */
 
 static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
-#line 1122
+#line 1173
 #if 1
 {
     &bool_sum_of_products_outstride0_any,
@@ -83314,7 +83747,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &byte_sum_of_products_outstride0_any,
@@ -83326,7 +83759,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &ubyte_sum_of_products_outstride0_any,
@@ -83338,7 +83771,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &short_sum_of_products_outstride0_any,
@@ -83350,7 +83783,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &ushort_sum_of_products_outstride0_any,
@@ -83362,7 +83795,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &int_sum_of_products_outstride0_any,
@@ -83374,7 +83807,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &uint_sum_of_products_outstride0_any,
@@ -83386,7 +83819,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &long_sum_of_products_outstride0_any,
@@ -83398,7 +83831,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &ulong_sum_of_products_outstride0_any,
@@ -83410,7 +83843,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &longlong_sum_of_products_outstride0_any,
@@ -83422,7 +83855,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &ulonglong_sum_of_products_outstride0_any,
@@ -83434,7 +83867,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &float_sum_of_products_outstride0_any,
@@ -83446,7 +83879,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &double_sum_of_products_outstride0_any,
@@ -83458,7 +83891,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &longdouble_sum_of_products_outstride0_any,
@@ -83470,7 +83903,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &cfloat_sum_of_products_outstride0_any,
@@ -83482,7 +83915,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &cdouble_sum_of_products_outstride0_any,
@@ -83494,7 +83927,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &clongdouble_sum_of_products_outstride0_any,
@@ -83506,8 +83939,8 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
-#if 0
+#line 1173
+#if 1
 {
     &object_sum_of_products_outstride0_any,
     &object_sum_of_products_outstride0_one,
@@ -83518,7 +83951,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 0
 {
     &string_sum_of_products_outstride0_any,
@@ -83530,7 +83963,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 0
 {
     &unicode_sum_of_products_outstride0_any,
@@ -83542,7 +83975,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 0
 {
     &void_sum_of_products_outstride0_any,
@@ -83554,7 +83987,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 0
 {
     &datetime_sum_of_products_outstride0_any,
@@ -83566,7 +83999,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 0
 {
     &timedelta_sum_of_products_outstride0_any,
@@ -83578,7 +84011,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1122
+#line 1173
 #if 1
 {
     &half_sum_of_products_outstride0_any,
@@ -83593,7 +84026,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
 }; /* End of _outstride0_specialized_table */
 
 static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
-#line 1158
+#line 1209
 #if 1
 {
     &bool_sum_of_products_contig_any,
@@ -83605,7 +84038,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &byte_sum_of_products_contig_any,
@@ -83617,7 +84050,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &ubyte_sum_of_products_contig_any,
@@ -83629,7 +84062,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &short_sum_of_products_contig_any,
@@ -83641,7 +84074,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &ushort_sum_of_products_contig_any,
@@ -83653,7 +84086,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &int_sum_of_products_contig_any,
@@ -83665,7 +84098,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &uint_sum_of_products_contig_any,
@@ -83677,7 +84110,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &long_sum_of_products_contig_any,
@@ -83689,7 +84122,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &ulong_sum_of_products_contig_any,
@@ -83701,7 +84134,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &longlong_sum_of_products_contig_any,
@@ -83713,7 +84146,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &ulonglong_sum_of_products_contig_any,
@@ -83725,7 +84158,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &float_sum_of_products_contig_any,
@@ -83737,7 +84170,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &double_sum_of_products_contig_any,
@@ -83749,7 +84182,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &longdouble_sum_of_products_contig_any,
@@ -83761,7 +84194,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &cfloat_sum_of_products_contig_any,
@@ -83773,7 +84206,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &cdouble_sum_of_products_contig_any,
@@ -83785,7 +84218,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &clongdouble_sum_of_products_contig_any,
@@ -83797,8 +84230,8 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
-#if 0
+#line 1209
+#if 1
 {
     &object_sum_of_products_contig_any,
     &object_sum_of_products_contig_one,
@@ -83809,7 +84242,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 0
 {
     &string_sum_of_products_contig_any,
@@ -83821,7 +84254,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 0
 {
     &unicode_sum_of_products_contig_any,
@@ -83833,7 +84266,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 0
 {
     &void_sum_of_products_contig_any,
@@ -83845,7 +84278,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 0
 {
     &datetime_sum_of_products_contig_any,
@@ -83857,7 +84290,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 0
 {
     &timedelta_sum_of_products_contig_any,
@@ -83869,7 +84302,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1158
+#line 1209
 #if 1
 {
     &half_sum_of_products_contig_any,
@@ -83884,7 +84317,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
 }; /* End of _allcontig_specialized_table */
 
 static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
-#line 1194
+#line 1245
 #if 1
 {
     &bool_sum_of_products_any,
@@ -83896,7 +84329,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &byte_sum_of_products_any,
@@ -83908,7 +84341,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &ubyte_sum_of_products_any,
@@ -83920,7 +84353,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &short_sum_of_products_any,
@@ -83932,7 +84365,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &ushort_sum_of_products_any,
@@ -83944,7 +84377,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &int_sum_of_products_any,
@@ -83956,7 +84389,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &uint_sum_of_products_any,
@@ -83968,7 +84401,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &long_sum_of_products_any,
@@ -83980,7 +84413,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &ulong_sum_of_products_any,
@@ -83992,7 +84425,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &longlong_sum_of_products_any,
@@ -84004,7 +84437,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &ulonglong_sum_of_products_any,
@@ -84016,7 +84449,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &float_sum_of_products_any,
@@ -84028,7 +84461,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &double_sum_of_products_any,
@@ -84040,7 +84473,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &longdouble_sum_of_products_any,
@@ -84052,7 +84485,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &cfloat_sum_of_products_any,
@@ -84064,7 +84497,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &cdouble_sum_of_products_any,
@@ -84076,7 +84509,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &clongdouble_sum_of_products_any,
@@ -84088,8 +84521,8 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
-#if 0
+#line 1245
+#if 1
 {
     &object_sum_of_products_any,
     &object_sum_of_products_one,
@@ -84100,7 +84533,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 0
 {
     &string_sum_of_products_any,
@@ -84112,7 +84545,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 0
 {
     &unicode_sum_of_products_any,
@@ -84124,7 +84557,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 0
 {
     &void_sum_of_products_any,
@@ -84136,7 +84569,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 0
 {
     &datetime_sum_of_products_any,
@@ -84148,7 +84581,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 0
 {
     &timedelta_sum_of_products_any,
@@ -84160,7 +84593,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
     {NULL, NULL, NULL, NULL},
 #endif
 
-#line 1194
+#line 1245
 #if 1
 {
     &half_sum_of_products_any,
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c.src
index e7b2f2c2c0c..5f2ccf2dc0e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/einsum_sumprod.c.src
@@ -1026,6 +1026,57 @@ bool_sum_of_products_outstride0_@noplabel@(int nop, char **dataptr,
 
 /**end repeat**/
 
+/**begin repeat
+ * #fn_name = 
+ *  object_sum_of_products_any,
+ *  object_sum_of_products_one,
+ *  object_sum_of_products_two,
+ *  object_sum_of_products_three,
+ *  object_sum_of_products_contig_any,
+ *  object_sum_of_products_contig_one,
+ *  object_sum_of_products_contig_two,
+ *  object_sum_of_products_contig_three,
+ *  object_sum_of_products_outstride0_any,
+ *  object_sum_of_products_outstride0_one,
+ *  object_sum_of_products_outstride0_two,
+ *  object_sum_of_products_outstride0_three#
+ */
+static void
+@fn_name@(int nop, char **dataptr,
+                                npy_intp const *strides, npy_intp count)
+{
+    while(count--){
+        PyObject *prod = *(PyObject **)dataptr[0];
+        if (!prod) {
+            prod = Py_None;  // convention is to treat nulls as None
+        }
+        Py_INCREF(prod);
+        for (int i = 1; i < nop; ++i){
+            PyObject *curr = *(PyObject **)dataptr[i];
+            if (!curr) {
+                curr = Py_None;  // convention is to treat nulls as None
+            }
+            Py_SETREF(prod, PyNumber_Multiply(prod, curr));
+            if (!prod) {
+                return;
+            }
+        }
+
+        PyObject *sum = PyNumber_Add(*(PyObject **)dataptr[nop], prod);
+        Py_DECREF(prod);
+        if (!sum) {
+            return;
+        }
+        
+        Py_XDECREF(*(PyObject **)dataptr[nop]);
+        *(PyObject **)dataptr[nop] = sum;
+        for (int i = 0; i <= nop; ++i) {
+            dataptr[i] += strides[i];
+        }
+    }
+}
+/**end repeat**/
+
 /* These tables need to match up with the type enum */
 static sum_of_products_fn
 _contig_outstride0_unary_specialization_table[NPY_NTYPES] = {
@@ -1116,7 +1167,7 @@ static sum_of_products_fn _outstride0_specialized_table[NPY_NTYPES][4] = {
  *        1, 1,
  *        1, 1, 1,
  *        1, 1, 1,
- *        0, 0, 0, 0,
+ *        1, 0, 0, 0,
  *        0, 0, 1#
  */
 #if @use@
@@ -1152,7 +1203,7 @@ static sum_of_products_fn _allcontig_specialized_table[NPY_NTYPES][4] = {
  *        1, 1,
  *        1, 1, 1,
  *        1, 1, 1,
- *        0, 0, 0, 0,
+ *        1, 0, 0, 0,
  *        0, 0, 1#
  */
 #if @use@
@@ -1188,7 +1239,7 @@ static sum_of_products_fn _unspecialized_table[NPY_NTYPES][4] = {
  *        1, 1,
  *        1, 1, 1,
  *        1, 1, 1,
- *        0, 0, 0, 0,
+ *        1, 0, 0, 0,
  *        0, 0, 1#
  */
 #if @use@
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/experimental_public_dtype_api.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/experimental_public_dtype_api.c
index 84507b4812b..a712ea5c892 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/experimental_public_dtype_api.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/experimental_public_dtype_api.c
@@ -16,19 +16,6 @@
 #include "common_dtype.h"
 
 
-#define EXPERIMENTAL_DTYPE_API_VERSION 5
-
-
-typedef struct{
-    PyTypeObject *typeobj;    /* type of python scalar or NULL */
-    int flags;                /* flags, including parametric and abstract */
-    /* NULL terminated cast definitions. Use NULL for the newly created DType */
-    PyArrayMethod_Spec **casts;
-    PyType_Slot *slots;
-} PyArrayDTypeMeta_Spec;
-
-
-
 static PyArray_DTypeMeta *
 dtype_does_not_promote(
         PyArray_DTypeMeta *NPY_UNUSED(self), PyArray_DTypeMeta *NPY_UNUSED(other))
@@ -116,10 +103,6 @@ PyArray_ArrFuncs default_funcs = {
 };
 
 
-/* other slots are in order, so keep only last around: */
-#define NUM_DTYPE_SLOTS 8
-
-
 int
 PyArrayInitDTypeMeta_FromSpec(
         PyArray_DTypeMeta *DType, PyArrayDTypeMeta_Spec *spec)
@@ -155,10 +138,12 @@ PyArrayInitDTypeMeta_FromSpec(
     }
 
     /* Check and handle flags: */
-    if (spec->flags & ~(NPY_DT_PARAMETRIC|NPY_DT_ABSTRACT)) {
+    int allowed_flags = NPY_DT_PARAMETRIC | NPY_DT_ABSTRACT | NPY_DT_NUMERIC;
+    if (spec->flags & ~(allowed_flags)) {
         PyErr_SetString(PyExc_RuntimeError,
-                "invalid DType flags specified, only parametric and abstract "
-                "are valid flags for user DTypes.");
+                "invalid DType flags specified, only NPY_DT_PARAMETRIC, "
+                "NPY_DT_ABSTRACT, and NPY_DT_NUMERIC are valid flags for "
+                "user DTypes.");
         return -1;
     }
 
@@ -178,6 +163,8 @@ PyArrayInitDTypeMeta_FromSpec(
     NPY_DT_SLOTS(DType)->common_instance = NULL;
     NPY_DT_SLOTS(DType)->setitem = NULL;
     NPY_DT_SLOTS(DType)->getitem = NULL;
+    NPY_DT_SLOTS(DType)->get_clear_loop = NULL;
+    NPY_DT_SLOTS(DType)->f = default_funcs;
 
     PyType_Slot *spec_slot = spec->slots;
     while (1) {
@@ -187,20 +174,100 @@ PyArrayInitDTypeMeta_FromSpec(
         if (slot == 0) {
             break;
         }
-        if (slot > NUM_DTYPE_SLOTS || slot < 0) {
+        if ((slot < 0) ||
+            ((slot > NPY_NUM_DTYPE_SLOTS) &&
+             (slot <= _NPY_DT_ARRFUNCS_OFFSET)) ||
+            (slot > NPY_DT_MAX_ARRFUNCS_SLOT)) {
             PyErr_Format(PyExc_RuntimeError,
                     "Invalid slot with value %d passed in.", slot);
             return -1;
         }
         /*
-         * It is up to the user to get this right, and slots are sorted
-         * exactly like they are stored right now:
+         * It is up to the user to get this right, the slots in the public API
+         * are sorted exactly like they are stored in the NPY_DT_Slots struct
+         * right now:
          */
-        void **current = (void **)(&(
-                NPY_DT_SLOTS(DType)->discover_descr_from_pyobject));
-        current += slot - 1;
-        *current = pfunc;
+        if (slot <= NPY_NUM_DTYPE_SLOTS) {
+            // slot > NPY_NUM_DTYPE_SLOTS are PyArray_ArrFuncs
+            void **current = (void **)(&(
+                    NPY_DT_SLOTS(DType)->discover_descr_from_pyobject));
+            current += slot - 1;
+            *current = pfunc;
+        }
+        else {
+            int f_slot = slot - _NPY_DT_ARRFUNCS_OFFSET;
+            if (1 <= f_slot && f_slot <= NPY_NUM_DTYPE_PYARRAY_ARRFUNCS_SLOTS) {
+                switch (f_slot) {
+                    case 1:
+                        NPY_DT_SLOTS(DType)->f.getitem = pfunc;
+                        break;
+                    case 2:
+                        NPY_DT_SLOTS(DType)->f.setitem = pfunc;
+                        break;
+                    case 3:
+                        NPY_DT_SLOTS(DType)->f.copyswapn = pfunc;
+                        break;
+                    case 4:
+                        NPY_DT_SLOTS(DType)->f.copyswap = pfunc;
+                        break;
+                    case 5:
+                        NPY_DT_SLOTS(DType)->f.compare = pfunc;
+                        break;
+                    case 6:
+                        NPY_DT_SLOTS(DType)->f.argmax = pfunc;
+                        break;
+                    case 7:
+                        NPY_DT_SLOTS(DType)->f.dotfunc = pfunc;
+                        break;
+                    case 8:
+                        NPY_DT_SLOTS(DType)->f.scanfunc = pfunc;
+                        break;
+                    case 9:
+                        NPY_DT_SLOTS(DType)->f.fromstr = pfunc;
+                        break;
+                    case 10:
+                        NPY_DT_SLOTS(DType)->f.nonzero = pfunc;
+                        break;
+                    case 11:
+                        NPY_DT_SLOTS(DType)->f.fill = pfunc;
+                        break;
+                    case 12:
+                        NPY_DT_SLOTS(DType)->f.fillwithscalar = pfunc;
+                        break;
+                    case 13:
+                        *NPY_DT_SLOTS(DType)->f.sort = pfunc;
+                        break;
+                    case 14:
+                        *NPY_DT_SLOTS(DType)->f.argsort = pfunc;
+                        break;
+                    case 15:
+                    case 16:
+                    case 17:
+                    case 18:
+                    case 19:
+                    case 20:
+                    case 21:
+                        PyErr_Format(
+                            PyExc_RuntimeError,
+                            "PyArray_ArrFunc casting slot with value %d is disabled.",
+                            f_slot
+                        );
+                        return -1;
+                    case 22:
+                        NPY_DT_SLOTS(DType)->f.argmin = pfunc;
+                        break;
+                }
+            } else {
+                    PyErr_Format(
+                        PyExc_RuntimeError,
+                        "Invalid PyArray_ArrFunc slot with value %d passed in.",
+                        f_slot
+                    );
+                    return -1;
+            }
+        }
     }
+
     if (NPY_DT_SLOTS(DType)->setitem == NULL
             || NPY_DT_SLOTS(DType)->getitem == NULL) {
         PyErr_SetString(PyExc_RuntimeError,
@@ -229,7 +296,7 @@ PyArrayInitDTypeMeta_FromSpec(
             return -1;
         }
     }
-    NPY_DT_SLOTS(DType)->f = default_funcs;
+
     /* invalid type num. Ideally, we get away with it! */
     DType->type_num = -1;
 
@@ -374,7 +441,7 @@ _PyArray_GetDefaultDescr(PyArray_DTypeMeta *DType)
 NPY_NO_EXPORT PyObject *
 _get_experimental_dtype_api(PyObject *NPY_UNUSED(mod), PyObject *arg)
 {
-    static void *experimental_api_table[42] = {
+    static void *experimental_api_table[44] = {
             &PyUFunc_AddLoopFromSpec,
             &PyUFunc_AddPromoter,
             &PyArrayDTypeMeta_Type,
@@ -426,6 +493,9 @@ _get_experimental_dtype_api(PyObject *NPY_UNUSED(mod), PyObject *arg)
         /* Datetime/Timedelta */
         experimental_api_table[40] = PyArray_DTypeFromTypeNum(NPY_DATETIME);
         experimental_api_table[41] = PyArray_DTypeFromTypeNum(NPY_TIMEDELTA);
+        /* Object and Structured */
+        experimental_api_table[42] = PyArray_DTypeFromTypeNum(NPY_OBJECT);
+        experimental_api_table[43] = PyArray_DTypeFromTypeNum(NPY_VOID);
     }
 
     char *env = getenv("NUMPY_EXPERIMENTAL_DTYPE_API");
@@ -443,12 +513,12 @@ _get_experimental_dtype_api(PyObject *NPY_UNUSED(mod), PyObject *arg)
     if (error_converting(version)) {
         return NULL;
     }
-    if (version != EXPERIMENTAL_DTYPE_API_VERSION) {
+    if (version != __EXPERIMENTAL_DTYPE_API_VERSION) {
         PyErr_Format(PyExc_RuntimeError,
                 "Experimental DType API version %d requested, but NumPy "
                 "is exporting version %d.  Recompile your DType and/or upgrade "
                 "NumPy to match.",
-                version, EXPERIMENTAL_DTYPE_API_VERSION);
+                version, __EXPERIMENTAL_DTYPE_API_VERSION);
         return NULL;
     }
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/getset.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/getset.c
index ab35548ed72..7022eb231ce 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/getset.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/getset.c
@@ -797,20 +797,17 @@ array_imag_get(PyArrayObject *self, void *NPY_UNUSED(ignored))
     }
     else {
         Py_INCREF(PyArray_DESCR(self));
-        ret = (PyArrayObject *)PyArray_NewFromDescr(Py_TYPE(self),
-                                                    PyArray_DESCR(self),
-                                                    PyArray_NDIM(self),
-                                                    PyArray_DIMS(self),
-                                                    NULL, NULL,
-                                                    PyArray_ISFORTRAN(self),
-                                                    (PyObject *)self);
+        ret = (PyArrayObject *)PyArray_NewFromDescr_int(
+                Py_TYPE(self),
+                PyArray_DESCR(self),
+                PyArray_NDIM(self),
+                PyArray_DIMS(self),
+                NULL, NULL,
+                PyArray_ISFORTRAN(self),
+                (PyObject *)self, NULL, _NPY_ARRAY_ZEROED);
         if (ret == NULL) {
             return NULL;
         }
-        if (_zerofill(ret) < 0) {
-            Py_DECREF(ret);
-            return NULL;
-        }
         PyArray_CLEARFLAGS(ret, NPY_ARRAY_WRITEABLE);
     }
     return (PyObject *) ret;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/item_selection.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/item_selection.c
index edb2f687163..625c649335e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/item_selection.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/item_selection.c
@@ -17,10 +17,12 @@
 
 #include "multiarraymodule.h"
 #include "common.h"
+#include "dtype_transfer.h"
 #include "arrayobject.h"
 #include "ctors.h"
 #include "lowlevel_strided_loops.h"
 #include "array_assign.h"
+#include "refcount.h"
 
 #include "npy_sort.h"
 #include "npy_partition.h"
@@ -30,7 +32,7 @@
 #include "array_coercion.h"
 #include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
 
-static NPY_GCC_OPT_3 NPY_INLINE int
+static NPY_GCC_OPT_3 inline int
 npy_fasttake_impl(
         char *dest, char *src, const npy_intp *indices,
         npy_intp n, npy_intp m, npy_intp max_item,
@@ -39,7 +41,26 @@ npy_fasttake_impl(
         PyArray_Descr *dtype, int axis)
 {
     NPY_BEGIN_THREADS_DEF;
-    NPY_BEGIN_THREADS_DESCR(dtype);
+
+    NPY_cast_info cast_info;
+    NPY_ARRAYMETHOD_FLAGS flags;
+    NPY_cast_info_init(&cast_info);
+
+    if (!needs_refcounting) {
+        /* if "refcounting" is not needed memcpy is safe for a simple copy  */
+        NPY_BEGIN_THREADS;
+    }
+    else {
+        if (PyArray_GetDTypeTransferFunction(
+                1, itemsize, itemsize, dtype, dtype, 0,
+                &cast_info, &flags) < 0) {
+            return -1;
+        }
+        if (!(flags & NPY_METH_REQUIRES_PYAPI)) {
+            NPY_BEGIN_THREADS;
+        }
+    }
+
     switch (clipmode) {
         case NPY_RAISE:
             for (npy_intp i = 0; i < n; i++) {
@@ -47,22 +68,23 @@ npy_fasttake_impl(
                     npy_intp tmp = indices[j];
                     if (check_and_adjust_index(&tmp, max_item, axis,
                                                _save) < 0) {
-                        return -1;
+                        goto fail;
                     }
                     char *tmp_src = src + tmp * chunk;
                     if (needs_refcounting) {
-                        for (npy_intp k = 0; k < nelem; k++) {
-                            PyArray_Item_INCREF(tmp_src, dtype);
-                            PyArray_Item_XDECREF(dest, dtype);
-                            memmove(dest, tmp_src, itemsize);
-                            dest += itemsize;
-                            tmp_src += itemsize;
+                        char *data[2] = {tmp_src, dest};
+                        npy_intp strides[2] = {itemsize, itemsize};
+                        if (cast_info.func(
+                                &cast_info.context, data, &nelem, strides,
+                                cast_info.auxdata) < 0) {
+                            NPY_END_THREADS;
+                            goto fail;
                         }
                     }
                     else {
-                        memmove(dest, tmp_src, chunk);
-                        dest += chunk;
+                        memcpy(dest, tmp_src, chunk);
                     }
+                    dest += chunk;
                 }
                 src += chunk*max_item;
             }
@@ -83,18 +105,19 @@ npy_fasttake_impl(
                     }
                     char *tmp_src = src + tmp * chunk;
                     if (needs_refcounting) {
-                        for (npy_intp k = 0; k < nelem; k++) {
-                            PyArray_Item_INCREF(tmp_src, dtype);
-                            PyArray_Item_XDECREF(dest, dtype);
-                            memmove(dest, tmp_src, itemsize);
-                            dest += itemsize;
-                            tmp_src += itemsize;
+                        char *data[2] = {tmp_src, dest};
+                        npy_intp strides[2] = {itemsize, itemsize};
+                        if (cast_info.func(
+                                &cast_info.context, data, &nelem, strides,
+                                cast_info.auxdata) < 0) {
+                            NPY_END_THREADS;
+                            goto fail;
                         }
                     }
                     else {
-                        memmove(dest, tmp_src, chunk);
-                        dest += chunk;
+                        memcpy(dest, tmp_src, chunk);
                     }
+                    dest += chunk;
                 }
                 src += chunk*max_item;
             }
@@ -111,18 +134,19 @@ npy_fasttake_impl(
                     }
                     char *tmp_src = src + tmp * chunk;
                     if (needs_refcounting) {
-                        for (npy_intp k = 0; k < nelem; k++) {
-                            PyArray_Item_INCREF(tmp_src, dtype);
-                            PyArray_Item_XDECREF(dest, dtype);
-                            memmove(dest, tmp_src, itemsize);
-                            dest += itemsize;
-                            tmp_src += itemsize;
+                        char *data[2] = {tmp_src, dest};
+                        npy_intp strides[2] = {itemsize, itemsize};
+                        if (cast_info.func(
+                                &cast_info.context, data, &nelem, strides,
+                                cast_info.auxdata) < 0) {
+                            NPY_END_THREADS;
+                            goto fail;
                         }
                     }
                     else {
-                        memmove(dest, tmp_src, chunk);
-                        dest += chunk;
+                        memcpy(dest, tmp_src, chunk);
                     }
+                    dest += chunk;
                 }
                 src += chunk*max_item;
             }
@@ -130,7 +154,13 @@ npy_fasttake_impl(
     }
 
     NPY_END_THREADS;
+    NPY_cast_info_xfree(&cast_info);
     return 0;
+
+  fail:
+    /* NPY_END_THREADS already ensured. */
+    NPY_cast_info_xfree(&cast_info);
+    return -1;
 }
 
 
@@ -322,11 +352,17 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
               NPY_CLIPMODE clipmode)
 {
     PyArrayObject  *indices, *values;
-    npy_intp i, chunk, ni, max_item, nv, tmp;
+    npy_intp i, itemsize, ni, max_item, nv, tmp;
     char *src, *dest;
     int copied = 0;
     int overlap = 0;
 
+    NPY_BEGIN_THREADS_DEF;
+    NPY_cast_info cast_info;
+    NPY_ARRAYMETHOD_FLAGS flags;
+
+    NPY_cast_info_init(&cast_info);
+
     indices = NULL;
     values = NULL;
     if (!PyArray_Check(self)) {
@@ -372,25 +408,51 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
     }
     max_item = PyArray_SIZE(self);
     dest = PyArray_DATA(self);
-    chunk = PyArray_DESCR(self)->elsize;
+    itemsize = PyArray_DESCR(self)->elsize;
+
+    int has_references = PyDataType_REFCHK(PyArray_DESCR(self));
+
+    if (!has_references) {
+        /* if has_references is not needed memcpy is safe for a simple copy  */
+        NPY_BEGIN_THREADS_THRESHOLDED(ni);
+    }
+    else {
+        PyArray_Descr *dtype = PyArray_DESCR(self);
+        if (PyArray_GetDTypeTransferFunction(
+                PyArray_ISALIGNED(self), itemsize, itemsize, dtype, dtype, 0,
+                &cast_info, &flags) < 0) {
+            goto fail;
+        }
+        if (!(flags & NPY_METH_REQUIRES_PYAPI)) {
+            NPY_BEGIN_THREADS_THRESHOLDED(ni);
+        }
+    }
+
+
+    if (has_references) {
+        const npy_intp one = 1;
+        const npy_intp strides[2] = {itemsize, itemsize};
 
-    if (PyDataType_REFCHK(PyArray_DESCR(self))) {
         switch(clipmode) {
         case NPY_RAISE:
             for (i = 0; i < ni; i++) {
-                src = PyArray_BYTES(values) + chunk*(i % nv);
+                src = PyArray_BYTES(values) + itemsize*(i % nv);
                 tmp = ((npy_intp *)(PyArray_DATA(indices)))[i];
-                if (check_and_adjust_index(&tmp, max_item, 0, NULL) < 0) {
+                if (check_and_adjust_index(&tmp, max_item, 0, _save) < 0) {
+                    goto fail;
+                }
+                char *data[2] = {src, dest + tmp*itemsize};
+                if (cast_info.func(
+                        &cast_info.context, data, &one, strides,
+                        cast_info.auxdata) < 0) {
+                    NPY_END_THREADS;
                     goto fail;
                 }
-                PyArray_Item_INCREF(src, PyArray_DESCR(self));
-                PyArray_Item_XDECREF(dest+tmp*chunk, PyArray_DESCR(self));
-                memmove(dest + tmp*chunk, src, chunk);
             }
             break;
         case NPY_WRAP:
             for (i = 0; i < ni; i++) {
-                src = PyArray_BYTES(values) + chunk * (i % nv);
+                src = PyArray_BYTES(values) + itemsize * (i % nv);
                 tmp = ((npy_intp *)(PyArray_DATA(indices)))[i];
                 if (tmp < 0) {
                     while (tmp < 0) {
@@ -402,14 +464,18 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
                         tmp -= max_item;
                     }
                 }
-                PyArray_Item_INCREF(src, PyArray_DESCR(self));
-                PyArray_Item_XDECREF(dest+tmp*chunk, PyArray_DESCR(self));
-                memmove(dest + tmp * chunk, src, chunk);
+                char *data[2] = {src, dest + tmp*itemsize};
+                if (cast_info.func(
+                        &cast_info.context, data, &one, strides,
+                        cast_info.auxdata) < 0) {
+                    NPY_END_THREADS;
+                    goto fail;
+                }
             }
             break;
         case NPY_CLIP:
             for (i = 0; i < ni; i++) {
-                src = PyArray_BYTES(values) + chunk * (i % nv);
+                src = PyArray_BYTES(values) + itemsize * (i % nv);
                 tmp = ((npy_intp *)(PyArray_DATA(indices)))[i];
                 if (tmp < 0) {
                     tmp = 0;
@@ -417,30 +483,32 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
                 else if (tmp >= max_item) {
                     tmp = max_item - 1;
                 }
-                PyArray_Item_INCREF(src, PyArray_DESCR(self));
-                PyArray_Item_XDECREF(dest+tmp*chunk, PyArray_DESCR(self));
-                memmove(dest + tmp * chunk, src, chunk);
+                char *data[2] = {src, dest + tmp*itemsize};
+                if (cast_info.func(
+                        &cast_info.context, data, &one, strides,
+                        cast_info.auxdata) < 0) {
+                    NPY_END_THREADS;
+                    goto fail;
+                }
             }
             break;
         }
     }
     else {
-        NPY_BEGIN_THREADS_DEF;
-        NPY_BEGIN_THREADS_THRESHOLDED(ni);
         switch(clipmode) {
         case NPY_RAISE:
             for (i = 0; i < ni; i++) {
-                src = PyArray_BYTES(values) + chunk * (i % nv);
+                src = PyArray_BYTES(values) + itemsize * (i % nv);
                 tmp = ((npy_intp *)(PyArray_DATA(indices)))[i];
                 if (check_and_adjust_index(&tmp, max_item, 0, _save) < 0) {
                     goto fail;
                 }
-                memmove(dest + tmp * chunk, src, chunk);
+                memmove(dest + tmp * itemsize, src, itemsize);
             }
             break;
         case NPY_WRAP:
             for (i = 0; i < ni; i++) {
-                src = PyArray_BYTES(values) + chunk * (i % nv);
+                src = PyArray_BYTES(values) + itemsize * (i % nv);
                 tmp = ((npy_intp *)(PyArray_DATA(indices)))[i];
                 if (tmp < 0) {
                     while (tmp < 0) {
@@ -452,12 +520,12 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
                         tmp -= max_item;
                     }
                 }
-                memmove(dest + tmp * chunk, src, chunk);
+                memmove(dest + tmp * itemsize, src, itemsize);
             }
             break;
         case NPY_CLIP:
             for (i = 0; i < ni; i++) {
-                src = PyArray_BYTES(values) + chunk * (i % nv);
+                src = PyArray_BYTES(values) + itemsize * (i % nv);
                 tmp = ((npy_intp *)(PyArray_DATA(indices)))[i];
                 if (tmp < 0) {
                     tmp = 0;
@@ -465,14 +533,16 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
                 else if (tmp >= max_item) {
                     tmp = max_item - 1;
                 }
-                memmove(dest + tmp * chunk, src, chunk);
+                memmove(dest + tmp * itemsize, src, itemsize);
             }
             break;
         }
-        NPY_END_THREADS;
     }
+    NPY_END_THREADS;
 
  finish:
+    NPY_cast_info_xfree(&cast_info);
+
     Py_XDECREF(values);
     Py_XDECREF(indices);
     if (copied) {
@@ -482,6 +552,8 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
     Py_RETURN_NONE;
 
  fail:
+    NPY_cast_info_xfree(&cast_info);
+
     Py_XDECREF(indices);
     Py_XDECREF(values);
     if (copied) {
@@ -492,7 +564,7 @@ PyArray_PutTo(PyArrayObject *self, PyObject* values0, PyObject *indices0,
 }
 
 
-static NPY_GCC_OPT_3 NPY_INLINE void
+static NPY_GCC_OPT_3 inline void
 npy_fastputmask_impl(
         char *dest, char *src, const npy_bool *mask_data,
         npy_intp ni, npy_intp nv, npy_intp chunk)
@@ -562,11 +634,12 @@ PyArray_PutMask(PyArrayObject *self, PyObject* values0, PyObject* mask0)
 {
     PyArrayObject *mask, *values;
     PyArray_Descr *dtype;
-    npy_intp chunk, ni, nv;
+    npy_intp itemsize, ni, nv;
     char *src, *dest;
     npy_bool *mask_data;
     int copied = 0;
     int overlap = 0;
+    NPY_BEGIN_THREADS_DEF;
 
     mask = NULL;
     values = NULL;
@@ -627,31 +700,49 @@ PyArray_PutMask(PyArrayObject *self, PyObject* values0, PyObject* mask0)
         self = obj;
     }
 
-    chunk = PyArray_DESCR(self)->elsize;
+    itemsize = PyArray_DESCR(self)->elsize;
     dest = PyArray_DATA(self);
 
     if (PyDataType_REFCHK(PyArray_DESCR(self))) {
+        NPY_cast_info cast_info;
+        NPY_ARRAYMETHOD_FLAGS flags;
+        const npy_intp one = 1;
+        const npy_intp strides[2] = {itemsize, itemsize};
+
+        NPY_cast_info_init(&cast_info);
+        if (PyArray_GetDTypeTransferFunction(
+                PyArray_ISALIGNED(self), itemsize, itemsize, dtype, dtype, 0,
+                &cast_info, &flags) < 0) {
+            goto fail;
+        }
+        if (!(flags & NPY_METH_REQUIRES_PYAPI)) {
+            NPY_BEGIN_THREADS;
+        }
+
         for (npy_intp i = 0, j = 0; i < ni; i++, j++) {
             if (j >= nv) {
                 j = 0;
             }
             if (mask_data[i]) {
-                char *src_ptr = src + j*chunk;
-                char *dest_ptr = dest + i*chunk;
-
-                PyArray_Item_INCREF(src_ptr, PyArray_DESCR(self));
-                PyArray_Item_XDECREF(dest_ptr, PyArray_DESCR(self));
-                memmove(dest_ptr, src_ptr, chunk);
+                char *data[2] = {src + j*itemsize, dest + i*itemsize};
+                if (cast_info.func(
+                        &cast_info.context, data, &one, strides,
+                        cast_info.auxdata) < 0) {
+                    NPY_END_THREADS;
+                    NPY_cast_info_xfree(&cast_info);
+                    goto fail;
+                }
             }
         }
+        NPY_cast_info_xfree(&cast_info);
     }
     else {
-        NPY_BEGIN_THREADS_DEF;
-        NPY_BEGIN_THREADS_DESCR(PyArray_DESCR(self));
-        npy_fastputmask(dest, src, mask_data, ni, nv, chunk);
-        NPY_END_THREADS;
+        NPY_BEGIN_THREADS;
+        npy_fastputmask(dest, src, mask_data, ni, nv, itemsize);
     }
 
+    NPY_END_THREADS;
+
     Py_XDECREF(values);
     Py_XDECREF(mask);
     if (copied) {
@@ -670,6 +761,81 @@ PyArray_PutMask(PyArrayObject *self, PyObject* values0, PyObject* mask0)
     return NULL;
 }
 
+static NPY_GCC_OPT_3 inline int
+npy_fastrepeat_impl(
+    npy_intp n_outer, npy_intp n, npy_intp nel, npy_intp chunk,
+    npy_bool broadcast, npy_intp* counts, char* new_data, char* old_data,
+    npy_intp elsize, NPY_cast_info cast_info, int needs_refcounting)
+{
+    npy_intp i, j, k;
+    for (i = 0; i < n_outer; i++) {
+        for (j = 0; j < n; j++) {
+            npy_intp tmp = broadcast ? counts[0] : counts[j];
+            for (k = 0; k < tmp; k++) {
+                if (!needs_refcounting) {
+                    memcpy(new_data, old_data, chunk);
+                }
+                else {
+                    char *data[2] = {old_data, new_data};
+                    npy_intp strides[2] = {elsize, elsize};
+                    if (cast_info.func(&cast_info.context, data, &nel,
+                                       strides, cast_info.auxdata) < 0) {
+                        return -1;
+                    }
+                }
+                new_data += chunk;
+            }
+            old_data += chunk;
+        }
+    }
+    return 0;
+}
+
+static NPY_GCC_OPT_3 int
+npy_fastrepeat(
+    npy_intp n_outer, npy_intp n, npy_intp nel, npy_intp chunk,
+    npy_bool broadcast, npy_intp* counts, char* new_data, char* old_data,
+    npy_intp elsize, NPY_cast_info cast_info, int needs_refcounting)
+{
+    if (!needs_refcounting) {
+        if (chunk == 1) {
+            return npy_fastrepeat_impl(
+                n_outer, n, nel, chunk, broadcast, counts, new_data, old_data,
+                elsize, cast_info, needs_refcounting);
+        }
+        if (chunk == 2) {
+            return npy_fastrepeat_impl(
+                n_outer, n, nel, chunk, broadcast, counts, new_data, old_data,
+                elsize, cast_info, needs_refcounting);
+        }
+        if (chunk == 4) {
+            return npy_fastrepeat_impl(
+                n_outer, n, nel, chunk, broadcast, counts, new_data, old_data,
+                elsize, cast_info, needs_refcounting);
+        }
+        if (chunk == 8) {
+            return npy_fastrepeat_impl(
+                n_outer, n, nel, chunk, broadcast, counts, new_data, old_data,
+                elsize, cast_info, needs_refcounting);
+        }
+        if (chunk == 16) {
+            return npy_fastrepeat_impl(
+                n_outer, n, nel, chunk, broadcast, counts, new_data, old_data,
+                elsize, cast_info, needs_refcounting);
+        }
+        if (chunk == 32) {
+            return npy_fastrepeat_impl(
+                n_outer, n, nel, chunk, broadcast, counts, new_data, old_data,
+                elsize, cast_info, needs_refcounting);
+        }
+    }
+
+    return npy_fastrepeat_impl(
+        n_outer, n, nel, chunk, broadcast, counts, new_data, old_data, elsize,
+        cast_info, needs_refcounting);    
+}
+
+
 /*NUMPY_API
  * Repeat the array.
  */
@@ -677,13 +843,16 @@ NPY_NO_EXPORT PyObject *
 PyArray_Repeat(PyArrayObject *aop, PyObject *op, int axis)
 {
     npy_intp *counts;
-    npy_intp n, n_outer, i, j, k, chunk;
+    npy_intp i, j, n, n_outer, chunk, elsize, nel;
     npy_intp total = 0;
     npy_bool broadcast = NPY_FALSE;
     PyArrayObject *repeats = NULL;
     PyObject *ap = NULL;
     PyArrayObject *ret = NULL;
     char *new_data, *old_data;
+    NPY_cast_info cast_info;
+    NPY_ARRAYMETHOD_FLAGS flags;
+    int needs_refcounting;
 
     repeats = (PyArrayObject *)PyArray_ContiguousFromAny(op, NPY_INTP, 0, 1);
     if (repeats == NULL) {
@@ -707,6 +876,8 @@ PyArray_Repeat(PyArrayObject *aop, PyObject *op, int axis)
 
     aop = (PyArrayObject *)ap;
     n = PyArray_DIM(aop, axis);
+    NPY_cast_info_init(&cast_info);
+    needs_refcounting = PyDataType_REFCHK(PyArray_DESCR(aop));
 
     if (!broadcast && PyArray_SIZE(repeats) != n) {
         PyErr_Format(PyExc_ValueError,
@@ -744,35 +915,41 @@ PyArray_Repeat(PyArrayObject *aop, PyObject *op, int axis)
     new_data = PyArray_DATA(ret);
     old_data = PyArray_DATA(aop);
 
-    chunk = PyArray_DESCR(aop)->elsize;
+    nel = 1;
+    elsize = PyArray_DESCR(aop)->elsize;
     for(i = axis + 1; i < PyArray_NDIM(aop); i++) {
-        chunk *= PyArray_DIMS(aop)[i];
+        nel *= PyArray_DIMS(aop)[i];
     }
+    chunk = nel*elsize;
 
     n_outer = 1;
     for (i = 0; i < axis; i++) {
         n_outer *= PyArray_DIMS(aop)[i];
     }
-    for (i = 0; i < n_outer; i++) {
-        for (j = 0; j < n; j++) {
-            npy_intp tmp = broadcast ? counts[0] : counts[j];
-            for (k = 0; k < tmp; k++) {
-                memcpy(new_data, old_data, chunk);
-                new_data += chunk;
-            }
-            old_data += chunk;
+
+    if (needs_refcounting) {
+        if (PyArray_GetDTypeTransferFunction(
+                1, elsize, elsize, PyArray_DESCR(aop), PyArray_DESCR(aop), 0,
+                &cast_info, &flags) < 0) {
+            goto fail;
         }
     }
 
+    if (npy_fastrepeat(n_outer, n, nel, chunk, broadcast, counts, new_data,
+                       old_data, elsize, cast_info, needs_refcounting) < 0) {
+        goto fail;
+    }
+
     Py_DECREF(repeats);
-    PyArray_INCREF(ret);
     Py_XDECREF(aop);
+    NPY_cast_info_xfree(&cast_info);
     return (PyObject *)ret;
 
  fail:
     Py_DECREF(repeats);
     Py_XDECREF(aop);
     Py_XDECREF(ret);
+    NPY_cast_info_xfree(&cast_info);
     return NULL;
 }
 
@@ -945,10 +1122,10 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort,
     npy_intp elsize = (npy_intp)PyArray_ITEMSIZE(op);
     npy_intp astride = PyArray_STRIDE(op, axis);
     int swap = PyArray_ISBYTESWAPPED(op);
-    int needcopy = !IsAligned(op) || swap || astride != elsize;
-    int hasrefs = PyDataType_REFCHK(PyArray_DESCR(op));
+    int is_aligned = IsAligned(op);
+    int needcopy = !is_aligned || swap || astride != elsize;
+    int needs_api = PyDataType_FLAGCHK(PyArray_DESCR(op), NPY_NEEDS_PYAPI);
 
-    PyArray_CopySwapNFunc *copyswapn = PyArray_DESCR(op)->f->copyswapn;
     char *buffer = NULL;
 
     PyArrayIterObject *it;
@@ -956,6 +1133,12 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort,
 
     int ret = 0;
 
+    PyArray_Descr *descr = PyArray_DESCR(op);
+    PyArray_Descr *odescr = NULL;
+
+    NPY_cast_info to_cast_info = {.func = NULL};
+    NPY_cast_info from_cast_info = {.func = NULL};
+
     NPY_BEGIN_THREADS_DEF;
 
     /* Check if there is any sorting to do */
@@ -980,32 +1163,48 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort,
             ret = -1;
             goto fail;
         }
+        if (PyDataType_FLAGCHK(descr, NPY_NEEDS_INIT)) {
+            memset(buffer, 0, N * elsize);
+        }
+
+        if (swap) {
+            odescr = PyArray_DescrNewByteorder(descr, NPY_SWAP);
+        }
+        else {
+            odescr = descr;
+            Py_INCREF(odescr);
+        }
+
+        NPY_ARRAYMETHOD_FLAGS to_transfer_flags;
+
+        if (PyArray_GetDTypeTransferFunction(
+                is_aligned, astride, elsize, descr, odescr, 0, &to_cast_info,
+                &to_transfer_flags) != NPY_SUCCEED) {
+            goto fail;
+        }
+
+        NPY_ARRAYMETHOD_FLAGS from_transfer_flags;
+
+        if (PyArray_GetDTypeTransferFunction(
+                is_aligned, elsize, astride, odescr, descr, 0, &from_cast_info,
+                &from_transfer_flags) != NPY_SUCCEED) {
+            goto fail;
+        }
     }
 
-    NPY_BEGIN_THREADS_DESCR(PyArray_DESCR(op));
+    NPY_BEGIN_THREADS_DESCR(descr);
 
     while (size--) {
         char *bufptr = it->dataptr;
 
         if (needcopy) {
-            if (hasrefs) {
-                /*
-                 * For dtype's with objects, copyswapn Py_XINCREF's src
-                 * and Py_XDECREF's dst. This would crash if called on
-                 * an uninitialized buffer, or leak a reference to each
-                 * object if initialized.
-                 *
-                 * So, first do the copy with no refcounting...
-                 */
-                _unaligned_strided_byte_copy(buffer, elsize,
-                                             it->dataptr, astride, N, elsize);
-                /* ...then swap in-place if needed */
-                if (swap) {
-                    copyswapn(buffer, elsize, NULL, 0, N, swap, op);
-                }
-            }
-            else {
-                copyswapn(buffer, elsize, it->dataptr, astride, N, swap, op);
+            char *args[2] = {it->dataptr, buffer};
+            npy_intp strides[2] = {astride, elsize};
+
+            if (NPY_UNLIKELY(to_cast_info.func(
+                                 &to_cast_info.context, args, &N, strides,
+                                 to_cast_info.auxdata) < 0)) {
+                goto fail;
             }
             bufptr = buffer;
         }
@@ -1019,7 +1218,7 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort,
 
         if (part == NULL) {
             ret = sort(bufptr, N, op);
-            if (hasrefs && PyErr_Occurred()) {
+            if (needs_api && PyErr_Occurred()) {
                 ret = -1;
             }
             if (ret < 0) {
@@ -1032,7 +1231,7 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort,
             npy_intp i;
             for (i = 0; i < nkth; ++i) {
                 ret = part(bufptr, N, kth[i], pivots, &npiv, op);
-                if (hasrefs && PyErr_Occurred()) {
+                if (needs_api && PyErr_Occurred()) {
                     ret = -1;
                 }
                 if (ret < 0) {
@@ -1042,15 +1241,13 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort,
         }
 
         if (needcopy) {
-            if (hasrefs) {
-                if (swap) {
-                    copyswapn(buffer, elsize, NULL, 0, N, swap, op);
-                }
-                _unaligned_strided_byte_copy(it->dataptr, astride,
-                                             buffer, elsize, N, elsize);
-            }
-            else {
-                copyswapn(it->dataptr, astride, buffer, elsize, N, swap, op);
+            char *args[2] = {buffer, it->dataptr};
+            npy_intp strides[2] = {elsize, astride};
+
+            if (NPY_UNLIKELY(from_cast_info.func(
+                                 &from_cast_info.context, args, &N, strides,
+                                 from_cast_info.auxdata) < 0)) {
+                goto fail;
             }
         }
 
@@ -1058,15 +1255,21 @@ _new_sortlike(PyArrayObject *op, int axis, PyArray_SortFunc *sort,
     }
 
 fail:
-    NPY_END_THREADS_DESCR(PyArray_DESCR(op));
+    NPY_END_THREADS_DESCR(descr);
     /* cleanup internal buffer */
-    PyDataMem_UserFREE(buffer, N * elsize, mem_handler);
+    if (needcopy) {
+        PyArray_ClearBuffer(odescr, buffer, elsize, N, 1);
+        PyDataMem_UserFREE(buffer, N * elsize, mem_handler);
+        Py_DECREF(odescr);
+    }
     if (ret < 0 && !PyErr_Occurred()) {
         /* Out of memory during sorting or buffer creation */
         PyErr_NoMemory();
     }
     Py_DECREF(it);
     Py_DECREF(mem_handler);
+    NPY_cast_info_xfree(&to_cast_info);
+    NPY_cast_info_xfree(&from_cast_info);
 
     return ret;
 }
@@ -1080,11 +1283,11 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
     npy_intp elsize = (npy_intp)PyArray_ITEMSIZE(op);
     npy_intp astride = PyArray_STRIDE(op, axis);
     int swap = PyArray_ISBYTESWAPPED(op);
-    int needcopy = !IsAligned(op) || swap || astride != elsize;
-    int hasrefs = PyDataType_REFCHK(PyArray_DESCR(op));
+    int is_aligned = IsAligned(op);
+    int needcopy = !is_aligned || swap || astride != elsize;
+    int needs_api = PyDataType_FLAGCHK(PyArray_DESCR(op), NPY_NEEDS_PYAPI);
     int needidxbuffer;
 
-    PyArray_CopySwapNFunc *copyswapn = PyArray_DESCR(op)->f->copyswapn;
     char *valbuffer = NULL;
     npy_intp *idxbuffer = NULL;
 
@@ -1096,6 +1299,12 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
 
     int ret = 0;
 
+    PyArray_Descr *descr = PyArray_DESCR(op);
+    PyArray_Descr *odescr = NULL;
+
+    NPY_ARRAYMETHOD_FLAGS transfer_flags;
+    NPY_cast_info cast_info = {.func = NULL};
+
     NPY_BEGIN_THREADS_DEF;
 
     PyObject *mem_handler = PyDataMem_GetHandler();
@@ -1134,6 +1343,23 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
             ret = -1;
             goto fail;
         }
+        if (PyDataType_FLAGCHK(descr, NPY_NEEDS_INIT)) {
+            memset(valbuffer, 0, N * elsize);
+        }
+
+        if (swap) {
+            odescr = PyArray_DescrNewByteorder(descr, NPY_SWAP);
+        }
+        else {
+            odescr = descr;
+            Py_INCREF(odescr);
+        }
+
+        if (PyArray_GetDTypeTransferFunction(
+                is_aligned, astride, elsize, descr, odescr, 0, &cast_info,
+                &transfer_flags) != NPY_SUCCEED) {
+            goto fail;
+        }
     }
 
     if (needidxbuffer) {
@@ -1145,7 +1371,7 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
         }
     }
 
-    NPY_BEGIN_THREADS_DESCR(PyArray_DESCR(op));
+    NPY_BEGIN_THREADS_DESCR(descr);
 
     while (size--) {
         char *valptr = it->dataptr;
@@ -1153,25 +1379,13 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
         npy_intp *iptr, i;
 
         if (needcopy) {
-            if (hasrefs) {
-                /*
-                 * For dtype's with objects, copyswapn Py_XINCREF's src
-                 * and Py_XDECREF's dst. This would crash if called on
-                 * an uninitialized valbuffer, or leak a reference to
-                 * each object item if initialized.
-                 *
-                 * So, first do the copy with no refcounting...
-                 */
-                 _unaligned_strided_byte_copy(valbuffer, elsize,
-                                              it->dataptr, astride, N, elsize);
-                /* ...then swap in-place if needed */
-                if (swap) {
-                    copyswapn(valbuffer, elsize, NULL, 0, N, swap, op);
-                }
-            }
-            else {
-                copyswapn(valbuffer, elsize,
-                          it->dataptr, astride, N, swap, op);
+            char *args[2] = {it->dataptr, valbuffer};
+            npy_intp strides[2] = {astride, elsize};
+
+            if (NPY_UNLIKELY(cast_info.func(
+                                 &cast_info.context, args, &N, strides,
+                                 cast_info.auxdata) < 0)) {
+                goto fail;
             }
             valptr = valbuffer;
         }
@@ -1188,7 +1402,7 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
         if (argpart == NULL) {
             ret = argsort(valptr, idxptr, N, op);
             /* Object comparisons may raise an exception in Python 3 */
-            if (hasrefs && PyErr_Occurred()) {
+            if (needs_api && PyErr_Occurred()) {
                 ret = -1;
             }
             if (ret < 0) {
@@ -1202,7 +1416,7 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
             for (i = 0; i < nkth; ++i) {
                 ret = argpart(valptr, idxptr, N, kth[i], pivots, &npiv, op);
                 /* Object comparisons may raise an exception in Python 3 */
-                if (hasrefs && PyErr_Occurred()) {
+                if (needs_api && PyErr_Occurred()) {
                     ret = -1;
                 }
                 if (ret < 0) {
@@ -1226,9 +1440,13 @@ _new_argsortlike(PyArrayObject *op, int axis, PyArray_ArgSortFunc *argsort,
     }
 
 fail:
-    NPY_END_THREADS_DESCR(PyArray_DESCR(op));
+    NPY_END_THREADS_DESCR(descr);
     /* cleanup internal buffers */
-    PyDataMem_UserFREE(valbuffer, N * elsize, mem_handler);
+    if (needcopy) {
+        PyArray_ClearBuffer(odescr, valbuffer, elsize, N, 1);
+        PyDataMem_UserFREE(valbuffer, N * elsize, mem_handler);
+        Py_DECREF(odescr);
+    }
     PyDataMem_UserFREE(idxbuffer, N * sizeof(npy_intp), mem_handler);
     if (ret < 0) {
         if (!PyErr_Occurred()) {
@@ -1241,6 +1459,7 @@ fail:
     Py_XDECREF(it);
     Py_XDECREF(rit);
     Py_DECREF(mem_handler);
+    NPY_cast_info_xfree(&cast_info);
 
     return (PyObject *)rop;
 }
@@ -2113,7 +2332,7 @@ PyArray_Compress(PyArrayObject *self, PyObject *condition, int axis,
  * even though it uses 64 bit types its faster than the bytewise sum on 32 bit
  * but a 32 bit type version would make it even faster on these platforms
  */
-static NPY_INLINE npy_intp
+static inline npy_intp
 count_nonzero_bytes_384(const npy_uint64 * w)
 {
     const npy_uint64 w1 = w[0];
@@ -2209,7 +2428,7 @@ count_zero_bytes_u16(const npy_uint8 **d, const npy_uint8 *end, npy_uint16 max_c
  *                          !!
  *                     (2*16-1)*16
 */
-static NPY_INLINE NPY_GCC_OPT_3 npy_intp
+static inline NPY_GCC_OPT_3 npy_intp
 count_nonzero_u8(const char *data, npy_intp bstride, npy_uintp len)
 {
     npy_intp count = 0;
@@ -2245,7 +2464,7 @@ count_nonzero_u8(const char *data, npy_intp bstride, npy_uintp len)
     return count;
 }
 
-static NPY_INLINE NPY_GCC_OPT_3 npy_intp
+static inline NPY_GCC_OPT_3 npy_intp
 count_nonzero_u16(const char *data, npy_intp bstride, npy_uintp len)
 {
     npy_intp count = 0;
@@ -2277,7 +2496,7 @@ count_nonzero_u16(const char *data, npy_intp bstride, npy_uintp len)
     return count;
 }
 
-static NPY_INLINE NPY_GCC_OPT_3 npy_intp
+static inline NPY_GCC_OPT_3 npy_intp
 count_nonzero_u32(const char *data, npy_intp bstride, npy_uintp len)
 {
     npy_intp count = 0;
@@ -2307,7 +2526,7 @@ count_nonzero_u32(const char *data, npy_intp bstride, npy_uintp len)
     return count;
 }
 
-static NPY_INLINE NPY_GCC_OPT_3 npy_intp
+static inline NPY_GCC_OPT_3 npy_intp
 count_nonzero_u64(const char *data, npy_intp bstride, npy_uintp len)
 {
     npy_intp count = 0;
@@ -2642,7 +2861,7 @@ PyArray_Nonzero(PyArrayObject *self)
                 }
             }
             /*
-             * Fallback to a branchless strategy to avoid branch misprediction 
+             * Fallback to a branchless strategy to avoid branch misprediction
              * stalls that are very expensive on most modern processors.
              */
             else {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/iterators.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/iterators.c
index 95aa11d2ded..4e6418bf9a0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/iterators.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/iterators.c
@@ -1241,7 +1241,7 @@ PyArray_Broadcast(PyArrayMultiIterObject *mit)
     return 0;
 }
 
-static NPY_INLINE PyObject*
+static inline PyObject*
 multiiter_wrong_number_of_args(void)
 {
     return PyErr_Format(PyExc_ValueError,
@@ -1615,7 +1615,7 @@ get_ptr_constant(PyArrayIterObject* _iter, const npy_intp *coordinates)
  * value  2  3  3  2  1 1 2 3 3 2 1 1
  *
  * _npy_pos_index_mirror(4, 3) will return 1, because x[4] = x[1]*/
-NPY_INLINE static npy_intp
+static inline npy_intp
 __npy_pos_remainder(npy_intp i, npy_intp n)
 {
     npy_intp k, l, j;
@@ -1661,7 +1661,7 @@ get_ptr_mirror(PyArrayIterObject* _iter, const npy_intp *coordinates)
 #undef _INF_SET_PTR_MIRROR
 
 /* compute l such as i = k * n + l, 0 <= l < |k| */
-NPY_INLINE static npy_intp
+static inline npy_intp
 __npy_euclidean_division(npy_intp i, npy_intp n)
 {
     npy_intp l;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c
index 4b824a2a1df..309ef1b89cb 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c
@@ -251456,7 +251456,9 @@ PyArray_TransferMaskedStridedToNDim(npy_intp ndim,
 /****************** MapIter (Advanced indexing) Get/Set ********************/
 /***************************************************************************/
 
-#line 1447
+typedef struct {npy_uint64 a; npy_uint64 b;} copytype128;
+
+#line 1449
 
 /*
  * Advanced indexing iteration of arrays when there is a single indexing
@@ -251464,8 +251466,9 @@ PyArray_TransferMaskedStridedToNDim(npy_intp ndim,
  * can be trivially iterated (single stride, aligned, no casting necessary).
  */
 NPY_NO_EXPORT int
-mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
-                       PyArrayObject *result)
+mapiter_trivial_set(
+        PyArrayObject *self, PyArrayObject *ind, PyArrayObject *result,
+        int is_aligned, NPY_cast_info *cast_info)
 {
     char *base_ptr, *ind_ptr, *result_ptr;
     npy_intp self_stride, ind_stride, result_stride;
@@ -251473,10 +251476,12 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
 
     npy_intp itersize;
 
-    int is_aligned = IsUintAligned(self) && IsUintAligned(result);
+    /* copying between the same dtype, we can assume this is correct: */
     int needs_api = PyDataType_REFCHK(PyArray_DESCR(self));
+    npy_intp itemsize = PyArray_ITEMSIZE(self);
+    npy_intp strides[2] = {itemsize, itemsize};
+    npy_intp one = 1;
 
-    PyArray_CopySwapFunc *copyswap = PyArray_DESCR(self)->f->copyswap;
     NPY_BEGIN_THREADS_DEF;
 
     base_ptr = PyArray_BYTES(self);
@@ -251508,9 +251513,9 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
 #endif
 
     /* Optimization for aligned types that do not need the api */
-    switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(self) : 0) {
+    switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
-#line 1504
+#line 1509
 
 #if 1
     case 1:
@@ -251538,7 +251543,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint8)));
             *(npy_uint8 *)result_ptr = *(npy_uint8 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !0 */
@@ -251547,7 +251558,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint8)));
             *(npy_uint8 *)self_ptr = *(npy_uint8 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -251557,7 +251574,7 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
 
 #if 2
     case 2:
@@ -251585,7 +251602,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint16)));
             *(npy_uint16 *)result_ptr = *(npy_uint16 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !0 */
@@ -251594,7 +251617,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint16)));
             *(npy_uint16 *)self_ptr = *(npy_uint16 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -251604,7 +251633,7 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
 
 #if 4
     case 4:
@@ -251632,7 +251661,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint32)));
             *(npy_uint32 *)result_ptr = *(npy_uint32 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !0 */
@@ -251641,7 +251676,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint32)));
             *(npy_uint32 *)self_ptr = *(npy_uint32 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -251651,7 +251692,7 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
 
 #if 8
     case 8:
@@ -251679,7 +251720,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint64)));
             *(npy_uint64 *)result_ptr = *(npy_uint64 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !0 */
@@ -251688,7 +251735,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint64)));
             *(npy_uint64 *)self_ptr = *(npy_uint64 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -251698,7 +251751,66 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
+
+#if 16
+    case 16:
+#else
+    default:
+#endif
+        while (itersize--) {
+            char * self_ptr;
+            npy_intp indval = *((npy_intp*)ind_ptr);
+            assert(npy_is_aligned(ind_ptr, _UINT_ALIGN(npy_intp)));
+#if 0
+            if (check_and_adjust_index(&indval, fancy_dim, 0, _save) < 0 ) {
+                return -1;
+            }
+#else
+            if (indval < 0) {
+                indval += fancy_dim;
+            }
+#endif
+            self_ptr = base_ptr + indval * self_stride;
+
+#if 0
+#if 16
+            assert(npy_is_aligned(result_ptr, _UINT_ALIGN(copytype128)));
+            assert(npy_is_aligned(self_ptr, _UINT_ALIGN(copytype128)));
+            *(copytype128 *)result_ptr = *(copytype128 *)self_ptr;
+#else
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
+#endif
+
+#else /* !0 */
+#if 16
+            assert(npy_is_aligned(result_ptr, _UINT_ALIGN(copytype128)));
+            assert(npy_is_aligned(self_ptr, _UINT_ALIGN(copytype128)));
+            *(copytype128 *)self_ptr = *(copytype128 *)result_ptr;
+#else
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
+#endif
+#endif
+
+            ind_ptr += ind_stride;
+            result_ptr += result_stride;
+        }
+        break;
+
+
+#line 1509
 
 #if 0
     case 0:
@@ -251726,7 +251838,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(0)));
             *(0 *)result_ptr = *(0 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !0 */
@@ -251735,7 +251853,13 @@ mapiter_trivial_set(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(0)));
             *(0 *)self_ptr = *(0 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -251795,18 +251919,20 @@ mapiter_set(
     if (mit->subspace_iter == NULL) {
         /*
          * Item by item copy situation, the operand is buffered
-         * so use copyswap.  The iterator may not do any transfers, so may
+         * so use a cast to copy.  The iterator may not do any transfers, so may
          * not have set `needs_api` yet, set it if necessary:
          */
         needs_api |= PyDataType_REFCHK(PyArray_DESCR(array));
-        PyArray_CopySwapFunc *copyswap = PyArray_DESCR(array)->f->copyswap;
+        npy_intp itemsize = PyArray_ITEMSIZE(array);
+        npy_intp strides[2] = {itemsize, itemsize};
+        npy_intp one = 1;
 
         /* We have only one iterator handling everything */
         counter = NpyIter_GetInnerLoopSizePtr(mit->outer);
 
         /************ Optimized inner loops without subspace *************/
 
-#line 1615
+#line 1634
 
 #if 1
         if (numiter == 1) {
@@ -251819,9 +251945,9 @@ mapiter_set(
             }
 
             /* Optimization for aligned types that do not need the api */
-            switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(array) : 0) {
+            switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
-#line 1633
+#line 1652
 
 #if 1
             case 1:
@@ -251871,7 +251997,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)(outer_ptrs[i]) = *(npy_uint8 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 1
@@ -251881,7 +252013,13 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)self_ptr = *(npy_uint8 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -251892,7 +252030,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 2
             case 2:
@@ -251942,7 +252080,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)(outer_ptrs[i]) = *(npy_uint16 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 2
@@ -251952,7 +252096,13 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)self_ptr = *(npy_uint16 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -251963,7 +252113,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 4
             case 4:
@@ -252013,7 +252163,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)(outer_ptrs[i]) = *(npy_uint32 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 4
@@ -252023,7 +252179,13 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)self_ptr = *(npy_uint32 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252034,7 +252196,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 8
             case 8:
@@ -252084,7 +252246,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)(outer_ptrs[i]) = *(npy_uint64 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 8
@@ -252094,7 +252262,96 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)self_ptr = *(npy_uint64 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#endif
+                        /* advance extra operand */
+                        outer_ptrs[i] += outer_strides[i];
+                    }
+                } while (mit->outer_next(mit->outer));
+
+                break;
+
+
+#line 1652
+
+#if 16
+            case 16:
+#else
+            default:
+#endif
+                /* Outer iteration (safe because mit->size != 0) */
+                do {
+#if !0
+                    /*
+                     * When the API is needed the casting might fail
+                     * TODO: (only if buffering is enabled).
+                     */
+                    if (needs_api && PyErr_Occurred()) {
+                        return -1;
+                    }
+#endif
+                    count = *counter;
+                    while (count--) {
+                        char * self_ptr = baseoffset;
+                        for (i=0; i < 1; i++) {
+                            npy_intp indval = *((npy_intp*)outer_ptrs[i]);
+                            assert(npy_is_aligned(outer_ptrs[i],
+                                                  _UINT_ALIGN(npy_intp)));
+
+#if 0 && 1
+                            if (check_and_adjust_index(&indval, fancy_dims[i],
+                                                       iteraxis, _save) < 0 ) {
+                                return -1;
+                            }
+#else
+                            if (indval < 0) {
+                                indval += fancy_dims[i];
+                            }
+#endif
+                            self_ptr += indval * fancy_strides[i];
+
+                            /* advance indexing arrays */
+                            outer_ptrs[i] += outer_strides[i];
+                        }
+
+#if 0
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                                              _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                                              _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)(outer_ptrs[i]) = *(copytype128 *)self_ptr;
+#else
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#else /* !0 */
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                               _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                               _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)self_ptr = *(copytype128 *)(outer_ptrs[i]);
+#else
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252105,7 +252362,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 0
             case 0:
@@ -252155,7 +252412,13 @@ mapiter_set(
                                               _UINT_ALIGN(0)));
                         *(0 *)(outer_ptrs[i]) = *(0 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 0
@@ -252165,7 +252428,13 @@ mapiter_set(
                                _UINT_ALIGN(0)));
                         *(0 *)self_ptr = *(0 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252180,7 +252449,7 @@ mapiter_set(
             NPY_END_THREADS;
         }
 
-#line 1615
+#line 1634
 
 #if 0
         if (numiter == 1) {
@@ -252193,9 +252462,9 @@ mapiter_set(
             }
 
             /* Optimization for aligned types that do not need the api */
-            switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(array) : 0) {
+            switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
-#line 1633
+#line 1652
 
 #if 1
             case 1:
@@ -252245,7 +252514,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)(outer_ptrs[i]) = *(npy_uint8 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 1
@@ -252255,7 +252530,13 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)self_ptr = *(npy_uint8 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252266,7 +252547,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 2
             case 2:
@@ -252316,7 +252597,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)(outer_ptrs[i]) = *(npy_uint16 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 2
@@ -252326,7 +252613,13 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)self_ptr = *(npy_uint16 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252337,7 +252630,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 4
             case 4:
@@ -252387,7 +252680,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)(outer_ptrs[i]) = *(npy_uint32 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 4
@@ -252397,7 +252696,13 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)self_ptr = *(npy_uint32 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252408,7 +252713,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 8
             case 8:
@@ -252458,7 +252763,13 @@ mapiter_set(
                                               _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)(outer_ptrs[i]) = *(npy_uint64 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 8
@@ -252468,7 +252779,96 @@ mapiter_set(
                                _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)self_ptr = *(npy_uint64 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#endif
+                        /* advance extra operand */
+                        outer_ptrs[i] += outer_strides[i];
+                    }
+                } while (mit->outer_next(mit->outer));
+
+                break;
+
+
+#line 1652
+
+#if 16
+            case 16:
+#else
+            default:
+#endif
+                /* Outer iteration (safe because mit->size != 0) */
+                do {
+#if !0
+                    /*
+                     * When the API is needed the casting might fail
+                     * TODO: (only if buffering is enabled).
+                     */
+                    if (needs_api && PyErr_Occurred()) {
+                        return -1;
+                    }
+#endif
+                    count = *counter;
+                    while (count--) {
+                        char * self_ptr = baseoffset;
+                        for (i=0; i < numiter; i++) {
+                            npy_intp indval = *((npy_intp*)outer_ptrs[i]);
+                            assert(npy_is_aligned(outer_ptrs[i],
+                                                  _UINT_ALIGN(npy_intp)));
+
+#if 0 && 0
+                            if (check_and_adjust_index(&indval, fancy_dims[i],
+                                                       iteraxis, _save) < 0 ) {
+                                return -1;
+                            }
+#else
+                            if (indval < 0) {
+                                indval += fancy_dims[i];
+                            }
+#endif
+                            self_ptr += indval * fancy_strides[i];
+
+                            /* advance indexing arrays */
+                            outer_ptrs[i] += outer_strides[i];
+                        }
+
+#if 0
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                                              _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                                              _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)(outer_ptrs[i]) = *(copytype128 *)self_ptr;
+#else
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#else /* !0 */
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                               _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                               _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)self_ptr = *(copytype128 *)(outer_ptrs[i]);
+#else
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252479,7 +252879,7 @@ mapiter_set(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 0
             case 0:
@@ -252529,7 +252929,13 @@ mapiter_set(
                                               _UINT_ALIGN(0)));
                         *(0 *)(outer_ptrs[i]) = *(0 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !0 */
 #if 0
@@ -252539,7 +252945,13 @@ mapiter_set(
                                _UINT_ALIGN(0)));
                         *(0 *)self_ptr = *(0 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -252577,7 +252989,7 @@ mapiter_set(
             */
             is_subiter_trivial = 1;
         }
-#line 1734
+#line 1765
 
 #if 1
         if (numiter == 1) {
@@ -252688,7 +253100,7 @@ mapiter_set(
             NPY_END_THREADS;
         }
 
-#line 1734
+#line 1765
 
 #if 0
         if (numiter == 1) {
@@ -252804,7 +253216,7 @@ mapiter_set(
 }
 
 
-#line 1447
+#line 1449
 
 /*
  * Advanced indexing iteration of arrays when there is a single indexing
@@ -252812,8 +253224,9 @@ mapiter_set(
  * can be trivially iterated (single stride, aligned, no casting necessary).
  */
 NPY_NO_EXPORT int
-mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
-                       PyArrayObject *result)
+mapiter_trivial_get(
+        PyArrayObject *self, PyArrayObject *ind, PyArrayObject *result,
+        int is_aligned, NPY_cast_info *cast_info)
 {
     char *base_ptr, *ind_ptr, *result_ptr;
     npy_intp self_stride, ind_stride, result_stride;
@@ -252821,10 +253234,12 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
 
     npy_intp itersize;
 
-    int is_aligned = IsUintAligned(self) && IsUintAligned(result);
+    /* copying between the same dtype, we can assume this is correct: */
     int needs_api = PyDataType_REFCHK(PyArray_DESCR(self));
+    npy_intp itemsize = PyArray_ITEMSIZE(self);
+    npy_intp strides[2] = {itemsize, itemsize};
+    npy_intp one = 1;
 
-    PyArray_CopySwapFunc *copyswap = PyArray_DESCR(self)->f->copyswap;
     NPY_BEGIN_THREADS_DEF;
 
     base_ptr = PyArray_BYTES(self);
@@ -252856,9 +253271,9 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
 #endif
 
     /* Optimization for aligned types that do not need the api */
-    switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(self) : 0) {
+    switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
-#line 1504
+#line 1509
 
 #if 1
     case 1:
@@ -252886,7 +253301,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint8)));
             *(npy_uint8 *)result_ptr = *(npy_uint8 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !1 */
@@ -252895,7 +253316,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint8)));
             *(npy_uint8 *)self_ptr = *(npy_uint8 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -252905,7 +253332,7 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
 
 #if 2
     case 2:
@@ -252933,7 +253360,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint16)));
             *(npy_uint16 *)result_ptr = *(npy_uint16 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !1 */
@@ -252942,7 +253375,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint16)));
             *(npy_uint16 *)self_ptr = *(npy_uint16 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -252952,7 +253391,7 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
 
 #if 4
     case 4:
@@ -252980,7 +253419,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint32)));
             *(npy_uint32 *)result_ptr = *(npy_uint32 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !1 */
@@ -252989,7 +253434,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint32)));
             *(npy_uint32 *)self_ptr = *(npy_uint32 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -252999,7 +253450,7 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
 
 #if 8
     case 8:
@@ -253027,7 +253478,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint64)));
             *(npy_uint64 *)result_ptr = *(npy_uint64 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !1 */
@@ -253036,7 +253493,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(npy_uint64)));
             *(npy_uint64 *)self_ptr = *(npy_uint64 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -253046,7 +253509,66 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
         break;
 
 
-#line 1504
+#line 1509
+
+#if 16
+    case 16:
+#else
+    default:
+#endif
+        while (itersize--) {
+            char * self_ptr;
+            npy_intp indval = *((npy_intp*)ind_ptr);
+            assert(npy_is_aligned(ind_ptr, _UINT_ALIGN(npy_intp)));
+#if 1
+            if (check_and_adjust_index(&indval, fancy_dim, 0, _save) < 0 ) {
+                return -1;
+            }
+#else
+            if (indval < 0) {
+                indval += fancy_dim;
+            }
+#endif
+            self_ptr = base_ptr + indval * self_stride;
+
+#if 1
+#if 16
+            assert(npy_is_aligned(result_ptr, _UINT_ALIGN(copytype128)));
+            assert(npy_is_aligned(self_ptr, _UINT_ALIGN(copytype128)));
+            *(copytype128 *)result_ptr = *(copytype128 *)self_ptr;
+#else
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
+#endif
+
+#else /* !1 */
+#if 16
+            assert(npy_is_aligned(result_ptr, _UINT_ALIGN(copytype128)));
+            assert(npy_is_aligned(self_ptr, _UINT_ALIGN(copytype128)));
+            *(copytype128 *)self_ptr = *(copytype128 *)result_ptr;
+#else
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
+#endif
+#endif
+
+            ind_ptr += ind_stride;
+            result_ptr += result_stride;
+        }
+        break;
+
+
+#line 1509
 
 #if 0
     case 0:
@@ -253074,7 +253596,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(0)));
             *(0 *)result_ptr = *(0 *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !1 */
@@ -253083,7 +253611,13 @@ mapiter_trivial_get(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(0)));
             *(0 *)self_ptr = *(0 *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -253143,18 +253677,20 @@ mapiter_get(
     if (mit->subspace_iter == NULL) {
         /*
          * Item by item copy situation, the operand is buffered
-         * so use copyswap.  The iterator may not do any transfers, so may
+         * so use a cast to copy.  The iterator may not do any transfers, so may
          * not have set `needs_api` yet, set it if necessary:
          */
         needs_api |= PyDataType_REFCHK(PyArray_DESCR(array));
-        PyArray_CopySwapFunc *copyswap = PyArray_DESCR(array)->f->copyswap;
+        npy_intp itemsize = PyArray_ITEMSIZE(array);
+        npy_intp strides[2] = {itemsize, itemsize};
+        npy_intp one = 1;
 
         /* We have only one iterator handling everything */
         counter = NpyIter_GetInnerLoopSizePtr(mit->outer);
 
         /************ Optimized inner loops without subspace *************/
 
-#line 1615
+#line 1634
 
 #if 1
         if (numiter == 1) {
@@ -253167,9 +253703,9 @@ mapiter_get(
             }
 
             /* Optimization for aligned types that do not need the api */
-            switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(array) : 0) {
+            switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
-#line 1633
+#line 1652
 
 #if 1
             case 1:
@@ -253219,7 +253755,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)(outer_ptrs[i]) = *(npy_uint8 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 1
@@ -253229,7 +253771,13 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)self_ptr = *(npy_uint8 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253240,7 +253788,7 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 2
             case 2:
@@ -253290,7 +253838,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)(outer_ptrs[i]) = *(npy_uint16 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 2
@@ -253300,7 +253854,13 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)self_ptr = *(npy_uint16 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253311,7 +253871,7 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 4
             case 4:
@@ -253361,7 +253921,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)(outer_ptrs[i]) = *(npy_uint32 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 4
@@ -253371,7 +253937,13 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)self_ptr = *(npy_uint32 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253382,7 +253954,7 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 8
             case 8:
@@ -253432,7 +254004,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)(outer_ptrs[i]) = *(npy_uint64 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 8
@@ -253442,7 +254020,13 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)self_ptr = *(npy_uint64 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253453,7 +254037,90 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
+
+#if 16
+            case 16:
+#else
+            default:
+#endif
+                /* Outer iteration (safe because mit->size != 0) */
+                do {
+#if !1
+                    /*
+                     * When the API is needed the casting might fail
+                     * TODO: (only if buffering is enabled).
+                     */
+                    if (needs_api && PyErr_Occurred()) {
+                        return -1;
+                    }
+#endif
+                    count = *counter;
+                    while (count--) {
+                        char * self_ptr = baseoffset;
+                        for (i=0; i < 1; i++) {
+                            npy_intp indval = *((npy_intp*)outer_ptrs[i]);
+                            assert(npy_is_aligned(outer_ptrs[i],
+                                                  _UINT_ALIGN(npy_intp)));
+
+#if 1 && 1
+                            if (check_and_adjust_index(&indval, fancy_dims[i],
+                                                       iteraxis, _save) < 0 ) {
+                                return -1;
+                            }
+#else
+                            if (indval < 0) {
+                                indval += fancy_dims[i];
+                            }
+#endif
+                            self_ptr += indval * fancy_strides[i];
+
+                            /* advance indexing arrays */
+                            outer_ptrs[i] += outer_strides[i];
+                        }
+
+#if 1
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                                              _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                                              _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)(outer_ptrs[i]) = *(copytype128 *)self_ptr;
+#else
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#else /* !1 */
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                               _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                               _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)self_ptr = *(copytype128 *)(outer_ptrs[i]);
+#else
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#endif
+                        /* advance extra operand */
+                        outer_ptrs[i] += outer_strides[i];
+                    }
+                } while (mit->outer_next(mit->outer));
+
+                break;
+
+
+#line 1652
 
 #if 0
             case 0:
@@ -253503,7 +254170,13 @@ mapiter_get(
                                               _UINT_ALIGN(0)));
                         *(0 *)(outer_ptrs[i]) = *(0 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 0
@@ -253513,7 +254186,13 @@ mapiter_get(
                                _UINT_ALIGN(0)));
                         *(0 *)self_ptr = *(0 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253528,7 +254207,7 @@ mapiter_get(
             NPY_END_THREADS;
         }
 
-#line 1615
+#line 1634
 
 #if 0
         if (numiter == 1) {
@@ -253541,9 +254220,9 @@ mapiter_get(
             }
 
             /* Optimization for aligned types that do not need the api */
-            switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(array) : 0) {
+            switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
-#line 1633
+#line 1652
 
 #if 1
             case 1:
@@ -253593,7 +254272,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)(outer_ptrs[i]) = *(npy_uint8 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 1
@@ -253603,7 +254288,13 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint8)));
                         *(npy_uint8 *)self_ptr = *(npy_uint8 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253614,7 +254305,7 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 2
             case 2:
@@ -253664,7 +254355,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)(outer_ptrs[i]) = *(npy_uint16 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 2
@@ -253674,7 +254371,13 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint16)));
                         *(npy_uint16 *)self_ptr = *(npy_uint16 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253685,7 +254388,7 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 4
             case 4:
@@ -253735,7 +254438,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)(outer_ptrs[i]) = *(npy_uint32 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 4
@@ -253745,7 +254454,13 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint32)));
                         *(npy_uint32 *)self_ptr = *(npy_uint32 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253756,7 +254471,7 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 8
             case 8:
@@ -253806,7 +254521,13 @@ mapiter_get(
                                               _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)(outer_ptrs[i]) = *(npy_uint64 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 8
@@ -253816,7 +254537,96 @@ mapiter_get(
                                _UINT_ALIGN(npy_uint64)));
                         *(npy_uint64 *)self_ptr = *(npy_uint64 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#endif
+                        /* advance extra operand */
+                        outer_ptrs[i] += outer_strides[i];
+                    }
+                } while (mit->outer_next(mit->outer));
+
+                break;
+
+
+#line 1652
+
+#if 16
+            case 16:
+#else
+            default:
+#endif
+                /* Outer iteration (safe because mit->size != 0) */
+                do {
+#if !1
+                    /*
+                     * When the API is needed the casting might fail
+                     * TODO: (only if buffering is enabled).
+                     */
+                    if (needs_api && PyErr_Occurred()) {
+                        return -1;
+                    }
+#endif
+                    count = *counter;
+                    while (count--) {
+                        char * self_ptr = baseoffset;
+                        for (i=0; i < numiter; i++) {
+                            npy_intp indval = *((npy_intp*)outer_ptrs[i]);
+                            assert(npy_is_aligned(outer_ptrs[i],
+                                                  _UINT_ALIGN(npy_intp)));
+
+#if 1 && 0
+                            if (check_and_adjust_index(&indval, fancy_dims[i],
+                                                       iteraxis, _save) < 0 ) {
+                                return -1;
+                            }
+#else
+                            if (indval < 0) {
+                                indval += fancy_dims[i];
+                            }
+#endif
+                            self_ptr += indval * fancy_strides[i];
+
+                            /* advance indexing arrays */
+                            outer_ptrs[i] += outer_strides[i];
+                        }
+
+#if 1
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                                              _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                                              _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)(outer_ptrs[i]) = *(copytype128 *)self_ptr;
+#else
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
+#endif
+#else /* !1 */
+#if 16
+                        assert(npy_is_aligned(outer_ptrs[i],
+                               _UINT_ALIGN(copytype128)));
+                        assert(npy_is_aligned(self_ptr,
+                               _UINT_ALIGN(copytype128)));
+                        *(copytype128 *)self_ptr = *(copytype128 *)(outer_ptrs[i]);
+#else
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253827,7 +254637,7 @@ mapiter_get(
                 break;
 
 
-#line 1633
+#line 1652
 
 #if 0
             case 0:
@@ -253877,7 +254687,13 @@ mapiter_get(
                                               _UINT_ALIGN(0)));
                         *(0 *)(outer_ptrs[i]) = *(0 *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !1 */
 #if 0
@@ -253887,7 +254703,13 @@ mapiter_get(
                                _UINT_ALIGN(0)));
                         *(0 *)self_ptr = *(0 *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
@@ -253925,7 +254747,7 @@ mapiter_get(
             */
             is_subiter_trivial = 1;
         }
-#line 1734
+#line 1765
 
 #if 1
         if (numiter == 1) {
@@ -254036,7 +254858,7 @@ mapiter_get(
             NPY_END_THREADS;
         }
 
-#line 1734
+#line 1765
 
 #if 0
         if (numiter == 1) {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c.src
index 8e3afd3cc65..2c48ae58bf2 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/lowlevel_strided_loops.c.src
@@ -1440,6 +1440,8 @@ PyArray_TransferMaskedStridedToNDim(npy_intp ndim,
 /****************** MapIter (Advanced indexing) Get/Set ********************/
 /***************************************************************************/
 
+typedef struct {npy_uint64 a; npy_uint64 b;} copytype128;
+
 /**begin repeat
  * #name = set, get#
  * #isget = 0, 1#
@@ -1451,8 +1453,9 @@ PyArray_TransferMaskedStridedToNDim(npy_intp ndim,
  * can be trivially iterated (single stride, aligned, no casting necessary).
  */
 NPY_NO_EXPORT int
-mapiter_trivial_@name@(PyArrayObject *self, PyArrayObject *ind,
-                       PyArrayObject *result)
+mapiter_trivial_@name@(
+        PyArrayObject *self, PyArrayObject *ind, PyArrayObject *result,
+        int is_aligned, NPY_cast_info *cast_info)
 {
     char *base_ptr, *ind_ptr, *result_ptr;
     npy_intp self_stride, ind_stride, result_stride;
@@ -1460,10 +1463,12 @@ mapiter_trivial_@name@(PyArrayObject *self, PyArrayObject *ind,
 
     npy_intp itersize;
 
-    int is_aligned = IsUintAligned(self) && IsUintAligned(result);
+    /* copying between the same dtype, we can assume this is correct: */
     int needs_api = PyDataType_REFCHK(PyArray_DESCR(self));
+    npy_intp itemsize = PyArray_ITEMSIZE(self);
+    npy_intp strides[2] = {itemsize, itemsize};
+    npy_intp one = 1;
 
-    PyArray_CopySwapFunc *copyswap = PyArray_DESCR(self)->f->copyswap;
     NPY_BEGIN_THREADS_DEF;
 
     base_ptr = PyArray_BYTES(self);
@@ -1495,11 +1500,11 @@ mapiter_trivial_@name@(PyArrayObject *self, PyArrayObject *ind,
 #endif
 
     /* Optimization for aligned types that do not need the api */
-    switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(self) : 0) {
+    switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
 /**begin repeat1
- * #elsize = 1, 2, 4, 8, 0#
- * #copytype = npy_uint8, npy_uint16, npy_uint32, npy_uint64, 0#
+ * #elsize = 1, 2, 4, 8, 16, 0#
+ * #copytype = npy_uint8, npy_uint16, npy_uint32, npy_uint64, copytype128, 0#
  */
 
 #if @elsize@
@@ -1528,7 +1533,13 @@ mapiter_trivial_@name@(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(@copytype@)));
             *(@copytype@ *)result_ptr = *(@copytype@ *)self_ptr;
 #else
-            copyswap(result_ptr, self_ptr, 0, self);
+            char *args[2] = {self_ptr, result_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 
 #else /* !@isget@ */
@@ -1537,7 +1548,13 @@ mapiter_trivial_@name@(PyArrayObject *self, PyArrayObject *ind,
             assert(npy_is_aligned(self_ptr, _UINT_ALIGN(@copytype@)));
             *(@copytype@ *)self_ptr = *(@copytype@ *)result_ptr;
 #else
-            copyswap(self_ptr, result_ptr, 0, self);
+            char *args[2] = {result_ptr, self_ptr};
+            if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                    args, &one, strides,
+                    cast_info->auxdata) < 0)) {
+                NPY_END_THREADS;
+                return -1;
+            }
 #endif
 #endif
 
@@ -1597,11 +1614,13 @@ mapiter_@name@(
     if (mit->subspace_iter == NULL) {
         /*
          * Item by item copy situation, the operand is buffered
-         * so use copyswap.  The iterator may not do any transfers, so may
+         * so use a cast to copy.  The iterator may not do any transfers, so may
          * not have set `needs_api` yet, set it if necessary:
          */
         needs_api |= PyDataType_REFCHK(PyArray_DESCR(array));
-        PyArray_CopySwapFunc *copyswap = PyArray_DESCR(array)->f->copyswap;
+        npy_intp itemsize = PyArray_ITEMSIZE(array);
+        npy_intp strides[2] = {itemsize, itemsize};
+        npy_intp one = 1;
 
         /* We have only one iterator handling everything */
         counter = NpyIter_GetInnerLoopSizePtr(mit->outer);
@@ -1624,11 +1643,11 @@ mapiter_@name@(
             }
 
             /* Optimization for aligned types that do not need the api */
-            switch ((is_aligned && !needs_api) ? PyArray_ITEMSIZE(array) : 0) {
+            switch ((is_aligned && !needs_api) ? itemsize : 0) {
 
 /**begin repeat2
- * #elsize = 1, 2, 4, 8, 0#
- * #copytype = npy_uint8, npy_uint16, npy_uint32, npy_uint64, 0#
+ * #elsize = 1, 2, 4, 8, 16, 0#
+ * #copytype = npy_uint8, npy_uint16, npy_uint32, npy_uint64, copytype128, 0#
  */
 
 #if @elsize@
@@ -1679,7 +1698,13 @@ mapiter_@name@(
                                               _UINT_ALIGN(@copytype@)));
                         *(@copytype@ *)(outer_ptrs[i]) = *(@copytype@ *)self_ptr;
 #else
-                        copyswap(outer_ptrs[i], self_ptr, 0, array);
+                        char *args[2] = {self_ptr, outer_ptrs[i]};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #else /* !@isget@ */
 #if @elsize@
@@ -1689,7 +1714,13 @@ mapiter_@name@(
                                _UINT_ALIGN(@copytype@)));
                         *(@copytype@ *)self_ptr = *(@copytype@ *)(outer_ptrs[i]);
 #else
-                        copyswap(self_ptr, outer_ptrs[i], 0, array);
+                        char *args[2] = {outer_ptrs[i], self_ptr};
+                        if (NPY_UNLIKELY(cast_info->func(&cast_info->context,
+                                               args, &one, strides,
+                                               cast_info->auxdata) < 0)) {
+                                           NPY_END_THREADS;
+                                           return -1;
+                                       }
 #endif
 #endif
                         /* advance extra operand */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/mapping.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/mapping.c
index 98c2d7edaa2..50899aed897 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/mapping.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/mapping.c
@@ -160,7 +160,7 @@ PyArray_MapIterSwapAxes(PyArrayMapIterObject *mit, PyArrayObject **ret, int getm
     *ret = (PyArrayObject *)new;
 }
 
-static NPY_INLINE void
+static inline void
 multi_DECREF(PyObject **objects, npy_intp n)
 {
     npy_intp i;
@@ -176,7 +176,7 @@ multi_DECREF(PyObject **objects, npy_intp n)
  * Useful if a tuple is being iterated over multiple times, or for a code path
  * that doesn't always want the overhead of allocating a tuple.
  */
-static NPY_INLINE npy_intp
+static inline npy_intp
 unpack_tuple(PyTupleObject *index, PyObject **result, npy_intp result_n)
 {
     npy_intp n, i;
@@ -194,7 +194,7 @@ unpack_tuple(PyTupleObject *index, PyObject **result, npy_intp result_n)
 }
 
 /* Unpack a single scalar index, taking a new reference to match unpack_tuple */
-static NPY_INLINE npy_intp
+static inline npy_intp
 unpack_scalar(PyObject *index, PyObject **result, npy_intp NPY_UNUSED(result_n))
 {
     Py_INCREF(index);
@@ -889,13 +889,13 @@ get_view_from_index(PyArrayObject *self, PyArrayObject **view,
 
     /* Create the new view and set the base array */
     Py_INCREF(PyArray_DESCR(self));
-    *view = (PyArrayObject *)PyArray_NewFromDescrAndBase(
+    *view = (PyArrayObject *)PyArray_NewFromDescr_int(
             ensure_array ? &PyArray_Type : Py_TYPE(self),
             PyArray_DESCR(self),
             new_dim, new_shape, new_strides, data_ptr,
             PyArray_FLAGS(self),
             ensure_array ? NULL : (PyObject *)self,
-            (PyObject *)self);
+            (PyObject *)self, _NPY_ARRAY_ENSURE_DTYPE_IDENTITY);
     if (*view == NULL) {
         return -1;
     }
@@ -1361,7 +1361,8 @@ _get_field_view(PyArrayObject *arr, PyObject *ind, PyArrayObject **view)
                 PyArray_BYTES(arr) + offset,
                 PyArray_FLAGS(arr),
                 (PyObject *)arr, (PyObject *)arr,
-                0, 1);
+                /* We do not preserve the dtype for a subarray one, only str */
+                _NPY_ARRAY_ALLOW_EMPTY_STRING);
         if (*view == NULL) {
             return 0;
         }
@@ -1415,7 +1416,8 @@ _get_field_view(PyArrayObject *arr, PyObject *ind, PyArrayObject **view)
                 PyArray_DATA(arr),
                 PyArray_FLAGS(arr),
                 (PyObject *)arr, (PyObject *)arr,
-                0, 1);
+                /* We do not preserve the dtype for a subarray one, only str */
+                _NPY_ARRAY_ALLOW_EMPTY_STRING);
 
         if (*view == NULL) {
             return 0;
@@ -1563,7 +1565,20 @@ array_subscript(PyArrayObject *self, PyObject *op)
                 goto finish;
             }
 
-            if (mapiter_trivial_get(self, ind, (PyArrayObject *)result) < 0) {
+            NPY_ARRAYMETHOD_FLAGS transfer_flags;
+            npy_intp itemsize = PyArray_ITEMSIZE(self);
+            /* We can assume the newly allocated result is aligned */
+            int is_aligned = IsUintAligned(self);
+
+            if (PyArray_GetDTypeTransferFunction(is_aligned,
+                    itemsize, itemsize,
+                    PyArray_DESCR(self), PyArray_DESCR(self),
+                    0, &cast_info, &transfer_flags) != NPY_SUCCEED) {
+                goto finish;
+            }
+
+            if (mapiter_trivial_get(
+                    self, ind, (PyArrayObject *)result, is_aligned, &cast_info) < 0) {
                 Py_DECREF(result);
                 result = NULL;
                 goto finish;
@@ -1637,6 +1652,19 @@ array_subscript(PyArrayObject *self, PyObject *op)
         }
         meth_flags = PyArrayMethod_COMBINED_FLAGS(meth_flags, transfer_flags);
     }
+    else {
+        /* May need a generic copy function (only for refs and odd sizes) */
+        NPY_ARRAYMETHOD_FLAGS transfer_flags;
+        npy_intp itemsize = PyArray_ITEMSIZE(self);
+
+        if (PyArray_GetDTypeTransferFunction(1,
+                itemsize, itemsize,
+                PyArray_DESCR(self), PyArray_DESCR(self),
+                0, &cast_info, &transfer_flags) != NPY_SUCCEED) {
+            goto finish;
+        }
+        meth_flags = PyArrayMethod_COMBINED_FLAGS(meth_flags, transfer_flags);
+    }
 
     if (mapiter_get(mit, &cast_info, meth_flags, is_aligned) < 0) {
         goto finish;
@@ -1952,8 +1980,20 @@ array_assign_subscript(PyArrayObject *self, PyObject *ind, PyObject *op)
                 IsUintAligned(ind) &&
                 PyDataType_ISNOTSWAPPED(PyArray_DESCR(ind))) {
 
+            NPY_ARRAYMETHOD_FLAGS transfer_flags;
+            npy_intp itemsize = PyArray_ITEMSIZE(self);
+            int is_aligned = IsUintAligned(self) && IsUintAligned(tmp_arr);
+
+            if (PyArray_GetDTypeTransferFunction(is_aligned,
+                    itemsize, itemsize,
+                    PyArray_DESCR(self), PyArray_DESCR(self),
+                    0, &cast_info, &transfer_flags) != NPY_SUCCEED) {
+                goto fail;
+            }
+
             /* trivial_set checks the index for us */
-            if (mapiter_trivial_set(self, ind, tmp_arr) < 0) {
+            if (mapiter_trivial_set(
+                    self, ind, tmp_arr, is_aligned, &cast_info) < 0) {
                 goto fail;
             }
             goto success;
@@ -2034,6 +2074,19 @@ array_assign_subscript(PyArrayObject *self, PyObject *ind, PyObject *op)
         }
         meth_flags = PyArrayMethod_COMBINED_FLAGS(meth_flags, transfer_flags);
     }
+    else {
+        /* May need a generic copy function (only for refs and odd sizes) */
+        NPY_ARRAYMETHOD_FLAGS transfer_flags;
+        npy_intp itemsize = PyArray_ITEMSIZE(self);
+
+        if (PyArray_GetDTypeTransferFunction(1,
+                itemsize, itemsize,
+                PyArray_DESCR(self), PyArray_DESCR(self),
+                0, &cast_info, &transfer_flags) != NPY_SUCCEED) {
+            goto fail;
+        }
+        meth_flags = PyArrayMethod_COMBINED_FLAGS(meth_flags, transfer_flags);
+    }
 
     if (!(meth_flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) {
         npy_clear_floatstatus_barrier((char *)mit);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/methods.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/methods.c
index fff772769eb..d97c6f87d05 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/methods.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/methods.c
@@ -21,12 +21,14 @@
 #include "ctors.h"
 #include "calculation.h"
 #include "convert_datatype.h"
+#include "descriptor.h"
 #include "item_selection.h"
 #include "conversion_utils.h"
 #include "shape.h"
 #include "strfuncs.h"
 #include "array_assign.h"
 #include "npy_dlpack.h"
+#include "multiarraymodule.h"
 
 #include "methods.h"
 #include "alloc.h"
@@ -435,7 +437,7 @@ PyArray_GetField(PyArrayObject *self, PyArray_Descr *typed, int offset)
             PyArray_BYTES(self) + offset,
             PyArray_FLAGS(self) & ~NPY_ARRAY_F_CONTIGUOUS,
             (PyObject *)self, (PyObject *)self,
-            0, 1);
+            _NPY_ARRAY_ALLOW_EMPTY_STRING);
     return ret;
 }
 
@@ -851,29 +853,35 @@ static PyObject *
 array_astype(PyArrayObject *self,
         PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
-    PyArray_Descr *dtype = NULL;
     /*
      * TODO: UNSAFE default for compatibility, I think
      *       switching to SAME_KIND by default would be good.
      */
+    npy_dtype_info dt_info = {NULL, NULL};
     NPY_CASTING casting = NPY_UNSAFE_CASTING;
     NPY_ORDER order = NPY_KEEPORDER;
     _PyArray_CopyMode forcecopy = 1;
     int subok = 1;
+
     NPY_PREPARE_ARGPARSER;
     if (npy_parse_arguments("astype", args, len_args, kwnames,
-            "dtype", &PyArray_DescrConverter, &dtype,
+            "dtype", &PyArray_DTypeOrDescrConverterRequired, &dt_info,
             "|order", &PyArray_OrderConverter, &order,
             "|casting", &PyArray_CastingConverter, &casting,
             "|subok", &PyArray_PythonPyIntFromInt, &subok,
             "|copy", &PyArray_CopyConverter, &forcecopy,
             NULL, NULL, NULL) < 0) {
-        Py_XDECREF(dtype);
+        Py_XDECREF(dt_info.descr);
+        Py_XDECREF(dt_info.dtype);
         return NULL;
     }
 
     /* If it is not a concrete dtype instance find the best one for the array */
-    Py_SETREF(dtype, PyArray_AdaptDescriptorToArray(self, (PyObject *)dtype));
+    PyArray_Descr *dtype;
+
+    dtype = PyArray_AdaptDescriptorToArray(self, dt_info.dtype, dt_info.descr);
+    Py_XDECREF(dt_info.descr);
+    Py_DECREF(dt_info.dtype);
     if (dtype == NULL) {
         return NULL;
     }
@@ -916,29 +924,29 @@ array_astype(PyArrayObject *self,
 
     PyArrayObject *ret;
 
-    /* This steals the reference to dtype, so no DECREF of dtype */
+    /* This steals the reference to dtype */
+    Py_INCREF(dtype);
     ret = (PyArrayObject *)PyArray_NewLikeArray(
                                 self, order, dtype, subok);
     if (ret == NULL) {
+        Py_DECREF(dtype);
         return NULL;
     }
-    /* NumPy 1.20, 2020-10-01 */
-    if ((PyArray_NDIM(self) != PyArray_NDIM(ret)) &&
-            DEPRECATE_FUTUREWARNING(
-                "casting an array to a subarray dtype "
-                "will not use broadcasting in the future, but cast each "
-                "element to the new dtype and then append the dtype's shape "
-                "to the new array. You can opt-in to the new behaviour, by "
-                "additional field to the cast: "
-                "`arr.astype(np.dtype([('f', dtype)]))['f']`.\n"
-                "This may lead to a different result or to current failures "
-                "succeeding.  "
-                "(FutureWarning since NumPy 1.20)") < 0) {
-        Py_DECREF(ret);
-        return NULL;
+
+    /* Decrease the number of dimensions removing subarray ones again */
+    int out_ndim = PyArray_NDIM(ret);
+    PyArray_Descr *out_descr = PyArray_DESCR(ret);
+    if (out_ndim != PyArray_NDIM(self)) {
+        ((PyArrayObject_fields *)ret)->nd = PyArray_NDIM(self);
+        ((PyArrayObject_fields *)ret)->descr = dtype;
     }
+    int success = PyArray_CopyInto(ret, self);
+
+    Py_DECREF(dtype);
+    ((PyArrayObject_fields *)ret)->nd = out_ndim;
+    ((PyArrayObject_fields *)ret)->descr = out_descr;
 
-    if (PyArray_CopyInto(ret, self) < 0) {
+    if (success < 0) {
         Py_DECREF(ret);
         return NULL;
     }
@@ -1095,7 +1103,7 @@ any_array_ufunc_overrides(PyObject *args, PyObject *kwds)
     int nin, nout;
     PyObject *out_kwd_obj;
     PyObject *fast;
-    PyObject **in_objs, **out_objs;
+    PyObject **in_objs, **out_objs, *where_obj;
 
     /* check inputs */
     nin = PyTuple_Size(args);
@@ -1114,6 +1122,9 @@ any_array_ufunc_overrides(PyObject *args, PyObject *kwds)
         }
     }
     Py_DECREF(fast);
+    if (kwds == NULL) {
+        return 0;
+    }
     /* check outputs, if any */
     nout = PyUFuncOverride_GetOutObjects(kwds, &out_kwd_obj, &out_objs);
     if (nout < 0) {
@@ -1126,6 +1137,17 @@ any_array_ufunc_overrides(PyObject *args, PyObject *kwds)
         }
     }
     Py_DECREF(out_kwd_obj);
+    /* check where if it exists */
+    where_obj = PyDict_GetItemWithError(kwds, npy_ma_str_where);
+    if (where_obj == NULL) {
+        if (PyErr_Occurred()) {
+            return -1;
+        }
+    } else {
+        if (PyUFunc_HasOverride(where_obj)){
+            return 1;
+        }
+    }
     return 0;
 }
 
@@ -2691,7 +2713,11 @@ array_setflags(PyArrayObject *self, PyObject *args, PyObject *kwds)
         return NULL;
 
     if (align_flag != Py_None) {
-        if (PyObject_Not(align_flag)) {
+        int isnot = PyObject_Not(align_flag);
+        if (isnot == -1) {
+            return NULL;
+        }
+        if (isnot) {
             PyArray_CLEARFLAGS(self, NPY_ARRAY_ALIGNED);
         }
         else if (IsAligned(self)) {
@@ -2706,7 +2732,11 @@ array_setflags(PyArrayObject *self, PyObject *args, PyObject *kwds)
     }
 
     if (uic != Py_None) {
-        if (PyObject_IsTrue(uic)) {
+        int istrue = PyObject_IsTrue(uic);
+        if (istrue == -1) {
+            return NULL;
+        }
+        if (istrue) {
             fa->flags = flagback;
             PyErr_SetString(PyExc_ValueError,
                             "cannot set WRITEBACKIFCOPY "
@@ -2721,7 +2751,11 @@ array_setflags(PyArrayObject *self, PyObject *args, PyObject *kwds)
     }
 
     if (write_flag != Py_None) {
-        if (PyObject_IsTrue(write_flag)) {
+        int istrue = PyObject_IsTrue(write_flag);
+        if (istrue == -1) {
+            return NULL;
+        }
+        else if (istrue == 1) {
             if (_IsWriteable(self)) {
                 /*
                  * _IsWritable (and PyArray_UpdateFlags) allows flipping this,
@@ -2785,9 +2819,7 @@ array_complex(PyArrayObject *self, PyObject *NPY_UNUSED(args))
     PyArray_Descr *dtype;
     PyObject *c;
 
-    if (PyArray_SIZE(self) != 1) {
-        PyErr_SetString(PyExc_TypeError,
-                "only length-1 arrays can be converted to Python scalars");
+    if (check_is_convertible_to_scalar(self) < 0) {
         return NULL;
     }
 
@@ -2832,25 +2864,15 @@ array_complex(PyArrayObject *self, PyObject *NPY_UNUSED(args))
 static PyObject *
 array_class_getitem(PyObject *cls, PyObject *args)
 {
-    PyObject *generic_alias;
-
-#ifdef Py_GENERICALIASOBJECT_H
-    Py_ssize_t args_len;
+    const Py_ssize_t args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
 
-    args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
     if ((args_len > 2) || (args_len == 0)) {
         return PyErr_Format(PyExc_TypeError,
                             "Too %s arguments for %s",
                             args_len > 2 ? "many" : "few",
                             ((PyTypeObject *)cls)->tp_name);
     }
-    generic_alias = Py_GenericAlias(cls, args);
-#else
-    PyErr_SetString(PyExc_TypeError,
-                    "Type subscription requires python >= 3.9");
-    generic_alias = NULL;
-#endif
-    return generic_alias;
+    return Py_GenericAlias(cls, args);
 }
 
 NPY_NO_EXPORT PyMethodDef array_methods[] = {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.c
index b2925f75871..66305722d14 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.c
@@ -49,6 +49,8 @@ NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0;
 #include "convert_datatype.h"
 #include "conversion_utils.h"
 #include "nditer_pywrap.h"
+#define NPY_ITERATOR_IMPLEMENTATION_CODE
+#include "nditer_impl.h"
 #include "methods.h"
 #include "_datetime.h"
 #include "datetime_strings.h"
@@ -66,6 +68,9 @@ NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0;
 #include "compiled_base.h"
 #include "mem_overlap.h"
 #include "typeinfo.h"
+#include "convert.h" /* for PyArray_AssignZero */
+#include "lowlevel_strided_loops.h"
+#include "dtype_transfer.h"
 
 #include "get_attr_string.h"
 #include "experimental_public_dtype_api.h"  /* _get_experimental_dtype_api */
@@ -98,6 +103,12 @@ _umath_strings_richcompare(
  * future.
  */
 int npy_legacy_print_mode = INT_MAX;
+/*
+ * Global variable to check whether NumPy 2.0 behavior is opted in.
+ * This flag is considered a runtime constant.
+ */
+int npy_numpy2_behavior = NPY_FALSE;
+
 
 static PyObject *
 set_legacy_print_mode(PyObject *NPY_UNUSED(self), PyObject *args)
@@ -467,7 +478,7 @@ PyArray_ConcatenateArrays(int narrays, PyArrayObject **arrays, int axis,
         /* Get the priority subtype for the array */
         PyTypeObject *subtype = PyArray_GetSubType(narrays, arrays);
         PyArray_Descr *descr = PyArray_FindConcatenationDescriptor(
-                narrays, arrays,  (PyObject *)dtype);
+                narrays, arrays, dtype);
         if (descr == NULL) {
             return NULL;
         }
@@ -488,7 +499,7 @@ PyArray_ConcatenateArrays(int narrays, PyArrayObject **arrays, int axis,
         /* Allocate the array for the result. This steals the 'dtype' reference. */
         ret = (PyArrayObject *)PyArray_NewFromDescr_int(
                 subtype, descr, ndim, shape, strides, NULL, 0, NULL,
-                NULL, 0, 1);
+                NULL, _NPY_ARRAY_ALLOW_EMPTY_STRING);
         if (ret == NULL) {
             return NULL;
         }
@@ -583,7 +594,7 @@ PyArray_ConcatenateFlattenedArrays(int narrays, PyArrayObject **arrays,
         PyTypeObject *subtype = PyArray_GetSubType(narrays, arrays);
 
         PyArray_Descr *descr = PyArray_FindConcatenationDescriptor(
-                narrays, arrays, (PyObject *)dtype);
+                narrays, arrays, dtype);
         if (descr == NULL) {
             return NULL;
         }
@@ -593,7 +604,7 @@ PyArray_ConcatenateFlattenedArrays(int narrays, PyArrayObject **arrays,
         /* Allocate the array for the result. This steals the 'dtype' reference. */
         ret = (PyArrayObject *)PyArray_NewFromDescr_int(
                 subtype, descr,  1, &shape, &stride, NULL, 0, NULL,
-                NULL, 0, 1);
+                NULL, _NPY_ARRAY_ALLOW_EMPTY_STRING);
         if (ret == NULL) {
             return NULL;
         }
@@ -707,11 +718,13 @@ PyArray_ConcatenateInto(PyObject *op,
             goto fail;
         }
         arrays[iarrays] = (PyArrayObject *)PyArray_FROM_O(item);
-        Py_DECREF(item);
         if (arrays[iarrays] == NULL) {
+            Py_DECREF(item);
             narrays = iarrays;
             goto fail;
         }
+        npy_mark_tmp_array_if_pyscalar(item, arrays[iarrays], NULL);
+        Py_DECREF(item);
     }
 
     if (axis >= NPY_MAXDIMS) {
@@ -1036,12 +1049,11 @@ PyArray_MatrixProduct2(PyObject *op1, PyObject *op2, PyArrayObject* out)
 #endif
 
     if (PyArray_NDIM(ap1) == 0 || PyArray_NDIM(ap2) == 0) {
-        result = (PyArray_NDIM(ap1) == 0 ? ap1 : ap2);
-        result = (PyArrayObject *)Py_TYPE(result)->tp_as_number->nb_multiply(
-                                        (PyObject *)ap1, (PyObject *)ap2);
+        PyObject *mul_res = PyObject_CallFunctionObjArgs(
+                n_ops.multiply, ap1, ap2, out, NULL);
         Py_DECREF(ap1);
         Py_DECREF(ap2);
-        return (PyObject *)result;
+        return mul_res;
     }
     l = PyArray_DIMS(ap1)[PyArray_NDIM(ap1) - 1];
     if (PyArray_NDIM(ap2) > 1) {
@@ -1079,7 +1091,9 @@ PyArray_MatrixProduct2(PyObject *op1, PyObject *op2, PyArrayObject* out)
     }
     /* Ensure that multiarray.dot(<Nx0>,<0xM>) -> zeros((N,M)) */
     if (PyArray_SIZE(ap1) == 0 && PyArray_SIZE(ap2) == 0) {
-        memset(PyArray_DATA(out_buf), 0, PyArray_NBYTES(out_buf));
+        if (PyArray_AssignZero(out_buf, NULL) < 0) {
+            goto fail;
+        }
     }
 
     dot = PyArray_DESCR(out_buf)->f->dotfunc;
@@ -1492,19 +1506,26 @@ fail:
     return NULL;
 }
 
-
 static PyObject *
-array_putmask(PyObject *NPY_UNUSED(module), PyObject *args, PyObject *kwds)
+array_putmask(PyObject *NPY_UNUSED(module), PyObject *const *args,
+                Py_ssize_t len_args, PyObject *kwnames )
 {
     PyObject *mask, *values;
     PyObject *array;
 
-    static char *kwlist[] = {"arr", "mask", "values", NULL};
-
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "O!OO:putmask", kwlist,
-                &PyArray_Type, &array, &mask, &values)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("putmask", args, len_args, kwnames,
+            "", NULL, &array,
+            "mask", NULL, &mask,
+            "values", NULL, &values,
+            NULL, NULL, NULL) < 0) {
         return NULL;
     }
+    if (!PyArray_Check(array)) {
+        PyErr_SetString(PyExc_TypeError,
+                "argument a of putmask must be a numpy array");
+    }
+
     return PyArray_PutMask((PyArrayObject *)array, values, mask);
 }
 
@@ -1622,25 +1643,35 @@ _prepend_ones(PyArrayObject *arr, int nd, int ndmin, NPY_ORDER order)
                  ((order) == NPY_CORDER && PyArray_IS_C_CONTIGUOUS(op)) || \
                  ((order) == NPY_FORTRANORDER && PyArray_IS_F_CONTIGUOUS(op)))
 
-static NPY_INLINE PyObject *
+
+static inline PyObject *
 _array_fromobject_generic(
-        PyObject *op, PyArray_Descr *type, _PyArray_CopyMode copy, NPY_ORDER order,
-        npy_bool subok, int ndmin)
+        PyObject *op, PyArray_Descr *in_descr, PyArray_DTypeMeta *in_DType,
+        _PyArray_CopyMode copy, NPY_ORDER order, npy_bool subok, int ndmin)
 {
     PyArrayObject *oparr = NULL, *ret = NULL;
     PyArray_Descr *oldtype = NULL;
     int nd, flags = 0;
 
+    /* Hold on to `in_descr` as `dtype`, since we may also set it below. */
+    Py_XINCREF(in_descr);
+    PyArray_Descr *dtype = in_descr;
+
     if (ndmin > NPY_MAXDIMS) {
         PyErr_Format(PyExc_ValueError,
                 "ndmin bigger than allowable number of dimensions "
                 "NPY_MAXDIMS (=%d)", NPY_MAXDIMS);
-        return NULL;
+        goto finish;
     }
     /* fast exit if simple call */
     if (PyArray_CheckExact(op) || (subok && PyArray_Check(op))) {
         oparr = (PyArrayObject *)op;
-        if (type == NULL) {
+
+        if (dtype == NULL && in_DType == NULL) {
+            /*
+             * User did not ask for a specific dtype instance or class. So
+             * we can return either self or a copy.
+             */
             if (copy != NPY_COPY_ALWAYS && STRIDING_OK(oparr, order)) {
                 ret = oparr;
                 Py_INCREF(ret);
@@ -1650,17 +1681,30 @@ _array_fromobject_generic(
                 if (copy == NPY_COPY_NEVER) {
                     PyErr_SetString(PyExc_ValueError,
                             "Unable to avoid copy while creating a new array.");
-                    return NULL;
+                    goto finish;
                 }
                 ret = (PyArrayObject *)PyArray_NewCopy(oparr, order);
                 goto finish;
             }
         }
+        else if (dtype == NULL) {
+            /*
+             * If the user passed a DType class but not a dtype instance,
+             * we must use `PyArray_AdaptDescriptorToArray` to find the
+             * correct dtype instance.
+             * Even if the fast-path doesn't work we will use this.
+             */
+            dtype = PyArray_AdaptDescriptorToArray(oparr, in_DType, NULL);
+            if (dtype == NULL) {
+                goto finish;
+            }
+        }
+
         /* One more chance for faster exit if user specified the dtype. */
         oldtype = PyArray_DESCR(oparr);
-        if (PyArray_EquivTypes(oldtype, type)) {
+        if (PyArray_EquivTypes(oldtype, dtype)) {
             if (copy != NPY_COPY_ALWAYS && STRIDING_OK(oparr, order)) {
-                if (oldtype == type) {
+                if (oldtype == dtype) {
                     Py_INCREF(op);
                     ret = oparr;
                 }
@@ -1668,10 +1712,10 @@ _array_fromobject_generic(
                     /* Create a new PyArrayObject from the caller's
                      * PyArray_Descr. Use the reference `op` as the base
                      * object. */
-                    Py_INCREF(type);
+                    Py_INCREF(dtype);
                     ret = (PyArrayObject *)PyArray_NewFromDescrAndBase(
                             Py_TYPE(op),
-                            type,
+                            dtype,
                             PyArray_NDIM(oparr),
                             PyArray_DIMS(oparr),
                             PyArray_STRIDES(oparr),
@@ -1687,10 +1731,10 @@ _array_fromobject_generic(
                 if (copy == NPY_COPY_NEVER) {
                     PyErr_SetString(PyExc_ValueError,
                             "Unable to avoid copy while creating a new array.");
-                    return NULL;
+                    goto finish;
                 }
                 ret = (PyArrayObject *)PyArray_NewCopy(oparr, order);
-                if (oldtype == type || ret == NULL) {
+                if (oldtype == dtype || ret == NULL) {
                     goto finish;
                 }
                 Py_INCREF(oldtype);
@@ -1721,11 +1765,13 @@ _array_fromobject_generic(
     }
 
     flags |= NPY_ARRAY_FORCECAST;
-    Py_XINCREF(type);
-    ret = (PyArrayObject *)PyArray_CheckFromAny(op, type,
-                                                0, 0, flags, NULL);
+
+    ret = (PyArrayObject *)PyArray_CheckFromAny_int(
+            op, dtype, in_DType, 0, 0, flags, NULL);
 
 finish:
+    Py_XDECREF(dtype);
+
     if (ret == NULL) {
         return NULL;
     }
@@ -1752,7 +1798,7 @@ array_array(PyObject *NPY_UNUSED(ignored),
     npy_bool subok = NPY_FALSE;
     _PyArray_CopyMode copy = NPY_COPY_ALWAYS;
     int ndmin = 0;
-    PyArray_Descr *type = NULL;
+    npy_dtype_info dt_info = {NULL, NULL};
     NPY_ORDER order = NPY_KEEPORDER;
     PyObject *like = Py_None;
     NPY_PREPARE_ARGPARSER;
@@ -1760,21 +1806,23 @@ array_array(PyObject *NPY_UNUSED(ignored),
     if (len_args != 1 || (kwnames != NULL)) {
         if (npy_parse_arguments("array", args, len_args, kwnames,
                 "object", NULL, &op,
-                "|dtype", &PyArray_DescrConverter2, &type,
+                "|dtype", &PyArray_DTypeOrDescrConverterOptional, &dt_info,
                 "$copy", &PyArray_CopyConverter, &copy,
                 "$order", &PyArray_OrderConverter, &order,
                 "$subok", &PyArray_BoolConverter, &subok,
                 "$ndmin", &PyArray_PythonPyIntFromInt, &ndmin,
                 "$like", NULL, &like,
                 NULL, NULL, NULL) < 0) {
-            Py_XDECREF(type);
+            Py_XDECREF(dt_info.descr);
+            Py_XDECREF(dt_info.dtype);
             return NULL;
         }
         if (like != Py_None) {
             PyObject *deferred = array_implement_c_array_function_creation(
                     "array", like, NULL, NULL, args, len_args, kwnames);
             if (deferred != Py_NotImplemented) {
-                Py_XDECREF(type);
+                Py_XDECREF(dt_info.descr);
+                Py_XDECREF(dt_info.dtype);
                 return deferred;
             }
         }
@@ -1785,8 +1833,9 @@ array_array(PyObject *NPY_UNUSED(ignored),
     }
 
     PyObject *res = _array_fromobject_generic(
-            op, type, copy, order, subok, ndmin);
-    Py_XDECREF(type);
+            op, dt_info.descr, dt_info.dtype, copy, order, subok, ndmin);
+    Py_XDECREF(dt_info.descr);
+    Py_XDECREF(dt_info.dtype);
     return res;
 }
 
@@ -1795,7 +1844,7 @@ array_asarray(PyObject *NPY_UNUSED(ignored),
         PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *op;
-    PyArray_Descr *type = NULL;
+    npy_dtype_info dt_info = {NULL, NULL};
     NPY_ORDER order = NPY_KEEPORDER;
     PyObject *like = Py_None;
     NPY_PREPARE_ARGPARSER;
@@ -1803,18 +1852,20 @@ array_asarray(PyObject *NPY_UNUSED(ignored),
     if (len_args != 1 || (kwnames != NULL)) {
         if (npy_parse_arguments("asarray", args, len_args, kwnames,
                 "a", NULL, &op,
-                "|dtype", &PyArray_DescrConverter2, &type,
+                "|dtype", &PyArray_DTypeOrDescrConverterOptional, &dt_info,
                 "|order", &PyArray_OrderConverter, &order,
                 "$like", NULL, &like,
                 NULL, NULL, NULL) < 0) {
-            Py_XDECREF(type);
+            Py_XDECREF(dt_info.descr);
+            Py_XDECREF(dt_info.dtype);
             return NULL;
         }
         if (like != Py_None) {
             PyObject *deferred = array_implement_c_array_function_creation(
                     "asarray", like, NULL, NULL, args, len_args, kwnames);
             if (deferred != Py_NotImplemented) {
-                Py_XDECREF(type);
+                Py_XDECREF(dt_info.descr);
+                Py_XDECREF(dt_info.dtype);
                 return deferred;
             }
         }
@@ -1824,8 +1875,9 @@ array_asarray(PyObject *NPY_UNUSED(ignored),
     }
 
     PyObject *res = _array_fromobject_generic(
-            op, type, NPY_FALSE, order, NPY_FALSE, 0);
-    Py_XDECREF(type);
+            op, dt_info.descr, dt_info.dtype, NPY_FALSE, order, NPY_FALSE, 0);
+    Py_XDECREF(dt_info.descr);
+    Py_XDECREF(dt_info.dtype);
     return res;
 }
 
@@ -1834,7 +1886,7 @@ array_asanyarray(PyObject *NPY_UNUSED(ignored),
         PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *op;
-    PyArray_Descr *type = NULL;
+    npy_dtype_info dt_info = {NULL, NULL};
     NPY_ORDER order = NPY_KEEPORDER;
     PyObject *like = Py_None;
     NPY_PREPARE_ARGPARSER;
@@ -1842,18 +1894,20 @@ array_asanyarray(PyObject *NPY_UNUSED(ignored),
     if (len_args != 1 || (kwnames != NULL)) {
         if (npy_parse_arguments("asanyarray", args, len_args, kwnames,
                 "a", NULL, &op,
-                "|dtype", &PyArray_DescrConverter2, &type,
+                "|dtype", &PyArray_DTypeOrDescrConverterOptional, &dt_info,
                 "|order", &PyArray_OrderConverter, &order,
                 "$like", NULL, &like,
                 NULL, NULL, NULL) < 0) {
-            Py_XDECREF(type);
+            Py_XDECREF(dt_info.descr);
+            Py_XDECREF(dt_info.dtype);
             return NULL;
         }
         if (like != Py_None) {
             PyObject *deferred = array_implement_c_array_function_creation(
                     "asanyarray", like, NULL, NULL, args, len_args, kwnames);
             if (deferred != Py_NotImplemented) {
-                Py_XDECREF(type);
+                Py_XDECREF(dt_info.descr);
+                Py_XDECREF(dt_info.dtype);
                 return deferred;
             }
         }
@@ -1863,8 +1917,9 @@ array_asanyarray(PyObject *NPY_UNUSED(ignored),
     }
 
     PyObject *res = _array_fromobject_generic(
-            op, type, NPY_FALSE, order, NPY_TRUE, 0);
-    Py_XDECREF(type);
+            op, dt_info.descr, dt_info.dtype, NPY_FALSE, order, NPY_TRUE, 0);
+    Py_XDECREF(dt_info.descr);
+    Py_XDECREF(dt_info.dtype);
     return res;
 }
 
@@ -1874,24 +1929,26 @@ array_ascontiguousarray(PyObject *NPY_UNUSED(ignored),
         PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *op;
-    PyArray_Descr *type = NULL;
+    npy_dtype_info dt_info = {NULL, NULL};
     PyObject *like = Py_None;
     NPY_PREPARE_ARGPARSER;
 
     if (len_args != 1 || (kwnames != NULL)) {
         if (npy_parse_arguments("ascontiguousarray", args, len_args, kwnames,
                 "a", NULL, &op,
-                "|dtype", &PyArray_DescrConverter2, &type,
+                "|dtype", &PyArray_DTypeOrDescrConverterOptional, &dt_info,
                 "$like", NULL, &like,
                 NULL, NULL, NULL) < 0) {
-            Py_XDECREF(type);
+            Py_XDECREF(dt_info.descr);
+            Py_XDECREF(dt_info.dtype);
             return NULL;
         }
         if (like != Py_None) {
             PyObject *deferred = array_implement_c_array_function_creation(
                     "ascontiguousarray", like, NULL, NULL, args, len_args, kwnames);
             if (deferred != Py_NotImplemented) {
-                Py_XDECREF(type);
+                Py_XDECREF(dt_info.descr);
+                Py_XDECREF(dt_info.dtype);
                 return deferred;
             }
         }
@@ -1901,8 +1958,10 @@ array_ascontiguousarray(PyObject *NPY_UNUSED(ignored),
     }
 
     PyObject *res = _array_fromobject_generic(
-            op, type, NPY_FALSE, NPY_CORDER, NPY_FALSE, 1);
-    Py_XDECREF(type);
+            op, dt_info.descr, dt_info.dtype, NPY_FALSE, NPY_CORDER, NPY_FALSE,
+            1);
+    Py_XDECREF(dt_info.descr);
+    Py_XDECREF(dt_info.dtype);
     return res;
 }
 
@@ -1912,24 +1971,26 @@ array_asfortranarray(PyObject *NPY_UNUSED(ignored),
         PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *op;
-    PyArray_Descr *type = NULL;
+    npy_dtype_info dt_info = {NULL, NULL};
     PyObject *like = Py_None;
     NPY_PREPARE_ARGPARSER;
 
     if (len_args != 1 || (kwnames != NULL)) {
         if (npy_parse_arguments("asfortranarray", args, len_args, kwnames,
                 "a", NULL, &op,
-                "|dtype", &PyArray_DescrConverter2, &type,
+                "|dtype", &PyArray_DTypeOrDescrConverterOptional, &dt_info,
                 "$like", NULL, &like,
                 NULL, NULL, NULL) < 0) {
-            Py_XDECREF(type);
+            Py_XDECREF(dt_info.descr);
+            Py_XDECREF(dt_info.dtype);
             return NULL;
         }
         if (like != Py_None) {
             PyObject *deferred = array_implement_c_array_function_creation(
                     "asfortranarray", like, NULL, NULL, args, len_args, kwnames);
             if (deferred != Py_NotImplemented) {
-                Py_XDECREF(type);
+                Py_XDECREF(dt_info.descr);
+                Py_XDECREF(dt_info.dtype);
                 return deferred;
             }
         }
@@ -1939,8 +2000,10 @@ array_asfortranarray(PyObject *NPY_UNUSED(ignored),
     }
 
     PyObject *res = _array_fromobject_generic(
-            op, type, NPY_FALSE, NPY_FORTRANORDER, NPY_FALSE, 1);
-    Py_XDECREF(type);
+            op, dt_info.descr, dt_info.dtype, NPY_FALSE, NPY_FORTRANORDER,
+            NPY_FALSE, 1);
+    Py_XDECREF(dt_info.descr);
+    Py_XDECREF(dt_info.dtype);
     return res;
 }
 
@@ -2046,10 +2109,9 @@ fail:
 }
 
 static PyObject *
-array_empty_like(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds)
+array_empty_like(PyObject *NPY_UNUSED(ignored),
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
-
-    static char *kwlist[] = {"prototype", "dtype", "order", "subok", "shape", NULL};
     PyArrayObject *prototype = NULL;
     PyArray_Descr *dtype = NULL;
     NPY_ORDER order = NPY_KEEPORDER;
@@ -2058,12 +2120,15 @@ array_empty_like(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds)
     /* -1 is a special value meaning "not specified" */
     PyArray_Dims shape = {NULL, -1};
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "O&|O&O&iO&:empty_like", kwlist,
-                &PyArray_Converter, &prototype,
-                &PyArray_DescrConverter2, &dtype,
-                &PyArray_OrderConverter, &order,
-                &subok,
-                &PyArray_OptionalIntpConverter, &shape)) {
+    NPY_PREPARE_ARGPARSER;
+
+    if (npy_parse_arguments("empty_like", args, len_args, kwnames,
+            "prototype", &PyArray_Converter, &prototype,
+            "|dtype", &PyArray_DescrConverter2, &dtype,
+            "|order", &PyArray_OrderConverter, &order,
+            "|subok", &PyArray_PythonPyIntFromInt, &subok,
+            "|shape", &PyArray_OptionalIntpConverter, &shape,
+            NULL, NULL, NULL) < 0) {
         goto fail;
     }
     /* steals the reference to dtype if it's not NULL */
@@ -2249,12 +2314,15 @@ fail:
 }
 
 static PyObject *
-array_count_nonzero(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds)
+array_count_nonzero(PyObject *NPY_UNUSED(self), PyObject *const *args, Py_ssize_t len_args)
 {
     PyArrayObject *array;
     npy_intp count;
 
-    if (!PyArg_ParseTuple(args, "O&:count_nonzero", PyArray_Converter, &array)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("count_nonzero", args, len_args, NULL,
+            "", PyArray_Converter, &array,
+            NULL, NULL, NULL) < 0) {
         return NULL;
     }
 
@@ -2460,7 +2528,8 @@ array_frombuffer(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *keywds
 }
 
 static PyObject *
-array_concatenate(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
+array_concatenate(PyObject *NPY_UNUSED(dummy),
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *a0;
     PyObject *out = NULL;
@@ -2469,10 +2538,15 @@ array_concatenate(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
     PyObject *casting_obj = NULL;
     PyObject *res;
     int axis = 0;
-    static char *kwlist[] = {"seq", "axis", "out", "dtype", "casting", NULL};
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|O&O$O&O:concatenate", kwlist,
-                &a0, PyArray_AxisConverter, &axis, &out,
-                PyArray_DescrConverter2, &dtype, &casting_obj)) {
+
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("concatenate", args, len_args, kwnames,
+            "seq", NULL, &a0,
+            "|axis", &PyArray_AxisConverter, &axis,
+            "|out", NULL, &out,
+            "$dtype", &PyArray_DescrConverter2, &dtype,
+            "$casting", NULL, &casting_obj,
+            NULL, NULL, NULL) < 0) {
         return NULL;
     }
     int casting_not_passed = 0;
@@ -2504,25 +2578,34 @@ array_concatenate(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
 }
 
 static PyObject *
-array_innerproduct(PyObject *NPY_UNUSED(dummy), PyObject *args)
+array_innerproduct(PyObject *NPY_UNUSED(dummy), PyObject *const *args, Py_ssize_t len_args)
 {
     PyObject *b0, *a0;
 
-    if (!PyArg_ParseTuple(args, "OO:innerproduct", &a0, &b0)) {
-        return NULL;
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("innerproduct", args, len_args, NULL,
+            "", NULL, &a0,
+            "", NULL, &b0,
+            NULL, NULL, NULL) < 0) {
+    return NULL;
     }
+
     return PyArray_Return((PyArrayObject *)PyArray_InnerProduct(a0, b0));
 }
 
 static PyObject *
-array_matrixproduct(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject* kwds)
+array_matrixproduct(PyObject *NPY_UNUSED(dummy),
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *v, *a, *o = NULL;
     PyArrayObject *ret;
-    static char* kwlist[] = {"a", "b", "out", NULL};
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "OO|O:matrixproduct",
-                                     kwlist, &a, &v, &o)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("dot", args, len_args, kwnames,
+            "a", NULL, &a,
+            "b", NULL, &v,
+            "|out", NULL, &o,
+            NULL, NULL, NULL) < 0) {
         return NULL;
     }
     if (o != NULL) {
@@ -2540,7 +2623,7 @@ array_matrixproduct(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject* kwds)
 
 
 static PyObject *
-array_vdot(PyObject *NPY_UNUSED(dummy), PyObject *args)
+array_vdot(PyObject *NPY_UNUSED(dummy), PyObject *const *args, Py_ssize_t len_args)
 {
     int typenum;
     char *ip1, *ip2, *op;
@@ -2553,7 +2636,11 @@ array_vdot(PyObject *NPY_UNUSED(dummy), PyObject *args)
     PyArray_DotFunc *vdot;
     NPY_BEGIN_THREADS_DEF;
 
-    if (!PyArg_ParseTuple(args, "OO:vdot", &op1, &op2)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("vdot", args, len_args, NULL,
+            "", NULL, &op1,
+            "", NULL, &op2,
+            NULL, NULL, NULL) < 0) {
         return NULL;
     }
 
@@ -3130,13 +3217,9 @@ PyArray_GetNDArrayCFeatureVersion(void)
 }
 
 static PyObject *
-array__get_ndarray_c_version(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
+array__get_ndarray_c_version(
+        PyObject *NPY_UNUSED(dummy), PyObject *NPY_UNUSED(arg))
 {
-    static char *kwlist[] = {NULL};
-
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "", kwlist )) {
-        return NULL;
-    }
     return PyLong_FromLong( (long) PyArray_GetNDArrayCVersion() );
 }
 
@@ -3304,6 +3387,8 @@ PyArray_Where(PyObject *condition, PyObject *x, PyObject *y)
         return NULL;
     }
 
+    NPY_cast_info cast_info = {.func = NULL};
+
     ax = (PyArrayObject*)PyArray_FROM_O(x);
     ay = (PyArrayObject*)PyArray_FROM_O(y);
     if (ax == NULL || ay == NULL) {
@@ -3317,14 +3402,15 @@ PyArray_Where(PyObject *condition, PyObject *x, PyObject *y)
         };
         npy_uint32 op_flags[4] = {
             NPY_ITER_WRITEONLY | NPY_ITER_ALLOCATE | NPY_ITER_NO_SUBTYPE,
-            NPY_ITER_READONLY, NPY_ITER_READONLY, NPY_ITER_READONLY
+            NPY_ITER_READONLY,
+            NPY_ITER_READONLY | NPY_ITER_ALIGNED,
+            NPY_ITER_READONLY | NPY_ITER_ALIGNED
         };
         PyArray_Descr * common_dt = PyArray_ResultType(2, &op_in[0] + 2,
                                                        0, NULL);
         PyArray_Descr * op_dt[4] = {common_dt, PyArray_DescrFromType(NPY_BOOL),
                                     common_dt, common_dt};
         NpyIter * iter;
-        int needs_api;
         NPY_BEGIN_THREADS_DEF;
 
         if (common_dt == NULL || op_dt[1] == NULL) {
@@ -3341,61 +3427,94 @@ PyArray_Where(PyObject *condition, PyObject *x, PyObject *y)
             goto fail;
         }
 
-        needs_api = NpyIter_IterationNeedsAPI(iter);
-
         /* Get the result from the iterator object array */
         ret = (PyObject*)NpyIter_GetOperandArray(iter)[0];
 
-        NPY_BEGIN_THREADS_NDITER(iter);
+        npy_intp itemsize = common_dt->elsize;
+
+        int has_ref = PyDataType_REFCHK(common_dt);
+
+        NPY_ARRAYMETHOD_FLAGS transfer_flags = 0;
+
+        npy_intp transfer_strides[2] = {itemsize, itemsize};
+        npy_intp one = 1;
+
+        if (has_ref || ((itemsize != 16) && (itemsize != 8) && (itemsize != 4) &&
+                        (itemsize != 2) && (itemsize != 1))) {
+            // The iterator has NPY_ITER_ALIGNED flag so no need to check alignment
+            // of the input arrays.
+            //
+            // There's also no need to set up a cast for y, since the iterator
+            // ensures both casts are identical.
+            if (PyArray_GetDTypeTransferFunction(
+                    1, itemsize, itemsize, common_dt, common_dt, 0,
+                    &cast_info, &transfer_flags) != NPY_SUCCEED) {
+                goto fail;
+            }
+        }
+
+        transfer_flags = PyArrayMethod_COMBINED_FLAGS(
+            transfer_flags, NpyIter_GetTransferFlags(iter));
+
+        if (!(transfer_flags & NPY_METH_REQUIRES_PYAPI)) {
+            NPY_BEGIN_THREADS_THRESHOLDED(NpyIter_GetIterSize(iter));
+        }
 
         if (NpyIter_GetIterSize(iter) != 0) {
             NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter, NULL);
             npy_intp * innersizeptr = NpyIter_GetInnerLoopSizePtr(iter);
             char **dataptrarray = NpyIter_GetDataPtrArray(iter);
+            npy_intp *strides = NpyIter_GetInnerStrideArray(iter);
 
             do {
-                PyArray_Descr * dtx = NpyIter_GetDescrArray(iter)[2];
-                PyArray_Descr * dty = NpyIter_GetDescrArray(iter)[3];
-                int axswap = PyDataType_ISBYTESWAPPED(dtx);
-                int ayswap = PyDataType_ISBYTESWAPPED(dty);
-                PyArray_CopySwapFunc *copyswapx = dtx->f->copyswap;
-                PyArray_CopySwapFunc *copyswapy = dty->f->copyswap;
-                int native = (axswap == ayswap) && (axswap == 0) && !needs_api;
                 npy_intp n = (*innersizeptr);
-                npy_intp itemsize = NpyIter_GetDescrArray(iter)[0]->elsize;
-                npy_intp cstride = NpyIter_GetInnerStrideArray(iter)[1];
-                npy_intp xstride = NpyIter_GetInnerStrideArray(iter)[2];
-                npy_intp ystride = NpyIter_GetInnerStrideArray(iter)[3];
                 char * dst = dataptrarray[0];
                 char * csrc = dataptrarray[1];
                 char * xsrc = dataptrarray[2];
                 char * ysrc = dataptrarray[3];
 
+                // the iterator might mutate these pointers,
+                // so need to update them every iteration
+                npy_intp cstride = strides[1];
+                npy_intp xstride = strides[2];
+                npy_intp ystride = strides[3];
+
                 /* constant sizes so compiler replaces memcpy */
-                if (native && itemsize == 16) {
+                if (!has_ref && itemsize == 16) {
                     INNER_WHERE_LOOP(16);
                 }
-                else if (native && itemsize == 8) {
+                else if (!has_ref && itemsize == 8) {
                     INNER_WHERE_LOOP(8);
                 }
-                else if (native && itemsize == 4) {
+                else if (!has_ref && itemsize == 4) {
                     INNER_WHERE_LOOP(4);
                 }
-                else if (native && itemsize == 2) {
+                else if (!has_ref && itemsize == 2) {
                     INNER_WHERE_LOOP(2);
                 }
-                else if (native && itemsize == 1) {
+                else if (!has_ref && itemsize == 1) {
                     INNER_WHERE_LOOP(1);
                 }
                 else {
-                    /* copyswap is faster than memcpy even if we are native */
                     npy_intp i;
                     for (i = 0; i < n; i++) {
                         if (*csrc) {
-                            copyswapx(dst, xsrc, axswap, ret);
+                            char *args[2] = {xsrc, dst};
+
+                            if (cast_info.func(
+                                    &cast_info.context, args, &one,
+                                    transfer_strides, cast_info.auxdata) < 0) {
+                                goto fail;
+                            }
                         }
                         else {
-                            copyswapy(dst, ysrc, ayswap, ret);
+                            char *args[2] = {ysrc, dst};
+
+                            if (cast_info.func(
+                                    &cast_info.context, args, &one,
+                                    transfer_strides, cast_info.auxdata) < 0) {
+                                goto fail;
+                            }
                         }
                         dst += itemsize;
                         xsrc += xstride;
@@ -3412,6 +3531,7 @@ PyArray_Where(PyObject *condition, PyObject *x, PyObject *y)
         Py_DECREF(arr);
         Py_DECREF(ax);
         Py_DECREF(ay);
+        NPY_cast_info_xfree(&cast_info);
 
         if (NpyIter_Deallocate(iter) != NPY_SUCCEED) {
             Py_DECREF(ret);
@@ -3425,38 +3545,49 @@ fail:
     Py_DECREF(arr);
     Py_XDECREF(ax);
     Py_XDECREF(ay);
+    NPY_cast_info_xfree(&cast_info);
     return NULL;
 }
 
 #undef INNER_WHERE_LOOP
 
 static PyObject *
-array_where(PyObject *NPY_UNUSED(ignored), PyObject *args)
+array_where(PyObject *NPY_UNUSED(ignored), PyObject *const *args, Py_ssize_t len_args)
 {
     PyObject *obj = NULL, *x = NULL, *y = NULL;
 
-    if (!PyArg_ParseTuple(args, "O|OO:where", &obj, &x, &y)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("where", args, len_args, NULL,
+            "", NULL, &obj,
+            "|x", NULL, &x,
+            "|y", NULL, &y,
+            NULL, NULL, NULL) < 0) {
         return NULL;
     }
+
     return PyArray_Where(obj, x, y);
 }
 
 static PyObject *
-array_lexsort(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds)
+array_lexsort(PyObject *NPY_UNUSED(ignored), PyObject *const *args, Py_ssize_t len_args,
+                             PyObject *kwnames)
 {
     int axis = -1;
     PyObject *obj;
-    static char *kwlist[] = {"keys", "axis", NULL};
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|i:lexsort", kwlist, &obj, &axis)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("lexsort", args, len_args, kwnames,
+            "keys", NULL, &obj,
+            "|axis", PyArray_PythonPyIntFromInt, &axis,
+            NULL, NULL, NULL) < 0) {
         return NULL;
     }
     return PyArray_Return((PyArrayObject *)PyArray_LexSort(obj, axis));
 }
 
 static PyObject *
-array_can_cast_safely(PyObject *NPY_UNUSED(self), PyObject *args,
-        PyObject *kwds)
+array_can_cast_safely(PyObject *NPY_UNUSED(self),
+        PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
 {
     PyObject *from_obj = NULL;
     PyArray_Descr *d1 = NULL;
@@ -3464,12 +3595,13 @@ array_can_cast_safely(PyObject *NPY_UNUSED(self), PyObject *args,
     int ret;
     PyObject *retobj = NULL;
     NPY_CASTING casting = NPY_SAFE_CASTING;
-    static char *kwlist[] = {"from_", "to", "casting", NULL};
 
-    if(!PyArg_ParseTupleAndKeywords(args, kwds, "OO&|O&:can_cast", kwlist,
-                &from_obj,
-                PyArray_DescrConverter2, &d2,
-                PyArray_CastingConverter, &casting)) {
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("can_cast", args, len_args, kwnames,
+            "from_", NULL, &from_obj,
+            "to", &PyArray_DescrConverter2, &d2,
+            "|casting", &PyArray_CastingConverter, &casting,
+            NULL, NULL, NULL) < 0) {
         goto finish;
     }
     if (d2 == NULL) {
@@ -3512,13 +3644,17 @@ array_can_cast_safely(PyObject *NPY_UNUSED(self), PyObject *args,
 }
 
 static PyObject *
-array_promote_types(PyObject *NPY_UNUSED(dummy), PyObject *args)
+array_promote_types(PyObject *NPY_UNUSED(dummy), PyObject *const *args, Py_ssize_t len_args)
 {
     PyArray_Descr *d1 = NULL;
     PyArray_Descr *d2 = NULL;
     PyObject *ret = NULL;
-    if (!PyArg_ParseTuple(args, "O&O&:promote_types",
-                PyArray_DescrConverter2, &d1, PyArray_DescrConverter2, &d2)) {
+
+    NPY_PREPARE_ARGPARSER;
+    if (npy_parse_arguments("promote_types", args, len_args, NULL,
+            "", PyArray_DescrConverter2, &d1,
+            "", PyArray_DescrConverter2, &d2,
+            NULL, NULL, NULL) < 0) {
         goto finish;
     }
 
@@ -3558,14 +3694,13 @@ array_min_scalar_type(PyObject *NPY_UNUSED(dummy), PyObject *args)
 }
 
 static PyObject *
-array_result_type(PyObject *NPY_UNUSED(dummy), PyObject *args)
+array_result_type(PyObject *NPY_UNUSED(dummy), PyObject *const *args, Py_ssize_t len)
 {
-    npy_intp i, len, narr = 0, ndtypes = 0;
+    npy_intp i, narr = 0, ndtypes = 0;
     PyArrayObject **arr = NULL;
     PyArray_Descr **dtypes = NULL;
     PyObject *ret = NULL;
 
-    len = PyTuple_GET_SIZE(args);
     if (len == 0) {
         PyErr_SetString(PyExc_ValueError,
                         "at least one array or dtype is required");
@@ -3579,7 +3714,7 @@ array_result_type(PyObject *NPY_UNUSED(dummy), PyObject *args)
     dtypes = (PyArray_Descr**)&arr[len];
 
     for (i = 0; i < len; ++i) {
-        PyObject *obj = PyTuple_GET_ITEM(args, i);
+        PyObject *obj = args[i];
         if (PyArray_Check(obj)) {
             Py_INCREF(obj);
             arr[narr] = (PyArrayObject *)obj;
@@ -3785,6 +3920,34 @@ format_longfloat(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
                               TrimMode_LeaveOneZero, -1, -1);
 }
 
+/*
+ * returns 1 if array is a user-defined string dtype, sets an error and
+ * returns 0 otherwise
+ */
+static int _is_user_defined_string_array(PyArrayObject* array)
+{
+    if (NPY_DT_is_user_defined(PyArray_DESCR(array))) {
+        PyTypeObject* scalar_type = NPY_DTYPE(PyArray_DESCR(array))->scalar_type;
+        if (PyType_IsSubtype(scalar_type, &PyBytes_Type) ||
+            PyType_IsSubtype(scalar_type, &PyUnicode_Type)) {
+            return 1;
+        }
+        else {
+            PyErr_SetString(
+                PyExc_TypeError,
+                "string comparisons are only allowed for dtypes with a "
+                "scalar type that is a subtype of str or bytes.");
+            return 0;
+        }
+    }
+    else {
+        PyErr_SetString(
+            PyExc_TypeError,
+            "string operation on non-string array");
+        return 0;
+    }
+}
+
 
 /*
  * The only purpose of this function is that it allows the "rstrip".
@@ -3861,6 +4024,9 @@ compare_chararrays(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds)
     else {
         PyErr_SetString(PyExc_TypeError,
                 "comparison of non-string arrays");
+        Py_DECREF(newarr);
+        Py_DECREF(newoth);
+        return NULL;
     }
     Py_DECREF(newarr);
     Py_DECREF(newoth);
@@ -4061,10 +4227,15 @@ _vec_string(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *NPY_UNUSED(kw
         method = PyObject_GetAttr((PyObject *)&PyUnicode_Type, method_name);
     }
     else {
-        PyErr_SetString(PyExc_TypeError,
-                "string operation on non-string array");
-        Py_DECREF(type);
-        goto err;
+        if (_is_user_defined_string_array(char_array)) {
+            PyTypeObject* scalar_type =
+                NPY_DTYPE(PyArray_DESCR(char_array))->scalar_type;
+            method = PyObject_GetAttr((PyObject*)scalar_type, method_name);
+        }
+        else {
+            Py_DECREF(type);
+            goto err;
+        }
     }
     if (method == NULL) {
         Py_DECREF(type);
@@ -4100,7 +4271,7 @@ _vec_string(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *NPY_UNUSED(kw
     return 0;
 }
 
-#ifndef __NPY_PRIVATE_NO_SIGNAL
+#ifndef NPY_NO_SIGNAL
 
 static NPY_TLS int sigint_buf_init = 0;
 static NPY_TLS NPY_SIGJMP_BUF _NPY_SIGINT_BUF;
@@ -4162,7 +4333,6 @@ array_shares_memory_impl(PyObject *args, PyObject *kwds, Py_ssize_t default_max_
     static char *kwlist[] = {"self", "other", "max_work", NULL};
 
     mem_overlap_t result;
-    static PyObject *too_hard_cls = NULL;
     Py_ssize_t max_work;
     NPY_BEGIN_THREADS_DEF;
 
@@ -4242,8 +4412,8 @@ array_shares_memory_impl(PyObject *args, PyObject *kwds, Py_ssize_t default_max_
     }
     else if (result == MEM_OVERLAP_TOO_HARD) {
         if (raise_exceptions) {
-            npy_cache_import("numpy.core._exceptions", "TooHardError",
-                             &too_hard_cls);
+            static PyObject *too_hard_cls = NULL;
+            npy_cache_import("numpy.exceptions", "TooHardError", &too_hard_cls);
             if (too_hard_cls) {
                 PyErr_SetString(too_hard_cls, "Exceeded max_work");
             }
@@ -4306,6 +4476,24 @@ normalize_axis_index(PyObject *NPY_UNUSED(self),
 
 
 static PyObject *
+_set_numpy_warn_if_no_mem_policy(PyObject *NPY_UNUSED(self), PyObject *arg)
+{
+    int res = PyObject_IsTrue(arg);
+    if (res < 0) {
+        return NULL;
+    }
+    int old_value = numpy_warn_if_no_mem_policy;
+    numpy_warn_if_no_mem_policy = res;
+    if (old_value) {
+        Py_RETURN_TRUE;
+    }
+    else {
+        Py_RETURN_FALSE;
+    }
+}
+
+
+static PyObject *
 _reload_guard(PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args)) {
     static int initialized = 0;
 
@@ -4340,13 +4528,22 @@ _reload_guard(PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args)) {
     Py_RETURN_NONE;
 }
 
+
+static PyObject *
+_using_numpy2_behavior(
+        PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args))
+{
+    return PyBool_FromLong(npy_numpy2_behavior);
+}
+
+
 static struct PyMethodDef array_module_methods[] = {
     {"_get_implementing_args",
         (PyCFunction)array__get_implementing_args,
         METH_VARARGS, NULL},
     {"_get_ndarray_c_version",
         (PyCFunction)array__get_ndarray_c_version,
-        METH_VARARGS|METH_KEYWORDS, NULL},
+        METH_NOARGS, NULL},
     {"_reconstruct",
         (PyCFunction)array__reconstruct,
         METH_VARARGS, NULL},
@@ -4391,25 +4588,25 @@ static struct PyMethodDef array_module_methods[] = {
         METH_FASTCALL | METH_KEYWORDS, NULL},
     {"count_nonzero",
         (PyCFunction)array_count_nonzero,
-        METH_VARARGS|METH_KEYWORDS, NULL},
+        METH_FASTCALL, NULL},
     {"empty",
         (PyCFunction)array_empty,
         METH_FASTCALL | METH_KEYWORDS, NULL},
     {"empty_like",
         (PyCFunction)array_empty_like,
-        METH_VARARGS|METH_KEYWORDS, NULL},
+        METH_FASTCALL|METH_KEYWORDS, NULL},
     {"scalar",
         (PyCFunction)array_scalar,
         METH_VARARGS|METH_KEYWORDS, NULL},
     {"where",
         (PyCFunction)array_where,
-        METH_VARARGS, NULL},
+        METH_FASTCALL, NULL},
     {"lexsort",
         (PyCFunction)array_lexsort,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"putmask",
         (PyCFunction)array_putmask,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"fromstring",
         (PyCFunction)array_fromstring,
         METH_VARARGS|METH_KEYWORDS, NULL},
@@ -4418,16 +4615,16 @@ static struct PyMethodDef array_module_methods[] = {
         METH_VARARGS|METH_KEYWORDS, NULL},
     {"concatenate",
         (PyCFunction)array_concatenate,
-        METH_VARARGS|METH_KEYWORDS, NULL},
+        METH_FASTCALL|METH_KEYWORDS, NULL},
     {"inner",
         (PyCFunction)array_innerproduct,
-        METH_VARARGS, NULL},
+        METH_FASTCALL, NULL},
     {"dot",
         (PyCFunction)array_matrixproduct,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"vdot",
         (PyCFunction)array_vdot,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL, NULL},
     {"c_einsum",
         (PyCFunction)array_einsum,
         METH_VARARGS|METH_KEYWORDS, NULL},
@@ -4448,16 +4645,16 @@ static struct PyMethodDef array_module_methods[] = {
         METH_VARARGS | METH_KEYWORDS, NULL},
     {"can_cast",
         (PyCFunction)array_can_cast_safely,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"promote_types",
         (PyCFunction)array_promote_types,
-        METH_VARARGS, NULL},
+        METH_FASTCALL, NULL},
     {"min_scalar_type",
         (PyCFunction)array_min_scalar_type,
         METH_VARARGS, NULL},
     {"result_type",
         (PyCFunction)array_result_type,
-        METH_VARARGS, NULL},
+        METH_FASTCALL, NULL},
     {"shares_memory",
         (PyCFunction)array_shares_memory,
         METH_VARARGS | METH_KEYWORDS, NULL},
@@ -4496,27 +4693,24 @@ static struct PyMethodDef array_module_methods[] = {
     {"_vec_string",
         (PyCFunction)_vec_string,
         METH_VARARGS | METH_KEYWORDS, NULL},
-    {"_insert", (PyCFunction)arr_insert,
+    {"_place", (PyCFunction)arr_place,
         METH_VARARGS | METH_KEYWORDS,
         "Insert vals sequentially into equivalent 1-d positions "
         "indicated by mask."},
     {"bincount", (PyCFunction)arr_bincount,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"_monotonicity", (PyCFunction)arr__monotonicity,
         METH_VARARGS | METH_KEYWORDS, NULL},
-    {"implement_array_function",
-        (PyCFunction)array_implement_array_function,
-        METH_VARARGS, NULL},
     {"interp", (PyCFunction)arr_interp,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"interp_complex", (PyCFunction)arr_interp_complex,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"ravel_multi_index", (PyCFunction)arr_ravel_multi_index,
         METH_VARARGS | METH_KEYWORDS, NULL},
     {"unravel_index", (PyCFunction)arr_unravel_index,
         METH_VARARGS | METH_KEYWORDS, NULL},
     {"add_docstring", (PyCFunction)arr_add_docstring,
-        METH_VARARGS, NULL},
+        METH_FASTCALL, NULL},
     {"packbits", (PyCFunction)io_pack,
         METH_VARARGS | METH_KEYWORDS, NULL},
     {"unpackbits", (PyCFunction)io_unpack,
@@ -4526,7 +4720,7 @@ static struct PyMethodDef array_module_methods[] = {
     {"set_legacy_print_mode", (PyCFunction)set_legacy_print_mode,
         METH_VARARGS, NULL},
     {"_discover_array_parameters", (PyCFunction)_discover_array_parameters,
-        METH_VARARGS | METH_KEYWORDS, NULL},
+        METH_FASTCALL | METH_KEYWORDS, NULL},
     {"_get_castingimpl",  (PyCFunction)_get_castingimpl,
         METH_VARARGS | METH_KEYWORDS, NULL},
     {"_get_experimental_dtype_api", (PyCFunction)_get_experimental_dtype_api,
@@ -4539,10 +4733,10 @@ static struct PyMethodDef array_module_methods[] = {
         METH_VARARGS | METH_KEYWORDS, NULL},
     {"seterrobj",
         (PyCFunction) ufunc_seterr,
-        METH_VARARGS, NULL},
+        METH_O, NULL},
     {"geterrobj",
         (PyCFunction) ufunc_geterr,
-        METH_VARARGS, NULL},
+        METH_NOARGS, NULL},
     {"get_handler_name",
         (PyCFunction) get_handler_name,
         METH_VARARGS, NULL},
@@ -4557,6 +4751,9 @@ static struct PyMethodDef array_module_methods[] = {
          METH_O, "Set the NEP 50 promotion state.  This is not thread-safe.\n"
                  "The optional warnings can be safely silenced using the \n"
                  "`np._no_nep50_warning()` context manager."},
+    {"_set_numpy_warn_if_no_mem_policy",
+         (PyCFunction)_set_numpy_warn_if_no_mem_policy,
+         METH_O, "Change the warn if no mem policy flag for testing."},
     {"_add_newdoc_ufunc", (PyCFunction)add_newdoc_ufunc,
         METH_VARARGS, NULL},
     {"_get_sfloat_dtype",
@@ -4568,6 +4765,8 @@ static struct PyMethodDef array_module_methods[] = {
     {"_reload_guard", (PyCFunction)_reload_guard,
         METH_NOARGS,
         "Give a warning on reload and big warning in sub-interpreters."},
+    {"_using_numpy2_behavior", (PyCFunction)_using_numpy2_behavior,
+        METH_NOARGS, NULL},
     {"from_dlpack", (PyCFunction)from_dlpack,
         METH_O, NULL},
     {NULL, NULL, 0, NULL}                /* sentinel */
@@ -4753,6 +4952,7 @@ NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_axis1 = NULL;
 NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_axis2 = NULL;
 NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_like = NULL;
 NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_numpy = NULL;
+NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_where = NULL;
 
 static int
 intern_strings(void)
@@ -4809,9 +5009,59 @@ intern_strings(void)
     if (npy_ma_str_numpy == NULL) {
         return -1;
     }
+    npy_ma_str_where = PyUnicode_InternFromString("where");
+    if (npy_ma_str_where == NULL) {
+        return -1;
+    }
     return 0;
 }
 
+
+/*
+ * Initializes global constants.  At some points these need to be cleaned
+ * up, and sometimes we also import them where they are needed.  But for
+ * some things, adding an `npy_cache_import` everywhere seems inconvenient.
+ *
+ * These globals should not need the C-layer at all and will be imported
+ * before anything on the C-side is initialized.
+ */
+static int
+initialize_static_globals(void)
+{
+    assert(npy_DTypePromotionError == NULL);
+    npy_cache_import(
+            "numpy.exceptions", "DTypePromotionError",
+            &npy_DTypePromotionError);
+    if (npy_DTypePromotionError == NULL) {
+        return -1;
+    }
+
+    assert(npy_UFuncNoLoopError == NULL);
+    npy_cache_import(
+            "numpy.core._exceptions", "_UFuncNoLoopError",
+            &npy_UFuncNoLoopError);
+    if (npy_UFuncNoLoopError == NULL) {
+        return -1;
+    }
+
+    /* Initialize from certain environment variabels: */
+    char *env = getenv("NPY_NUMPY_2_BEHAVIOR");
+    if (env != NULL && strcmp(env, "0") != 0) {
+        npy_numpy2_behavior = NPY_TRUE;
+    }
+
+    env = getenv("NUMPY_WARN_IF_NO_MEM_POLICY");
+    if ((env != NULL) && (strncmp(env, "1", 1) == 0)) {
+        numpy_warn_if_no_mem_policy = 1;
+    }
+    else {
+        numpy_warn_if_no_mem_policy = 0;
+    }
+
+    return 0;
+}
+
+
 static struct PyModuleDef moduledef = {
         PyModuleDef_HEAD_INIT,
         "_multiarray_umath",
@@ -4862,6 +5112,14 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) {
         goto err;
     }
 
+    if (intern_strings() < 0) {
+        goto err;
+    }
+
+    if (initialize_static_globals() < 0) {
+        goto err;
+    }
+
     if (PyType_Ready(&PyUFunc_Type) < 0) {
         goto err;
     }
@@ -5034,13 +5292,15 @@ PyMODINIT_FUNC PyInit__multiarray_umath(void) {
         goto err;
     }
 
-    if (intern_strings() < 0) {
+    if (set_typeinfo(d) != 0) {
         goto err;
     }
-
-    if (set_typeinfo(d) != 0) {
+    if (PyType_Ready(&PyArrayFunctionDispatcher_Type) < 0) {
         goto err;
     }
+    PyDict_SetItemString(
+            d, "_ArrayFunctionDispatcher",
+            (PyObject *)&PyArrayFunctionDispatcher_Type);
     if (PyType_Ready(&PyArrayMethod_Type) < 0) {
         goto err;
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.h
index 809736cd2df..9ba2a1831c8 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/multiarraymodule.h
@@ -1,6 +1,8 @@
 #ifndef NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_
 #define NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_
 
+NPY_VISIBILITY_HIDDEN extern int npy_numpy2_behavior;
+
 NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_current_allocator;
 NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array;
 NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_function;
@@ -14,5 +16,6 @@ NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_axis1;
 NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_axis2;
 NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_like;
 NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_numpy;
+NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_where;
 
 #endif  /* NUMPY_CORE_SRC_MULTIARRAY_MULTIARRAYMODULE_H_ */
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_api.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_api.c
index 2de888aaa22..0501c146b1f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_api.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_api.c
@@ -17,6 +17,7 @@
 #include "nditer_impl.h"
 #include "templ_common.h"
 #include "ctors.h"
+#include "refcount.h"
 
 /* Internal helper functions private to this file */
 static npy_intp
@@ -1950,8 +1951,7 @@ npyiter_copy_from_buffers(NpyIter *iter)
          * only copy back when this flag is on.
          */
         if ((transferinfo[iop].write.func != NULL) &&
-               (op_itflags[iop]&(NPY_OP_ITFLAG_WRITE|NPY_OP_ITFLAG_USINGBUFFER))
-                        == (NPY_OP_ITFLAG_WRITE|NPY_OP_ITFLAG_USINGBUFFER)) {
+               (op_itflags[iop]&NPY_OP_ITFLAG_USINGBUFFER)) {
             npy_intp op_transfersize;
 
             npy_intp src_stride, *dst_strides, *dst_coords, *dst_shape;
@@ -2064,27 +2064,18 @@ npyiter_copy_from_buffers(NpyIter *iter)
          * The flag USINGBUFFER is set when the buffer was used, so
          * only decrement refs when this flag is on.
          */
-        else if (transferinfo[iop].write.func != NULL &&
-                       (op_itflags[iop]&NPY_OP_ITFLAG_USINGBUFFER) != 0) {
-            NPY_IT_DBG_PRINT1("Iterator: Freeing refs and zeroing buffer "
-                                "of operand %d\n", (int)iop);
-            /* Decrement refs */
+        else if (transferinfo[iop].clear.func != NULL &&
+                    (op_itflags[iop]&NPY_OP_ITFLAG_USINGBUFFER)) {
+            NPY_IT_DBG_PRINT1(
+                    "Iterator: clearing refs of operand %d\n", (int)iop);
             npy_intp buf_stride = dtypes[iop]->elsize;
-            if (transferinfo[iop].write.func(
-                    &transferinfo[iop].write.context,
-                    &buffer, &transfersize, &buf_stride,
-                    transferinfo[iop].write.auxdata) < 0) {
+            if (transferinfo[iop].clear.func(
+                    NULL, transferinfo[iop].clear.descr, buffer, transfersize,
+                    buf_stride, transferinfo[iop].clear.auxdata) < 0) {
                 /* Since this should only decrement, it should never error */
                 assert(0);
                 return -1;
             }
-            /*
-             * Zero out the memory for safety.  For instance,
-             * if during iteration some Python code copied an
-             * array pointing into the buffer, it will get None
-             * values for its references after this.
-             */
-            memset(buffer, 0, dtypes[iop]->elsize*transfersize);
         }
     }
 
@@ -2631,54 +2622,36 @@ npyiter_clear_buffers(NpyIter *iter)
         return;
     }
 
-    if (!(NIT_ITFLAGS(iter) & NPY_ITFLAG_NEEDSAPI)) {
-        /* Buffers do not require clearing, but should not be copied back */
-        NBF_SIZE(bufferdata) = 0;
-        return;
-    }
-
     /*
-     * The iterator may be using a dtype with references, which always
-     * requires the API. In that case, further cleanup may be necessary.
-     *
-     * TODO: At this time, we assume that a dtype having references
-     *       implies the need to hold the GIL at all times. In theory
-     *       we could broaden this definition for a new
-     *       `PyArray_Item_XDECREF` API and the assumption may become
-     *       incorrect.
+     * The iterator may be using a dtype with references (as of writing this
+     * means Python objects, but does not need to stay that way).
+     * In that case, further cleanup may be necessary and we clear buffers
+     * explicitly.
      */
     PyObject *type, *value, *traceback;
     PyErr_Fetch(&type,  &value, &traceback);
 
     /* Cleanup any buffers with references */
     char **buffers = NBF_BUFFERS(bufferdata);
+
+    NpyIter_TransferInfo *transferinfo = NBF_TRANSFERINFO(bufferdata);
     PyArray_Descr **dtypes = NIT_DTYPES(iter);
     npyiter_opitflags *op_itflags = NIT_OPITFLAGS(iter);
     for (int iop = 0; iop < nop; ++iop, ++buffers) {
-        /*
-         * We may want to find a better way to do this, on the other hand,
-         * this cleanup seems rare and fairly special.  A dtype using
-         * references (right now only us) must always keep the buffer in
-         * a well defined state (either NULL or owning the reference).
-         * Only we implement cleanup
-         */
-        if (!PyDataType_REFCHK(dtypes[iop]) ||
-                !(op_itflags[iop]&NPY_OP_ITFLAG_USINGBUFFER)) {
+        if (transferinfo[iop].clear.func == NULL ||
+                 !(op_itflags[iop]&NPY_OP_ITFLAG_USINGBUFFER)) {
             continue;
         }
         if (*buffers == 0) {
             continue;
         }
         int itemsize = dtypes[iop]->elsize;
-        for (npy_intp i = 0; i < NBF_SIZE(bufferdata); i++) {
-            /*
-             * See above comment, if this API is expanded the GIL assumption
-             * could become incorrect.
-             */
-            PyArray_Item_XDECREF(*buffers + (itemsize * i), dtypes[iop]);
+        if (transferinfo[iop].clear.func(NULL,
+                dtypes[iop], *buffers, NBF_SIZE(bufferdata), itemsize,
+                transferinfo[iop].clear.auxdata) < 0) {
+            /* This should never fail; if it does write it out */
+            PyErr_WriteUnraisable(NULL);
         }
-        /* Clear out the buffer just to be sure */
-        memset(*buffers, 0, NBF_SIZE(bufferdata) * itemsize);
     }
     /* Signal that the buffers are empty */
     NBF_SIZE(bufferdata) = 0;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_constr.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_constr.c
index 93d2bc961ea..dfa84c51cda 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_constr.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_constr.c
@@ -19,6 +19,8 @@
 #include "array_coercion.h"
 #include "templ_common.h"
 #include "array_assign.h"
+#include "dtype_traversal.h"
+
 
 /* Internal helper functions private to this file */
 static int
@@ -58,7 +60,7 @@ npyiter_fill_axisdata(NpyIter *iter, npy_uint32 flags, npyiter_opitflags *op_itf
                     char **op_dataptr,
                     const npy_uint32 *op_flags, int **op_axes,
                     npy_intp const *itershape);
-static NPY_INLINE int
+static inline int
 npyiter_get_op_axis(int axis, npy_bool *reduction_axis);
 static void
 npyiter_replace_axisdata(
@@ -630,6 +632,18 @@ NpyIter_Copy(NpyIter *iter)
                     }
                 }
             }
+
+            if (transferinfo[iop].clear.func != NULL) {
+                if (out_of_memory) {
+                    transferinfo[iop].clear.func = NULL;  /* No cleanup */
+                }
+                else {
+                    if (NPY_traverse_info_copy(&transferinfo[iop].clear,
+                                               &transferinfo[iop].clear) < 0) {
+                        out_of_memory = 1;
+                    }
+                }
+            }
         }
 
         /* Initialize the buffers to the current iterindex */
@@ -706,6 +720,7 @@ NpyIter_Deallocate(NpyIter *iter)
         for (iop = 0; iop < nop; ++iop, ++transferinfo) {
             NPY_cast_info_xfree(&transferinfo->read);
             NPY_cast_info_xfree(&transferinfo->write);
+            NPY_traverse_info_xfree(&transferinfo->clear);
         }
     }
 
@@ -1106,7 +1121,8 @@ npyiter_prepare_one_operand(PyArrayObject **op,
                                              NPY_ITEM_IS_POINTER))) != 0)) {
                 PyErr_SetString(PyExc_TypeError,
                         "Iterator operand or requested dtype holds "
-                        "references, but the REFS_OK flag was not enabled");
+                        "references, but the NPY_ITER_REFS_OK flag was not "
+                        "enabled");
                 return 0;
             }
         }
@@ -1118,7 +1134,7 @@ npyiter_prepare_one_operand(PyArrayObject **op,
         if (op_request_dtype != NULL) {
             /* We just have a borrowed reference to op_request_dtype */
             Py_SETREF(*op_dtype, PyArray_AdaptDescriptorToArray(
-                                            *op, (PyObject *)op_request_dtype));
+                                            *op, NULL, op_request_dtype));
             if (*op_dtype == NULL) {
                 return 0;
             }
@@ -1133,7 +1149,7 @@ npyiter_prepare_one_operand(PyArrayObject **op,
                           PyArray_DescrNewByteorder(*op_dtype, NPY_NATIVE));
                 if (*op_dtype == NULL) {
                     return 0;
-                }                
+                }
                 NPY_IT_DBG_PRINT("Iterator: Setting NPY_OP_ITFLAG_CAST "
                                     "because of NPY_ITER_NBO\n");
                 /* Indicate that byte order or alignment needs fixing */
@@ -1445,7 +1461,7 @@ npyiter_check_reduce_ok_and_set_flags(
  * @param reduction_axis Output 1 if a reduction axis, otherwise 0.
  * @returns The normalized axis (without reduce axis flag).
  */
-static NPY_INLINE int
+static inline int
 npyiter_get_op_axis(int axis, npy_bool *reduction_axis) {
     npy_bool forced_broadcast = axis >= NPY_ITER_REDUCTION_AXIS(-1);
 
@@ -2249,7 +2265,7 @@ npyiter_reverse_axis_ordering(NpyIter *iter)
     NIT_ITFLAGS(iter) &= ~NPY_ITFLAG_IDENTPERM;
 }
 
-static NPY_INLINE npy_intp
+static inline npy_intp
 intp_abs(npy_intp x)
 {
     return (x < 0) ? -x : x;
@@ -3186,31 +3202,33 @@ npyiter_allocate_transfer_functions(NpyIter *iter)
                     cflags = PyArrayMethod_COMBINED_FLAGS(cflags, nc_flags);
                 }
             }
-            /* If no write back but there are references make a decref fn */
-            else if (PyDataType_REFCHK(op_dtype[iop])) {
+            else {
+                transferinfo[iop].write.func = NULL;
+            }
+
+            /* Get the decref function, used for no-writeback and on error */
+            if (PyDataType_REFCHK(op_dtype[iop])) {
                 /*
                  * By passing NULL to dst_type and setting move_references
                  * to 1, we get back a function that just decrements the
                  * src references.
                  */
-                if (PyArray_GetDTypeTransferFunction(
+                if (PyArray_GetClearFunction(
                         (flags & NPY_OP_ITFLAG_ALIGNED) != 0,
-                        op_dtype[iop]->elsize, 0,
-                        op_dtype[iop], NULL,
-                        1,
-                        &transferinfo[iop].write,
-                        &nc_flags) != NPY_SUCCEED) {
+                        op_dtype[iop]->elsize, op_dtype[iop],
+                        &transferinfo[iop].clear, &nc_flags) < 0) {
                     goto fail;
                 }
                 cflags = PyArrayMethod_COMBINED_FLAGS(cflags, nc_flags);
             }
             else {
-                transferinfo[iop].write.func = NULL;
+                transferinfo[iop].clear.func = NULL;
             }
         }
         else {
             transferinfo[iop].read.func = NULL;
             transferinfo[iop].write.func = NULL;
+            transferinfo[iop].clear.func = NULL;
         }
     }
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_impl.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_impl.h
index 6650fc0a84c..a33e4f90fa4 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_impl.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/nditer_impl.h
@@ -23,6 +23,8 @@
 
 #include "lowlevel_strided_loops.h"
 #include "dtype_transfer.h"
+#include "dtype_traversal.h"
+
 
 /********** ITERATOR CONSTRUCTION TIMING **************/
 #define NPY_IT_CONSTRUCTION_TIMING 0
@@ -242,6 +244,7 @@ typedef npy_int16 npyiter_opitflags;
 struct NpyIter_TransferInfo_tag {
     NPY_cast_info read;
     NPY_cast_info write;
+    NPY_traverse_info clear;
     /* Probably unnecessary, but make sure what follows is intp aligned: */
     npy_intp _unused_ensure_alignment[];
 };
@@ -329,7 +332,7 @@ struct NpyIter_AxisData_tag {
  * @return The unpermuted axis. Without `op_axes` this is correct, with
  *         `op_axes` this indexes into `op_axes` (unpermuted iterator axis)
  */
-static NPY_INLINE int
+static inline int
 npyiter_undo_iter_axis_perm(
         int axis, int ndim, const npy_int8 *perm, npy_bool *axis_flipped)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/number.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/number.c
index df814a79625..35e4af79c8d 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/number.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/number.c
@@ -53,6 +53,8 @@ static PyObject *
 array_inplace_remainder(PyArrayObject *m1, PyObject *m2);
 static PyObject *
 array_inplace_power(PyArrayObject *a1, PyObject *o2, PyObject *NPY_UNUSED(modulo));
+static PyObject *
+array_inplace_matrix_multiply(PyArrayObject *m1, PyObject *m2);
 
 /*
  * Dictionary can contain any of the numeric operations, by name.
@@ -339,7 +341,6 @@ array_divmod(PyObject *m1, PyObject *m2)
     return PyArray_GenericBinaryFunction(m1, m2, n_ops.divmod);
 }
 
-/* Need this to be version dependent on account of the slot check */
 static PyObject *
 array_matrix_multiply(PyObject *m1, PyObject *m2)
 {
@@ -348,13 +349,70 @@ array_matrix_multiply(PyObject *m1, PyObject *m2)
 }
 
 static PyObject *
-array_inplace_matrix_multiply(
-        PyArrayObject *NPY_UNUSED(m1), PyObject *NPY_UNUSED(m2))
+array_inplace_matrix_multiply(PyArrayObject *self, PyObject *other)
 {
-    PyErr_SetString(PyExc_TypeError,
-                    "In-place matrix multiplication is not (yet) supported. "
-                    "Use 'a = a @ b' instead of 'a @= b'.");
-    return NULL;
+    static PyObject *AxisError_cls = NULL;
+    npy_cache_import("numpy.exceptions", "AxisError", &AxisError_cls);
+    if (AxisError_cls == NULL) {
+        return NULL;
+    }
+
+    INPLACE_GIVE_UP_IF_NEEDED(self, other,
+            nb_inplace_matrix_multiply, array_inplace_matrix_multiply);
+
+    /*
+     * Unlike `matmul(a, b, out=a)` we ensure that the result is not broadcast
+     * if the result without `out` would have less dimensions than `a`.
+     * Since the signature of matmul is '(n?,k),(k,m?)->(n?,m?)' this is the
+     * case exactly when the second operand has both core dimensions.
+     *
+     * The error here will be confusing, but for now, we enforce this by
+     * passing the correct `axes=`.
+     */
+    static PyObject *axes_1d_obj_kwargs = NULL;
+    static PyObject *axes_2d_obj_kwargs = NULL;
+    if (NPY_UNLIKELY(axes_1d_obj_kwargs == NULL)) {
+        axes_1d_obj_kwargs = Py_BuildValue(
+                "{s, [(i), (i, i), (i)]}", "axes", -1, -2, -1, -1);
+        if (axes_1d_obj_kwargs == NULL) {
+            return NULL;
+        }
+    }
+    if (NPY_UNLIKELY(axes_2d_obj_kwargs == NULL)) {
+        axes_2d_obj_kwargs = Py_BuildValue(
+                "{s, [(i, i), (i, i), (i, i)]}", "axes", -2, -1, -2, -1, -2, -1);
+        if (axes_2d_obj_kwargs == NULL) {
+            return NULL;
+        }
+    }
+
+    PyObject *args = PyTuple_Pack(3, self, other, self);
+    if (args == NULL) {
+        return NULL;
+    }
+    PyObject *kwargs;
+    if (PyArray_NDIM(self) == 1) {
+        kwargs = axes_1d_obj_kwargs;
+    }
+    else {
+        kwargs = axes_2d_obj_kwargs;
+    }
+    PyObject *res = PyObject_Call(n_ops.matmul, args, kwargs);
+    Py_DECREF(args);
+
+    if (res == NULL) {
+        /*
+         * AxisError should indicate that the axes argument didn't work out
+         * which should mean the second operand not being 2 dimensional.
+         */
+        if (PyErr_ExceptionMatches(AxisError_cls)) {
+            PyErr_SetString(PyExc_ValueError,
+                "inplace matrix multiplication requires the first operand to "
+                "have at least one and the second at least two dimensions.");
+        }
+    }
+
+    return res;
 }
 
 /*
@@ -539,47 +597,10 @@ array_power(PyObject *a1, PyObject *o2, PyObject *modulo)
 static PyObject *
 array_positive(PyArrayObject *m1)
 {
-    /*
-     * For backwards compatibility, where + just implied a copy,
-     * we cannot just call n_ops.positive.  Instead, we do the following
-     * 1. Try n_ops.positive
-     * 2. If we get an exception, check whether __array_ufunc__ is
-     *    overridden; if so, we live in the future and we allow the
-     *    TypeError to be passed on.
-     * 3. If not, give a deprecation warning and return a copy.
-     */
-    PyObject *value;
     if (can_elide_temp_unary(m1)) {
-        value = PyArray_GenericInplaceUnaryFunction(m1, n_ops.positive);
-    }
-    else {
-        value = PyArray_GenericUnaryFunction(m1, n_ops.positive);
+        return PyArray_GenericInplaceUnaryFunction(m1, n_ops.positive);
     }
-    if (value == NULL) {
-        /*
-         * We first fetch the error, as it needs to be clear to check
-         * for the override.  When the deprecation is removed,
-         * this whole stanza can be deleted.
-         */
-        PyObject *exc, *val, *tb;
-        PyErr_Fetch(&exc, &val, &tb);
-        if (PyUFunc_HasOverride((PyObject *)m1)) {
-            PyErr_Restore(exc, val, tb);
-            return NULL;
-        }
-        Py_XDECREF(exc);
-        Py_XDECREF(val);
-        Py_XDECREF(tb);
-
-        /* 2018-06-28, 1.16.0 */
-        if (DEPRECATE("Applying '+' to a non-numerical array is "
-                      "ill-defined. Returning a copy, but in the future "
-                      "this will error.") < 0) {
-            return NULL;
-        }
-        value = PyArray_Return((PyArrayObject *)PyArray_Copy(m1));
-    }
-    return value;
+    return PyArray_GenericUnaryFunction(m1, n_ops.positive);
 }
 
 static PyObject *
@@ -848,13 +869,11 @@ array_scalar_forward(PyArrayObject *v,
                      PyObject *(*builtin_func)(PyObject *),
                      const char *where)
 {
-    PyObject *scalar;
-    if (PyArray_SIZE(v) != 1) {
-        PyErr_SetString(PyExc_TypeError, "only size-1 arrays can be"\
-                        " converted to Python scalars");
+    if (check_is_convertible_to_scalar(v) < 0) {
         return NULL;
     }
 
+    PyObject *scalar;
     scalar = PyArray_GETITEM(v, PyArray_DATA(v));
     if (scalar == NULL) {
         return NULL;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.c
index a1c310700fa..876bb53e165 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.c
@@ -2,6 +2,10 @@
  * This module corresponds to the `Special functions for NPY_OBJECT`
  * section in the numpy reference for C-API.
  */
+#include "array_method.h"
+#include "dtype_traversal.h"
+#include "lowlevel_strided_loops.h"
+#include "pyerrors.h"
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
 #define _MULTIARRAYMODULE
 
@@ -12,13 +16,101 @@
 #include "numpy/arrayobject.h"
 #include "numpy/arrayscalars.h"
 #include "iterators.h"
+#include "dtypemeta.h"
+#include "refcount.h"
 
 #include "npy_config.h"
 
 #include "npy_pycompat.h"
 
-static void
-_fillobject(char *optr, PyObject *obj, PyArray_Descr *dtype);
+/*
+ * Helper function to clear a strided memory (normally or always contiguous)
+ * from all Python (or other) references.  The function does nothing if the
+ * array dtype does not indicate holding references.
+ *
+ * It is safe to call this function more than once, failing here is usually
+ * critical (during cleanup) and should be set up to minimize the risk or
+ * avoid it fully.
+ */
+NPY_NO_EXPORT int
+PyArray_ClearBuffer(
+        PyArray_Descr *descr, char *data,
+        npy_intp stride, npy_intp size, int aligned)
+{
+    if (!PyDataType_REFCHK(descr)) {
+        return 0;
+    }
+
+    NPY_traverse_info clear_info;
+    /* Flags unused: float errors do not matter and we do not release GIL */
+    NPY_ARRAYMETHOD_FLAGS flags_unused;
+    if (PyArray_GetClearFunction(
+            aligned, stride, descr, &clear_info, &flags_unused) < 0) {
+        return -1;
+    }
+
+    int res = clear_info.func(
+            NULL, clear_info.descr, data, size, stride, clear_info.auxdata);
+    NPY_traverse_info_xfree(&clear_info);
+    return res;
+}
+
+
+/*
+ * Helper function to clear whole array.  It seems plausible that we should
+ * be able to get away with assuming the the array is contiguous.
+ *
+ * Must only be called on arrays which own their data (and asserts this).
+ */
+ NPY_NO_EXPORT int
+ PyArray_ClearArray(PyArrayObject *arr)
+ {
+    assert(PyArray_FLAGS(arr) & NPY_ARRAY_OWNDATA);
+
+    PyArray_Descr *descr = PyArray_DESCR(arr);
+    if (!PyDataType_REFCHK(descr)) {
+        return 0;
+    }
+    /*
+     * The contiguous path should cover practically all important cases since
+     * it is difficult to create a non-contiguous array which owns its memory
+     * and only arrays which own their memory should clear it.
+     */
+    int aligned = PyArray_ISALIGNED(arr);
+    if (PyArray_ISCONTIGUOUS(arr)) {
+        return PyArray_ClearBuffer(
+                descr, PyArray_BYTES(arr), descr->elsize,
+                PyArray_SIZE(arr), aligned);
+    }
+    int idim, ndim;
+    npy_intp shape_it[NPY_MAXDIMS], strides_it[NPY_MAXDIMS];
+    npy_intp coord[NPY_MAXDIMS];
+    char *data_it;
+    if (PyArray_PrepareOneRawArrayIter(
+                    PyArray_NDIM(arr), PyArray_DIMS(arr),
+                    PyArray_BYTES(arr), PyArray_STRIDES(arr),
+                    &ndim, shape_it, &data_it, strides_it) < 0) {
+        return -1;
+    }
+    npy_intp inner_stride = strides_it[0];
+    npy_intp inner_shape = shape_it[0];
+    NPY_traverse_info clear_info;
+    /* Flags unused: float errors do not matter and we do not release GIL */
+    NPY_ARRAYMETHOD_FLAGS flags_unused;
+    if (PyArray_GetClearFunction(
+            aligned, inner_stride, descr, &clear_info, &flags_unused) < 0) {
+        return -1;
+    }
+    NPY_RAW_ITER_START(idim, ndim, coord, shape_it) {
+        /* Process the innermost dimension */
+        if (clear_info.func(NULL, clear_info.descr,
+                data_it, inner_shape, inner_stride, clear_info.auxdata) < 0) {
+            return -1;
+        }
+    } NPY_RAW_ITER_ONE_NEXT(idim, ndim, coord,
+                            shape_it, data_it, strides_it);
+    return 0;
+}
 
 
 /*NUMPY_API
@@ -204,6 +296,10 @@ PyArray_INCREF(PyArrayObject *mp)
 /*NUMPY_API
   Decrement all internal references for object arrays.
   (or arrays with object fields)
+
+  The use of this function is strongly discouraged, within NumPy
+  use PyArray_Clear, which DECREF's and sets everything to NULL and can
+  work with any dtype.
 */
 NPY_NO_EXPORT int
 PyArray_XDECREF(PyArrayObject *mp)
@@ -254,15 +350,28 @@ PyArray_XDECREF(PyArrayObject *mp)
     return 0;
 }
 
+
+static void
+_fillobject(char *optr, PyObject *obj, PyArray_Descr *dtype);
+
+
 /*NUMPY_API
  * Assumes contiguous
  */
 NPY_NO_EXPORT void
 PyArray_FillObjectArray(PyArrayObject *arr, PyObject *obj)
 {
+    PyArray_Descr* descr = PyArray_DESCR(arr);
+
+    // non-legacy dtypes are responsible for initializing
+    // their own internal references
+    if (!NPY_DT_is_legacy(NPY_DTYPE(descr))) {
+        return;
+    }
+
     npy_intp i,n;
     n = PyArray_SIZE(arr);
-    if (PyArray_DESCR(arr)->type_num == NPY_OBJECT) {
+    if (descr->type_num == NPY_OBJECT) {
         PyObject **optr;
         optr = (PyObject **)(PyArray_DATA(arr));
         n = PyArray_SIZE(arr);
@@ -282,8 +391,8 @@ PyArray_FillObjectArray(PyArrayObject *arr, PyObject *obj)
         char *optr;
         optr = PyArray_DATA(arr);
         for (i = 0; i < n; i++) {
-            _fillobject(optr, obj, PyArray_DESCR(arr));
-            optr += PyArray_DESCR(arr)->elsize;
+            _fillobject(optr, obj, descr);
+            optr += descr->elsize;
         }
     }
 }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.h
index 959eef5bacf..16d34e292fe 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/refcount.h
@@ -1,6 +1,14 @@
 #ifndef NUMPY_CORE_SRC_MULTIARRAY_REFCOUNT_H_
 #define NUMPY_CORE_SRC_MULTIARRAY_REFCOUNT_H_
 
+NPY_NO_EXPORT int
+PyArray_ClearBuffer(
+        PyArray_Descr *descr, char *data,
+        npy_intp stride, npy_intp size, int aligned);
+
+NPY_NO_EXPORT int
+PyArray_ClearArray(PyArrayObject *arr);
+
 NPY_NO_EXPORT void
 PyArray_Item_INCREF(char *data, PyArray_Descr *descr);
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c
index a9e03040287..f1ebd93c83c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c
@@ -25,6 +25,7 @@
 
 #include "npy_pycompat.h"
 
+#include "arraytypes.h"
 #include "npy_config.h"
 #include "mapping.h"
 #include "ctors.h"
@@ -68,63 +69,63 @@ NPY_NO_EXPORT PyTypeObject PyTimeIntegerArrType_Type;
  * single inheritance)
  */
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyNumberArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.number",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyIntegerArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.integer",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PySignedIntegerArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.signedinteger",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyUnsignedIntegerArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.unsignedinteger",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyInexactArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.inexact",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyFloatingArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.floating",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyComplexFloatingArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.complexfloating",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyFlexibleArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.flexible",
     .tp_basicsize = sizeof(PyObject),
 };
 
-#line 67
+#line 68
 NPY_NO_EXPORT PyTypeObject PyCharacterArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
     .tp_name = "numpy.character",
@@ -232,7 +233,7 @@ gentype_add(PyObject *m1, PyObject* m2)
     return PyArray_Type.tp_as_number->nb_add(m1, m2);
 }
 
-#line 179
+#line 180
 static PyObject *
 gentype_subtract(PyObject *m1, PyObject *m2)
 {
@@ -241,7 +242,7 @@ gentype_subtract(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_remainder(PyObject *m1, PyObject *m2)
 {
@@ -250,7 +251,7 @@ gentype_remainder(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_divmod(PyObject *m1, PyObject *m2)
 {
@@ -259,7 +260,7 @@ gentype_divmod(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_lshift(PyObject *m1, PyObject *m2)
 {
@@ -268,7 +269,7 @@ gentype_lshift(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_rshift(PyObject *m1, PyObject *m2)
 {
@@ -277,7 +278,7 @@ gentype_rshift(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_and(PyObject *m1, PyObject *m2)
 {
@@ -286,7 +287,7 @@ gentype_and(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_xor(PyObject *m1, PyObject *m2)
 {
@@ -295,7 +296,7 @@ gentype_xor(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_or(PyObject *m1, PyObject *m2)
 {
@@ -304,7 +305,7 @@ gentype_or(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_floor_divide(PyObject *m1, PyObject *m2)
 {
@@ -313,7 +314,7 @@ gentype_floor_divide(PyObject *m1, PyObject *m2)
 }
 
 
-#line 179
+#line 180
 static PyObject *
 gentype_true_divide(PyObject *m1, PyObject *m2)
 {
@@ -357,7 +358,7 @@ gentype_multiply(PyObject *m1, PyObject *m2)
     return PyArray_Type.tp_as_number->nb_multiply(m1, m2);
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_byte_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -368,7 +369,7 @@ npy_byte_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_ubyte_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -379,7 +380,7 @@ npy_ubyte_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_short_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -390,7 +391,7 @@ npy_short_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_ushort_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -401,7 +402,7 @@ npy_ushort_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_int_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -412,7 +413,7 @@ npy_int_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_uint_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -423,7 +424,7 @@ npy_uint_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_long_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -434,7 +435,7 @@ npy_long_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_ulong_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -445,7 +446,7 @@ npy_ulong_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_longlong_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -456,7 +457,7 @@ npy_longlong_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
     return result;
 }
 
-#line 232
+#line 233
 static PyObject *
 npy_ulonglong_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -468,7 +469,7 @@ npy_ulonglong_bit_count(PyObject *self, PyObject *NPY_UNUSED(args))
 }
 
 
-#line 247
+#line 248
 static PyObject *
 gentype_positive(PyObject *m1)
 {
@@ -483,7 +484,7 @@ gentype_positive(PyObject *m1)
     return ret;
 }
 
-#line 247
+#line 248
 static PyObject *
 gentype_negative(PyObject *m1)
 {
@@ -498,7 +499,7 @@ gentype_negative(PyObject *m1)
     return ret;
 }
 
-#line 247
+#line 248
 static PyObject *
 gentype_absolute(PyObject *m1)
 {
@@ -513,7 +514,7 @@ gentype_absolute(PyObject *m1)
     return ret;
 }
 
-#line 247
+#line 248
 static PyObject *
 gentype_invert(PyObject *m1)
 {
@@ -528,7 +529,7 @@ gentype_invert(PyObject *m1)
     return ret;
 }
 
-#line 247
+#line 248
 static PyObject *
 gentype_int(PyObject *m1)
 {
@@ -543,7 +544,7 @@ gentype_int(PyObject *m1)
     return ret;
 }
 
-#line 247
+#line 248
 static PyObject *
 gentype_float(PyObject *m1)
 {
@@ -578,7 +579,44 @@ static PyObject *
 genint_type_str(PyObject *self)
 {
     PyObject  *item, *item_str;
-    item = gentype_generic_method(self, NULL, NULL, "item");
+    PyArray_Descr *descr = PyArray_DescrFromTypeObject((PyObject *)Py_TYPE(self));
+    void *val = scalar_value(self, descr);
+    switch (descr->type_num) {
+        case NPY_BYTE:
+            item = PyLong_FromLong(*(npy_byte *)val);
+            break;
+        case NPY_UBYTE:
+            item = PyLong_FromUnsignedLong(*(npy_ubyte *)val);
+            break;
+        case NPY_SHORT:
+            item = PyLong_FromLong(*(npy_short *)val);
+            break;
+        case NPY_USHORT:
+            item = PyLong_FromUnsignedLong(*(npy_ushort *)val);
+            break;
+        case NPY_INT:
+            item = PyLong_FromLong(*(npy_int *)val);
+            break;
+        case NPY_UINT:
+            item = PyLong_FromUnsignedLong(*(npy_uint *)val);
+            break;
+        case NPY_LONG:
+            item = PyLong_FromLong(*(npy_long *)val);
+            break;
+        case NPY_ULONG:
+            item = PyLong_FromUnsignedLong(*(npy_ulong *)val);
+            break;
+        case NPY_LONGLONG:
+            item = PyLong_FromLongLong(*(npy_longlong *)val);
+            break;
+        case NPY_ULONGLONG:
+            item = PyLong_FromUnsignedLongLong(*(npy_ulonglong *)val);
+            break;
+        default:
+            item = gentype_generic_method(self, NULL, NULL, "item");
+            break;
+    }
+    Py_DECREF(descr);
     if (item == NULL) {
         return NULL;
     }
@@ -648,7 +686,7 @@ gentype_format(PyObject *self, PyObject *args)
 #define NPY_LONGDOUBLE_FMT NPY_DOUBLE_FMT
 #endif
 
-#line 358
+#line 396
 
 NPY_NO_EXPORT PyObject *
 format_half(npy_half val, npy_bool scientific,
@@ -669,7 +707,7 @@ format_half(npy_half val, npy_bool scientific,
 
 
 
-#line 358
+#line 396
 
 NPY_NO_EXPORT PyObject *
 format_float(npy_float val, npy_bool scientific,
@@ -690,7 +728,7 @@ format_float(npy_float val, npy_bool scientific,
 
 
 
-#line 358
+#line 396
 
 NPY_NO_EXPORT PyObject *
 format_double(npy_double val, npy_bool scientific,
@@ -711,7 +749,7 @@ format_double(npy_double val, npy_bool scientific,
 
 
 
-#line 358
+#line 396
 
 NPY_NO_EXPORT PyObject *
 format_longdouble(npy_longdouble val, npy_bool scientific,
@@ -738,7 +776,7 @@ format_longdouble(npy_longdouble val, npy_bool scientific,
  * then call the corresponding functions of PyBytes_Type to generate the string
  */
 
-#line 387
+#line 425
 static PyObject *
 stringtype_repr(PyObject *self)
 {
@@ -759,7 +797,7 @@ stringtype_repr(PyObject *self)
     return ret;
 }
 
-#line 387
+#line 425
 static PyObject *
 stringtype_str(PyObject *self)
 {
@@ -786,7 +824,7 @@ stringtype_str(PyObject *self)
  * then call the corresponding functions of PyUnicode_Type to generate the string
  */
 
-#line 416
+#line 454
 static PyObject *
 unicodetype_repr(PyObject *self)
 {
@@ -813,7 +851,7 @@ unicodetype_repr(PyObject *self)
     return ret;
 }
 
-#line 416
+#line 454
 static PyObject *
 unicodetype_str(PyObject *self)
 {
@@ -848,7 +886,7 @@ unicodetype_str(PyObject *self)
  *
  * Largely copied from _Py_strhex_impl in CPython implementation
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 _void_to_hex(const char* argbuf, const Py_ssize_t arglen,
              const char *schars, const char *bprefix, const char *echars)
 {
@@ -1139,9 +1177,9 @@ extern int npy_legacy_print_mode;
 #define LONGDOUBLEPREC_STR 12
 #endif
 
-#line 745
+#line 783
 
-#line 752
+#line 790
 
 #define _FMT1 "%%.%i" NPY_FLOAT_FMT
 #define _FMT2 "%%+.%i" NPY_FLOAT_FMT
@@ -1225,7 +1263,7 @@ legacy_cfloat_formatstr(npy_cfloat val)
 #undef _FMT2
 
 
-#line 752
+#line 790
 
 #define _FMT1 "%%.%i" NPY_DOUBLE_FMT
 #define _FMT2 "%%+.%i" NPY_DOUBLE_FMT
@@ -1309,7 +1347,7 @@ legacy_cdouble_formatstr(npy_cdouble val)
 #undef _FMT2
 
 
-#line 752
+#line 790
 
 #define _FMT1 "%%.%i" NPY_LONGDOUBLE_FMT
 #define _FMT2 "%%+.%i" NPY_LONGDOUBLE_FMT
@@ -1394,7 +1432,7 @@ legacy_clongdouble_formatstr(npy_clongdouble val)
 
 
 
-#line 842
+#line 880
 
 #define _FMT1 "%%.%i" NPY_FLOAT_FMT
 
@@ -1428,7 +1466,7 @@ legacy_float_formatstr(npy_float val){
 #undef _FMT1
 
 
-#line 842
+#line 880
 
 #define _FMT1 "%%.%i" NPY_DOUBLE_FMT
 
@@ -1462,7 +1500,7 @@ legacy_double_formatstr(npy_double val){
 #undef _FMT1
 
 
-#line 842
+#line 880
 
 #define _FMT1 "%%.%i" NPY_LONGDOUBLE_FMT
 
@@ -1498,9 +1536,9 @@ legacy_longdouble_formatstr(npy_longdouble val){
 
 
 
-#line 745
+#line 783
 
-#line 752
+#line 790
 
 #define _FMT1 "%%.%i" NPY_FLOAT_FMT
 #define _FMT2 "%%+.%i" NPY_FLOAT_FMT
@@ -1584,7 +1622,7 @@ legacy_cfloat_formatrepr(npy_cfloat val)
 #undef _FMT2
 
 
-#line 752
+#line 790
 
 #define _FMT1 "%%.%i" NPY_DOUBLE_FMT
 #define _FMT2 "%%+.%i" NPY_DOUBLE_FMT
@@ -1668,7 +1706,7 @@ legacy_cdouble_formatrepr(npy_cdouble val)
 #undef _FMT2
 
 
-#line 752
+#line 790
 
 #define _FMT1 "%%.%i" NPY_LONGDOUBLE_FMT
 #define _FMT2 "%%+.%i" NPY_LONGDOUBLE_FMT
@@ -1753,7 +1791,7 @@ legacy_clongdouble_formatrepr(npy_clongdouble val)
 
 
 
-#line 842
+#line 880
 
 #define _FMT1 "%%.%i" NPY_FLOAT_FMT
 
@@ -1787,7 +1825,7 @@ legacy_float_formatrepr(npy_float val){
 #undef _FMT1
 
 
-#line 842
+#line 880
 
 #define _FMT1 "%%.%i" NPY_DOUBLE_FMT
 
@@ -1821,7 +1859,7 @@ legacy_double_formatrepr(npy_double val){
 #undef _FMT1
 
 
-#line 842
+#line 880
 
 #define _FMT1 "%%.%i" NPY_LONGDOUBLE_FMT
 
@@ -1864,9 +1902,9 @@ legacy_longdouble_formatrepr(npy_longdouble val){
  */
 
 
-#line 887
+#line 925
 
-#line 893
+#line 931
 
 /* helper function choose scientific of fractional output, based on a cutoff */
 static PyObject *
@@ -1963,7 +2001,7 @@ cfloattype_str(PyObject *self)
 #undef PREC
 
 
-#line 893
+#line 931
 
 /* helper function choose scientific of fractional output, based on a cutoff */
 static PyObject *
@@ -2060,7 +2098,7 @@ cdoubletype_str(PyObject *self)
 #undef PREC
 
 
-#line 893
+#line 931
 
 /* helper function choose scientific of fractional output, based on a cutoff */
 static PyObject *
@@ -2180,9 +2218,9 @@ halftype_str(PyObject *self)
 
 
 
-#line 887
+#line 925
 
-#line 893
+#line 931
 
 /* helper function choose scientific of fractional output, based on a cutoff */
 static PyObject *
@@ -2279,7 +2317,7 @@ cfloattype_repr(PyObject *self)
 #undef PREC
 
 
-#line 893
+#line 931
 
 /* helper function choose scientific of fractional output, based on a cutoff */
 static PyObject *
@@ -2376,7 +2414,7 @@ cdoubletype_repr(PyObject *self)
 #undef PREC
 
 
-#line 893
+#line 931
 
 /* helper function choose scientific of fractional output, based on a cutoff */
 static PyObject *
@@ -2497,7 +2535,7 @@ halftype_repr(PyObject *self)
 
 
 
-#line 1018
+#line 1056
 static PyObject *
 longdoubletype_float(PyObject *self)
 {
@@ -2513,7 +2551,7 @@ longdoubletype_long(PyObject *self)
 }
 
 
-#line 1018
+#line 1056
 static PyObject *
 clongdoubletype_float(PyObject *self)
 {
@@ -3002,77 +3040,77 @@ gentype_wraparray(PyObject *NPY_UNUSED(scalar), PyObject *args)
  * These gentype_* functions do not take keyword arguments.
  * The proper flag is METH_VARARGS.
  */
-#line 1512
+#line 1550
 static PyObject *
 gentype_tolist(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "tolist");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_item(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "item");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype___deepcopy__(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "__deepcopy__");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype___copy__(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "__copy__");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_swapaxes(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "swapaxes");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_conj(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "conj");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_conjugate(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "conjugate");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_nonzero(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "nonzero");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_fill(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "fill");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_transpose(PyObject *self, PyObject *args)
 {
     return gentype_generic_method(self, args, NULL, "transpose");
 }
 
-#line 1512
+#line 1550
 static PyObject *
 gentype_newbyteorder(PyObject *self, PyObject *args)
 {
@@ -3132,266 +3170,266 @@ gentype_byteswap(PyObject *self, PyObject *args, PyObject *kwds)
  * These gentype_* functions take keyword arguments.
  * The proper flag is METH_VARARGS | METH_KEYWORDS.
  */
-#line 1579
+#line 1617
 static PyObject *
 gentype_take(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "take");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_getfield(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "getfield");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_put(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "put");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_repeat(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "repeat");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_tofile(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "tofile");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_mean(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "mean");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_trace(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "trace");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_diagonal(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "diagonal");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_clip(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "clip");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_std(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "std");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_var(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "var");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_sum(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "sum");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_cumsum(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "cumsum");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_prod(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "prod");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_cumprod(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "cumprod");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_compress(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "compress");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_sort(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "sort");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_argsort(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "argsort");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_round(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "round");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_argmax(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "argmax");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_argmin(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "argmin");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_max(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "max");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_min(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "min");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_ptp(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "ptp");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_any(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "any");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_all(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "all");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_astype(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "astype");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_resize(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "resize");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_reshape(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "reshape");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_choose(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "choose");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_tostring(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "tostring");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_tobytes(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "tobytes");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_copy(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "copy");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_searchsorted(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "searchsorted");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_view(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "view");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_flatten(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "flatten");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_ravel(PyObject *self, PyObject *args, PyObject *kwds)
 {
     return gentype_generic_method(self, args, kwds, "ravel");
 }
 
-#line 1579
+#line 1617
 static PyObject *
 gentype_squeeze(PyObject *self, PyObject *args, PyObject *kwds)
 {
@@ -3400,7 +3438,7 @@ gentype_squeeze(PyObject *self, PyObject *args, PyObject *kwds)
 
 
 
-#line 1591
+#line 1629
 static PyObject *
 integertype_dunder_round(PyObject *self, PyObject *args, PyObject *kwds)
 {
@@ -3447,7 +3485,7 @@ integertype_dunder_round(PyObject *self, PyObject *args, PyObject *kwds)
     return obj;
 }
 
-#line 1591
+#line 1629
 static PyObject *
 floatingtype_dunder_round(PyObject *self, PyObject *args, PyObject *kwds)
 {
@@ -3494,7 +3532,7 @@ floatingtype_dunder_round(PyObject *self, PyObject *args, PyObject *kwds)
     return obj;
 }
 
-#line 1591
+#line 1629
 static PyObject *
 complexfloatingtype_dunder_round(PyObject *self, PyObject *args, PyObject *kwds)
 {
@@ -3759,10 +3797,7 @@ gentype_setflags(PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args),
 static PyObject *
 numbertype_class_getitem_abc(PyObject *cls, PyObject *args)
 {
-    PyObject *generic_alias;
-
-#ifdef Py_GENERICALIASOBJECT_H
-    Py_ssize_t args_len;
+    const Py_ssize_t args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
     int args_len_expected;
 
     /* complexfloating should take 2 parameters, all others take 1 */
@@ -3774,20 +3809,13 @@ numbertype_class_getitem_abc(PyObject *cls, PyObject *args)
         args_len_expected = 1;
     }
 
-    args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
     if ((args_len > args_len_expected) || (args_len == 0)) {
         return PyErr_Format(PyExc_TypeError,
                             "Too %s arguments for %s",
                             args_len > args_len_expected ? "many" : "few",
                             ((PyTypeObject *)cls)->tp_name);
     }
-    generic_alias = Py_GenericAlias(cls, args);
-#else
-    PyErr_SetString(PyExc_TypeError,
-                    "Type subscription requires python >= 3.9");
-    generic_alias = NULL;
-#endif
-    return generic_alias;
+    return Py_GenericAlias(cls, args);
 }
 
 /*
@@ -3798,14 +3826,9 @@ numbertype_class_getitem_abc(PyObject *cls, PyObject *args)
 static PyObject *
 numbertype_class_getitem(PyObject *cls, PyObject *args)
 {
-#ifdef Py_GENERICALIASOBJECT_H
     PyErr_Format(PyExc_TypeError,
                  "There are no type variables left in %s",
                  ((PyTypeObject *)cls)->tp_name);
-#else
-    PyErr_SetString(PyExc_TypeError,
-                    "Type subscription requires python >= 3.9");
-#endif
     return NULL;
 }
 
@@ -3814,7 +3837,7 @@ numbertype_class_getitem(PyObject *cls, PyObject *args)
  * to Python complex
  */
 
-#line 1914
+#line 1937
 static PyObject *
 cfloat_complex(PyObject *self, PyObject *NPY_UNUSED(args),
                PyObject *NPY_UNUSED(kwds))
@@ -3823,7 +3846,7 @@ cfloat_complex(PyObject *self, PyObject *NPY_UNUSED(args),
                                  PyArrayScalar_VAL(self, CFloat).imag);
 }
 
-#line 1914
+#line 1937
 static PyObject *
 clongdouble_complex(PyObject *self, PyObject *NPY_UNUSED(args),
                PyObject *NPY_UNUSED(kwds))
@@ -3833,7 +3856,7 @@ clongdouble_complex(PyObject *self, PyObject *NPY_UNUSED(args),
 }
 
 
-#line 1932
+#line 1955
 /* Heavily copied from the builtin float.as_integer_ratio */
 static PyObject *
 half_as_integer_ratio(PyObject *self, PyObject *NPY_UNUSED(args))
@@ -3909,7 +3932,7 @@ error:
     return result_pair;
 }
 
-#line 1932
+#line 1955
 /* Heavily copied from the builtin float.as_integer_ratio */
 static PyObject *
 float_as_integer_ratio(PyObject *self, PyObject *NPY_UNUSED(args))
@@ -3985,7 +4008,7 @@ error:
     return result_pair;
 }
 
-#line 1932
+#line 1955
 /* Heavily copied from the builtin float.as_integer_ratio */
 static PyObject *
 double_as_integer_ratio(PyObject *self, PyObject *NPY_UNUSED(args))
@@ -4061,7 +4084,7 @@ error:
     return result_pair;
 }
 
-#line 1932
+#line 1955
 /* Heavily copied from the builtin float.as_integer_ratio */
 static PyObject *
 longdouble_as_integer_ratio(PyObject *self, PyObject *NPY_UNUSED(args))
@@ -4138,7 +4161,7 @@ error:
 }
 
 
-#line 2014
+#line 2037
 static PyObject *
 half_is_integer(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -4161,7 +4184,7 @@ half_is_integer(PyObject *self, PyObject *NPY_UNUSED(args))
     return ret;
 }
 
-#line 2014
+#line 2037
 static PyObject *
 float_is_integer(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -4184,7 +4207,7 @@ float_is_integer(PyObject *self, PyObject *NPY_UNUSED(args))
     return ret;
 }
 
-#line 2014
+#line 2037
 static PyObject *
 double_is_integer(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -4207,7 +4230,7 @@ double_is_integer(PyObject *self, PyObject *NPY_UNUSED(args))
     return ret;
 }
 
-#line 2014
+#line 2037
 static PyObject *
 longdouble_is_integer(PyObject *self, PyObject *NPY_UNUSED(args))
 {
@@ -4483,31 +4506,31 @@ static PyGetSetDef inttype_getsets[] = {
 };
 
 static PyMethodDef numbertype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem_abc,
         METH_CLASS | METH_O, NULL},
     {NULL, NULL, 0, NULL}  /* sentinel */
 };
 
-#line 2299
+#line 2322
 static PyMethodDef cfloattype_methods[] = {
     {"__complex__",
         (PyCFunction)cfloat_complex,
         METH_VARARGS | METH_KEYWORDS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
     {NULL, NULL, 0, NULL}
 };
 
-#line 2299
+#line 2322
 static PyMethodDef clongdoubletype_methods[] = {
     {"__complex__",
         (PyCFunction)clongdouble_complex,
         METH_VARARGS | METH_KEYWORDS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4515,7 +4538,7 @@ static PyMethodDef clongdoubletype_methods[] = {
 };
 
 
-#line 2314
+#line 2337
 static PyMethodDef floatingtype_methods[] = {
     /* Hook for the round() builtin */
     {"__round__",
@@ -4524,7 +4547,7 @@ static PyMethodDef floatingtype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2314
+#line 2337
 static PyMethodDef complexfloatingtype_methods[] = {
     /* Hook for the round() builtin */
     {"__round__",
@@ -4545,7 +4568,7 @@ static PyMethodDef integertype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2337
+#line 2360
 static PyMethodDef halftype_methods[] = {
     {"as_integer_ratio",
         (PyCFunction)half_as_integer_ratio,
@@ -4553,14 +4576,14 @@ static PyMethodDef halftype_methods[] = {
     {"is_integer",
         (PyCFunction)half_is_integer,
         METH_NOARGS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
     {NULL, NULL, 0, NULL}
 };
 
-#line 2337
+#line 2360
 static PyMethodDef floattype_methods[] = {
     {"as_integer_ratio",
         (PyCFunction)float_as_integer_ratio,
@@ -4568,14 +4591,14 @@ static PyMethodDef floattype_methods[] = {
     {"is_integer",
         (PyCFunction)float_is_integer,
         METH_NOARGS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
     {NULL, NULL, 0, NULL}
 };
 
-#line 2337
+#line 2360
 static PyMethodDef doubletype_methods[] = {
     {"as_integer_ratio",
         (PyCFunction)double_as_integer_ratio,
@@ -4583,14 +4606,14 @@ static PyMethodDef doubletype_methods[] = {
     {"is_integer",
         (PyCFunction)double_is_integer,
         METH_NOARGS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
     {NULL, NULL, 0, NULL}
 };
 
-#line 2337
+#line 2360
 static PyMethodDef longdoubletype_methods[] = {
     {"as_integer_ratio",
         (PyCFunction)longdouble_as_integer_ratio,
@@ -4598,7 +4621,7 @@ static PyMethodDef longdoubletype_methods[] = {
     {"is_integer",
         (PyCFunction)longdouble_is_integer,
         METH_NOARGS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4606,18 +4629,18 @@ static PyMethodDef longdoubletype_methods[] = {
 };
 
 
-#line 2355
+#line 2378
 static PyMethodDef timedeltatype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
     {NULL, NULL, 0, NULL}
 };
 
-#line 2355
+#line 2378
 static PyMethodDef cdoubletype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4625,9 +4648,9 @@ static PyMethodDef cdoubletype_methods[] = {
 };
 
 
-#line 2368
+#line 2391
 static PyMethodDef bytetype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4637,9 +4660,9 @@ static PyMethodDef bytetype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef ubytetype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4649,9 +4672,9 @@ static PyMethodDef ubytetype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef shorttype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4661,9 +4684,9 @@ static PyMethodDef shorttype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef ushorttype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4673,9 +4696,9 @@ static PyMethodDef ushorttype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef inttype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4685,9 +4708,9 @@ static PyMethodDef inttype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef uinttype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4697,9 +4720,9 @@ static PyMethodDef uinttype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef longtype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4709,9 +4732,9 @@ static PyMethodDef longtype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef ulongtype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4721,9 +4744,9 @@ static PyMethodDef ulongtype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef longlongtype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4733,9 +4756,9 @@ static PyMethodDef longlongtype_methods[] = {
     {NULL, NULL, 0, NULL} /* sentinel */
 };
 
-#line 2368
+#line 2391
 static PyMethodDef ulonglongtype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -4991,7 +5014,7 @@ static PyBufferProcs gentype_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 bool_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5032,7 +5055,7 @@ static PyBufferProcs bool_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 byte_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5073,7 +5096,7 @@ static PyBufferProcs byte_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 short_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5114,7 +5137,7 @@ static PyBufferProcs short_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 int_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5155,7 +5178,7 @@ static PyBufferProcs int_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 long_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5196,7 +5219,7 @@ static PyBufferProcs long_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 longlong_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5237,7 +5260,7 @@ static PyBufferProcs longlong_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 ubyte_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5278,7 +5301,7 @@ static PyBufferProcs ubyte_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 ushort_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5319,7 +5342,7 @@ static PyBufferProcs ushort_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 uint_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5360,7 +5383,7 @@ static PyBufferProcs uint_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 ulong_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5401,7 +5424,7 @@ static PyBufferProcs ulong_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 ulonglong_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5442,7 +5465,7 @@ static PyBufferProcs ulonglong_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 half_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5483,7 +5506,7 @@ static PyBufferProcs half_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 float_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5524,7 +5547,7 @@ static PyBufferProcs float_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 double_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5565,7 +5588,7 @@ static PyBufferProcs double_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 longdouble_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5606,7 +5629,7 @@ static PyBufferProcs longdouble_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 cfloat_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5647,7 +5670,7 @@ static PyBufferProcs cfloat_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 cdouble_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5688,7 +5711,7 @@ static PyBufferProcs cdouble_arrtype_as_buffer = {
 };
 
 
-#line 2637
+#line 2660
 
 static int
 clongdouble_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5794,7 +5817,7 @@ static PyBufferProcs unicode_arrtype_as_buffer = {
 };
 
 
-#line 2746
+#line 2769
 
 static int
 datetime_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5836,7 +5859,7 @@ static PyBufferProcs datetime_arrtype_as_buffer = {
 };
 
 
-#line 2746
+#line 2769
 
 static int
 timedelta_getbuffer(PyObject *self, Py_buffer *view, int flags)
@@ -5951,7 +5974,7 @@ unicode_arrtype_dealloc(PyObject *v)
     PyUnicode_Type.tp_dealloc(v);
 }
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _BYTE_IS_BYTE
@@ -6068,7 +6091,7 @@ byte_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _BYTE_IS_BYTE
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _SHORT_IS_SHORT
@@ -6185,7 +6208,7 @@ short_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _SHORT_IS_SHORT
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _INT_IS_INT
@@ -6302,7 +6325,7 @@ int_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _INT_IS_INT
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _LONG_IS_LONG
@@ -6419,7 +6442,7 @@ long_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _LONG_IS_LONG
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _LONGLONG_IS_LONGLONG
@@ -6536,7 +6559,7 @@ longlong_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _LONGLONG_IS_LONGLONG
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _UBYTE_IS_UBYTE
@@ -6653,7 +6676,7 @@ ubyte_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _UBYTE_IS_UBYTE
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _USHORT_IS_USHORT
@@ -6770,7 +6793,7 @@ ushort_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _USHORT_IS_USHORT
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _UINT_IS_UINT
@@ -6887,7 +6910,7 @@ uint_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _UINT_IS_UINT
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _ULONG_IS_ULONG
@@ -7004,7 +7027,7 @@ ulong_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _ULONG_IS_ULONG
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _ULONGLONG_IS_ULONGLONG
@@ -7121,7 +7144,7 @@ ulonglong_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _ULONGLONG_IS_ULONGLONG
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _HALF_IS_HALF
@@ -7238,7 +7261,7 @@ half_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _HALF_IS_HALF
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _FLOAT_IS_FLOAT
@@ -7355,7 +7378,7 @@ float_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _FLOAT_IS_FLOAT
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _DOUBLE_IS_DOUBLE
@@ -7472,7 +7495,7 @@ double_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _DOUBLE_IS_DOUBLE
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _LONGDOUBLE_IS_LONGDOUBLE
@@ -7589,7 +7612,7 @@ longdouble_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _LONGDOUBLE_IS_LONGDOUBLE
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _CFLOAT_IS_CFLOAT
@@ -7706,7 +7729,7 @@ cfloat_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _CFLOAT_IS_CFLOAT
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _CDOUBLE_IS_CDOUBLE
@@ -7823,7 +7846,7 @@ cdouble_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _CDOUBLE_IS_CDOUBLE
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _CLONGDOUBLE_IS_CLONGDOUBLE
@@ -7940,7 +7963,7 @@ clongdouble_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _CLONGDOUBLE_IS_CLONGDOUBLE
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _STRING_IS_STRING
@@ -8057,7 +8080,7 @@ string_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 #undef _STRING_IS_STRING
 
 
-#line 2871
+#line 2894
 
 /* used as a pattern for testing token equality */
 #define _UNICODE_IS_UNICODE
@@ -8192,7 +8215,7 @@ object_arrtype_new(PyTypeObject *NPY_UNUSED(type), PyObject *args, PyObject *kwd
     return PyArray_Return(arr);
 }
 
-#line 3011
+#line 3034
 
 static PyObject *
 datetime_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
@@ -8251,7 +8274,7 @@ datetime_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
     return (PyObject *)ret;
 }
 
-#line 3011
+#line 3034
 
 static PyObject *
 timedelta_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
@@ -8377,7 +8400,7 @@ bool_arrtype_nonzero(PyObject *a)
     return a == PyArrayScalar_True;
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods byte_arrtype_as_number;
 static PyObject *
 byte_index(PyObject *self)
@@ -8385,7 +8408,7 @@ byte_index(PyObject *self)
     return PyLong_FromLong(PyArrayScalar_VAL(self, Byte));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods short_arrtype_as_number;
 static PyObject *
 short_index(PyObject *self)
@@ -8393,7 +8416,7 @@ short_index(PyObject *self)
     return PyLong_FromLong(PyArrayScalar_VAL(self, Short));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods int_arrtype_as_number;
 static PyObject *
 int_index(PyObject *self)
@@ -8401,7 +8424,7 @@ int_index(PyObject *self)
     return PyLong_FromLong(PyArrayScalar_VAL(self, Int));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods long_arrtype_as_number;
 static PyObject *
 long_index(PyObject *self)
@@ -8409,7 +8432,7 @@ long_index(PyObject *self)
     return PyLong_FromLong(PyArrayScalar_VAL(self, Long));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods ubyte_arrtype_as_number;
 static PyObject *
 ubyte_index(PyObject *self)
@@ -8417,7 +8440,7 @@ ubyte_index(PyObject *self)
     return PyLong_FromLong(PyArrayScalar_VAL(self, UByte));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods ushort_arrtype_as_number;
 static PyObject *
 ushort_index(PyObject *self)
@@ -8425,7 +8448,7 @@ ushort_index(PyObject *self)
     return PyLong_FromLong(PyArrayScalar_VAL(self, UShort));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods longlong_arrtype_as_number;
 static PyObject *
 longlong_index(PyObject *self)
@@ -8433,7 +8456,7 @@ longlong_index(PyObject *self)
     return PyLong_FromLongLong(PyArrayScalar_VAL(self, LongLong));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods uint_arrtype_as_number;
 static PyObject *
 uint_index(PyObject *self)
@@ -8441,7 +8464,7 @@ uint_index(PyObject *self)
     return PyLong_FromUnsignedLong(PyArrayScalar_VAL(self, UInt));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods ulong_arrtype_as_number;
 static PyObject *
 ulong_index(PyObject *self)
@@ -8449,7 +8472,7 @@ ulong_index(PyObject *self)
     return PyLong_FromUnsignedLong(PyArrayScalar_VAL(self, ULong));
 }
 
-#line 3144
+#line 3167
 static PyNumberMethods ulonglong_arrtype_as_number;
 static PyObject *
 ulonglong_index(PyObject *self)
@@ -8458,25 +8481,25 @@ ulonglong_index(PyObject *self)
 }
 
 
-#line 3158
+#line 3181
 static PyNumberMethods half_arrtype_as_number;
 
-#line 3158
+#line 3181
 static PyNumberMethods float_arrtype_as_number;
 
-#line 3158
+#line 3181
 static PyNumberMethods double_arrtype_as_number;
 
-#line 3158
+#line 3181
 static PyNumberMethods longdouble_arrtype_as_number;
 
-#line 3158
+#line 3181
 static PyNumberMethods cfloat_arrtype_as_number;
 
-#line 3158
+#line 3181
 static PyNumberMethods cdouble_arrtype_as_number;
 
-#line 3158
+#line 3181
 static PyNumberMethods clongdouble_arrtype_as_number;
 
 
@@ -8590,21 +8613,21 @@ void_arrtype_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
 
 /****************  Define Hash functions ********************/
 
-#line 3275
+#line 3298
 static npy_hash_t
 bool_arrtype_hash(PyObject *obj)
 {
     return (npy_hash_t)(PyArrayScalar_VAL(obj, Bool));
 }
 
-#line 3275
+#line 3298
 static npy_hash_t
 ubyte_arrtype_hash(PyObject *obj)
 {
     return (npy_hash_t)(PyArrayScalar_VAL(obj, UByte));
 }
 
-#line 3275
+#line 3298
 static npy_hash_t
 ushort_arrtype_hash(PyObject *obj)
 {
@@ -8612,7 +8635,7 @@ ushort_arrtype_hash(PyObject *obj)
 }
 
 
-#line 3286
+#line 3309
 static npy_hash_t
 byte_arrtype_hash(PyObject *obj)
 {
@@ -8623,7 +8646,7 @@ byte_arrtype_hash(PyObject *obj)
     return x;
 }
 
-#line 3286
+#line 3309
 static npy_hash_t
 short_arrtype_hash(PyObject *obj)
 {
@@ -8634,7 +8657,7 @@ short_arrtype_hash(PyObject *obj)
     return x;
 }
 
-#line 3286
+#line 3309
 static npy_hash_t
 uint_arrtype_hash(PyObject *obj)
 {
@@ -8674,8 +8697,8 @@ long_arrtype_hash(PyObject *obj)
     return x;
 }
 
-#line 3330
-static NPY_INLINE npy_hash_t
+#line 3353
+static inline npy_hash_t
 longlong_arrtype_hash(PyObject *obj)
 {
     PyObject * l = PyLong_FromLongLong(
@@ -8685,8 +8708,8 @@ longlong_arrtype_hash(PyObject *obj)
     return x;
 }
 
-#line 3330
-static NPY_INLINE npy_hash_t
+#line 3353
+static inline npy_hash_t
 ulonglong_arrtype_hash(PyObject *obj)
 {
     PyObject * l = PyLong_FromUnsignedLongLong(
@@ -8698,7 +8721,7 @@ ulonglong_arrtype_hash(PyObject *obj)
 
 
 
-#line 3346
+#line 3369
 #if NPY_SIZEOF_HASH_T==NPY_SIZEOF_DATETIME
 static npy_hash_t
 datetime_arrtype_hash(PyObject *obj)
@@ -8735,7 +8758,7 @@ datetime_arrtype_hash(PyObject *obj)
 }
 #endif
 
-#line 3346
+#line 3369
 #if NPY_SIZEOF_HASH_T==NPY_SIZEOF_DATETIME
 static npy_hash_t
 timedelta_arrtype_hash(PyObject *obj)
@@ -8777,7 +8800,7 @@ timedelta_arrtype_hash(PyObject *obj)
 
 /* Wrong thing to do for longdouble, but....*/
 
-#line 3391
+#line 3414
 static npy_hash_t
 float_arrtype_hash(PyObject *obj)
 {
@@ -8807,7 +8830,7 @@ cfloat_arrtype_hash(PyObject *obj)
     return combined;
 }
 
-#line 3391
+#line 3414
 static npy_hash_t
 longdouble_arrtype_hash(PyObject *obj)
 {
@@ -9016,7 +9039,7 @@ object_arrtype_call(PyObjectScalarObject *obj, PyObject *args, PyObject *kwds)
 
 NPY_NO_EXPORT PyTypeObject PyObjectArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.object_",
+    .tp_name = "numpy." NPY_OBJECT_name,
     .tp_basicsize = sizeof(PyObjectScalarObject),
     .tp_dealloc = (destructor)object_arrtype_dealloc,
     .tp_as_sequence = &object_arrtype_as_sequence,
@@ -9052,563 +9075,220 @@ gen_arrtype_subscript(PyObject *self, PyObject *key)
 }
 
 
-#define NAME_bool "bool"
-#define NAME_void "void"
-#define NAME_string "bytes"
-#define NAME_unicode "str"
+#line 3674
 
-#line 3645
 NPY_NO_EXPORT PyTypeObject PyBoolArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy." NAME_bool "_",
+    .tp_name = "numpy." NPY_BOOL_name,
     .tp_basicsize = sizeof(PyBoolScalarObject),
 };
 
-#line 3645
-NPY_NO_EXPORT PyTypeObject PyStringArrType_Type = {
-    PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy." NAME_string "_",
-    .tp_basicsize = sizeof(PyStringScalarObject),
-};
 
-#line 3645
-NPY_NO_EXPORT PyTypeObject PyUnicodeArrType_Type = {
-    PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy." NAME_unicode "_",
-    .tp_basicsize = sizeof(PyUnicodeScalarObject),
-};
+#line 3674
 
-#line 3645
-NPY_NO_EXPORT PyTypeObject PyVoidArrType_Type = {
-    PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy." NAME_void "",
-    .tp_basicsize = sizeof(PyVoidScalarObject),
-};
-
-
-#undef NAME_bool
-#undef NAME_void
-#undef NAME_string
-#undef NAME_unicode
-
-#line 3664
-#if NPY_BITSOF_CHAR == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_CHAR == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_CHAR == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_CHAR == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_CHAR == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_CHAR == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_CHAR == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_CHAR == 256
-#define _THIS_SIZE "256"
-#endif
 NPY_NO_EXPORT PyTypeObject PyByteArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.int" _THIS_SIZE,
+    .tp_name = "numpy." NPY_BYTE_name,
     .tp_basicsize = sizeof(PyByteScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_SHORT == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_SHORT == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_SHORT == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_SHORT == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_SHORT == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_SHORT == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_SHORT == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_SHORT == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyShortArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.int" _THIS_SIZE,
+    .tp_name = "numpy." NPY_SHORT_name,
     .tp_basicsize = sizeof(PyShortScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_INT == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_INT == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_INT == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_INT == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_INT == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_INT == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_INT == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_INT == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyIntArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.int" _THIS_SIZE,
+    .tp_name = "numpy." NPY_INT_name,
     .tp_basicsize = sizeof(PyIntScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_LONG == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_LONG == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_LONG == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_LONG == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_LONG == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_LONG == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_LONG == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_LONG == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyLongArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.int" _THIS_SIZE,
+    .tp_name = "numpy." NPY_LONG_name,
     .tp_basicsize = sizeof(PyLongScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_LONGLONG == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_LONGLONG == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_LONGLONG == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_LONGLONG == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_LONGLONG == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_LONGLONG == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_LONGLONG == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_LONGLONG == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyLongLongArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.int" _THIS_SIZE,
+    .tp_name = "numpy." NPY_LONGLONG_name,
     .tp_basicsize = sizeof(PyLongLongScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_CHAR == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_CHAR == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_CHAR == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_CHAR == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_CHAR == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_CHAR == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_CHAR == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_CHAR == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyUByteArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.uint" _THIS_SIZE,
+    .tp_name = "numpy." NPY_UBYTE_name,
     .tp_basicsize = sizeof(PyUByteScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_SHORT == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_SHORT == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_SHORT == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_SHORT == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_SHORT == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_SHORT == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_SHORT == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_SHORT == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyUShortArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.uint" _THIS_SIZE,
+    .tp_name = "numpy." NPY_USHORT_name,
     .tp_basicsize = sizeof(PyUShortScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_INT == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_INT == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_INT == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_INT == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_INT == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_INT == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_INT == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_INT == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyUIntArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.uint" _THIS_SIZE,
+    .tp_name = "numpy." NPY_UINT_name,
     .tp_basicsize = sizeof(PyUIntScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_LONG == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_LONG == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_LONG == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_LONG == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_LONG == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_LONG == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_LONG == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_LONG == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyULongArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.uint" _THIS_SIZE,
+    .tp_name = "numpy." NPY_ULONG_name,
     .tp_basicsize = sizeof(PyULongScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_LONGLONG == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_LONGLONG == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_LONGLONG == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_LONGLONG == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_LONGLONG == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_LONGLONG == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_LONGLONG == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_LONGLONG == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyULongLongArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.uint" _THIS_SIZE,
+    .tp_name = "numpy." NPY_ULONGLONG_name,
     .tp_basicsize = sizeof(PyULongLongScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_HALF == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_HALF == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_HALF == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_HALF == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_HALF == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_HALF == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_HALF == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_HALF == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyHalfArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.float" _THIS_SIZE,
+    .tp_name = "numpy." NPY_HALF_name,
     .tp_basicsize = sizeof(PyHalfScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_FLOAT == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_FLOAT == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_FLOAT == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_FLOAT == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_FLOAT == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_FLOAT == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_FLOAT == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_FLOAT == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyFloatArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.float" _THIS_SIZE,
+    .tp_name = "numpy." NPY_FLOAT_name,
     .tp_basicsize = sizeof(PyFloatScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_DOUBLE == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_DOUBLE == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_DOUBLE == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_DOUBLE == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_DOUBLE == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_DOUBLE == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_DOUBLE == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_DOUBLE == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyDoubleArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.float" _THIS_SIZE,
+    .tp_name = "numpy." NPY_DOUBLE_name,
     .tp_basicsize = sizeof(PyDoubleScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_LONGDOUBLE == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_LONGDOUBLE == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_LONGDOUBLE == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_LONGDOUBLE == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_LONGDOUBLE == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_LONGDOUBLE == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_LONGDOUBLE == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_LONGDOUBLE == 256
-#define _THIS_SIZE "256"
-#endif
+#line 3674
+
 NPY_NO_EXPORT PyTypeObject PyLongDoubleArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.float" _THIS_SIZE,
+    .tp_name = "numpy." NPY_LONGDOUBLE_name,
     .tp_basicsize = sizeof(PyLongDoubleScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_DATETIME == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_DATETIME == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_DATETIME == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_DATETIME == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_DATETIME == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_DATETIME == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_DATETIME == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_DATETIME == 256
-#define _THIS_SIZE "256"
-#endif
-NPY_NO_EXPORT PyTypeObject PyDatetimeArrType_Type = {
+#line 3674
+
+NPY_NO_EXPORT PyTypeObject PyCFloatArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.datetime" _THIS_SIZE,
-    .tp_basicsize = sizeof(PyDatetimeScalarObject),
+    .tp_name = "numpy." NPY_CFLOAT_name,
+    .tp_basicsize = sizeof(PyCFloatScalarObject),
 };
 
 
-#undef _THIS_SIZE
-
-#line 3664
-#if NPY_BITSOF_TIMEDELTA == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_TIMEDELTA == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_TIMEDELTA == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_TIMEDELTA == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_TIMEDELTA == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_TIMEDELTA == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_TIMEDELTA == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_TIMEDELTA == 256
-#define _THIS_SIZE "256"
-#endif
-NPY_NO_EXPORT PyTypeObject PyTimedeltaArrType_Type = {
+#line 3674
+
+NPY_NO_EXPORT PyTypeObject PyCDoubleArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.timedelta" _THIS_SIZE,
-    .tp_basicsize = sizeof(PyTimedeltaScalarObject),
+    .tp_name = "numpy." NPY_CDOUBLE_name,
+    .tp_basicsize = sizeof(PyCDoubleScalarObject),
 };
 
 
-#undef _THIS_SIZE
+#line 3674
 
+NPY_NO_EXPORT PyTypeObject PyCLongDoubleArrType_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "numpy." NPY_CLONGDOUBLE_name,
+    .tp_basicsize = sizeof(PyCLongDoubleScalarObject),
+};
 
 
-static PyMappingMethods gentype_as_mapping = {
-    .mp_subscript = (binaryfunc)gen_arrtype_subscript,
+#line 3674
+
+NPY_NO_EXPORT PyTypeObject PyStringArrType_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "numpy." NPY_STRING_name,
+    .tp_basicsize = sizeof(PyStringScalarObject),
 };
 
 
-#line 3702
-#if NPY_BITSOF_FLOAT == 16
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_FLOAT == 32
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_FLOAT == 64
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_FLOAT == 80
-#define _THIS_SIZE "160"
-#elif NPY_BITSOF_FLOAT == 96
-#define _THIS_SIZE "192"
-#elif NPY_BITSOF_FLOAT == 128
-#define _THIS_SIZE "256"
-#elif NPY_BITSOF_FLOAT == 256
-#define _THIS_SIZE "512"
-#endif
+#line 3674
 
-NPY_NO_EXPORT PyTypeObject PyCFloatArrType_Type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    .tp_name = "numpy.complex" _THIS_SIZE,
-    .tp_basicsize = sizeof(PyCFloatScalarObject),
-    .tp_flags = Py_TPFLAGS_DEFAULT,
+NPY_NO_EXPORT PyTypeObject PyUnicodeArrType_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "numpy." NPY_UNICODE_name,
+    .tp_basicsize = sizeof(PyUnicodeScalarObject),
 };
-#undef _THIS_SIZE
-
-
-#line 3702
-#if NPY_BITSOF_DOUBLE == 16
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_DOUBLE == 32
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_DOUBLE == 64
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_DOUBLE == 80
-#define _THIS_SIZE "160"
-#elif NPY_BITSOF_DOUBLE == 96
-#define _THIS_SIZE "192"
-#elif NPY_BITSOF_DOUBLE == 128
-#define _THIS_SIZE "256"
-#elif NPY_BITSOF_DOUBLE == 256
-#define _THIS_SIZE "512"
-#endif
 
-NPY_NO_EXPORT PyTypeObject PyCDoubleArrType_Type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    .tp_name = "numpy.complex" _THIS_SIZE,
-    .tp_basicsize = sizeof(PyCDoubleScalarObject),
-    .tp_flags = Py_TPFLAGS_DEFAULT,
+
+#line 3674
+
+NPY_NO_EXPORT PyTypeObject PyVoidArrType_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "numpy." NPY_VOID_name,
+    .tp_basicsize = sizeof(PyVoidScalarObject),
 };
-#undef _THIS_SIZE
-
-
-#line 3702
-#if NPY_BITSOF_LONGDOUBLE == 16
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_LONGDOUBLE == 32
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_LONGDOUBLE == 64
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_LONGDOUBLE == 80
-#define _THIS_SIZE "160"
-#elif NPY_BITSOF_LONGDOUBLE == 96
-#define _THIS_SIZE "192"
-#elif NPY_BITSOF_LONGDOUBLE == 128
-#define _THIS_SIZE "256"
-#elif NPY_BITSOF_LONGDOUBLE == 256
-#define _THIS_SIZE "512"
-#endif
 
-NPY_NO_EXPORT PyTypeObject PyCLongDoubleArrType_Type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    .tp_name = "numpy.complex" _THIS_SIZE,
-    .tp_basicsize = sizeof(PyCLongDoubleScalarObject),
-    .tp_flags = Py_TPFLAGS_DEFAULT,
+
+#line 3674
+
+NPY_NO_EXPORT PyTypeObject PyDatetimeArrType_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "numpy." NPY_DATETIME_name,
+    .tp_basicsize = sizeof(PyDatetimeScalarObject),
+};
+
+
+#line 3674
+
+NPY_NO_EXPORT PyTypeObject PyTimedeltaArrType_Type = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "numpy." NPY_TIMEDELTA_name,
+    .tp_basicsize = sizeof(PyTimedeltaScalarObject),
 };
-#undef _THIS_SIZE
 
 
 
+
+static PyMappingMethods gentype_as_mapping = {
+    .mp_subscript = (binaryfunc)gen_arrtype_subscript,
+};
+
+
 /*
  * This table maps the built-in type numbers to their scalar
  * type numbers.  Note that signed integers are mapped to INTNEG_SCALAR,
@@ -9659,109 +9339,109 @@ initialize_casting_tables(void)
 
     /* Compile-time loop of scalar kinds */
 
-    #line 3794
+    #line 3755
 
     _npy_scalar_kinds_table[NPY_BOOL] = NPY_BOOL_SCALAR;
     _npy_next_larger_type_table[NPY_BOOL] = -1;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_BYTE] = NPY_INTNEG_SCALAR;
     _npy_next_larger_type_table[NPY_BYTE] = NPY_SHORT;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_UBYTE] = NPY_INTPOS_SCALAR;
     _npy_next_larger_type_table[NPY_UBYTE] = NPY_USHORT;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_SHORT] = NPY_INTNEG_SCALAR;
     _npy_next_larger_type_table[NPY_SHORT] = NPY_INT;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_USHORT] = NPY_INTPOS_SCALAR;
     _npy_next_larger_type_table[NPY_USHORT] = NPY_UINT;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_INT] = NPY_INTNEG_SCALAR;
     _npy_next_larger_type_table[NPY_INT] = NPY_LONG;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_UINT] = NPY_INTPOS_SCALAR;
     _npy_next_larger_type_table[NPY_UINT] = NPY_ULONG;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_LONG] = NPY_INTNEG_SCALAR;
     _npy_next_larger_type_table[NPY_LONG] = NPY_LONGLONG;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_ULONG] = NPY_INTPOS_SCALAR;
     _npy_next_larger_type_table[NPY_ULONG] = NPY_ULONGLONG;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_LONGLONG] = NPY_INTNEG_SCALAR;
     _npy_next_larger_type_table[NPY_LONGLONG] = -1;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_ULONGLONG] = NPY_INTPOS_SCALAR;
     _npy_next_larger_type_table[NPY_ULONGLONG] = -1;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_HALF] = NPY_FLOAT_SCALAR;
     _npy_next_larger_type_table[NPY_HALF] = NPY_FLOAT;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_FLOAT] = NPY_FLOAT_SCALAR;
     _npy_next_larger_type_table[NPY_FLOAT] = NPY_DOUBLE;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_DOUBLE] = NPY_FLOAT_SCALAR;
     _npy_next_larger_type_table[NPY_DOUBLE] = NPY_LONGDOUBLE;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_LONGDOUBLE] = NPY_FLOAT_SCALAR;
     _npy_next_larger_type_table[NPY_LONGDOUBLE] = -1;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_CFLOAT] = NPY_COMPLEX_SCALAR;
     _npy_next_larger_type_table[NPY_CFLOAT] = NPY_CDOUBLE;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_CDOUBLE] = NPY_COMPLEX_SCALAR;
     _npy_next_larger_type_table[NPY_CDOUBLE] = NPY_CLONGDOUBLE;
 
     
-#line 3794
+#line 3755
 
     _npy_scalar_kinds_table[NPY_CLONGDOUBLE] = NPY_COMPLEX_SCALAR;
     _npy_next_larger_type_table[NPY_CLONGDOUBLE] = -1;
@@ -9889,98 +9569,98 @@ initialize_numeric_types(void)
      * also fill array_type_as_number struct with reasonable defaults
      */
 
-    #line 3927
+    #line 3888
     byte_arrtype_as_number = gentype_as_number;
     PyByteArrType_Type.tp_as_number = &byte_arrtype_as_number;
     PyByteArrType_Type.tp_as_number->nb_index = (unaryfunc)byte_index;
 
     
-#line 3927
+#line 3888
     short_arrtype_as_number = gentype_as_number;
     PyShortArrType_Type.tp_as_number = &short_arrtype_as_number;
     PyShortArrType_Type.tp_as_number->nb_index = (unaryfunc)short_index;
 
     
-#line 3927
+#line 3888
     int_arrtype_as_number = gentype_as_number;
     PyIntArrType_Type.tp_as_number = &int_arrtype_as_number;
     PyIntArrType_Type.tp_as_number->nb_index = (unaryfunc)int_index;
 
     
-#line 3927
+#line 3888
     long_arrtype_as_number = gentype_as_number;
     PyLongArrType_Type.tp_as_number = &long_arrtype_as_number;
     PyLongArrType_Type.tp_as_number->nb_index = (unaryfunc)long_index;
 
     
-#line 3927
+#line 3888
     longlong_arrtype_as_number = gentype_as_number;
     PyLongLongArrType_Type.tp_as_number = &longlong_arrtype_as_number;
     PyLongLongArrType_Type.tp_as_number->nb_index = (unaryfunc)longlong_index;
 
     
-#line 3927
+#line 3888
     ubyte_arrtype_as_number = gentype_as_number;
     PyUByteArrType_Type.tp_as_number = &ubyte_arrtype_as_number;
     PyUByteArrType_Type.tp_as_number->nb_index = (unaryfunc)ubyte_index;
 
     
-#line 3927
+#line 3888
     ushort_arrtype_as_number = gentype_as_number;
     PyUShortArrType_Type.tp_as_number = &ushort_arrtype_as_number;
     PyUShortArrType_Type.tp_as_number->nb_index = (unaryfunc)ushort_index;
 
     
-#line 3927
+#line 3888
     uint_arrtype_as_number = gentype_as_number;
     PyUIntArrType_Type.tp_as_number = &uint_arrtype_as_number;
     PyUIntArrType_Type.tp_as_number->nb_index = (unaryfunc)uint_index;
 
     
-#line 3927
+#line 3888
     ulong_arrtype_as_number = gentype_as_number;
     PyULongArrType_Type.tp_as_number = &ulong_arrtype_as_number;
     PyULongArrType_Type.tp_as_number->nb_index = (unaryfunc)ulong_index;
 
     
-#line 3927
+#line 3888
     ulonglong_arrtype_as_number = gentype_as_number;
     PyULongLongArrType_Type.tp_as_number = &ulonglong_arrtype_as_number;
     PyULongLongArrType_Type.tp_as_number->nb_index = (unaryfunc)ulonglong_index;
 
     
 
-    #line 3939
+    #line 3900
     half_arrtype_as_number = gentype_as_number;
     PyHalfArrType_Type.tp_as_number = &half_arrtype_as_number;
 
     
-#line 3939
+#line 3900
     float_arrtype_as_number = gentype_as_number;
     PyFloatArrType_Type.tp_as_number = &float_arrtype_as_number;
 
     
-#line 3939
+#line 3900
     double_arrtype_as_number = gentype_as_number;
     PyDoubleArrType_Type.tp_as_number = &double_arrtype_as_number;
 
     
-#line 3939
+#line 3900
     longdouble_arrtype_as_number = gentype_as_number;
     PyLongDoubleArrType_Type.tp_as_number = &longdouble_arrtype_as_number;
 
     
-#line 3939
+#line 3900
     cfloat_arrtype_as_number = gentype_as_number;
     PyCFloatArrType_Type.tp_as_number = &cfloat_arrtype_as_number;
 
     
-#line 3939
+#line 3900
     cdouble_arrtype_as_number = gentype_as_number;
     PyCDoubleArrType_Type.tp_as_number = &cdouble_arrtype_as_number;
 
     
-#line 3939
+#line 3900
     clongdouble_arrtype_as_number = gentype_as_number;
     PyCLongDoubleArrType_Type.tp_as_number = &clongdouble_arrtype_as_number;
 
@@ -10008,53 +9688,53 @@ initialize_numeric_types(void)
 
     PyNumberArrType_Type.tp_methods = numbertype_methods;
 
-    #line 3970
+    #line 3931
 
     PyNumberArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PyIntegerArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PySignedIntegerArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PyUnsignedIntegerArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PyInexactArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PyFloatingArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PyComplexFloatingArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PyFlexibleArrType_Type.tp_flags = BASEFLAGS;
 
     
-#line 3970
+#line 3931
 
     PyCharacterArrType_Type.tp_flags = BASEFLAGS;
 
     
 
-    #line 3985
+    #line 3946
 
     PyBoolArrType_Type.tp_flags = BASEFLAGS;
     PyBoolArrType_Type.tp_new = bool_arrtype_new;
@@ -10067,7 +9747,7 @@ initialize_numeric_types(void)
 #undef _IS_Bool
 
     
-#line 3985
+#line 3946
 
     PyByteArrType_Type.tp_flags = BASEFLAGS;
     PyByteArrType_Type.tp_new = byte_arrtype_new;
@@ -10080,7 +9760,7 @@ initialize_numeric_types(void)
 #undef _IS_Byte
 
     
-#line 3985
+#line 3946
 
     PyShortArrType_Type.tp_flags = BASEFLAGS;
     PyShortArrType_Type.tp_new = short_arrtype_new;
@@ -10093,7 +9773,7 @@ initialize_numeric_types(void)
 #undef _IS_Short
 
     
-#line 3985
+#line 3946
 
     PyIntArrType_Type.tp_flags = BASEFLAGS;
     PyIntArrType_Type.tp_new = int_arrtype_new;
@@ -10106,7 +9786,7 @@ initialize_numeric_types(void)
 #undef _IS_Int
 
     
-#line 3985
+#line 3946
 
     PyLongArrType_Type.tp_flags = BASEFLAGS;
     PyLongArrType_Type.tp_new = long_arrtype_new;
@@ -10119,7 +9799,7 @@ initialize_numeric_types(void)
 #undef _IS_Long
 
     
-#line 3985
+#line 3946
 
     PyLongLongArrType_Type.tp_flags = BASEFLAGS;
     PyLongLongArrType_Type.tp_new = longlong_arrtype_new;
@@ -10132,7 +9812,7 @@ initialize_numeric_types(void)
 #undef _IS_LongLong
 
     
-#line 3985
+#line 3946
 
     PyUByteArrType_Type.tp_flags = BASEFLAGS;
     PyUByteArrType_Type.tp_new = ubyte_arrtype_new;
@@ -10145,7 +9825,7 @@ initialize_numeric_types(void)
 #undef _IS_UByte
 
     
-#line 3985
+#line 3946
 
     PyUShortArrType_Type.tp_flags = BASEFLAGS;
     PyUShortArrType_Type.tp_new = ushort_arrtype_new;
@@ -10158,7 +9838,7 @@ initialize_numeric_types(void)
 #undef _IS_UShort
 
     
-#line 3985
+#line 3946
 
     PyUIntArrType_Type.tp_flags = BASEFLAGS;
     PyUIntArrType_Type.tp_new = uint_arrtype_new;
@@ -10171,7 +9851,7 @@ initialize_numeric_types(void)
 #undef _IS_UInt
 
     
-#line 3985
+#line 3946
 
     PyULongArrType_Type.tp_flags = BASEFLAGS;
     PyULongArrType_Type.tp_new = ulong_arrtype_new;
@@ -10184,7 +9864,7 @@ initialize_numeric_types(void)
 #undef _IS_ULong
 
     
-#line 3985
+#line 3946
 
     PyULongLongArrType_Type.tp_flags = BASEFLAGS;
     PyULongLongArrType_Type.tp_new = ulonglong_arrtype_new;
@@ -10197,7 +9877,7 @@ initialize_numeric_types(void)
 #undef _IS_ULongLong
 
     
-#line 3985
+#line 3946
 
     PyHalfArrType_Type.tp_flags = BASEFLAGS;
     PyHalfArrType_Type.tp_new = half_arrtype_new;
@@ -10210,7 +9890,7 @@ initialize_numeric_types(void)
 #undef _IS_Half
 
     
-#line 3985
+#line 3946
 
     PyFloatArrType_Type.tp_flags = BASEFLAGS;
     PyFloatArrType_Type.tp_new = float_arrtype_new;
@@ -10223,7 +9903,7 @@ initialize_numeric_types(void)
 #undef _IS_Float
 
     
-#line 3985
+#line 3946
 
     PyDoubleArrType_Type.tp_flags = BASEFLAGS;
     PyDoubleArrType_Type.tp_new = double_arrtype_new;
@@ -10236,7 +9916,7 @@ initialize_numeric_types(void)
 #undef _IS_Double
 
     
-#line 3985
+#line 3946
 
     PyLongDoubleArrType_Type.tp_flags = BASEFLAGS;
     PyLongDoubleArrType_Type.tp_new = longdouble_arrtype_new;
@@ -10249,7 +9929,7 @@ initialize_numeric_types(void)
 #undef _IS_LongDouble
 
     
-#line 3985
+#line 3946
 
     PyCFloatArrType_Type.tp_flags = BASEFLAGS;
     PyCFloatArrType_Type.tp_new = cfloat_arrtype_new;
@@ -10262,7 +9942,7 @@ initialize_numeric_types(void)
 #undef _IS_CFloat
 
     
-#line 3985
+#line 3946
 
     PyCDoubleArrType_Type.tp_flags = BASEFLAGS;
     PyCDoubleArrType_Type.tp_new = cdouble_arrtype_new;
@@ -10275,7 +9955,7 @@ initialize_numeric_types(void)
 #undef _IS_CDouble
 
     
-#line 3985
+#line 3946
 
     PyCLongDoubleArrType_Type.tp_flags = BASEFLAGS;
     PyCLongDoubleArrType_Type.tp_new = clongdouble_arrtype_new;
@@ -10288,7 +9968,7 @@ initialize_numeric_types(void)
 #undef _IS_CLongDouble
 
     
-#line 3985
+#line 3946
 
     PyStringArrType_Type.tp_flags = BASEFLAGS;
     PyStringArrType_Type.tp_new = string_arrtype_new;
@@ -10301,7 +9981,7 @@ initialize_numeric_types(void)
 #undef _IS_String
 
     
-#line 3985
+#line 3946
 
     PyUnicodeArrType_Type.tp_flags = BASEFLAGS;
     PyUnicodeArrType_Type.tp_new = unicode_arrtype_new;
@@ -10314,7 +9994,7 @@ initialize_numeric_types(void)
 #undef _IS_Unicode
 
     
-#line 3985
+#line 3946
 
     PyVoidArrType_Type.tp_flags = BASEFLAGS;
     PyVoidArrType_Type.tp_new = void_arrtype_new;
@@ -10327,7 +10007,7 @@ initialize_numeric_types(void)
 #undef _IS_Void
 
     
-#line 3985
+#line 3946
 
     PyObjectArrType_Type.tp_flags = BASEFLAGS;
     PyObjectArrType_Type.tp_new = object_arrtype_new;
@@ -10340,7 +10020,7 @@ initialize_numeric_types(void)
 #undef _IS_Object
 
     
-#line 3985
+#line 3946
 
     PyDatetimeArrType_Type.tp_flags = BASEFLAGS;
     PyDatetimeArrType_Type.tp_new = datetime_arrtype_new;
@@ -10353,7 +10033,7 @@ initialize_numeric_types(void)
 #undef _IS_Datetime
 
     
-#line 3985
+#line 3946
 
     PyTimedeltaArrType_Type.tp_flags = BASEFLAGS;
     PyTimedeltaArrType_Type.tp_new = timedelta_arrtype_new;
@@ -10369,246 +10049,246 @@ initialize_numeric_types(void)
 
     PyUnicodeArrType_Type.tp_dealloc = unicode_arrtype_dealloc;
 
-    #line 4008
+    #line 3969
 
     PyBoolArrType_Type.tp_hash = bool_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyByteArrType_Type.tp_hash = byte_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyShortArrType_Type.tp_hash = short_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyUByteArrType_Type.tp_hash = ubyte_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyUShortArrType_Type.tp_hash = ushort_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyUIntArrType_Type.tp_hash = uint_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyULongArrType_Type.tp_hash = ulong_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyULongLongArrType_Type.tp_hash = ulonglong_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyHalfArrType_Type.tp_hash = half_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyFloatArrType_Type.tp_hash = float_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyLongDoubleArrType_Type.tp_hash = longdouble_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyCFloatArrType_Type.tp_hash = cfloat_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyCLongDoubleArrType_Type.tp_hash = clongdouble_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyVoidArrType_Type.tp_hash = void_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyObjectArrType_Type.tp_hash = object_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyDatetimeArrType_Type.tp_hash = datetime_arrtype_hash;
 
     
-#line 4008
+#line 3969
 
     PyTimedeltaArrType_Type.tp_hash = timedelta_arrtype_hash;
 
     
 
-    #line 4017
+    #line 3978
 
     PyCFloatArrType_Type.tp_methods = cfloattype_methods;
 
     
-#line 4017
+#line 3978
 
     PyCLongDoubleArrType_Type.tp_methods = clongdoubletype_methods;
 
     
-#line 4017
+#line 3978
 
     PyFloatingArrType_Type.tp_methods = floatingtype_methods;
 
     
-#line 4017
+#line 3978
 
     PyIntegerArrType_Type.tp_methods = integertype_methods;
 
     
-#line 4017
+#line 3978
 
     PyComplexFloatingArrType_Type.tp_methods = complexfloatingtype_methods;
 
     
 
-    #line 4028
+    #line 3989
 
     PyByteArrType_Type.tp_methods = bytetype_methods;
 
     
-#line 4028
+#line 3989
 
     PyShortArrType_Type.tp_methods = shorttype_methods;
 
     
-#line 4028
+#line 3989
 
     PyIntArrType_Type.tp_methods = inttype_methods;
 
     
-#line 4028
+#line 3989
 
     PyLongArrType_Type.tp_methods = longtype_methods;
 
     
-#line 4028
+#line 3989
 
     PyLongLongArrType_Type.tp_methods = longlongtype_methods;
 
     
-#line 4028
+#line 3989
 
     PyUByteArrType_Type.tp_methods = ubytetype_methods;
 
     
-#line 4028
+#line 3989
 
     PyUShortArrType_Type.tp_methods = ushorttype_methods;
 
     
-#line 4028
+#line 3989
 
     PyUIntArrType_Type.tp_methods = uinttype_methods;
 
     
-#line 4028
+#line 3989
 
     PyULongArrType_Type.tp_methods = ulongtype_methods;
 
     
-#line 4028
+#line 3989
 
     PyULongLongArrType_Type.tp_methods = ulonglongtype_methods;
 
     
 
-    #line 4037
+    #line 3998
 
     PyHalfArrType_Type.tp_methods = halftype_methods;
 
     
-#line 4037
+#line 3998
 
     PyFloatArrType_Type.tp_methods = floattype_methods;
 
     
-#line 4037
+#line 3998
 
     PyDoubleArrType_Type.tp_methods = doubletype_methods;
 
     
-#line 4037
+#line 3998
 
     PyLongDoubleArrType_Type.tp_methods = longdoubletype_methods;
 
     
 
-    #line 4048
+    #line 4009
 
     PyByteArrType_Type.tp_methods = bytetype_methods;
 
     
-#line 4048
+#line 4009
 
     PyShortArrType_Type.tp_methods = shorttype_methods;
 
     
-#line 4048
+#line 4009
 
     PyIntArrType_Type.tp_methods = inttype_methods;
 
     
-#line 4048
+#line 4009
 
     PyLongArrType_Type.tp_methods = longtype_methods;
 
     
-#line 4048
+#line 4009
 
     PyLongLongArrType_Type.tp_methods = longlongtype_methods;
 
     
-#line 4048
+#line 4009
 
     PyUByteArrType_Type.tp_methods = ubytetype_methods;
 
     
-#line 4048
+#line 4009
 
     PyUShortArrType_Type.tp_methods = ushorttype_methods;
 
     
-#line 4048
+#line 4009
 
     PyUIntArrType_Type.tp_methods = uinttype_methods;
 
     
-#line 4048
+#line 4009
 
     PyULongArrType_Type.tp_methods = ulongtype_methods;
 
     
-#line 4048
+#line 4009
 
     PyULongLongArrType_Type.tp_methods = ulonglongtype_methods;
 
     
-#line 4048
+#line 4009
 
     PyTimedeltaArrType_Type.tp_methods = timedeltatype_methods;
 
     
-#line 4048
+#line 4009
 
     PyCDoubleArrType_Type.tp_methods = cdoubletype_methods;
 
@@ -10623,7 +10303,7 @@ initialize_numeric_types(void)
     /* We won't be inheriting from Python Int type. */
     PyLongLongArrType_Type.tp_hash = longlong_arrtype_hash;
 
-    #line 4065
+    #line 4026
 
     PyHalfArrType_Type.tp_repr = halftype_repr;
 
@@ -10637,7 +10317,7 @@ initialize_numeric_types(void)
     PyTimedeltaArrType_Type.tp_repr = timedeltatype_repr;
 
     
-#line 4065
+#line 4026
 
     PyHalfArrType_Type.tp_str = halftype_str;
 
@@ -10653,77 +10333,77 @@ initialize_numeric_types(void)
     
 
 
-    #line 4084
+    #line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyBoolArrType_Type.tp_str = genint_type_str;
     PyBoolArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyByteArrType_Type.tp_str = genint_type_str;
     PyByteArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyUByteArrType_Type.tp_str = genint_type_str;
     PyUByteArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyShortArrType_Type.tp_str = genint_type_str;
     PyShortArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyUShortArrType_Type.tp_str = genint_type_str;
     PyUShortArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyIntArrType_Type.tp_str = genint_type_str;
     PyIntArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyUIntArrType_Type.tp_str = genint_type_str;
     PyUIntArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyLongArrType_Type.tp_str = genint_type_str;
     PyLongArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyULongArrType_Type.tp_str = genint_type_str;
     PyULongArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyLongLongArrType_Type.tp_str = genint_type_str;
     PyLongLongArrType_Type.tp_repr = genint_type_str;
 
     
-#line 4084
+#line 4045
 
     /* both str/repr use genint_type_str to avoid trailing "L" of longs */
     PyULongLongArrType_Type.tp_str = genint_type_str;
@@ -10733,7 +10413,7 @@ initialize_numeric_types(void)
 
 
 
-    #line 4097
+    #line 4058
 
     /*
      * These need to be coded specially because longdouble/clongdouble getitem
@@ -10747,7 +10427,7 @@ initialize_numeric_types(void)
     PyLongDoubleArrType_Type.tp_str = longdoubletype_str;
 
     
-#line 4097
+#line 4058
 
     /*
      * These need to be coded specially because longdouble/clongdouble getitem
@@ -10767,36 +10447,6 @@ initialize_numeric_types(void)
 
     PyArrayIter_Type.tp_iter = PyObject_SelfIter;
     PyArrayMapIter_Type.tp_iter = PyObject_SelfIter;
-
-    /*
-     * Give types different names when they are the same size (gh-9799).
-     * `np.intX` always refers to the first int of that size in the sequence
-     * `['LONG', 'LONGLONG', 'INT', 'SHORT', 'BYTE']`.
-     */
-#if (NPY_SIZEOF_BYTE == NPY_SIZEOF_SHORT)
-    PyByteArrType_Type.tp_name = "numpy.byte";
-    PyUByteArrType_Type.tp_name = "numpy.ubyte";
-#endif
-#if (NPY_SIZEOF_SHORT == NPY_SIZEOF_INT)
-    PyShortArrType_Type.tp_name = "numpy.short";
-    PyUShortArrType_Type.tp_name = "numpy.ushort";
-#endif
-#if (NPY_SIZEOF_INT == NPY_SIZEOF_LONG)
-    PyIntArrType_Type.tp_name = "numpy.intc";
-    PyUIntArrType_Type.tp_name = "numpy.uintc";
-#endif
-#if (NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONG)
-    PyLongLongArrType_Type.tp_name = "numpy.longlong";
-    PyULongLongArrType_Type.tp_name = "numpy.ulonglong";
-#endif
-
-    /*
-    Do the same for longdouble
-    */
-#if (NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE)
-    PyLongDoubleArrType_Type.tp_name = "numpy.longdouble";
-    PyCLongDoubleArrType_Type.tp_name = "numpy.clongdouble";
-#endif
 }
 
 typedef struct {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c.src b/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c.src
index 16a78aec57d..f3382e51142 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/scalartypes.c.src
@@ -15,6 +15,7 @@
 
 #include "npy_pycompat.h"
 
+#include "arraytypes.h"
 #include "npy_config.h"
 #include "mapping.h"
 #include "ctors.h"
@@ -278,7 +279,44 @@ static PyObject *
 genint_type_str(PyObject *self)
 {
     PyObject  *item, *item_str;
-    item = gentype_generic_method(self, NULL, NULL, "item");
+    PyArray_Descr *descr = PyArray_DescrFromTypeObject((PyObject *)Py_TYPE(self));
+    void *val = scalar_value(self, descr);
+    switch (descr->type_num) {
+        case NPY_BYTE:
+            item = PyLong_FromLong(*(npy_byte *)val);
+            break;
+        case NPY_UBYTE:
+            item = PyLong_FromUnsignedLong(*(npy_ubyte *)val);
+            break;
+        case NPY_SHORT:
+            item = PyLong_FromLong(*(npy_short *)val);
+            break;
+        case NPY_USHORT:
+            item = PyLong_FromUnsignedLong(*(npy_ushort *)val);
+            break;
+        case NPY_INT:
+            item = PyLong_FromLong(*(npy_int *)val);
+            break;
+        case NPY_UINT:
+            item = PyLong_FromUnsignedLong(*(npy_uint *)val);
+            break;
+        case NPY_LONG:
+            item = PyLong_FromLong(*(npy_long *)val);
+            break;
+        case NPY_ULONG:
+            item = PyLong_FromUnsignedLong(*(npy_ulong *)val);
+            break;
+        case NPY_LONGLONG:
+            item = PyLong_FromLongLong(*(npy_longlong *)val);
+            break;
+        case NPY_ULONGLONG:
+            item = PyLong_FromUnsignedLongLong(*(npy_ulonglong *)val);
+            break;
+        default:
+            item = gentype_generic_method(self, NULL, NULL, "item");
+            break;
+    }
+    Py_DECREF(descr);
     if (item == NULL) {
         return NULL;
     }
@@ -447,7 +485,7 @@ unicodetype_@form@(PyObject *self)
  *
  * Largely copied from _Py_strhex_impl in CPython implementation
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 _void_to_hex(const char* argbuf, const Py_ssize_t arglen,
              const char *schars, const char *bprefix, const char *echars)
 {
@@ -1852,10 +1890,7 @@ gentype_setflags(PyObject *NPY_UNUSED(self), PyObject *NPY_UNUSED(args),
 static PyObject *
 numbertype_class_getitem_abc(PyObject *cls, PyObject *args)
 {
-    PyObject *generic_alias;
-
-#ifdef Py_GENERICALIASOBJECT_H
-    Py_ssize_t args_len;
+    const Py_ssize_t args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
     int args_len_expected;
 
     /* complexfloating should take 2 parameters, all others take 1 */
@@ -1867,20 +1902,13 @@ numbertype_class_getitem_abc(PyObject *cls, PyObject *args)
         args_len_expected = 1;
     }
 
-    args_len = PyTuple_Check(args) ? PyTuple_Size(args) : 1;
     if ((args_len > args_len_expected) || (args_len == 0)) {
         return PyErr_Format(PyExc_TypeError,
                             "Too %s arguments for %s",
                             args_len > args_len_expected ? "many" : "few",
                             ((PyTypeObject *)cls)->tp_name);
     }
-    generic_alias = Py_GenericAlias(cls, args);
-#else
-    PyErr_SetString(PyExc_TypeError,
-                    "Type subscription requires python >= 3.9");
-    generic_alias = NULL;
-#endif
-    return generic_alias;
+    return Py_GenericAlias(cls, args);
 }
 
 /*
@@ -1891,14 +1919,9 @@ numbertype_class_getitem_abc(PyObject *cls, PyObject *args)
 static PyObject *
 numbertype_class_getitem(PyObject *cls, PyObject *args)
 {
-#ifdef Py_GENERICALIASOBJECT_H
     PyErr_Format(PyExc_TypeError,
                  "There are no type variables left in %s",
                  ((PyTypeObject *)cls)->tp_name);
-#else
-    PyErr_SetString(PyExc_TypeError,
-                    "Type subscription requires python >= 3.9");
-#endif
     return NULL;
 }
 
@@ -2286,7 +2309,7 @@ static PyGetSetDef inttype_getsets[] = {
 };
 
 static PyMethodDef numbertype_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem_abc,
         METH_CLASS | METH_O, NULL},
@@ -2300,7 +2323,7 @@ static PyMethodDef @name@type_methods[] = {
     {"__complex__",
         (PyCFunction)@name@_complex,
         METH_VARARGS | METH_KEYWORDS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -2341,7 +2364,7 @@ static PyMethodDef @name@type_methods[] = {
     {"is_integer",
         (PyCFunction)@name@_is_integer,
         METH_NOARGS, NULL},
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -2353,7 +2376,7 @@ static PyMethodDef @name@type_methods[] = {
  * #name = timedelta, cdouble#
  */
 static PyMethodDef @name@type_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -2366,7 +2389,7 @@ static PyMethodDef @name@type_methods[] = {
  *         long, ulong, longlong, ulonglong#
  */
 static PyMethodDef @name@type_methods[] = {
-    /* for typing; requires python >= 3.9 */
+    /* for typing */
     {"__class_getitem__",
         (PyCFunction)numbertype_class_getitem,
         METH_CLASS | METH_O, NULL},
@@ -3327,7 +3350,7 @@ long_arrtype_hash(PyObject *obj)
  * #Char = ,U#
  * #Word = ,Unsigned#
  */
-static NPY_INLINE npy_hash_t
+static inline npy_hash_t
 @char@longlong_arrtype_hash(PyObject *obj)
 {
     PyObject * l = PyLong_From@Word@LongLong(
@@ -3596,7 +3619,7 @@ object_arrtype_call(PyObjectScalarObject *obj, PyObject *args, PyObject *kwds)
 
 NPY_NO_EXPORT PyTypeObject PyObjectArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.object_",
+    .tp_name = "numpy." NPY_OBJECT_name,
     .tp_basicsize = sizeof(PyObjectScalarObject),
     .tp_dealloc = (destructor)object_arrtype_dealloc,
     .tp_as_sequence = &object_arrtype_as_sequence,
@@ -3632,60 +3655,29 @@ gen_arrtype_subscript(PyObject *self, PyObject *key)
 }
 
 
-#define NAME_bool "bool"
-#define NAME_void "void"
-#define NAME_string "bytes"
-#define NAME_unicode "str"
-
 /**begin repeat
- * #name = bool, string, unicode, void#
- * #NAME = Bool, String, Unicode, Void#
- * #ex = _,_,_,#
+ * #Name = Bool,
+ *         Byte, Short, Int, Long, LongLong,
+ *         UByte, UShort, UInt, ULong, ULongLong,
+ *         Half, Float, Double, LongDouble,
+ *         CFloat, CDouble, CLongDouble,
+ *         String, Unicode, Void,
+ *         Datetime, Timedelta#
+ * #NAME = BOOL,
+ *         BYTE, SHORT, INT, LONG, LONGLONG,
+ *         UBYTE, USHORT, UINT, ULONG, ULONGLONG,
+ *         HALF, FLOAT, DOUBLE, LONGDOUBLE,
+ *         CFLOAT, CDOUBLE, CLONGDOUBLE,
+ *         STRING, UNICODE, VOID,
+ *         DATETIME, TIMEDELTA#
  */
-NPY_NO_EXPORT PyTypeObject Py@NAME@ArrType_Type = {
-    PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy." NAME_@name@ "@ex@",
-    .tp_basicsize = sizeof(Py@NAME@ScalarObject),
-};
-/**end repeat**/
 
-#undef NAME_bool
-#undef NAME_void
-#undef NAME_string
-#undef NAME_unicode
-
-/**begin repeat
- * #NAME = Byte, Short, Int, Long, LongLong, UByte, UShort, UInt, ULong,
- *         ULongLong, Half, Float, Double, LongDouble, Datetime, Timedelta#
- * #name = int*5, uint*5, float*4, datetime, timedelta#
- * #CNAME = (CHAR, SHORT, INT, LONG, LONGLONG)*2, HALF, FLOAT, DOUBLE,
- *          LONGDOUBLE, DATETIME, TIMEDELTA#
- */
-#if NPY_BITSOF_@CNAME@ == 8
-#define _THIS_SIZE "8"
-#elif NPY_BITSOF_@CNAME@ == 16
-#define _THIS_SIZE "16"
-#elif NPY_BITSOF_@CNAME@ == 32
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_@CNAME@ == 64
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_@CNAME@ == 80
-#define _THIS_SIZE "80"
-#elif NPY_BITSOF_@CNAME@ == 96
-#define _THIS_SIZE "96"
-#elif NPY_BITSOF_@CNAME@ == 128
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_@CNAME@ == 256
-#define _THIS_SIZE "256"
-#endif
-NPY_NO_EXPORT PyTypeObject Py@NAME@ArrType_Type = {
+NPY_NO_EXPORT PyTypeObject Py@Name@ArrType_Type = {
     PyVarObject_HEAD_INIT(NULL, 0)
-    .tp_name = "numpy.@name@" _THIS_SIZE,
-    .tp_basicsize = sizeof(Py@NAME@ScalarObject),
+    .tp_name = "numpy." NPY_@NAME@_name,
+    .tp_basicsize = sizeof(Py@Name@ScalarObject),
 };
 
-
-#undef _THIS_SIZE
 /**end repeat**/
 
 
@@ -3694,37 +3686,6 @@ static PyMappingMethods gentype_as_mapping = {
 };
 
 
-/**begin repeat
- * #NAME = CFloat, CDouble, CLongDouble#
- * #name = complex*3#
- * #CNAME = FLOAT, DOUBLE, LONGDOUBLE#
- */
-#if NPY_BITSOF_@CNAME@ == 16
-#define _THIS_SIZE "32"
-#elif NPY_BITSOF_@CNAME@ == 32
-#define _THIS_SIZE "64"
-#elif NPY_BITSOF_@CNAME@ == 64
-#define _THIS_SIZE "128"
-#elif NPY_BITSOF_@CNAME@ == 80
-#define _THIS_SIZE "160"
-#elif NPY_BITSOF_@CNAME@ == 96
-#define _THIS_SIZE "192"
-#elif NPY_BITSOF_@CNAME@ == 128
-#define _THIS_SIZE "256"
-#elif NPY_BITSOF_@CNAME@ == 256
-#define _THIS_SIZE "512"
-#endif
-
-NPY_NO_EXPORT PyTypeObject Py@NAME@ArrType_Type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    .tp_name = "numpy.@name@" _THIS_SIZE,
-    .tp_basicsize = sizeof(Py@NAME@ScalarObject),
-    .tp_flags = Py_TPFLAGS_DEFAULT,
-};
-#undef _THIS_SIZE
-
-/**end repeat**/
-
 /*
  * This table maps the built-in type numbers to their scalar
  * type numbers.  Note that signed integers are mapped to INTNEG_SCALAR,
@@ -4113,36 +4074,6 @@ initialize_numeric_types(void)
 
     PyArrayIter_Type.tp_iter = PyObject_SelfIter;
     PyArrayMapIter_Type.tp_iter = PyObject_SelfIter;
-
-    /*
-     * Give types different names when they are the same size (gh-9799).
-     * `np.intX` always refers to the first int of that size in the sequence
-     * `['LONG', 'LONGLONG', 'INT', 'SHORT', 'BYTE']`.
-     */
-#if (NPY_SIZEOF_BYTE == NPY_SIZEOF_SHORT)
-    PyByteArrType_Type.tp_name = "numpy.byte";
-    PyUByteArrType_Type.tp_name = "numpy.ubyte";
-#endif
-#if (NPY_SIZEOF_SHORT == NPY_SIZEOF_INT)
-    PyShortArrType_Type.tp_name = "numpy.short";
-    PyUShortArrType_Type.tp_name = "numpy.ushort";
-#endif
-#if (NPY_SIZEOF_INT == NPY_SIZEOF_LONG)
-    PyIntArrType_Type.tp_name = "numpy.intc";
-    PyUIntArrType_Type.tp_name = "numpy.uintc";
-#endif
-#if (NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONG)
-    PyLongLongArrType_Type.tp_name = "numpy.longlong";
-    PyULongLongArrType_Type.tp_name = "numpy.ulonglong";
-#endif
-
-    /*
-    Do the same for longdouble
-    */
-#if (NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE)
-    PyLongDoubleArrType_Type.tp_name = "numpy.longdouble";
-    PyCLongDoubleArrType_Type.tp_name = "numpy.clongdouble";
-#endif
 }
 
 typedef struct {
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/shape.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/shape.c
index dc7151a9bef..15b1664237a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/shape.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/shape.c
@@ -285,7 +285,7 @@ PyArray_Newshape(PyArrayObject *self, PyArray_Dims *newdims,
             Py_TYPE(self), PyArray_DESCR(self),
             ndim, dimensions, strides, PyArray_DATA(self),
             flags, (PyObject *)self, (PyObject *)self,
-            0, 1);
+            _NPY_ARRAY_ENSURE_DTYPE_IDENTITY);
     Py_DECREF(self);
     return (PyObject *)ret;
 }
@@ -787,7 +787,7 @@ PyArray_CreateSortedStridePerm(int ndim, npy_intp const *strides,
                                     &_npy_stride_sort_item_comparator);
 }
 
-static NPY_INLINE npy_intp
+static inline npy_intp
 s_intp_abs(npy_intp x)
 {
     return (x < 0) ? -x : x;
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/conversions.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/conversions.c
index 6c93e02d879..67a4803b9ea 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/conversions.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/conversions.c
@@ -47,7 +47,7 @@ npy_to_bool(PyArray_Descr *NPY_UNUSED(descr),
  *        (used by the complex parser).
  * @param result Output stored as double value.
  */
-static NPY_INLINE int
+static inline int
 double_from_ucs4(
         const Py_UCS4 *str, const Py_UCS4 *end,
         bool strip_whitespace, double *result, const Py_UCS4 **p_end)
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/field_types.h b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/field_types.h
index d335a366595..078b740cfe0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/field_types.h
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/field_types.h
@@ -2,6 +2,9 @@
 #ifndef NUMPY_CORE_SRC_MULTIARRAY_TEXTREADING_FIELD_TYPES_H_
 #define NUMPY_CORE_SRC_MULTIARRAY_TEXTREADING_FIELD_TYPES_H_
 
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
 #include <stdint.h>
 #include <stdbool.h>
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/readtext.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/readtext.c
index 5646b3d3002..e8defcc4dd2 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/readtext.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/readtext.c
@@ -1,9 +1,9 @@
-#include <stdio.h>
-#include <stdbool.h>
-
 #define PY_SSIZE_T_CLEAN
 #include <Python.h>
 
+#include <stdio.h>
+#include <stdbool.h>
+
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
 #define _MULTIARRAYMODULE
 #include "numpy/arrayobject.h"
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/rows.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/rows.c
index 9c490b3f710..b8a6943dced 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/rows.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/rows.c
@@ -318,10 +318,19 @@ read_rows(stream *s,
         }
 
         if (!usecols && (actual_num_fields != current_num_fields)) {
-            PyErr_Format(PyExc_ValueError,
+            if (homogeneous) {
+                PyErr_Format(PyExc_ValueError,
                     "the number of columns changed from %zd to %zd at row %zd; "
                     "use `usecols` to select a subset and avoid this error",
                     actual_num_fields, current_num_fields, row_count+1);
+            }
+            else {
+                PyErr_Format(PyExc_ValueError,
+                    "the dtype passed requires %zd columns but %zd were found "
+                    "at row %zd; "
+                    "use `usecols` to select a subset and avoid this error",
+                    actual_num_fields, current_num_fields, row_count+1);
+            }
             goto error;
         }
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/stream_pyobject.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/stream_pyobject.c
index 6f84ff01ddb..3832ee84a4f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/stream_pyobject.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/stream_pyobject.c
@@ -4,11 +4,12 @@
  * single line of a file.
  */
 
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
 #include <stdio.h>
 #include <stdlib.h>
 
-#define PY_SSIZE_T_CLEAN
-#include <Python.h>
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
 #define _MULTIARRAYMODULE
 #include "numpy/arrayobject.h"
@@ -41,7 +42,7 @@ typedef struct {
  *
  * NOTE: Steals a reference to `str` (although usually returns it unmodified).
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 process_stringlike(PyObject *str, const char *encoding)
 {
     if (PyBytes_Check(str)) {
@@ -63,7 +64,7 @@ process_stringlike(PyObject *str, const char *encoding)
 }
 
 
-static NPY_INLINE void
+static inline void
 buffer_info_from_unicode(PyObject *str, char **start, char **end, int *kind)
 {
     Py_ssize_t length = PyUnicode_GET_LENGTH(str);
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/tokenize.cpp b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/tokenize.cpp
index 5a85fd402dd..e0ddc393d88 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/tokenize.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/textreading/tokenize.cpp
@@ -290,7 +290,7 @@ NPY_NO_EXPORT int
 npy_tokenize(stream *s, tokenizer_state *ts, parser_config *const config)
 {
     assert(ts->fields_size >= 2);
-    assert(ts->field_buffer_length >= 2*sizeof(Py_UCS4));
+    assert(ts->field_buffer_length >= 2*(Py_ssize_t)sizeof(Py_UCS4));
 
     int finished_reading_file = 0;
 
@@ -449,6 +449,8 @@ npy_tokenizer_init(tokenizer_state *ts, parser_config *config)
 
     ts->fields = (field_info *)PyMem_Malloc(4 * sizeof(*ts->fields));
     if (ts->fields == nullptr) {
+        PyMem_Free(ts->field_buffer);
+        ts->field_buffer = nullptr;
         PyErr_NoMemory();
         return -1;
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/multiarray/usertypes.c b/contrib/python/numpy/py3/numpy/core/src/multiarray/usertypes.c
index a338d712dad..5e36c914b22 100644
--- a/contrib/python/numpy/py3/numpy/core/src/multiarray/usertypes.c
+++ b/contrib/python/numpy/py3/numpy/core/src/multiarray/usertypes.c
@@ -41,6 +41,7 @@ maintainer email:  oliphant.travis@ieee.org
 #include "scalartypes.h"
 #include "array_method.h"
 #include "convert_datatype.h"
+#include "dtype_traversal.h"
 #include "legacy_dtype_implementation.h"
 
 
@@ -223,6 +224,8 @@ PyArray_RegisterDataType(PyArray_Descr *descr)
         PyErr_SetString(PyExc_ValueError, "missing typeobject");
         return -1;
     }
+
+    int use_void_clearimpl = 0;
     if (descr->flags & (NPY_ITEM_IS_POINTER | NPY_ITEM_REFCOUNT)) {
         /*
          * User dtype can't actually do reference counting, however, there
@@ -231,6 +234,8 @@ PyArray_RegisterDataType(PyArray_Descr *descr)
          * so we have to support this. But such a structure must be constant
          * (i.e. fixed at registration time, this is the case for `xpress`).
          */
+        use_void_clearimpl = 1;
+
         if (descr->names == NULL || descr->fields == NULL ||
             !PyDict_CheckExact(descr->fields)) {
             PyErr_Format(PyExc_ValueError,
@@ -256,14 +261,53 @@ PyArray_RegisterDataType(PyArray_Descr *descr)
         return -1;
     }
 
+    /*
+     * Legacy user DTypes classes cannot have a name, since the user never
+     * defined one.  So we create a name for them here. These DTypes are
+     * effectively static types.
+     *
+     * Note: we have no intention of freeing the memory again since this
+     * behaves identically to static type definition.
+     */
+
+    const char *scalar_name = descr->typeobj->tp_name;
+    /*
+     * We have to take only the name, and ignore the module to get
+     * a reasonable __name__, since static types are limited in this regard
+     * (this is not ideal, but not a big issue in practice).
+     * This is what Python does to print __name__ for static types.
+     */
+    const char *dot = strrchr(scalar_name, '.');
+    if (dot) {
+        scalar_name = dot + 1;
+    }
+    Py_ssize_t name_length = strlen(scalar_name) + 14;
+
+    char *name = PyMem_Malloc(name_length);
+    if (name == NULL) {
+        PyErr_NoMemory();
+        return -1;
+    }
+
+    snprintf(name, name_length, "numpy.dtype[%s]", scalar_name);
+
     userdescrs[NPY_NUMUSERTYPES++] = descr;
 
     descr->type_num = typenum;
-    if (dtypemeta_wrap_legacy_descriptor(descr) < 0) {
+    if (dtypemeta_wrap_legacy_descriptor(descr, name, NULL) < 0) {
         descr->type_num = -1;
         NPY_NUMUSERTYPES--;
+        PyMem_Free(name);  /* free the name only on failure */
         return -1;
     }
+    if (use_void_clearimpl) {
+        /* See comment where use_void_clearimpl is set... */
+        NPY_DT_SLOTS(NPY_DTYPE(descr))->get_clear_loop = (
+                &npy_get_clear_void_and_legacy_user_dtype_loop);
+        /* Also use the void zerofill since there may be objects */
+        NPY_DT_SLOTS(NPY_DTYPE(descr))->get_clear_loop = (
+                &npy_get_zerofill_void_and_legacy_user_dtype_loop);
+    }
 
     return typenum;
 }
@@ -597,7 +641,7 @@ PyArray_AddLegacyWrapping_CastingImpl(
     if (from == to) {
         spec.flags = NPY_METH_REQUIRES_PYAPI | NPY_METH_SUPPORTS_UNALIGNED;
         PyType_Slot slots[] = {
-            {NPY_METH_get_loop, &legacy_cast_get_strided_loop},
+            {_NPY_METH_get_loop, &legacy_cast_get_strided_loop},
             {NPY_METH_resolve_descriptors, &legacy_same_dtype_resolve_descriptors},
             {0, NULL}};
         spec.slots = slots;
@@ -606,7 +650,7 @@ PyArray_AddLegacyWrapping_CastingImpl(
     else {
         spec.flags = NPY_METH_REQUIRES_PYAPI;
         PyType_Slot slots[] = {
-            {NPY_METH_get_loop, &legacy_cast_get_strided_loop},
+            {_NPY_METH_get_loop, &legacy_cast_get_strided_loop},
             {NPY_METH_resolve_descriptors, &simple_cast_resolve_descriptors},
             {0, NULL}};
         spec.slots = slots;
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/_signbit.c b/contrib/python/numpy/py3/numpy/core/src/npymath/_signbit.c
deleted file mode 100644
index 12af5538740..00000000000
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/_signbit.c
+++ /dev/null
@@ -1,32 +0,0 @@
-/* Adapted from cephes */
-
-int
-_npy_signbit_d(double x)
-{
-    union
-    {
-        double d;
-        short s[4];
-        int i[2];
-    } u;
-
-    u.d = x;
-
-#if NPY_SIZEOF_INT == 4
-
-#ifdef WORDS_BIGENDIAN /* defined in pyconfig.h */
-    return u.i[0] < 0;
-#else
-    return u.i[1] < 0;
-#endif
-
-#else  /* NPY_SIZEOF_INT != 4 */
-
-#ifdef WORDS_BIGENDIAN
-    return u.s[0] < 0;
-#else
-    return u.s[3] < 0;
-#endif
-
-#endif  /* NPY_SIZEOF_INT */
-}
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/arm64_exports.c b/contrib/python/numpy/py3/numpy/core/src/npymath/arm64_exports.c
index c9f0fbf746e..dfa8054d7e9 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/arm64_exports.c
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/arm64_exports.c
@@ -23,4 +23,8 @@ double npy_log1p(double x) {
     return log1p(x);
 }
 
+double npy_nextafter(double x, double y) {
+    return nextafter(x, y);
+}
+
 #endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/halffloat.c b/contrib/python/numpy/py3/numpy/core/src/npymath/halffloat.c
deleted file mode 100644
index 51948c73627..00000000000
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/halffloat.c
+++ /dev/null
@@ -1,555 +0,0 @@
-#define NPY_NO_DEPRECATED_API NPY_API_VERSION
-
-#include "numpy/halffloat.h"
-
-/*
- * This chooses between 'ties to even' and 'ties away from zero'.
- */
-#define NPY_HALF_ROUND_TIES_TO_EVEN 1
-/*
- * If these are 1, the conversions try to trigger underflow,
- * overflow, and invalid exceptions in the FP system when needed.
- */
-#define NPY_HALF_GENERATE_OVERFLOW 1
-#define NPY_HALF_GENERATE_UNDERFLOW 1
-#define NPY_HALF_GENERATE_INVALID 1
-
-/*
- ********************************************************************
- *                   HALF-PRECISION ROUTINES                        *
- ********************************************************************
- */
-
-float npy_half_to_float(npy_half h)
-{
-    union { float ret; npy_uint32 retbits; } conv;
-    conv.retbits = npy_halfbits_to_floatbits(h);
-    return conv.ret;
-}
-
-double npy_half_to_double(npy_half h)
-{
-    union { double ret; npy_uint64 retbits; } conv;
-    conv.retbits = npy_halfbits_to_doublebits(h);
-    return conv.ret;
-}
-
-npy_half npy_float_to_half(float f)
-{
-    union { float f; npy_uint32 fbits; } conv;
-    conv.f = f;
-    return npy_floatbits_to_halfbits(conv.fbits);
-}
-
-npy_half npy_double_to_half(double d)
-{
-    union { double d; npy_uint64 dbits; } conv;
-    conv.d = d;
-    return npy_doublebits_to_halfbits(conv.dbits);
-}
-
-int npy_half_iszero(npy_half h)
-{
-    return (h&0x7fff) == 0;
-}
-
-int npy_half_isnan(npy_half h)
-{
-    return ((h&0x7c00u) == 0x7c00u) && ((h&0x03ffu) != 0x0000u);
-}
-
-int npy_half_isinf(npy_half h)
-{
-    return ((h&0x7fffu) == 0x7c00u);
-}
-
-int npy_half_isfinite(npy_half h)
-{
-    return ((h&0x7c00u) != 0x7c00u);
-}
-
-int npy_half_signbit(npy_half h)
-{
-    return (h&0x8000u) != 0;
-}
-
-npy_half npy_half_spacing(npy_half h)
-{
-    npy_half ret;
-    npy_uint16 h_exp = h&0x7c00u;
-    npy_uint16 h_sig = h&0x03ffu;
-    if (h_exp == 0x7c00u) {
-#if NPY_HALF_GENERATE_INVALID
-        npy_set_floatstatus_invalid();
-#endif
-        ret = NPY_HALF_NAN;
-    } else if (h == 0x7bffu) {
-#if NPY_HALF_GENERATE_OVERFLOW
-        npy_set_floatstatus_overflow();
-#endif
-        ret = NPY_HALF_PINF;
-    } else if ((h&0x8000u) && h_sig == 0) { /* Negative boundary case */
-        if (h_exp > 0x2c00u) { /* If result is normalized */
-            ret = h_exp - 0x2c00u;
-        } else if(h_exp > 0x0400u) { /* The result is a subnormal, but not the smallest */
-            ret = 1 << ((h_exp >> 10) - 2);
-        } else {
-            ret = 0x0001u; /* Smallest subnormal half */
-        }
-    } else if (h_exp > 0x2800u) { /* If result is still normalized */
-        ret = h_exp - 0x2800u;
-    } else if (h_exp > 0x0400u) { /* The result is a subnormal, but not the smallest */
-        ret = 1 << ((h_exp >> 10) - 1);
-    } else {
-        ret = 0x0001u;
-    }
-
-    return ret;
-}
-
-npy_half npy_half_copysign(npy_half x, npy_half y)
-{
-    return (x&0x7fffu) | (y&0x8000u);
-}
-
-npy_half npy_half_nextafter(npy_half x, npy_half y)
-{
-    npy_half ret;
-
-    if (npy_half_isnan(x) || npy_half_isnan(y)) {
-        ret = NPY_HALF_NAN;
-    } else if (npy_half_eq_nonan(x, y)) {
-        ret = x;
-    } else if (npy_half_iszero(x)) {
-        ret = (y&0x8000u) + 1; /* Smallest subnormal half */
-    } else if (!(x&0x8000u)) { /* x > 0 */
-        if ((npy_int16)x > (npy_int16)y) { /* x > y */
-            ret = x-1;
-        } else {
-            ret = x+1;
-        }
-    } else {
-        if (!(y&0x8000u) || (x&0x7fffu) > (y&0x7fffu)) { /* x < y */
-            ret = x-1;
-        } else {
-            ret = x+1;
-        }
-    }
-#if NPY_HALF_GENERATE_OVERFLOW
-    if (npy_half_isinf(ret) && npy_half_isfinite(x)) {
-        npy_set_floatstatus_overflow();
-    }
-#endif
-
-    return ret;
-}
-
-int npy_half_eq_nonan(npy_half h1, npy_half h2)
-{
-    return (h1 == h2 || ((h1 | h2) & 0x7fff) == 0);
-}
-
-int npy_half_eq(npy_half h1, npy_half h2)
-{
-    /*
-     * The equality cases are as follows:
-     *   - If either value is NaN, never equal.
-     *   - If the values are equal, equal.
-     *   - If the values are both signed zeros, equal.
-     */
-    return (!npy_half_isnan(h1) && !npy_half_isnan(h2)) &&
-           (h1 == h2 || ((h1 | h2) & 0x7fff) == 0);
-}
-
-int npy_half_ne(npy_half h1, npy_half h2)
-{
-    return !npy_half_eq(h1, h2);
-}
-
-int npy_half_lt_nonan(npy_half h1, npy_half h2)
-{
-    if (h1&0x8000u) {
-        if (h2&0x8000u) {
-            return (h1&0x7fffu) > (h2&0x7fffu);
-        } else {
-            /* Signed zeros are equal, have to check for it */
-            return (h1 != 0x8000u) || (h2 != 0x0000u);
-        }
-    } else {
-        if (h2&0x8000u) {
-            return 0;
-        } else {
-            return (h1&0x7fffu) < (h2&0x7fffu);
-        }
-    }
-}
-
-int npy_half_lt(npy_half h1, npy_half h2)
-{
-    return (!npy_half_isnan(h1) && !npy_half_isnan(h2)) && npy_half_lt_nonan(h1, h2);
-}
-
-int npy_half_gt(npy_half h1, npy_half h2)
-{
-    return npy_half_lt(h2, h1);
-}
-
-int npy_half_le_nonan(npy_half h1, npy_half h2)
-{
-    if (h1&0x8000u) {
-        if (h2&0x8000u) {
-            return (h1&0x7fffu) >= (h2&0x7fffu);
-        } else {
-            return 1;
-        }
-    } else {
-        if (h2&0x8000u) {
-            /* Signed zeros are equal, have to check for it */
-            return (h1 == 0x0000u) && (h2 == 0x8000u);
-        } else {
-            return (h1&0x7fffu) <= (h2&0x7fffu);
-        }
-    }
-}
-
-int npy_half_le(npy_half h1, npy_half h2)
-{
-    return (!npy_half_isnan(h1) && !npy_half_isnan(h2)) && npy_half_le_nonan(h1, h2);
-}
-
-int npy_half_ge(npy_half h1, npy_half h2)
-{
-    return npy_half_le(h2, h1);
-}
-
-npy_half npy_half_divmod(npy_half h1, npy_half h2, npy_half *modulus)
-{
-    float fh1 = npy_half_to_float(h1);
-    float fh2 = npy_half_to_float(h2);
-    float div, mod;
-
-    div = npy_divmodf(fh1, fh2, &mod);
-    *modulus = npy_float_to_half(mod);
-    return npy_float_to_half(div);
-}
-
-
-
-/*
- ********************************************************************
- *                     BIT-LEVEL CONVERSIONS                        *
- ********************************************************************
- */
-
-npy_uint16 npy_floatbits_to_halfbits(npy_uint32 f)
-{
-    npy_uint32 f_exp, f_sig;
-    npy_uint16 h_sgn, h_exp, h_sig;
-
-    h_sgn = (npy_uint16) ((f&0x80000000u) >> 16);
-    f_exp = (f&0x7f800000u);
-
-    /* Exponent overflow/NaN converts to signed inf/NaN */
-    if (f_exp >= 0x47800000u) {
-        if (f_exp == 0x7f800000u) {
-            /* Inf or NaN */
-            f_sig = (f&0x007fffffu);
-            if (f_sig != 0) {
-                /* NaN - propagate the flag in the significand... */
-                npy_uint16 ret = (npy_uint16) (0x7c00u + (f_sig >> 13));
-                /* ...but make sure it stays a NaN */
-                if (ret == 0x7c00u) {
-                    ret++;
-                }
-                return h_sgn + ret;
-            } else {
-                /* signed inf */
-                return (npy_uint16) (h_sgn + 0x7c00u);
-            }
-        } else {
-            /* overflow to signed inf */
-#if NPY_HALF_GENERATE_OVERFLOW
-            npy_set_floatstatus_overflow();
-#endif
-            return (npy_uint16) (h_sgn + 0x7c00u);
-        }
-    }
-
-    /* Exponent underflow converts to a subnormal half or signed zero */
-    if (f_exp <= 0x38000000u) {
-        /*
-         * Signed zeros, subnormal floats, and floats with small
-         * exponents all convert to signed zero half-floats.
-         */
-        if (f_exp < 0x33000000u) {
-#if NPY_HALF_GENERATE_UNDERFLOW
-            /* If f != 0, it underflowed to 0 */
-            if ((f&0x7fffffff) != 0) {
-                npy_set_floatstatus_underflow();
-            }
-#endif
-            return h_sgn;
-        }
-        /* Make the subnormal significand */
-        f_exp >>= 23;
-        f_sig = (0x00800000u + (f&0x007fffffu));
-#if NPY_HALF_GENERATE_UNDERFLOW
-        /* If it's not exactly represented, it underflowed */
-        if ((f_sig&(((npy_uint32)1 << (126 - f_exp)) - 1)) != 0) {
-            npy_set_floatstatus_underflow();
-        }
-#endif
-        /*
-         * Usually the significand is shifted by 13. For subnormals an
-         * additional shift needs to occur. This shift is one for the largest
-         * exponent giving a subnormal `f_exp = 0x38000000 >> 23 = 112`, which
-         * offsets the new first bit. At most the shift can be 1+10 bits.
-         */
-        f_sig >>= (113 - f_exp);
-        /* Handle rounding by adding 1 to the bit beyond half precision */
-#if NPY_HALF_ROUND_TIES_TO_EVEN
-        /*
-         * If the last bit in the half significand is 0 (already even), and
-         * the remaining bit pattern is 1000...0, then we do not add one
-         * to the bit after the half significand. However, the (113 - f_exp)
-         * shift can lose up to 11 bits, so the || checks them in the original.
-         * In all other cases, we can just add one.
-         */
-        if (((f_sig&0x00003fffu) != 0x00001000u) || (f&0x000007ffu)) {
-            f_sig += 0x00001000u;
-        }
-#else
-        f_sig += 0x00001000u;
-#endif
-        h_sig = (npy_uint16) (f_sig >> 13);
-        /*
-         * If the rounding causes a bit to spill into h_exp, it will
-         * increment h_exp from zero to one and h_sig will be zero.
-         * This is the correct result.
-         */
-        return (npy_uint16) (h_sgn + h_sig);
-    }
-
-    /* Regular case with no overflow or underflow */
-    h_exp = (npy_uint16) ((f_exp - 0x38000000u) >> 13);
-    /* Handle rounding by adding 1 to the bit beyond half precision */
-    f_sig = (f&0x007fffffu);
-#if NPY_HALF_ROUND_TIES_TO_EVEN
-    /*
-     * If the last bit in the half significand is 0 (already even), and
-     * the remaining bit pattern is 1000...0, then we do not add one
-     * to the bit after the half significand.  In all other cases, we do.
-     */
-    if ((f_sig&0x00003fffu) != 0x00001000u) {
-        f_sig += 0x00001000u;
-    }
-#else
-    f_sig += 0x00001000u;
-#endif
-    h_sig = (npy_uint16) (f_sig >> 13);
-    /*
-     * If the rounding causes a bit to spill into h_exp, it will
-     * increment h_exp by one and h_sig will be zero.  This is the
-     * correct result.  h_exp may increment to 15, at greatest, in
-     * which case the result overflows to a signed inf.
-     */
-#if NPY_HALF_GENERATE_OVERFLOW
-    h_sig += h_exp;
-    if (h_sig == 0x7c00u) {
-        npy_set_floatstatus_overflow();
-    }
-    return h_sgn + h_sig;
-#else
-    return h_sgn + h_exp + h_sig;
-#endif
-}
-
-npy_uint16 npy_doublebits_to_halfbits(npy_uint64 d)
-{
-    npy_uint64 d_exp, d_sig;
-    npy_uint16 h_sgn, h_exp, h_sig;
-
-    h_sgn = (d&0x8000000000000000ULL) >> 48;
-    d_exp = (d&0x7ff0000000000000ULL);
-
-    /* Exponent overflow/NaN converts to signed inf/NaN */
-    if (d_exp >= 0x40f0000000000000ULL) {
-        if (d_exp == 0x7ff0000000000000ULL) {
-            /* Inf or NaN */
-            d_sig = (d&0x000fffffffffffffULL);
-            if (d_sig != 0) {
-                /* NaN - propagate the flag in the significand... */
-                npy_uint16 ret = (npy_uint16) (0x7c00u + (d_sig >> 42));
-                /* ...but make sure it stays a NaN */
-                if (ret == 0x7c00u) {
-                    ret++;
-                }
-                return h_sgn + ret;
-            } else {
-                /* signed inf */
-                return h_sgn + 0x7c00u;
-            }
-        } else {
-            /* overflow to signed inf */
-#if NPY_HALF_GENERATE_OVERFLOW
-            npy_set_floatstatus_overflow();
-#endif
-            return h_sgn + 0x7c00u;
-        }
-    }
-
-    /* Exponent underflow converts to subnormal half or signed zero */
-    if (d_exp <= 0x3f00000000000000ULL) {
-        /*
-         * Signed zeros, subnormal floats, and floats with small
-         * exponents all convert to signed zero half-floats.
-         */
-        if (d_exp < 0x3e60000000000000ULL) {
-#if NPY_HALF_GENERATE_UNDERFLOW
-            /* If d != 0, it underflowed to 0 */
-            if ((d&0x7fffffffffffffffULL) != 0) {
-                npy_set_floatstatus_underflow();
-            }
-#endif
-            return h_sgn;
-        }
-        /* Make the subnormal significand */
-        d_exp >>= 52;
-        d_sig = (0x0010000000000000ULL + (d&0x000fffffffffffffULL));
-#if NPY_HALF_GENERATE_UNDERFLOW
-        /* If it's not exactly represented, it underflowed */
-        if ((d_sig&(((npy_uint64)1 << (1051 - d_exp)) - 1)) != 0) {
-            npy_set_floatstatus_underflow();
-        }
-#endif
-        /*
-         * Unlike floats, doubles have enough room to shift left to align
-         * the subnormal significand leading to no loss of the last bits.
-         * The smallest possible exponent giving a subnormal is:
-         * `d_exp = 0x3e60000000000000 >> 52 = 998`. All larger subnormals are
-         * shifted with respect to it. This adds a shift of 10+1 bits the final
-         * right shift when comparing it to the one in the normal branch.
-         */
-        assert(d_exp - 998 >= 0);
-        d_sig <<= (d_exp - 998);
-        /* Handle rounding by adding 1 to the bit beyond half precision */
-#if NPY_HALF_ROUND_TIES_TO_EVEN
-        /*
-         * If the last bit in the half significand is 0 (already even), and
-         * the remaining bit pattern is 1000...0, then we do not add one
-         * to the bit after the half significand.  In all other cases, we do.
-         */
-        if ((d_sig&0x003fffffffffffffULL) != 0x0010000000000000ULL) {
-            d_sig += 0x0010000000000000ULL;
-        }
-#else
-        d_sig += 0x0010000000000000ULL;
-#endif
-        h_sig = (npy_uint16) (d_sig >> 53);
-        /*
-         * If the rounding causes a bit to spill into h_exp, it will
-         * increment h_exp from zero to one and h_sig will be zero.
-         * This is the correct result.
-         */
-        return h_sgn + h_sig;
-    }
-
-    /* Regular case with no overflow or underflow */
-    h_exp = (npy_uint16) ((d_exp - 0x3f00000000000000ULL) >> 42);
-    /* Handle rounding by adding 1 to the bit beyond half precision */
-    d_sig = (d&0x000fffffffffffffULL);
-#if NPY_HALF_ROUND_TIES_TO_EVEN
-    /*
-     * If the last bit in the half significand is 0 (already even), and
-     * the remaining bit pattern is 1000...0, then we do not add one
-     * to the bit after the half significand.  In all other cases, we do.
-     */
-    if ((d_sig&0x000007ffffffffffULL) != 0x0000020000000000ULL) {
-        d_sig += 0x0000020000000000ULL;
-    }
-#else
-    d_sig += 0x0000020000000000ULL;
-#endif
-    h_sig = (npy_uint16) (d_sig >> 42);
-
-    /*
-     * If the rounding causes a bit to spill into h_exp, it will
-     * increment h_exp by one and h_sig will be zero.  This is the
-     * correct result.  h_exp may increment to 15, at greatest, in
-     * which case the result overflows to a signed inf.
-     */
-#if NPY_HALF_GENERATE_OVERFLOW
-    h_sig += h_exp;
-    if (h_sig == 0x7c00u) {
-        npy_set_floatstatus_overflow();
-    }
-    return h_sgn + h_sig;
-#else
-    return h_sgn + h_exp + h_sig;
-#endif
-}
-
-npy_uint32 npy_halfbits_to_floatbits(npy_uint16 h)
-{
-    npy_uint16 h_exp, h_sig;
-    npy_uint32 f_sgn, f_exp, f_sig;
-
-    h_exp = (h&0x7c00u);
-    f_sgn = ((npy_uint32)h&0x8000u) << 16;
-    switch (h_exp) {
-        case 0x0000u: /* 0 or subnormal */
-            h_sig = (h&0x03ffu);
-            /* Signed zero */
-            if (h_sig == 0) {
-                return f_sgn;
-            }
-            /* Subnormal */
-            h_sig <<= 1;
-            while ((h_sig&0x0400u) == 0) {
-                h_sig <<= 1;
-                h_exp++;
-            }
-            f_exp = ((npy_uint32)(127 - 15 - h_exp)) << 23;
-            f_sig = ((npy_uint32)(h_sig&0x03ffu)) << 13;
-            return f_sgn + f_exp + f_sig;
-        case 0x7c00u: /* inf or NaN */
-            /* All-ones exponent and a copy of the significand */
-            return f_sgn + 0x7f800000u + (((npy_uint32)(h&0x03ffu)) << 13);
-        default: /* normalized */
-            /* Just need to adjust the exponent and shift */
-            return f_sgn + (((npy_uint32)(h&0x7fffu) + 0x1c000u) << 13);
-    }
-}
-
-npy_uint64 npy_halfbits_to_doublebits(npy_uint16 h)
-{
-    npy_uint16 h_exp, h_sig;
-    npy_uint64 d_sgn, d_exp, d_sig;
-
-    h_exp = (h&0x7c00u);
-    d_sgn = ((npy_uint64)h&0x8000u) << 48;
-    switch (h_exp) {
-        case 0x0000u: /* 0 or subnormal */
-            h_sig = (h&0x03ffu);
-            /* Signed zero */
-            if (h_sig == 0) {
-                return d_sgn;
-            }
-            /* Subnormal */
-            h_sig <<= 1;
-            while ((h_sig&0x0400u) == 0) {
-                h_sig <<= 1;
-                h_exp++;
-            }
-            d_exp = ((npy_uint64)(1023 - 15 - h_exp)) << 52;
-            d_sig = ((npy_uint64)(h_sig&0x03ffu)) << 42;
-            return d_sgn + d_exp + d_sig;
-        case 0x7c00u: /* inf or NaN */
-            /* All-ones exponent and a copy of the significand */
-            return d_sgn + 0x7ff0000000000000ULL +
-                                (((npy_uint64)(h&0x03ffu)) << 42);
-        default: /* normalized */
-            /* Just need to adjust the exponent and shift */
-            return d_sgn + (((npy_uint64)(h&0x7fffu) + 0xfc000u) << 42);
-    }
-}
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/halffloat.cpp b/contrib/python/numpy/py3/numpy/core/src/npymath/halffloat.cpp
new file mode 100644
index 00000000000..aa582c1b951
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/halffloat.cpp
@@ -0,0 +1,238 @@
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+/*
+ * If these are 1, the conversions try to trigger underflow,
+ * overflow, and invalid exceptions in the FP system when needed.
+ */
+#define NPY_HALF_GENERATE_OVERFLOW 1
+#define NPY_HALF_GENERATE_INVALID 1
+
+#include "numpy/halffloat.h"
+
+#include "common.hpp"
+/*
+ ********************************************************************
+ *                   HALF-PRECISION ROUTINES                        *
+ ********************************************************************
+ */
+using namespace np;
+
+float npy_half_to_float(npy_half h)
+{
+    return static_cast<float>(Half::FromBits(h));
+}
+
+double npy_half_to_double(npy_half h)
+{
+    return static_cast<double>(Half::FromBits(h));
+}
+
+npy_half npy_float_to_half(float f)
+{
+    return Half(f).Bits();
+}
+
+npy_half npy_double_to_half(double d)
+{
+    return Half(d).Bits();
+}
+
+int npy_half_iszero(npy_half h)
+{
+    return (h&0x7fff) == 0;
+}
+
+int npy_half_isnan(npy_half h)
+{
+    return Half::FromBits(h).IsNaN();
+}
+
+int npy_half_isinf(npy_half h)
+{
+    return ((h&0x7fffu) == 0x7c00u);
+}
+
+int npy_half_isfinite(npy_half h)
+{
+    return ((h&0x7c00u) != 0x7c00u);
+}
+
+int npy_half_signbit(npy_half h)
+{
+    return (h&0x8000u) != 0;
+}
+
+npy_half npy_half_spacing(npy_half h)
+{
+    npy_half ret;
+    npy_uint16 h_exp = h&0x7c00u;
+    npy_uint16 h_sig = h&0x03ffu;
+    if (h_exp == 0x7c00u) {
+#if NPY_HALF_GENERATE_INVALID
+        npy_set_floatstatus_invalid();
+#endif
+        ret = NPY_HALF_NAN;
+    } else if (h == 0x7bffu) {
+#if NPY_HALF_GENERATE_OVERFLOW
+        npy_set_floatstatus_overflow();
+#endif
+        ret = NPY_HALF_PINF;
+    } else if ((h&0x8000u) && h_sig == 0) { /* Negative boundary case */
+        if (h_exp > 0x2c00u) { /* If result is normalized */
+            ret = h_exp - 0x2c00u;
+        } else if(h_exp > 0x0400u) { /* The result is a subnormal, but not the smallest */
+            ret = 1 << ((h_exp >> 10) - 2);
+        } else {
+            ret = 0x0001u; /* Smallest subnormal half */
+        }
+    } else if (h_exp > 0x2800u) { /* If result is still normalized */
+        ret = h_exp - 0x2800u;
+    } else if (h_exp > 0x0400u) { /* The result is a subnormal, but not the smallest */
+        ret = 1 << ((h_exp >> 10) - 1);
+    } else {
+        ret = 0x0001u;
+    }
+
+    return ret;
+}
+
+npy_half npy_half_copysign(npy_half x, npy_half y)
+{
+    return (x&0x7fffu) | (y&0x8000u);
+}
+
+npy_half npy_half_nextafter(npy_half x, npy_half y)
+{
+    npy_half ret;
+
+    if (npy_half_isnan(x) || npy_half_isnan(y)) {
+        ret = NPY_HALF_NAN;
+    } else if (npy_half_eq_nonan(x, y)) {
+        ret = x;
+    } else if (npy_half_iszero(x)) {
+        ret = (y&0x8000u) + 1; /* Smallest subnormal half */
+    } else if (!(x&0x8000u)) { /* x > 0 */
+        if ((npy_int16)x > (npy_int16)y) { /* x > y */
+            ret = x-1;
+        } else {
+            ret = x+1;
+        }
+    } else {
+        if (!(y&0x8000u) || (x&0x7fffu) > (y&0x7fffu)) { /* x < y */
+            ret = x-1;
+        } else {
+            ret = x+1;
+        }
+    }
+#if NPY_HALF_GENERATE_OVERFLOW
+    if (npy_half_isinf(ret) && npy_half_isfinite(x)) {
+        npy_set_floatstatus_overflow();
+    }
+#endif
+
+    return ret;
+}
+
+int npy_half_eq_nonan(npy_half h1, npy_half h2)
+{
+    return Half::FromBits(h1).Equal(Half::FromBits(h2));
+}
+
+int npy_half_eq(npy_half h1, npy_half h2)
+{
+    return Half::FromBits(h1) == Half::FromBits(h2);
+}
+
+int npy_half_ne(npy_half h1, npy_half h2)
+{
+    return Half::FromBits(h1) != Half::FromBits(h2);
+}
+
+int npy_half_lt_nonan(npy_half h1, npy_half h2)
+{
+    return Half::FromBits(h1).Less(Half::FromBits(h2));
+}
+
+int npy_half_lt(npy_half h1, npy_half h2)
+{
+    return Half::FromBits(h1) < Half::FromBits(h2);
+}
+
+int npy_half_gt(npy_half h1, npy_half h2)
+{
+    return npy_half_lt(h2, h1);
+}
+
+int npy_half_le_nonan(npy_half h1, npy_half h2)
+{
+    return Half::FromBits(h1).LessEqual(Half::FromBits(h2));
+}
+
+int npy_half_le(npy_half h1, npy_half h2)
+{
+    return Half::FromBits(h1) <= Half::FromBits(h2);
+}
+
+int npy_half_ge(npy_half h1, npy_half h2)
+{
+    return npy_half_le(h2, h1);
+}
+
+npy_half npy_half_divmod(npy_half h1, npy_half h2, npy_half *modulus)
+{
+    float fh1 = npy_half_to_float(h1);
+    float fh2 = npy_half_to_float(h2);
+    float div, mod;
+
+    div = npy_divmodf(fh1, fh2, &mod);
+    *modulus = npy_float_to_half(mod);
+    return npy_float_to_half(div);
+}
+
+
+/*
+ ********************************************************************
+ *                     BIT-LEVEL CONVERSIONS                        *
+ ********************************************************************
+ */
+
+npy_uint16 npy_floatbits_to_halfbits(npy_uint32 f)
+{
+    if constexpr (Half::kNativeConversion<float>) {
+        return BitCast<uint16_t>(Half(BitCast<float>(f)));
+    }
+    else {
+        return half_private::FromFloatBits(f);
+    }
+}
+
+npy_uint16 npy_doublebits_to_halfbits(npy_uint64 d)
+{
+    if constexpr (Half::kNativeConversion<double>) {
+        return BitCast<uint16_t>(Half(BitCast<double>(d)));
+    }
+    else {
+        return half_private::FromDoubleBits(d);
+    }
+}
+
+npy_uint32 npy_halfbits_to_floatbits(npy_uint16 h)
+{
+    if constexpr (Half::kNativeConversion<float>) {
+        return BitCast<uint32_t>(static_cast<float>(Half::FromBits(h)));
+    }
+    else {
+        return half_private::ToFloatBits(h);
+    }
+}
+
+npy_uint64 npy_halfbits_to_doublebits(npy_uint16 h)
+{
+    if constexpr (Half::kNativeConversion<double>) {
+        return BitCast<uint64_t>(static_cast<double>(Half::FromBits(h)));
+    }
+    else {
+        return half_private::ToDoubleBits(h);
+    }
+}
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c b/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c
index 8c1a75ad34d..ee9bf8532fa 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c
@@ -25,20 +25,6 @@
 #define LDBL_TRUE_MIN __LDBL_DENORM_MIN__
 #endif
 
-#if !defined(HAVE_DECL_SIGNBIT)
-#include "_signbit.c"
-
-int _npy_signbit_f(float x)
-{
-    return _npy_signbit_d((double) x);
-}
-
-int _npy_signbit_ld(long double x)
-{
-    return _npy_signbit_d((double) x);
-}
-#endif
-
 /*
  * FIXME: There is a lot of redundancy between _next* and npy_nextafter*.
  * refactor this at some point
@@ -320,7 +306,7 @@ static npy_longdouble _nextl(npy_longdouble x, int p)
 }
 #endif
 
-#line 318
+#line 304
 npy_float npy_spacingf(npy_float x)
 {
     /* XXX: npy isnan/isinf may be optimized by bit twiddling */
@@ -331,7 +317,7 @@ npy_float npy_spacingf(npy_float x)
     return _nextf(x, 1) - x;
 }
 
-#line 318
+#line 304
 npy_double npy_spacing(npy_double x)
 {
     /* XXX: npy isnan/isinf may be optimized by bit twiddling */
@@ -342,7 +328,7 @@ npy_double npy_spacing(npy_double x)
     return _next(x, 1) - x;
 }
 
-#line 318
+#line 304
 npy_longdouble npy_spacingl(npy_longdouble x)
 {
     /* XXX: npy isnan/isinf may be optimized by bit twiddling */
@@ -354,25 +340,6 @@ npy_longdouble npy_spacingl(npy_longdouble x)
 }
 
 
-/*
- * Decorate all the math functions which are available on the current platform
- */
-
-float npy_nextafterf(float x, float y)
-{
-    return nextafterf(x, y);
-}
-
-double npy_nextafter(double x, double y)
-{
-    return nextafter(x, y);
-}
-
-npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y)
-{
-    return nextafterl(x, y);
-}
-
 int npy_clear_floatstatus() {
     char x=0;
     return npy_clear_floatstatus_barrier(&x);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c.src b/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c.src
index 5d8cb06a4de..8fccc9a69ba 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.c.src
@@ -15,20 +15,6 @@
 #define LDBL_TRUE_MIN __LDBL_DENORM_MIN__
 #endif
 
-#if !defined(HAVE_DECL_SIGNBIT)
-#include "_signbit.c"
-
-int _npy_signbit_f(float x)
-{
-    return _npy_signbit_d((double) x);
-}
-
-int _npy_signbit_ld(long double x)
-{
-    return _npy_signbit_d((double) x);
-}
-#endif
-
 /*
  * FIXME: There is a lot of redundancy between _next* and npy_nextafter*.
  * refactor this at some point
@@ -326,25 +312,6 @@ static npy_longdouble _nextl(npy_longdouble x, int p)
 }
 /**end repeat**/
 
-/*
- * Decorate all the math functions which are available on the current platform
- */
-
-float npy_nextafterf(float x, float y)
-{
-    return nextafterf(x, y);
-}
-
-double npy_nextafter(double x, double y)
-{
-    return nextafter(x, y);
-}
-
-npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y)
-{
-    return nextafterl(x, y);
-}
-
 int npy_clear_floatstatus() {
     char x=0;
     return npy_clear_floatstatus_barrier(&x);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.cpp b/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.cpp
index ebc1dbeba72..1c59bf300b0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/ieee754.cpp
@@ -17,21 +17,6 @@
 #define LDBL_TRUE_MIN __LDBL_DENORM_MIN__
 #endif
 
-#if !defined(HAVE_DECL_SIGNBIT)
-#include "_signbit.c"
-
-int
-_npy_signbit_f(float x)
-{
-    return _npy_signbit_d((double)x);
-}
-
-int
-_npy_signbit_ld(long double x)
-{
-    return _npy_signbit_d((double)x);
-}
-#endif
 
 /*
  * FIXME: There is a lot of redundancy between _next* and npy_nextafter*.
@@ -388,28 +373,6 @@ npy_spacingl(npy_longdouble x)
 }
 }
 
-/*
- * Decorate all the math functions which are available on the current platform
- */
-
-extern "C" float
-npy_nextafterf(float x, float y)
-{
-    return nextafterf(x, y);
-}
-
-extern "C" double
-npy_nextafter(double x, double y)
-{
-    return nextafter(x, y);
-}
-
-extern "C" npy_longdouble
-npy_nextafterl(npy_longdouble x, npy_longdouble y)
-{
-    return nextafterl(x, y);
-}
-
 extern "C" int
 npy_clear_floatstatus()
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c b/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c
index bd39f78c486..5f6babfd2c3 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c
@@ -72,7 +72,7 @@ static const npy_cfloat c_1f = {1.0F, 0.0};
  * These are necessary because we do not count on using a
  * C99 compiler.
  *=========================================================*/
-static NPY_INLINE
+static inline
 npy_cfloat
 cmulf(npy_cfloat a, npy_cfloat b)
 {
@@ -84,7 +84,7 @@ cmulf(npy_cfloat a, npy_cfloat b)
     return npy_cpackf(ar*br - ai*bi, ar*bi + ai*br);
 }
 
-static NPY_INLINE
+static inline
 npy_cfloat
 cdivf(npy_cfloat a, npy_cfloat b)
 {
@@ -443,29 +443,43 @@ npy_cpowf (npy_cfloat a, npy_cfloat b)
     npy_float bi = npy_cimagf(b);
     npy_cfloat r;
 
+    /*
+     * Checking if in a^b, if b is zero.
+     * If a is not zero then by definition of logarithm a^0 is 1.
+     * If a is also zero then 0^0 is best defined as 1.
+     */
     if (br == 0. && bi == 0.) {
         return npy_cpackf(1., 0.);
     }
-    if (ar == 0. && ai == 0.) {
-        if (br > 0 && bi == 0) {
-            return npy_cpackf(0., 0.);
-        }
-        else {
-            volatile npy_float tmp = NPY_INFINITYF;
-            /*
-             * NB: there are four complex zeros; c0 = (+-0, +-0), so that
-             * unlike for reals, c0**p, with `p` negative is in general
-             * ill-defined.
-             *
-             *     c0**z with z complex is also ill-defined.
-             */
-            r = npy_cpackf(NPY_NANF, NPY_NANF);
-
-            /* Raise invalid */
-            tmp -= NPY_INFINITYF;
-            ar = tmp;
-            return r;
-        }
+    /* case 0^b
+     * If a is a complex zero (ai=ar=0), then the result depends 
+     * upon values of br and bi. The result is either:
+     * 0 (in magnitude), undefined or 1.
+     * The later case is for br=bi=0 and independent of ar and ai
+     * but is handled above).
+     */
+    else if (ar == 0. && ai == 0.) {
+        /* 
+         * If the real part of b is positive (br>0) then this is
+         * the zero complex with positive sign on both the
+         * real and imaginary part.
+         */
+         if (br > 0) {
+             return npy_cpackf(0., 0.);
+         }
+        /* else we are in the case where the
+         * real part of b is negative (br<0).
+         * Here we should return a complex nan
+         * and raise FloatingPointError: invalid value...
+         */
+         
+         /* Raise invalid value by calling inf - inf*/
+          volatile npy_float tmp = NPY_INFINITYF;
+          tmp -= NPY_INFINITYF;
+          ar = tmp;
+          
+          r = npy_cpackf(NPY_NANF, NPY_NANF);
+          return r;
     }
     if (bi == 0 && (n=(npy_intp)br) == br) {
         if (n == 1) {
@@ -1010,7 +1024,7 @@ npy_ctanhf(npy_cfloat z)
  * Function f(a, b, hypot_a_b) = (hypot(a, b) - b) / 2.
  * Pass hypot(a, b) as the third argument.
  */
-static NPY_INLINE npy_float
+static inline npy_float
 _ff(npy_float a, npy_float b, npy_float hypot_a_b)
 {
     if (b < 0) {
@@ -1032,7 +1046,7 @@ _ff(npy_float a, npy_float b, npy_float hypot_a_b)
  * If returning sqrt_A2my2 has potential to result in an underflow, it is
  * rescaled, and new_y is similarly rescaled.
  */
-static NPY_INLINE void
+static inline void
 _do_hard_workf(npy_float x, npy_float y, npy_float *rx,
     npy_int *B_is_usable, npy_float *B, npy_float *sqrt_A2my2, npy_float *new_y)
 {
@@ -1176,7 +1190,7 @@ _do_hard_workf(npy_float x, npy_float y, npy_float *rx,
 /*
  * Optimized version of clog() for |z| finite and larger than ~RECIP_EPSILON.
  */
-static NPY_INLINE void
+static inline void
 _clog_for_large_valuesf(npy_float x, npy_float y,
     npy_float *rr, npy_float *ri)
 {
@@ -1477,7 +1491,7 @@ npy_casinhf(npy_cfloat z)
  * Assumes y is non-negative.
  * Assumes fabs(x) >= DBL_EPSILON.
  */
-static NPY_INLINE npy_float
+static inline npy_float
 _sum_squaresf(npy_float x, npy_float y)
 {
 #if 1 == 1
@@ -1514,7 +1528,7 @@ const npy_longdouble SQRT_MIN = 1.8336038675548471656e-2466l;
 #if 1 == 1
 #define BIAS (FLT_MAX_EXP - 1)
 #define CUTOFF (FLT_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_float
+static inline npy_float
 _real_part_reciprocalf(npy_float x, npy_float y)
 {
     npy_float scale;
@@ -1547,7 +1561,7 @@ _real_part_reciprocalf(npy_float x, npy_float y)
 #define BIAS (DBL_MAX_EXP - 1)
 /*  more guard digits are useful iff there is extra precision. */
 #define CUTOFF (DBL_MANT_DIG / 2 + 1)  /* just half or 1 guard digit */
-static NPY_INLINE npy_double
+static inline npy_double
 _real_part_reciprocal(npy_double x, npy_double y)
 {
     npy_double scale;
@@ -1589,7 +1603,7 @@ _real_part_reciprocal(npy_double x, npy_double y)
 
 #define BIAS (LDBL_MAX_EXP - 1)
 #define CUTOFF (LDBL_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
@@ -1622,7 +1636,7 @@ _real_part_reciprocall(npy_longdouble x,
 
 #else
 
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
@@ -1735,7 +1749,7 @@ static const npy_cdouble c_1 = {1.0, 0.0};
  * These are necessary because we do not count on using a
  * C99 compiler.
  *=========================================================*/
-static NPY_INLINE
+static inline
 npy_cdouble
 cmul(npy_cdouble a, npy_cdouble b)
 {
@@ -1747,7 +1761,7 @@ cmul(npy_cdouble a, npy_cdouble b)
     return npy_cpack(ar*br - ai*bi, ar*bi + ai*br);
 }
 
-static NPY_INLINE
+static inline
 npy_cdouble
 cdiv(npy_cdouble a, npy_cdouble b)
 {
@@ -2106,29 +2120,43 @@ npy_cpow (npy_cdouble a, npy_cdouble b)
     npy_double bi = npy_cimag(b);
     npy_cdouble r;
 
+    /*
+     * Checking if in a^b, if b is zero.
+     * If a is not zero then by definition of logarithm a^0 is 1.
+     * If a is also zero then 0^0 is best defined as 1.
+     */
     if (br == 0. && bi == 0.) {
         return npy_cpack(1., 0.);
     }
-    if (ar == 0. && ai == 0.) {
-        if (br > 0 && bi == 0) {
-            return npy_cpack(0., 0.);
-        }
-        else {
-            volatile npy_double tmp = NPY_INFINITY;
-            /*
-             * NB: there are four complex zeros; c0 = (+-0, +-0), so that
-             * unlike for reals, c0**p, with `p` negative is in general
-             * ill-defined.
-             *
-             *     c0**z with z complex is also ill-defined.
-             */
-            r = npy_cpack(NPY_NAN, NPY_NAN);
-
-            /* Raise invalid */
-            tmp -= NPY_INFINITY;
-            ar = tmp;
-            return r;
-        }
+    /* case 0^b
+     * If a is a complex zero (ai=ar=0), then the result depends 
+     * upon values of br and bi. The result is either:
+     * 0 (in magnitude), undefined or 1.
+     * The later case is for br=bi=0 and independent of ar and ai
+     * but is handled above).
+     */
+    else if (ar == 0. && ai == 0.) {
+        /* 
+         * If the real part of b is positive (br>0) then this is
+         * the zero complex with positive sign on both the
+         * real and imaginary part.
+         */
+         if (br > 0) {
+             return npy_cpack(0., 0.);
+         }
+        /* else we are in the case where the
+         * real part of b is negative (br<0).
+         * Here we should return a complex nan
+         * and raise FloatingPointError: invalid value...
+         */
+         
+         /* Raise invalid value by calling inf - inf*/
+          volatile npy_double tmp = NPY_INFINITY;
+          tmp -= NPY_INFINITY;
+          ar = tmp;
+          
+          r = npy_cpack(NPY_NAN, NPY_NAN);
+          return r;
     }
     if (bi == 0 && (n=(npy_intp)br) == br) {
         if (n == 1) {
@@ -2673,7 +2701,7 @@ npy_ctanh(npy_cdouble z)
  * Function f(a, b, hypot_a_b) = (hypot(a, b) - b) / 2.
  * Pass hypot(a, b) as the third argument.
  */
-static NPY_INLINE npy_double
+static inline npy_double
 _f(npy_double a, npy_double b, npy_double hypot_a_b)
 {
     if (b < 0) {
@@ -2695,7 +2723,7 @@ _f(npy_double a, npy_double b, npy_double hypot_a_b)
  * If returning sqrt_A2my2 has potential to result in an underflow, it is
  * rescaled, and new_y is similarly rescaled.
  */
-static NPY_INLINE void
+static inline void
 _do_hard_work(npy_double x, npy_double y, npy_double *rx,
     npy_int *B_is_usable, npy_double *B, npy_double *sqrt_A2my2, npy_double *new_y)
 {
@@ -2839,7 +2867,7 @@ _do_hard_work(npy_double x, npy_double y, npy_double *rx,
 /*
  * Optimized version of clog() for |z| finite and larger than ~RECIP_EPSILON.
  */
-static NPY_INLINE void
+static inline void
 _clog_for_large_values(npy_double x, npy_double y,
     npy_double *rr, npy_double *ri)
 {
@@ -3140,7 +3168,7 @@ npy_casinh(npy_cdouble z)
  * Assumes y is non-negative.
  * Assumes fabs(x) >= DBL_EPSILON.
  */
-static NPY_INLINE npy_double
+static inline npy_double
 _sum_squares(npy_double x, npy_double y)
 {
 #if 2 == 1
@@ -3177,7 +3205,7 @@ const npy_longdouble SQRT_MIN = 1.8336038675548471656e-2466l;
 #if 2 == 1
 #define BIAS (FLT_MAX_EXP - 1)
 #define CUTOFF (FLT_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_float
+static inline npy_float
 _real_part_reciprocalf(npy_float x, npy_float y)
 {
     npy_float scale;
@@ -3210,7 +3238,7 @@ _real_part_reciprocalf(npy_float x, npy_float y)
 #define BIAS (DBL_MAX_EXP - 1)
 /*  more guard digits are useful iff there is extra precision. */
 #define CUTOFF (DBL_MANT_DIG / 2 + 1)  /* just half or 1 guard digit */
-static NPY_INLINE npy_double
+static inline npy_double
 _real_part_reciprocal(npy_double x, npy_double y)
 {
     npy_double scale;
@@ -3252,7 +3280,7 @@ _real_part_reciprocal(npy_double x, npy_double y)
 
 #define BIAS (LDBL_MAX_EXP - 1)
 #define CUTOFF (LDBL_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
@@ -3285,7 +3313,7 @@ _real_part_reciprocall(npy_longdouble x,
 
 #else
 
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
@@ -3398,7 +3426,7 @@ static const npy_clongdouble c_1l = {1.0L, 0.0};
  * These are necessary because we do not count on using a
  * C99 compiler.
  *=========================================================*/
-static NPY_INLINE
+static inline
 npy_clongdouble
 cmull(npy_clongdouble a, npy_clongdouble b)
 {
@@ -3410,7 +3438,7 @@ cmull(npy_clongdouble a, npy_clongdouble b)
     return npy_cpackl(ar*br - ai*bi, ar*bi + ai*br);
 }
 
-static NPY_INLINE
+static inline
 npy_clongdouble
 cdivl(npy_clongdouble a, npy_clongdouble b)
 {
@@ -3769,29 +3797,43 @@ npy_cpowl (npy_clongdouble a, npy_clongdouble b)
     npy_longdouble bi = npy_cimagl(b);
     npy_clongdouble r;
 
+    /*
+     * Checking if in a^b, if b is zero.
+     * If a is not zero then by definition of logarithm a^0 is 1.
+     * If a is also zero then 0^0 is best defined as 1.
+     */
     if (br == 0. && bi == 0.) {
         return npy_cpackl(1., 0.);
     }
-    if (ar == 0. && ai == 0.) {
-        if (br > 0 && bi == 0) {
-            return npy_cpackl(0., 0.);
-        }
-        else {
-            volatile npy_longdouble tmp = NPY_INFINITYL;
-            /*
-             * NB: there are four complex zeros; c0 = (+-0, +-0), so that
-             * unlike for reals, c0**p, with `p` negative is in general
-             * ill-defined.
-             *
-             *     c0**z with z complex is also ill-defined.
-             */
-            r = npy_cpackl(NPY_NANL, NPY_NANL);
-
-            /* Raise invalid */
-            tmp -= NPY_INFINITYL;
-            ar = tmp;
-            return r;
-        }
+    /* case 0^b
+     * If a is a complex zero (ai=ar=0), then the result depends 
+     * upon values of br and bi. The result is either:
+     * 0 (in magnitude), undefined or 1.
+     * The later case is for br=bi=0 and independent of ar and ai
+     * but is handled above).
+     */
+    else if (ar == 0. && ai == 0.) {
+        /* 
+         * If the real part of b is positive (br>0) then this is
+         * the zero complex with positive sign on both the
+         * real and imaginary part.
+         */
+         if (br > 0) {
+             return npy_cpackl(0., 0.);
+         }
+        /* else we are in the case where the
+         * real part of b is negative (br<0).
+         * Here we should return a complex nan
+         * and raise FloatingPointError: invalid value...
+         */
+         
+         /* Raise invalid value by calling inf - inf*/
+          volatile npy_longdouble tmp = NPY_INFINITYL;
+          tmp -= NPY_INFINITYL;
+          ar = tmp;
+          
+          r = npy_cpackl(NPY_NANL, NPY_NANL);
+          return r;
     }
     if (bi == 0 && (n=(npy_intp)br) == br) {
         if (n == 1) {
@@ -4336,7 +4378,7 @@ npy_ctanhl(npy_clongdouble z)
  * Function f(a, b, hypot_a_b) = (hypot(a, b) - b) / 2.
  * Pass hypot(a, b) as the third argument.
  */
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _fl(npy_longdouble a, npy_longdouble b, npy_longdouble hypot_a_b)
 {
     if (b < 0) {
@@ -4358,7 +4400,7 @@ _fl(npy_longdouble a, npy_longdouble b, npy_longdouble hypot_a_b)
  * If returning sqrt_A2my2 has potential to result in an underflow, it is
  * rescaled, and new_y is similarly rescaled.
  */
-static NPY_INLINE void
+static inline void
 _do_hard_workl(npy_longdouble x, npy_longdouble y, npy_longdouble *rx,
     npy_int *B_is_usable, npy_longdouble *B, npy_longdouble *sqrt_A2my2, npy_longdouble *new_y)
 {
@@ -4502,7 +4544,7 @@ _do_hard_workl(npy_longdouble x, npy_longdouble y, npy_longdouble *rx,
 /*
  * Optimized version of clog() for |z| finite and larger than ~RECIP_EPSILON.
  */
-static NPY_INLINE void
+static inline void
 _clog_for_large_valuesl(npy_longdouble x, npy_longdouble y,
     npy_longdouble *rr, npy_longdouble *ri)
 {
@@ -4803,7 +4845,7 @@ npy_casinhl(npy_clongdouble z)
  * Assumes y is non-negative.
  * Assumes fabs(x) >= DBL_EPSILON.
  */
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _sum_squaresl(npy_longdouble x, npy_longdouble y)
 {
 #if 3 == 1
@@ -4840,7 +4882,7 @@ const npy_longdouble SQRT_MIN = 1.8336038675548471656e-2466l;
 #if 3 == 1
 #define BIAS (FLT_MAX_EXP - 1)
 #define CUTOFF (FLT_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_float
+static inline npy_float
 _real_part_reciprocalf(npy_float x, npy_float y)
 {
     npy_float scale;
@@ -4873,7 +4915,7 @@ _real_part_reciprocalf(npy_float x, npy_float y)
 #define BIAS (DBL_MAX_EXP - 1)
 /*  more guard digits are useful iff there is extra precision. */
 #define CUTOFF (DBL_MANT_DIG / 2 + 1)  /* just half or 1 guard digit */
-static NPY_INLINE npy_double
+static inline npy_double
 _real_part_reciprocal(npy_double x, npy_double y)
 {
     npy_double scale;
@@ -4915,7 +4957,7 @@ _real_part_reciprocal(npy_double x, npy_double y)
 
 #define BIAS (LDBL_MAX_EXP - 1)
 #define CUTOFF (LDBL_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
@@ -4948,7 +4990,7 @@ _real_part_reciprocall(npy_longdouble x,
 
 #else
 
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
@@ -5053,9 +5095,9 @@ npy_catanhl(npy_clongdouble z)
  * Decorate all the functions which are available natively
  *=========================================================*/
 
-#line 1736
+#line 1750
 
-#line 1741
+#line 1755
 #ifdef HAVE_CABSF
 npy_float
 npy_cabsf(npy_cfloat z)
@@ -5066,7 +5108,7 @@ npy_cabsf(npy_cfloat z)
 }
 #endif
 
-#line 1741
+#line 1755
 #ifdef HAVE_CARGF
 npy_float
 npy_cargf(npy_cfloat z)
@@ -5078,7 +5120,7 @@ npy_cargf(npy_cfloat z)
 #endif
 
 
-#line 1758
+#line 1772
 #ifdef HAVE_CEXPF
 npy_cfloat
 npy_cexpf(npy_cfloat z)
@@ -5091,7 +5133,7 @@ npy_cexpf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CLOGF
 npy_cfloat
 npy_clogf(npy_cfloat z)
@@ -5104,7 +5146,7 @@ npy_clogf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSQRTF
 npy_cfloat
 npy_csqrtf(npy_cfloat z)
@@ -5117,7 +5159,7 @@ npy_csqrtf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CCOSF
 npy_cfloat
 npy_ccosf(npy_cfloat z)
@@ -5130,7 +5172,7 @@ npy_ccosf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSINF
 npy_cfloat
 npy_csinf(npy_cfloat z)
@@ -5143,7 +5185,7 @@ npy_csinf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CTANF
 npy_cfloat
 npy_ctanf(npy_cfloat z)
@@ -5156,7 +5198,7 @@ npy_ctanf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CCOSHF
 npy_cfloat
 npy_ccoshf(npy_cfloat z)
@@ -5169,7 +5211,7 @@ npy_ccoshf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSINHF
 npy_cfloat
 npy_csinhf(npy_cfloat z)
@@ -5182,7 +5224,7 @@ npy_csinhf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CTANHF
 npy_cfloat
 npy_ctanhf(npy_cfloat z)
@@ -5195,7 +5237,7 @@ npy_ctanhf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CACOSF
 npy_cfloat
 npy_cacosf(npy_cfloat z)
@@ -5208,7 +5250,7 @@ npy_cacosf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CASINF
 npy_cfloat
 npy_casinf(npy_cfloat z)
@@ -5221,7 +5263,7 @@ npy_casinf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CATANF
 npy_cfloat
 npy_catanf(npy_cfloat z)
@@ -5234,7 +5276,7 @@ npy_catanf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CACOSHF
 npy_cfloat
 npy_cacoshf(npy_cfloat z)
@@ -5247,7 +5289,7 @@ npy_cacoshf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CASINHF
 npy_cfloat
 npy_casinhf(npy_cfloat z)
@@ -5260,7 +5302,7 @@ npy_casinhf(npy_cfloat z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CATANHF
 npy_cfloat
 npy_catanhf(npy_cfloat z)
@@ -5276,9 +5318,9 @@ npy_catanhf(npy_cfloat z)
 
 
 
-#line 1736
+#line 1750
 
-#line 1741
+#line 1755
 #ifdef HAVE_CABS
 npy_double
 npy_cabs(npy_cdouble z)
@@ -5289,7 +5331,7 @@ npy_cabs(npy_cdouble z)
 }
 #endif
 
-#line 1741
+#line 1755
 #ifdef HAVE_CARG
 npy_double
 npy_carg(npy_cdouble z)
@@ -5301,7 +5343,7 @@ npy_carg(npy_cdouble z)
 #endif
 
 
-#line 1758
+#line 1772
 #ifdef HAVE_CEXP
 npy_cdouble
 npy_cexp(npy_cdouble z)
@@ -5314,7 +5356,7 @@ npy_cexp(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CLOG
 npy_cdouble
 npy_clog(npy_cdouble z)
@@ -5327,7 +5369,7 @@ npy_clog(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSQRT
 npy_cdouble
 npy_csqrt(npy_cdouble z)
@@ -5340,7 +5382,7 @@ npy_csqrt(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CCOS
 npy_cdouble
 npy_ccos(npy_cdouble z)
@@ -5353,7 +5395,7 @@ npy_ccos(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSIN
 npy_cdouble
 npy_csin(npy_cdouble z)
@@ -5366,7 +5408,7 @@ npy_csin(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CTAN
 npy_cdouble
 npy_ctan(npy_cdouble z)
@@ -5379,7 +5421,7 @@ npy_ctan(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CCOSH
 npy_cdouble
 npy_ccosh(npy_cdouble z)
@@ -5392,7 +5434,7 @@ npy_ccosh(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSINH
 npy_cdouble
 npy_csinh(npy_cdouble z)
@@ -5405,7 +5447,7 @@ npy_csinh(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CTANH
 npy_cdouble
 npy_ctanh(npy_cdouble z)
@@ -5418,7 +5460,7 @@ npy_ctanh(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CACOS
 npy_cdouble
 npy_cacos(npy_cdouble z)
@@ -5431,7 +5473,7 @@ npy_cacos(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CASIN
 npy_cdouble
 npy_casin(npy_cdouble z)
@@ -5444,7 +5486,7 @@ npy_casin(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CATAN
 npy_cdouble
 npy_catan(npy_cdouble z)
@@ -5457,7 +5499,7 @@ npy_catan(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CACOSH
 npy_cdouble
 npy_cacosh(npy_cdouble z)
@@ -5470,7 +5512,7 @@ npy_cacosh(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CASINH
 npy_cdouble
 npy_casinh(npy_cdouble z)
@@ -5483,7 +5525,7 @@ npy_casinh(npy_cdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CATANH
 npy_cdouble
 npy_catanh(npy_cdouble z)
@@ -5499,9 +5541,9 @@ npy_catanh(npy_cdouble z)
 
 
 
-#line 1736
+#line 1750
 
-#line 1741
+#line 1755
 #ifdef HAVE_CABSL
 npy_longdouble
 npy_cabsl(npy_clongdouble z)
@@ -5512,7 +5554,7 @@ npy_cabsl(npy_clongdouble z)
 }
 #endif
 
-#line 1741
+#line 1755
 #ifdef HAVE_CARGL
 npy_longdouble
 npy_cargl(npy_clongdouble z)
@@ -5524,7 +5566,7 @@ npy_cargl(npy_clongdouble z)
 #endif
 
 
-#line 1758
+#line 1772
 #ifdef HAVE_CEXPL
 npy_clongdouble
 npy_cexpl(npy_clongdouble z)
@@ -5537,7 +5579,7 @@ npy_cexpl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CLOGL
 npy_clongdouble
 npy_clogl(npy_clongdouble z)
@@ -5550,7 +5592,7 @@ npy_clogl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSQRTL
 npy_clongdouble
 npy_csqrtl(npy_clongdouble z)
@@ -5563,7 +5605,7 @@ npy_csqrtl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CCOSL
 npy_clongdouble
 npy_ccosl(npy_clongdouble z)
@@ -5576,7 +5618,7 @@ npy_ccosl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSINL
 npy_clongdouble
 npy_csinl(npy_clongdouble z)
@@ -5589,7 +5631,7 @@ npy_csinl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CTANL
 npy_clongdouble
 npy_ctanl(npy_clongdouble z)
@@ -5602,7 +5644,7 @@ npy_ctanl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CCOSHL
 npy_clongdouble
 npy_ccoshl(npy_clongdouble z)
@@ -5615,7 +5657,7 @@ npy_ccoshl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CSINHL
 npy_clongdouble
 npy_csinhl(npy_clongdouble z)
@@ -5628,7 +5670,7 @@ npy_csinhl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CTANHL
 npy_clongdouble
 npy_ctanhl(npy_clongdouble z)
@@ -5641,7 +5683,7 @@ npy_ctanhl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CACOSL
 npy_clongdouble
 npy_cacosl(npy_clongdouble z)
@@ -5654,7 +5696,7 @@ npy_cacosl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CASINL
 npy_clongdouble
 npy_casinl(npy_clongdouble z)
@@ -5667,7 +5709,7 @@ npy_casinl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CATANL
 npy_clongdouble
 npy_catanl(npy_clongdouble z)
@@ -5680,7 +5722,7 @@ npy_catanl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CACOSHL
 npy_clongdouble
 npy_cacoshl(npy_clongdouble z)
@@ -5693,7 +5735,7 @@ npy_cacoshl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CASINHL
 npy_clongdouble
 npy_casinhl(npy_clongdouble z)
@@ -5706,7 +5748,7 @@ npy_casinhl(npy_clongdouble z)
 }
 #endif
 
-#line 1758
+#line 1772
 #ifdef HAVE_CATANHL
 npy_clongdouble
 npy_catanhl(npy_clongdouble z)
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c.src b/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c.src
index fff541ee53a..eb9d810b7d8 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_complex.c.src
@@ -72,7 +72,7 @@ static const @ctype@ c_1@c@ = {1.0@C@, 0.0};
  * These are necessary because we do not count on using a
  * C99 compiler.
  *=========================================================*/
-static NPY_INLINE
+static inline
 @ctype@
 cmul@c@(@ctype@ a, @ctype@ b)
 {
@@ -84,7 +84,7 @@ cmul@c@(@ctype@ a, @ctype@ b)
     return npy_cpack@c@(ar*br - ai*bi, ar*bi + ai*br);
 }
 
-static NPY_INLINE
+static inline
 @ctype@
 cdiv@c@(@ctype@ a, @ctype@ b)
 {
@@ -443,29 +443,43 @@ npy_cpow@c@ (@ctype@ a, @ctype@ b)
     @type@ bi = npy_cimag@c@(b);
     @ctype@ r;
 
+    /*
+     * Checking if in a^b, if b is zero.
+     * If a is not zero then by definition of logarithm a^0 is 1.
+     * If a is also zero then 0^0 is best defined as 1.
+     */
     if (br == 0. && bi == 0.) {
         return npy_cpack@c@(1., 0.);
     }
-    if (ar == 0. && ai == 0.) {
-        if (br > 0 && bi == 0) {
-            return npy_cpack@c@(0., 0.);
-        }
-        else {
-            volatile @type@ tmp = NPY_INFINITY@C@;
-            /*
-             * NB: there are four complex zeros; c0 = (+-0, +-0), so that
-             * unlike for reals, c0**p, with `p` negative is in general
-             * ill-defined.
-             *
-             *     c0**z with z complex is also ill-defined.
-             */
-            r = npy_cpack@c@(NPY_NAN@C@, NPY_NAN@C@);
-
-            /* Raise invalid */
-            tmp -= NPY_INFINITY@C@;
-            ar = tmp;
-            return r;
-        }
+    /* case 0^b
+     * If a is a complex zero (ai=ar=0), then the result depends 
+     * upon values of br and bi. The result is either:
+     * 0 (in magnitude), undefined or 1.
+     * The later case is for br=bi=0 and independent of ar and ai
+     * but is handled above).
+     */
+    else if (ar == 0. && ai == 0.) {
+        /* 
+         * If the real part of b is positive (br>0) then this is
+         * the zero complex with positive sign on both the
+         * real and imaginary part.
+         */
+         if (br > 0) {
+             return npy_cpack@c@(0., 0.);
+         }
+        /* else we are in the case where the
+         * real part of b is negative (br<0).
+         * Here we should return a complex nan
+         * and raise FloatingPointError: invalid value...
+         */
+         
+         /* Raise invalid value by calling inf - inf*/
+          volatile @type@ tmp = NPY_INFINITY@C@;
+          tmp -= NPY_INFINITY@C@;
+          ar = tmp;
+          
+          r = npy_cpack@c@(NPY_NAN@C@, NPY_NAN@C@);
+          return r;
     }
     if (bi == 0 && (n=(npy_intp)br) == br) {
         if (n == 1) {
@@ -1010,7 +1024,7 @@ npy_ctanh@c@(@ctype@ z)
  * Function f(a, b, hypot_a_b) = (hypot(a, b) - b) / 2.
  * Pass hypot(a, b) as the third argument.
  */
-static NPY_INLINE @type@
+static inline @type@
 _f@c@(@type@ a, @type@ b, @type@ hypot_a_b)
 {
     if (b < 0) {
@@ -1032,7 +1046,7 @@ _f@c@(@type@ a, @type@ b, @type@ hypot_a_b)
  * If returning sqrt_A2my2 has potential to result in an underflow, it is
  * rescaled, and new_y is similarly rescaled.
  */
-static NPY_INLINE void
+static inline void
 _do_hard_work@c@(@type@ x, @type@ y, @type@ *rx,
     npy_int *B_is_usable, @type@ *B, @type@ *sqrt_A2my2, @type@ *new_y)
 {
@@ -1176,7 +1190,7 @@ _do_hard_work@c@(@type@ x, @type@ y, @type@ *rx,
 /*
  * Optimized version of clog() for |z| finite and larger than ~RECIP_EPSILON.
  */
-static NPY_INLINE void
+static inline void
 _clog_for_large_values@c@(@type@ x, @type@ y,
     @type@ *rr, @type@ *ri)
 {
@@ -1477,7 +1491,7 @@ npy_casinh@c@(@ctype@ z)
  * Assumes y is non-negative.
  * Assumes fabs(x) >= DBL_EPSILON.
  */
-static NPY_INLINE @type@
+static inline @type@
 _sum_squares@c@(@type@ x, @type@ y)
 {
 #if @precision@ == 1
@@ -1514,7 +1528,7 @@ const npy_longdouble SQRT_MIN = 1.8336038675548471656e-2466l;
 #if @precision@ == 1
 #define BIAS (FLT_MAX_EXP - 1)
 #define CUTOFF (FLT_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_float
+static inline npy_float
 _real_part_reciprocalf(npy_float x, npy_float y)
 {
     npy_float scale;
@@ -1547,7 +1561,7 @@ _real_part_reciprocalf(npy_float x, npy_float y)
 #define BIAS (DBL_MAX_EXP - 1)
 /*  more guard digits are useful iff there is extra precision. */
 #define CUTOFF (DBL_MANT_DIG / 2 + 1)  /* just half or 1 guard digit */
-static NPY_INLINE npy_double
+static inline npy_double
 _real_part_reciprocal(npy_double x, npy_double y)
 {
     npy_double scale;
@@ -1589,7 +1603,7 @@ _real_part_reciprocal(npy_double x, npy_double y)
 
 #define BIAS (LDBL_MAX_EXP - 1)
 #define CUTOFF (LDBL_MANT_DIG / 2 + 1)
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
@@ -1622,7 +1636,7 @@ _real_part_reciprocall(npy_longdouble x,
 
 #else
 
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 _real_part_reciprocall(npy_longdouble x,
     npy_longdouble y)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_private.h b/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_private.h
index 936e526310e..7190c8a5a17 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_private.h
+++ b/contrib/python/numpy/py3/numpy/core/src/npymath/npy_math_private.h
@@ -21,6 +21,7 @@
 #include <Python.h>
 #ifdef __cplusplus
 #include <cmath>
+#include <complex>
 using std::isgreater;
 using std::isless;
 #else
@@ -498,8 +499,9 @@ do {                                                            \
  * Microsoft C defines _MSC_VER
  * Intel compiler does not use MSVC complex types, but defines _MSC_VER by
  * default.
+ * since c++17 msvc is no longer support them.
  */
-#if defined(_MSC_VER) && !defined(__INTEL_COMPILER)
+#if !defined(__cplusplus) && defined(_MSC_VER) && !defined(__INTEL_COMPILER)
 typedef union {
         npy_cdouble npy_z;
 #ifdef __cplusplus
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/heapsort.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/heapsort.cpp
index de39367c22f..77a4bda7422 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/heapsort.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/heapsort.cpp
@@ -21,7 +21,7 @@
  *
  * The merge sort is *stable*, meaning that equal components
  * are unmoved from their entry versions, so it can be used to
- * implement lexigraphic sorting on multiple keys.
+ * implement lexicographic sorting on multiple keys.
  *
  * The heap sort is included for completeness.
  */
@@ -55,6 +55,9 @@ npy_heapsort(void *start, npy_intp num, void *varr)
     PyArrayObject *arr = (PyArrayObject *)varr;
     npy_intp elsize = PyArray_ITEMSIZE(arr);
     PyArray_CompareFunc *cmp = PyArray_DESCR(arr)->f->compare;
+    if (elsize == 0) {
+        return 0;  /* no need for sorting elements of no size */
+    }
     char *tmp = (char *)malloc(elsize);
     char *a = (char *)start - elsize;
     npy_intp i, j, l;
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/mergesort.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/mergesort.cpp
index f892dd185b8..f53feb3207b 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/mergesort.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/mergesort.cpp
@@ -21,7 +21,7 @@
  *
  * The merge sort is *stable*, meaning that equal components
  * are unmoved from their entry versions, so it can be used to
- * implement lexigraphic sorting on multiple keys.
+ * implement lexicographic sorting on multiple keys.
  *
  * The heap sort is included for completeness.
  */
@@ -171,6 +171,7 @@ amergesort_(type *v, npy_intp *tosort, npy_intp num)
 }
 
 /*
+ 
  *****************************************************************************
  **                             STRING SORTS                                **
  *****************************************************************************
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_common.h b/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_common.h
index ab9f456b65d..851eb89daae 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_common.h
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_common.h
@@ -44,112 +44,112 @@ extern "C" {
  *****************************************************************************
  */
 
-NPY_INLINE static int
+static inline int
 BOOL_LT(npy_bool a, npy_bool b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 BYTE_LT(npy_byte a, npy_byte b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 UBYTE_LT(npy_ubyte a, npy_ubyte b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 SHORT_LT(npy_short a, npy_short b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 USHORT_LT(npy_ushort a, npy_ushort b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 INT_LT(npy_int a, npy_int b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 UINT_LT(npy_uint a, npy_uint b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 LONG_LT(npy_long a, npy_long b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 ULONG_LT(npy_ulong a, npy_ulong b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 LONGLONG_LT(npy_longlong a, npy_longlong b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 ULONGLONG_LT(npy_ulonglong a, npy_ulonglong b)
 {
     return a < b;
 }
 
 
-NPY_INLINE static int
+static inline int
 FLOAT_LT(npy_float a, npy_float b)
 {
     return a < b || (b != b && a == a);
 }
 
 
-NPY_INLINE static int
+static inline int
 DOUBLE_LT(npy_double a, npy_double b)
 {
     return a < b || (b != b && a == a);
 }
 
 
-NPY_INLINE static int
+static inline int
 LONGDOUBLE_LT(npy_longdouble a, npy_longdouble b)
 {
     return a < b || (b != b && a == a);
 }
 
 
-NPY_INLINE static int
+static inline int
 _npy_half_isnan(npy_half h)
 {
     return ((h&0x7c00u) == 0x7c00u) && ((h&0x03ffu) != 0x0000u);
 }
 
 
-NPY_INLINE static int
+static inline int
 _npy_half_lt_nonan(npy_half h1, npy_half h2)
 {
     if (h1&0x8000u) {
@@ -172,7 +172,7 @@ _npy_half_lt_nonan(npy_half h1, npy_half h2)
 }
 
 
-NPY_INLINE static int
+static inline int
 HALF_LT(npy_half a, npy_half b)
 {
     int ret;
@@ -192,7 +192,7 @@ HALF_LT(npy_half a, npy_half b)
  * of an if statement. It's a SUN compiler thing, so assign the return value
  * to a variable instead.
  */
-NPY_INLINE static int
+static inline int
 CFLOAT_LT(npy_cfloat a, npy_cfloat b)
 {
     int ret;
@@ -214,7 +214,7 @@ CFLOAT_LT(npy_cfloat a, npy_cfloat b)
 }
 
 
-NPY_INLINE static int
+static inline int
 CDOUBLE_LT(npy_cdouble a, npy_cdouble b)
 {
     int ret;
@@ -236,7 +236,7 @@ CDOUBLE_LT(npy_cdouble a, npy_cdouble b)
 }
 
 
-NPY_INLINE static int
+static inline int
 CLONGDOUBLE_LT(npy_clongdouble a, npy_clongdouble b)
 {
     int ret;
@@ -258,14 +258,14 @@ CLONGDOUBLE_LT(npy_clongdouble a, npy_clongdouble b)
 }
 
 
-NPY_INLINE static void
+static inline void
 STRING_COPY(char *s1, char const*s2, size_t len)
 {
     memcpy(s1, s2, len);
 }
 
 
-NPY_INLINE static void
+static inline void
 STRING_SWAP(char *s1, char *s2, size_t len)
 {
     while(len--) {
@@ -276,7 +276,7 @@ STRING_SWAP(char *s1, char *s2, size_t len)
 }
 
 
-NPY_INLINE static int
+static inline int
 STRING_LT(const char *s1, const char *s2, size_t len)
 {
     const unsigned char *c1 = (const unsigned char *)s1;
@@ -294,7 +294,7 @@ STRING_LT(const char *s1, const char *s2, size_t len)
 }
 
 
-NPY_INLINE static void
+static inline void
 UNICODE_COPY(npy_ucs4 *s1, npy_ucs4 const *s2, size_t len)
 {
     while(len--) {
@@ -303,7 +303,7 @@ UNICODE_COPY(npy_ucs4 *s1, npy_ucs4 const *s2, size_t len)
 }
 
 
-NPY_INLINE static void
+static inline void
 UNICODE_SWAP(npy_ucs4 *s1, npy_ucs4 *s2, size_t len)
 {
     while(len--) {
@@ -314,7 +314,7 @@ UNICODE_SWAP(npy_ucs4 *s1, npy_ucs4 *s2, size_t len)
 }
 
 
-NPY_INLINE static int
+static inline int
 UNICODE_LT(const npy_ucs4 *s1, const npy_ucs4 *s2, size_t len)
 {
     size_t i;
@@ -330,7 +330,7 @@ UNICODE_LT(const npy_ucs4 *s1, const npy_ucs4 *s2, size_t len)
 }
 
 
-NPY_INLINE static int
+static inline int
 DATETIME_LT(npy_datetime a, npy_datetime b)
 {
     if (a == NPY_DATETIME_NAT) {
@@ -345,7 +345,7 @@ DATETIME_LT(npy_datetime a, npy_datetime b)
 }
 
 
-NPY_INLINE static int
+static inline int
 TIMEDELTA_LT(npy_timedelta a, npy_timedelta b)
 {
     if (a == NPY_DATETIME_NAT) {
@@ -360,14 +360,14 @@ TIMEDELTA_LT(npy_timedelta a, npy_timedelta b)
 }
 
 
-NPY_INLINE static void
+static inline void
 GENERIC_COPY(char *a, char *b, size_t len)
 {
     memcpy(a, b, len);
 }
 
 
-NPY_INLINE static void
+static inline void
 GENERIC_SWAP(char *a, char *b, size_t len)
 {
     while(len--) {
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_heapsort.h b/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_heapsort.h
index 762d89b7be7..16750b81738 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_heapsort.h
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/npysort_heapsort.h
@@ -16,7 +16,7 @@
  */
 
 template <typename Tag, typename type>
-NPY_INLINE NPY_NO_EXPORT
+inline NPY_NO_EXPORT
 int heapsort_(type *start, npy_intp n)
 {
     type tmp, *a;
@@ -67,7 +67,7 @@ int heapsort_(type *start, npy_intp n)
 }
 
 template <typename Tag, typename type>
-NPY_INLINE NPY_NO_EXPORT
+inline NPY_NO_EXPORT
 int aheapsort_(type *vv, npy_intp *tosort, npy_intp n)
 {
     type *v = vv;
@@ -123,11 +123,15 @@ int aheapsort_(type *vv, npy_intp *tosort, npy_intp n)
  */
 
 template <typename Tag, typename type>
-NPY_INLINE NPY_NO_EXPORT
+inline NPY_NO_EXPORT
 int string_heapsort_(type *start, npy_intp n, void *varr)
 {
     PyArrayObject *arr = (PyArrayObject *)varr;
     size_t len = PyArray_ITEMSIZE(arr) / sizeof(type);
+    if (len == 0) {
+        return 0;  /* no need for sorting if strings are empty */
+    }
+
     type *tmp = (type *)malloc(PyArray_ITEMSIZE(arr));
     type *a = (type *)start - len;
     npy_intp i, j, l;
@@ -177,7 +181,7 @@ int string_heapsort_(type *start, npy_intp n, void *varr)
 }
 
 template <typename Tag, typename type>
-NPY_INLINE NPY_NO_EXPORT
+inline NPY_NO_EXPORT
 int string_aheapsort_(type *vv, npy_intp *tosort, npy_intp n, void *varr)
 {
     type *v = vv;
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/quicksort.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/quicksort.cpp
index 3e351dd846c..e9fa792ff9a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/quicksort.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/quicksort.cpp
@@ -54,15 +54,11 @@
 #include "npysort_common.h"
 #include "npysort_heapsort.h"
 #include "numpy_tag.h"
+#include "simd_qsort.hpp"
 
-#include "x86-qsort.h"
 #include <cstdlib>
 #include <utility>
 
-#ifndef NPY_DISABLE_OPTIMIZATION
-#include "x86-qsort.dispatch.h"
-#endif
-
 #define NOT_USED NPY_UNUSED(unused)
 /*
  * pushing largest partition has upper bound of log2(n) space
@@ -73,70 +69,75 @@
 #define SMALL_MERGESORT 20
 #define SMALL_STRING 16
 
+// Temporarily disable AVX512 sorting on WIN32 and CYGWIN until we can figure
+// out why it has test failures
+#if defined(_MSC_VER) || defined(__CYGWIN__)
+template<typename T>
+inline bool quicksort_dispatch(T*, npy_intp)
+{
+    return false;
+}
+template<typename T>
+inline bool aquicksort_dispatch(T*, npy_intp*, npy_intp)
+{
+    return false;
+}
+#else
+template<typename T>
+inline bool quicksort_dispatch(T *start, npy_intp num)
+{
+    using TF = typename np::meta::FixedWidth<T>::Type;
+    void (*dispfunc)(TF*, intptr_t) = nullptr;
+    if (sizeof(T) == sizeof(uint16_t)) {
+        #ifndef NPY_DISABLE_OPTIMIZATION
+            #include "simd_qsort_16bit.dispatch.h"
+        #endif
+        NPY_CPU_DISPATCH_CALL_XB(dispfunc = np::qsort_simd::template QSort, <TF>);
+    }
+    else if (sizeof(T) == sizeof(uint32_t) || sizeof(T) == sizeof(uint64_t)) {
+        #ifndef NPY_DISABLE_OPTIMIZATION
+            #include "simd_qsort.dispatch.h"
+        #endif
+        NPY_CPU_DISPATCH_CALL_XB(dispfunc = np::qsort_simd::template QSort, <TF>);
+    }
+    if (dispfunc) {
+        (*dispfunc)(reinterpret_cast<TF*>(start), static_cast<intptr_t>(num));
+        return true;
+    }
+    return false;
+}
+
+template<typename T>
+inline bool aquicksort_dispatch(T *start, npy_intp* arg, npy_intp num)
+{
+    using TF = typename np::meta::FixedWidth<T>::Type;
+    void (*dispfunc)(TF*, npy_intp*, npy_intp) = nullptr;
+    #ifndef NPY_DISABLE_OPTIMIZATION
+        #include "simd_qsort.dispatch.h"
+    #endif
+    /* x86-simd-sort uses 8-byte int to store arg values, npy_intp is 4 bytes
+     * in 32-bit*/
+    if (sizeof(npy_intp) == sizeof(int64_t)) {
+        NPY_CPU_DISPATCH_CALL_XB(dispfunc = np::qsort_simd::template ArgQSort, <TF>);
+    }
+    if (dispfunc) {
+        (*dispfunc)(reinterpret_cast<TF*>(start), arg, num);
+        return true;
+    }
+    return false;
+}
+#endif // _MSC_VER || CYGWIN
+
 /*
  *****************************************************************************
  **                            NUMERIC SORTS                                **
  *****************************************************************************
  */
 
-namespace {
-
-template <typename Tag>
-struct x86_dispatch {
-    static bool quicksort(typename Tag::type *, npy_intp) { return false; }
-};
-
-template <>
-struct x86_dispatch<npy::int_tag> {
-    static bool quicksort(npy_int *start, npy_intp num)
-    {
-        void (*dispfunc)(void *, npy_intp) = nullptr;
-        NPY_CPU_DISPATCH_CALL_XB(dispfunc = x86_quicksort_int);
-        if (dispfunc) {
-            (*dispfunc)(start, num);
-            return true;
-        }
-        return false;
-    }
-};
-
-template <>
-struct x86_dispatch<npy::uint_tag> {
-    static bool quicksort(npy_uint *start, npy_intp num)
-    {
-        void (*dispfunc)(void *, npy_intp) = nullptr;
-        NPY_CPU_DISPATCH_CALL_XB(dispfunc = x86_quicksort_uint);
-        if (dispfunc) {
-            (*dispfunc)(start, num);
-            return true;
-        }
-        return false;
-    }
-};
-
-template <>
-struct x86_dispatch<npy::float_tag> {
-    static bool quicksort(npy_float *start, npy_intp num)
-    {
-        void (*dispfunc)(void *, npy_intp) = nullptr;
-        NPY_CPU_DISPATCH_CALL_XB(dispfunc = x86_quicksort_float);
-        if (dispfunc) {
-            (*dispfunc)(start, num);
-            return true;
-        }
-        return false;
-    }
-};
-
-}  // namespace
-
 template <typename Tag, typename type>
 static int
 quicksort_(type *start, npy_intp num)
 {
-    if (x86_dispatch<Tag>::quicksort(start, num))
-        return 0;
-
     type vp;
     type *pl = start;
     type *pr = pl + num - 1;
@@ -729,56 +730,89 @@ quicksort_ubyte(void *start, npy_intp n, void *NPY_UNUSED(varr))
 NPY_NO_EXPORT int
 quicksort_short(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_short *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::short_tag>((npy_short *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_ushort(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_ushort *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::ushort_tag>((npy_ushort *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_int(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_int *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::int_tag>((npy_int *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_uint(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_uint *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::uint_tag>((npy_uint *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_long(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_long *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::long_tag>((npy_long *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_ulong(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_ulong *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::ulong_tag>((npy_ulong *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_longlong(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_longlong *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::longlong_tag>((npy_longlong *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_ulonglong(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_ulonglong *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::ulonglong_tag>((npy_ulonglong *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_half(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((np::Half *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::half_tag>((npy_half *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_float(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_float *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::float_tag>((npy_float *)start, n);
 }
 NPY_NO_EXPORT int
 quicksort_double(void *start, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (quicksort_dispatch((npy_double *)start, n)) {
+        return 0;
+    }
     return quicksort_<npy::double_tag>((npy_double *)start, n);
 }
 NPY_NO_EXPORT int
@@ -843,34 +877,52 @@ aquicksort_ushort(void *vv, npy_intp *tosort, npy_intp n,
 NPY_NO_EXPORT int
 aquicksort_int(void *vv, npy_intp *tosort, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_int *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::int_tag>((npy_int *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
 aquicksort_uint(void *vv, npy_intp *tosort, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_uint *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::uint_tag>((npy_uint *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
 aquicksort_long(void *vv, npy_intp *tosort, npy_intp n, void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_long *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::long_tag>((npy_long *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
 aquicksort_ulong(void *vv, npy_intp *tosort, npy_intp n,
                  void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_ulong *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::ulong_tag>((npy_ulong *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
 aquicksort_longlong(void *vv, npy_intp *tosort, npy_intp n,
                     void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_longlong *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::longlong_tag>((npy_longlong *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
 aquicksort_ulonglong(void *vv, npy_intp *tosort, npy_intp n,
                      void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_ulonglong *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::ulonglong_tag>((npy_ulonglong *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
@@ -882,12 +934,18 @@ NPY_NO_EXPORT int
 aquicksort_float(void *vv, npy_intp *tosort, npy_intp n,
                  void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_float *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::float_tag>((npy_float *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
 aquicksort_double(void *vv, npy_intp *tosort, npy_intp n,
                   void *NPY_UNUSED(varr))
 {
+    if (aquicksort_dispatch((npy_double *)vv, tosort, n)) {
+        return 0;
+    }
     return aquicksort_<npy::double_tag>((npy_double *)vv, tosort, n);
 }
 NPY_NO_EXPORT int
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/selection.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/selection.cpp
index ebe188b719c..450858df9e0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/selection.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/selection.cpp
@@ -423,8 +423,8 @@ static constexpr std::array<arg_map, sizeof...(Tags)>
 make_partition_map(npy::taglist<Tags...>)
 {
     return std::array<arg_map, sizeof...(Tags)>{
-            arg_map{Tags::type_value, &introselect_noarg<Tags>,
-                    &introselect_arg<Tags>}...};
+            arg_map{Tags::type_value, {&introselect_noarg<Tags>},
+                {&introselect_arg<Tags>}}...};
 }
 
 struct partition_t {
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.avx512_skx.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.avx512_skx.cpp
new file mode 100644
index 00000000000..8f86eb2aa8b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.avx512_skx.cpp
@@ -0,0 +1,21 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT AVX512_SKX
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#define NPY__CPU_TARGET_POPCNT
+#define NPY__CPU_TARGET_SSE42
+#define NPY__CPU_TARGET_AVX
+#define NPY__CPU_TARGET_F16C
+#define NPY__CPU_TARGET_FMA3
+#define NPY__CPU_TARGET_AVX2
+#define NPY__CPU_TARGET_AVX512F
+#define NPY__CPU_TARGET_AVX512CD
+#define NPY__CPU_TARGET_AVX512_SKX
+#include "numpy/core/src/npysort/simd_qsort.dispatch.cpp"
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.cpp
new file mode 100644
index 00000000000..86bc64b4360
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.cpp
@@ -0,0 +1,69 @@
+/*@targets
+ * $maxopt $keep_baseline avx512_skx
+ */
+// policy $keep_baseline is used to avoid skip building avx512_skx
+// when its part of baseline features (--cpu-baseline), since
+// 'baseline' option isn't specified within targets.
+
+#include "simd_qsort.hpp"
+
+#if defined(NPY_HAVE_AVX512_SKX) && !defined(_MSC_VER)
+    #include "x86-simd-sort/src/avx512-32bit-qsort.hpp"
+    #include "x86-simd-sort/src/avx512-64bit-qsort.hpp"
+    #include "x86-simd-sort/src/avx512-64bit-argsort.hpp"
+#endif
+
+namespace np { namespace qsort_simd {
+
+#if defined(NPY_HAVE_AVX512_SKX) && !defined(_MSC_VER)
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(int32_t *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(uint32_t *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(int64_t *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(uint64_t *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(float *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(double *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(ArgQSort)(int32_t *arr, npy_intp *arg, npy_intp size)
+{
+    avx512_argsort(arr, reinterpret_cast<int64_t*>(arg), size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(ArgQSort)(uint32_t *arr, npy_intp *arg, npy_intp size)
+{
+    avx512_argsort(arr, reinterpret_cast<int64_t*>(arg), size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(ArgQSort)(int64_t *arr, npy_intp *arg, npy_intp size)
+{
+    avx512_argsort(arr, reinterpret_cast<int64_t*>(arg), size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(ArgQSort)(uint64_t *arr, npy_intp *arg, npy_intp size)
+{
+    avx512_argsort(arr, reinterpret_cast<int64_t*>(arg), size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(ArgQSort)(float *arr, npy_intp *arg, npy_intp size)
+{
+    avx512_argsort(arr, reinterpret_cast<int64_t*>(arg), size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(ArgQSort)(double *arr, npy_intp *arg, npy_intp size)
+{
+    avx512_argsort(arr, reinterpret_cast<int64_t*>(arg), size);
+}
+#endif  // NPY_HAVE_AVX512_SKX
+
+}} // namespace np::simd
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.h
index 4e94f102a3d..4e94f102a3d 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.h
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.dispatch.h
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.hpp
new file mode 100644
index 00000000000..672503155d2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort.hpp
@@ -0,0 +1,20 @@
+#ifndef NUMPY_SRC_COMMON_NPYSORT_SIMD_QSORT_HPP
+#define NUMPY_SRC_COMMON_NPYSORT_SIMD_QSORT_HPP
+
+#include "common.hpp"
+
+namespace np { namespace qsort_simd {
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "simd_qsort.dispatch.h"
+#endif
+NPY_CPU_DISPATCH_DECLARE(template <typename T> void QSort, (T *arr, intptr_t size))
+NPY_CPU_DISPATCH_DECLARE(template <typename T> void ArgQSort, (T *arr, npy_intp* arg, npy_intp size))
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "simd_qsort_16bit.dispatch.h"
+#endif
+NPY_CPU_DISPATCH_DECLARE(template <typename T> void QSort, (T *arr, intptr_t size))
+
+} } // np::qsort_simd
+#endif // NUMPY_SRC_COMMON_NPYSORT_SIMD_QSORT_HPP
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.avx512_icl.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.avx512_icl.cpp
new file mode 100644
index 00000000000..3863e07035d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.avx512_icl.cpp
@@ -0,0 +1,24 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT AVX512_ICL
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#define NPY__CPU_TARGET_POPCNT
+#define NPY__CPU_TARGET_SSE42
+#define NPY__CPU_TARGET_AVX
+#define NPY__CPU_TARGET_F16C
+#define NPY__CPU_TARGET_FMA3
+#define NPY__CPU_TARGET_AVX2
+#define NPY__CPU_TARGET_AVX512F
+#define NPY__CPU_TARGET_AVX512CD
+#define NPY__CPU_TARGET_AVX512_SKX
+#define NPY__CPU_TARGET_AVX512_CLX
+#define NPY__CPU_TARGET_AVX512_CNL
+#define NPY__CPU_TARGET_AVX512_ICL
+#include "numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp"
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp
new file mode 100644
index 00000000000..3f5099758c5
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp
@@ -0,0 +1,39 @@
+/*@targets
+ * $maxopt $keep_baseline avx512_icl avx512_spr
+ */
+// policy $keep_baseline is used to avoid skip building avx512_skx
+// when its part of baseline features (--cpu-baseline), since
+// 'baseline' option isn't specified within targets.
+
+#include "simd_qsort.hpp"
+
+#if defined(NPY_HAVE_AVX512_SPR) && !defined(_MSC_VER)
+    #include "x86-simd-sort/src/avx512fp16-16bit-qsort.hpp"
+#elif defined(NPY_HAVE_AVX512_ICL) && !defined(_MSC_VER)
+    #include "x86-simd-sort/src/avx512-16bit-qsort.hpp"
+#endif
+
+namespace np { namespace qsort_simd {
+
+#if !defined(_MSC_VER)
+#if defined(NPY_HAVE_AVX512_ICL) || defined(NPY_HAVE_AVX512_SPR)
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(Half *arr, intptr_t size)
+{
+#if defined(NPY_HAVE_AVX512_SPR)
+    avx512_qsort(reinterpret_cast<_Float16*>(arr), size);
+#else
+    avx512_qsort_fp16(reinterpret_cast<uint16_t*>(arr), size);
+#endif
+}
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(uint16_t *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+template<> void NPY_CPU_DISPATCH_CURFX(QSort)(int16_t *arr, intptr_t size)
+{
+    avx512_qsort(arr, size);
+}
+#endif // NPY_HAVE_AVX512_ICL || SPR
+#endif // _MSC_VER
+
+}} // namespace np::qsort_simd
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.h
new file mode 100644
index 00000000000..993987e10a1
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/simd_qsort_16bit.dispatch.h
@@ -0,0 +1,19 @@
+// cache_hash:3628888795
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator (distutils/ccompiler_opt.py)
+ */
+#ifndef NPY__CPU_DISPATCH_EXPAND_
+    #define NPY__CPU_DISPATCH_EXPAND_(X) X
+#endif
+#undef NPY__CPU_DISPATCH_BASELINE_CALL
+#undef NPY__CPU_DISPATCH_CALL
+#define NPY__CPU_DISPATCH_BASELINE_CALL(CB, ...) \
+
+#if defined(__x86_64__) || defined(_M_X64)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX512_ICL)), AVX512_ICL, __VA_ARGS__))
+#elif defined(__aarch64__)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/timsort.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/timsort.cpp
index 27294af0c8a..9438fb293d4 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/timsort.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/timsort.cpp
@@ -21,7 +21,7 @@
  *
  * The merge sort is *stable*, meaning that equal components
  * are unmoved from their entry versions, so it can be used to
- * implement lexigraphic sorting on multiple keys.
+ * implement lexicographic sorting on multiple keys.
  *
  * The heap sort is included for completeness.
  */
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.cpp
deleted file mode 100644
index 507f40a625d..00000000000
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.cpp
+++ /dev/null
@@ -1,835 +0,0 @@
-/*@targets
- * $maxopt $keep_baseline avx512_skx
- */
-// policy $keep_baseline is used to avoid skip building avx512_skx
-// when its part of baseline features (--cpu-baseline), since
-// 'baseline' option isn't specified within targets.
-
-#include "x86-qsort.h"
-#define NPY_NO_DEPRECATED_API NPY_API_VERSION
-
-#ifdef NPY_HAVE_AVX512_SKX
-#include "numpy/npy_math.h"
-
-#include "npy_sort.h"
-#include "numpy_tag.h"
-
-#include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
-#include <immintrin.h>
-
-template <typename Tag, typename type>
-NPY_NO_EXPORT int
-heapsort_(type *start, npy_intp n);
-
-/*
- * Quicksort using AVX-512 for int, uint32 and float. The ideas and code are
- * based on these two research papers:
- * (1) Fast and Robust Vectorized In-Place Sorting of Primitive Types
- *     https://drops.dagstuhl.de/opus/volltexte/2021/13775/
- * (2) A Novel Hybrid Quicksort Algorithm Vectorized using AVX-512 on Intel
- * Skylake https://arxiv.org/pdf/1704.08579.pdf
- *
- * High level idea: Vectorize the quicksort partitioning using AVX-512
- * compressstore instructions. The algorithm to pick the pivot is to use median
- * of 72 elements picked at random. If the array size is < 128, then use
- * Bitonic sorting network. Good resource for bitonic sorting network:
- * http://mitp-content-server.mit.edu:18180/books/content/sectbyfn?collid=books_pres_0&fn=Chapter%2027.pdf&id=8030
- *
- * Refer to https://github.com/numpy/numpy/pull/20133#issuecomment-958110340
- * for potential problems when converting this code to universal intrinsics
- * framework.
- */
-
-/*
- * Constants used in sorting 16 elements in a ZMM registers. Based on Bitonic
- * sorting network (see
- * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg)
- */
-#define NETWORK1 14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1
-#define NETWORK2 12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3
-#define NETWORK3 8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7
-#define NETWORK4 13, 12, 15, 14, 9, 8, 11, 10, 5, 4, 7, 6, 1, 0, 3, 2
-#define NETWORK5 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
-#define NETWORK6 11, 10, 9, 8, 15, 14, 13, 12, 3, 2, 1, 0, 7, 6, 5, 4
-#define NETWORK7 7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8
-#define ZMM_MAX_FLOAT _mm512_set1_ps(NPY_INFINITYF)
-#define ZMM_MAX_UINT _mm512_set1_epi32(NPY_MAX_UINT32)
-#define ZMM_MAX_INT _mm512_set1_epi32(NPY_MAX_INT32)
-#define SHUFFLE_MASK(a, b, c, d) (a << 6) | (b << 4) | (c << 2) | d
-#define SHUFFLE_ps(ZMM, MASK) _mm512_shuffle_ps(zmm, zmm, MASK)
-#define SHUFFLE_epi32(ZMM, MASK) _mm512_shuffle_epi32(zmm, MASK)
-
-#define MAX(x, y) (((x) > (y)) ? (x) : (y))
-#define MIN(x, y) (((x) < (y)) ? (x) : (y))
-
-/*
- * Vectorized random number generator xoroshiro128+. Broken into 2 parts:
- * (1) vnext generates 2 64-bit random integers
- * (2) rnd_epu32 converts this to 4 32-bit random integers and bounds it to
- *     the length of the array
- */
-#define VROTL(x, k) /* rotate each uint64_t value in vector */ \
-    _mm256_or_si256(_mm256_slli_epi64((x), (k)),               \
-                    _mm256_srli_epi64((x), 64 - (k)))
-
-static inline __m256i
-vnext(__m256i *s0, __m256i *s1)
-{
-    *s1 = _mm256_xor_si256(*s0, *s1); /* modify vectors s1 and s0 */
-    *s0 = _mm256_xor_si256(_mm256_xor_si256(VROTL(*s0, 24), *s1),
-                           _mm256_slli_epi64(*s1, 16));
-    *s1 = VROTL(*s1, 37);
-    return _mm256_add_epi64(*s0, *s1); /* return random vector */
-}
-
-/* transform random numbers to the range between 0 and bound - 1 */
-static inline __m256i
-rnd_epu32(__m256i rnd_vec, __m256i bound)
-{
-    __m256i even = _mm256_srli_epi64(_mm256_mul_epu32(rnd_vec, bound), 32);
-    __m256i odd = _mm256_mul_epu32(_mm256_srli_epi64(rnd_vec, 32), bound);
-    return _mm256_blend_epi32(odd, even, 0b01010101);
-}
-
-template <typename type>
-struct vector;
-
-template <>
-struct vector<npy_int> {
-    using tag = npy::int_tag;
-    using type_t = npy_int;
-    using zmm_t = __m512i;
-    using ymm_t = __m256i;
-
-    static type_t type_max() { return NPY_MAX_INT32; }
-    static type_t type_min() { return NPY_MIN_INT32; }
-    static zmm_t zmm_max() { return _mm512_set1_epi32(type_max()); }
-
-    static __mmask16 ge(zmm_t x, zmm_t y)
-    {
-        return _mm512_cmp_epi32_mask(x, y, _MM_CMPINT_NLT);
-    }
-    template <int scale>
-    static ymm_t i64gather(__m512i index, void const *base)
-    {
-        return _mm512_i64gather_epi32(index, base, scale);
-    }
-    static zmm_t loadu(void const *mem) { return _mm512_loadu_si512(mem); }
-    static zmm_t max(zmm_t x, zmm_t y) { return _mm512_max_epi32(x, y); }
-    static void mask_compressstoreu(void *mem, __mmask16 mask, zmm_t x)
-    {
-        return _mm512_mask_compressstoreu_epi32(mem, mask, x);
-    }
-    static zmm_t mask_loadu(zmm_t x, __mmask16 mask, void const *mem)
-    {
-        return _mm512_mask_loadu_epi32(x, mask, mem);
-    }
-    static zmm_t mask_mov(zmm_t x, __mmask16 mask, zmm_t y)
-    {
-        return _mm512_mask_mov_epi32(x, mask, y);
-    }
-    static void mask_storeu(void *mem, __mmask16 mask, zmm_t x)
-    {
-        return _mm512_mask_storeu_epi32(mem, mask, x);
-    }
-    static zmm_t min(zmm_t x, zmm_t y) { return _mm512_min_epi32(x, y); }
-    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
-    {
-        return _mm512_permutexvar_epi32(idx, zmm);
-    }
-    static type_t reducemax(zmm_t v) { return npyv_reduce_max_s32(v); }
-    static type_t reducemin(zmm_t v) { return npyv_reduce_min_s32(v); }
-    static zmm_t set1(type_t v) { return _mm512_set1_epi32(v); }
-    template<__mmask16 mask>
-    static zmm_t shuffle(zmm_t zmm)
-    {
-        return _mm512_shuffle_epi32(zmm, (_MM_PERM_ENUM)mask);
-    }
-    static void storeu(void *mem, zmm_t x)
-    {
-        return _mm512_storeu_si512(mem, x);
-    }
-
-    static ymm_t max(ymm_t x, ymm_t y) { return _mm256_max_epi32(x, y); }
-    static ymm_t min(ymm_t x, ymm_t y) { return _mm256_min_epi32(x, y); }
-};
-template <>
-struct vector<npy_uint> {
-    using tag = npy::uint_tag;
-    using type_t = npy_uint;
-    using zmm_t = __m512i;
-    using ymm_t = __m256i;
-
-    static type_t type_max() { return NPY_MAX_UINT32; }
-    static type_t type_min() { return 0; }
-    static zmm_t zmm_max() { return _mm512_set1_epi32(type_max()); }
-
-    template<int scale>
-    static ymm_t i64gather(__m512i index, void const *base)
-    {
-        return _mm512_i64gather_epi32(index, base, scale);
-    }
-    static __mmask16 ge(zmm_t x, zmm_t y)
-    {
-        return _mm512_cmp_epu32_mask(x, y, _MM_CMPINT_NLT);
-    }
-    static zmm_t loadu(void const *mem) { return _mm512_loadu_si512(mem); }
-    static zmm_t max(zmm_t x, zmm_t y) { return _mm512_max_epu32(x, y); }
-    static void mask_compressstoreu(void *mem, __mmask16 mask, zmm_t x)
-    {
-        return _mm512_mask_compressstoreu_epi32(mem, mask, x);
-    }
-    static zmm_t mask_loadu(zmm_t x, __mmask16 mask, void const *mem)
-    {
-        return _mm512_mask_loadu_epi32(x, mask, mem);
-    }
-    static zmm_t mask_mov(zmm_t x, __mmask16 mask, zmm_t y)
-    {
-        return _mm512_mask_mov_epi32(x, mask, y);
-    }
-    static void mask_storeu(void *mem, __mmask16 mask, zmm_t x)
-    {
-        return _mm512_mask_storeu_epi32(mem, mask, x);
-    }
-    static zmm_t min(zmm_t x, zmm_t y) { return _mm512_min_epu32(x, y); }
-    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
-    {
-        return _mm512_permutexvar_epi32(idx, zmm);
-    }
-    static type_t reducemax(zmm_t v) { return npyv_reduce_max_u32(v); }
-    static type_t reducemin(zmm_t v) { return npyv_reduce_min_u32(v); }
-    static zmm_t set1(type_t v) { return _mm512_set1_epi32(v); }
-    template<__mmask16 mask>
-    static zmm_t shuffle(zmm_t zmm)
-    {
-        return _mm512_shuffle_epi32(zmm, (_MM_PERM_ENUM)mask);
-    }
-    static void storeu(void *mem, zmm_t x)
-    {
-        return _mm512_storeu_si512(mem, x);
-    }
-
-    static ymm_t max(ymm_t x, ymm_t y) { return _mm256_max_epu32(x, y); }
-    static ymm_t min(ymm_t x, ymm_t y) { return _mm256_min_epu32(x, y); }
-};
-template <>
-struct vector<npy_float> {
-    using tag = npy::float_tag;
-    using type_t = npy_float;
-    using zmm_t = __m512;
-    using ymm_t = __m256;
-
-    static type_t type_max() { return NPY_INFINITYF; }
-    static type_t type_min() { return -NPY_INFINITYF; }
-    static zmm_t zmm_max() { return _mm512_set1_ps(type_max()); }
-
-    static __mmask16 ge(zmm_t x, zmm_t y)
-    {
-        return _mm512_cmp_ps_mask(x, y, _CMP_GE_OQ);
-    }
-    template<int scale>
-    static ymm_t i64gather(__m512i index, void const *base)
-    {
-        return _mm512_i64gather_ps(index, base, scale);
-    }
-    static zmm_t loadu(void const *mem) { return _mm512_loadu_ps(mem); }
-    static zmm_t max(zmm_t x, zmm_t y) { return _mm512_max_ps(x, y); }
-    static void mask_compressstoreu(void *mem, __mmask16 mask, zmm_t x)
-    {
-        return _mm512_mask_compressstoreu_ps(mem, mask, x);
-    }
-    static zmm_t mask_loadu(zmm_t x, __mmask16 mask, void const *mem)
-    {
-        return _mm512_mask_loadu_ps(x, mask, mem);
-    }
-    static zmm_t mask_mov(zmm_t x, __mmask16 mask, zmm_t y)
-    {
-        return _mm512_mask_mov_ps(x, mask, y);
-    }
-    static void mask_storeu(void *mem, __mmask16 mask, zmm_t x)
-    {
-        return _mm512_mask_storeu_ps(mem, mask, x);
-    }
-    static zmm_t min(zmm_t x, zmm_t y) { return _mm512_min_ps(x, y); }
-    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
-    {
-        return _mm512_permutexvar_ps(idx, zmm);
-    }
-    static type_t reducemax(zmm_t v) { return npyv_reduce_max_f32(v); }
-    static type_t reducemin(zmm_t v) { return npyv_reduce_min_f32(v); }
-    static zmm_t set1(type_t v) { return _mm512_set1_ps(v); }
-    template<__mmask16 mask>
-    static zmm_t shuffle(zmm_t zmm)
-    {
-        return _mm512_shuffle_ps(zmm, zmm, (_MM_PERM_ENUM)mask);
-    }
-    static void storeu(void *mem, zmm_t x) { return _mm512_storeu_ps(mem, x); }
-
-    static ymm_t max(ymm_t x, ymm_t y) { return _mm256_max_ps(x, y); }
-    static ymm_t min(ymm_t x, ymm_t y) { return _mm256_min_ps(x, y); }
-};
-
-/*
- * COEX == Compare and Exchange two registers by swapping min and max values
- */
-template <typename vtype, typename mm_t>
-void
-COEX(mm_t &a, mm_t &b)
-{
-    mm_t temp = a;
-    a = vtype::min(a, b);
-    b = vtype::max(temp, b);
-}
-
-template <typename vtype, typename zmm_t = typename vtype::zmm_t>
-static inline zmm_t
-cmp_merge(zmm_t in1, zmm_t in2, __mmask16 mask)
-{
-    zmm_t min = vtype::min(in2, in1);
-    zmm_t max = vtype::max(in2, in1);
-    return vtype::mask_mov(min, mask, max);  // 0 -> min, 1 -> max
-}
-
-/*
- * Assumes zmm is random and performs a full sorting network defined in
- * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg
- */
-template <typename vtype, typename zmm_t = typename vtype::zmm_t>
-static inline zmm_t
-sort_zmm(zmm_t zmm)
-{
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
-                           0xAAAA);
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(0, 1, 2, 3)>(zmm),
-                           0xCCCC);
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
-                           0xAAAA);
-    zmm = cmp_merge<vtype>(
-            zmm, vtype::permutexvar(_mm512_set_epi32(NETWORK3), zmm), 0xF0F0);
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
-                           0xCCCC);
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
-                           0xAAAA);
-    zmm = cmp_merge<vtype>(
-            zmm, vtype::permutexvar(_mm512_set_epi32(NETWORK5), zmm), 0xFF00);
-    zmm = cmp_merge<vtype>(
-            zmm, vtype::permutexvar(_mm512_set_epi32(NETWORK6), zmm), 0xF0F0);
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
-                           0xCCCC);
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
-                           0xAAAA);
-    return zmm;
-}
-
-// Assumes zmm is bitonic and performs a recursive half cleaner
-template <typename vtype, typename zmm_t = typename vtype::zmm_t>
-static inline zmm_t
-bitonic_merge_zmm(zmm_t zmm)
-{
-    // 1) half_cleaner[16]: compare 1-9, 2-10, 3-11 etc ..
-    zmm = cmp_merge<vtype>(
-            zmm, vtype::permutexvar(_mm512_set_epi32(NETWORK7), zmm), 0xFF00);
-    // 2) half_cleaner[8]: compare 1-5, 2-6, 3-7 etc ..
-    zmm = cmp_merge<vtype>(
-            zmm, vtype::permutexvar(_mm512_set_epi32(NETWORK6), zmm), 0xF0F0);
-    // 3) half_cleaner[4]
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
-                           0xCCCC);
-    // 3) half_cleaner[1]
-    zmm = cmp_merge<vtype>(zmm, vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
-                           0xAAAA);
-    return zmm;
-}
-
-// Assumes zmm1 and zmm2 are sorted and performs a recursive half cleaner
-template <typename vtype, typename zmm_t = typename vtype::zmm_t>
-static inline void
-bitonic_merge_two_zmm(zmm_t *zmm1, zmm_t *zmm2)
-{
-    // 1) First step of a merging network: coex of zmm1 and zmm2 reversed
-    *zmm2 = vtype::permutexvar(_mm512_set_epi32(NETWORK5), *zmm2);
-    zmm_t zmm3 = vtype::min(*zmm1, *zmm2);
-    zmm_t zmm4 = vtype::max(*zmm1, *zmm2);
-    // 2) Recursive half cleaner for each
-    *zmm1 = bitonic_merge_zmm<vtype>(zmm3);
-    *zmm2 = bitonic_merge_zmm<vtype>(zmm4);
-}
-
-// Assumes [zmm0, zmm1] and [zmm2, zmm3] are sorted and performs a recursive
-// half cleaner
-template <typename vtype, typename zmm_t = typename vtype::zmm_t>
-static inline void
-bitonic_merge_four_zmm(zmm_t *zmm)
-{
-    zmm_t zmm2r = vtype::permutexvar(_mm512_set_epi32(NETWORK5), zmm[2]);
-    zmm_t zmm3r = vtype::permutexvar(_mm512_set_epi32(NETWORK5), zmm[3]);
-    zmm_t zmm_t1 = vtype::min(zmm[0], zmm3r);
-    zmm_t zmm_t2 = vtype::min(zmm[1], zmm2r);
-    zmm_t zmm_t3 = vtype::permutexvar(_mm512_set_epi32(NETWORK5),
-                                      vtype::max(zmm[1], zmm2r));
-    zmm_t zmm_t4 = vtype::permutexvar(_mm512_set_epi32(NETWORK5),
-                                      vtype::max(zmm[0], zmm3r));
-    zmm_t zmm0 = vtype::min(zmm_t1, zmm_t2);
-    zmm_t zmm1 = vtype::max(zmm_t1, zmm_t2);
-    zmm_t zmm2 = vtype::min(zmm_t3, zmm_t4);
-    zmm_t zmm3 = vtype::max(zmm_t3, zmm_t4);
-    zmm[0] = bitonic_merge_zmm<vtype>(zmm0);
-    zmm[1] = bitonic_merge_zmm<vtype>(zmm1);
-    zmm[2] = bitonic_merge_zmm<vtype>(zmm2);
-    zmm[3] = bitonic_merge_zmm<vtype>(zmm3);
-}
-
-template <typename vtype, typename zmm_t = typename vtype::zmm_t>
-static inline void
-bitonic_merge_eight_zmm(zmm_t *zmm)
-{
-    zmm_t zmm4r = vtype::permutexvar(_mm512_set_epi32(NETWORK5), zmm[4]);
-    zmm_t zmm5r = vtype::permutexvar(_mm512_set_epi32(NETWORK5), zmm[5]);
-    zmm_t zmm6r = vtype::permutexvar(_mm512_set_epi32(NETWORK5), zmm[6]);
-    zmm_t zmm7r = vtype::permutexvar(_mm512_set_epi32(NETWORK5), zmm[7]);
-    zmm_t zmm_t1 = vtype::min(zmm[0], zmm7r);
-    zmm_t zmm_t2 = vtype::min(zmm[1], zmm6r);
-    zmm_t zmm_t3 = vtype::min(zmm[2], zmm5r);
-    zmm_t zmm_t4 = vtype::min(zmm[3], zmm4r);
-    zmm_t zmm_t5 = vtype::permutexvar(_mm512_set_epi32(NETWORK5),
-                                      vtype::max(zmm[3], zmm4r));
-    zmm_t zmm_t6 = vtype::permutexvar(_mm512_set_epi32(NETWORK5),
-                                      vtype::max(zmm[2], zmm5r));
-    zmm_t zmm_t7 = vtype::permutexvar(_mm512_set_epi32(NETWORK5),
-                                      vtype::max(zmm[1], zmm6r));
-    zmm_t zmm_t8 = vtype::permutexvar(_mm512_set_epi32(NETWORK5),
-                                      vtype::max(zmm[0], zmm7r));
-    COEX<vtype>(zmm_t1, zmm_t3);
-    COEX<vtype>(zmm_t2, zmm_t4);
-    COEX<vtype>(zmm_t5, zmm_t7);
-    COEX<vtype>(zmm_t6, zmm_t8);
-    COEX<vtype>(zmm_t1, zmm_t2);
-    COEX<vtype>(zmm_t3, zmm_t4);
-    COEX<vtype>(zmm_t5, zmm_t6);
-    COEX<vtype>(zmm_t7, zmm_t8);
-    zmm[0] = bitonic_merge_zmm<vtype>(zmm_t1);
-    zmm[1] = bitonic_merge_zmm<vtype>(zmm_t2);
-    zmm[2] = bitonic_merge_zmm<vtype>(zmm_t3);
-    zmm[3] = bitonic_merge_zmm<vtype>(zmm_t4);
-    zmm[4] = bitonic_merge_zmm<vtype>(zmm_t5);
-    zmm[5] = bitonic_merge_zmm<vtype>(zmm_t6);
-    zmm[6] = bitonic_merge_zmm<vtype>(zmm_t7);
-    zmm[7] = bitonic_merge_zmm<vtype>(zmm_t8);
-}
-
-template <typename vtype, typename type_t>
-static inline void
-sort_16(type_t *arr, npy_int N)
-{
-    __mmask16 load_mask = (0x0001 << N) - 0x0001;
-    typename vtype::zmm_t zmm =
-            vtype::mask_loadu(vtype::zmm_max(), load_mask, arr);
-    vtype::mask_storeu(arr, load_mask, sort_zmm<vtype>(zmm));
-}
-
-template <typename vtype, typename type_t>
-static inline void
-sort_32(type_t *arr, npy_int N)
-{
-    if (N <= 16) {
-        sort_16<vtype>(arr, N);
-        return;
-    }
-    using zmm_t = typename vtype::zmm_t;
-    zmm_t zmm1 = vtype::loadu(arr);
-    __mmask16 load_mask = (0x0001 << (N - 16)) - 0x0001;
-    zmm_t zmm2 = vtype::mask_loadu(vtype::zmm_max(), load_mask, arr + 16);
-    zmm1 = sort_zmm<vtype>(zmm1);
-    zmm2 = sort_zmm<vtype>(zmm2);
-    bitonic_merge_two_zmm<vtype>(&zmm1, &zmm2);
-    vtype::storeu(arr, zmm1);
-    vtype::mask_storeu(arr + 16, load_mask, zmm2);
-}
-
-template <typename vtype, typename type_t>
-static inline void
-sort_64(type_t *arr, npy_int N)
-{
-    if (N <= 32) {
-        sort_32<vtype>(arr, N);
-        return;
-    }
-    using zmm_t = typename vtype::zmm_t;
-    zmm_t zmm[4];
-    zmm[0] = vtype::loadu(arr);
-    zmm[1] = vtype::loadu(arr + 16);
-    __mmask16 load_mask1 = 0xFFFF, load_mask2 = 0xFFFF;
-    if (N < 48) {
-        load_mask1 = (0x0001 << (N - 32)) - 0x0001;
-        load_mask2 = 0x0000;
-    }
-    else if (N < 64) {
-        load_mask2 = (0x0001 << (N - 48)) - 0x0001;
-    }
-    zmm[2] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 32);
-    zmm[3] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 48);
-    zmm[0] = sort_zmm<vtype>(zmm[0]);
-    zmm[1] = sort_zmm<vtype>(zmm[1]);
-    zmm[2] = sort_zmm<vtype>(zmm[2]);
-    zmm[3] = sort_zmm<vtype>(zmm[3]);
-    bitonic_merge_two_zmm<vtype>(&zmm[0], &zmm[1]);
-    bitonic_merge_two_zmm<vtype>(&zmm[2], &zmm[3]);
-    bitonic_merge_four_zmm<vtype>(zmm);
-    vtype::storeu(arr, zmm[0]);
-    vtype::storeu(arr + 16, zmm[1]);
-    vtype::mask_storeu(arr + 32, load_mask1, zmm[2]);
-    vtype::mask_storeu(arr + 48, load_mask2, zmm[3]);
-}
-
-template <typename vtype, typename type_t>
-static inline void
-sort_128(type_t *arr, npy_int N)
-{
-    if (N <= 64) {
-        sort_64<vtype>(arr, N);
-        return;
-    }
-    using zmm_t = typename vtype::zmm_t;
-    zmm_t zmm[8];
-    zmm[0] = vtype::loadu(arr);
-    zmm[1] = vtype::loadu(arr + 16);
-    zmm[2] = vtype::loadu(arr + 32);
-    zmm[3] = vtype::loadu(arr + 48);
-    zmm[0] = sort_zmm<vtype>(zmm[0]);
-    zmm[1] = sort_zmm<vtype>(zmm[1]);
-    zmm[2] = sort_zmm<vtype>(zmm[2]);
-    zmm[3] = sort_zmm<vtype>(zmm[3]);
-    __mmask16 load_mask1 = 0xFFFF, load_mask2 = 0xFFFF;
-    __mmask16 load_mask3 = 0xFFFF, load_mask4 = 0xFFFF;
-    if (N < 80) {
-        load_mask1 = (0x0001 << (N - 64)) - 0x0001;
-        load_mask2 = 0x0000;
-        load_mask3 = 0x0000;
-        load_mask4 = 0x0000;
-    }
-    else if (N < 96) {
-        load_mask2 = (0x0001 << (N - 80)) - 0x0001;
-        load_mask3 = 0x0000;
-        load_mask4 = 0x0000;
-    }
-    else if (N < 112) {
-        load_mask3 = (0x0001 << (N - 96)) - 0x0001;
-        load_mask4 = 0x0000;
-    }
-    else {
-        load_mask4 = (0x0001 << (N - 112)) - 0x0001;
-    }
-    zmm[4] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 64);
-    zmm[5] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 80);
-    zmm[6] = vtype::mask_loadu(vtype::zmm_max(), load_mask3, arr + 96);
-    zmm[7] = vtype::mask_loadu(vtype::zmm_max(), load_mask4, arr + 112);
-    zmm[4] = sort_zmm<vtype>(zmm[4]);
-    zmm[5] = sort_zmm<vtype>(zmm[5]);
-    zmm[6] = sort_zmm<vtype>(zmm[6]);
-    zmm[7] = sort_zmm<vtype>(zmm[7]);
-    bitonic_merge_two_zmm<vtype>(&zmm[0], &zmm[1]);
-    bitonic_merge_two_zmm<vtype>(&zmm[2], &zmm[3]);
-    bitonic_merge_two_zmm<vtype>(&zmm[4], &zmm[5]);
-    bitonic_merge_two_zmm<vtype>(&zmm[6], &zmm[7]);
-    bitonic_merge_four_zmm<vtype>(zmm);
-    bitonic_merge_four_zmm<vtype>(zmm + 4);
-    bitonic_merge_eight_zmm<vtype>(zmm);
-    vtype::storeu(arr, zmm[0]);
-    vtype::storeu(arr + 16, zmm[1]);
-    vtype::storeu(arr + 32, zmm[2]);
-    vtype::storeu(arr + 48, zmm[3]);
-    vtype::mask_storeu(arr + 64, load_mask1, zmm[4]);
-    vtype::mask_storeu(arr + 80, load_mask2, zmm[5]);
-    vtype::mask_storeu(arr + 96, load_mask3, zmm[6]);
-    vtype::mask_storeu(arr + 112, load_mask4, zmm[7]);
-}
-
-template <typename type_t>
-static inline void
-swap(type_t *arr, npy_intp ii, npy_intp jj)
-{
-    type_t temp = arr[ii];
-    arr[ii] = arr[jj];
-    arr[jj] = temp;
-}
-
-// Median of 3 strategy
-// template<typename type_t>
-// static inline
-// npy_intp get_pivot_index(type_t *arr, const npy_intp left, const npy_intp
-// right) {
-//    return (rand() % (right + 1 - left)) + left;
-//    //npy_intp middle = ((right-left)/2) + left;
-//    //type_t a = arr[left], b = arr[middle], c = arr[right];
-//    //if ((b >= a && b <= c) || (b <= a && b >= c))
-//    //    return middle;
-//    //if ((a >= b && a <= c) || (a <= b && a >= c))
-//    //    return left;
-//    //else
-//    //    return right;
-//}
-
-/*
- * Picking the pivot: Median of 72 array elements chosen at random.
- */
-
-template <typename vtype, typename type_t>
-static inline type_t
-get_pivot(type_t *arr, const npy_intp left, const npy_intp right)
-{
-    /* seeds for vectorized random number generator */
-    __m256i s0 = _mm256_setr_epi64x(8265987198341093849, 3762817312854612374,
-                                    1324281658759788278, 6214952190349879213);
-    __m256i s1 = _mm256_setr_epi64x(2874178529384792648, 1257248936691237653,
-                                    7874578921548791257, 1998265912745817298);
-    s0 = _mm256_add_epi64(s0, _mm256_set1_epi64x(left));
-    s1 = _mm256_sub_epi64(s1, _mm256_set1_epi64x(right));
-
-    npy_intp arrsize = right - left + 1;
-    __m256i bound =
-            _mm256_set1_epi32(arrsize > INT32_MAX ? INT32_MAX : arrsize);
-    __m512i left_vec = _mm512_set1_epi64(left);
-    __m512i right_vec = _mm512_set1_epi64(right);
-    using ymm_t = typename vtype::ymm_t;
-    ymm_t v[9];
-    /* fill 9 vectors with random numbers */
-    for (npy_int i = 0; i < 9; ++i) {
-        __m256i rand_64 = vnext(&s0, &s1); /* vector with 4 random uint64_t */
-        __m512i rand_32 = _mm512_cvtepi32_epi64(rnd_epu32(
-                rand_64, bound)); /* random numbers between 0 and bound - 1 */
-        __m512i indices;
-        if (i < 5)
-            indices =
-                    _mm512_add_epi64(left_vec, rand_32); /* indices for arr */
-        else
-            indices =
-                    _mm512_sub_epi64(right_vec, rand_32); /* indices for arr */
-
-        v[i] = vtype::template i64gather<sizeof(type_t)>(indices, arr);
-    }
-
-    /* median network for 9 elements */
-    COEX<vtype>(v[0], v[1]);
-    COEX<vtype>(v[2], v[3]);
-    COEX<vtype>(v[4], v[5]);
-    COEX<vtype>(v[6], v[7]);
-    COEX<vtype>(v[0], v[2]);
-    COEX<vtype>(v[1], v[3]);
-    COEX<vtype>(v[4], v[6]);
-    COEX<vtype>(v[5], v[7]);
-    COEX<vtype>(v[0], v[4]);
-    COEX<vtype>(v[1], v[2]);
-    COEX<vtype>(v[5], v[6]);
-    COEX<vtype>(v[3], v[7]);
-    COEX<vtype>(v[1], v[5]);
-    COEX<vtype>(v[2], v[6]);
-    COEX<vtype>(v[3], v[5]);
-    COEX<vtype>(v[2], v[4]);
-    COEX<vtype>(v[3], v[4]);
-    COEX<vtype>(v[3], v[8]);
-    COEX<vtype>(v[4], v[8]);
-
-    // technically v[4] needs to be sorted before we pick the correct median,
-    // picking the 4th element works just as well for performance
-    type_t *temp = (type_t *)&v[4];
-
-    return temp[4];
-}
-
-/*
- * Partition one ZMM register based on the pivot and returns the index of the
- * last element that is less than equal to the pivot.
- */
-template <typename vtype, typename type_t, typename zmm_t>
-static inline npy_int
-partition_vec(type_t *arr, npy_intp left, npy_intp right, const zmm_t curr_vec,
-              const zmm_t pivot_vec, zmm_t *smallest_vec, zmm_t *biggest_vec)
-{
-    /* which elements are larger than the pivot */
-    __mmask16 gt_mask = vtype::ge(curr_vec, pivot_vec);
-    npy_int amount_gt_pivot = _mm_popcnt_u32((npy_int)gt_mask);
-    vtype::mask_compressstoreu(arr + left, _mm512_knot(gt_mask), curr_vec);
-    vtype::mask_compressstoreu(arr + right - amount_gt_pivot, gt_mask,
-                               curr_vec);
-    *smallest_vec = vtype::min(curr_vec, *smallest_vec);
-    *biggest_vec = vtype::max(curr_vec, *biggest_vec);
-    return amount_gt_pivot;
-}
-
-/*
- * Partition an array based on the pivot and returns the index of the
- * last element that is less than equal to the pivot.
- */
-template <typename vtype, typename type_t>
-static inline npy_intp
-partition_avx512(type_t *arr, npy_intp left, npy_intp right, type_t pivot,
-                 type_t *smallest, type_t *biggest)
-{
-    /* make array length divisible by 16 , shortening the array */
-    for (npy_int i = (right - left) % 16; i > 0; --i) {
-        *smallest = MIN(*smallest, arr[left]);
-        *biggest = MAX(*biggest, arr[left]);
-        if (arr[left] > pivot) {
-            swap(arr, left, --right);
-        }
-        else {
-            ++left;
-        }
-    }
-
-    if (left == right)
-        return left; /* less than 16 elements in the array */
-
-    using zmm_t = typename vtype::zmm_t;
-    zmm_t pivot_vec = vtype::set1(pivot);
-    zmm_t min_vec = vtype::set1(*smallest);
-    zmm_t max_vec = vtype::set1(*biggest);
-
-    if (right - left == 16) {
-        zmm_t vec = vtype::loadu(arr + left);
-        npy_int amount_gt_pivot = partition_vec<vtype>(
-                arr, left, left + 16, vec, pivot_vec, &min_vec, &max_vec);
-        *smallest = vtype::reducemin(min_vec);
-        *biggest = vtype::reducemax(max_vec);
-        return left + (16 - amount_gt_pivot);
-    }
-
-    // first and last 16 values are partitioned at the end
-    zmm_t vec_left = vtype::loadu(arr + left);
-    zmm_t vec_right = vtype::loadu(arr + (right - 16));
-    // store points of the vectors
-    npy_intp r_store = right - 16;
-    npy_intp l_store = left;
-    // indices for loading the elements
-    left += 16;
-    right -= 16;
-    while (right - left != 0) {
-        zmm_t curr_vec;
-        /*
-         * if fewer elements are stored on the right side of the array,
-         * then next elements are loaded from the right side,
-         * otherwise from the left side
-         */
-        if ((r_store + 16) - right < left - l_store) {
-            right -= 16;
-            curr_vec = vtype::loadu(arr + right);
-        }
-        else {
-            curr_vec = vtype::loadu(arr + left);
-            left += 16;
-        }
-        // partition the current vector and save it on both sides of the array
-        npy_int amount_gt_pivot =
-                partition_vec<vtype>(arr, l_store, r_store + 16, curr_vec,
-                                     pivot_vec, &min_vec, &max_vec);
-        ;
-        r_store -= amount_gt_pivot;
-        l_store += (16 - amount_gt_pivot);
-    }
-
-    /* partition and save vec_left and vec_right */
-    npy_int amount_gt_pivot =
-            partition_vec<vtype>(arr, l_store, r_store + 16, vec_left,
-                                 pivot_vec, &min_vec, &max_vec);
-    l_store += (16 - amount_gt_pivot);
-    amount_gt_pivot =
-            partition_vec<vtype>(arr, l_store, l_store + 16, vec_right,
-                                 pivot_vec, &min_vec, &max_vec);
-    l_store += (16 - amount_gt_pivot);
-    *smallest = vtype::reducemin(min_vec);
-    *biggest = vtype::reducemax(max_vec);
-    return l_store;
-}
-
-template <typename vtype, typename type_t>
-static inline void
-qsort_(type_t *arr, npy_intp left, npy_intp right, npy_int max_iters)
-{
-    /*
-     * Resort to heapsort if quicksort isn't making any progress
-     */
-    if (max_iters <= 0) {
-        heapsort_<typename vtype::tag>(arr + left, right + 1 - left);
-        return;
-    }
-    /*
-     * Base case: use bitonic networks to sort arrays <= 128
-     */
-    if (right + 1 - left <= 128) {
-        sort_128<vtype>(arr + left, (npy_int)(right + 1 - left));
-        return;
-    }
-
-    type_t pivot = get_pivot<vtype>(arr, left, right);
-    type_t smallest = vtype::type_max();
-    type_t biggest = vtype::type_min();
-    npy_intp pivot_index = partition_avx512<vtype>(arr, left, right + 1, pivot,
-                                                   &smallest, &biggest);
-    if (pivot != smallest)
-        qsort_<vtype>(arr, left, pivot_index - 1, max_iters - 1);
-    if (pivot != biggest)
-        qsort_<vtype>(arr, pivot_index, right, max_iters - 1);
-}
-
-static inline npy_intp
-replace_nan_with_inf(npy_float *arr, npy_intp arrsize)
-{
-    npy_intp nan_count = 0;
-    __mmask16 loadmask = 0xFFFF;
-    while (arrsize > 0) {
-        if (arrsize < 16) {
-            loadmask = (0x0001 << arrsize) - 0x0001;
-        }
-        __m512 in_zmm = _mm512_maskz_loadu_ps(loadmask, arr);
-        __mmask16 nanmask = _mm512_cmp_ps_mask(in_zmm, in_zmm, _CMP_NEQ_UQ);
-        nan_count += _mm_popcnt_u32((npy_int)nanmask);
-        _mm512_mask_storeu_ps(arr, nanmask, ZMM_MAX_FLOAT);
-        arr += 16;
-        arrsize -= 16;
-    }
-    return nan_count;
-}
-
-static inline void
-replace_inf_with_nan(npy_float *arr, npy_intp arrsize, npy_intp nan_count)
-{
-    for (npy_intp ii = arrsize - 1; nan_count > 0; --ii) {
-        arr[ii] = NPY_NANF;
-        nan_count -= 1;
-    }
-}
-
-/***************************************
- * C > C++ dispatch
- ***************************************/
-
-NPY_NO_EXPORT void
-NPY_CPU_DISPATCH_CURFX(x86_quicksort_int)(void *arr, npy_intp arrsize)
-{
-    if (arrsize > 1) {
-        qsort_<vector<npy_int>, npy_int>((npy_int *)arr, 0, arrsize - 1,
-                                         2 * (npy_int)log2(arrsize));
-    }
-}
-
-NPY_NO_EXPORT void
-NPY_CPU_DISPATCH_CURFX(x86_quicksort_uint)(void *arr, npy_intp arrsize)
-{
-    if (arrsize > 1) {
-        qsort_<vector<npy_uint>, npy_uint>((npy_uint *)arr, 0, arrsize - 1,
-                                           2 * (npy_int)log2(arrsize));
-    }
-}
-
-NPY_NO_EXPORT void
-NPY_CPU_DISPATCH_CURFX(x86_quicksort_float)(void *arr, npy_intp arrsize)
-{
-    if (arrsize > 1) {
-        npy_intp nan_count = replace_nan_with_inf((npy_float *)arr, arrsize);
-        qsort_<vector<npy_float>, npy_float>((npy_float *)arr, 0, arrsize - 1,
-                                             2 * (npy_int)log2(arrsize));
-        replace_inf_with_nan((npy_float *)arr, arrsize, nan_count);
-    }
-}
-
-#endif  // NPY_HAVE_AVX512_SKX
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.h
deleted file mode 100644
index 6340e2bc7c3..00000000000
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.h
+++ /dev/null
@@ -1,28 +0,0 @@
-#include "numpy/npy_common.h"
-
-#include "npy_cpu_dispatch.h"
-
-#ifndef NPY_NO_EXPORT
-#define NPY_NO_EXPORT NPY_VISIBILITY_HIDDEN
-#endif
-
-#ifndef NPY_DISABLE_OPTIMIZATION
-#include "x86-qsort.dispatch.h"
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void x86_quicksort_int,
-                         (void *start, npy_intp num))
-
-NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void x86_quicksort_uint,
-                         (void *start, npy_intp num))
-
-NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void x86_quicksort_float,
-                         (void *start, npy_intp num))
-
-#ifdef __cplusplus
-}
-#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/.github/workflows/build-numpy.yml b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/.github/workflows/build-numpy.yml
new file mode 100644
index 00000000000..19cf006ff68
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/.github/workflows/build-numpy.yml
@@ -0,0 +1,67 @@
+name: NumPy
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+  schedule:
+  - cron: '0 5 * * *'
+
+jobs:
+  NumPyMultiarrayTests:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout x86-simd-sort
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        path: x86-simd-sort
+
+    - name: Specify branch name
+      working-directory: ${{ github.workspace }}/x86-simd-sort
+      run: git switch -c pr-branch
+
+    - name: Install build dependencies
+      run: |
+        sudo apt update
+        sudo apt -y install g++-12 gcc-12 git
+
+    - name: Checkout NumPy main
+      uses: actions/checkout@v3
+      with:
+        repository: numpy/numpy
+        submodules: recursive
+        fetch-depth: 0
+        ref: main
+        path: numpy
+
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+
+    - name: Install NumPy dependencies
+      working-directory: ${{ github.workspace }}/numpy
+      run: |
+        pip install -r build_requirements.txt
+        pip install -r test_requirements.txt
+
+    - name: Update x86-simd-sort
+      working-directory: ${{ github.workspace }}/numpy
+      run: |
+        cd numpy/core/src/npysort/x86-simd-sort
+        git remote add temp ${{ github.workspace }}/x86-simd-sort
+        git fetch temp
+        git checkout temp/pr-branch
+
+    - name: Build & run NumPy sort tests
+      working-directory: ${{ github.workspace }}/numpy
+      env:
+        CXX: g++-12
+        CC: gcc-12
+      run: |
+        python runtests.py -t numpy/core/tests/test_multiarray.py
+
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/.github/workflows/c-cpp.yml b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/.github/workflows/c-cpp.yml
new file mode 100644
index 00000000000..27519b7666a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/.github/workflows/c-cpp.yml
@@ -0,0 +1,97 @@
+name: Tests and benchmarks
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  BasicBuildAndTest:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Install dependencies
+      run: |
+        sudo apt update
+        sudo apt -y install g++-12 g++-10 libgtest-dev meson curl git cmake
+
+    - name: Install google benchmarks
+      run: |
+        git clone https://github.com/google/benchmark.git
+        cd benchmark
+        cmake -E make_directory "build"
+        cmake -E chdir "build" cmake -DBENCHMARK_ENABLE_GTEST_TESTS=OFF -DBENCHMARK_ENABLE_TESTING=OFF -DCMAKE_BUILD_TYPE=Release ../
+        sudo cmake --build "build" --config Release --target install
+
+    - name: Install Intel SDE
+      run: |
+        curl -o /tmp/sde.tar.xz https://downloadmirror.intel.com/732268/sde-external-9.7.0-2022-05-09-lin.tar.xz
+        mkdir /tmp/sde && tar -xvf /tmp/sde.tar.xz -C /tmp/sde/
+        sudo mv /tmp/sde/* /opt/sde && sudo ln -s /opt/sde/sde64 /usr/bin/sde
+
+    - name: Build with gcc-10
+      env:
+        CXX: g++-10
+      run: |
+        make clean
+        meson setup --warnlevel 0 --buildtype plain builddir
+        cd builddir
+        ninja
+
+    - name: Run test suite on SKX
+      run: sde -skx -- ./builddir/testexe
+
+    - name: Build with gcc-12
+      env:
+        CXX: g++-12
+      run: |
+        make clean
+        meson setup --warnlevel 0 --buildtype plain builddir
+        cd builddir
+        ninja
+
+    - name: Run test suite on SPR
+      run: sde -spr -- ./builddir/testexe
+
+  compare-benchmarks-with-main:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        path: x86-simd-sort
+
+    - name: Specify branch name
+      working-directory: ${{ github.workspace }}/x86-simd-sort
+      run: git switch -c pr-branch
+
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.9'
+
+    - name: Install dependencies
+      run: |
+        sudo apt update
+        sudo apt -y install g++-12 libgtest-dev meson curl git cmake
+
+    - name: Install google benchmarks
+      run: |
+        git clone https://github.com/google/benchmark.git
+        cd benchmark
+        pip3 install -r requirements.txt
+        cmake -E make_directory "build"
+        cmake -E chdir "build" cmake -DBENCHMARK_ENABLE_GTEST_TESTS=OFF -DBENCHMARK_ENABLE_TESTING=OFF -DCMAKE_BUILD_TYPE=Release ../
+        sudo cmake --build "build" --config Release --target install
+
+    - name: Run bench-compare
+      working-directory: ${{ github.workspace }}/x86-simd-sort
+      env:
+        CXX: g++-12
+        GBENCH: ${{ github.workspace }}/benchmark
+      run: bash -x bench-compare.sh
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/CODE_OF_CONDUCT.md b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/CODE_OF_CONDUCT.md
new file mode 100644
index 00000000000..58dba18db6d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/CODE_OF_CONDUCT.md
@@ -0,0 +1,131 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+CommunityCodeOfConduct AT intel DOT com.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
+
+[homepage]: https://www.contributor-covenant.org
+[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
+[Mozilla CoC]: https://github.com/mozilla/diversity
+[FAQ]: https://www.contributor-covenant.org/faq
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/CONTRIBUTING.md b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/CONTRIBUTING.md
new file mode 100644
index 00000000000..9568cf5562a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/CONTRIBUTING.md
@@ -0,0 +1,62 @@
+# Contributing
+
+### License
+
+x86-simd-sort is licensed under the terms in
+[LICENSE]<https://github.com/intel/x86-simd-sort/blob/main/LICENSE.md>. By
+contributing to the project, you agree to the license and copyright terms
+therein and release your contribution under these terms.
+
+### Sign your work
+
+Please use the sign-off line at the end of the patch. Your signature certifies
+that you wrote the patch or otherwise have the right to pass it on as an
+open-source patch. The rules are pretty simple: if you can certify the below
+(from [developercertificate.org](http://developercertificate.org/)):
+
+```
+Developer Certificate of Origin
+Version 1.1
+
+Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
+660 York Street, Suite 102,
+San Francisco, CA 94110 USA
+
+Everyone is permitted to copy and distribute verbatim copies of this
+license document, but changing it is not allowed.
+
+Developer's Certificate of Origin 1.1
+
+By making a contribution to this project, I certify that:
+
+(a) The contribution was created in whole or in part by me and I
+    have the right to submit it under the open source license
+    indicated in the file; or
+
+(b) The contribution is based upon previous work that, to the best
+    of my knowledge, is covered under an appropriate open source
+    license and I have the right under that license to submit that
+    work with modifications, whether created in whole or in part
+    by me, under the same open source license (unless I am
+    permitted to submit under a different license), as indicated
+    in the file; or
+
+(c) The contribution was provided directly to me by some other
+    person who certified (a), (b) or (c) and I have not modified
+    it.
+
+(d) I understand and agree that this project and the contribution
+    are public and that a record of the contribution (including all
+    personal information I submit with it, including my sign-off) is
+    maintained indefinitely and may be redistributed consistent with
+    this project or the open source license(s) involved.
+```
+
+Then you just add a line to every git commit message:
+
+    Signed-off-by: Joe Smith <joe.smith@email.com>
+
+Use your real name (sorry, no pseudonyms or anonymous contributions.)
+
+If you set your `user.name` and `user.email` git configs, you can sign your
+commit automatically with `git commit -s`.
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/LICENSE.md b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/LICENSE.md
new file mode 100644
index 00000000000..3e32165ed47
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/LICENSE.md
@@ -0,0 +1,28 @@
+BSD 3-Clause License
+
+Copyright (c) 2022, Intel. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/Makefile b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/Makefile
new file mode 100644
index 00000000000..52a1c60dd51
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/Makefile
@@ -0,0 +1,51 @@
+CXX		?= g++-12
+CXXFLAGS	+= -I$(SRCDIR) -I$(UTILS) -O3
+GTESTCFLAGS	= `pkg-config --cflags gtest_main`
+GTESTLDFLAGS	= `pkg-config --static --libs gtest_main`
+GBENCHCFLAGS	= `pkg-config --cflags benchmark`
+GBENCHLDFLAGS	= `pkg-config --static --libs benchmark`
+MARCHFLAG	= -march=sapphirerapids
+
+SRCDIR		= ./src
+TESTDIR		= ./tests
+BENCHDIR	= ./benchmarks
+UTILS		= ./utils
+SRCS		= $(wildcard $(SRCDIR)/*.hpp)
+TESTS		= $(wildcard $(TESTDIR)/*.cpp)
+BENCHS		= $(wildcard $(BENCHDIR)/*.cpp)
+TESTOBJS	= $(patsubst $(TESTDIR)/%.cpp,$(TESTDIR)/%.o,$(TESTS))
+BENCHOBJS	= $(patsubst $(BENCHDIR)/%.cpp,$(BENCHDIR)/%.o,$(BENCHS))
+
+# Compiling AVX512-FP16 instructions isn't possible for g++ < 12
+ifeq ($(shell expr `$(CXX) -dumpversion | cut -d '.' -f 1` \< 12), 1)
+	MARCHFLAG = -march=icelake-client
+	BENCHOBJS_SKIP += bench-qsortfp16.o
+	TESTOBJS_SKIP += test-qsortfp16.o
+endif
+
+BENCHOBJS	:= $(filter-out $(addprefix $(BENCHDIR)/, $(BENCHOBJS_SKIP)) ,$(BENCHOBJS))
+TESTOBJS	:= $(filter-out $(addprefix $(TESTDIR)/, $(TESTOBJS_SKIP)) ,$(TESTOBJS))
+
+all : test bench
+
+$(UTILS)/cpuinfo.o : $(UTILS)/cpuinfo.cpp
+	$(CXX) $(CXXFLAGS) -c $(UTILS)/cpuinfo.cpp -o $(UTILS)/cpuinfo.o
+
+$(TESTDIR)/%.o : $(TESTDIR)/%.cpp $(SRCS)
+	$(CXX) $(CXXFLAGS) $(MARCHFLAG) $(GTESTCFLAGS) -c $< -o $@
+
+test: $(TESTOBJS) $(UTILS)/cpuinfo.o $(SRCS)
+	$(CXX) $(TESTOBJS) $(UTILS)/cpuinfo.o $(MARCHFLAG) $(CXXFLAGS) -lgtest_main $(GTESTLDFLAGS) -o testexe
+
+$(BENCHDIR)/%.o : $(BENCHDIR)/%.cpp $(SRCS)
+	$(CXX) $(CXXFLAGS) $(MARCHFLAG) $(GBENCHCFLAGS) -c $< -o $@
+
+bench: $(BENCHOBJS) $(UTILS)/cpuinfo.o
+	$(CXX) $(BENCHOBJS) $(UTILS)/cpuinfo.o $(MARCHFLAG) $(CXXFLAGS) -lbenchmark_main $(GBENCHLDFLAGS) -o benchexe
+
+meson:
+	meson setup --warnlevel 0 --buildtype plain builddir
+	cd builddir && ninja
+
+clean:
+	$(RM) -rf $(TESTDIR)/*.o $(BENCHDIR)/*.o $(UTILS)/*.o testexe benchexe builddir
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/README.md b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/README.md
new file mode 100644
index 00000000000..a28838a9aa3
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/README.md
@@ -0,0 +1,117 @@
+# x86-simd-sort
+
+C++ header file library for SIMD based 16-bit, 32-bit and 64-bit data type
+sorting on x86 processors. Source header files are available in src directory.
+We currently only have AVX-512 based implementation of quicksort. This
+repository also includes a test suite which can be built and run to test the
+sorting algorithms for correctness. It also has benchmarking code to compare
+its performance relative to std::sort.
+
+## Algorithm details
+
+The ideas and code are based on these two research papers [1] and [2]. On a
+high level, the idea is to vectorize quicksort partitioning using AVX-512
+compressstore instructions. If the array size is < 128, then use Bitonic
+sorting network implemented on 512-bit registers.  The precise network
+definitions depend on the size of the dtype and are defined in separate files:
+`avx512-16bit-qsort.hpp`, `avx512-32bit-qsort.hpp` and
+`avx512-64bit-qsort.hpp`. Article [4] is a good resource for bitonic sorting
+network. The core implementations of the vectorized qsort functions
+`avx512_qsort<T>(T*, int64_t)` are modified versions of avx2 quicksort
+presented in the paper [2] and source code associated with that paper [3].
+
+## Handling NAN in float and double arrays
+
+If you expect your array to contain NANs, please be aware that the these
+routines **do not preserve your NANs as you pass them**. The
+`avx512_qsort<T>()` routine will put all your NAN's at the end of the sorted
+array and replace them with `std::nan("1")`. Please take a look at
+`avx512_qsort<float>()` and `avx512_qsort<double>()` functions for details.
+
+## Example to include and build this in a C++ code
+
+### Sample code `main.cpp`
+
+```cpp
+#include "src/avx512-32bit-qsort.hpp"
+
+int main() {
+    const int ARRSIZE = 10;
+    std::vector<float> arr;
+
+    /* Initialize elements is reverse order */
+    for (int ii = 0; ii < ARRSIZE; ++ii) {
+        arr.push_back(ARRSIZE - ii);
+    }
+
+    /* call avx512 quicksort */
+    avx512_qsort<float>(arr.data(), ARRSIZE);
+    return 0;
+}
+
+```
+
+### Build using gcc
+
+```
+gcc main.cpp -mavx512f -mavx512dq -O3
+```
+
+This is a header file only library and we do not provide any compile time and
+run time checks which is recommended while including this your source code. A
+slightly modified version of this source code has been contributed to
+[NumPy](https://github.com/numpy/numpy) (see this [pull
+request](https://github.com/numpy/numpy/pull/22315) for details). This NumPy
+pull request is a good reference for how to include and build this library with
+your source code.
+
+## Build requirements
+
+None, its header files only. However you will need `make` or `meson` to build
+the unit tests and benchmarking suite. You will need a relatively modern
+compiler to build.
+
+```
+gcc >= 8.x
+```
+
+### Build using Make
+
+`make` command builds two executables:
+- `testexe`: runs a bunch of tests written in ./tests directory.
+- `benchexe`: measures performance of these algorithms for various data types
+  and compares them to std::sort.
+
+You can use `make test` and `make bench` to build just the `testexe` and
+`benchexe` respectively.
+
+### Build using Meson
+
+You can also build `testexe` and `benchexe` using Meson/Ninja with the following
+command:
+
+```
+meson setup builddir && cd builddir && ninja
+```
+
+## Requirements and dependencies
+
+The sorting routines relies only on the C++ Standard Library and requires a
+relatively modern compiler to build (gcc 8.x and above). Since they use the
+AVX-512 instruction set, they can only run on processors that have AVX-512.
+Specifically, the 32-bit and 64-bit require AVX-512F and AVX-512DQ instruction
+set. The 16-bit sorting requires the AVX-512F, AVX-512BW and AVX-512 VMBI2
+instruction set. The test suite is written using the Google test framework.
+
+## References
+
+* [1] Fast and Robust Vectorized In-Place Sorting of Primitive Types
+    https://drops.dagstuhl.de/opus/volltexte/2021/13775/
+
+* [2] A Novel Hybrid Quicksort Algorithm Vectorized using AVX-512 on Intel
+Skylake https://arxiv.org/pdf/1704.08579.pdf
+
+* [3] https://github.com/simd-sorting/fast-and-robust: SPDX-License-Identifier: MIT
+
+* [4] http://mitp-content-server.mit.edu:18180/books/content/sectbyfn?collid=books_pres_0&fn=Chapter%2027.pdf&id=8030
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/_clang-format b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/_clang-format
new file mode 100644
index 00000000000..30f080641dc
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/_clang-format
@@ -0,0 +1,98 @@
+---
+Language:        Cpp
+AccessModifierOffset: -4
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: false
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlines: DontAlign
+AlignOperands:   false
+AlignTrailingComments: false
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortBlocksOnASingleLine: true
+AllowShortCaseLabelsOnASingleLine: true
+AllowShortFunctionsOnASingleLine: Empty
+AllowShortIfStatementsOnASingleLine: true
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterDefinitionReturnType: None
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: Yes
+BinPackArguments: false
+BinPackParameters: false
+BraceWrapping:
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   true
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  AfterExternBlock: false
+  BeforeCatch:     false
+  BeforeElse:      true
+  IndentBraces:    false
+  SplitEmptyFunction: true
+  SplitEmptyRecord: true
+  SplitEmptyNamespace: true
+BreakBeforeBinaryOperators: All
+BreakBeforeBraces: Custom
+BreakBeforeInheritanceComma: false
+BreakInheritanceList: BeforeColon
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializers: BeforeComma
+BreakAfterJavaFieldAnnotations: false
+BreakStringLiterals: true
+ColumnLimit:     80
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 8
+Cpp11BracedListStyle: true
+DerivePointerAlignment: false
+FixNamespaceComments: true
+ForEachMacros:
+IncludeBlocks:   Preserve
+IndentCaseLabels: true
+# IndentPPDirectives: AfterHash
+IndentPPDirectives: None
+IndentWidth:     4
+IndentWrappedFunctionNames: false
+KeepEmptyLinesAtTheStartOfBlocks: true
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 19
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 60
+PointerAlignment: Right
+ReflowComments:  false
+SortIncludes:    true
+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: true
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCpp11BracedList: true
+SpaceBeforeCtorInitializerColon: true
+SpaceBeforeInheritanceColon: true
+SpaceBeforeParens: ControlStatements
+SpaceBeforeRangeBasedForLoopColon: true
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 1
+SpacesInAngles:  false
+SpacesInContainerLiterals: false
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp11
+TabWidth:        4
+UseTab:          Never
+...
+# vim:ft=conf et ts=2 sw=2
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/bench-compare.sh b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/bench-compare.sh
new file mode 100644
index 00000000000..7b8da319961
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/bench-compare.sh
@@ -0,0 +1,38 @@
+#~/bin/bash
+set -e
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+cd $SCRIPT_DIR
+branch=$(git rev-parse --abbrev-ref HEAD)
+echo "Comparing main branch with $branch"
+
+if [[ -z "${GBENCH}" ]]; then
+    echo "Please set env variable GBENCH and re run"
+    exit 1
+fi
+
+compare=$GBENCH/tools/compare.py
+if [ ! -f $compare ]; then
+    echo "Unable to locate $GBENCH/tools/compare.py"
+    exit 1
+fi
+
+rm -rf .bench-compare
+mkdir .bench-compare
+cd .bench-compare
+echo "Fetching and build $branch .."
+git clone ${SCRIPT_DIR} -b $branch .
+git fetch origin
+meson setup --warnlevel 0 --buildtype plain builddir-${branch}
+cd builddir-${branch}
+ninja
+echo "Fetching and build main .."
+cd ..
+git remote add upstream https://github.com/intel/x86-simd-sort.git
+git fetch upstream
+git checkout upstream/main
+meson setup --warnlevel 0 --buildtype plain builddir-main
+cd builddir-main
+ninja
+cd ..
+echo "Running benchmarks .."
+$compare benchmarks ./builddir-main/benchexe ./builddir-${branch}/benchexe
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-argsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-argsort.hpp
new file mode 100644
index 00000000000..24ffea76e27
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-argsort.hpp
@@ -0,0 +1,96 @@
+#include "bench-qsort-common.h"
+
+template <typename T>
+std::vector<int64_t> stdargsort(const std::vector<T> &array)
+{
+    std::vector<int64_t> indices(array.size());
+    std::iota(indices.begin(), indices.end(), 0);
+    std::sort(indices.begin(),
+              indices.end(),
+              [&array](int64_t left, int64_t right) -> bool {
+                  // sort indices according to corresponding array element
+                  return array[left] < array[right];
+              });
+
+    return indices;
+}
+
+template <typename T, class... Args>
+static void stdargsort(benchmark::State &state, Args &&...args)
+{
+    auto args_tuple = std::make_tuple(std::move(args)...);
+    // Perform setup here
+    size_t ARRSIZE = std::get<0>(args_tuple);
+    std::vector<T> arr;
+    std::vector<int64_t> inx;
+
+    std::string arrtype = std::get<1>(args_tuple);
+    if (arrtype == "random") { arr = get_uniform_rand_array<T>(ARRSIZE); }
+    else if (arrtype == "sorted") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+    }
+    else if (arrtype == "constant") {
+        T temp = get_uniform_rand_array<T>(1)[0];
+        for (size_t ii = 0; ii < ARRSIZE; ++ii) {
+            arr.push_back(temp);
+        }
+    }
+    else if (arrtype == "reverse") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+        std::reverse(arr.begin(), arr.end());
+    }
+
+    /* call avx512 quicksort */
+    for (auto _ : state) {
+        inx = stdargsort(arr);
+    }
+}
+
+template <typename T, class... Args>
+static void avx512argsort(benchmark::State &state, Args &&...args)
+{
+    auto args_tuple = std::make_tuple(std::move(args)...);
+    if (!cpu_has_avx512bw()) {
+        state.SkipWithMessage("Requires AVX512 BW ISA");
+    }
+    // Perform setup here
+    size_t ARRSIZE = std::get<0>(args_tuple);
+    std::vector<T> arr;
+    std::vector<int64_t> inx;
+
+    std::string arrtype = std::get<1>(args_tuple);
+    if (arrtype == "random") { arr = get_uniform_rand_array<T>(ARRSIZE); }
+    else if (arrtype == "sorted") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+    }
+    else if (arrtype == "constant") {
+        T temp = get_uniform_rand_array<T>(1)[0];
+        for (size_t ii = 0; ii < ARRSIZE; ++ii) {
+            arr.push_back(temp);
+        }
+    }
+    else if (arrtype == "reverse") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+        std::reverse(arr.begin(), arr.end());
+    }
+
+    /* call avx512 quicksort */
+    for (auto _ : state) {
+        inx = avx512_argsort<T>(arr.data(), ARRSIZE);
+    }
+}
+
+#define BENCH_BOTH(type)\
+    BENCH(avx512argsort, type)\
+    BENCH(stdargsort, type)\
+
+BENCH_BOTH(int64_t)
+BENCH_BOTH(uint64_t)
+BENCH_BOTH(double)
+BENCH_BOTH(int32_t)
+BENCH_BOTH(uint32_t)
+BENCH_BOTH(float)
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-partial-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-partial-qsort.hpp
new file mode 100644
index 00000000000..2560107d311
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-partial-qsort.hpp
@@ -0,0 +1,102 @@
+#include "bench-qsort-common.h"
+
+template <typename T>
+static void avx512_partial_qsort(benchmark::State &state)
+{
+    if (!cpu_has_avx512bw()) {
+        state.SkipWithMessage("Requires AVX512 BW ISA");
+    }
+    if ((sizeof(T) == 2) && (!cpu_has_avx512_vbmi2())) {
+        state.SkipWithMessage("Requires AVX512 VBMI2 ISA");
+    }
+    // Perform setup here
+    int64_t K = state.range(0);
+    size_t ARRSIZE = 10000;
+    std::vector<T> arr;
+    std::vector<T> arr_bkp;
+
+    /* Initialize elements */
+    arr = get_uniform_rand_array<T>(ARRSIZE);
+    arr_bkp = arr;
+
+    /* call avx512_partial_qsort */
+    for (auto _ : state) {
+        avx512_partial_qsort<T>(arr.data(), K, ARRSIZE);
+
+        state.PauseTiming();
+        arr = arr_bkp;
+        state.ResumeTiming();
+    }
+}
+
+template <typename T>
+static void stdpartialsort(benchmark::State &state)
+{
+    // Perform setup here
+    int64_t K = state.range(0);
+    size_t ARRSIZE = 10000;
+    std::vector<T> arr;
+    std::vector<T> arr_bkp;
+
+    /* Initialize elements */
+    arr = get_uniform_rand_array<T>(ARRSIZE);
+    arr_bkp = arr;
+
+    /* call std::partial_sort */
+    for (auto _ : state) {
+        std::partial_sort(arr.begin(), arr.begin() + K, arr.end());
+
+        state.PauseTiming();
+        arr = arr_bkp;
+        state.ResumeTiming();
+    }
+}
+
+// Register the function as a benchmark
+BENCHMARK(avx512_partial_qsort<float>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdpartialsort<float>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_partial_qsort<uint32_t>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<uint32_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_partial_qsort<int32_t>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<int32_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+
+BENCHMARK(avx512_partial_qsort<double>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<double>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_partial_qsort<uint64_t>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<uint64_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_partial_qsort<int64_t>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<int64_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+
+//BENCHMARK(avx512_partial_qsort<float16>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_partial_qsort<uint16_t>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<uint16_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_partial_qsort<int16_t>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<int16_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qselect.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qselect.hpp
new file mode 100644
index 00000000000..0f6ad8c709f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qselect.hpp
@@ -0,0 +1,74 @@
+#include "bench-qsort-common.h"
+
+template <typename T>
+static void avx512_qselect(benchmark::State &state)
+{
+    if (!cpu_has_avx512bw()) {
+        state.SkipWithMessage("Requires AVX512 BW ISA");
+    }
+    if ((sizeof(T) == 2) && (!cpu_has_avx512_vbmi2())) {
+        state.SkipWithMessage("Requires AVX512 VBMI2 ISA");
+    }
+    // Perform setup here
+    int64_t K = state.range(0);
+    size_t ARRSIZE = 10000;
+    std::vector<T> arr;
+    std::vector<T> arr_bkp;
+
+    /* Initialize elements */
+    arr = get_uniform_rand_array<T>(ARRSIZE);
+    arr_bkp = arr;
+
+    /* call avx512 quickselect */
+    for (auto _ : state) {
+        avx512_qselect<T>(arr.data(), K, ARRSIZE);
+
+        state.PauseTiming();
+        arr = arr_bkp;
+        state.ResumeTiming();
+    }
+}
+
+template <typename T>
+static void stdnthelement(benchmark::State &state)
+{
+    // Perform setup here
+    int64_t K = state.range(0);
+    size_t ARRSIZE = 10000;
+    std::vector<T> arr;
+    std::vector<T> arr_bkp;
+
+    /* Initialize elements */
+    arr = get_uniform_rand_array<T>(ARRSIZE);
+    arr_bkp = arr;
+
+    /* call std::nth_element */
+    for (auto _ : state) {
+        std::nth_element(arr.begin(), arr.begin() + K, arr.end());
+
+        state.PauseTiming();
+        arr = arr_bkp;
+        state.ResumeTiming();
+    }
+}
+
+// Register the function as a benchmark
+BENCHMARK(avx512_qselect<float>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<float>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_qselect<uint32_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<uint32_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_qselect<int32_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<int32_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+
+BENCHMARK(avx512_qselect<double>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<double>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_qselect<uint64_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<uint64_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_qselect<int64_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<int64_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+
+//BENCHMARK(avx512_qselect<float16>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_qselect<uint16_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<uint16_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(avx512_qselect<int16_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<int16_t>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort-common.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort-common.h
new file mode 100644
index 00000000000..208b16c9f33
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort-common.h
@@ -0,0 +1,38 @@
+#ifndef AVX512_BENCH_COMMON
+#define AVX512_BENCH_COMMON
+
+#include "avx512-16bit-qsort.hpp"
+#include "avx512-32bit-qsort.hpp"
+#include "avx512-64bit-argsort.hpp"
+#include "avx512-64bit-qsort.hpp"
+#include "cpuinfo.h"
+#include "rand_array.h"
+#include <benchmark/benchmark.h>
+
+#define MY_BENCHMARK_CAPTURE(func, T, test_case_name, ...) \
+    BENCHMARK_PRIVATE_DECLARE(func) \
+            = (::benchmark::internal::RegisterBenchmarkInternal( \
+                    new ::benchmark::internal::FunctionBenchmark( \
+                            #func "/" #test_case_name "/" #T, \
+                            [](::benchmark::State &st) { \
+                                func<T>(st, __VA_ARGS__); \
+                            })))
+
+#define BENCH(func, type) \
+    MY_BENCHMARK_CAPTURE( \
+            func, type, random_5k, 5000, std::string("random")); \
+    MY_BENCHMARK_CAPTURE( \
+            func, type, random_100k, 100000, std::string("random")); \
+    MY_BENCHMARK_CAPTURE( \
+            func, type, random_1m, 1000000, std::string("random")); \
+    MY_BENCHMARK_CAPTURE( \
+            func, type, random_10m, 10000000, std::string("random")); \
+    MY_BENCHMARK_CAPTURE( \
+            func, type, sorted_10k, 10000, std::string("sorted")); \
+    MY_BENCHMARK_CAPTURE( \
+            func, type, constant_10k, 10000, std::string("constant")); \
+    MY_BENCHMARK_CAPTURE( \
+            func, type, reverse_10k, 10000, std::string("reverse"));
+
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort.cpp
new file mode 100644
index 00000000000..97e78ffcc41
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort.cpp
@@ -0,0 +1,4 @@
+#include "bench-qsort.hpp"
+#include "bench-argsort.hpp"
+#include "bench-partial-qsort.hpp"
+#include "bench-qselect.hpp"
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort.hpp
new file mode 100644
index 00000000000..ae02ac9da10
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsort.hpp
@@ -0,0 +1,94 @@
+#include "bench-qsort-common.h"
+
+template <typename T, class... Args>
+static void stdsort(benchmark::State &state, Args &&...args)
+{
+    auto args_tuple = std::make_tuple(std::move(args)...);
+    // Perform setup here
+    size_t ARRSIZE = std::get<0>(args_tuple);
+    std::vector<T> arr;
+    std::vector<T> arr_bkp;
+
+    std::string arrtype = std::get<1>(args_tuple);
+    if (arrtype == "random") { arr = get_uniform_rand_array<T>(ARRSIZE); }
+    else if (arrtype == "sorted") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+    }
+    else if (arrtype == "constant") {
+        T temp = get_uniform_rand_array<T>(1)[0];
+        for (size_t ii = 0; ii < ARRSIZE; ++ii) {
+            arr.push_back(temp);
+        }
+    }
+    else if (arrtype == "reverse") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+        std::reverse(arr.begin(), arr.end());
+    }
+    arr_bkp = arr;
+
+    /* call avx512 quicksort */
+    for (auto _ : state) {
+        std::sort(arr.begin(), arr.end());
+        state.PauseTiming();
+        arr = arr_bkp;
+        state.ResumeTiming();
+    }
+}
+
+template <typename T, class... Args>
+static void avx512qsort(benchmark::State &state, Args &&...args)
+{
+    auto args_tuple = std::make_tuple(std::move(args)...);
+    if (!cpu_has_avx512bw()) {
+        state.SkipWithMessage("Requires AVX512 BW ISA");
+    }
+    if ((sizeof(T) == 2) && (!cpu_has_avx512_vbmi2())) {
+        state.SkipWithMessage("Requires AVX512 VBMI2");
+    }
+    // Perform setup here
+    size_t ARRSIZE = std::get<0>(args_tuple);
+    std::vector<T> arr;
+    std::vector<T> arr_bkp;
+
+    std::string arrtype = std::get<1>(args_tuple);
+    if (arrtype == "random") { arr = get_uniform_rand_array<T>(ARRSIZE); }
+    else if (arrtype == "sorted") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+    }
+    else if (arrtype == "constant") {
+        T temp = get_uniform_rand_array<T>(1)[0];
+        for (size_t ii = 0; ii < ARRSIZE; ++ii) {
+            arr.push_back(temp);
+        }
+    }
+    else if (arrtype == "reverse") {
+        arr = get_uniform_rand_array<T>(ARRSIZE);
+        std::sort(arr.begin(), arr.end());
+        std::reverse(arr.begin(), arr.end());
+    }
+    arr_bkp = arr;
+
+    /* call avx512 quicksort */
+    for (auto _ : state) {
+        avx512_qsort<T>(arr.data(), ARRSIZE);
+        state.PauseTiming();
+        arr = arr_bkp;
+        state.ResumeTiming();
+    }
+}
+
+#define BENCH_BOTH_QSORT(type)\
+    BENCH(avx512qsort, type)\
+    BENCH(stdsort, type)
+
+BENCH_BOTH_QSORT(uint64_t)
+BENCH_BOTH_QSORT(int64_t)
+BENCH_BOTH_QSORT(uint32_t)
+BENCH_BOTH_QSORT(int32_t)
+BENCH_BOTH_QSORT(uint16_t)
+BENCH_BOTH_QSORT(int16_t)
+BENCH_BOTH_QSORT(float)
+BENCH_BOTH_QSORT(double)
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsortfp16.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsortfp16.cpp
new file mode 100644
index 00000000000..9a90d9d6abe
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-qsortfp16.cpp
@@ -0,0 +1,201 @@
+#include "avx512fp16-16bit-qsort.hpp"
+#include "cpuinfo.h"
+#include "rand_array.h"
+#include <benchmark/benchmark.h>
+
+template <typename T>
+static void avx512_qsort(benchmark::State &state)
+{
+    if (cpu_has_avx512fp16()) {
+        // Perform setup here
+        size_t ARRSIZE = state.range(0);
+        std::vector<T> arr;
+        std::vector<T> arr_bkp;
+
+        /* Initialize elements */
+        for (size_t jj = 0; jj < ARRSIZE; ++jj) {
+            _Float16 temp = (float)rand() / (float)(RAND_MAX);
+            arr.push_back(temp);
+        }
+        arr_bkp = arr;
+
+        /* call avx512 quicksort */
+        for (auto _ : state) {
+            avx512_qsort<T>(arr.data(), ARRSIZE);
+            state.PauseTiming();
+            arr = arr_bkp;
+            state.ResumeTiming();
+        }
+    }
+    else {
+        state.SkipWithMessage("Requires AVX512-FP16 ISA");
+    }
+}
+
+template <typename T>
+static void stdsort(benchmark::State &state)
+{
+    if (cpu_has_avx512fp16()) {
+        // Perform setup here
+        size_t ARRSIZE = state.range(0);
+        std::vector<T> arr;
+        std::vector<T> arr_bkp;
+
+        for (size_t jj = 0; jj < ARRSIZE; ++jj) {
+            _Float16 temp = (float)rand() / (float)(RAND_MAX);
+            arr.push_back(temp);
+        }
+        arr_bkp = arr;
+
+        /* call std::sort */
+        for (auto _ : state) {
+            std::sort(arr.begin(), arr.end());
+            state.PauseTiming();
+            arr = arr_bkp;
+            state.ResumeTiming();
+        }
+    }
+    else {
+        state.SkipWithMessage("Requires AVX512-FP16 ISA");
+    }
+}
+
+// Register the function as a benchmark
+BENCHMARK(avx512_qsort<_Float16>)->Arg(10000)->Arg(1000000);
+BENCHMARK(stdsort<_Float16>)->Arg(10000)->Arg(1000000);
+
+template <typename T>
+static void avx512_qselect(benchmark::State &state)
+{
+    if (cpu_has_avx512fp16()) {
+        // Perform setup here
+        int64_t K = state.range(0);
+        size_t ARRSIZE = 10000;
+        std::vector<T> arr;
+        std::vector<T> arr_bkp;
+
+        /* Initialize elements */
+        for (size_t jj = 0; jj < ARRSIZE; ++jj) {
+            _Float16 temp = (float)rand() / (float)(RAND_MAX);
+            arr.push_back(temp);
+        }
+        arr_bkp = arr;
+
+        /* call avx512 quickselect */
+        for (auto _ : state) {
+            avx512_qselect<T>(arr.data(), K, ARRSIZE);
+
+            state.PauseTiming();
+            arr = arr_bkp;
+            state.ResumeTiming();
+        }
+    }
+    else {
+        state.SkipWithMessage("Requires AVX512-FP16 ISA");
+    }
+}
+
+template <typename T>
+static void stdnthelement(benchmark::State &state)
+{
+    if (cpu_has_avx512fp16()) {
+        // Perform setup here
+        int64_t K = state.range(0);
+        size_t ARRSIZE = 10000;
+        std::vector<T> arr;
+        std::vector<T> arr_bkp;
+
+        /* Initialize elements */
+        for (size_t jj = 0; jj < ARRSIZE; ++jj) {
+            _Float16 temp = (float)rand() / (float)(RAND_MAX);
+            arr.push_back(temp);
+        }
+        arr_bkp = arr;
+
+        /* call std::nth_element */
+        for (auto _ : state) {
+            std::nth_element(arr.begin(), arr.begin() + K, arr.end());
+
+            state.PauseTiming();
+            arr = arr_bkp;
+            state.ResumeTiming();
+        }
+    }
+    else {
+        state.SkipWithMessage("Requires AVX512-FP16 ISA");
+    }
+}
+
+// Register the function as a benchmark
+BENCHMARK(avx512_qselect<_Float16>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+BENCHMARK(stdnthelement<_Float16>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
+
+template <typename T>
+static void avx512_partial_qsort(benchmark::State &state)
+{
+    if (cpu_has_avx512fp16()) {
+        // Perform setup here
+        int64_t K = state.range(0);
+        size_t ARRSIZE = 10000;
+        std::vector<T> arr;
+        std::vector<T> arr_bkp;
+
+        /* Initialize elements */
+        for (size_t jj = 0; jj < ARRSIZE; ++jj) {
+            _Float16 temp = (float)rand() / (float)(RAND_MAX);
+            arr.push_back(temp);
+        }
+        arr_bkp = arr;
+
+        /* call avx512_partial_qsort */
+        for (auto _ : state) {
+            avx512_partial_qsort<T>(arr.data(), K, ARRSIZE);
+
+            state.PauseTiming();
+            arr = arr_bkp;
+            state.ResumeTiming();
+        }
+    }
+    else {
+        state.SkipWithMessage("Requires AVX512-FP16 ISA");
+    }
+}
+
+template <typename T>
+static void stdpartialsort(benchmark::State &state)
+{
+    if (cpu_has_avx512fp16()) {
+        // Perform setup here
+        int64_t K = state.range(0);
+        size_t ARRSIZE = 10000;
+        std::vector<T> arr;
+        std::vector<T> arr_bkp;
+
+        /* Initialize elements */
+        for (size_t jj = 0; jj < ARRSIZE; ++jj) {
+            _Float16 temp = (float)rand() / (float)(RAND_MAX);
+            arr.push_back(temp);
+        }
+        arr_bkp = arr;
+
+        /* call std::partial_sort */
+        for (auto _ : state) {
+            std::partial_sort(arr.begin(), arr.begin() + K, arr.end());
+
+            state.PauseTiming();
+            arr = arr_bkp;
+            state.ResumeTiming();
+        }
+    }
+    else {
+        state.SkipWithMessage("Requires AVX512-FP16 ISA");
+    }
+}
+
+// Register the function as a benchmark
+BENCHMARK(avx512_partial_qsort<_Float16>)
+        ->Arg(10)
+        ->Arg(100)
+        ->Arg(1000)
+        ->Arg(5000);
+BENCHMARK(stdpartialsort<_Float16>)->Arg(10)->Arg(100)->Arg(1000)->Arg(5000);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-tgl.out b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-tgl.out
new file mode 100644
index 00000000000..1bb03936e4b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/bench-tgl.out
@@ -0,0 +1,28 @@
+|-----------------+-------------+------------+-----------------+-----------+----------|
+| Array data type | typeid name | array size | avx512_qsort<T> | std::sort | speed up |
+|-----------------+-------------+------------+-----------------+-----------+----------|
+| uniform random  | uint32_t    | 10000      | 115697          | 1579118   | 13.6     |
+| uniform random  | uint32_t    | 100000     | 1786812         | 19973203  | 11.2     |
+| uniform random  | uint32_t    | 1000000    | 22536966        | 233470422 | 10.4     |
+| uniform random  | int32_t     | 10000      | 95591           | 1569108   | 16.4     |
+| uniform random  | int32_t     | 100000     | 1790362         | 19785007  | 11.1     |
+| uniform random  | int32_t     | 1000000    | 22874571        | 233358497 | 10.2     |
+| uniform random  | float       | 10000      | 113316          | 1668407   | 14.7     |
+| uniform random  | float       | 100000     | 1920018         | 21815024  | 11.4     |
+| uniform random  | float       | 1000000    | 24776954        | 256867990 | 10.4     |
+| uniform random  | uint64_t    | 10000      | 233501          | 1537649   | 6.6      |
+| uniform random  | uint64_t    | 100000     | 3991372         | 19559859  | 4.9      |
+| uniform random  | uint64_t    | 1000000    | 49818870        | 232687666 | 4.7      |
+| uniform random  | int64_t     | 10000      | 228000          | 1445131   | 6.3      |
+| uniform random  | int64_t     | 100000     | 3892092         | 18917322  | 4.9      |
+| uniform random  | int64_t     | 1000000    | 48957088        | 235100259 | 4.8      |
+| uniform random  | double      | 10000      | 180307          | 1702801   | 9.4      |
+| uniform random  | double      | 100000     | 3596886         | 21849587  | 6.1      |
+| uniform random  | double      | 1000000    | 47724381        | 258014177 | 5.4      |
+| uniform random  | uint16_t    | 10000      | 84732           | 1548275   | 18.3     |
+| uniform random  | uint16_t    | 100000     | 1406417         | 19632858  | 14.0     |
+| uniform random  | uint16_t    | 1000000    | 17119960        | 214085305 | 12.5     |
+| uniform random  | int16_t     | 10000      | 84703           | 1547726   | 18.3     |
+| uniform random  | int16_t     | 100000     | 1442726         | 19705242  | 13.7     |
+| uniform random  | int16_t     | 1000000    | 20210224        | 212137465 | 10.5     |
+|-----------------+-------------+------------+-----------------+-----------+----------|
\ No newline at end of file
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/meson.build b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/meson.build
new file mode 100644
index 00000000000..d7b62b0723b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/benchmarks/meson.build
@@ -0,0 +1,19 @@
+libbench = []
+
+if cpp.has_argument('-march=icelake-client')
+  libbench += static_library('bench_qsort',
+    files('bench-qsort.cpp', ),
+    dependencies: gbench_dep,
+    include_directories : [src, utils],
+    cpp_args : ['-O3', '-march=icelake-client'],
+    )
+endif
+
+if cancompilefp16
+  libbench += static_library('bench_qsortfp16',
+    files('bench-qsortfp16.cpp', ),
+    dependencies: gbench_dep,
+    include_directories : [src, utils],
+    cpp_args : ['-O3', '-march=sapphirerapids'],
+    )
+endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/meson.build b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/meson.build
new file mode 100644
index 00000000000..b5a40cbca96
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/meson.build
@@ -0,0 +1,44 @@
+project('x86-simd-sort', 'cpp',
+        version : '1.0.0',
+        license : 'BSD 3-clause')
+cpp = meson.get_compiler('cpp')
+src = include_directories('src')
+bench = include_directories('benchmarks')
+utils = include_directories('utils')
+tests = include_directories('tests')
+gtest_dep = dependency('gtest_main', required : true, static: true)
+gbench_dep = dependency('benchmark', required : true, static: true)
+
+fp16code = '''#include<immintrin.h>
+int main() { 
+  __m512h temp = _mm512_set1_ph(1.0f);
+  __m512h var2 = _mm512_min_ph(temp, temp);
+  return 0;
+}
+'''
+cancompilefp16 = cpp.compiles(fp16code, args:'-march=sapphirerapids')
+
+subdir('utils')
+subdir('tests')
+subdir('benchmarks')
+
+testexe = executable('testexe',
+                     include_directories : [src, utils],
+                     dependencies : gtest_dep,
+                     link_whole : [libtests, libcpuinfo]
+                    )
+
+benchexe = executable('benchexe',
+                      include_directories : [src, utils, bench],
+                      dependencies : [gbench_dep],
+                      link_args: ['-lbenchmark_main'],
+                      link_whole : [libbench, libcpuinfo],
+                     )
+
+summary({
+  'Can compile AVX-512 FP16 ISA': cancompilefp16,
+  },
+  section: 'Configuration',
+  bool_yn: true
+  )
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-16bit-common.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-16bit-common.h
new file mode 100644
index 00000000000..0c8199466f2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-16bit-common.h
@@ -0,0 +1,325 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_16BIT_COMMON
+#define AVX512_16BIT_COMMON
+
+#include "avx512-common-qsort.h"
+
+/*
+ * Constants used in sorting 32 elements in a ZMM registers. Based on Bitonic
+ * sorting network (see
+ * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg)
+ */
+// ZMM register: 31,30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,0
+static const uint16_t network[6][32]
+        = {{7,  6,  5,  4,  3,  2,  1,  0,  15, 14, 13, 12, 11, 10, 9,  8,
+            23, 22, 21, 20, 19, 18, 17, 16, 31, 30, 29, 28, 27, 26, 25, 24},
+           {15, 14, 13, 12, 11, 10, 9,  8,  7,  6,  5,  4,  3,  2,  1,  0,
+            31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16},
+           {4,  5,  6,  7,  0,  1,  2,  3,  12, 13, 14, 15, 8,  9,  10, 11,
+            20, 21, 22, 23, 16, 17, 18, 19, 28, 29, 30, 31, 24, 25, 26, 27},
+           {31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16,
+            15, 14, 13, 12, 11, 10, 9,  8,  7,  6,  5,  4,  3,  2,  1,  0},
+           {8,  9,  10, 11, 12, 13, 14, 15, 0,  1,  2,  3,  4,  5,  6,  7,
+            24, 25, 26, 27, 28, 29, 30, 31, 16, 17, 18, 19, 20, 21, 22, 23},
+           {16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
+            0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15}};
+
+/*
+ * Assumes zmm is random and performs a full sorting network defined in
+ * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg
+ */
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t sort_zmm_16bit(zmm_t zmm)
+{
+    // Level 1
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAAAAAA);
+    // Level 2
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(0, 1, 2, 3)>(zmm),
+            0xCCCCCCCC);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAAAAAA);
+    // Level 3
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(1), zmm), 0xF0F0F0F0);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
+            0xCCCCCCCC);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAAAAAA);
+    // Level 4
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(2), zmm), 0xFF00FF00);
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(3), zmm), 0xF0F0F0F0);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
+            0xCCCCCCCC);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAAAAAA);
+    // Level 5
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(4), zmm), 0xFFFF0000);
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(5), zmm), 0xFF00FF00);
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(3), zmm), 0xF0F0F0F0);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
+            0xCCCCCCCC);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAAAAAA);
+    return zmm;
+}
+
+// Assumes zmm is bitonic and performs a recursive half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t bitonic_merge_zmm_16bit(zmm_t zmm)
+{
+    // 1) half_cleaner[32]: compare 1-17, 2-18, 3-19 etc ..
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(6), zmm), 0xFFFF0000);
+    // 2) half_cleaner[16]: compare 1-9, 2-10, 3-11 etc ..
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(5), zmm), 0xFF00FF00);
+    // 3) half_cleaner[8]
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::permutexvar(vtype::get_network(3), zmm), 0xF0F0F0F0);
+    // 3) half_cleaner[4]
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
+            0xCCCCCCCC);
+    // 3) half_cleaner[2]
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAAAAAA);
+    return zmm;
+}
+
+// Assumes zmm1 and zmm2 are sorted and performs a recursive half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_two_zmm_16bit(zmm_t &zmm1, zmm_t &zmm2)
+{
+    // 1) First step of a merging network: coex of zmm1 and zmm2 reversed
+    zmm2 = vtype::permutexvar(vtype::get_network(4), zmm2);
+    zmm_t zmm3 = vtype::min(zmm1, zmm2);
+    zmm_t zmm4 = vtype::max(zmm1, zmm2);
+    // 2) Recursive half cleaner for each
+    zmm1 = bitonic_merge_zmm_16bit<vtype>(zmm3);
+    zmm2 = bitonic_merge_zmm_16bit<vtype>(zmm4);
+}
+
+// Assumes [zmm0, zmm1] and [zmm2, zmm3] are sorted and performs a recursive
+// half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_four_zmm_16bit(zmm_t *zmm)
+{
+    zmm_t zmm2r = vtype::permutexvar(vtype::get_network(4), zmm[2]);
+    zmm_t zmm3r = vtype::permutexvar(vtype::get_network(4), zmm[3]);
+    zmm_t zmm_t1 = vtype::min(zmm[0], zmm3r);
+    zmm_t zmm_t2 = vtype::min(zmm[1], zmm2r);
+    zmm_t zmm_t3 = vtype::permutexvar(vtype::get_network(4),
+                                      vtype::max(zmm[1], zmm2r));
+    zmm_t zmm_t4 = vtype::permutexvar(vtype::get_network(4),
+                                      vtype::max(zmm[0], zmm3r));
+    zmm_t zmm0 = vtype::min(zmm_t1, zmm_t2);
+    zmm_t zmm1 = vtype::max(zmm_t1, zmm_t2);
+    zmm_t zmm2 = vtype::min(zmm_t3, zmm_t4);
+    zmm_t zmm3 = vtype::max(zmm_t3, zmm_t4);
+    zmm[0] = bitonic_merge_zmm_16bit<vtype>(zmm0);
+    zmm[1] = bitonic_merge_zmm_16bit<vtype>(zmm1);
+    zmm[2] = bitonic_merge_zmm_16bit<vtype>(zmm2);
+    zmm[3] = bitonic_merge_zmm_16bit<vtype>(zmm3);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_32_16bit(type_t *arr, int32_t N)
+{
+    typename vtype::opmask_t load_mask = ((0x1ull << N) - 0x1ull) & 0xFFFFFFFF;
+    typename vtype::zmm_t zmm
+            = vtype::mask_loadu(vtype::zmm_max(), load_mask, arr);
+    vtype::mask_storeu(arr, load_mask, sort_zmm_16bit<vtype>(zmm));
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_64_16bit(type_t *arr, int32_t N)
+{
+    if (N <= 32) {
+        sort_32_16bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    typename vtype::opmask_t load_mask
+            = ((0x1ull << (N - 32)) - 0x1ull) & 0xFFFFFFFF;
+    zmm_t zmm1 = vtype::loadu(arr);
+    zmm_t zmm2 = vtype::mask_loadu(vtype::zmm_max(), load_mask, arr + 32);
+    zmm1 = sort_zmm_16bit<vtype>(zmm1);
+    zmm2 = sort_zmm_16bit<vtype>(zmm2);
+    bitonic_merge_two_zmm_16bit<vtype>(zmm1, zmm2);
+    vtype::storeu(arr, zmm1);
+    vtype::mask_storeu(arr + 32, load_mask, zmm2);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_128_16bit(type_t *arr, int32_t N)
+{
+    if (N <= 64) {
+        sort_64_16bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t zmm[4];
+    zmm[0] = vtype::loadu(arr);
+    zmm[1] = vtype::loadu(arr + 32);
+    opmask_t load_mask1 = 0xFFFFFFFF, load_mask2 = 0xFFFFFFFF;
+    if (N != 128) {
+        uint64_t combined_mask = (0x1ull << (N - 64)) - 0x1ull;
+        load_mask1 = combined_mask & 0xFFFFFFFF;
+        load_mask2 = (combined_mask >> 32) & 0xFFFFFFFF;
+    }
+    zmm[2] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 64);
+    zmm[3] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 96);
+    zmm[0] = sort_zmm_16bit<vtype>(zmm[0]);
+    zmm[1] = sort_zmm_16bit<vtype>(zmm[1]);
+    zmm[2] = sort_zmm_16bit<vtype>(zmm[2]);
+    zmm[3] = sort_zmm_16bit<vtype>(zmm[3]);
+    bitonic_merge_two_zmm_16bit<vtype>(zmm[0], zmm[1]);
+    bitonic_merge_two_zmm_16bit<vtype>(zmm[2], zmm[3]);
+    bitonic_merge_four_zmm_16bit<vtype>(zmm);
+    vtype::storeu(arr, zmm[0]);
+    vtype::storeu(arr + 32, zmm[1]);
+    vtype::mask_storeu(arr + 64, load_mask1, zmm[2]);
+    vtype::mask_storeu(arr + 96, load_mask2, zmm[3]);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE type_t get_pivot_16bit(type_t *arr,
+                                            const int64_t left,
+                                            const int64_t right)
+{
+    // median of 32
+    int64_t size = (right - left) / 32;
+    type_t vec_arr[32] = {arr[left],
+                          arr[left + size],
+                          arr[left + 2 * size],
+                          arr[left + 3 * size],
+                          arr[left + 4 * size],
+                          arr[left + 5 * size],
+                          arr[left + 6 * size],
+                          arr[left + 7 * size],
+                          arr[left + 8 * size],
+                          arr[left + 9 * size],
+                          arr[left + 10 * size],
+                          arr[left + 11 * size],
+                          arr[left + 12 * size],
+                          arr[left + 13 * size],
+                          arr[left + 14 * size],
+                          arr[left + 15 * size],
+                          arr[left + 16 * size],
+                          arr[left + 17 * size],
+                          arr[left + 18 * size],
+                          arr[left + 19 * size],
+                          arr[left + 20 * size],
+                          arr[left + 21 * size],
+                          arr[left + 22 * size],
+                          arr[left + 23 * size],
+                          arr[left + 24 * size],
+                          arr[left + 25 * size],
+                          arr[left + 26 * size],
+                          arr[left + 27 * size],
+                          arr[left + 28 * size],
+                          arr[left + 29 * size],
+                          arr[left + 30 * size],
+                          arr[left + 31 * size]};
+    typename vtype::zmm_t rand_vec = vtype::loadu(vec_arr);
+    typename vtype::zmm_t sort = sort_zmm_16bit<vtype>(rand_vec);
+    return ((type_t *)&sort)[16];
+}
+
+template <typename vtype, typename type_t>
+static void
+qsort_16bit_(type_t *arr, int64_t left, int64_t right, int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        std::sort(arr + left, arr + right + 1, comparison_func<vtype>);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 128
+     */
+    if (right + 1 - left <= 128) {
+        sort_128_16bit<vtype>(arr + left, (int32_t)(right + 1 - left));
+        return;
+    }
+
+    type_t pivot = get_pivot_16bit<vtype>(arr, left, right);
+    type_t smallest = vtype::type_max();
+    type_t biggest = vtype::type_min();
+    int64_t pivot_index = partition_avx512<vtype>(
+            arr, left, right + 1, pivot, &smallest, &biggest);
+    if (pivot != smallest)
+        qsort_16bit_<vtype>(arr, left, pivot_index - 1, max_iters - 1);
+    if (pivot != biggest)
+        qsort_16bit_<vtype>(arr, pivot_index, right, max_iters - 1);
+}
+
+template <typename vtype, typename type_t>
+static void qselect_16bit_(type_t *arr,
+                           int64_t pos,
+                           int64_t left,
+                           int64_t right,
+                           int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        std::sort(arr + left, arr + right + 1, comparison_func<vtype>);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 128
+     */
+    if (right + 1 - left <= 128) {
+        sort_128_16bit<vtype>(arr + left, (int32_t)(right + 1 - left));
+        return;
+    }
+
+    type_t pivot = get_pivot_16bit<vtype>(arr, left, right);
+    type_t smallest = vtype::type_max();
+    type_t biggest = vtype::type_min();
+    int64_t pivot_index = partition_avx512<vtype>(
+            arr, left, right + 1, pivot, &smallest, &biggest);
+    if ((pivot != smallest) && (pos < pivot_index))
+        qselect_16bit_<vtype>(arr, pos, left, pivot_index - 1, max_iters - 1);
+    else if ((pivot != biggest) && (pos >= pivot_index))
+        qselect_16bit_<vtype>(arr, pos, pivot_index, right, max_iters - 1);
+}
+
+#endif // AVX512_16BIT_COMMON
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-16bit-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-16bit-qsort.hpp
new file mode 100644
index 00000000000..606f8706457
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-16bit-qsort.hpp
@@ -0,0 +1,464 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_QSORT_16BIT
+#define AVX512_QSORT_16BIT
+
+#include "avx512-16bit-common.h"
+
+struct float16 {
+    uint16_t val;
+};
+
+template <>
+struct zmm_vector<float16> {
+    using type_t = uint16_t;
+    using zmm_t = __m512i;
+    using ymm_t = __m256i;
+    using opmask_t = __mmask32;
+    static const uint8_t numlanes = 32;
+
+    static zmm_t get_network(int index)
+    {
+        return _mm512_loadu_si512(&network[index - 1][0]);
+    }
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_INFINITYH;
+    }
+    static type_t type_min()
+    {
+        return X86_SIMD_SORT_NEGINFINITYH;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_epi16(type_max());
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask32(x);
+    }
+
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        zmm_t sign_x = _mm512_and_si512(x, _mm512_set1_epi16(0x8000));
+        zmm_t sign_y = _mm512_and_si512(y, _mm512_set1_epi16(0x8000));
+        zmm_t exp_x = _mm512_and_si512(x, _mm512_set1_epi16(0x7c00));
+        zmm_t exp_y = _mm512_and_si512(y, _mm512_set1_epi16(0x7c00));
+        zmm_t mant_x = _mm512_and_si512(x, _mm512_set1_epi16(0x3ff));
+        zmm_t mant_y = _mm512_and_si512(y, _mm512_set1_epi16(0x3ff));
+
+        __mmask32 mask_ge = _mm512_cmp_epu16_mask(
+                sign_x, sign_y, _MM_CMPINT_LT); // only greater than
+        __mmask32 sign_eq = _mm512_cmpeq_epu16_mask(sign_x, sign_y);
+        __mmask32 neg = _mm512_mask_cmpeq_epu16_mask(
+                sign_eq,
+                sign_x,
+                _mm512_set1_epi16(0x8000)); // both numbers are -ve
+
+        // compare exponents only if signs are equal:
+        mask_ge = mask_ge
+                | _mm512_mask_cmp_epu16_mask(
+                          sign_eq, exp_x, exp_y, _MM_CMPINT_NLE);
+        // get mask for elements for which both sign and exponents are equal:
+        __mmask32 exp_eq = _mm512_mask_cmpeq_epu16_mask(sign_eq, exp_x, exp_y);
+
+        // compare mantissa for elements for which both sign and expponent are equal:
+        mask_ge = mask_ge
+                | _mm512_mask_cmp_epu16_mask(
+                          exp_eq, mant_x, mant_y, _MM_CMPINT_NLT);
+        return _kxor_mask32(mask_ge, neg);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_si512(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_mask_mov_epi16(y, ge(x, y), x);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        // AVX512_VBMI2
+        return _mm512_mask_compressstoreu_epi16(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        // AVX512BW
+        return _mm512_mask_loadu_epi16(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_epi16(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi16(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_mask_mov_epi16(x, ge(x, y), y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_epi16(idx, zmm);
+    }
+    // Apparently this is a terrible for perf, npy_half_to_float seems to work
+    // better
+    //static float uint16_to_float(uint16_t val)
+    //{
+    //    // Ideally use _mm_loadu_si16, but its only gcc > 11.x
+    //    // TODO: use inline ASM? https://godbolt.org/z/aGYvh7fMM
+    //    __m128i xmm = _mm_maskz_loadu_epi16(0x01, &val);
+    //    __m128 xmm2 = _mm_cvtph_ps(xmm);
+    //    return _mm_cvtss_f32(xmm2);
+    //}
+    static type_t float_to_uint16(float val)
+    {
+        __m128 xmm = _mm_load_ss(&val);
+        __m128i xmm2 = _mm_cvtps_ph(xmm, _MM_FROUND_NO_EXC);
+        return _mm_extract_epi16(xmm2, 0);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        __m512 lo = _mm512_cvtph_ps(_mm512_extracti64x4_epi64(v, 0));
+        __m512 hi = _mm512_cvtph_ps(_mm512_extracti64x4_epi64(v, 1));
+        float lo_max = _mm512_reduce_max_ps(lo);
+        float hi_max = _mm512_reduce_max_ps(hi);
+        return float_to_uint16(std::max(lo_max, hi_max));
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        __m512 lo = _mm512_cvtph_ps(_mm512_extracti64x4_epi64(v, 0));
+        __m512 hi = _mm512_cvtph_ps(_mm512_extracti64x4_epi64(v, 1));
+        float lo_max = _mm512_reduce_min_ps(lo);
+        float hi_max = _mm512_reduce_min_ps(hi);
+        return float_to_uint16(std::min(lo_max, hi_max));
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_epi16(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        zmm = _mm512_shufflehi_epi16(zmm, (_MM_PERM_ENUM)mask);
+        return _mm512_shufflelo_epi16(zmm, (_MM_PERM_ENUM)mask);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        return _mm512_storeu_si512(mem, x);
+    }
+};
+
+template <>
+struct zmm_vector<int16_t> {
+    using type_t = int16_t;
+    using zmm_t = __m512i;
+    using ymm_t = __m256i;
+    using opmask_t = __mmask32;
+    static const uint8_t numlanes = 32;
+
+    static zmm_t get_network(int index)
+    {
+        return _mm512_loadu_si512(&network[index - 1][0]);
+    }
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_INT16;
+    }
+    static type_t type_min()
+    {
+        return X86_SIMD_SORT_MIN_INT16;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_epi16(type_max());
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask32(x);
+    }
+
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epi16_mask(x, y, _MM_CMPINT_NLT);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_si512(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_epi16(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        // AVX512_VBMI2
+        return _mm512_mask_compressstoreu_epi16(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        // AVX512BW
+        return _mm512_mask_loadu_epi16(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_epi16(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi16(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_epi16(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_epi16(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        zmm_t lo = _mm512_cvtepi16_epi32(_mm512_extracti64x4_epi64(v, 0));
+        zmm_t hi = _mm512_cvtepi16_epi32(_mm512_extracti64x4_epi64(v, 1));
+        type_t lo_max = (type_t)_mm512_reduce_max_epi32(lo);
+        type_t hi_max = (type_t)_mm512_reduce_max_epi32(hi);
+        return std::max(lo_max, hi_max);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        zmm_t lo = _mm512_cvtepi16_epi32(_mm512_extracti64x4_epi64(v, 0));
+        zmm_t hi = _mm512_cvtepi16_epi32(_mm512_extracti64x4_epi64(v, 1));
+        type_t lo_min = (type_t)_mm512_reduce_min_epi32(lo);
+        type_t hi_min = (type_t)_mm512_reduce_min_epi32(hi);
+        return std::min(lo_min, hi_min);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_epi16(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        zmm = _mm512_shufflehi_epi16(zmm, (_MM_PERM_ENUM)mask);
+        return _mm512_shufflelo_epi16(zmm, (_MM_PERM_ENUM)mask);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        return _mm512_storeu_si512(mem, x);
+    }
+};
+template <>
+struct zmm_vector<uint16_t> {
+    using type_t = uint16_t;
+    using zmm_t = __m512i;
+    using ymm_t = __m256i;
+    using opmask_t = __mmask32;
+    static const uint8_t numlanes = 32;
+
+    static zmm_t get_network(int index)
+    {
+        return _mm512_loadu_si512(&network[index - 1][0]);
+    }
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_UINT16;
+    }
+    static type_t type_min()
+    {
+        return 0;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_epi16(type_max());
+    }
+
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask32(x);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epu16_mask(x, y, _MM_CMPINT_NLT);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_si512(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_epu16(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_compressstoreu_epi16(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm512_mask_loadu_epi16(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_epi16(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi16(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_epu16(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_epi16(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        zmm_t lo = _mm512_cvtepu16_epi32(_mm512_extracti64x4_epi64(v, 0));
+        zmm_t hi = _mm512_cvtepu16_epi32(_mm512_extracti64x4_epi64(v, 1));
+        type_t lo_max = (type_t)_mm512_reduce_max_epi32(lo);
+        type_t hi_max = (type_t)_mm512_reduce_max_epi32(hi);
+        return std::max(lo_max, hi_max);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        zmm_t lo = _mm512_cvtepu16_epi32(_mm512_extracti64x4_epi64(v, 0));
+        zmm_t hi = _mm512_cvtepu16_epi32(_mm512_extracti64x4_epi64(v, 1));
+        type_t lo_min = (type_t)_mm512_reduce_min_epi32(lo);
+        type_t hi_min = (type_t)_mm512_reduce_min_epi32(hi);
+        return std::min(lo_min, hi_min);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_epi16(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        zmm = _mm512_shufflehi_epi16(zmm, (_MM_PERM_ENUM)mask);
+        return _mm512_shufflelo_epi16(zmm, (_MM_PERM_ENUM)mask);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        return _mm512_storeu_si512(mem, x);
+    }
+};
+
+template <>
+bool comparison_func<zmm_vector<float16>>(const uint16_t &a, const uint16_t &b)
+{
+    uint16_t signa = a & 0x8000, signb = b & 0x8000;
+    uint16_t expa = a & 0x7c00, expb = b & 0x7c00;
+    uint16_t manta = a & 0x3ff, mantb = b & 0x3ff;
+    if (signa != signb) {
+        // opposite signs
+        return a > b;
+    }
+    else if (signa > 0) {
+        // both -ve
+        if (expa != expb) { return expa > expb; }
+        else {
+            return manta > mantb;
+        }
+    }
+    else {
+        // both +ve
+        if (expa != expb) { return expa < expb; }
+        else {
+            return manta < mantb;
+        }
+    }
+
+    //return npy_half_to_float(a) < npy_half_to_float(b);
+}
+
+X86_SIMD_SORT_INLINE int64_t replace_nan_with_inf(uint16_t *arr,
+                                                  int64_t arrsize)
+{
+    int64_t nan_count = 0;
+    __mmask16 loadmask = 0xFFFF;
+    while (arrsize > 0) {
+        if (arrsize < 16) { loadmask = (0x0001 << arrsize) - 0x0001; }
+        __m256i in_zmm = _mm256_maskz_loadu_epi16(loadmask, arr);
+        __m512 in_zmm_asfloat = _mm512_cvtph_ps(in_zmm);
+        __mmask16 nanmask = _mm512_cmp_ps_mask(
+                in_zmm_asfloat, in_zmm_asfloat, _CMP_NEQ_UQ);
+        nan_count += _mm_popcnt_u32((int32_t)nanmask);
+        _mm256_mask_storeu_epi16(arr, nanmask, YMM_MAX_HALF);
+        arr += 16;
+        arrsize -= 16;
+    }
+    return nan_count;
+}
+
+X86_SIMD_SORT_INLINE void
+replace_inf_with_nan(uint16_t *arr, int64_t arrsize, int64_t nan_count)
+{
+    for (int64_t ii = arrsize - 1; nan_count > 0; --ii) {
+        arr[ii] = 0xFFFF;
+        nan_count -= 1;
+    }
+}
+
+template <>
+void avx512_qselect(int16_t *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qselect_16bit_<zmm_vector<int16_t>, int16_t>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qselect(uint16_t *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qselect_16bit_<zmm_vector<uint16_t>, uint16_t>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+void avx512_qselect_fp16(uint16_t *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qselect_16bit_<zmm_vector<float16>, uint16_t>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+
+template <>
+void avx512_qsort(int16_t *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_16bit_<zmm_vector<int16_t>, int16_t>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qsort(uint16_t *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_16bit_<zmm_vector<uint16_t>, uint16_t>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+void avx512_qsort_fp16(uint16_t *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qsort_16bit_<zmm_vector<float16>, uint16_t>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+
+#endif // AVX512_QSORT_16BIT
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-32bit-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-32bit-qsort.hpp
new file mode 100644
index 00000000000..c4061ddf1fe
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-32bit-qsort.hpp
@@ -0,0 +1,775 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * Copyright (C) 2021 Serge Sans Paille
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ *          Serge Sans Paille <serge.guelton@telecom-bretagne.eu>
+ * ****************************************************************/
+#ifndef AVX512_QSORT_32BIT
+#define AVX512_QSORT_32BIT
+
+#include "avx512-common-qsort.h"
+
+/*
+ * Constants used in sorting 16 elements in a ZMM registers. Based on Bitonic
+ * sorting network (see
+ * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg)
+ */
+#define NETWORK_32BIT_1 14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1
+#define NETWORK_32BIT_2 12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3
+#define NETWORK_32BIT_3 8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7
+#define NETWORK_32BIT_4 13, 12, 15, 14, 9, 8, 11, 10, 5, 4, 7, 6, 1, 0, 3, 2
+#define NETWORK_32BIT_5 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
+#define NETWORK_32BIT_6 11, 10, 9, 8, 15, 14, 13, 12, 3, 2, 1, 0, 7, 6, 5, 4
+#define NETWORK_32BIT_7 7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8
+
+template <>
+struct zmm_vector<int32_t> {
+    using type_t = int32_t;
+    using zmm_t = __m512i;
+    using ymm_t = __m256i;
+    using opmask_t = __mmask16;
+    static const uint8_t numlanes = 16;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_INT32;
+    }
+    static type_t type_min()
+    {
+        return X86_SIMD_SORT_MIN_INT32;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_epi32(type_max());
+    }
+
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _mm512_knot(x);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epi32_mask(x, y, _MM_CMPINT_NLT);
+    }
+    template <int scale>
+    static ymm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_epi32(index, base, scale);
+    }
+    static zmm_t merge(ymm_t y1, ymm_t y2)
+    {
+        zmm_t z1 = _mm512_castsi256_si512(y1);
+        return _mm512_inserti32x8(z1, y2, 1);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_si512(mem);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_compressstoreu_epi32(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm512_mask_loadu_epi32(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_epi32(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi32(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_epi32(x, y);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_epi32(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_epi32(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        return _mm512_reduce_max_epi32(v);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        return _mm512_reduce_min_epi32(v);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_epi32(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        return _mm512_shuffle_epi32(zmm, (_MM_PERM_ENUM)mask);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        return _mm512_storeu_si512(mem, x);
+    }
+
+    static ymm_t max(ymm_t x, ymm_t y)
+    {
+        return _mm256_max_epi32(x, y);
+    }
+    static ymm_t min(ymm_t x, ymm_t y)
+    {
+        return _mm256_min_epi32(x, y);
+    }
+};
+template <>
+struct zmm_vector<uint32_t> {
+    using type_t = uint32_t;
+    using zmm_t = __m512i;
+    using ymm_t = __m256i;
+    using opmask_t = __mmask16;
+    static const uint8_t numlanes = 16;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_UINT32;
+    }
+    static type_t type_min()
+    {
+        return 0;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_epi32(type_max());
+    } // TODO: this should broadcast bits as is?
+
+    template <int scale>
+    static ymm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_epi32(index, base, scale);
+    }
+    static zmm_t merge(ymm_t y1, ymm_t y2)
+    {
+        zmm_t z1 = _mm512_castsi256_si512(y1);
+        return _mm512_inserti32x8(z1, y2, 1);
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _mm512_knot(x);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epu32_mask(x, y, _MM_CMPINT_NLT);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_si512(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_epu32(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_compressstoreu_epi32(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm512_mask_loadu_epi32(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_epi32(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi32(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_epu32(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_epi32(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        return _mm512_reduce_max_epu32(v);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        return _mm512_reduce_min_epu32(v);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_epi32(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        return _mm512_shuffle_epi32(zmm, (_MM_PERM_ENUM)mask);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        return _mm512_storeu_si512(mem, x);
+    }
+
+    static ymm_t max(ymm_t x, ymm_t y)
+    {
+        return _mm256_max_epu32(x, y);
+    }
+    static ymm_t min(ymm_t x, ymm_t y)
+    {
+        return _mm256_min_epu32(x, y);
+    }
+};
+template <>
+struct zmm_vector<float> {
+    using type_t = float;
+    using zmm_t = __m512;
+    using ymm_t = __m256;
+    using opmask_t = __mmask16;
+    static const uint8_t numlanes = 16;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_INFINITYF;
+    }
+    static type_t type_min()
+    {
+        return -X86_SIMD_SORT_INFINITYF;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_ps(type_max());
+    }
+
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _mm512_knot(x);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_ps_mask(x, y, _CMP_GE_OQ);
+    }
+    template <int scale>
+    static ymm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_ps(index, base, scale);
+    }
+    static zmm_t merge(ymm_t y1, ymm_t y2)
+    {
+        zmm_t z1 = _mm512_castsi512_ps(
+                _mm512_castsi256_si512(_mm256_castps_si256(y1)));
+        return _mm512_insertf32x8(z1, y2, 1);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_ps(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_ps(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_compressstoreu_ps(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm512_mask_loadu_ps(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_ps(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_ps(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_ps(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_ps(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        return _mm512_reduce_max_ps(v);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        return _mm512_reduce_min_ps(v);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_ps(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        return _mm512_shuffle_ps(zmm, zmm, (_MM_PERM_ENUM)mask);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        return _mm512_storeu_ps(mem, x);
+    }
+
+    static ymm_t max(ymm_t x, ymm_t y)
+    {
+        return _mm256_max_ps(x, y);
+    }
+    static ymm_t min(ymm_t x, ymm_t y)
+    {
+        return _mm256_min_ps(x, y);
+    }
+};
+
+/*
+ * Assumes zmm is random and performs a full sorting network defined in
+ * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg
+ */
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t sort_zmm_32bit(zmm_t zmm)
+{
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAA);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(0, 1, 2, 3)>(zmm),
+            0xCCCC);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAA);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_3), zmm),
+            0xF0F0);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
+            0xCCCC);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAA);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), zmm),
+            0xFF00);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_6), zmm),
+            0xF0F0);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
+            0xCCCC);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAA);
+    return zmm;
+}
+
+// Assumes zmm is bitonic and performs a recursive half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t bitonic_merge_zmm_32bit(zmm_t zmm)
+{
+    // 1) half_cleaner[16]: compare 1-9, 2-10, 3-11 etc ..
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_7), zmm),
+            0xFF00);
+    // 2) half_cleaner[8]: compare 1-5, 2-6, 3-7 etc ..
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_6), zmm),
+            0xF0F0);
+    // 3) half_cleaner[4]
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(1, 0, 3, 2)>(zmm),
+            0xCCCC);
+    // 3) half_cleaner[1]
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::template shuffle<SHUFFLE_MASK(2, 3, 0, 1)>(zmm),
+            0xAAAA);
+    return zmm;
+}
+
+// Assumes zmm1 and zmm2 are sorted and performs a recursive half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_two_zmm_32bit(zmm_t *zmm1, zmm_t *zmm2)
+{
+    // 1) First step of a merging network: coex of zmm1 and zmm2 reversed
+    *zmm2 = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), *zmm2);
+    zmm_t zmm3 = vtype::min(*zmm1, *zmm2);
+    zmm_t zmm4 = vtype::max(*zmm1, *zmm2);
+    // 2) Recursive half cleaner for each
+    *zmm1 = bitonic_merge_zmm_32bit<vtype>(zmm3);
+    *zmm2 = bitonic_merge_zmm_32bit<vtype>(zmm4);
+}
+
+// Assumes [zmm0, zmm1] and [zmm2, zmm3] are sorted and performs a recursive
+// half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_four_zmm_32bit(zmm_t *zmm)
+{
+    zmm_t zmm2r = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), zmm[2]);
+    zmm_t zmm3r = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), zmm[3]);
+    zmm_t zmm_t1 = vtype::min(zmm[0], zmm3r);
+    zmm_t zmm_t2 = vtype::min(zmm[1], zmm2r);
+    zmm_t zmm_t3 = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5),
+                                      vtype::max(zmm[1], zmm2r));
+    zmm_t zmm_t4 = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5),
+                                      vtype::max(zmm[0], zmm3r));
+    zmm_t zmm0 = vtype::min(zmm_t1, zmm_t2);
+    zmm_t zmm1 = vtype::max(zmm_t1, zmm_t2);
+    zmm_t zmm2 = vtype::min(zmm_t3, zmm_t4);
+    zmm_t zmm3 = vtype::max(zmm_t3, zmm_t4);
+    zmm[0] = bitonic_merge_zmm_32bit<vtype>(zmm0);
+    zmm[1] = bitonic_merge_zmm_32bit<vtype>(zmm1);
+    zmm[2] = bitonic_merge_zmm_32bit<vtype>(zmm2);
+    zmm[3] = bitonic_merge_zmm_32bit<vtype>(zmm3);
+}
+
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_eight_zmm_32bit(zmm_t *zmm)
+{
+    zmm_t zmm4r = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), zmm[4]);
+    zmm_t zmm5r = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), zmm[5]);
+    zmm_t zmm6r = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), zmm[6]);
+    zmm_t zmm7r = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5), zmm[7]);
+    zmm_t zmm_t1 = vtype::min(zmm[0], zmm7r);
+    zmm_t zmm_t2 = vtype::min(zmm[1], zmm6r);
+    zmm_t zmm_t3 = vtype::min(zmm[2], zmm5r);
+    zmm_t zmm_t4 = vtype::min(zmm[3], zmm4r);
+    zmm_t zmm_t5 = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5),
+                                      vtype::max(zmm[3], zmm4r));
+    zmm_t zmm_t6 = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5),
+                                      vtype::max(zmm[2], zmm5r));
+    zmm_t zmm_t7 = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5),
+                                      vtype::max(zmm[1], zmm6r));
+    zmm_t zmm_t8 = vtype::permutexvar(_mm512_set_epi32(NETWORK_32BIT_5),
+                                      vtype::max(zmm[0], zmm7r));
+    COEX<vtype>(zmm_t1, zmm_t3);
+    COEX<vtype>(zmm_t2, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t7);
+    COEX<vtype>(zmm_t6, zmm_t8);
+    COEX<vtype>(zmm_t1, zmm_t2);
+    COEX<vtype>(zmm_t3, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t6);
+    COEX<vtype>(zmm_t7, zmm_t8);
+    zmm[0] = bitonic_merge_zmm_32bit<vtype>(zmm_t1);
+    zmm[1] = bitonic_merge_zmm_32bit<vtype>(zmm_t2);
+    zmm[2] = bitonic_merge_zmm_32bit<vtype>(zmm_t3);
+    zmm[3] = bitonic_merge_zmm_32bit<vtype>(zmm_t4);
+    zmm[4] = bitonic_merge_zmm_32bit<vtype>(zmm_t5);
+    zmm[5] = bitonic_merge_zmm_32bit<vtype>(zmm_t6);
+    zmm[6] = bitonic_merge_zmm_32bit<vtype>(zmm_t7);
+    zmm[7] = bitonic_merge_zmm_32bit<vtype>(zmm_t8);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_16_32bit(type_t *arr, int32_t N)
+{
+    typename vtype::opmask_t load_mask = (0x0001 << N) - 0x0001;
+    typename vtype::zmm_t zmm
+            = vtype::mask_loadu(vtype::zmm_max(), load_mask, arr);
+    vtype::mask_storeu(arr, load_mask, sort_zmm_32bit<vtype>(zmm));
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_32_32bit(type_t *arr, int32_t N)
+{
+    if (N <= 16) {
+        sort_16_32bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    zmm_t zmm1 = vtype::loadu(arr);
+    typename vtype::opmask_t load_mask = (0x0001 << (N - 16)) - 0x0001;
+    zmm_t zmm2 = vtype::mask_loadu(vtype::zmm_max(), load_mask, arr + 16);
+    zmm1 = sort_zmm_32bit<vtype>(zmm1);
+    zmm2 = sort_zmm_32bit<vtype>(zmm2);
+    bitonic_merge_two_zmm_32bit<vtype>(&zmm1, &zmm2);
+    vtype::storeu(arr, zmm1);
+    vtype::mask_storeu(arr + 16, load_mask, zmm2);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_64_32bit(type_t *arr, int32_t N)
+{
+    if (N <= 32) {
+        sort_32_32bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t zmm[4];
+    zmm[0] = vtype::loadu(arr);
+    zmm[1] = vtype::loadu(arr + 16);
+    opmask_t load_mask1 = 0xFFFF, load_mask2 = 0xFFFF;
+    uint64_t combined_mask = (0x1ull << (N - 32)) - 0x1ull;
+    load_mask1 &= combined_mask & 0xFFFF;
+    load_mask2 &= (combined_mask >> 16) & 0xFFFF;
+    zmm[2] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 32);
+    zmm[3] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 48);
+    zmm[0] = sort_zmm_32bit<vtype>(zmm[0]);
+    zmm[1] = sort_zmm_32bit<vtype>(zmm[1]);
+    zmm[2] = sort_zmm_32bit<vtype>(zmm[2]);
+    zmm[3] = sort_zmm_32bit<vtype>(zmm[3]);
+    bitonic_merge_two_zmm_32bit<vtype>(&zmm[0], &zmm[1]);
+    bitonic_merge_two_zmm_32bit<vtype>(&zmm[2], &zmm[3]);
+    bitonic_merge_four_zmm_32bit<vtype>(zmm);
+    vtype::storeu(arr, zmm[0]);
+    vtype::storeu(arr + 16, zmm[1]);
+    vtype::mask_storeu(arr + 32, load_mask1, zmm[2]);
+    vtype::mask_storeu(arr + 48, load_mask2, zmm[3]);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_128_32bit(type_t *arr, int32_t N)
+{
+    if (N <= 64) {
+        sort_64_32bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t zmm[8];
+    zmm[0] = vtype::loadu(arr);
+    zmm[1] = vtype::loadu(arr + 16);
+    zmm[2] = vtype::loadu(arr + 32);
+    zmm[3] = vtype::loadu(arr + 48);
+    zmm[0] = sort_zmm_32bit<vtype>(zmm[0]);
+    zmm[1] = sort_zmm_32bit<vtype>(zmm[1]);
+    zmm[2] = sort_zmm_32bit<vtype>(zmm[2]);
+    zmm[3] = sort_zmm_32bit<vtype>(zmm[3]);
+    opmask_t load_mask1 = 0xFFFF, load_mask2 = 0xFFFF;
+    opmask_t load_mask3 = 0xFFFF, load_mask4 = 0xFFFF;
+    if (N != 128) {
+        uint64_t combined_mask = (0x1ull << (N - 64)) - 0x1ull;
+        load_mask1 &= combined_mask & 0xFFFF;
+        load_mask2 &= (combined_mask >> 16) & 0xFFFF;
+        load_mask3 &= (combined_mask >> 32) & 0xFFFF;
+        load_mask4 &= (combined_mask >> 48) & 0xFFFF;
+    }
+    zmm[4] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 64);
+    zmm[5] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 80);
+    zmm[6] = vtype::mask_loadu(vtype::zmm_max(), load_mask3, arr + 96);
+    zmm[7] = vtype::mask_loadu(vtype::zmm_max(), load_mask4, arr + 112);
+    zmm[4] = sort_zmm_32bit<vtype>(zmm[4]);
+    zmm[5] = sort_zmm_32bit<vtype>(zmm[5]);
+    zmm[6] = sort_zmm_32bit<vtype>(zmm[6]);
+    zmm[7] = sort_zmm_32bit<vtype>(zmm[7]);
+    bitonic_merge_two_zmm_32bit<vtype>(&zmm[0], &zmm[1]);
+    bitonic_merge_two_zmm_32bit<vtype>(&zmm[2], &zmm[3]);
+    bitonic_merge_two_zmm_32bit<vtype>(&zmm[4], &zmm[5]);
+    bitonic_merge_two_zmm_32bit<vtype>(&zmm[6], &zmm[7]);
+    bitonic_merge_four_zmm_32bit<vtype>(zmm);
+    bitonic_merge_four_zmm_32bit<vtype>(zmm + 4);
+    bitonic_merge_eight_zmm_32bit<vtype>(zmm);
+    vtype::storeu(arr, zmm[0]);
+    vtype::storeu(arr + 16, zmm[1]);
+    vtype::storeu(arr + 32, zmm[2]);
+    vtype::storeu(arr + 48, zmm[3]);
+    vtype::mask_storeu(arr + 64, load_mask1, zmm[4]);
+    vtype::mask_storeu(arr + 80, load_mask2, zmm[5]);
+    vtype::mask_storeu(arr + 96, load_mask3, zmm[6]);
+    vtype::mask_storeu(arr + 112, load_mask4, zmm[7]);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE type_t get_pivot_32bit(type_t *arr,
+                                            const int64_t left,
+                                            const int64_t right)
+{
+    // median of 16
+    int64_t size = (right - left) / 16;
+    using zmm_t = typename vtype::zmm_t;
+    using ymm_t = typename vtype::ymm_t;
+    __m512i rand_index1 = _mm512_set_epi64(left + size,
+                                           left + 2 * size,
+                                           left + 3 * size,
+                                           left + 4 * size,
+                                           left + 5 * size,
+                                           left + 6 * size,
+                                           left + 7 * size,
+                                           left + 8 * size);
+    __m512i rand_index2 = _mm512_set_epi64(left + 9 * size,
+                                           left + 10 * size,
+                                           left + 11 * size,
+                                           left + 12 * size,
+                                           left + 13 * size,
+                                           left + 14 * size,
+                                           left + 15 * size,
+                                           left + 16 * size);
+    ymm_t rand_vec1
+            = vtype::template i64gather<sizeof(type_t)>(rand_index1, arr);
+    ymm_t rand_vec2
+            = vtype::template i64gather<sizeof(type_t)>(rand_index2, arr);
+    zmm_t rand_vec = vtype::merge(rand_vec1, rand_vec2);
+    zmm_t sort = sort_zmm_32bit<vtype>(rand_vec);
+    // pivot will never be a nan, since there are no nan's!
+    return ((type_t *)&sort)[8];
+}
+
+template <typename vtype, typename type_t>
+static void
+qsort_32bit_(type_t *arr, int64_t left, int64_t right, int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        std::sort(arr + left, arr + right + 1);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 128
+     */
+    if (right + 1 - left <= 128) {
+        sort_128_32bit<vtype>(arr + left, (int32_t)(right + 1 - left));
+        return;
+    }
+
+    type_t pivot = get_pivot_32bit<vtype>(arr, left, right);
+    type_t smallest = vtype::type_max();
+    type_t biggest = vtype::type_min();
+    int64_t pivot_index = partition_avx512_unrolled<vtype, 2>(
+            arr, left, right + 1, pivot, &smallest, &biggest);
+    if (pivot != smallest)
+        qsort_32bit_<vtype>(arr, left, pivot_index - 1, max_iters - 1);
+    if (pivot != biggest)
+        qsort_32bit_<vtype>(arr, pivot_index, right, max_iters - 1);
+}
+
+template <typename vtype, typename type_t>
+static void qselect_32bit_(type_t *arr,
+                           int64_t pos,
+                           int64_t left,
+                           int64_t right,
+                           int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        std::sort(arr + left, arr + right + 1);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 128
+     */
+    if (right + 1 - left <= 128) {
+        sort_128_32bit<vtype>(arr + left, (int32_t)(right + 1 - left));
+        return;
+    }
+
+    type_t pivot = get_pivot_32bit<vtype>(arr, left, right);
+    type_t smallest = vtype::type_max();
+    type_t biggest = vtype::type_min();
+    int64_t pivot_index = partition_avx512_unrolled<vtype, 2>(
+            arr, left, right + 1, pivot, &smallest, &biggest);
+    if ((pivot != smallest) && (pos < pivot_index))
+        qselect_32bit_<vtype>(arr, pos, left, pivot_index - 1, max_iters - 1);
+    else if ((pivot != biggest) && (pos >= pivot_index))
+        qselect_32bit_<vtype>(arr, pos, pivot_index, right, max_iters - 1);
+}
+
+X86_SIMD_SORT_INLINE int64_t replace_nan_with_inf(float *arr, int64_t arrsize)
+{
+    int64_t nan_count = 0;
+    __mmask16 loadmask = 0xFFFF;
+    while (arrsize > 0) {
+        if (arrsize < 16) { loadmask = (0x0001 << arrsize) - 0x0001; }
+        __m512 in_zmm = _mm512_maskz_loadu_ps(loadmask, arr);
+        __mmask16 nanmask = _mm512_cmp_ps_mask(in_zmm, in_zmm, _CMP_NEQ_UQ);
+        nan_count += _mm_popcnt_u32((int32_t)nanmask);
+        _mm512_mask_storeu_ps(arr, nanmask, ZMM_MAX_FLOAT);
+        arr += 16;
+        arrsize -= 16;
+    }
+    return nan_count;
+}
+
+X86_SIMD_SORT_INLINE void
+replace_inf_with_nan(float *arr, int64_t arrsize, int64_t nan_count)
+{
+    for (int64_t ii = arrsize - 1; nan_count > 0; --ii) {
+        arr[ii] = std::nanf("1");
+        nan_count -= 1;
+    }
+}
+
+template <>
+void avx512_qselect<int32_t>(int32_t *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qselect_32bit_<zmm_vector<int32_t>, int32_t>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qselect<uint32_t>(uint32_t *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qselect_32bit_<zmm_vector<uint32_t>, uint32_t>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qselect<float>(float *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qselect_32bit_<zmm_vector<float>, float>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+
+template <>
+void avx512_qsort<int32_t>(int32_t *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_32bit_<zmm_vector<int32_t>, int32_t>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qsort<uint32_t>(uint32_t *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_32bit_<zmm_vector<uint32_t>, uint32_t>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qsort<float>(float *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qsort_32bit_<zmm_vector<float>, float>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+
+#endif //AVX512_QSORT_32BIT
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-argsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-argsort.hpp
new file mode 100644
index 00000000000..80c6ce4ae91
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-argsort.hpp
@@ -0,0 +1,378 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_ARGSORT_64BIT
+#define AVX512_ARGSORT_64BIT
+
+#include "avx512-64bit-common.h"
+#include "avx512-common-argsort.h"
+#include "avx512-64bit-keyvalue-networks.hpp"
+
+/* argsort using std::sort */
+template <typename T>
+void std_argsort_withnan(T *arr, int64_t *arg, int64_t left, int64_t right)
+{
+    std::sort(arg + left,
+              arg + right,
+              [arr](int64_t left, int64_t right) -> bool {
+              if ((!std::isnan(arr[left])) && (!std::isnan(arr[right]))) {return arr[left] < arr[right];}
+              else if (std::isnan(arr[left])) {return false;}
+              else {return true;}
+              });
+}
+
+/* argsort using std::sort */
+template <typename T>
+void std_argsort(T *arr, int64_t *arg, int64_t left, int64_t right)
+{
+    std::sort(arg + left,
+              arg + right,
+              [arr](int64_t left, int64_t right) -> bool {
+                  // sort indices according to corresponding array element
+                  return arr[left] < arr[right];
+              });
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void argsort_8_64bit(type_t *arr, int64_t *arg, int32_t N)
+{
+    using zmm_t = typename vtype::zmm_t;
+    typename vtype::opmask_t load_mask = (0x01 << N) - 0x01;
+    argzmm_t argzmm = argtype::maskz_loadu(load_mask, arg);
+    zmm_t arrzmm = vtype::template mask_i64gather<sizeof(type_t)>(
+            vtype::zmm_max(), load_mask, argzmm, arr);
+    arrzmm = sort_zmm_64bit<vtype, argtype>(arrzmm, argzmm);
+    argtype::mask_storeu(arg, load_mask, argzmm);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void argsort_16_64bit(type_t *arr, int64_t *arg, int32_t N)
+{
+    if (N <= 8) {
+        argsort_8_64bit<vtype>(arr, arg, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    typename vtype::opmask_t load_mask = (0x01 << (N - 8)) - 0x01;
+    argzmm_t argzmm1 = argtype::loadu(arg);
+    argzmm_t argzmm2 = argtype::maskz_loadu(load_mask, arg + 8);
+    zmm_t arrzmm1 = vtype::template i64gather<sizeof(type_t)>(argzmm1, arr);
+    zmm_t arrzmm2 = vtype::template mask_i64gather<sizeof(type_t)>(
+            vtype::zmm_max(), load_mask, argzmm2, arr);
+    arrzmm1 = sort_zmm_64bit<vtype, argtype>(arrzmm1, argzmm1);
+    arrzmm2 = sort_zmm_64bit<vtype, argtype>(arrzmm2, argzmm2);
+    bitonic_merge_two_zmm_64bit<vtype, argtype>(
+            arrzmm1, arrzmm2, argzmm1, argzmm2);
+    argtype::storeu(arg, argzmm1);
+    argtype::mask_storeu(arg + 8, load_mask, argzmm2);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void argsort_32_64bit(type_t *arr, int64_t *arg, int32_t N)
+{
+    if (N <= 16) {
+        argsort_16_64bit<vtype>(arr, arg, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t arrzmm[4];
+    argzmm_t argzmm[4];
+
+#pragma GCC unroll 2
+    for (int ii = 0; ii < 2; ++ii) {
+        argzmm[ii] = argtype::loadu(arg + 8 * ii);
+        arrzmm[ii] = vtype::template i64gather<sizeof(type_t)>(argzmm[ii], arr);
+        arrzmm[ii] = sort_zmm_64bit<vtype, argtype>(arrzmm[ii], argzmm[ii]);
+    }
+
+    uint64_t combined_mask = (0x1ull << (N - 16)) - 0x1ull;
+    opmask_t load_mask[2] = {0xFF, 0xFF};
+#pragma GCC unroll 2
+    for (int ii = 0; ii < 2; ++ii) {
+        load_mask[ii] = (combined_mask >> (ii * 8)) & 0xFF;
+        argzmm[ii + 2] = argtype::maskz_loadu(load_mask[ii], arg + 16 + 8 * ii);
+        arrzmm[ii + 2] = vtype::template mask_i64gather<sizeof(type_t)>(
+                vtype::zmm_max(), load_mask[ii], argzmm[ii + 2], arr);
+        arrzmm[ii + 2] = sort_zmm_64bit<vtype, argtype>(arrzmm[ii + 2],
+                                                        argzmm[ii + 2]);
+    }
+
+    bitonic_merge_two_zmm_64bit<vtype, argtype>(
+            arrzmm[0], arrzmm[1], argzmm[0], argzmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype, argtype>(
+            arrzmm[2], arrzmm[3], argzmm[2], argzmm[3]);
+    bitonic_merge_four_zmm_64bit<vtype, argtype>(arrzmm, argzmm);
+
+    argtype::storeu(arg, argzmm[0]);
+    argtype::storeu(arg + 8, argzmm[1]);
+    argtype::mask_storeu(arg + 16, load_mask[0], argzmm[2]);
+    argtype::mask_storeu(arg + 24, load_mask[1], argzmm[3]);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void argsort_64_64bit(type_t *arr, int64_t *arg, int32_t N)
+{
+    if (N <= 32) {
+        argsort_32_64bit<vtype>(arr, arg, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t arrzmm[8];
+    argzmm_t argzmm[8];
+
+#pragma GCC unroll 4
+    for (int ii = 0; ii < 4; ++ii) {
+        argzmm[ii] = argtype::loadu(arg + 8 * ii);
+        arrzmm[ii] = vtype::template i64gather<sizeof(type_t)>(argzmm[ii], arr);
+        arrzmm[ii] = sort_zmm_64bit<vtype, argtype>(arrzmm[ii], argzmm[ii]);
+    }
+
+    opmask_t load_mask[4] = {0xFF, 0xFF, 0xFF, 0xFF};
+    uint64_t combined_mask = (0x1ull << (N - 32)) - 0x1ull;
+#pragma GCC unroll 4
+    for (int ii = 0; ii < 4; ++ii) {
+        load_mask[ii] = (combined_mask >> (ii * 8)) & 0xFF;
+        argzmm[ii + 4] = argtype::maskz_loadu(load_mask[ii], arg + 32 + 8 * ii);
+        arrzmm[ii + 4] = vtype::template mask_i64gather<sizeof(type_t)>(
+                vtype::zmm_max(), load_mask[ii], argzmm[ii + 4], arr);
+        arrzmm[ii + 4] = sort_zmm_64bit<vtype, argtype>(arrzmm[ii + 4],
+                                                        argzmm[ii + 4]);
+    }
+
+#pragma GCC unroll 4
+    for (int ii = 0; ii < 8; ii = ii + 2) {
+        bitonic_merge_two_zmm_64bit<vtype, argtype>(
+                arrzmm[ii], arrzmm[ii + 1], argzmm[ii], argzmm[ii + 1]);
+    }
+    bitonic_merge_four_zmm_64bit<vtype, argtype>(arrzmm, argzmm);
+    bitonic_merge_four_zmm_64bit<vtype, argtype>(arrzmm + 4, argzmm + 4);
+    bitonic_merge_eight_zmm_64bit<vtype, argtype>(arrzmm, argzmm);
+
+#pragma GCC unroll 4
+    for (int ii = 0; ii < 4; ++ii) {
+        argtype::storeu(arg + 8 * ii, argzmm[ii]);
+    }
+#pragma GCC unroll 4
+    for (int ii = 0; ii < 4; ++ii) {
+        argtype::mask_storeu(arg + 32 + 8 * ii, load_mask[ii], argzmm[ii + 4]);
+    }
+}
+
+/* arsort 128 doesn't seem to make much of a difference to perf*/
+//template <typename vtype, typename type_t>
+//X86_SIMD_SORT_INLINE void
+//argsort_128_64bit(type_t *arr, int64_t *arg, int32_t N)
+//{
+//    if (N <= 64) {
+//        argsort_64_64bit<vtype>(arr, arg, N);
+//        return;
+//    }
+//    using zmm_t = typename vtype::zmm_t;
+//    using opmask_t = typename vtype::opmask_t;
+//    zmm_t arrzmm[16];
+//    argzmm_t argzmm[16];
+//
+//#pragma GCC unroll 8
+//    for (int ii = 0; ii < 8; ++ii) {
+//        argzmm[ii] = argtype::loadu(arg + 8*ii);
+//        arrzmm[ii] = vtype::template i64gather<sizeof(type_t)>(argzmm[ii], arr);
+//        arrzmm[ii] = sort_zmm_64bit<vtype, argtype>(arrzmm[ii], argzmm[ii]);
+//    }
+//
+//    opmask_t load_mask[8] = {0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF};
+//    if (N != 128) {
+//    uint64_t combined_mask = (0x1ull << (N - 64)) - 0x1ull;
+//#pragma GCC unroll 8
+//        for (int ii = 0; ii < 8; ++ii) {
+//            load_mask[ii] = (combined_mask >> (ii*8)) & 0xFF;
+//        }
+//    }
+//#pragma GCC unroll 8
+//    for (int ii = 0; ii < 8; ++ii) {
+//        argzmm[ii+8] = argtype::maskz_loadu(load_mask[ii], arg + 64 + 8*ii);
+//        arrzmm[ii+8] = vtype::template mask_i64gather<sizeof(type_t)>(vtype::zmm_max(), load_mask[ii], argzmm[ii+8], arr);
+//        arrzmm[ii+8] = sort_zmm_64bit<vtype, argtype>(arrzmm[ii+8], argzmm[ii+8]);
+//    }
+//
+//#pragma GCC unroll 8
+//    for (int ii = 0; ii < 16; ii = ii + 2) {
+//        bitonic_merge_two_zmm_64bit<vtype, argtype>(arrzmm[ii], arrzmm[ii + 1], argzmm[ii], argzmm[ii + 1]);
+//    }
+//    bitonic_merge_four_zmm_64bit<vtype, argtype>(arrzmm, argzmm);
+//    bitonic_merge_four_zmm_64bit<vtype, argtype>(arrzmm + 4, argzmm + 4);
+//    bitonic_merge_four_zmm_64bit<vtype, argtype>(arrzmm + 8, argzmm + 8);
+//    bitonic_merge_four_zmm_64bit<vtype, argtype>(arrzmm + 12, argzmm + 12);
+//    bitonic_merge_eight_zmm_64bit<vtype, argtype>(arrzmm, argzmm);
+//    bitonic_merge_eight_zmm_64bit<vtype, argtype>(arrzmm+8, argzmm+8);
+//    bitonic_merge_sixteen_zmm_64bit<vtype, argtype>(arrzmm, argzmm);
+//
+//#pragma GCC unroll 8
+//    for (int ii = 0; ii < 8; ++ii) {
+//        argtype::storeu(arg + 8*ii, argzmm[ii]);
+//    }
+//#pragma GCC unroll 8
+//    for (int ii = 0; ii < 8; ++ii) {
+//        argtype::mask_storeu(arg + 64 + 8*ii, load_mask[ii], argzmm[ii + 8]);
+//    }
+//}
+
+template <typename vtype, typename type_t>
+type_t get_pivot_64bit(type_t *arr,
+                       int64_t *arg,
+                       const int64_t left,
+                       const int64_t right)
+{
+    if (right - left >= vtype::numlanes) {
+        // median of 8
+        int64_t size = (right - left) / 8;
+        using zmm_t = typename vtype::zmm_t;
+        // TODO: Use gather here too:
+        __m512i rand_index = _mm512_set_epi64(arg[left + size],
+                                              arg[left + 2 * size],
+                                              arg[left + 3 * size],
+                                              arg[left + 4 * size],
+                                              arg[left + 5 * size],
+                                              arg[left + 6 * size],
+                                              arg[left + 7 * size],
+                                              arg[left + 8 * size]);
+        zmm_t rand_vec
+                = vtype::template i64gather<sizeof(type_t)>(rand_index, arr);
+        // pivot will never be a nan, since there are no nan's!
+        zmm_t sort = sort_zmm_64bit<vtype>(rand_vec);
+        return ((type_t *)&sort)[4];
+    }
+    else {
+        return arr[arg[right]];
+    }
+}
+
+template <typename vtype, typename type_t>
+inline void argsort_64bit_(type_t *arr,
+                           int64_t *arg,
+                           int64_t left,
+                           int64_t right,
+                           int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        std_argsort(arr, arg, left, right + 1);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 64
+     */
+    if (right + 1 - left <= 64) {
+        argsort_64_64bit<vtype>(arr, arg + left, (int32_t)(right + 1 - left));
+        return;
+    }
+    type_t pivot = get_pivot_64bit<vtype>(arr, arg, left, right);
+    type_t smallest = vtype::type_max();
+    type_t biggest = vtype::type_min();
+    int64_t pivot_index = partition_avx512_unrolled<vtype, 4>(
+            arr, arg, left, right + 1, pivot, &smallest, &biggest);
+    if (pivot != smallest)
+        argsort_64bit_<vtype>(arr, arg, left, pivot_index - 1, max_iters - 1);
+    if (pivot != biggest)
+        argsort_64bit_<vtype>(arr, arg, pivot_index, right, max_iters - 1);
+}
+
+template <typename vtype, typename type_t>
+bool has_nan(type_t* arr, int64_t arrsize)
+{
+    using opmask_t = typename vtype::opmask_t;
+    using zmm_t = typename vtype::zmm_t;
+    bool found_nan = false;
+    opmask_t loadmask = 0xFF;
+    zmm_t in;
+    while (arrsize > 0) {
+        if (arrsize < vtype::numlanes) {
+            loadmask = (0x01 << arrsize) - 0x01;
+            in = vtype::maskz_loadu(loadmask, arr);
+        }
+        else {
+            in = vtype::loadu(arr);
+        }
+        opmask_t nanmask = vtype::template fpclass<0x01|0x80>(in);
+        arr += vtype::numlanes;
+        arrsize -= vtype::numlanes;
+        if (nanmask != 0x00) {
+            found_nan = true;
+            break;
+        }
+    }
+    return found_nan;
+}
+
+template <typename T>
+void avx512_argsort(T* arr, int64_t *arg, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        argsort_64bit_<zmm_vector<T>>(
+                arr, arg, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_argsort(double* arr, int64_t *arg, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        if (has_nan<zmm_vector<double>>(arr, arrsize)) {
+            std_argsort_withnan(arr, arg, 0, arrsize);
+        }
+        else {
+            argsort_64bit_<zmm_vector<double>>(
+                    arr, arg, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        }
+    }
+}
+
+
+template <>
+void avx512_argsort(int32_t* arr, int64_t *arg, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        argsort_64bit_<ymm_vector<int32_t>>(
+                arr, arg, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_argsort(uint32_t* arr, int64_t *arg, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        argsort_64bit_<ymm_vector<uint32_t>>(
+                arr, arg, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_argsort(float* arr, int64_t *arg, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        if (has_nan<ymm_vector<float>>(arr, arrsize)) {
+            std_argsort_withnan(arr, arg, 0, arrsize);
+        }
+        else {
+            argsort_64bit_<ymm_vector<float>>(
+                    arr, arg, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        }
+    }
+}
+
+template <typename T>
+std::vector<int64_t> avx512_argsort(T* arr, int64_t arrsize)
+{
+    std::vector<int64_t> indices(arrsize);
+    std::iota(indices.begin(), indices.end(), 0);
+    avx512_argsort<T>(arr, indices.data(), arrsize);
+    return indices;
+}
+
+#endif // AVX512_ARGSORT_64BIT
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-common.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-common.h
new file mode 100644
index 00000000000..d12684c1f9a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-common.h
@@ -0,0 +1,848 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_64BIT_COMMON
+#define AVX512_64BIT_COMMON
+#include "avx512-common-qsort.h"
+
+/*
+ * Constants used in sorting 8 elements in a ZMM registers. Based on Bitonic
+ * sorting network (see
+ * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg)
+ */
+// ZMM                  7, 6, 5, 4, 3, 2, 1, 0
+#define NETWORK_64BIT_1 4, 5, 6, 7, 0, 1, 2, 3
+#define NETWORK_64BIT_2 0, 1, 2, 3, 4, 5, 6, 7
+#define NETWORK_64BIT_3 5, 4, 7, 6, 1, 0, 3, 2
+#define NETWORK_64BIT_4 3, 2, 1, 0, 7, 6, 5, 4
+
+template <>
+struct ymm_vector<float> {
+    using type_t = float;
+    using zmm_t = __m256;
+    using zmmi_t = __m256i;
+    using opmask_t = __mmask8;
+    static const uint8_t numlanes = 8;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_INFINITYF;
+    }
+    static type_t type_min()
+    {
+        return -X86_SIMD_SORT_INFINITYF;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm256_set1_ps(type_max());
+    }
+
+    static zmmi_t seti(int v1,
+                       int v2,
+                       int v3,
+                       int v4,
+                       int v5,
+                       int v6,
+                       int v7,
+                       int v8)
+    {
+        return _mm256_set_epi32(v1, v2, v3, v4, v5, v6, v7, v8);
+    }
+    static opmask_t kxor_opmask(opmask_t x, opmask_t y)
+    {
+        return _kxor_mask8(x, y);
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask8(x);
+    }
+    static opmask_t le(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_ps_mask(x, y, _CMP_LE_OQ);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_ps_mask(x, y, _CMP_GE_OQ);
+    }
+    static opmask_t eq(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_ps_mask(x, y, _CMP_EQ_OQ);
+    }
+    template <int type>
+    static opmask_t fpclass(zmm_t x)
+    {
+        return _mm256_fpclass_ps_mask(x, type);
+    }
+    template <int scale>
+    static zmm_t
+    mask_i64gather(zmm_t src, opmask_t mask, __m512i index, void const *base)
+    {
+        return _mm512_mask_i64gather_ps(src, mask, index, base, scale);
+    }
+    template <int scale>
+    static zmm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_ps(index, base, scale);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm256_loadu_ps((float*) mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm256_max_ps(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm256_mask_compressstoreu_ps(mem, mask, x);
+    }
+    static zmm_t maskz_loadu(opmask_t mask, void const *mem)
+    {
+        return _mm256_maskz_loadu_ps(mask, mem);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm256_mask_loadu_ps(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm256_mask_mov_ps(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm256_mask_storeu_ps(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm256_min_ps(x, y);
+    }
+    static zmm_t permutexvar(__m256i idx, zmm_t zmm)
+    {
+        return _mm256_permutexvar_ps(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        __m128 v128 = _mm_max_ps(_mm256_castps256_ps128(v), _mm256_extractf32x4_ps (v, 1));
+        __m128 v64 =  _mm_max_ps(v128, _mm_shuffle_ps(v128, v128, _MM_SHUFFLE(1, 0, 3, 2)));
+        __m128 v32 = _mm_max_ps(v64, _mm_shuffle_ps(v64, v64, _MM_SHUFFLE(0, 0, 0, 1)));
+        return _mm_cvtss_f32(v32);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        __m128 v128 = _mm_min_ps(_mm256_castps256_ps128(v), _mm256_extractf32x4_ps(v, 1));
+        __m128 v64 =  _mm_min_ps(v128, _mm_shuffle_ps(v128, v128,_MM_SHUFFLE(1, 0, 3, 2)));
+        __m128 v32 = _mm_min_ps(v64, _mm_shuffle_ps(v64, v64,_MM_SHUFFLE(0, 0, 0, 1)));
+        return _mm_cvtss_f32(v32);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm256_set1_ps(v);
+    }
+    template <uint8_t mask, bool = (mask == 0b01010101)>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        /* Hack!: have to make shuffles within 128-bit lanes work for both
+         * 32-bit and 64-bit */
+        return _mm256_shuffle_ps(zmm, zmm, 0b10110001);
+        //if constexpr (mask == 0b01010101) {
+        //}
+        //else {
+        //    /* Not used, so far */
+        //    return _mm256_shuffle_ps(zmm, zmm, mask);
+        //}
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        _mm256_storeu_ps((float*)mem, x);
+    }
+};
+template <>
+struct ymm_vector<uint32_t> {
+    using type_t = uint32_t;
+    using zmm_t = __m256i;
+    using zmmi_t = __m256i;
+    using opmask_t = __mmask8;
+    static const uint8_t numlanes = 8;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_UINT32;
+    }
+    static type_t type_min()
+    {
+        return 0;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm256_set1_epi32(type_max());
+    }
+
+    static zmmi_t seti(int v1,
+                       int v2,
+                       int v3,
+                       int v4,
+                       int v5,
+                       int v6,
+                       int v7,
+                       int v8)
+    {
+        return _mm256_set_epi32(v1, v2, v3, v4, v5, v6, v7, v8);
+    }
+    static opmask_t kxor_opmask(opmask_t x, opmask_t y)
+    {
+        return _kxor_mask8(x, y);
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask8(x);
+    }
+    static opmask_t le(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_epu32_mask(x, y, _MM_CMPINT_LE);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_epu32_mask(x, y, _MM_CMPINT_NLT);
+    }
+    static opmask_t eq(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_epu32_mask(x, y, _MM_CMPINT_EQ);
+    }
+    template <int scale>
+    static zmm_t
+    mask_i64gather(zmm_t src, opmask_t mask, __m512i index, void const *base)
+    {
+        return _mm512_mask_i64gather_epi32(src, mask, index, base, scale);
+    }
+    template <int scale>
+    static zmm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_epi32(index, base, scale);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm256_loadu_si256((__m256i*) mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm256_max_epu32(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm256_mask_compressstoreu_epi32(mem, mask, x);
+    }
+    static zmm_t maskz_loadu(opmask_t mask, void const *mem)
+    {
+        return _mm256_maskz_loadu_epi32(mask, mem);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm256_mask_loadu_epi32(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm256_mask_mov_epi32(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm256_mask_storeu_epi32(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm256_min_epu32(x, y);
+    }
+    static zmm_t permutexvar(__m256i idx, zmm_t zmm)
+    {
+        return _mm256_permutexvar_epi32(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        __m128i v128 = _mm_max_epu32(_mm256_castsi256_si128(v), _mm256_extracti128_si256(v, 1));
+        __m128i v64 =  _mm_max_epu32(v128, _mm_shuffle_epi32(v128, _MM_SHUFFLE(1, 0, 3, 2)));
+        __m128i v32 = _mm_max_epu32(v64, _mm_shuffle_epi32(v64, _MM_SHUFFLE(0, 0, 0, 1)));
+        return (type_t)_mm_cvtsi128_si32(v32);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        __m128i v128 = _mm_min_epu32(_mm256_castsi256_si128(v), _mm256_extracti128_si256(v, 1));
+        __m128i v64 =  _mm_min_epu32(v128, _mm_shuffle_epi32(v128, _MM_SHUFFLE(1, 0, 3, 2)));
+        __m128i v32 = _mm_min_epu32(v64, _mm_shuffle_epi32(v64, _MM_SHUFFLE(0, 0, 0, 1)));
+        return (type_t)_mm_cvtsi128_si32(v32);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm256_set1_epi32(v);
+    }
+    template <uint8_t mask, bool = (mask == 0b01010101)>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        /* Hack!: have to make shuffles within 128-bit lanes work for both
+         * 32-bit and 64-bit */
+        return _mm256_shuffle_epi32(zmm, 0b10110001);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        _mm256_storeu_si256((__m256i*) mem, x);
+    }
+};
+template <>
+struct ymm_vector<int32_t> {
+    using type_t = int32_t;
+    using zmm_t = __m256i;
+    using zmmi_t = __m256i;
+    using opmask_t = __mmask8;
+    static const uint8_t numlanes = 8;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_INT32;
+    }
+    static type_t type_min()
+    {
+        return X86_SIMD_SORT_MIN_INT32;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm256_set1_epi32(type_max());
+    } // TODO: this should broadcast bits as is?
+
+    static zmmi_t seti(int v1,
+                       int v2,
+                       int v3,
+                       int v4,
+                       int v5,
+                       int v6,
+                       int v7,
+                       int v8)
+    {
+        return _mm256_set_epi32(v1, v2, v3, v4, v5, v6, v7, v8);
+    }
+    static opmask_t kxor_opmask(opmask_t x, opmask_t y)
+    {
+        return _kxor_mask8(x, y);
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask8(x);
+    }
+    static opmask_t le(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_epi32_mask(x, y, _MM_CMPINT_LE);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_epi32_mask(x, y, _MM_CMPINT_NLT);
+    }
+    static opmask_t eq(zmm_t x, zmm_t y)
+    {
+        return _mm256_cmp_epi32_mask(x, y, _MM_CMPINT_EQ);
+    }
+    template <int scale>
+    static zmm_t
+    mask_i64gather(zmm_t src, opmask_t mask, __m512i index, void const *base)
+    {
+        return _mm512_mask_i64gather_epi32(src, mask, index, base, scale);
+    }
+    template <int scale>
+    static zmm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_epi32(index, base, scale);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm256_loadu_si256((__m256i*) mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm256_max_epi32(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm256_mask_compressstoreu_epi32(mem, mask, x);
+    }
+    static zmm_t maskz_loadu(opmask_t mask, void const *mem)
+    {
+        return _mm256_maskz_loadu_epi32(mask, mem);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm256_mask_loadu_epi32(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm256_mask_mov_epi32(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm256_mask_storeu_epi32(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm256_min_epi32(x, y);
+    }
+    static zmm_t permutexvar(__m256i idx, zmm_t zmm)
+    {
+        return _mm256_permutexvar_epi32(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        __m128i v128 = _mm_max_epi32(_mm256_castsi256_si128(v), _mm256_extracti128_si256(v, 1));
+        __m128i v64 =  _mm_max_epi32(v128, _mm_shuffle_epi32(v128, _MM_SHUFFLE(1, 0, 3, 2)));
+        __m128i v32 = _mm_max_epi32(v64, _mm_shuffle_epi32(v64, _MM_SHUFFLE(0, 0, 0, 1)));
+        return (type_t)_mm_cvtsi128_si32(v32);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        __m128i v128 = _mm_min_epi32(_mm256_castsi256_si128(v), _mm256_extracti128_si256(v, 1));
+        __m128i v64 =  _mm_min_epi32(v128, _mm_shuffle_epi32(v128, _MM_SHUFFLE(1, 0, 3, 2)));
+        __m128i v32 = _mm_min_epi32(v64, _mm_shuffle_epi32(v64, _MM_SHUFFLE(0, 0, 0, 1)));
+        return (type_t)_mm_cvtsi128_si32(v32);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm256_set1_epi32(v);
+    }
+    template <uint8_t mask, bool = (mask == 0b01010101)>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        /* Hack!: have to make shuffles within 128-bit lanes work for both
+         * 32-bit and 64-bit */
+        return _mm256_shuffle_epi32(zmm, 0b10110001);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        _mm256_storeu_si256((__m256i*) mem, x);
+    }
+};
+template <>
+struct zmm_vector<int64_t> {
+    using type_t = int64_t;
+    using zmm_t = __m512i;
+    using zmmi_t = __m512i;
+    using ymm_t = __m512i;
+    using opmask_t = __mmask8;
+    static const uint8_t numlanes = 8;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_INT64;
+    }
+    static type_t type_min()
+    {
+        return X86_SIMD_SORT_MIN_INT64;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_epi64(type_max());
+    } // TODO: this should broadcast bits as is?
+
+    static zmmi_t seti(int v1,
+                       int v2,
+                       int v3,
+                       int v4,
+                       int v5,
+                       int v6,
+                       int v7,
+                       int v8)
+    {
+        return _mm512_set_epi64(v1, v2, v3, v4, v5, v6, v7, v8);
+    }
+    static opmask_t kxor_opmask(opmask_t x, opmask_t y)
+    {
+        return _kxor_mask8(x, y);
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask8(x);
+    }
+    static opmask_t le(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epi64_mask(x, y, _MM_CMPINT_LE);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epi64_mask(x, y, _MM_CMPINT_NLT);
+    }
+    static opmask_t eq(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epi64_mask(x, y, _MM_CMPINT_EQ);
+    }
+    template <int scale>
+    static zmm_t
+    mask_i64gather(zmm_t src, opmask_t mask, __m512i index, void const *base)
+    {
+        return _mm512_mask_i64gather_epi64(src, mask, index, base, scale);
+    }
+    template <int scale>
+    static zmm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_epi64(index, base, scale);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_si512(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_epi64(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_compressstoreu_epi64(mem, mask, x);
+    }
+    static zmm_t maskz_loadu(opmask_t mask, void const *mem)
+    {
+        return _mm512_maskz_loadu_epi64(mask, mem);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm512_mask_loadu_epi64(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_epi64(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi64(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_epi64(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_epi64(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        return _mm512_reduce_max_epi64(v);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        return _mm512_reduce_min_epi64(v);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_epi64(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        __m512d temp = _mm512_castsi512_pd(zmm);
+        return _mm512_castpd_si512(
+                _mm512_shuffle_pd(temp, temp, (_MM_PERM_ENUM)mask));
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        _mm512_storeu_si512(mem, x);
+    }
+};
+template <>
+struct zmm_vector<uint64_t> {
+    using type_t = uint64_t;
+    using zmm_t = __m512i;
+    using zmmi_t = __m512i;
+    using ymm_t = __m512i;
+    using opmask_t = __mmask8;
+    static const uint8_t numlanes = 8;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_MAX_UINT64;
+    }
+    static type_t type_min()
+    {
+        return 0;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_epi64(type_max());
+    }
+
+    static zmmi_t seti(int v1,
+                       int v2,
+                       int v3,
+                       int v4,
+                       int v5,
+                       int v6,
+                       int v7,
+                       int v8)
+    {
+        return _mm512_set_epi64(v1, v2, v3, v4, v5, v6, v7, v8);
+    }
+    template <int scale>
+    static zmm_t
+    mask_i64gather(zmm_t src, opmask_t mask, __m512i index, void const *base)
+    {
+        return _mm512_mask_i64gather_epi64(src, mask, index, base, scale);
+    }
+    template <int scale>
+    static zmm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_epi64(index, base, scale);
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask8(x);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epu64_mask(x, y, _MM_CMPINT_NLT);
+    }
+    static opmask_t eq(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_epu64_mask(x, y, _MM_CMPINT_EQ);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_si512(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_epu64(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_compressstoreu_epi64(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm512_mask_loadu_epi64(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_epi64(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi64(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_epu64(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_epi64(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        return _mm512_reduce_max_epu64(v);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        return _mm512_reduce_min_epu64(v);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_epi64(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        __m512d temp = _mm512_castsi512_pd(zmm);
+        return _mm512_castpd_si512(
+                _mm512_shuffle_pd(temp, temp, (_MM_PERM_ENUM)mask));
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        _mm512_storeu_si512(mem, x);
+    }
+};
+template <>
+struct zmm_vector<double> {
+    using type_t = double;
+    using zmm_t = __m512d;
+    using zmmi_t = __m512i;
+    using ymm_t = __m512d;
+    using opmask_t = __mmask8;
+    static const uint8_t numlanes = 8;
+
+    static type_t type_max()
+    {
+        return X86_SIMD_SORT_INFINITY;
+    }
+    static type_t type_min()
+    {
+        return -X86_SIMD_SORT_INFINITY;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_pd(type_max());
+    }
+
+    static zmmi_t seti(int v1,
+                       int v2,
+                       int v3,
+                       int v4,
+                       int v5,
+                       int v6,
+                       int v7,
+                       int v8)
+    {
+        return _mm512_set_epi64(v1, v2, v3, v4, v5, v6, v7, v8);
+    }
+
+    static zmm_t maskz_loadu(opmask_t mask, void const *mem)
+    {
+        return _mm512_maskz_loadu_pd(mask, mem);
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask8(x);
+    }
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_pd_mask(x, y, _CMP_GE_OQ);
+    }
+    static opmask_t eq(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_pd_mask(x, y, _CMP_EQ_OQ);
+    }
+    template <int type>
+    static opmask_t fpclass(zmm_t x)
+    {
+        return _mm512_fpclass_pd_mask(x, type);
+    }
+    template <int scale>
+    static zmm_t
+    mask_i64gather(zmm_t src, opmask_t mask, __m512i index, void const *base)
+    {
+        return _mm512_mask_i64gather_pd(src, mask, index, base, scale);
+    }
+    template <int scale>
+    static zmm_t i64gather(__m512i index, void const *base)
+    {
+        return _mm512_i64gather_pd(index, base, scale);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_pd(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_pd(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_compressstoreu_pd(mem, mask, x);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        return _mm512_mask_loadu_pd(x, mask, mem);
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_mask_mov_pd(x, mask, y);
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_pd(mem, mask, x);
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_pd(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_pd(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        return _mm512_reduce_max_pd(v);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        return _mm512_reduce_min_pd(v);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_pd(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        return _mm512_shuffle_pd(zmm, zmm, (_MM_PERM_ENUM)mask);
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        _mm512_storeu_pd(mem, x);
+    }
+};
+X86_SIMD_SORT_INLINE int64_t replace_nan_with_inf(double *arr, int64_t arrsize)
+{
+    int64_t nan_count = 0;
+    __mmask8 loadmask = 0xFF;
+    while (arrsize > 0) {
+        if (arrsize < 8) { loadmask = (0x01 << arrsize) - 0x01; }
+        __m512d in_zmm = _mm512_maskz_loadu_pd(loadmask, arr);
+        __mmask8 nanmask = _mm512_cmp_pd_mask(in_zmm, in_zmm, _CMP_NEQ_UQ);
+        nan_count += _mm_popcnt_u32((int32_t)nanmask);
+        _mm512_mask_storeu_pd(arr, nanmask, ZMM_MAX_DOUBLE);
+        arr += 8;
+        arrsize -= 8;
+    }
+    return nan_count;
+}
+
+X86_SIMD_SORT_INLINE void
+replace_inf_with_nan(double *arr, int64_t arrsize, int64_t nan_count)
+{
+    for (int64_t ii = arrsize - 1; nan_count > 0; --ii) {
+        arr[ii] = std::nan("1");
+        nan_count -= 1;
+    }
+}
+/*
+ * Assumes zmm is random and performs a full sorting network defined in
+ * https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort.svg
+ */
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t sort_zmm_64bit(zmm_t zmm)
+{
+    const typename vtype::zmmi_t rev_index = vtype::seti(NETWORK_64BIT_2);
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(zmm), 0xAA);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(vtype::seti(NETWORK_64BIT_1), zmm),
+            0xCC);
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(zmm), 0xAA);
+    zmm = cmp_merge<vtype>(zmm, vtype::permutexvar(rev_index, zmm), 0xF0);
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(vtype::seti(NETWORK_64BIT_3), zmm),
+            0xCC);
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(zmm), 0xAA);
+    return zmm;
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE type_t get_pivot_64bit(type_t *arr,
+                                            const int64_t left,
+                                            const int64_t right)
+{
+    // median of 8
+    int64_t size = (right - left) / 8;
+    using zmm_t = typename vtype::zmm_t;
+    __m512i rand_index = _mm512_set_epi64(left + size,
+                                          left + 2 * size,
+                                          left + 3 * size,
+                                          left + 4 * size,
+                                          left + 5 * size,
+                                          left + 6 * size,
+                                          left + 7 * size,
+                                          left + 8 * size);
+    zmm_t rand_vec = vtype::template i64gather<sizeof(type_t)>(rand_index, arr);
+    // pivot will never be a nan, since there are no nan's!
+    zmm_t sort = sort_zmm_64bit<vtype>(rand_vec);
+    return ((type_t *)&sort)[4];
+}
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-keyvalue-networks.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-keyvalue-networks.hpp
new file mode 100644
index 00000000000..af3a2a98893
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-keyvalue-networks.hpp
@@ -0,0 +1,457 @@
+
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t = typename vtype1::zmm_t,
+          typename index_type = typename vtype2::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t sort_zmm_64bit(zmm_t key_zmm, index_type &index_zmm)
+{
+    const typename vtype1::zmmi_t rev_index1 = vtype1::seti(NETWORK_64BIT_2);
+    const typename vtype2::zmmi_t rev_index2 = vtype2::seti(NETWORK_64BIT_2);
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(key_zmm),
+            index_zmm,
+            vtype2::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(index_zmm),
+            0xAA);
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::permutexvar(vtype1::seti(NETWORK_64BIT_1), key_zmm),
+            index_zmm,
+            vtype2::permutexvar(vtype2::seti(NETWORK_64BIT_1), index_zmm),
+            0xCC);
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(key_zmm),
+            index_zmm,
+            vtype2::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(index_zmm),
+            0xAA);
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::permutexvar(rev_index1, key_zmm),
+            index_zmm,
+            vtype2::permutexvar(rev_index2, index_zmm),
+            0xF0);
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::permutexvar(vtype1::seti(NETWORK_64BIT_3), key_zmm),
+            index_zmm,
+            vtype2::permutexvar(vtype2::seti(NETWORK_64BIT_3), index_zmm),
+            0xCC);
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(key_zmm),
+            index_zmm,
+            vtype2::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(index_zmm),
+            0xAA);
+    return key_zmm;
+}
+// Assumes zmm is bitonic and performs a recursive half cleaner
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t = typename vtype1::zmm_t,
+          typename index_type = typename vtype2::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t bitonic_merge_zmm_64bit(zmm_t key_zmm,
+                                                   index_type &index_zmm)
+{
+
+    // 1) half_cleaner[8]: compare 0-4, 1-5, 2-6, 3-7
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::permutexvar(vtype1::seti(NETWORK_64BIT_4), key_zmm),
+            index_zmm,
+            vtype2::permutexvar(vtype2::seti(NETWORK_64BIT_4), index_zmm),
+            0xF0);
+    // 2) half_cleaner[4]
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::permutexvar(vtype1::seti(NETWORK_64BIT_3), key_zmm),
+            index_zmm,
+            vtype2::permutexvar(vtype2::seti(NETWORK_64BIT_3), index_zmm),
+            0xCC);
+    // 3) half_cleaner[1]
+    key_zmm = cmp_merge<vtype1, vtype2>(
+            key_zmm,
+            vtype1::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(key_zmm),
+            index_zmm,
+            vtype2::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(index_zmm),
+            0xAA);
+    return key_zmm;
+}
+// Assumes zmm1 and zmm2 are sorted and performs a recursive half cleaner
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t = typename vtype1::zmm_t,
+          typename index_type = typename vtype2::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_two_zmm_64bit(zmm_t &key_zmm1,
+                                                      zmm_t &key_zmm2,
+                                                      index_type &index_zmm1,
+                                                      index_type &index_zmm2)
+{
+    const typename vtype1::zmmi_t rev_index1 = vtype1::seti(NETWORK_64BIT_2);
+    const typename vtype2::zmmi_t rev_index2 = vtype2::seti(NETWORK_64BIT_2);
+    // 1) First step of a merging network: coex of zmm1 and zmm2 reversed
+    key_zmm2 = vtype1::permutexvar(rev_index1, key_zmm2);
+    index_zmm2 = vtype2::permutexvar(rev_index2, index_zmm2);
+
+    zmm_t key_zmm3 = vtype1::min(key_zmm1, key_zmm2);
+    zmm_t key_zmm4 = vtype1::max(key_zmm1, key_zmm2);
+
+    typename vtype1::opmask_t movmask = vtype1::eq(key_zmm3, key_zmm1);
+
+    index_type index_zmm3 = vtype2::mask_mov(index_zmm2, movmask, index_zmm1);
+    index_type index_zmm4 = vtype2::mask_mov(index_zmm1, movmask, index_zmm2);
+
+    /* need to reverse the lower registers to keep the correct order */
+    key_zmm4 = vtype1::permutexvar(rev_index1, key_zmm4);
+    index_zmm4 = vtype2::permutexvar(rev_index2, index_zmm4);
+
+    // 2) Recursive half cleaner for each
+    key_zmm1 = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm3, index_zmm3);
+    key_zmm2 = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm4, index_zmm4);
+    index_zmm1 = index_zmm3;
+    index_zmm2 = index_zmm4;
+}
+// Assumes [zmm0, zmm1] and [zmm2, zmm3] are sorted and performs a recursive
+// half cleaner
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t = typename vtype1::zmm_t,
+          typename index_type = typename vtype2::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_four_zmm_64bit(zmm_t *key_zmm,
+                                                       index_type *index_zmm)
+{
+    const typename vtype1::zmmi_t rev_index1 = vtype1::seti(NETWORK_64BIT_2);
+    const typename vtype2::zmmi_t rev_index2 = vtype2::seti(NETWORK_64BIT_2);
+    // 1) First step of a merging network
+    zmm_t key_zmm2r = vtype1::permutexvar(rev_index1, key_zmm[2]);
+    zmm_t key_zmm3r = vtype1::permutexvar(rev_index1, key_zmm[3]);
+    index_type index_zmm2r = vtype2::permutexvar(rev_index2, index_zmm[2]);
+    index_type index_zmm3r = vtype2::permutexvar(rev_index2, index_zmm[3]);
+
+    zmm_t key_zmm_t1 = vtype1::min(key_zmm[0], key_zmm3r);
+    zmm_t key_zmm_t2 = vtype1::min(key_zmm[1], key_zmm2r);
+    zmm_t key_zmm_m1 = vtype1::max(key_zmm[0], key_zmm3r);
+    zmm_t key_zmm_m2 = vtype1::max(key_zmm[1], key_zmm2r);
+
+    typename vtype1::opmask_t movmask1 = vtype1::eq(key_zmm_t1, key_zmm[0]);
+    typename vtype1::opmask_t movmask2 = vtype1::eq(key_zmm_t2, key_zmm[1]);
+
+    index_type index_zmm_t1 = vtype2::mask_mov(
+            index_zmm3r, movmask1, index_zmm[0]);
+    index_type index_zmm_m1 = vtype2::mask_mov(
+            index_zmm[0], movmask1, index_zmm3r);
+    index_type index_zmm_t2 = vtype2::mask_mov(
+            index_zmm2r, movmask2, index_zmm[1]);
+    index_type index_zmm_m2 = vtype2::mask_mov(
+            index_zmm[1], movmask2, index_zmm2r);
+
+    // 2) Recursive half clearer: 16
+    zmm_t key_zmm_t3 = vtype1::permutexvar(rev_index1, key_zmm_m2);
+    zmm_t key_zmm_t4 = vtype1::permutexvar(rev_index1, key_zmm_m1);
+    index_type index_zmm_t3 = vtype2::permutexvar(rev_index2, index_zmm_m2);
+    index_type index_zmm_t4 = vtype2::permutexvar(rev_index2, index_zmm_m1);
+
+    zmm_t key_zmm0 = vtype1::min(key_zmm_t1, key_zmm_t2);
+    zmm_t key_zmm1 = vtype1::max(key_zmm_t1, key_zmm_t2);
+    zmm_t key_zmm2 = vtype1::min(key_zmm_t3, key_zmm_t4);
+    zmm_t key_zmm3 = vtype1::max(key_zmm_t3, key_zmm_t4);
+
+    movmask1 = vtype1::eq(key_zmm0, key_zmm_t1);
+    movmask2 = vtype1::eq(key_zmm2, key_zmm_t3);
+
+    index_type index_zmm0 = vtype2::mask_mov(
+            index_zmm_t2, movmask1, index_zmm_t1);
+    index_type index_zmm1 = vtype2::mask_mov(
+            index_zmm_t1, movmask1, index_zmm_t2);
+    index_type index_zmm2 = vtype2::mask_mov(
+            index_zmm_t4, movmask2, index_zmm_t3);
+    index_type index_zmm3 = vtype2::mask_mov(
+            index_zmm_t3, movmask2, index_zmm_t4);
+
+    key_zmm[0] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm0, index_zmm0);
+    key_zmm[1] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm1, index_zmm1);
+    key_zmm[2] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm2, index_zmm2);
+    key_zmm[3] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm3, index_zmm3);
+
+    index_zmm[0] = index_zmm0;
+    index_zmm[1] = index_zmm1;
+    index_zmm[2] = index_zmm2;
+    index_zmm[3] = index_zmm3;
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t = typename vtype1::zmm_t,
+          typename index_type = typename vtype2::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_eight_zmm_64bit(zmm_t *key_zmm,
+                                                        index_type *index_zmm)
+{
+    const typename vtype1::zmmi_t rev_index1 = vtype1::seti(NETWORK_64BIT_2);
+    const typename vtype2::zmmi_t rev_index2 = vtype2::seti(NETWORK_64BIT_2);
+    zmm_t key_zmm4r = vtype1::permutexvar(rev_index1, key_zmm[4]);
+    zmm_t key_zmm5r = vtype1::permutexvar(rev_index1, key_zmm[5]);
+    zmm_t key_zmm6r = vtype1::permutexvar(rev_index1, key_zmm[6]);
+    zmm_t key_zmm7r = vtype1::permutexvar(rev_index1, key_zmm[7]);
+    index_type index_zmm4r = vtype2::permutexvar(rev_index2, index_zmm[4]);
+    index_type index_zmm5r = vtype2::permutexvar(rev_index2, index_zmm[5]);
+    index_type index_zmm6r = vtype2::permutexvar(rev_index2, index_zmm[6]);
+    index_type index_zmm7r = vtype2::permutexvar(rev_index2, index_zmm[7]);
+
+    zmm_t key_zmm_t1 = vtype1::min(key_zmm[0], key_zmm7r);
+    zmm_t key_zmm_t2 = vtype1::min(key_zmm[1], key_zmm6r);
+    zmm_t key_zmm_t3 = vtype1::min(key_zmm[2], key_zmm5r);
+    zmm_t key_zmm_t4 = vtype1::min(key_zmm[3], key_zmm4r);
+
+    zmm_t key_zmm_m1 = vtype1::max(key_zmm[0], key_zmm7r);
+    zmm_t key_zmm_m2 = vtype1::max(key_zmm[1], key_zmm6r);
+    zmm_t key_zmm_m3 = vtype1::max(key_zmm[2], key_zmm5r);
+    zmm_t key_zmm_m4 = vtype1::max(key_zmm[3], key_zmm4r);
+
+    typename vtype1::opmask_t movmask1 = vtype1::eq(key_zmm_t1, key_zmm[0]);
+    typename vtype1::opmask_t movmask2 = vtype1::eq(key_zmm_t2, key_zmm[1]);
+    typename vtype1::opmask_t movmask3 = vtype1::eq(key_zmm_t3, key_zmm[2]);
+    typename vtype1::opmask_t movmask4 = vtype1::eq(key_zmm_t4, key_zmm[3]);
+
+    index_type index_zmm_t1 = vtype2::mask_mov(
+            index_zmm7r, movmask1, index_zmm[0]);
+    index_type index_zmm_m1 = vtype2::mask_mov(
+            index_zmm[0], movmask1, index_zmm7r);
+    index_type index_zmm_t2 = vtype2::mask_mov(
+            index_zmm6r, movmask2, index_zmm[1]);
+    index_type index_zmm_m2 = vtype2::mask_mov(
+            index_zmm[1], movmask2, index_zmm6r);
+    index_type index_zmm_t3 = vtype2::mask_mov(
+            index_zmm5r, movmask3, index_zmm[2]);
+    index_type index_zmm_m3 = vtype2::mask_mov(
+            index_zmm[2], movmask3, index_zmm5r);
+    index_type index_zmm_t4 = vtype2::mask_mov(
+            index_zmm4r, movmask4, index_zmm[3]);
+    index_type index_zmm_m4 = vtype2::mask_mov(
+            index_zmm[3], movmask4, index_zmm4r);
+
+    zmm_t key_zmm_t5 = vtype1::permutexvar(rev_index1, key_zmm_m4);
+    zmm_t key_zmm_t6 = vtype1::permutexvar(rev_index1, key_zmm_m3);
+    zmm_t key_zmm_t7 = vtype1::permutexvar(rev_index1, key_zmm_m2);
+    zmm_t key_zmm_t8 = vtype1::permutexvar(rev_index1, key_zmm_m1);
+    index_type index_zmm_t5 = vtype2::permutexvar(rev_index2, index_zmm_m4);
+    index_type index_zmm_t6 = vtype2::permutexvar(rev_index2, index_zmm_m3);
+    index_type index_zmm_t7 = vtype2::permutexvar(rev_index2, index_zmm_m2);
+    index_type index_zmm_t8 = vtype2::permutexvar(rev_index2, index_zmm_m1);
+
+    COEX<vtype1, vtype2>(key_zmm_t1, key_zmm_t3, index_zmm_t1, index_zmm_t3);
+    COEX<vtype1, vtype2>(key_zmm_t2, key_zmm_t4, index_zmm_t2, index_zmm_t4);
+    COEX<vtype1, vtype2>(key_zmm_t5, key_zmm_t7, index_zmm_t5, index_zmm_t7);
+    COEX<vtype1, vtype2>(key_zmm_t6, key_zmm_t8, index_zmm_t6, index_zmm_t8);
+    COEX<vtype1, vtype2>(key_zmm_t1, key_zmm_t2, index_zmm_t1, index_zmm_t2);
+    COEX<vtype1, vtype2>(key_zmm_t3, key_zmm_t4, index_zmm_t3, index_zmm_t4);
+    COEX<vtype1, vtype2>(key_zmm_t5, key_zmm_t6, index_zmm_t5, index_zmm_t6);
+    COEX<vtype1, vtype2>(key_zmm_t7, key_zmm_t8, index_zmm_t7, index_zmm_t8);
+    key_zmm[0]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t1, index_zmm_t1);
+    key_zmm[1]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t2, index_zmm_t2);
+    key_zmm[2]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t3, index_zmm_t3);
+    key_zmm[3]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t4, index_zmm_t4);
+    key_zmm[4]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t5, index_zmm_t5);
+    key_zmm[5]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t6, index_zmm_t6);
+    key_zmm[6]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t7, index_zmm_t7);
+    key_zmm[7]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t8, index_zmm_t8);
+
+    index_zmm[0] = index_zmm_t1;
+    index_zmm[1] = index_zmm_t2;
+    index_zmm[2] = index_zmm_t3;
+    index_zmm[3] = index_zmm_t4;
+    index_zmm[4] = index_zmm_t5;
+    index_zmm[5] = index_zmm_t6;
+    index_zmm[6] = index_zmm_t7;
+    index_zmm[7] = index_zmm_t8;
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t = typename vtype1::zmm_t,
+          typename index_type = typename vtype2::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_sixteen_zmm_64bit(zmm_t *key_zmm,
+                                                          index_type *index_zmm)
+{
+    const typename vtype1::zmmi_t rev_index1 = vtype1::seti(NETWORK_64BIT_2);
+    const typename vtype2::zmmi_t rev_index2 = vtype2::seti(NETWORK_64BIT_2);
+    zmm_t key_zmm8r = vtype1::permutexvar(rev_index1, key_zmm[8]);
+    zmm_t key_zmm9r = vtype1::permutexvar(rev_index1, key_zmm[9]);
+    zmm_t key_zmm10r = vtype1::permutexvar(rev_index1, key_zmm[10]);
+    zmm_t key_zmm11r = vtype1::permutexvar(rev_index1, key_zmm[11]);
+    zmm_t key_zmm12r = vtype1::permutexvar(rev_index1, key_zmm[12]);
+    zmm_t key_zmm13r = vtype1::permutexvar(rev_index1, key_zmm[13]);
+    zmm_t key_zmm14r = vtype1::permutexvar(rev_index1, key_zmm[14]);
+    zmm_t key_zmm15r = vtype1::permutexvar(rev_index1, key_zmm[15]);
+
+    index_type index_zmm8r = vtype2::permutexvar(rev_index2, index_zmm[8]);
+    index_type index_zmm9r = vtype2::permutexvar(rev_index2, index_zmm[9]);
+    index_type index_zmm10r = vtype2::permutexvar(rev_index2, index_zmm[10]);
+    index_type index_zmm11r = vtype2::permutexvar(rev_index2, index_zmm[11]);
+    index_type index_zmm12r = vtype2::permutexvar(rev_index2, index_zmm[12]);
+    index_type index_zmm13r = vtype2::permutexvar(rev_index2, index_zmm[13]);
+    index_type index_zmm14r = vtype2::permutexvar(rev_index2, index_zmm[14]);
+    index_type index_zmm15r = vtype2::permutexvar(rev_index2, index_zmm[15]);
+
+    zmm_t key_zmm_t1 = vtype1::min(key_zmm[0], key_zmm15r);
+    zmm_t key_zmm_t2 = vtype1::min(key_zmm[1], key_zmm14r);
+    zmm_t key_zmm_t3 = vtype1::min(key_zmm[2], key_zmm13r);
+    zmm_t key_zmm_t4 = vtype1::min(key_zmm[3], key_zmm12r);
+    zmm_t key_zmm_t5 = vtype1::min(key_zmm[4], key_zmm11r);
+    zmm_t key_zmm_t6 = vtype1::min(key_zmm[5], key_zmm10r);
+    zmm_t key_zmm_t7 = vtype1::min(key_zmm[6], key_zmm9r);
+    zmm_t key_zmm_t8 = vtype1::min(key_zmm[7], key_zmm8r);
+
+    zmm_t key_zmm_m1 = vtype1::max(key_zmm[0], key_zmm15r);
+    zmm_t key_zmm_m2 = vtype1::max(key_zmm[1], key_zmm14r);
+    zmm_t key_zmm_m3 = vtype1::max(key_zmm[2], key_zmm13r);
+    zmm_t key_zmm_m4 = vtype1::max(key_zmm[3], key_zmm12r);
+    zmm_t key_zmm_m5 = vtype1::max(key_zmm[4], key_zmm11r);
+    zmm_t key_zmm_m6 = vtype1::max(key_zmm[5], key_zmm10r);
+    zmm_t key_zmm_m7 = vtype1::max(key_zmm[6], key_zmm9r);
+    zmm_t key_zmm_m8 = vtype1::max(key_zmm[7], key_zmm8r);
+
+    index_type index_zmm_t1 = vtype2::mask_mov(
+            index_zmm15r, vtype1::eq(key_zmm_t1, key_zmm[0]), index_zmm[0]);
+    index_type index_zmm_m1 = vtype2::mask_mov(
+            index_zmm[0], vtype1::eq(key_zmm_t1, key_zmm[0]), index_zmm15r);
+    index_type index_zmm_t2 = vtype2::mask_mov(
+            index_zmm14r, vtype1::eq(key_zmm_t2, key_zmm[1]), index_zmm[1]);
+    index_type index_zmm_m2 = vtype2::mask_mov(
+            index_zmm[1], vtype1::eq(key_zmm_t2, key_zmm[1]), index_zmm14r);
+    index_type index_zmm_t3 = vtype2::mask_mov(
+            index_zmm13r, vtype1::eq(key_zmm_t3, key_zmm[2]), index_zmm[2]);
+    index_type index_zmm_m3 = vtype2::mask_mov(
+            index_zmm[2], vtype1::eq(key_zmm_t3, key_zmm[2]), index_zmm13r);
+    index_type index_zmm_t4 = vtype2::mask_mov(
+            index_zmm12r, vtype1::eq(key_zmm_t4, key_zmm[3]), index_zmm[3]);
+    index_type index_zmm_m4 = vtype2::mask_mov(
+            index_zmm[3], vtype1::eq(key_zmm_t4, key_zmm[3]), index_zmm12r);
+
+    index_type index_zmm_t5 = vtype2::mask_mov(
+            index_zmm11r, vtype1::eq(key_zmm_t5, key_zmm[4]), index_zmm[4]);
+    index_type index_zmm_m5 = vtype2::mask_mov(
+            index_zmm[4], vtype1::eq(key_zmm_t5, key_zmm[4]), index_zmm11r);
+    index_type index_zmm_t6 = vtype2::mask_mov(
+            index_zmm10r, vtype1::eq(key_zmm_t6, key_zmm[5]), index_zmm[5]);
+    index_type index_zmm_m6 = vtype2::mask_mov(
+            index_zmm[5], vtype1::eq(key_zmm_t6, key_zmm[5]), index_zmm10r);
+    index_type index_zmm_t7 = vtype2::mask_mov(
+            index_zmm9r, vtype1::eq(key_zmm_t7, key_zmm[6]), index_zmm[6]);
+    index_type index_zmm_m7 = vtype2::mask_mov(
+            index_zmm[6], vtype1::eq(key_zmm_t7, key_zmm[6]), index_zmm9r);
+    index_type index_zmm_t8 = vtype2::mask_mov(
+            index_zmm8r, vtype1::eq(key_zmm_t8, key_zmm[7]), index_zmm[7]);
+    index_type index_zmm_m8 = vtype2::mask_mov(
+            index_zmm[7], vtype1::eq(key_zmm_t8, key_zmm[7]), index_zmm8r);
+
+    zmm_t key_zmm_t9 = vtype1::permutexvar(rev_index1, key_zmm_m8);
+    zmm_t key_zmm_t10 = vtype1::permutexvar(rev_index1, key_zmm_m7);
+    zmm_t key_zmm_t11 = vtype1::permutexvar(rev_index1, key_zmm_m6);
+    zmm_t key_zmm_t12 = vtype1::permutexvar(rev_index1, key_zmm_m5);
+    zmm_t key_zmm_t13 = vtype1::permutexvar(rev_index1, key_zmm_m4);
+    zmm_t key_zmm_t14 = vtype1::permutexvar(rev_index1, key_zmm_m3);
+    zmm_t key_zmm_t15 = vtype1::permutexvar(rev_index1, key_zmm_m2);
+    zmm_t key_zmm_t16 = vtype1::permutexvar(rev_index1, key_zmm_m1);
+    index_type index_zmm_t9 = vtype2::permutexvar(rev_index2, index_zmm_m8);
+    index_type index_zmm_t10 = vtype2::permutexvar(rev_index2, index_zmm_m7);
+    index_type index_zmm_t11 = vtype2::permutexvar(rev_index2, index_zmm_m6);
+    index_type index_zmm_t12 = vtype2::permutexvar(rev_index2, index_zmm_m5);
+    index_type index_zmm_t13 = vtype2::permutexvar(rev_index2, index_zmm_m4);
+    index_type index_zmm_t14 = vtype2::permutexvar(rev_index2, index_zmm_m3);
+    index_type index_zmm_t15 = vtype2::permutexvar(rev_index2, index_zmm_m2);
+    index_type index_zmm_t16 = vtype2::permutexvar(rev_index2, index_zmm_m1);
+
+    COEX<vtype1, vtype2>(key_zmm_t1, key_zmm_t5, index_zmm_t1, index_zmm_t5);
+    COEX<vtype1, vtype2>(key_zmm_t2, key_zmm_t6, index_zmm_t2, index_zmm_t6);
+    COEX<vtype1, vtype2>(key_zmm_t3, key_zmm_t7, index_zmm_t3, index_zmm_t7);
+    COEX<vtype1, vtype2>(key_zmm_t4, key_zmm_t8, index_zmm_t4, index_zmm_t8);
+    COEX<vtype1, vtype2>(key_zmm_t9, key_zmm_t13, index_zmm_t9, index_zmm_t13);
+    COEX<vtype1, vtype2>(
+            key_zmm_t10, key_zmm_t14, index_zmm_t10, index_zmm_t14);
+    COEX<vtype1, vtype2>(
+            key_zmm_t11, key_zmm_t15, index_zmm_t11, index_zmm_t15);
+    COEX<vtype1, vtype2>(
+            key_zmm_t12, key_zmm_t16, index_zmm_t12, index_zmm_t16);
+
+    COEX<vtype1, vtype2>(key_zmm_t1, key_zmm_t3, index_zmm_t1, index_zmm_t3);
+    COEX<vtype1, vtype2>(key_zmm_t2, key_zmm_t4, index_zmm_t2, index_zmm_t4);
+    COEX<vtype1, vtype2>(key_zmm_t5, key_zmm_t7, index_zmm_t5, index_zmm_t7);
+    COEX<vtype1, vtype2>(key_zmm_t6, key_zmm_t8, index_zmm_t6, index_zmm_t8);
+    COEX<vtype1, vtype2>(key_zmm_t9, key_zmm_t11, index_zmm_t9, index_zmm_t11);
+    COEX<vtype1, vtype2>(
+            key_zmm_t10, key_zmm_t12, index_zmm_t10, index_zmm_t12);
+    COEX<vtype1, vtype2>(
+            key_zmm_t13, key_zmm_t15, index_zmm_t13, index_zmm_t15);
+    COEX<vtype1, vtype2>(
+            key_zmm_t14, key_zmm_t16, index_zmm_t14, index_zmm_t16);
+
+    COEX<vtype1, vtype2>(key_zmm_t1, key_zmm_t2, index_zmm_t1, index_zmm_t2);
+    COEX<vtype1, vtype2>(key_zmm_t3, key_zmm_t4, index_zmm_t3, index_zmm_t4);
+    COEX<vtype1, vtype2>(key_zmm_t5, key_zmm_t6, index_zmm_t5, index_zmm_t6);
+    COEX<vtype1, vtype2>(key_zmm_t7, key_zmm_t8, index_zmm_t7, index_zmm_t8);
+    COEX<vtype1, vtype2>(key_zmm_t9, key_zmm_t10, index_zmm_t9, index_zmm_t10);
+    COEX<vtype1, vtype2>(
+            key_zmm_t11, key_zmm_t12, index_zmm_t11, index_zmm_t12);
+    COEX<vtype1, vtype2>(
+            key_zmm_t13, key_zmm_t14, index_zmm_t13, index_zmm_t14);
+    COEX<vtype1, vtype2>(
+            key_zmm_t15, key_zmm_t16, index_zmm_t15, index_zmm_t16);
+    //
+    key_zmm[0]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t1, index_zmm_t1);
+    key_zmm[1]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t2, index_zmm_t2);
+    key_zmm[2]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t3, index_zmm_t3);
+    key_zmm[3]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t4, index_zmm_t4);
+    key_zmm[4]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t5, index_zmm_t5);
+    key_zmm[5]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t6, index_zmm_t6);
+    key_zmm[6]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t7, index_zmm_t7);
+    key_zmm[7]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t8, index_zmm_t8);
+    key_zmm[8]
+            = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t9, index_zmm_t9);
+    key_zmm[9] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t10,
+                                                         index_zmm_t10);
+    key_zmm[10] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t11,
+                                                          index_zmm_t11);
+    key_zmm[11] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t12,
+                                                          index_zmm_t12);
+    key_zmm[12] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t13,
+                                                          index_zmm_t13);
+    key_zmm[13] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t14,
+                                                          index_zmm_t14);
+    key_zmm[14] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t15,
+                                                          index_zmm_t15);
+    key_zmm[15] = bitonic_merge_zmm_64bit<vtype1, vtype2>(key_zmm_t16,
+                                                          index_zmm_t16);
+
+    index_zmm[0] = index_zmm_t1;
+    index_zmm[1] = index_zmm_t2;
+    index_zmm[2] = index_zmm_t3;
+    index_zmm[3] = index_zmm_t4;
+    index_zmm[4] = index_zmm_t5;
+    index_zmm[5] = index_zmm_t6;
+    index_zmm[6] = index_zmm_t7;
+    index_zmm[7] = index_zmm_t8;
+    index_zmm[8] = index_zmm_t9;
+    index_zmm[9] = index_zmm_t10;
+    index_zmm[10] = index_zmm_t11;
+    index_zmm[11] = index_zmm_t12;
+    index_zmm[12] = index_zmm_t13;
+    index_zmm[13] = index_zmm_t14;
+    index_zmm[14] = index_zmm_t15;
+    index_zmm[15] = index_zmm_t16;
+}
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-keyvaluesort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-keyvaluesort.hpp
new file mode 100644
index 00000000000..f721f5c8518
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-keyvaluesort.hpp
@@ -0,0 +1,472 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Liu Zhuan <zhuan.liu@intel.com>
+ *          Tang Xi <xi.tang@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_QSORT_64BIT_KV
+#define AVX512_QSORT_64BIT_KV
+
+#include "avx512-64bit-common.h"
+#include "avx512-64bit-keyvalue-networks.hpp"
+
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+X86_SIMD_SORT_INLINE void
+sort_8_64bit(type1_t *keys, type2_t *indexes, int32_t N)
+{
+    typename vtype1::opmask_t load_mask = (0x01 << N) - 0x01;
+    typename vtype1::zmm_t key_zmm
+            = vtype1::mask_loadu(vtype1::zmm_max(), load_mask, keys);
+
+    typename vtype2::zmm_t index_zmm
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask, indexes);
+    vtype1::mask_storeu(keys,
+                        load_mask,
+                        sort_zmm_64bit<vtype1, vtype2>(key_zmm, index_zmm));
+    vtype2::mask_storeu(indexes, load_mask, index_zmm);
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+X86_SIMD_SORT_INLINE void
+sort_16_64bit(type1_t *keys, type2_t *indexes, int32_t N)
+{
+    if (N <= 8) {
+        sort_8_64bit<vtype1, vtype2>(keys, indexes, N);
+        return;
+    }
+    using zmm_t = typename vtype1::zmm_t;
+    using index_type = typename vtype2::zmm_t;
+
+    typename vtype1::opmask_t load_mask = (0x01 << (N - 8)) - 0x01;
+
+    zmm_t key_zmm1 = vtype1::loadu(keys);
+    zmm_t key_zmm2 = vtype1::mask_loadu(vtype1::zmm_max(), load_mask, keys + 8);
+
+    index_type index_zmm1 = vtype2::loadu(indexes);
+    index_type index_zmm2
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask, indexes + 8);
+
+    key_zmm1 = sort_zmm_64bit<vtype1, vtype2>(key_zmm1, index_zmm1);
+    key_zmm2 = sort_zmm_64bit<vtype1, vtype2>(key_zmm2, index_zmm2);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm1, key_zmm2, index_zmm1, index_zmm2);
+
+    vtype2::storeu(indexes, index_zmm1);
+    vtype2::mask_storeu(indexes + 8, load_mask, index_zmm2);
+
+    vtype1::storeu(keys, key_zmm1);
+    vtype1::mask_storeu(keys + 8, load_mask, key_zmm2);
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+X86_SIMD_SORT_INLINE void
+sort_32_64bit(type1_t *keys, type2_t *indexes, int32_t N)
+{
+    if (N <= 16) {
+        sort_16_64bit<vtype1, vtype2>(keys, indexes, N);
+        return;
+    }
+    using zmm_t = typename vtype1::zmm_t;
+    using opmask_t = typename vtype2::opmask_t;
+    using index_type = typename vtype2::zmm_t;
+    zmm_t key_zmm[4];
+    index_type index_zmm[4];
+
+    key_zmm[0] = vtype1::loadu(keys);
+    key_zmm[1] = vtype1::loadu(keys + 8);
+
+    index_zmm[0] = vtype2::loadu(indexes);
+    index_zmm[1] = vtype2::loadu(indexes + 8);
+
+    key_zmm[0] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[0], index_zmm[0]);
+    key_zmm[1] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[1], index_zmm[1]);
+
+    opmask_t load_mask1 = 0xFF, load_mask2 = 0xFF;
+    uint64_t combined_mask = (0x1ull << (N - 16)) - 0x1ull;
+    load_mask1 = (combined_mask)&0xFF;
+    load_mask2 = (combined_mask >> 8) & 0xFF;
+    key_zmm[2] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask1, keys + 16);
+    key_zmm[3] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask2, keys + 24);
+
+    index_zmm[2]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask1, indexes + 16);
+    index_zmm[3]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask2, indexes + 24);
+
+    key_zmm[2] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[2], index_zmm[2]);
+    key_zmm[3] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[3], index_zmm[3]);
+
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[0], key_zmm[1], index_zmm[0], index_zmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[2], key_zmm[3], index_zmm[2], index_zmm[3]);
+    bitonic_merge_four_zmm_64bit<vtype1, vtype2>(key_zmm, index_zmm);
+
+    vtype2::storeu(indexes, index_zmm[0]);
+    vtype2::storeu(indexes + 8, index_zmm[1]);
+    vtype2::mask_storeu(indexes + 16, load_mask1, index_zmm[2]);
+    vtype2::mask_storeu(indexes + 24, load_mask2, index_zmm[3]);
+
+    vtype1::storeu(keys, key_zmm[0]);
+    vtype1::storeu(keys + 8, key_zmm[1]);
+    vtype1::mask_storeu(keys + 16, load_mask1, key_zmm[2]);
+    vtype1::mask_storeu(keys + 24, load_mask2, key_zmm[3]);
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+X86_SIMD_SORT_INLINE void
+sort_64_64bit(type1_t *keys, type2_t *indexes, int32_t N)
+{
+    if (N <= 32) {
+        sort_32_64bit<vtype1, vtype2>(keys, indexes, N);
+        return;
+    }
+    using zmm_t = typename vtype1::zmm_t;
+    using opmask_t = typename vtype1::opmask_t;
+    using index_type = typename vtype2::zmm_t;
+    zmm_t key_zmm[8];
+    index_type index_zmm[8];
+
+    key_zmm[0] = vtype1::loadu(keys);
+    key_zmm[1] = vtype1::loadu(keys + 8);
+    key_zmm[2] = vtype1::loadu(keys + 16);
+    key_zmm[3] = vtype1::loadu(keys + 24);
+
+    index_zmm[0] = vtype2::loadu(indexes);
+    index_zmm[1] = vtype2::loadu(indexes + 8);
+    index_zmm[2] = vtype2::loadu(indexes + 16);
+    index_zmm[3] = vtype2::loadu(indexes + 24);
+    key_zmm[0] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[0], index_zmm[0]);
+    key_zmm[1] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[1], index_zmm[1]);
+    key_zmm[2] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[2], index_zmm[2]);
+    key_zmm[3] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[3], index_zmm[3]);
+
+    opmask_t load_mask1 = 0xFF, load_mask2 = 0xFF;
+    opmask_t load_mask3 = 0xFF, load_mask4 = 0xFF;
+    // N-32 >= 1
+    uint64_t combined_mask = (0x1ull << (N - 32)) - 0x1ull;
+    load_mask1 = (combined_mask)&0xFF;
+    load_mask2 = (combined_mask >> 8) & 0xFF;
+    load_mask3 = (combined_mask >> 16) & 0xFF;
+    load_mask4 = (combined_mask >> 24) & 0xFF;
+    key_zmm[4] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask1, keys + 32);
+    key_zmm[5] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask2, keys + 40);
+    key_zmm[6] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask3, keys + 48);
+    key_zmm[7] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask4, keys + 56);
+
+    index_zmm[4]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask1, indexes + 32);
+    index_zmm[5]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask2, indexes + 40);
+    index_zmm[6]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask3, indexes + 48);
+    index_zmm[7]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask4, indexes + 56);
+    key_zmm[4] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[4], index_zmm[4]);
+    key_zmm[5] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[5], index_zmm[5]);
+    key_zmm[6] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[6], index_zmm[6]);
+    key_zmm[7] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[7], index_zmm[7]);
+
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[0], key_zmm[1], index_zmm[0], index_zmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[2], key_zmm[3], index_zmm[2], index_zmm[3]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[4], key_zmm[5], index_zmm[4], index_zmm[5]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[6], key_zmm[7], index_zmm[6], index_zmm[7]);
+    bitonic_merge_four_zmm_64bit<vtype1, vtype2>(key_zmm, index_zmm);
+    bitonic_merge_four_zmm_64bit<vtype1, vtype2>(key_zmm + 4, index_zmm + 4);
+    bitonic_merge_eight_zmm_64bit<vtype1, vtype2>(key_zmm, index_zmm);
+
+    vtype2::storeu(indexes, index_zmm[0]);
+    vtype2::storeu(indexes + 8, index_zmm[1]);
+    vtype2::storeu(indexes + 16, index_zmm[2]);
+    vtype2::storeu(indexes + 24, index_zmm[3]);
+    vtype2::mask_storeu(indexes + 32, load_mask1, index_zmm[4]);
+    vtype2::mask_storeu(indexes + 40, load_mask2, index_zmm[5]);
+    vtype2::mask_storeu(indexes + 48, load_mask3, index_zmm[6]);
+    vtype2::mask_storeu(indexes + 56, load_mask4, index_zmm[7]);
+
+    vtype1::storeu(keys, key_zmm[0]);
+    vtype1::storeu(keys + 8, key_zmm[1]);
+    vtype1::storeu(keys + 16, key_zmm[2]);
+    vtype1::storeu(keys + 24, key_zmm[3]);
+    vtype1::mask_storeu(keys + 32, load_mask1, key_zmm[4]);
+    vtype1::mask_storeu(keys + 40, load_mask2, key_zmm[5]);
+    vtype1::mask_storeu(keys + 48, load_mask3, key_zmm[6]);
+    vtype1::mask_storeu(keys + 56, load_mask4, key_zmm[7]);
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+X86_SIMD_SORT_INLINE void
+sort_128_64bit(type1_t *keys, type2_t *indexes, int32_t N)
+{
+    if (N <= 64) {
+        sort_64_64bit<vtype1, vtype2>(keys, indexes, N);
+        return;
+    }
+    using zmm_t = typename vtype1::zmm_t;
+    using index_type = typename vtype2::zmm_t;
+    using opmask_t = typename vtype1::opmask_t;
+    zmm_t key_zmm[16];
+    index_type index_zmm[16];
+
+    key_zmm[0] = vtype1::loadu(keys);
+    key_zmm[1] = vtype1::loadu(keys + 8);
+    key_zmm[2] = vtype1::loadu(keys + 16);
+    key_zmm[3] = vtype1::loadu(keys + 24);
+    key_zmm[4] = vtype1::loadu(keys + 32);
+    key_zmm[5] = vtype1::loadu(keys + 40);
+    key_zmm[6] = vtype1::loadu(keys + 48);
+    key_zmm[7] = vtype1::loadu(keys + 56);
+
+    index_zmm[0] = vtype2::loadu(indexes);
+    index_zmm[1] = vtype2::loadu(indexes + 8);
+    index_zmm[2] = vtype2::loadu(indexes + 16);
+    index_zmm[3] = vtype2::loadu(indexes + 24);
+    index_zmm[4] = vtype2::loadu(indexes + 32);
+    index_zmm[5] = vtype2::loadu(indexes + 40);
+    index_zmm[6] = vtype2::loadu(indexes + 48);
+    index_zmm[7] = vtype2::loadu(indexes + 56);
+    key_zmm[0] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[0], index_zmm[0]);
+    key_zmm[1] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[1], index_zmm[1]);
+    key_zmm[2] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[2], index_zmm[2]);
+    key_zmm[3] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[3], index_zmm[3]);
+    key_zmm[4] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[4], index_zmm[4]);
+    key_zmm[5] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[5], index_zmm[5]);
+    key_zmm[6] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[6], index_zmm[6]);
+    key_zmm[7] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[7], index_zmm[7]);
+
+    opmask_t load_mask1 = 0xFF, load_mask2 = 0xFF;
+    opmask_t load_mask3 = 0xFF, load_mask4 = 0xFF;
+    opmask_t load_mask5 = 0xFF, load_mask6 = 0xFF;
+    opmask_t load_mask7 = 0xFF, load_mask8 = 0xFF;
+    if (N != 128) {
+        uint64_t combined_mask = (0x1ull << (N - 64)) - 0x1ull;
+        load_mask1 = (combined_mask)&0xFF;
+        load_mask2 = (combined_mask >> 8) & 0xFF;
+        load_mask3 = (combined_mask >> 16) & 0xFF;
+        load_mask4 = (combined_mask >> 24) & 0xFF;
+        load_mask5 = (combined_mask >> 32) & 0xFF;
+        load_mask6 = (combined_mask >> 40) & 0xFF;
+        load_mask7 = (combined_mask >> 48) & 0xFF;
+        load_mask8 = (combined_mask >> 56) & 0xFF;
+    }
+    key_zmm[8] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask1, keys + 64);
+    key_zmm[9] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask2, keys + 72);
+    key_zmm[10] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask3, keys + 80);
+    key_zmm[11] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask4, keys + 88);
+    key_zmm[12] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask5, keys + 96);
+    key_zmm[13] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask6, keys + 104);
+    key_zmm[14] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask7, keys + 112);
+    key_zmm[15] = vtype1::mask_loadu(vtype1::zmm_max(), load_mask8, keys + 120);
+
+    index_zmm[8]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask1, indexes + 64);
+    index_zmm[9]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask2, indexes + 72);
+    index_zmm[10]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask3, indexes + 80);
+    index_zmm[11]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask4, indexes + 88);
+    index_zmm[12]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask5, indexes + 96);
+    index_zmm[13]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask6, indexes + 104);
+    index_zmm[14]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask7, indexes + 112);
+    index_zmm[15]
+            = vtype2::mask_loadu(vtype2::zmm_max(), load_mask8, indexes + 120);
+    key_zmm[8] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[8], index_zmm[8]);
+    key_zmm[9] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[9], index_zmm[9]);
+    key_zmm[10] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[10], index_zmm[10]);
+    key_zmm[11] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[11], index_zmm[11]);
+    key_zmm[12] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[12], index_zmm[12]);
+    key_zmm[13] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[13], index_zmm[13]);
+    key_zmm[14] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[14], index_zmm[14]);
+    key_zmm[15] = sort_zmm_64bit<vtype1, vtype2>(key_zmm[15], index_zmm[15]);
+
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[0], key_zmm[1], index_zmm[0], index_zmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[2], key_zmm[3], index_zmm[2], index_zmm[3]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[4], key_zmm[5], index_zmm[4], index_zmm[5]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[6], key_zmm[7], index_zmm[6], index_zmm[7]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[8], key_zmm[9], index_zmm[8], index_zmm[9]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[10], key_zmm[11], index_zmm[10], index_zmm[11]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[12], key_zmm[13], index_zmm[12], index_zmm[13]);
+    bitonic_merge_two_zmm_64bit<vtype1, vtype2>(
+            key_zmm[14], key_zmm[15], index_zmm[14], index_zmm[15]);
+    bitonic_merge_four_zmm_64bit<vtype1, vtype2>(key_zmm, index_zmm);
+    bitonic_merge_four_zmm_64bit<vtype1, vtype2>(key_zmm + 4, index_zmm + 4);
+    bitonic_merge_four_zmm_64bit<vtype1, vtype2>(key_zmm + 8, index_zmm + 8);
+    bitonic_merge_four_zmm_64bit<vtype1, vtype2>(key_zmm + 12, index_zmm + 12);
+    bitonic_merge_eight_zmm_64bit<vtype1, vtype2>(key_zmm, index_zmm);
+    bitonic_merge_eight_zmm_64bit<vtype1, vtype2>(key_zmm + 8, index_zmm + 8);
+    bitonic_merge_sixteen_zmm_64bit<vtype1, vtype2>(key_zmm, index_zmm);
+    vtype2::storeu(indexes, index_zmm[0]);
+    vtype2::storeu(indexes + 8, index_zmm[1]);
+    vtype2::storeu(indexes + 16, index_zmm[2]);
+    vtype2::storeu(indexes + 24, index_zmm[3]);
+    vtype2::storeu(indexes + 32, index_zmm[4]);
+    vtype2::storeu(indexes + 40, index_zmm[5]);
+    vtype2::storeu(indexes + 48, index_zmm[6]);
+    vtype2::storeu(indexes + 56, index_zmm[7]);
+    vtype2::mask_storeu(indexes + 64, load_mask1, index_zmm[8]);
+    vtype2::mask_storeu(indexes + 72, load_mask2, index_zmm[9]);
+    vtype2::mask_storeu(indexes + 80, load_mask3, index_zmm[10]);
+    vtype2::mask_storeu(indexes + 88, load_mask4, index_zmm[11]);
+    vtype2::mask_storeu(indexes + 96, load_mask5, index_zmm[12]);
+    vtype2::mask_storeu(indexes + 104, load_mask6, index_zmm[13]);
+    vtype2::mask_storeu(indexes + 112, load_mask7, index_zmm[14]);
+    vtype2::mask_storeu(indexes + 120, load_mask8, index_zmm[15]);
+
+    vtype1::storeu(keys, key_zmm[0]);
+    vtype1::storeu(keys + 8, key_zmm[1]);
+    vtype1::storeu(keys + 16, key_zmm[2]);
+    vtype1::storeu(keys + 24, key_zmm[3]);
+    vtype1::storeu(keys + 32, key_zmm[4]);
+    vtype1::storeu(keys + 40, key_zmm[5]);
+    vtype1::storeu(keys + 48, key_zmm[6]);
+    vtype1::storeu(keys + 56, key_zmm[7]);
+    vtype1::mask_storeu(keys + 64, load_mask1, key_zmm[8]);
+    vtype1::mask_storeu(keys + 72, load_mask2, key_zmm[9]);
+    vtype1::mask_storeu(keys + 80, load_mask3, key_zmm[10]);
+    vtype1::mask_storeu(keys + 88, load_mask4, key_zmm[11]);
+    vtype1::mask_storeu(keys + 96, load_mask5, key_zmm[12]);
+    vtype1::mask_storeu(keys + 104, load_mask6, key_zmm[13]);
+    vtype1::mask_storeu(keys + 112, load_mask7, key_zmm[14]);
+    vtype1::mask_storeu(keys + 120, load_mask8, key_zmm[15]);
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+void heapify(type1_t *keys, type2_t *indexes, int64_t idx, int64_t size)
+{
+    int64_t i = idx;
+    while (true) {
+        int64_t j = 2 * i + 1;
+        if (j >= size || j < 0) { break; }
+        int k = j + 1;
+        if (k < size && keys[j] < keys[k]) { j = k; }
+        if (keys[j] < keys[i]) { break; }
+        std::swap(keys[i], keys[j]);
+        std::swap(indexes[i], indexes[j]);
+        i = j;
+    }
+}
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+void heap_sort(type1_t *keys, type2_t *indexes, int64_t size)
+{
+    for (int64_t i = size / 2 - 1; i >= 0; i--) {
+        heapify<vtype1, vtype2>(keys, indexes, i, size);
+    }
+    for (int64_t i = size - 1; i > 0; i--) {
+        std::swap(keys[0], keys[i]);
+        std::swap(indexes[0], indexes[i]);
+        heapify<vtype1, vtype2>(keys, indexes, 0, i);
+    }
+}
+
+template <typename vtype1,
+          typename vtype2,
+          typename type1_t = typename vtype1::type_t,
+          typename type2_t = typename vtype2::type_t>
+void qsort_64bit_(type1_t *keys,
+                  type2_t *indexes,
+                  int64_t left,
+                  int64_t right,
+                  int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        //std::sort(keys+left,keys+right+1);
+        heap_sort<vtype1, vtype2>(
+                keys + left, indexes + left, right - left + 1);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 128
+     */
+    if (right + 1 - left <= 128) {
+
+        sort_128_64bit<vtype1, vtype2>(
+                keys + left, indexes + left, (int32_t)(right + 1 - left));
+        return;
+    }
+
+    type1_t pivot = get_pivot_64bit<vtype1>(keys, left, right);
+    type1_t smallest = vtype1::type_max();
+    type1_t biggest = vtype1::type_min();
+    int64_t pivot_index = partition_avx512<vtype1, vtype2>(
+            keys, indexes, left, right + 1, pivot, &smallest, &biggest);
+    if (pivot != smallest) {
+        qsort_64bit_<vtype1, vtype2>(
+                keys, indexes, left, pivot_index - 1, max_iters - 1);
+    }
+    if (pivot != biggest) {
+        qsort_64bit_<vtype1, vtype2>(
+                keys, indexes, pivot_index, right, max_iters - 1);
+    }
+}
+
+template <>
+void avx512_qsort_kv<int64_t>(int64_t *keys, uint64_t *indexes, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_64bit_<zmm_vector<int64_t>, zmm_vector<uint64_t>>(
+                keys, indexes, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qsort_kv<uint64_t>(uint64_t *keys,
+                               uint64_t *indexes,
+                               int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_64bit_<zmm_vector<uint64_t>, zmm_vector<uint64_t>>(
+                keys, indexes, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qsort_kv<double>(double *keys, uint64_t *indexes, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(keys, arrsize);
+        qsort_64bit_<zmm_vector<double>, zmm_vector<uint64_t>>(
+                keys, indexes, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(keys, arrsize, nan_count);
+    }
+}
+#endif // AVX512_QSORT_64BIT_KV
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-qsort.hpp
new file mode 100644
index 00000000000..1cbcd388aad
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-64bit-qsort.hpp
@@ -0,0 +1,843 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_QSORT_64BIT
+#define AVX512_QSORT_64BIT
+
+#include "avx512-64bit-common.h"
+
+// Assumes zmm is bitonic and performs a recursive half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE zmm_t bitonic_merge_zmm_64bit(zmm_t zmm)
+{
+
+    // 1) half_cleaner[8]: compare 0-4, 1-5, 2-6, 3-7
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(_mm512_set_epi64(NETWORK_64BIT_4), zmm),
+            0xF0);
+    // 2) half_cleaner[4]
+    zmm = cmp_merge<vtype>(
+            zmm,
+            vtype::permutexvar(_mm512_set_epi64(NETWORK_64BIT_3), zmm),
+            0xCC);
+    // 3) half_cleaner[1]
+    zmm = cmp_merge<vtype>(
+            zmm, vtype::template shuffle<SHUFFLE_MASK(1, 1, 1, 1)>(zmm), 0xAA);
+    return zmm;
+}
+// Assumes zmm1 and zmm2 are sorted and performs a recursive half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_two_zmm_64bit(zmm_t &zmm1, zmm_t &zmm2)
+{
+    const __m512i rev_index = _mm512_set_epi64(NETWORK_64BIT_2);
+    // 1) First step of a merging network: coex of zmm1 and zmm2 reversed
+    zmm2 = vtype::permutexvar(rev_index, zmm2);
+    zmm_t zmm3 = vtype::min(zmm1, zmm2);
+    zmm_t zmm4 = vtype::max(zmm1, zmm2);
+    // 2) Recursive half cleaner for each
+    zmm1 = bitonic_merge_zmm_64bit<vtype>(zmm3);
+    zmm2 = bitonic_merge_zmm_64bit<vtype>(zmm4);
+}
+// Assumes [zmm0, zmm1] and [zmm2, zmm3] are sorted and performs a recursive
+// half cleaner
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_four_zmm_64bit(zmm_t *zmm)
+{
+    const __m512i rev_index = _mm512_set_epi64(NETWORK_64BIT_2);
+    // 1) First step of a merging network
+    zmm_t zmm2r = vtype::permutexvar(rev_index, zmm[2]);
+    zmm_t zmm3r = vtype::permutexvar(rev_index, zmm[3]);
+    zmm_t zmm_t1 = vtype::min(zmm[0], zmm3r);
+    zmm_t zmm_t2 = vtype::min(zmm[1], zmm2r);
+    // 2) Recursive half clearer: 16
+    zmm_t zmm_t3 = vtype::permutexvar(rev_index, vtype::max(zmm[1], zmm2r));
+    zmm_t zmm_t4 = vtype::permutexvar(rev_index, vtype::max(zmm[0], zmm3r));
+    zmm_t zmm0 = vtype::min(zmm_t1, zmm_t2);
+    zmm_t zmm1 = vtype::max(zmm_t1, zmm_t2);
+    zmm_t zmm2 = vtype::min(zmm_t3, zmm_t4);
+    zmm_t zmm3 = vtype::max(zmm_t3, zmm_t4);
+    zmm[0] = bitonic_merge_zmm_64bit<vtype>(zmm0);
+    zmm[1] = bitonic_merge_zmm_64bit<vtype>(zmm1);
+    zmm[2] = bitonic_merge_zmm_64bit<vtype>(zmm2);
+    zmm[3] = bitonic_merge_zmm_64bit<vtype>(zmm3);
+}
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_eight_zmm_64bit(zmm_t *zmm)
+{
+    const __m512i rev_index = _mm512_set_epi64(NETWORK_64BIT_2);
+    zmm_t zmm4r = vtype::permutexvar(rev_index, zmm[4]);
+    zmm_t zmm5r = vtype::permutexvar(rev_index, zmm[5]);
+    zmm_t zmm6r = vtype::permutexvar(rev_index, zmm[6]);
+    zmm_t zmm7r = vtype::permutexvar(rev_index, zmm[7]);
+    zmm_t zmm_t1 = vtype::min(zmm[0], zmm7r);
+    zmm_t zmm_t2 = vtype::min(zmm[1], zmm6r);
+    zmm_t zmm_t3 = vtype::min(zmm[2], zmm5r);
+    zmm_t zmm_t4 = vtype::min(zmm[3], zmm4r);
+    zmm_t zmm_t5 = vtype::permutexvar(rev_index, vtype::max(zmm[3], zmm4r));
+    zmm_t zmm_t6 = vtype::permutexvar(rev_index, vtype::max(zmm[2], zmm5r));
+    zmm_t zmm_t7 = vtype::permutexvar(rev_index, vtype::max(zmm[1], zmm6r));
+    zmm_t zmm_t8 = vtype::permutexvar(rev_index, vtype::max(zmm[0], zmm7r));
+    COEX<vtype>(zmm_t1, zmm_t3);
+    COEX<vtype>(zmm_t2, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t7);
+    COEX<vtype>(zmm_t6, zmm_t8);
+    COEX<vtype>(zmm_t1, zmm_t2);
+    COEX<vtype>(zmm_t3, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t6);
+    COEX<vtype>(zmm_t7, zmm_t8);
+    zmm[0] = bitonic_merge_zmm_64bit<vtype>(zmm_t1);
+    zmm[1] = bitonic_merge_zmm_64bit<vtype>(zmm_t2);
+    zmm[2] = bitonic_merge_zmm_64bit<vtype>(zmm_t3);
+    zmm[3] = bitonic_merge_zmm_64bit<vtype>(zmm_t4);
+    zmm[4] = bitonic_merge_zmm_64bit<vtype>(zmm_t5);
+    zmm[5] = bitonic_merge_zmm_64bit<vtype>(zmm_t6);
+    zmm[6] = bitonic_merge_zmm_64bit<vtype>(zmm_t7);
+    zmm[7] = bitonic_merge_zmm_64bit<vtype>(zmm_t8);
+}
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_sixteen_zmm_64bit(zmm_t *zmm)
+{
+    const __m512i rev_index = _mm512_set_epi64(NETWORK_64BIT_2);
+    zmm_t zmm8r = vtype::permutexvar(rev_index, zmm[8]);
+    zmm_t zmm9r = vtype::permutexvar(rev_index, zmm[9]);
+    zmm_t zmm10r = vtype::permutexvar(rev_index, zmm[10]);
+    zmm_t zmm11r = vtype::permutexvar(rev_index, zmm[11]);
+    zmm_t zmm12r = vtype::permutexvar(rev_index, zmm[12]);
+    zmm_t zmm13r = vtype::permutexvar(rev_index, zmm[13]);
+    zmm_t zmm14r = vtype::permutexvar(rev_index, zmm[14]);
+    zmm_t zmm15r = vtype::permutexvar(rev_index, zmm[15]);
+    zmm_t zmm_t1 = vtype::min(zmm[0], zmm15r);
+    zmm_t zmm_t2 = vtype::min(zmm[1], zmm14r);
+    zmm_t zmm_t3 = vtype::min(zmm[2], zmm13r);
+    zmm_t zmm_t4 = vtype::min(zmm[3], zmm12r);
+    zmm_t zmm_t5 = vtype::min(zmm[4], zmm11r);
+    zmm_t zmm_t6 = vtype::min(zmm[5], zmm10r);
+    zmm_t zmm_t7 = vtype::min(zmm[6], zmm9r);
+    zmm_t zmm_t8 = vtype::min(zmm[7], zmm8r);
+    zmm_t zmm_t9 = vtype::permutexvar(rev_index, vtype::max(zmm[7], zmm8r));
+    zmm_t zmm_t10 = vtype::permutexvar(rev_index, vtype::max(zmm[6], zmm9r));
+    zmm_t zmm_t11 = vtype::permutexvar(rev_index, vtype::max(zmm[5], zmm10r));
+    zmm_t zmm_t12 = vtype::permutexvar(rev_index, vtype::max(zmm[4], zmm11r));
+    zmm_t zmm_t13 = vtype::permutexvar(rev_index, vtype::max(zmm[3], zmm12r));
+    zmm_t zmm_t14 = vtype::permutexvar(rev_index, vtype::max(zmm[2], zmm13r));
+    zmm_t zmm_t15 = vtype::permutexvar(rev_index, vtype::max(zmm[1], zmm14r));
+    zmm_t zmm_t16 = vtype::permutexvar(rev_index, vtype::max(zmm[0], zmm15r));
+    // Recusive half clear 16 zmm regs
+    COEX<vtype>(zmm_t1, zmm_t5);
+    COEX<vtype>(zmm_t2, zmm_t6);
+    COEX<vtype>(zmm_t3, zmm_t7);
+    COEX<vtype>(zmm_t4, zmm_t8);
+    COEX<vtype>(zmm_t9, zmm_t13);
+    COEX<vtype>(zmm_t10, zmm_t14);
+    COEX<vtype>(zmm_t11, zmm_t15);
+    COEX<vtype>(zmm_t12, zmm_t16);
+    //
+    COEX<vtype>(zmm_t1, zmm_t3);
+    COEX<vtype>(zmm_t2, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t7);
+    COEX<vtype>(zmm_t6, zmm_t8);
+    COEX<vtype>(zmm_t9, zmm_t11);
+    COEX<vtype>(zmm_t10, zmm_t12);
+    COEX<vtype>(zmm_t13, zmm_t15);
+    COEX<vtype>(zmm_t14, zmm_t16);
+    //
+    COEX<vtype>(zmm_t1, zmm_t2);
+    COEX<vtype>(zmm_t3, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t6);
+    COEX<vtype>(zmm_t7, zmm_t8);
+    COEX<vtype>(zmm_t9, zmm_t10);
+    COEX<vtype>(zmm_t11, zmm_t12);
+    COEX<vtype>(zmm_t13, zmm_t14);
+    COEX<vtype>(zmm_t15, zmm_t16);
+    //
+    zmm[0] = bitonic_merge_zmm_64bit<vtype>(zmm_t1);
+    zmm[1] = bitonic_merge_zmm_64bit<vtype>(zmm_t2);
+    zmm[2] = bitonic_merge_zmm_64bit<vtype>(zmm_t3);
+    zmm[3] = bitonic_merge_zmm_64bit<vtype>(zmm_t4);
+    zmm[4] = bitonic_merge_zmm_64bit<vtype>(zmm_t5);
+    zmm[5] = bitonic_merge_zmm_64bit<vtype>(zmm_t6);
+    zmm[6] = bitonic_merge_zmm_64bit<vtype>(zmm_t7);
+    zmm[7] = bitonic_merge_zmm_64bit<vtype>(zmm_t8);
+    zmm[8] = bitonic_merge_zmm_64bit<vtype>(zmm_t9);
+    zmm[9] = bitonic_merge_zmm_64bit<vtype>(zmm_t10);
+    zmm[10] = bitonic_merge_zmm_64bit<vtype>(zmm_t11);
+    zmm[11] = bitonic_merge_zmm_64bit<vtype>(zmm_t12);
+    zmm[12] = bitonic_merge_zmm_64bit<vtype>(zmm_t13);
+    zmm[13] = bitonic_merge_zmm_64bit<vtype>(zmm_t14);
+    zmm[14] = bitonic_merge_zmm_64bit<vtype>(zmm_t15);
+    zmm[15] = bitonic_merge_zmm_64bit<vtype>(zmm_t16);
+}
+
+template <typename vtype, typename zmm_t = typename vtype::zmm_t>
+X86_SIMD_SORT_INLINE void bitonic_merge_32_zmm_64bit(zmm_t *zmm)
+{
+    const __m512i rev_index = _mm512_set_epi64(NETWORK_64BIT_2);
+    zmm_t zmm16r = vtype::permutexvar(rev_index, zmm[16]);
+    zmm_t zmm17r = vtype::permutexvar(rev_index, zmm[17]);
+    zmm_t zmm18r = vtype::permutexvar(rev_index, zmm[18]);
+    zmm_t zmm19r = vtype::permutexvar(rev_index, zmm[19]);
+    zmm_t zmm20r = vtype::permutexvar(rev_index, zmm[20]);
+    zmm_t zmm21r = vtype::permutexvar(rev_index, zmm[21]);
+    zmm_t zmm22r = vtype::permutexvar(rev_index, zmm[22]);
+    zmm_t zmm23r = vtype::permutexvar(rev_index, zmm[23]);
+    zmm_t zmm24r = vtype::permutexvar(rev_index, zmm[24]);
+    zmm_t zmm25r = vtype::permutexvar(rev_index, zmm[25]);
+    zmm_t zmm26r = vtype::permutexvar(rev_index, zmm[26]);
+    zmm_t zmm27r = vtype::permutexvar(rev_index, zmm[27]);
+    zmm_t zmm28r = vtype::permutexvar(rev_index, zmm[28]);
+    zmm_t zmm29r = vtype::permutexvar(rev_index, zmm[29]);
+    zmm_t zmm30r = vtype::permutexvar(rev_index, zmm[30]);
+    zmm_t zmm31r = vtype::permutexvar(rev_index, zmm[31]);
+    zmm_t zmm_t1 = vtype::min(zmm[0], zmm31r);
+    zmm_t zmm_t2 = vtype::min(zmm[1], zmm30r);
+    zmm_t zmm_t3 = vtype::min(zmm[2], zmm29r);
+    zmm_t zmm_t4 = vtype::min(zmm[3], zmm28r);
+    zmm_t zmm_t5 = vtype::min(zmm[4], zmm27r);
+    zmm_t zmm_t6 = vtype::min(zmm[5], zmm26r);
+    zmm_t zmm_t7 = vtype::min(zmm[6], zmm25r);
+    zmm_t zmm_t8 = vtype::min(zmm[7], zmm24r);
+    zmm_t zmm_t9 = vtype::min(zmm[8], zmm23r);
+    zmm_t zmm_t10 = vtype::min(zmm[9], zmm22r);
+    zmm_t zmm_t11 = vtype::min(zmm[10], zmm21r);
+    zmm_t zmm_t12 = vtype::min(zmm[11], zmm20r);
+    zmm_t zmm_t13 = vtype::min(zmm[12], zmm19r);
+    zmm_t zmm_t14 = vtype::min(zmm[13], zmm18r);
+    zmm_t zmm_t15 = vtype::min(zmm[14], zmm17r);
+    zmm_t zmm_t16 = vtype::min(zmm[15], zmm16r);
+    zmm_t zmm_t17 = vtype::permutexvar(rev_index, vtype::max(zmm[15], zmm16r));
+    zmm_t zmm_t18 = vtype::permutexvar(rev_index, vtype::max(zmm[14], zmm17r));
+    zmm_t zmm_t19 = vtype::permutexvar(rev_index, vtype::max(zmm[13], zmm18r));
+    zmm_t zmm_t20 = vtype::permutexvar(rev_index, vtype::max(zmm[12], zmm19r));
+    zmm_t zmm_t21 = vtype::permutexvar(rev_index, vtype::max(zmm[11], zmm20r));
+    zmm_t zmm_t22 = vtype::permutexvar(rev_index, vtype::max(zmm[10], zmm21r));
+    zmm_t zmm_t23 = vtype::permutexvar(rev_index, vtype::max(zmm[9], zmm22r));
+    zmm_t zmm_t24 = vtype::permutexvar(rev_index, vtype::max(zmm[8], zmm23r));
+    zmm_t zmm_t25 = vtype::permutexvar(rev_index, vtype::max(zmm[7], zmm24r));
+    zmm_t zmm_t26 = vtype::permutexvar(rev_index, vtype::max(zmm[6], zmm25r));
+    zmm_t zmm_t27 = vtype::permutexvar(rev_index, vtype::max(zmm[5], zmm26r));
+    zmm_t zmm_t28 = vtype::permutexvar(rev_index, vtype::max(zmm[4], zmm27r));
+    zmm_t zmm_t29 = vtype::permutexvar(rev_index, vtype::max(zmm[3], zmm28r));
+    zmm_t zmm_t30 = vtype::permutexvar(rev_index, vtype::max(zmm[2], zmm29r));
+    zmm_t zmm_t31 = vtype::permutexvar(rev_index, vtype::max(zmm[1], zmm30r));
+    zmm_t zmm_t32 = vtype::permutexvar(rev_index, vtype::max(zmm[0], zmm31r));
+    // Recusive half clear 16 zmm regs
+    COEX<vtype>(zmm_t1, zmm_t9);
+    COEX<vtype>(zmm_t2, zmm_t10);
+    COEX<vtype>(zmm_t3, zmm_t11);
+    COEX<vtype>(zmm_t4, zmm_t12);
+    COEX<vtype>(zmm_t5, zmm_t13);
+    COEX<vtype>(zmm_t6, zmm_t14);
+    COEX<vtype>(zmm_t7, zmm_t15);
+    COEX<vtype>(zmm_t8, zmm_t16);
+    COEX<vtype>(zmm_t17, zmm_t25);
+    COEX<vtype>(zmm_t18, zmm_t26);
+    COEX<vtype>(zmm_t19, zmm_t27);
+    COEX<vtype>(zmm_t20, zmm_t28);
+    COEX<vtype>(zmm_t21, zmm_t29);
+    COEX<vtype>(zmm_t22, zmm_t30);
+    COEX<vtype>(zmm_t23, zmm_t31);
+    COEX<vtype>(zmm_t24, zmm_t32);
+    //
+    COEX<vtype>(zmm_t1, zmm_t5);
+    COEX<vtype>(zmm_t2, zmm_t6);
+    COEX<vtype>(zmm_t3, zmm_t7);
+    COEX<vtype>(zmm_t4, zmm_t8);
+    COEX<vtype>(zmm_t9, zmm_t13);
+    COEX<vtype>(zmm_t10, zmm_t14);
+    COEX<vtype>(zmm_t11, zmm_t15);
+    COEX<vtype>(zmm_t12, zmm_t16);
+    COEX<vtype>(zmm_t17, zmm_t21);
+    COEX<vtype>(zmm_t18, zmm_t22);
+    COEX<vtype>(zmm_t19, zmm_t23);
+    COEX<vtype>(zmm_t20, zmm_t24);
+    COEX<vtype>(zmm_t25, zmm_t29);
+    COEX<vtype>(zmm_t26, zmm_t30);
+    COEX<vtype>(zmm_t27, zmm_t31);
+    COEX<vtype>(zmm_t28, zmm_t32);
+    //
+    COEX<vtype>(zmm_t1, zmm_t3);
+    COEX<vtype>(zmm_t2, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t7);
+    COEX<vtype>(zmm_t6, zmm_t8);
+    COEX<vtype>(zmm_t9, zmm_t11);
+    COEX<vtype>(zmm_t10, zmm_t12);
+    COEX<vtype>(zmm_t13, zmm_t15);
+    COEX<vtype>(zmm_t14, zmm_t16);
+    COEX<vtype>(zmm_t17, zmm_t19);
+    COEX<vtype>(zmm_t18, zmm_t20);
+    COEX<vtype>(zmm_t21, zmm_t23);
+    COEX<vtype>(zmm_t22, zmm_t24);
+    COEX<vtype>(zmm_t25, zmm_t27);
+    COEX<vtype>(zmm_t26, zmm_t28);
+    COEX<vtype>(zmm_t29, zmm_t31);
+    COEX<vtype>(zmm_t30, zmm_t32);
+    //
+    COEX<vtype>(zmm_t1, zmm_t2);
+    COEX<vtype>(zmm_t3, zmm_t4);
+    COEX<vtype>(zmm_t5, zmm_t6);
+    COEX<vtype>(zmm_t7, zmm_t8);
+    COEX<vtype>(zmm_t9, zmm_t10);
+    COEX<vtype>(zmm_t11, zmm_t12);
+    COEX<vtype>(zmm_t13, zmm_t14);
+    COEX<vtype>(zmm_t15, zmm_t16);
+    COEX<vtype>(zmm_t17, zmm_t18);
+    COEX<vtype>(zmm_t19, zmm_t20);
+    COEX<vtype>(zmm_t21, zmm_t22);
+    COEX<vtype>(zmm_t23, zmm_t24);
+    COEX<vtype>(zmm_t25, zmm_t26);
+    COEX<vtype>(zmm_t27, zmm_t28);
+    COEX<vtype>(zmm_t29, zmm_t30);
+    COEX<vtype>(zmm_t31, zmm_t32);
+    //
+    zmm[0] = bitonic_merge_zmm_64bit<vtype>(zmm_t1);
+    zmm[1] = bitonic_merge_zmm_64bit<vtype>(zmm_t2);
+    zmm[2] = bitonic_merge_zmm_64bit<vtype>(zmm_t3);
+    zmm[3] = bitonic_merge_zmm_64bit<vtype>(zmm_t4);
+    zmm[4] = bitonic_merge_zmm_64bit<vtype>(zmm_t5);
+    zmm[5] = bitonic_merge_zmm_64bit<vtype>(zmm_t6);
+    zmm[6] = bitonic_merge_zmm_64bit<vtype>(zmm_t7);
+    zmm[7] = bitonic_merge_zmm_64bit<vtype>(zmm_t8);
+    zmm[8] = bitonic_merge_zmm_64bit<vtype>(zmm_t9);
+    zmm[9] = bitonic_merge_zmm_64bit<vtype>(zmm_t10);
+    zmm[10] = bitonic_merge_zmm_64bit<vtype>(zmm_t11);
+    zmm[11] = bitonic_merge_zmm_64bit<vtype>(zmm_t12);
+    zmm[12] = bitonic_merge_zmm_64bit<vtype>(zmm_t13);
+    zmm[13] = bitonic_merge_zmm_64bit<vtype>(zmm_t14);
+    zmm[14] = bitonic_merge_zmm_64bit<vtype>(zmm_t15);
+    zmm[15] = bitonic_merge_zmm_64bit<vtype>(zmm_t16);
+    zmm[16] = bitonic_merge_zmm_64bit<vtype>(zmm_t17);
+    zmm[17] = bitonic_merge_zmm_64bit<vtype>(zmm_t18);
+    zmm[18] = bitonic_merge_zmm_64bit<vtype>(zmm_t19);
+    zmm[19] = bitonic_merge_zmm_64bit<vtype>(zmm_t20);
+    zmm[20] = bitonic_merge_zmm_64bit<vtype>(zmm_t21);
+    zmm[21] = bitonic_merge_zmm_64bit<vtype>(zmm_t22);
+    zmm[22] = bitonic_merge_zmm_64bit<vtype>(zmm_t23);
+    zmm[23] = bitonic_merge_zmm_64bit<vtype>(zmm_t24);
+    zmm[24] = bitonic_merge_zmm_64bit<vtype>(zmm_t25);
+    zmm[25] = bitonic_merge_zmm_64bit<vtype>(zmm_t26);
+    zmm[26] = bitonic_merge_zmm_64bit<vtype>(zmm_t27);
+    zmm[27] = bitonic_merge_zmm_64bit<vtype>(zmm_t28);
+    zmm[28] = bitonic_merge_zmm_64bit<vtype>(zmm_t29);
+    zmm[29] = bitonic_merge_zmm_64bit<vtype>(zmm_t30);
+    zmm[30] = bitonic_merge_zmm_64bit<vtype>(zmm_t31);
+    zmm[31] = bitonic_merge_zmm_64bit<vtype>(zmm_t32);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_8_64bit(type_t *arr, int32_t N)
+{
+    typename vtype::opmask_t load_mask = (0x01 << N) - 0x01;
+    typename vtype::zmm_t zmm
+            = vtype::mask_loadu(vtype::zmm_max(), load_mask, arr);
+    vtype::mask_storeu(arr, load_mask, sort_zmm_64bit<vtype>(zmm));
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_16_64bit(type_t *arr, int32_t N)
+{
+    if (N <= 8) {
+        sort_8_64bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    zmm_t zmm1 = vtype::loadu(arr);
+    typename vtype::opmask_t load_mask = (0x01 << (N - 8)) - 0x01;
+    zmm_t zmm2 = vtype::mask_loadu(vtype::zmm_max(), load_mask, arr + 8);
+    zmm1 = sort_zmm_64bit<vtype>(zmm1);
+    zmm2 = sort_zmm_64bit<vtype>(zmm2);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm1, zmm2);
+    vtype::storeu(arr, zmm1);
+    vtype::mask_storeu(arr + 8, load_mask, zmm2);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_32_64bit(type_t *arr, int32_t N)
+{
+    if (N <= 16) {
+        sort_16_64bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t zmm[4];
+    zmm[0] = vtype::loadu(arr);
+    zmm[1] = vtype::loadu(arr + 8);
+    opmask_t load_mask1 = 0xFF, load_mask2 = 0xFF;
+    uint64_t combined_mask = (0x1ull << (N - 16)) - 0x1ull;
+    load_mask1 = (combined_mask)&0xFF;
+    load_mask2 = (combined_mask >> 8) & 0xFF;
+    zmm[2] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 16);
+    zmm[3] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 24);
+    zmm[0] = sort_zmm_64bit<vtype>(zmm[0]);
+    zmm[1] = sort_zmm_64bit<vtype>(zmm[1]);
+    zmm[2] = sort_zmm_64bit<vtype>(zmm[2]);
+    zmm[3] = sort_zmm_64bit<vtype>(zmm[3]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[0], zmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[2], zmm[3]);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm);
+    vtype::storeu(arr, zmm[0]);
+    vtype::storeu(arr + 8, zmm[1]);
+    vtype::mask_storeu(arr + 16, load_mask1, zmm[2]);
+    vtype::mask_storeu(arr + 24, load_mask2, zmm[3]);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_64_64bit(type_t *arr, int32_t N)
+{
+    if (N <= 32) {
+        sort_32_64bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t zmm[8];
+    zmm[0] = vtype::loadu(arr);
+    zmm[1] = vtype::loadu(arr + 8);
+    zmm[2] = vtype::loadu(arr + 16);
+    zmm[3] = vtype::loadu(arr + 24);
+    zmm[0] = sort_zmm_64bit<vtype>(zmm[0]);
+    zmm[1] = sort_zmm_64bit<vtype>(zmm[1]);
+    zmm[2] = sort_zmm_64bit<vtype>(zmm[2]);
+    zmm[3] = sort_zmm_64bit<vtype>(zmm[3]);
+    opmask_t load_mask1 = 0xFF, load_mask2 = 0xFF;
+    opmask_t load_mask3 = 0xFF, load_mask4 = 0xFF;
+    // N-32 >= 1
+    uint64_t combined_mask = (0x1ull << (N - 32)) - 0x1ull;
+    load_mask1 = (combined_mask)&0xFF;
+    load_mask2 = (combined_mask >> 8) & 0xFF;
+    load_mask3 = (combined_mask >> 16) & 0xFF;
+    load_mask4 = (combined_mask >> 24) & 0xFF;
+    zmm[4] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 32);
+    zmm[5] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 40);
+    zmm[6] = vtype::mask_loadu(vtype::zmm_max(), load_mask3, arr + 48);
+    zmm[7] = vtype::mask_loadu(vtype::zmm_max(), load_mask4, arr + 56);
+    zmm[4] = sort_zmm_64bit<vtype>(zmm[4]);
+    zmm[5] = sort_zmm_64bit<vtype>(zmm[5]);
+    zmm[6] = sort_zmm_64bit<vtype>(zmm[6]);
+    zmm[7] = sort_zmm_64bit<vtype>(zmm[7]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[0], zmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[2], zmm[3]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[4], zmm[5]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[6], zmm[7]);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 4);
+    bitonic_merge_eight_zmm_64bit<vtype>(zmm);
+    vtype::storeu(arr, zmm[0]);
+    vtype::storeu(arr + 8, zmm[1]);
+    vtype::storeu(arr + 16, zmm[2]);
+    vtype::storeu(arr + 24, zmm[3]);
+    vtype::mask_storeu(arr + 32, load_mask1, zmm[4]);
+    vtype::mask_storeu(arr + 40, load_mask2, zmm[5]);
+    vtype::mask_storeu(arr + 48, load_mask3, zmm[6]);
+    vtype::mask_storeu(arr + 56, load_mask4, zmm[7]);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_128_64bit(type_t *arr, int32_t N)
+{
+    if (N <= 64) {
+        sort_64_64bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t zmm[16];
+    zmm[0] = vtype::loadu(arr);
+    zmm[1] = vtype::loadu(arr + 8);
+    zmm[2] = vtype::loadu(arr + 16);
+    zmm[3] = vtype::loadu(arr + 24);
+    zmm[4] = vtype::loadu(arr + 32);
+    zmm[5] = vtype::loadu(arr + 40);
+    zmm[6] = vtype::loadu(arr + 48);
+    zmm[7] = vtype::loadu(arr + 56);
+    zmm[0] = sort_zmm_64bit<vtype>(zmm[0]);
+    zmm[1] = sort_zmm_64bit<vtype>(zmm[1]);
+    zmm[2] = sort_zmm_64bit<vtype>(zmm[2]);
+    zmm[3] = sort_zmm_64bit<vtype>(zmm[3]);
+    zmm[4] = sort_zmm_64bit<vtype>(zmm[4]);
+    zmm[5] = sort_zmm_64bit<vtype>(zmm[5]);
+    zmm[6] = sort_zmm_64bit<vtype>(zmm[6]);
+    zmm[7] = sort_zmm_64bit<vtype>(zmm[7]);
+    opmask_t load_mask1 = 0xFF, load_mask2 = 0xFF;
+    opmask_t load_mask3 = 0xFF, load_mask4 = 0xFF;
+    opmask_t load_mask5 = 0xFF, load_mask6 = 0xFF;
+    opmask_t load_mask7 = 0xFF, load_mask8 = 0xFF;
+    if (N != 128) {
+        uint64_t combined_mask = (0x1ull << (N - 64)) - 0x1ull;
+        load_mask1 = (combined_mask)&0xFF;
+        load_mask2 = (combined_mask >> 8) & 0xFF;
+        load_mask3 = (combined_mask >> 16) & 0xFF;
+        load_mask4 = (combined_mask >> 24) & 0xFF;
+        load_mask5 = (combined_mask >> 32) & 0xFF;
+        load_mask6 = (combined_mask >> 40) & 0xFF;
+        load_mask7 = (combined_mask >> 48) & 0xFF;
+        load_mask8 = (combined_mask >> 56) & 0xFF;
+    }
+    zmm[8] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 64);
+    zmm[9] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 72);
+    zmm[10] = vtype::mask_loadu(vtype::zmm_max(), load_mask3, arr + 80);
+    zmm[11] = vtype::mask_loadu(vtype::zmm_max(), load_mask4, arr + 88);
+    zmm[12] = vtype::mask_loadu(vtype::zmm_max(), load_mask5, arr + 96);
+    zmm[13] = vtype::mask_loadu(vtype::zmm_max(), load_mask6, arr + 104);
+    zmm[14] = vtype::mask_loadu(vtype::zmm_max(), load_mask7, arr + 112);
+    zmm[15] = vtype::mask_loadu(vtype::zmm_max(), load_mask8, arr + 120);
+    zmm[8] = sort_zmm_64bit<vtype>(zmm[8]);
+    zmm[9] = sort_zmm_64bit<vtype>(zmm[9]);
+    zmm[10] = sort_zmm_64bit<vtype>(zmm[10]);
+    zmm[11] = sort_zmm_64bit<vtype>(zmm[11]);
+    zmm[12] = sort_zmm_64bit<vtype>(zmm[12]);
+    zmm[13] = sort_zmm_64bit<vtype>(zmm[13]);
+    zmm[14] = sort_zmm_64bit<vtype>(zmm[14]);
+    zmm[15] = sort_zmm_64bit<vtype>(zmm[15]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[0], zmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[2], zmm[3]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[4], zmm[5]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[6], zmm[7]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[8], zmm[9]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[10], zmm[11]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[12], zmm[13]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[14], zmm[15]);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 4);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 8);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 12);
+    bitonic_merge_eight_zmm_64bit<vtype>(zmm);
+    bitonic_merge_eight_zmm_64bit<vtype>(zmm + 8);
+    bitonic_merge_sixteen_zmm_64bit<vtype>(zmm);
+    vtype::storeu(arr, zmm[0]);
+    vtype::storeu(arr + 8, zmm[1]);
+    vtype::storeu(arr + 16, zmm[2]);
+    vtype::storeu(arr + 24, zmm[3]);
+    vtype::storeu(arr + 32, zmm[4]);
+    vtype::storeu(arr + 40, zmm[5]);
+    vtype::storeu(arr + 48, zmm[6]);
+    vtype::storeu(arr + 56, zmm[7]);
+    vtype::mask_storeu(arr + 64, load_mask1, zmm[8]);
+    vtype::mask_storeu(arr + 72, load_mask2, zmm[9]);
+    vtype::mask_storeu(arr + 80, load_mask3, zmm[10]);
+    vtype::mask_storeu(arr + 88, load_mask4, zmm[11]);
+    vtype::mask_storeu(arr + 96, load_mask5, zmm[12]);
+    vtype::mask_storeu(arr + 104, load_mask6, zmm[13]);
+    vtype::mask_storeu(arr + 112, load_mask7, zmm[14]);
+    vtype::mask_storeu(arr + 120, load_mask8, zmm[15]);
+}
+
+template <typename vtype, typename type_t>
+X86_SIMD_SORT_INLINE void sort_256_64bit(type_t *arr, int32_t N)
+{
+    if (N <= 128) {
+        sort_128_64bit<vtype>(arr, N);
+        return;
+    }
+    using zmm_t = typename vtype::zmm_t;
+    using opmask_t = typename vtype::opmask_t;
+    zmm_t zmm[32];
+    zmm[0] = vtype::loadu(arr);
+    zmm[1] = vtype::loadu(arr + 8);
+    zmm[2] = vtype::loadu(arr + 16);
+    zmm[3] = vtype::loadu(arr + 24);
+    zmm[4] = vtype::loadu(arr + 32);
+    zmm[5] = vtype::loadu(arr + 40);
+    zmm[6] = vtype::loadu(arr + 48);
+    zmm[7] = vtype::loadu(arr + 56);
+    zmm[8] = vtype::loadu(arr + 64);
+    zmm[9] = vtype::loadu(arr + 72);
+    zmm[10] = vtype::loadu(arr + 80);
+    zmm[11] = vtype::loadu(arr + 88);
+    zmm[12] = vtype::loadu(arr + 96);
+    zmm[13] = vtype::loadu(arr + 104);
+    zmm[14] = vtype::loadu(arr + 112);
+    zmm[15] = vtype::loadu(arr + 120);
+    zmm[0] = sort_zmm_64bit<vtype>(zmm[0]);
+    zmm[1] = sort_zmm_64bit<vtype>(zmm[1]);
+    zmm[2] = sort_zmm_64bit<vtype>(zmm[2]);
+    zmm[3] = sort_zmm_64bit<vtype>(zmm[3]);
+    zmm[4] = sort_zmm_64bit<vtype>(zmm[4]);
+    zmm[5] = sort_zmm_64bit<vtype>(zmm[5]);
+    zmm[6] = sort_zmm_64bit<vtype>(zmm[6]);
+    zmm[7] = sort_zmm_64bit<vtype>(zmm[7]);
+    zmm[8] = sort_zmm_64bit<vtype>(zmm[8]);
+    zmm[9] = sort_zmm_64bit<vtype>(zmm[9]);
+    zmm[10] = sort_zmm_64bit<vtype>(zmm[10]);
+    zmm[11] = sort_zmm_64bit<vtype>(zmm[11]);
+    zmm[12] = sort_zmm_64bit<vtype>(zmm[12]);
+    zmm[13] = sort_zmm_64bit<vtype>(zmm[13]);
+    zmm[14] = sort_zmm_64bit<vtype>(zmm[14]);
+    zmm[15] = sort_zmm_64bit<vtype>(zmm[15]);
+    opmask_t load_mask1 = 0xFF, load_mask2 = 0xFF;
+    opmask_t load_mask3 = 0xFF, load_mask4 = 0xFF;
+    opmask_t load_mask5 = 0xFF, load_mask6 = 0xFF;
+    opmask_t load_mask7 = 0xFF, load_mask8 = 0xFF;
+    opmask_t load_mask9 = 0xFF, load_mask10 = 0xFF;
+    opmask_t load_mask11 = 0xFF, load_mask12 = 0xFF;
+    opmask_t load_mask13 = 0xFF, load_mask14 = 0xFF;
+    opmask_t load_mask15 = 0xFF, load_mask16 = 0xFF;
+    if (N != 256) {
+        uint64_t combined_mask;
+        if (N < 192) {
+            combined_mask = (0x1ull << (N - 128)) - 0x1ull;
+            load_mask1 = (combined_mask)&0xFF;
+            load_mask2 = (combined_mask >> 8) & 0xFF;
+            load_mask3 = (combined_mask >> 16) & 0xFF;
+            load_mask4 = (combined_mask >> 24) & 0xFF;
+            load_mask5 = (combined_mask >> 32) & 0xFF;
+            load_mask6 = (combined_mask >> 40) & 0xFF;
+            load_mask7 = (combined_mask >> 48) & 0xFF;
+            load_mask8 = (combined_mask >> 56) & 0xFF;
+            load_mask9 = 0x00;
+            load_mask10 = 0x0;
+            load_mask11 = 0x00;
+            load_mask12 = 0x00;
+            load_mask13 = 0x00;
+            load_mask14 = 0x00;
+            load_mask15 = 0x00;
+            load_mask16 = 0x00;
+        }
+        else {
+            combined_mask = (0x1ull << (N - 192)) - 0x1ull;
+            load_mask9 = (combined_mask)&0xFF;
+            load_mask10 = (combined_mask >> 8) & 0xFF;
+            load_mask11 = (combined_mask >> 16) & 0xFF;
+            load_mask12 = (combined_mask >> 24) & 0xFF;
+            load_mask13 = (combined_mask >> 32) & 0xFF;
+            load_mask14 = (combined_mask >> 40) & 0xFF;
+            load_mask15 = (combined_mask >> 48) & 0xFF;
+            load_mask16 = (combined_mask >> 56) & 0xFF;
+        }
+    }
+    zmm[16] = vtype::mask_loadu(vtype::zmm_max(), load_mask1, arr + 128);
+    zmm[17] = vtype::mask_loadu(vtype::zmm_max(), load_mask2, arr + 136);
+    zmm[18] = vtype::mask_loadu(vtype::zmm_max(), load_mask3, arr + 144);
+    zmm[19] = vtype::mask_loadu(vtype::zmm_max(), load_mask4, arr + 152);
+    zmm[20] = vtype::mask_loadu(vtype::zmm_max(), load_mask5, arr + 160);
+    zmm[21] = vtype::mask_loadu(vtype::zmm_max(), load_mask6, arr + 168);
+    zmm[22] = vtype::mask_loadu(vtype::zmm_max(), load_mask7, arr + 176);
+    zmm[23] = vtype::mask_loadu(vtype::zmm_max(), load_mask8, arr + 184);
+    if (N < 192) {
+        zmm[24] = vtype::zmm_max();
+        zmm[25] = vtype::zmm_max();
+        zmm[26] = vtype::zmm_max();
+        zmm[27] = vtype::zmm_max();
+        zmm[28] = vtype::zmm_max();
+        zmm[29] = vtype::zmm_max();
+        zmm[30] = vtype::zmm_max();
+        zmm[31] = vtype::zmm_max();
+    }
+    else {
+        zmm[24] = vtype::mask_loadu(vtype::zmm_max(), load_mask9, arr + 192);
+        zmm[25] = vtype::mask_loadu(vtype::zmm_max(), load_mask10, arr + 200);
+        zmm[26] = vtype::mask_loadu(vtype::zmm_max(), load_mask11, arr + 208);
+        zmm[27] = vtype::mask_loadu(vtype::zmm_max(), load_mask12, arr + 216);
+        zmm[28] = vtype::mask_loadu(vtype::zmm_max(), load_mask13, arr + 224);
+        zmm[29] = vtype::mask_loadu(vtype::zmm_max(), load_mask14, arr + 232);
+        zmm[30] = vtype::mask_loadu(vtype::zmm_max(), load_mask15, arr + 240);
+        zmm[31] = vtype::mask_loadu(vtype::zmm_max(), load_mask16, arr + 248);
+    }
+    zmm[16] = sort_zmm_64bit<vtype>(zmm[16]);
+    zmm[17] = sort_zmm_64bit<vtype>(zmm[17]);
+    zmm[18] = sort_zmm_64bit<vtype>(zmm[18]);
+    zmm[19] = sort_zmm_64bit<vtype>(zmm[19]);
+    zmm[20] = sort_zmm_64bit<vtype>(zmm[20]);
+    zmm[21] = sort_zmm_64bit<vtype>(zmm[21]);
+    zmm[22] = sort_zmm_64bit<vtype>(zmm[22]);
+    zmm[23] = sort_zmm_64bit<vtype>(zmm[23]);
+    zmm[24] = sort_zmm_64bit<vtype>(zmm[24]);
+    zmm[25] = sort_zmm_64bit<vtype>(zmm[25]);
+    zmm[26] = sort_zmm_64bit<vtype>(zmm[26]);
+    zmm[27] = sort_zmm_64bit<vtype>(zmm[27]);
+    zmm[28] = sort_zmm_64bit<vtype>(zmm[28]);
+    zmm[29] = sort_zmm_64bit<vtype>(zmm[29]);
+    zmm[30] = sort_zmm_64bit<vtype>(zmm[30]);
+    zmm[31] = sort_zmm_64bit<vtype>(zmm[31]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[0], zmm[1]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[2], zmm[3]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[4], zmm[5]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[6], zmm[7]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[8], zmm[9]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[10], zmm[11]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[12], zmm[13]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[14], zmm[15]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[16], zmm[17]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[18], zmm[19]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[20], zmm[21]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[22], zmm[23]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[24], zmm[25]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[26], zmm[27]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[28], zmm[29]);
+    bitonic_merge_two_zmm_64bit<vtype>(zmm[30], zmm[31]);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 4);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 8);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 12);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 16);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 20);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 24);
+    bitonic_merge_four_zmm_64bit<vtype>(zmm + 28);
+    bitonic_merge_eight_zmm_64bit<vtype>(zmm);
+    bitonic_merge_eight_zmm_64bit<vtype>(zmm + 8);
+    bitonic_merge_eight_zmm_64bit<vtype>(zmm + 16);
+    bitonic_merge_eight_zmm_64bit<vtype>(zmm + 24);
+    bitonic_merge_sixteen_zmm_64bit<vtype>(zmm);
+    bitonic_merge_sixteen_zmm_64bit<vtype>(zmm + 16);
+    bitonic_merge_32_zmm_64bit<vtype>(zmm);
+    vtype::storeu(arr, zmm[0]);
+    vtype::storeu(arr + 8, zmm[1]);
+    vtype::storeu(arr + 16, zmm[2]);
+    vtype::storeu(arr + 24, zmm[3]);
+    vtype::storeu(arr + 32, zmm[4]);
+    vtype::storeu(arr + 40, zmm[5]);
+    vtype::storeu(arr + 48, zmm[6]);
+    vtype::storeu(arr + 56, zmm[7]);
+    vtype::storeu(arr + 64, zmm[8]);
+    vtype::storeu(arr + 72, zmm[9]);
+    vtype::storeu(arr + 80, zmm[10]);
+    vtype::storeu(arr + 88, zmm[11]);
+    vtype::storeu(arr + 96, zmm[12]);
+    vtype::storeu(arr + 104, zmm[13]);
+    vtype::storeu(arr + 112, zmm[14]);
+    vtype::storeu(arr + 120, zmm[15]);
+    vtype::mask_storeu(arr + 128, load_mask1, zmm[16]);
+    vtype::mask_storeu(arr + 136, load_mask2, zmm[17]);
+    vtype::mask_storeu(arr + 144, load_mask3, zmm[18]);
+    vtype::mask_storeu(arr + 152, load_mask4, zmm[19]);
+    vtype::mask_storeu(arr + 160, load_mask5, zmm[20]);
+    vtype::mask_storeu(arr + 168, load_mask6, zmm[21]);
+    vtype::mask_storeu(arr + 176, load_mask7, zmm[22]);
+    vtype::mask_storeu(arr + 184, load_mask8, zmm[23]);
+    if (N > 192) {
+        vtype::mask_storeu(arr + 192, load_mask9, zmm[24]);
+        vtype::mask_storeu(arr + 200, load_mask10, zmm[25]);
+        vtype::mask_storeu(arr + 208, load_mask11, zmm[26]);
+        vtype::mask_storeu(arr + 216, load_mask12, zmm[27]);
+        vtype::mask_storeu(arr + 224, load_mask13, zmm[28]);
+        vtype::mask_storeu(arr + 232, load_mask14, zmm[29]);
+        vtype::mask_storeu(arr + 240, load_mask15, zmm[30]);
+        vtype::mask_storeu(arr + 248, load_mask16, zmm[31]);
+    }
+}
+
+template <typename vtype, typename type_t>
+static void
+qsort_64bit_(type_t *arr, int64_t left, int64_t right, int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        std::sort(arr + left, arr + right + 1);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 128
+     */
+    if (right + 1 - left <= 256) {
+        sort_256_64bit<vtype>(arr + left, (int32_t)(right + 1 - left));
+        return;
+    }
+
+    type_t pivot = get_pivot_64bit<vtype>(arr, left, right);
+    type_t smallest = vtype::type_max();
+    type_t biggest = vtype::type_min();
+    int64_t pivot_index = partition_avx512_unrolled<vtype, 8>(
+            arr, left, right + 1, pivot, &smallest, &biggest);
+    if (pivot != smallest)
+        qsort_64bit_<vtype>(arr, left, pivot_index - 1, max_iters - 1);
+    if (pivot != biggest)
+        qsort_64bit_<vtype>(arr, pivot_index, right, max_iters - 1);
+}
+
+template <typename vtype, typename type_t>
+static void qselect_64bit_(type_t *arr,
+                           int64_t pos,
+                           int64_t left,
+                           int64_t right,
+                           int64_t max_iters)
+{
+    /*
+     * Resort to std::sort if quicksort isnt making any progress
+     */
+    if (max_iters <= 0) {
+        std::sort(arr + left, arr + right + 1);
+        return;
+    }
+    /*
+     * Base case: use bitonic networks to sort arrays <= 128
+     */
+    if (right + 1 - left <= 128) {
+        sort_128_64bit<vtype>(arr + left, (int32_t)(right + 1 - left));
+        return;
+    }
+
+    type_t pivot = get_pivot_64bit<vtype>(arr, left, right);
+    type_t smallest = vtype::type_max();
+    type_t biggest = vtype::type_min();
+    int64_t pivot_index = partition_avx512_unrolled<vtype, 8>(
+            arr, left, right + 1, pivot, &smallest, &biggest);
+    if ((pivot != smallest) && (pos < pivot_index))
+        qselect_64bit_<vtype>(arr, pos, left, pivot_index - 1, max_iters - 1);
+    else if ((pivot != biggest) && (pos >= pivot_index))
+        qselect_64bit_<vtype>(arr, pos, pivot_index, right, max_iters - 1);
+}
+
+template <>
+void avx512_qselect<int64_t>(int64_t *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qselect_64bit_<zmm_vector<int64_t>, int64_t>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qselect<uint64_t>(uint64_t *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qselect_64bit_<zmm_vector<uint64_t>, uint64_t>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qselect<double>(double *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qselect_64bit_<zmm_vector<double>, double>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+
+template <>
+void avx512_qsort<int64_t>(int64_t *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_64bit_<zmm_vector<int64_t>, int64_t>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qsort<uint64_t>(uint64_t *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        qsort_64bit_<zmm_vector<uint64_t>, uint64_t>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+    }
+}
+
+template <>
+void avx512_qsort<double>(double *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qsort_64bit_<zmm_vector<double>, double>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+#endif // AVX512_QSORT_64BIT
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-common-argsort.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-common-argsort.h
new file mode 100644
index 00000000000..e0dcaccc233
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-common-argsort.h
@@ -0,0 +1,298 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_ARGSORT_COMMON
+#define AVX512_ARGSORT_COMMON
+
+#include "avx512-64bit-common.h"
+#include <numeric>
+#include <stdio.h>
+#include <vector>
+
+using argtype = zmm_vector<int64_t>;
+using argzmm_t = typename argtype::zmm_t;
+
+template <typename T>
+void avx512_argsort(T *arr, int64_t *arg, int64_t arrsize);
+
+template <typename T>
+std::vector<int64_t> avx512_argsort(T *arr, int64_t arrsize);
+
+/*
+ * Parition one ZMM register based on the pivot and returns the index of the
+ * last element that is less than equal to the pivot.
+ */
+template <typename vtype, typename type_t, typename zmm_t>
+static inline int32_t partition_vec(type_t *arg,
+                                    int64_t left,
+                                    int64_t right,
+                                    const argzmm_t arg_vec,
+                                    const zmm_t curr_vec,
+                                    const zmm_t pivot_vec,
+                                    zmm_t *smallest_vec,
+                                    zmm_t *biggest_vec)
+{
+    /* which elements are larger than the pivot */
+    typename vtype::opmask_t gt_mask = vtype::ge(curr_vec, pivot_vec);
+    int32_t amount_gt_pivot = _mm_popcnt_u32((int32_t)gt_mask);
+    argtype::mask_compressstoreu(
+            arg + left, vtype::knot_opmask(gt_mask), arg_vec);
+    argtype::mask_compressstoreu(arg + right - amount_gt_pivot, gt_mask, arg_vec);
+    *smallest_vec = vtype::min(curr_vec, *smallest_vec);
+    *biggest_vec = vtype::max(curr_vec, *biggest_vec);
+    return amount_gt_pivot;
+}
+/*
+ * Parition an array based on the pivot and returns the index of the
+ * last element that is less than equal to the pivot.
+ */
+template <typename vtype, typename type_t>
+static inline int64_t partition_avx512(type_t *arr,
+                                       int64_t *arg,
+                                       int64_t left,
+                                       int64_t right,
+                                       type_t pivot,
+                                       type_t *smallest,
+                                       type_t *biggest)
+{
+    /* make array length divisible by vtype::numlanes , shortening the array */
+    for (int32_t i = (right - left) % vtype::numlanes; i > 0; --i) {
+        *smallest = std::min(*smallest, arr[arg[left]], comparison_func<vtype>);
+        *biggest = std::max(*biggest, arr[arg[left]], comparison_func<vtype>);
+        if (!comparison_func<vtype>(arr[arg[left]], pivot)) {
+            std::swap(arg[left], arg[--right]);
+        }
+        else {
+            ++left;
+        }
+    }
+
+    if (left == right)
+        return left; /* less than vtype::numlanes elements in the array */
+
+    using zmm_t = typename vtype::zmm_t;
+    zmm_t pivot_vec = vtype::set1(pivot);
+    zmm_t min_vec = vtype::set1(*smallest);
+    zmm_t max_vec = vtype::set1(*biggest);
+
+    if (right - left == vtype::numlanes) {
+        argzmm_t argvec = argtype::loadu(arg + left);
+        zmm_t vec = vtype::template i64gather<sizeof(type_t)>(argvec, arr);
+        int32_t amount_gt_pivot = partition_vec<vtype>(arg,
+                                                       left,
+                                                       left + vtype::numlanes,
+                                                       argvec,
+                                                       vec,
+                                                       pivot_vec,
+                                                       &min_vec,
+                                                       &max_vec);
+        *smallest = vtype::reducemin(min_vec);
+        *biggest = vtype::reducemax(max_vec);
+        return left + (vtype::numlanes - amount_gt_pivot);
+    }
+
+    // first and last vtype::numlanes values are partitioned at the end
+    argzmm_t argvec_left = argtype::loadu(arg + left);
+    zmm_t vec_left
+            = vtype::template i64gather<sizeof(type_t)>(argvec_left, arr);
+    argzmm_t argvec_right = argtype::loadu(arg + (right - vtype::numlanes));
+    zmm_t vec_right
+            = vtype::template i64gather<sizeof(type_t)>(argvec_right, arr);
+    // store points of the vectors
+    int64_t r_store = right - vtype::numlanes;
+    int64_t l_store = left;
+    // indices for loading the elements
+    left += vtype::numlanes;
+    right -= vtype::numlanes;
+    while (right - left != 0) {
+        argzmm_t arg_vec;
+        zmm_t curr_vec;
+        /*
+         * if fewer elements are stored on the right side of the array,
+         * then next elements are loaded from the right side,
+         * otherwise from the left side
+         */
+        if ((r_store + vtype::numlanes) - right < left - l_store) {
+            right -= vtype::numlanes;
+            arg_vec = argtype::loadu(arg + right);
+            curr_vec = vtype::template i64gather<sizeof(type_t)>(arg_vec, arr);
+        }
+        else {
+            arg_vec = argtype::loadu(arg + left);
+            curr_vec = vtype::template i64gather<sizeof(type_t)>(arg_vec, arr);
+            left += vtype::numlanes;
+        }
+        // partition the current vector and save it on both sides of the array
+        int32_t amount_gt_pivot
+                = partition_vec<vtype>(arg,
+                                       l_store,
+                                       r_store + vtype::numlanes,
+                                       arg_vec,
+                                       curr_vec,
+                                       pivot_vec,
+                                       &min_vec,
+                                       &max_vec);
+        ;
+        r_store -= amount_gt_pivot;
+        l_store += (vtype::numlanes - amount_gt_pivot);
+    }
+
+    /* partition and save vec_left and vec_right */
+    int32_t amount_gt_pivot = partition_vec<vtype>(arg,
+                                                   l_store,
+                                                   r_store + vtype::numlanes,
+                                                   argvec_left,
+                                                   vec_left,
+                                                   pivot_vec,
+                                                   &min_vec,
+                                                   &max_vec);
+    l_store += (vtype::numlanes - amount_gt_pivot);
+    amount_gt_pivot = partition_vec<vtype>(arg,
+                                           l_store,
+                                           l_store + vtype::numlanes,
+                                           argvec_right,
+                                           vec_right,
+                                           pivot_vec,
+                                           &min_vec,
+                                           &max_vec);
+    l_store += (vtype::numlanes - amount_gt_pivot);
+    *smallest = vtype::reducemin(min_vec);
+    *biggest = vtype::reducemax(max_vec);
+    return l_store;
+}
+
+template <typename vtype,
+          int num_unroll,
+          typename type_t = typename vtype::type_t>
+static inline int64_t partition_avx512_unrolled(type_t *arr,
+                                                int64_t *arg,
+                                                int64_t left,
+                                                int64_t right,
+                                                type_t pivot,
+                                                type_t *smallest,
+                                                type_t *biggest)
+{
+    if (right - left <= 8 * num_unroll * vtype::numlanes) {
+        return partition_avx512<vtype>(
+                arr, arg, left, right, pivot, smallest, biggest);
+    }
+    /* make array length divisible by vtype::numlanes , shortening the array */
+    for (int32_t i = ((right - left) % (num_unroll * vtype::numlanes)); i > 0;
+         --i) {
+        *smallest = std::min(*smallest, arr[arg[left]], comparison_func<vtype>);
+        *biggest = std::max(*biggest, arr[arg[left]], comparison_func<vtype>);
+        if (!comparison_func<vtype>(arr[arg[left]], pivot)) {
+            std::swap(arg[left], arg[--right]);
+        }
+        else {
+            ++left;
+        }
+    }
+
+    if (left == right)
+        return left; /* less than vtype::numlanes elements in the array */
+
+    using zmm_t = typename vtype::zmm_t;
+    zmm_t pivot_vec = vtype::set1(pivot);
+    zmm_t min_vec = vtype::set1(*smallest);
+    zmm_t max_vec = vtype::set1(*biggest);
+
+    // first and last vtype::numlanes values are partitioned at the end
+    zmm_t vec_left[num_unroll], vec_right[num_unroll];
+    argzmm_t argvec_left[num_unroll], argvec_right[num_unroll];
+#pragma GCC unroll 8
+    for (int ii = 0; ii < num_unroll; ++ii) {
+        argvec_left[ii] = argtype::loadu(arg + left + vtype::numlanes * ii);
+        vec_left[ii] = vtype::template i64gather<sizeof(type_t)>(
+                argvec_left[ii], arr);
+        argvec_right[ii] = argtype::loadu(
+                arg + (right - vtype::numlanes * (num_unroll - ii)));
+        vec_right[ii] = vtype::template i64gather<sizeof(type_t)>(
+                argvec_right[ii], arr);
+    }
+    // store points of the vectors
+    int64_t r_store = right - vtype::numlanes;
+    int64_t l_store = left;
+    // indices for loading the elements
+    left += num_unroll * vtype::numlanes;
+    right -= num_unroll * vtype::numlanes;
+    while (right - left != 0) {
+        argzmm_t arg_vec[num_unroll];
+        zmm_t curr_vec[num_unroll];
+        /*
+         * if fewer elements are stored on the right side of the array,
+         * then next elements are loaded from the right side,
+         * otherwise from the left side
+         */
+        if ((r_store + vtype::numlanes) - right < left - l_store) {
+            right -= num_unroll * vtype::numlanes;
+#pragma GCC unroll 8
+            for (int ii = 0; ii < num_unroll; ++ii) {
+                arg_vec[ii] = argtype::loadu(arg + right + ii * vtype::numlanes);
+                curr_vec[ii] = vtype::template i64gather<sizeof(type_t)>(
+                        arg_vec[ii], arr);
+            }
+        }
+        else {
+#pragma GCC unroll 8
+            for (int ii = 0; ii < num_unroll; ++ii) {
+                arg_vec[ii] = argtype::loadu(arg + left + ii * vtype::numlanes);
+                curr_vec[ii] = vtype::template i64gather<sizeof(type_t)>(
+                        arg_vec[ii], arr);
+            }
+            left += num_unroll * vtype::numlanes;
+        }
+        // partition the current vector and save it on both sides of the array
+#pragma GCC unroll 8
+        for (int ii = 0; ii < num_unroll; ++ii) {
+            int32_t amount_gt_pivot
+                    = partition_vec<vtype>(arg,
+                                           l_store,
+                                           r_store + vtype::numlanes,
+                                           arg_vec[ii],
+                                           curr_vec[ii],
+                                           pivot_vec,
+                                           &min_vec,
+                                           &max_vec);
+            l_store += (vtype::numlanes - amount_gt_pivot);
+            r_store -= amount_gt_pivot;
+        }
+    }
+
+    /* partition and save vec_left and vec_right */
+#pragma GCC unroll 8
+    for (int ii = 0; ii < num_unroll; ++ii) {
+        int32_t amount_gt_pivot
+                = partition_vec<vtype>(arg,
+                                       l_store,
+                                       r_store + vtype::numlanes,
+                                       argvec_left[ii],
+                                       vec_left[ii],
+                                       pivot_vec,
+                                       &min_vec,
+                                       &max_vec);
+        l_store += (vtype::numlanes - amount_gt_pivot);
+        r_store -= amount_gt_pivot;
+    }
+#pragma GCC unroll 8
+    for (int ii = 0; ii < num_unroll; ++ii) {
+        int32_t amount_gt_pivot
+                = partition_vec<vtype>(arg,
+                                       l_store,
+                                       r_store + vtype::numlanes,
+                                       argvec_right[ii],
+                                       vec_right[ii],
+                                       pivot_vec,
+                                       &min_vec,
+                                       &max_vec);
+        l_store += (vtype::numlanes - amount_gt_pivot);
+        r_store -= amount_gt_pivot;
+    }
+    *smallest = vtype::reducemin(min_vec);
+    *biggest = vtype::reducemax(max_vec);
+    return l_store;
+}
+#endif // AVX512_ARGSORT_COMMON
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-common-qsort.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-common-qsort.h
new file mode 100644
index 00000000000..959352e668c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512-common-qsort.h
@@ -0,0 +1,599 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * Copyright (C) 2021 Serge Sans Paille
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ *          Serge Sans Paille <serge.guelton@telecom-bretagne.eu>
+ *          Liu Zhuan <zhuan.liu@intel.com>
+ *          Tang Xi <xi.tang@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512_QSORT_COMMON
+#define AVX512_QSORT_COMMON
+
+/*
+ * Quicksort using AVX-512. The ideas and code are based on these two research
+ * papers [1] and [2]. On a high level, the idea is to vectorize quicksort
+ * partitioning using AVX-512 compressstore instructions. If the array size is
+ * < 128, then use Bitonic sorting network implemented on 512-bit registers.
+ * The precise network definitions depend on the dtype and are defined in
+ * separate files: avx512-16bit-qsort.hpp, avx512-32bit-qsort.hpp and
+ * avx512-64bit-qsort.hpp. Article [4] is a good resource for bitonic sorting
+ * network. The core implementations of the vectorized qsort functions
+ * avx512_qsort<T>(T*, int64_t) are modified versions of avx2 quicksort
+ * presented in the paper [2] and source code associated with that paper [3].
+ *
+ * [1] Fast and Robust Vectorized In-Place Sorting of Primitive Types
+ *     https://drops.dagstuhl.de/opus/volltexte/2021/13775/
+ *
+ * [2] A Novel Hybrid Quicksort Algorithm Vectorized using AVX-512 on Intel
+ * Skylake https://arxiv.org/pdf/1704.08579.pdf
+ *
+ * [3] https://github.com/simd-sorting/fast-and-robust: SPDX-License-Identifier: MIT
+ *
+ * [4] http://mitp-content-server.mit.edu:18180/books/content/sectbyfn?collid=books_pres_0&fn=Chapter%2027.pdf&id=8030
+ *
+ */
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <cstring>
+#include <immintrin.h>
+#include <limits>
+
+#define X86_SIMD_SORT_INFINITY std::numeric_limits<double>::infinity()
+#define X86_SIMD_SORT_INFINITYF std::numeric_limits<float>::infinity()
+#define X86_SIMD_SORT_INFINITYH 0x7c00
+#define X86_SIMD_SORT_NEGINFINITYH 0xfc00
+#define X86_SIMD_SORT_MAX_UINT16 std::numeric_limits<uint16_t>::max()
+#define X86_SIMD_SORT_MAX_INT16 std::numeric_limits<int16_t>::max()
+#define X86_SIMD_SORT_MIN_INT16 std::numeric_limits<int16_t>::min()
+#define X86_SIMD_SORT_MAX_UINT32 std::numeric_limits<uint32_t>::max()
+#define X86_SIMD_SORT_MAX_INT32 std::numeric_limits<int32_t>::max()
+#define X86_SIMD_SORT_MIN_INT32 std::numeric_limits<int32_t>::min()
+#define X86_SIMD_SORT_MAX_UINT64 std::numeric_limits<uint64_t>::max()
+#define X86_SIMD_SORT_MAX_INT64 std::numeric_limits<int64_t>::max()
+#define X86_SIMD_SORT_MIN_INT64 std::numeric_limits<int64_t>::min()
+#define ZMM_MAX_DOUBLE _mm512_set1_pd(X86_SIMD_SORT_INFINITY)
+#define ZMM_MAX_UINT64 _mm512_set1_epi64(X86_SIMD_SORT_MAX_UINT64)
+#define ZMM_MAX_INT64 _mm512_set1_epi64(X86_SIMD_SORT_MAX_INT64)
+#define ZMM_MAX_FLOAT _mm512_set1_ps(X86_SIMD_SORT_INFINITYF)
+#define ZMM_MAX_UINT _mm512_set1_epi32(X86_SIMD_SORT_MAX_UINT32)
+#define ZMM_MAX_INT _mm512_set1_epi32(X86_SIMD_SORT_MAX_INT32)
+#define ZMM_MAX_HALF _mm512_set1_epi16(X86_SIMD_SORT_INFINITYH)
+#define YMM_MAX_HALF _mm256_set1_epi16(X86_SIMD_SORT_INFINITYH)
+#define ZMM_MAX_UINT16 _mm512_set1_epi16(X86_SIMD_SORT_MAX_UINT16)
+#define ZMM_MAX_INT16 _mm512_set1_epi16(X86_SIMD_SORT_MAX_INT16)
+#define SHUFFLE_MASK(a, b, c, d) (a << 6) | (b << 4) | (c << 2) | d
+
+#ifdef _MSC_VER
+#define X86_SIMD_SORT_INLINE static inline
+#define X86_SIMD_SORT_FINLINE static __forceinline
+#elif defined(__CYGWIN__)
+/*
+ * Force inline in cygwin to work around a compiler bug. See
+ * https://github.com/numpy/numpy/pull/22315#issuecomment-1267757584
+ */
+#define X86_SIMD_SORT_INLINE static __attribute__((always_inline))
+#define X86_SIMD_SORT_FINLINE static __attribute__((always_inline))
+#elif defined(__GNUC__)
+#define X86_SIMD_SORT_INLINE static inline
+#define X86_SIMD_SORT_FINLINE static __attribute__((always_inline))
+#else
+#define X86_SIMD_SORT_INLINE static
+#define X86_SIMD_SORT_FINLINE static
+#endif
+
+template <typename type>
+struct zmm_vector;
+
+template <typename type>
+struct ymm_vector;
+
+// Regular quicksort routines:
+template <typename T>
+void avx512_qsort(T *arr, int64_t arrsize);
+void avx512_qsort_fp16(uint16_t *arr, int64_t arrsize);
+
+template <typename T>
+void avx512_qselect(T *arr, int64_t k, int64_t arrsize);
+void avx512_qselect_fp16(uint16_t *arr, int64_t k, int64_t arrsize);
+
+template <typename T>
+inline void avx512_partial_qsort(T *arr, int64_t k, int64_t arrsize)
+{
+    avx512_qselect<T>(arr, k - 1, arrsize);
+    avx512_qsort<T>(arr, k - 1);
+}
+inline void avx512_partial_qsort_fp16(uint16_t *arr, int64_t k, int64_t arrsize)
+{
+    avx512_qselect_fp16(arr, k - 1, arrsize);
+    avx512_qsort_fp16(arr, k - 1);
+}
+
+// key-value sort routines
+template <typename T>
+void avx512_qsort_kv(T *keys, uint64_t *indexes, int64_t arrsize);
+
+template <typename vtype, typename T = typename vtype::type_t>
+bool comparison_func(const T &a, const T &b)
+{
+    return a < b;
+}
+
+/*
+ * COEX == Compare and Exchange two registers by swapping min and max values
+ */
+template <typename vtype, typename mm_t>
+static void COEX(mm_t &a, mm_t &b)
+{
+    mm_t temp = a;
+    a = vtype::min(a, b);
+    b = vtype::max(temp, b);
+}
+template <typename vtype,
+          typename zmm_t = typename vtype::zmm_t,
+          typename opmask_t = typename vtype::opmask_t>
+static inline zmm_t cmp_merge(zmm_t in1, zmm_t in2, opmask_t mask)
+{
+    zmm_t min = vtype::min(in2, in1);
+    zmm_t max = vtype::max(in2, in1);
+    return vtype::mask_mov(min, mask, max); // 0 -> min, 1 -> max
+}
+/*
+ * Parition one ZMM register based on the pivot and returns the
+ * number of elements that are greater than or equal to the pivot.
+ */
+template <typename vtype, typename type_t, typename zmm_t>
+static inline int32_t partition_vec(type_t *arr,
+                                    int64_t left,
+                                    int64_t right,
+                                    const zmm_t curr_vec,
+                                    const zmm_t pivot_vec,
+                                    zmm_t *smallest_vec,
+                                    zmm_t *biggest_vec)
+{
+    /* which elements are larger than or equal to the pivot */
+    typename vtype::opmask_t ge_mask = vtype::ge(curr_vec, pivot_vec);
+    int32_t amount_ge_pivot = _mm_popcnt_u32((int32_t)ge_mask);
+    vtype::mask_compressstoreu(
+            arr + left, vtype::knot_opmask(ge_mask), curr_vec);
+    vtype::mask_compressstoreu(
+            arr + right - amount_ge_pivot, ge_mask, curr_vec);
+    *smallest_vec = vtype::min(curr_vec, *smallest_vec);
+    *biggest_vec = vtype::max(curr_vec, *biggest_vec);
+    return amount_ge_pivot;
+}
+/*
+ * Parition an array based on the pivot and returns the index of the
+ * first element that is greater than or equal to the pivot.
+ */
+template <typename vtype, typename type_t>
+static inline int64_t partition_avx512(type_t *arr,
+                                       int64_t left,
+                                       int64_t right,
+                                       type_t pivot,
+                                       type_t *smallest,
+                                       type_t *biggest)
+{
+    /* make array length divisible by vtype::numlanes , shortening the array */
+    for (int32_t i = (right - left) % vtype::numlanes; i > 0; --i) {
+        *smallest = std::min(*smallest, arr[left], comparison_func<vtype>);
+        *biggest = std::max(*biggest, arr[left], comparison_func<vtype>);
+        if (!comparison_func<vtype>(arr[left], pivot)) {
+            std::swap(arr[left], arr[--right]);
+        }
+        else {
+            ++left;
+        }
+    }
+
+    if (left == right)
+        return left; /* less than vtype::numlanes elements in the array */
+
+    using zmm_t = typename vtype::zmm_t;
+    zmm_t pivot_vec = vtype::set1(pivot);
+    zmm_t min_vec = vtype::set1(*smallest);
+    zmm_t max_vec = vtype::set1(*biggest);
+
+    if (right - left == vtype::numlanes) {
+        zmm_t vec = vtype::loadu(arr + left);
+        int32_t amount_ge_pivot = partition_vec<vtype>(arr,
+                                                       left,
+                                                       left + vtype::numlanes,
+                                                       vec,
+                                                       pivot_vec,
+                                                       &min_vec,
+                                                       &max_vec);
+        *smallest = vtype::reducemin(min_vec);
+        *biggest = vtype::reducemax(max_vec);
+        return left + (vtype::numlanes - amount_ge_pivot);
+    }
+
+    // first and last vtype::numlanes values are partitioned at the end
+    zmm_t vec_left = vtype::loadu(arr + left);
+    zmm_t vec_right = vtype::loadu(arr + (right - vtype::numlanes));
+    // store points of the vectors
+    int64_t r_store = right - vtype::numlanes;
+    int64_t l_store = left;
+    // indices for loading the elements
+    left += vtype::numlanes;
+    right -= vtype::numlanes;
+    while (right - left != 0) {
+        zmm_t curr_vec;
+        /*
+         * if fewer elements are stored on the right side of the array,
+         * then next elements are loaded from the right side,
+         * otherwise from the left side
+         */
+        if ((r_store + vtype::numlanes) - right < left - l_store) {
+            right -= vtype::numlanes;
+            curr_vec = vtype::loadu(arr + right);
+        }
+        else {
+            curr_vec = vtype::loadu(arr + left);
+            left += vtype::numlanes;
+        }
+        // partition the current vector and save it on both sides of the array
+        int32_t amount_ge_pivot
+                = partition_vec<vtype>(arr,
+                                       l_store,
+                                       r_store + vtype::numlanes,
+                                       curr_vec,
+                                       pivot_vec,
+                                       &min_vec,
+                                       &max_vec);
+        ;
+        r_store -= amount_ge_pivot;
+        l_store += (vtype::numlanes - amount_ge_pivot);
+    }
+
+    /* partition and save vec_left and vec_right */
+    int32_t amount_ge_pivot = partition_vec<vtype>(arr,
+                                                   l_store,
+                                                   r_store + vtype::numlanes,
+                                                   vec_left,
+                                                   pivot_vec,
+                                                   &min_vec,
+                                                   &max_vec);
+    l_store += (vtype::numlanes - amount_ge_pivot);
+    amount_ge_pivot = partition_vec<vtype>(arr,
+                                           l_store,
+                                           l_store + vtype::numlanes,
+                                           vec_right,
+                                           pivot_vec,
+                                           &min_vec,
+                                           &max_vec);
+    l_store += (vtype::numlanes - amount_ge_pivot);
+    *smallest = vtype::reducemin(min_vec);
+    *biggest = vtype::reducemax(max_vec);
+    return l_store;
+}
+
+template <typename vtype,
+          int num_unroll,
+          typename type_t = typename vtype::type_t>
+static inline int64_t partition_avx512_unrolled(type_t *arr,
+                                                int64_t left,
+                                                int64_t right,
+                                                type_t pivot,
+                                                type_t *smallest,
+                                                type_t *biggest)
+{
+    if (right - left <= 2 * num_unroll * vtype::numlanes) {
+        return partition_avx512<vtype>(
+                arr, left, right, pivot, smallest, biggest);
+    }
+    /* make array length divisible by 8*vtype::numlanes , shortening the array */
+    for (int32_t i = ((right - left) % (num_unroll * vtype::numlanes)); i > 0;
+         --i) {
+        *smallest = std::min(*smallest, arr[left], comparison_func<vtype>);
+        *biggest = std::max(*biggest, arr[left], comparison_func<vtype>);
+        if (!comparison_func<vtype>(arr[left], pivot)) {
+            std::swap(arr[left], arr[--right]);
+        }
+        else {
+            ++left;
+        }
+    }
+
+    if (left == right)
+        return left; /* less than vtype::numlanes elements in the array */
+
+    using zmm_t = typename vtype::zmm_t;
+    zmm_t pivot_vec = vtype::set1(pivot);
+    zmm_t min_vec = vtype::set1(*smallest);
+    zmm_t max_vec = vtype::set1(*biggest);
+
+    // We will now have atleast 16 registers worth of data to process:
+    // left and right vtype::numlanes values are partitioned at the end
+    zmm_t vec_left[num_unroll], vec_right[num_unroll];
+#pragma GCC unroll 8
+    for (int ii = 0; ii < num_unroll; ++ii) {
+        vec_left[ii] = vtype::loadu(arr + left + vtype::numlanes * ii);
+        vec_right[ii] = vtype::loadu(
+                arr + (right - vtype::numlanes * (num_unroll - ii)));
+    }
+    // store points of the vectors
+    int64_t r_store = right - vtype::numlanes;
+    int64_t l_store = left;
+    // indices for loading the elements
+    left += num_unroll * vtype::numlanes;
+    right -= num_unroll * vtype::numlanes;
+    while (right - left != 0) {
+        zmm_t curr_vec[num_unroll];
+        /*
+         * if fewer elements are stored on the right side of the array,
+         * then next elements are loaded from the right side,
+         * otherwise from the left side
+         */
+        if ((r_store + vtype::numlanes) - right < left - l_store) {
+            right -= num_unroll * vtype::numlanes;
+#pragma GCC unroll 8
+            for (int ii = 0; ii < num_unroll; ++ii) {
+                curr_vec[ii] = vtype::loadu(arr + right + ii * vtype::numlanes);
+            }
+        }
+        else {
+#pragma GCC unroll 8
+            for (int ii = 0; ii < num_unroll; ++ii) {
+                curr_vec[ii] = vtype::loadu(arr + left + ii * vtype::numlanes);
+            }
+            left += num_unroll * vtype::numlanes;
+        }
+// partition the current vector and save it on both sides of the array
+#pragma GCC unroll 8
+        for (int ii = 0; ii < num_unroll; ++ii) {
+            int32_t amount_ge_pivot
+                    = partition_vec<vtype>(arr,
+                                           l_store,
+                                           r_store + vtype::numlanes,
+                                           curr_vec[ii],
+                                           pivot_vec,
+                                           &min_vec,
+                                           &max_vec);
+            l_store += (vtype::numlanes - amount_ge_pivot);
+            r_store -= amount_ge_pivot;
+        }
+    }
+
+/* partition and save vec_left[8] and vec_right[8] */
+#pragma GCC unroll 8
+    for (int ii = 0; ii < num_unroll; ++ii) {
+        int32_t amount_ge_pivot
+                = partition_vec<vtype>(arr,
+                                       l_store,
+                                       r_store + vtype::numlanes,
+                                       vec_left[ii],
+                                       pivot_vec,
+                                       &min_vec,
+                                       &max_vec);
+        l_store += (vtype::numlanes - amount_ge_pivot);
+        r_store -= amount_ge_pivot;
+    }
+#pragma GCC unroll 8
+    for (int ii = 0; ii < num_unroll; ++ii) {
+        int32_t amount_ge_pivot
+                = partition_vec<vtype>(arr,
+                                       l_store,
+                                       r_store + vtype::numlanes,
+                                       vec_right[ii],
+                                       pivot_vec,
+                                       &min_vec,
+                                       &max_vec);
+        l_store += (vtype::numlanes - amount_ge_pivot);
+        r_store -= amount_ge_pivot;
+    }
+    *smallest = vtype::reducemin(min_vec);
+    *biggest = vtype::reducemax(max_vec);
+    return l_store;
+}
+
+// Key-value sort helper functions
+
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t1 = typename vtype1::zmm_t,
+          typename zmm_t2 = typename vtype2::zmm_t>
+static void COEX(zmm_t1 &key1, zmm_t1 &key2, zmm_t2 &index1, zmm_t2 &index2)
+{
+    zmm_t1 key_t1 = vtype1::min(key1, key2);
+    zmm_t1 key_t2 = vtype1::max(key1, key2);
+
+    zmm_t2 index_t1
+            = vtype2::mask_mov(index2, vtype1::eq(key_t1, key1), index1);
+    zmm_t2 index_t2
+            = vtype2::mask_mov(index1, vtype1::eq(key_t1, key1), index2);
+
+    key1 = key_t1;
+    key2 = key_t2;
+    index1 = index_t1;
+    index2 = index_t2;
+}
+template <typename vtype1,
+          typename vtype2,
+          typename zmm_t1 = typename vtype1::zmm_t,
+          typename zmm_t2 = typename vtype2::zmm_t,
+          typename opmask_t = typename vtype1::opmask_t>
+static inline zmm_t1 cmp_merge(zmm_t1 in1,
+                               zmm_t1 in2,
+                               zmm_t2 &indexes1,
+                               zmm_t2 indexes2,
+                               opmask_t mask)
+{
+    zmm_t1 tmp_keys = cmp_merge<vtype1>(in1, in2, mask);
+    indexes1 = vtype2::mask_mov(indexes2, vtype1::eq(tmp_keys, in1), indexes1);
+    return tmp_keys; // 0 -> min, 1 -> max
+}
+
+/*
+ * Parition one ZMM register based on the pivot and returns the index of the
+ * last element that is less than equal to the pivot.
+ */
+template <typename vtype1,
+          typename vtype2,
+          typename type_t1 = typename vtype1::type_t,
+          typename type_t2 = typename vtype2::type_t,
+          typename zmm_t1 = typename vtype1::zmm_t,
+          typename zmm_t2 = typename vtype2::zmm_t>
+static inline int32_t partition_vec(type_t1 *keys,
+                                    type_t2 *indexes,
+                                    int64_t left,
+                                    int64_t right,
+                                    const zmm_t1 keys_vec,
+                                    const zmm_t2 indexes_vec,
+                                    const zmm_t1 pivot_vec,
+                                    zmm_t1 *smallest_vec,
+                                    zmm_t1 *biggest_vec)
+{
+    /* which elements are larger than the pivot */
+    typename vtype1::opmask_t gt_mask = vtype1::ge(keys_vec, pivot_vec);
+    int32_t amount_gt_pivot = _mm_popcnt_u32((int32_t)gt_mask);
+    vtype1::mask_compressstoreu(
+            keys + left, vtype1::knot_opmask(gt_mask), keys_vec);
+    vtype1::mask_compressstoreu(
+            keys + right - amount_gt_pivot, gt_mask, keys_vec);
+    vtype2::mask_compressstoreu(
+            indexes + left, vtype2::knot_opmask(gt_mask), indexes_vec);
+    vtype2::mask_compressstoreu(
+            indexes + right - amount_gt_pivot, gt_mask, indexes_vec);
+    *smallest_vec = vtype1::min(keys_vec, *smallest_vec);
+    *biggest_vec = vtype1::max(keys_vec, *biggest_vec);
+    return amount_gt_pivot;
+}
+/*
+ * Parition an array based on the pivot and returns the index of the
+ * last element that is less than equal to the pivot.
+ */
+template <typename vtype1,
+          typename vtype2,
+          typename type_t1 = typename vtype1::type_t,
+          typename type_t2 = typename vtype2::type_t,
+          typename zmm_t1 = typename vtype1::zmm_t,
+          typename zmm_t2 = typename vtype2::zmm_t>
+static inline int64_t partition_avx512(type_t1 *keys,
+                                       type_t2 *indexes,
+                                       int64_t left,
+                                       int64_t right,
+                                       type_t1 pivot,
+                                       type_t1 *smallest,
+                                       type_t1 *biggest)
+{
+    /* make array length divisible by vtype1::numlanes , shortening the array */
+    for (int32_t i = (right - left) % vtype1::numlanes; i > 0; --i) {
+        *smallest = std::min(*smallest, keys[left]);
+        *biggest = std::max(*biggest, keys[left]);
+        if (keys[left] > pivot) {
+            right--;
+            std::swap(keys[left], keys[right]);
+            std::swap(indexes[left], indexes[right]);
+        }
+        else {
+            ++left;
+        }
+    }
+
+    if (left == right)
+        return left; /* less than vtype1::numlanes elements in the array */
+
+    zmm_t1 pivot_vec = vtype1::set1(pivot);
+    zmm_t1 min_vec = vtype1::set1(*smallest);
+    zmm_t1 max_vec = vtype1::set1(*biggest);
+
+    if (right - left == vtype1::numlanes) {
+        zmm_t1 keys_vec = vtype1::loadu(keys + left);
+        int32_t amount_gt_pivot;
+
+        zmm_t2 indexes_vec = vtype2::loadu(indexes + left);
+        amount_gt_pivot = partition_vec<vtype1, vtype2>(keys,
+                                                        indexes,
+                                                        left,
+                                                        left + vtype1::numlanes,
+                                                        keys_vec,
+                                                        indexes_vec,
+                                                        pivot_vec,
+                                                        &min_vec,
+                                                        &max_vec);
+
+        *smallest = vtype1::reducemin(min_vec);
+        *biggest = vtype1::reducemax(max_vec);
+        return left + (vtype1::numlanes - amount_gt_pivot);
+    }
+
+    // first and last vtype1::numlanes values are partitioned at the end
+    zmm_t1 keys_vec_left = vtype1::loadu(keys + left);
+    zmm_t1 keys_vec_right = vtype1::loadu(keys + (right - vtype1::numlanes));
+    zmm_t2 indexes_vec_left;
+    zmm_t2 indexes_vec_right;
+    indexes_vec_left = vtype2::loadu(indexes + left);
+    indexes_vec_right = vtype2::loadu(indexes + (right - vtype1::numlanes));
+
+    // store points of the vectors
+    int64_t r_store = right - vtype1::numlanes;
+    int64_t l_store = left;
+    // indices for loading the elements
+    left += vtype1::numlanes;
+    right -= vtype1::numlanes;
+    while (right - left != 0) {
+        zmm_t1 keys_vec;
+        zmm_t2 indexes_vec;
+        /*
+         * if fewer elements are stored on the right side of the array,
+         * then next elements are loaded from the right side,
+         * otherwise from the left side
+         */
+        if ((r_store + vtype1::numlanes) - right < left - l_store) {
+            right -= vtype1::numlanes;
+            keys_vec = vtype1::loadu(keys + right);
+            indexes_vec = vtype2::loadu(indexes + right);
+        }
+        else {
+            keys_vec = vtype1::loadu(keys + left);
+            indexes_vec = vtype2::loadu(indexes + left);
+            left += vtype1::numlanes;
+        }
+        // partition the current vector and save it on both sides of the array
+        int32_t amount_gt_pivot;
+
+        amount_gt_pivot
+                = partition_vec<vtype1, vtype2>(keys,
+                                                indexes,
+                                                l_store,
+                                                r_store + vtype1::numlanes,
+                                                keys_vec,
+                                                indexes_vec,
+                                                pivot_vec,
+                                                &min_vec,
+                                                &max_vec);
+        r_store -= amount_gt_pivot;
+        l_store += (vtype1::numlanes - amount_gt_pivot);
+    }
+
+    /* partition and save vec_left and vec_right */
+    int32_t amount_gt_pivot;
+    amount_gt_pivot = partition_vec<vtype1, vtype2>(keys,
+                                                    indexes,
+                                                    l_store,
+                                                    r_store + vtype1::numlanes,
+                                                    keys_vec_left,
+                                                    indexes_vec_left,
+                                                    pivot_vec,
+                                                    &min_vec,
+                                                    &max_vec);
+    l_store += (vtype1::numlanes - amount_gt_pivot);
+    amount_gt_pivot = partition_vec<vtype1, vtype2>(keys,
+                                                    indexes,
+                                                    l_store,
+                                                    l_store + vtype1::numlanes,
+                                                    keys_vec_right,
+                                                    indexes_vec_right,
+                                                    pivot_vec,
+                                                    &min_vec,
+                                                    &max_vec);
+    l_store += (vtype1::numlanes - amount_gt_pivot);
+    *smallest = vtype1::reducemin(min_vec);
+    *biggest = vtype1::reducemax(max_vec);
+    return l_store;
+}
+#endif // AVX512_QSORT_COMMON
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512fp16-16bit-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512fp16-16bit-qsort.hpp
new file mode 100644
index 00000000000..8a9a49ede9d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/src/avx512fp16-16bit-qsort.hpp
@@ -0,0 +1,168 @@
+/*******************************************************************
+ * Copyright (C) 2022 Intel Corporation
+ * SPDX-License-Identifier: BSD-3-Clause
+ * Authors: Raghuveer Devulapalli <raghuveer.devulapalli@intel.com>
+ * ****************************************************************/
+
+#ifndef AVX512FP16_QSORT_16BIT
+#define AVX512FP16_QSORT_16BIT
+
+#include "avx512-16bit-common.h"
+
+typedef union {
+    _Float16 f_;
+    uint16_t i_;
+} Fp16Bits;
+
+template <>
+struct zmm_vector<_Float16> {
+    using type_t = _Float16;
+    using zmm_t = __m512h;
+    using ymm_t = __m256h;
+    using opmask_t = __mmask32;
+    static const uint8_t numlanes = 32;
+
+    static __m512i get_network(int index)
+    {
+        return _mm512_loadu_si512(&network[index - 1][0]);
+    }
+    static type_t type_max()
+    {
+        Fp16Bits val;
+        val.i_ = X86_SIMD_SORT_INFINITYH;
+        return val.f_;
+    }
+    static type_t type_min()
+    {
+        Fp16Bits val;
+        val.i_ = X86_SIMD_SORT_NEGINFINITYH;
+        return val.f_;
+    }
+    static zmm_t zmm_max()
+    {
+        return _mm512_set1_ph(type_max());
+    }
+    static opmask_t knot_opmask(opmask_t x)
+    {
+        return _knot_mask32(x);
+    }
+
+    static opmask_t ge(zmm_t x, zmm_t y)
+    {
+        return _mm512_cmp_ph_mask(x, y, _CMP_GE_OQ);
+    }
+    static zmm_t loadu(void const *mem)
+    {
+        return _mm512_loadu_ph(mem);
+    }
+    static zmm_t max(zmm_t x, zmm_t y)
+    {
+        return _mm512_max_ph(x, y);
+    }
+    static void mask_compressstoreu(void *mem, opmask_t mask, zmm_t x)
+    {
+        __m512i temp = _mm512_castph_si512(x);
+        // AVX512_VBMI2
+        return _mm512_mask_compressstoreu_epi16(mem, mask, temp);
+    }
+    static zmm_t mask_loadu(zmm_t x, opmask_t mask, void const *mem)
+    {
+        // AVX512BW
+        return _mm512_castsi512_ph(
+                _mm512_mask_loadu_epi16(_mm512_castph_si512(x), mask, mem));
+    }
+    static zmm_t mask_mov(zmm_t x, opmask_t mask, zmm_t y)
+    {
+        return _mm512_castsi512_ph(_mm512_mask_mov_epi16(
+                _mm512_castph_si512(x), mask, _mm512_castph_si512(y)));
+    }
+    static void mask_storeu(void *mem, opmask_t mask, zmm_t x)
+    {
+        return _mm512_mask_storeu_epi16(mem, mask, _mm512_castph_si512(x));
+    }
+    static zmm_t min(zmm_t x, zmm_t y)
+    {
+        return _mm512_min_ph(x, y);
+    }
+    static zmm_t permutexvar(__m512i idx, zmm_t zmm)
+    {
+        return _mm512_permutexvar_ph(idx, zmm);
+    }
+    static type_t reducemax(zmm_t v)
+    {
+        return _mm512_reduce_max_ph(v);
+    }
+    static type_t reducemin(zmm_t v)
+    {
+        return _mm512_reduce_min_ph(v);
+    }
+    static zmm_t set1(type_t v)
+    {
+        return _mm512_set1_ph(v);
+    }
+    template <uint8_t mask>
+    static zmm_t shuffle(zmm_t zmm)
+    {
+        __m512i temp = _mm512_shufflehi_epi16(_mm512_castph_si512(zmm),
+                                              (_MM_PERM_ENUM)mask);
+        return _mm512_castsi512_ph(
+                _mm512_shufflelo_epi16(temp, (_MM_PERM_ENUM)mask));
+    }
+    static void storeu(void *mem, zmm_t x)
+    {
+        return _mm512_storeu_ph(mem, x);
+    }
+};
+
+X86_SIMD_SORT_INLINE int64_t replace_nan_with_inf(_Float16 *arr,
+                                                  int64_t arrsize)
+{
+    int64_t nan_count = 0;
+    __mmask32 loadmask = 0xFFFFFFFF;
+    __m512h in_zmm;
+    while (arrsize > 0) {
+        if (arrsize < 32) {
+            loadmask = (0x00000001 << arrsize) - 0x00000001;
+            in_zmm = _mm512_castsi512_ph(
+                    _mm512_maskz_loadu_epi16(loadmask, arr));
+        }
+        else {
+            in_zmm = _mm512_loadu_ph(arr);
+        }
+        __mmask32 nanmask = _mm512_cmp_ph_mask(in_zmm, in_zmm, _CMP_NEQ_UQ);
+        nan_count += _mm_popcnt_u32((int32_t)nanmask);
+        _mm512_mask_storeu_epi16(arr, nanmask, ZMM_MAX_HALF);
+        arr += 32;
+        arrsize -= 32;
+    }
+    return nan_count;
+}
+
+X86_SIMD_SORT_INLINE void
+replace_inf_with_nan(_Float16 *arr, int64_t arrsize, int64_t nan_count)
+{
+    memset(arr + arrsize - nan_count, 0xFF, nan_count * 2);
+}
+
+template <>
+void avx512_qselect(_Float16 *arr, int64_t k, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qselect_16bit_<zmm_vector<_Float16>, _Float16>(
+                arr, k, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+
+template <>
+void avx512_qsort(_Float16 *arr, int64_t arrsize)
+{
+    if (arrsize > 1) {
+        int64_t nan_count = replace_nan_with_inf(arr, arrsize);
+        qsort_16bit_<zmm_vector<_Float16>, _Float16>(
+                arr, 0, arrsize - 1, 2 * (int64_t)log2(arrsize));
+        replace_inf_with_nan(arr, arrsize, nan_count);
+    }
+}
+#endif // AVX512FP16_QSORT_16BIT
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/meson.build b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/meson.build
new file mode 100644
index 00000000000..ac0ce341522
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/meson.build
@@ -0,0 +1,31 @@
+libtests = []
+
+if cpp.has_argument('-march=skylake-avx512')
+  libtests += static_library('tests_kv',
+    files(
+      'test-keyvalue.cpp',
+      'test-argsort.cpp',
+    ),
+    dependencies: gtest_dep,
+    include_directories : [src, utils],
+    cpp_args : ['-O3', '-march=skylake-avx512'],
+    )
+endif
+
+if cpp.has_argument('-march=icelake-client')
+  libtests += static_library('tests_qsort',
+    files('test-qsort.cpp', ),
+    dependencies: gtest_dep,
+    include_directories : [src, utils],
+    cpp_args : ['-O3', '-march=icelake-client'],
+    )
+endif
+
+if cancompilefp16
+  libtests += static_library('tests_qsortfp16',
+    files('test-qsortfp16.cpp', ),
+    dependencies: gtest_dep,
+    include_directories : [src, utils],
+    cpp_args : ['-O3', '-march=sapphirerapids'],
+    )
+endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-argsort.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-argsort.cpp
new file mode 100644
index 00000000000..8048d7516cb
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-argsort.cpp
@@ -0,0 +1,305 @@
+/*******************************************
+ * * Copyright (C) 2023 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include "avx512-64bit-argsort.hpp"
+#include "cpuinfo.h"
+#include "rand_array.h"
+#include <algorithm>
+#include <gtest/gtest.h>
+#include <vector>
+
+template <typename T>
+class avx512argsort : public ::testing::Test {
+};
+TYPED_TEST_SUITE_P(avx512argsort);
+
+template <typename T>
+std::vector<int64_t> std_argsort(const std::vector<T> &array)
+{
+    std::vector<int64_t> indices(array.size());
+    std::iota(indices.begin(), indices.end(), 0);
+    std::sort(indices.begin(),
+              indices.end(),
+              [&array](int left, int right) -> bool {
+                  // sort indices according to corresponding array sizeent
+                  return array[left] < array[right];
+              });
+
+    return indices;
+}
+
+#define EXPECT_UNIQUE(sorted_arg) \
+    std::sort(sorted_arg.begin(), sorted_arg.end()); \
+    std::vector<int64_t> expected_arg(sorted_arg.size()); \
+    std::iota(expected_arg.begin(), expected_arg.end(), 0); \
+    EXPECT_EQ(sorted_arg, expected_arg) << "Indices aren't unique. Array size = " << sorted_arg.size();
+
+TYPED_TEST_P(avx512argsort, test_random)
+{
+    if (cpu_has_avx512bw()) {
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii <= 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<TypeParam> arr;
+        for (auto &size : arrsizes) {
+            /* Random array */
+            arr = get_uniform_rand_array<TypeParam>(size);
+            std::vector<int64_t> inx1 = std_argsort(arr);
+            std::vector<int64_t> inx2
+                    = avx512_argsort<TypeParam>(arr.data(), arr.size());
+            std::vector<TypeParam> sort1, sort2;
+            for (size_t jj = 0; jj < size; ++jj) {
+                sort1.push_back(arr[inx1[jj]]);
+                sort2.push_back(arr[inx2[jj]]);
+            }
+            EXPECT_EQ(sort1, sort2) << "Array size =" << size;
+            EXPECT_UNIQUE(inx2)
+            arr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+}
+
+TYPED_TEST_P(avx512argsort, test_constant)
+{
+    if (cpu_has_avx512bw()) {
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii <= 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<TypeParam> arr;
+        for (auto &size : arrsizes) {
+            /* constant array */
+            auto elem = get_uniform_rand_array<TypeParam>(1)[0];
+            for (int64_t jj = 0; jj < size; ++jj) {
+                arr.push_back(elem);
+            }
+            std::vector<int64_t> inx1 = std_argsort(arr);
+            std::vector<int64_t> inx2
+                    = avx512_argsort<TypeParam>(arr.data(), arr.size());
+            std::vector<TypeParam> sort1, sort2;
+            for (size_t jj = 0; jj < size; ++jj) {
+                sort1.push_back(arr[inx1[jj]]);
+                sort2.push_back(arr[inx2[jj]]);
+            }
+            EXPECT_EQ(sort1, sort2) << "Array size =" << size;
+            EXPECT_UNIQUE(inx2)
+            arr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+}
+
+TYPED_TEST_P(avx512argsort, test_small_range)
+{
+    if (cpu_has_avx512bw()) {
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii <= 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<TypeParam> arr;
+        for (auto &size : arrsizes) {
+            /* array with a smaller range of values */
+            arr = get_uniform_rand_array<TypeParam>(size, 20, 1);
+            std::vector<int64_t> inx1 = std_argsort(arr);
+            std::vector<int64_t> inx2
+                    = avx512_argsort<TypeParam>(arr.data(), arr.size());
+            std::vector<TypeParam> sort1, sort2;
+            for (size_t jj = 0; jj < size; ++jj) {
+                sort1.push_back(arr[inx1[jj]]);
+                sort2.push_back(arr[inx2[jj]]);
+            }
+            EXPECT_EQ(sort1, sort2) << "Array size = " << size;
+            EXPECT_UNIQUE(inx2)
+            arr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+}
+
+TYPED_TEST_P(avx512argsort, test_sorted)
+{
+    if (cpu_has_avx512bw()) {
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii <= 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<TypeParam> arr;
+        for (auto &size : arrsizes) {
+            arr = get_uniform_rand_array<TypeParam>(size);
+            std::sort(arr.begin(), arr.end());
+            std::vector<int64_t> inx1 = std_argsort(arr);
+            std::vector<int64_t> inx2
+                    = avx512_argsort<TypeParam>(arr.data(), arr.size());
+            std::vector<TypeParam> sort1, sort2;
+            for (size_t jj = 0; jj < size; ++jj) {
+                sort1.push_back(arr[inx1[jj]]);
+                sort2.push_back(arr[inx2[jj]]);
+            }
+            EXPECT_EQ(sort1, sort2) << "Array size =" << size;
+            EXPECT_UNIQUE(inx2)
+            arr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+}
+
+TYPED_TEST_P(avx512argsort, test_reverse)
+{
+    if (cpu_has_avx512bw()) {
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii <= 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<TypeParam> arr;
+        for (auto &size : arrsizes) {
+            arr = get_uniform_rand_array<TypeParam>(size);
+            std::sort(arr.begin(), arr.end());
+            std::reverse(arr.begin(), arr.end());
+            std::vector<int64_t> inx1 = std_argsort(arr);
+            std::vector<int64_t> inx2
+                    = avx512_argsort<TypeParam>(arr.data(), arr.size());
+            std::vector<TypeParam> sort1, sort2;
+            for (size_t jj = 0; jj < size; ++jj) {
+                sort1.push_back(arr[inx1[jj]]);
+                sort2.push_back(arr[inx2[jj]]);
+            }
+            EXPECT_EQ(sort1, sort2) << "Array size =" << size;
+            EXPECT_UNIQUE(inx2)
+            arr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+}
+
+TYPED_TEST_P(avx512argsort, test_array_with_nan)
+{
+    if (!cpu_has_avx512bw()) {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+    if (!std::is_floating_point<TypeParam>::value) {
+        GTEST_SKIP() << "Skipping this test, it is meant for float/double";
+    }
+    std::vector<int64_t> arrsizes;
+    for (int64_t ii = 2; ii <= 1024; ++ii) {
+        arrsizes.push_back(ii);
+    }
+    std::vector<TypeParam> arr;
+    for (auto &size : arrsizes) {
+        arr = get_uniform_rand_array<TypeParam>(size);
+        arr[0] = std::numeric_limits<TypeParam>::quiet_NaN();
+        arr[1] = std::numeric_limits<TypeParam>::quiet_NaN();
+        std::vector<int64_t> inx
+                = avx512_argsort<TypeParam>(arr.data(), arr.size());
+        std::vector<TypeParam> sort1;
+        for (size_t jj = 0; jj < size; ++jj) {
+            sort1.push_back(arr[inx[jj]]);
+        }
+        if ((!std::isnan(sort1[size - 1])) || (!std::isnan(sort1[size - 2]))) {
+            FAIL() << "NAN's aren't sorted to the end";
+        }
+        if (!std::is_sorted(sort1.begin(), sort1.end() - 2)) {
+            FAIL() << "Array isn't sorted";
+        }
+        EXPECT_UNIQUE(inx)
+        arr.clear();
+    }
+}
+
+TYPED_TEST_P(avx512argsort, test_max_value_at_end_of_array)
+{
+    if (!cpu_has_avx512bw()) {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+    std::vector<int64_t> arrsizes;
+    for (int64_t ii = 1; ii <= 256; ++ii) {
+        arrsizes.push_back(ii);
+    }
+    std::vector<TypeParam> arr;
+    for (auto &size : arrsizes) {
+        arr = get_uniform_rand_array<TypeParam>(size);
+        if (std::numeric_limits<TypeParam>::has_infinity) {
+            arr[size - 1] = std::numeric_limits<TypeParam>::infinity();
+        }
+        else {
+            arr[size - 1] = std::numeric_limits<TypeParam>::max();
+        }
+        std::vector<int64_t> inx = avx512_argsort(arr.data(), arr.size());
+        std::vector<TypeParam> sorted;
+        for (size_t jj = 0; jj < size; ++jj) {
+            sorted.push_back(arr[inx[jj]]);
+        }
+        if (!std::is_sorted(sorted.begin(), sorted.end())) {
+            EXPECT_TRUE(false) << "Array of size " << size << "is not sorted";
+        }
+        EXPECT_UNIQUE(inx)
+        arr.clear();
+    }
+}
+
+TYPED_TEST_P(avx512argsort, test_all_inf_array)
+{
+    if (!cpu_has_avx512bw()) {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+    std::vector<int64_t> arrsizes;
+    for (int64_t ii = 1; ii <= 256; ++ii) {
+        arrsizes.push_back(ii);
+    }
+    std::vector<TypeParam> arr;
+    for (auto &size : arrsizes) {
+        arr = get_uniform_rand_array<TypeParam>(size);
+        if (std::numeric_limits<TypeParam>::has_infinity) {
+            for (int64_t jj = 1; jj <= size; ++jj) {
+                if (rand() % 0x1) {
+                    arr.push_back(std::numeric_limits<TypeParam>::infinity());
+                }
+            }
+        }
+        else {
+            for (int64_t jj = 1; jj <= size; ++jj) {
+                if (rand() % 0x1) {
+                    arr.push_back(std::numeric_limits<TypeParam>::max());
+                }
+            }
+        }
+        std::vector<int64_t> inx = avx512_argsort(arr.data(), arr.size());
+        std::vector<TypeParam> sorted;
+        for (size_t jj = 0; jj < size; ++jj) {
+            sorted.push_back(arr[inx[jj]]);
+        }
+        if (!std::is_sorted(sorted.begin(), sorted.end())) {
+            EXPECT_TRUE(false) << "Array of size " << size << "is not sorted";
+        }
+        EXPECT_UNIQUE(inx)
+        arr.clear();
+    }
+}
+
+REGISTER_TYPED_TEST_SUITE_P(avx512argsort,
+                            test_random,
+                            test_reverse,
+                            test_constant,
+                            test_sorted,
+                            test_small_range,
+                            test_all_inf_array,
+                            test_array_with_nan,
+                            test_max_value_at_end_of_array);
+
+using ArgSortTestTypes
+        = testing::Types<int32_t, uint32_t, float, uint64_t, int64_t, double>;
+
+INSTANTIATE_TYPED_TEST_SUITE_P(T, avx512argsort, ArgSortTestTypes);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-keyvalue.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-keyvalue.cpp
new file mode 100644
index 00000000000..2099ba5d313
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-keyvalue.cpp
@@ -0,0 +1,87 @@
+/*******************************************
+ * * Copyright (C) 2022 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include "avx512-64bit-keyvaluesort.hpp"
+#include "cpuinfo.h"
+#include "rand_array.h"
+#include <gtest/gtest.h>
+#include <vector>
+#define inf X86_SIMD_SORT_INFINITY
+
+template <typename K, typename V = uint64_t>
+struct sorted_t {
+    K key;
+    K value;
+};
+
+template <typename K, typename V = uint64_t>
+bool compare(sorted_t<K, V> a, sorted_t<K, V> b)
+{
+    return a.key == b.key ? a.value < b.value : a.key < b.key;
+}
+
+template <typename K>
+class KeyValueSort : public ::testing::Test {
+};
+
+TYPED_TEST_SUITE_P(KeyValueSort);
+
+TYPED_TEST_P(KeyValueSort, test_64bit_random_data)
+{
+    if (cpu_has_avx512bw()) {
+        std::vector<int64_t> keysizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            keysizes.push_back((TypeParam)ii);
+        }
+        std::vector<TypeParam> keys;
+        std::vector<uint64_t> values;
+        std::vector<sorted_t<TypeParam, uint64_t>> sortedarr;
+
+        for (size_t ii = 0; ii < keysizes.size(); ++ii) {
+            /* Random array */
+            keys = get_uniform_rand_array_with_uniquevalues<TypeParam>(
+                    keysizes[ii]);
+            values = get_uniform_rand_array<uint64_t>(keysizes[ii]);
+            for (size_t i = 0; i < keys.size(); i++) {
+                sorted_t<TypeParam, uint64_t> tmp_s;
+                tmp_s.key = keys[i];
+                tmp_s.value = values[i];
+                sortedarr.emplace_back(tmp_s);
+            }
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(),
+                      sortedarr.end(),
+                      compare<TypeParam, uint64_t>);
+            avx512_qsort_kv<TypeParam>(keys.data(), values.data(), keys.size());
+            for (size_t i = 0; i < keys.size(); i++) {
+                ASSERT_EQ(keys[i], sortedarr[i].key);
+                ASSERT_EQ(values[i], sortedarr[i].value);
+            }
+            keys.clear();
+            values.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+TEST(KeyValueSort, test_inf_at_endofarray)
+{
+    std::vector<double> key = {8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, inf};
+    std::vector<double> key_sorted
+            = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, inf};
+    std::vector<uint64_t> val = {7, 6, 5, 4, 3, 2, 1, 0, 8};
+    std::vector<uint64_t> val_sorted = {0, 1, 2, 3, 4, 5, 6, 7, 8};
+    avx512_qsort_kv(key.data(), val.data(), key.size());
+    ASSERT_EQ(key, key_sorted);
+    ASSERT_EQ(val, val_sorted);
+}
+
+REGISTER_TYPED_TEST_SUITE_P(KeyValueSort, test_64bit_random_data);
+
+using TypesKv = testing::Types<double, uint64_t, int64_t>;
+INSTANTIATE_TYPED_TEST_SUITE_P(T, KeyValueSort, TypesKv);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-partial-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-partial-qsort.hpp
new file mode 100644
index 00000000000..4ba5caa8087
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-partial-qsort.hpp
@@ -0,0 +1,50 @@
+#include "test-qsort-common.h"
+
+template <typename T>
+class avx512_partial_sort : public ::testing::Test {
+};
+TYPED_TEST_SUITE_P(avx512_partial_sort);
+
+TYPED_TEST_P(avx512_partial_sort, test_ranges)
+{
+    int64_t arrsize = 1024;
+    int64_t nranges = 500;
+
+    if (cpu_has_avx512bw()) {
+        if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+            GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+        }
+        std::vector<TypeParam> arr;
+        std::vector<TypeParam> sortedarr;
+        std::vector<TypeParam> psortedarr;
+        /* Random array */
+        arr = get_uniform_rand_array<TypeParam>(arrsize);
+        sortedarr = arr;
+        /* Sort with std::sort for comparison */
+        std::sort(sortedarr.begin(), sortedarr.end());
+
+        for (size_t ii = 0; ii < nranges; ++ii) {
+            psortedarr = arr;
+
+            /* Pick a random number of elements to sort at the beginning of the array */
+            int k = get_uniform_rand_array<int64_t>(1, arrsize, 1).front();
+
+            /* Sort the range and verify all the required elements match the presorted set */
+            avx512_partial_qsort<TypeParam>(
+                    psortedarr.data(), k, psortedarr.size());
+            for (size_t jj = 0; jj < k; jj++) {
+                ASSERT_EQ(sortedarr[jj], psortedarr[jj]);
+            }
+
+            psortedarr.clear();
+        }
+
+        arr.clear();
+        sortedarr.clear();
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+REGISTER_TYPED_TEST_SUITE_P(avx512_partial_sort, test_ranges);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qselect.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qselect.hpp
new file mode 100644
index 00000000000..f0c0c242535
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qselect.hpp
@@ -0,0 +1,96 @@
+#include "test-qsort-common.h"
+
+template <typename T>
+class avx512_select : public ::testing::Test {
+};
+TYPED_TEST_SUITE_P(avx512_select);
+
+TYPED_TEST_P(avx512_select, test_random)
+{
+    if (cpu_has_avx512bw()) {
+        if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+            GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+        }
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<TypeParam> arr;
+        std::vector<TypeParam> sortedarr;
+        std::vector<TypeParam> psortedarr;
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            /* Random array */
+            arr = get_uniform_rand_array<TypeParam>(arrsizes[ii]);
+            sortedarr = arr;
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            for (size_t k = 0; k < arr.size(); ++k) {
+                psortedarr = arr;
+                avx512_qselect<TypeParam>(
+                        psortedarr.data(), k, psortedarr.size());
+                /* index k is correct */
+                ASSERT_EQ(sortedarr[k], psortedarr[k]);
+                /* Check left partition */
+                for (size_t jj = 0; jj < k; jj++) {
+                    ASSERT_LE(psortedarr[jj], psortedarr[k]);
+                }
+                /* Check right partition */
+                for (size_t jj = k + 1; jj < arr.size(); jj++) {
+                    ASSERT_GE(psortedarr[jj], psortedarr[k]);
+                }
+                psortedarr.clear();
+            }
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+TYPED_TEST_P(avx512_select, test_small_range)
+{
+    if (cpu_has_avx512bw()) {
+        if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+            GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+        }
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<TypeParam> arr;
+        std::vector<TypeParam> sortedarr;
+        std::vector<TypeParam> psortedarr;
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            /* Random array */
+            arr = get_uniform_rand_array<TypeParam>(arrsizes[ii], 20, 1);
+            sortedarr = arr;
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            for (size_t k = 0; k < arr.size(); ++k) {
+                psortedarr = arr;
+                avx512_qselect<TypeParam>(
+                        psortedarr.data(), k, psortedarr.size());
+                /* index k is correct */
+                ASSERT_EQ(sortedarr[k], psortedarr[k]);
+                /* Check left partition */
+                for (size_t jj = 0; jj < k; jj++) {
+                    ASSERT_LE(psortedarr[jj], psortedarr[k]);
+                }
+                /* Check right partition */
+                for (size_t jj = k + 1; jj < arr.size(); jj++) {
+                    ASSERT_GE(psortedarr[jj], psortedarr[k]);
+                }
+                psortedarr.clear();
+            }
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+REGISTER_TYPED_TEST_SUITE_P(avx512_select, test_random, test_small_range);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort-common.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort-common.h
new file mode 100644
index 00000000000..a41b2c63419
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort-common.h
@@ -0,0 +1,11 @@
+#ifndef AVX512_TEST_COMMON
+#define AVX512_TEST_COMMON
+
+#include "avx512-16bit-qsort.hpp"
+#include "avx512-32bit-qsort.hpp"
+#include "avx512-64bit-qsort.hpp"
+#include "cpuinfo.h"
+#include "rand_array.h"
+#include <gtest/gtest.h>
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort-fp.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort-fp.hpp
new file mode 100644
index 00000000000..9000fb386c5
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort-fp.hpp
@@ -0,0 +1,47 @@
+/*******************************************
+ * * Copyright (C) 2022 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include "test-qsort-common.h"
+
+template <typename T>
+class avx512_sort_fp : public ::testing::Test {
+};
+TYPED_TEST_SUITE_P(avx512_sort_fp);
+
+TYPED_TEST_P(avx512_sort_fp, test_random_nan)
+{
+    const int num_nans = 3;
+    if (!cpu_has_avx512bw()) {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+    std::vector<int64_t> arrsizes;
+    for (int64_t ii = num_nans; ii < 1024; ++ii) {
+        arrsizes.push_back((TypeParam)ii);
+    }
+    std::vector<TypeParam> arr;
+    std::vector<TypeParam> sortedarr;
+    for (auto &size : arrsizes) {
+        /* Random array */
+        arr = get_uniform_rand_array<TypeParam>(size);
+        for (auto ii = 1; ii <= num_nans; ++ii) {
+            arr[size-ii] = std::numeric_limits<TypeParam>::quiet_NaN();
+        }
+        sortedarr = arr;
+        std::sort(sortedarr.begin(), sortedarr.end()-3);
+        std::random_shuffle(arr.begin(), arr.end());
+        avx512_qsort<TypeParam>(arr.data(), arr.size());
+        for (auto ii = 1; ii <= num_nans; ++ii) {
+            if (!std::isnan(arr[size-ii])) {
+                ASSERT_TRUE(false) << "NAN's aren't sorted to the end. Arr size = " << size;
+            }
+        }
+        if (!std::is_sorted(arr.begin(), arr.end() - num_nans)) {
+            ASSERT_TRUE(true) << "Array isn't sorted";
+        }
+        arr.clear();
+        sortedarr.clear();
+    }
+}
+REGISTER_TYPED_TEST_SUITE_P(avx512_sort_fp, test_random_nan);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort.cpp
new file mode 100644
index 00000000000..eb3d5f77be7
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort.cpp
@@ -0,0 +1,20 @@
+#include "test-partial-qsort.hpp"
+#include "test-qselect.hpp"
+#include "test-qsort-fp.hpp"
+#include "test-qsort.hpp"
+
+using QSortTestTypes = testing::Types<uint16_t,
+                                      int16_t,
+                                      float,
+                                      double,
+                                      uint32_t,
+                                      int32_t,
+                                      uint64_t,
+                                      int64_t>;
+
+using QSortTestFPTypes = testing::Types<float, double>;
+
+INSTANTIATE_TYPED_TEST_SUITE_P(T, avx512_sort, QSortTestTypes);
+INSTANTIATE_TYPED_TEST_SUITE_P(T, avx512_sort_fp, QSortTestFPTypes);
+INSTANTIATE_TYPED_TEST_SUITE_P(T, avx512_select, QSortTestTypes);
+INSTANTIATE_TYPED_TEST_SUITE_P(T, avx512_partial_sort, QSortTestTypes);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort.hpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort.hpp
new file mode 100644
index 00000000000..91f0e9b9d35
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsort.hpp
@@ -0,0 +1,168 @@
+/*******************************************
+ * * Copyright (C) 2022 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include "test-qsort-common.h"
+
+template <typename T>
+class avx512_sort : public ::testing::Test {
+};
+TYPED_TEST_SUITE_P(avx512_sort);
+
+TYPED_TEST_P(avx512_sort, test_random)
+{
+    if (cpu_has_avx512bw()) {
+        if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+            GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+        }
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back((TypeParam)ii);
+        }
+        std::vector<TypeParam> arr;
+        std::vector<TypeParam> sortedarr;
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            /* Random array */
+            arr = get_uniform_rand_array<TypeParam>(arrsizes[ii]);
+            sortedarr = arr;
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            avx512_qsort<TypeParam>(arr.data(), arr.size());
+            ASSERT_EQ(sortedarr, arr) << "Array size = " << arrsizes[ii];
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+TYPED_TEST_P(avx512_sort, test_reverse)
+{
+    if (cpu_has_avx512bw()) {
+        if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+            GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+        }
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back((TypeParam)(ii + 1));
+        }
+        std::vector<TypeParam> arr;
+        std::vector<TypeParam> sortedarr;
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            /* reverse array */
+            for (int jj = 0; jj < arrsizes[ii]; ++jj) {
+                arr.push_back((TypeParam)(arrsizes[ii] - jj));
+            }
+            sortedarr = arr;
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            avx512_qsort<TypeParam>(arr.data(), arr.size());
+            ASSERT_EQ(sortedarr, arr) << "Array size = " << arrsizes[ii];
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+TYPED_TEST_P(avx512_sort, test_constant)
+{
+    if (cpu_has_avx512bw()) {
+        if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+            GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+        }
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back((TypeParam)(ii + 1));
+        }
+        std::vector<TypeParam> arr;
+        std::vector<TypeParam> sortedarr;
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            /* constant array */
+            for (int jj = 0; jj < arrsizes[ii]; ++jj) {
+                arr.push_back(ii);
+            }
+            sortedarr = arr;
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            avx512_qsort<TypeParam>(arr.data(), arr.size());
+            ASSERT_EQ(sortedarr, arr) << "Array size = " << arrsizes[ii];
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+TYPED_TEST_P(avx512_sort, test_small_range)
+{
+    if (cpu_has_avx512bw()) {
+        if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+            GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+        }
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back((TypeParam)(ii + 1));
+        }
+        std::vector<TypeParam> arr;
+        std::vector<TypeParam> sortedarr;
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            arr = get_uniform_rand_array<TypeParam>(arrsizes[ii], 20, 1);
+            sortedarr = arr;
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            avx512_qsort<TypeParam>(arr.data(), arr.size());
+            ASSERT_EQ(sortedarr, arr) << "Array size = " << arrsizes[ii];
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw";
+    }
+}
+
+TYPED_TEST_P(avx512_sort, test_max_value_at_end_of_array)
+{
+    if (!cpu_has_avx512bw()) {
+        GTEST_SKIP() << "Skipping this test, it requires avx512bw ISA";
+    }
+    if ((sizeof(TypeParam) == 2) && (!cpu_has_avx512_vbmi2())) {
+        GTEST_SKIP() << "Skipping this test, it requires avx512_vbmi2";
+    }
+    std::vector<int64_t> arrsizes;
+    for (int64_t ii = 1; ii <= 1024; ++ii) {
+        arrsizes.push_back(ii);
+    }
+    std::vector<TypeParam> arr;
+    std::vector<TypeParam> sortedarr;
+    for (auto &size : arrsizes) {
+        arr = get_uniform_rand_array<TypeParam>(size);
+        if (std::numeric_limits<TypeParam>::has_infinity) {
+            arr[size - 1] = std::numeric_limits<TypeParam>::infinity();
+        }
+        else {
+            arr[size - 1] = std::numeric_limits<TypeParam>::max();
+        }
+        sortedarr = arr;
+        avx512_qsort(arr.data(), arr.size());
+        std::sort(sortedarr.begin(), sortedarr.end());
+        EXPECT_EQ(sortedarr, arr) << "Array size = " << size;
+        arr.clear();
+        sortedarr.clear();
+    }
+}
+
+REGISTER_TYPED_TEST_SUITE_P(avx512_sort,
+                            test_random,
+                            test_reverse,
+                            test_constant,
+                            test_small_range,
+                            test_max_value_at_end_of_array);
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsortfp16.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsortfp16.cpp
new file mode 100644
index 00000000000..d6a45f7bcf2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/tests/test-qsortfp16.cpp
@@ -0,0 +1,161 @@
+/*******************************************
+ * * Copyright (C) 2022 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include "avx512fp16-16bit-qsort.hpp"
+#include "cpuinfo.h"
+#include "rand_array.h"
+#include <gtest/gtest.h>
+#include <vector>
+
+TEST(avx512_qsort_float16, test_arrsizes)
+{
+    if (cpu_has_avx512fp16()) {
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<_Float16> arr;
+        std::vector<_Float16> sortedarr;
+
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            /* Random array */
+            for (size_t jj = 0; jj < arrsizes[ii]; ++jj) {
+                _Float16 temp = (float)rand() / (float)(RAND_MAX);
+                arr.push_back(temp);
+                sortedarr.push_back(temp);
+            }
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            avx512_qsort<_Float16>(arr.data(), arr.size());
+            ASSERT_EQ(sortedarr, arr);
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512fp16 ISA";
+    }
+}
+
+TEST(avx512_qsort_float16, test_special_floats)
+{
+    if (cpu_has_avx512fp16()) {
+        const int arrsize = 1111;
+        std::vector<_Float16> arr;
+        std::vector<_Float16> sortedarr;
+        Fp16Bits temp;
+        for (size_t jj = 0; jj < arrsize; ++jj) {
+            temp.f_ = (float)rand() / (float)(RAND_MAX);
+            switch (rand() % 10) {
+                case 0: temp.i_ = 0xFFFF; break;
+                case 1: temp.i_ = X86_SIMD_SORT_INFINITYH; break;
+                case 2: temp.i_ = X86_SIMD_SORT_NEGINFINITYH; break;
+                default: break;
+            }
+            arr.push_back(temp.f_);
+            sortedarr.push_back(temp.f_);
+        }
+        /* Cannot use std::sort because it treats NAN differently */
+        avx512_qsort_fp16(reinterpret_cast<uint16_t *>(sortedarr.data()),
+                          sortedarr.size());
+        avx512_qsort<_Float16>(arr.data(), arr.size());
+        // Cannot rely on ASSERT_EQ since it returns false if there are NAN's
+        if (memcmp(arr.data(), sortedarr.data(), arrsize * 2) != 0) {
+            ASSERT_EQ(sortedarr, arr);
+        }
+        arr.clear();
+        sortedarr.clear();
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512fp16 ISA";
+    }
+}
+
+TEST(avx512_qselect_float16, test_arrsizes)
+{
+    if (cpu_has_avx512fp16()) {
+        std::vector<int64_t> arrsizes;
+        for (int64_t ii = 0; ii < 1024; ++ii) {
+            arrsizes.push_back(ii);
+        }
+        std::vector<_Float16> arr;
+        std::vector<_Float16> sortedarr;
+        std::vector<_Float16> psortedarr;
+
+        for (size_t ii = 0; ii < arrsizes.size(); ++ii) {
+            /* Random array */
+            for (size_t jj = 0; jj < arrsizes[ii]; ++jj) {
+                _Float16 temp = (float)rand() / (float)(RAND_MAX);
+                arr.push_back(temp);
+                sortedarr.push_back(temp);
+            }
+            /* Sort with std::sort for comparison */
+            std::sort(sortedarr.begin(), sortedarr.end());
+            for (size_t k = 0; k < arr.size(); ++k) {
+                psortedarr = arr;
+                avx512_qselect<_Float16>(
+                        psortedarr.data(), k, psortedarr.size());
+                /* index k is correct */
+                ASSERT_EQ(sortedarr[k], psortedarr[k]);
+                /* Check left partition */
+                for (size_t jj = 0; jj < k; jj++) {
+                    ASSERT_LE(psortedarr[jj], psortedarr[k]);
+                }
+                /* Check right partition */
+                for (size_t jj = k + 1; jj < arr.size(); jj++) {
+                    ASSERT_GE(psortedarr[jj], psortedarr[k]);
+                }
+                psortedarr.clear();
+            }
+            arr.clear();
+            sortedarr.clear();
+        }
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512fp16 ISA";
+    }
+}
+
+TEST(avx512_partial_qsort_float16, test_ranges)
+{
+    if (cpu_has_avx512fp16()) {
+        int64_t arrsize = 1024;
+        int64_t nranges = 500;
+
+        std::vector<_Float16> arr;
+        std::vector<_Float16> sortedarr;
+        std::vector<_Float16> psortedarr;
+
+        /* Random array */
+        for (size_t ii = 0; ii < arrsize; ++ii) {
+            _Float16 temp = (float)rand() / (float)(RAND_MAX);
+            arr.push_back(temp);
+            sortedarr.push_back(temp);
+        }
+        /* Sort with std::sort for comparison */
+        std::sort(sortedarr.begin(), sortedarr.end());
+
+        for (size_t ii = 0; ii < nranges; ++ii) {
+            psortedarr = arr;
+
+            int k = get_uniform_rand_array<int64_t>(1, arrsize, 1).front();
+
+            /* Sort the range and verify all the required elements match the presorted set */
+            avx512_partial_qsort<_Float16>(
+                    psortedarr.data(), k, psortedarr.size());
+            for (size_t jj = 0; jj < k; jj++) {
+                ASSERT_EQ(sortedarr[jj], psortedarr[jj]);
+            }
+
+            psortedarr.clear();
+        }
+
+        arr.clear();
+        sortedarr.clear();
+    }
+    else {
+        GTEST_SKIP() << "Skipping this test, it requires avx512fp16 ISA";
+    }
+}
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/cpuinfo.cpp b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/cpuinfo.cpp
new file mode 100644
index 00000000000..c05acf34d29
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/cpuinfo.cpp
@@ -0,0 +1,50 @@
+/*******************************************
+ * * Copyright (C) 2022 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include "cpuinfo.h"
+
+static void
+cpuid(uint32_t feature, uint32_t *a, uint32_t *b, uint32_t *c, uint32_t *d)
+{
+    __asm__ volatile(
+            "cpuid"
+            "\n\t"
+            : "=a"(*a), "=b"(*b), "=c"(*c), "=d"(*d)
+            : "a"(feature), "c"(0));
+}
+
+int cpu_has_avx512_vbmi2()
+{
+    uint32_t eax(0), ebx(0), ecx(0), edx(0);
+    cpuid(0x07, &eax, &ebx, &ecx, &edx);
+    return (ecx >> 6) & 0x1;
+}
+
+int cpu_has_avx512bw()
+{
+    uint32_t eax(0), ebx(0), ecx(0), edx(0);
+    cpuid(0x07, &eax, &ebx, &ecx, &edx);
+    return (ebx >> 30) & 0x1;
+}
+
+int cpu_has_avx512fp16()
+{
+    uint32_t eax(0), ebx(0), ecx(0), edx(0);
+    cpuid(0x07, &eax, &ebx, &ecx, &edx);
+    return (edx >> 23) & 0x1;
+}
+
+// TODO:
+//int check_os_supports_avx512()
+//{
+//    uint32_t eax(0), ebx(0), ecx(0), edx(0);
+//    cpuid(0x01, &eax, &ebx, &ecx, &edx);
+//    // XSAVE:
+//    if ((ecx >> 27) & 0x1) {
+//	uint32_t xget_eax, xget_edx, index(0);
+//	__asm__ ("xgetbv" : "=a"(xget_eax), "=d"(xget_edx) : "c" (index))
+//    }
+//
+//}
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/cpuinfo.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/cpuinfo.h
new file mode 100644
index 00000000000..96f167e58e4
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/cpuinfo.h
@@ -0,0 +1,13 @@
+/*******************************************
+ * * Copyright (C) 2022 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include <cpuid.h>
+#include <stdint.h>
+
+int cpu_has_avx512_vbmi2();
+
+int cpu_has_avx512bw();
+
+int cpu_has_avx512fp16();
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/meson.build b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/meson.build
new file mode 100644
index 00000000000..5f344536580
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/meson.build
@@ -0,0 +1,6 @@
+libcpuinfo = []
+
+libcpuinfo += static_library('cpuinfo',
+  files('cpuinfo.cpp'),
+  cpp_args : ['-O3'],
+)
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/rand_array.h b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/rand_array.h
new file mode 100644
index 00000000000..a780f50de85
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/npysort/x86-simd-sort/utils/rand_array.h
@@ -0,0 +1,58 @@
+/*******************************************
+ * * Copyright (C) 2022 Intel Corporation
+ * * SPDX-License-Identifier: BSD-3-Clause
+ * *******************************************/
+
+#include <iostream>
+#include <random>
+#include <type_traits>
+#include <vector>
+
+template <typename T>
+static std::vector<T> get_uniform_rand_array(
+        int64_t arrsize,
+        T max = std::numeric_limits<T>::max(),
+        T min = std::numeric_limits<T>::min(),
+        typename std::enable_if<std::is_integral<T>::value>::type * = 0)
+{
+    std::vector<T> arr;
+    std::random_device r;
+    std::default_random_engine e1(r());
+    e1.seed(42);
+    std::uniform_int_distribution<T> uniform_dist(min, max);
+    for (int64_t ii = 0; ii < arrsize; ++ii) {
+        arr.emplace_back(uniform_dist(e1));
+    }
+    return arr;
+}
+
+template <typename T>
+static std::vector<T> get_uniform_rand_array(
+        int64_t arrsize,
+        T max = std::numeric_limits<T>::max(),
+        T min = std::numeric_limits<T>::min(),
+        typename std::enable_if<std::is_floating_point<T>::value>::type * = 0)
+{
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    gen.seed(42);
+    std::uniform_real_distribution<T> dis(min, max);
+    std::vector<T> arr;
+    for (int64_t ii = 0; ii < arrsize; ++ii) {
+        arr.emplace_back(dis(gen));
+    }
+    return arr;
+}
+
+template <typename T>
+static std::vector<T>
+get_uniform_rand_array_with_uniquevalues(int64_t arrsize,
+                                         T max = std::numeric_limits<T>::max(),
+                                         T min = std::numeric_limits<T>::min())
+{
+    std::vector<T> arr = get_uniform_rand_array<T>(arrsize, max, min);
+    typename std::vector<T>::iterator ip
+            = std::unique(arr.begin(), arr.begin() + arrsize);
+    arr.resize(std::distance(arr.begin(), ip));
+    return arr;
+}
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/_rational_tests.c b/contrib/python/numpy/py3/numpy/core/src/umath/_rational_tests.c
index 0444eaea533..7228040c926 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/_rational_tests.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/_rational_tests.c
@@ -49,7 +49,7 @@ set_zero_divide(void) {
 
 /* Integer arithmetic utilities */
 
-static NPY_INLINE npy_int32
+static inline npy_int32
 safe_neg(npy_int32 x) {
     if (x==(npy_int32)1<<31) {
         set_overflow();
@@ -57,7 +57,7 @@ safe_neg(npy_int32 x) {
     return -x;
 }
 
-static NPY_INLINE npy_int32
+static inline npy_int32
 safe_abs32(npy_int32 x) {
     npy_int32 nx;
     if (x>=0) {
@@ -70,7 +70,7 @@ safe_abs32(npy_int32 x) {
     return nx;
 }
 
-static NPY_INLINE npy_int64
+static inline npy_int64
 safe_abs64(npy_int64 x) {
     npy_int64 nx;
     if (x>=0) {
@@ -83,7 +83,7 @@ safe_abs64(npy_int64 x) {
     return nx;
 }
 
-static NPY_INLINE npy_int64
+static inline npy_int64
 gcd(npy_int64 x, npy_int64 y) {
     x = safe_abs64(x);
     y = safe_abs64(y);
@@ -102,7 +102,7 @@ gcd(npy_int64 x, npy_int64 y) {
     return x;
 }
 
-static NPY_INLINE npy_int64
+static inline npy_int64
 lcm(npy_int64 x, npy_int64 y) {
     npy_int64 lcm;
     if (!x || !y) {
@@ -128,7 +128,7 @@ typedef struct {
     npy_int32 dmm;
 } rational;
 
-static NPY_INLINE rational
+static inline rational
 make_rational_int(npy_int64 n) {
     rational r = {(npy_int32)n,0};
     if (r.n != n) {
@@ -164,7 +164,7 @@ make_rational_slow(npy_int64 n_, npy_int64 d_) {
     return r;
 }
 
-static NPY_INLINE npy_int32
+static inline npy_int32
 d(rational r) {
     return r.dmm+1;
 }
@@ -184,7 +184,7 @@ make_rational_fast(npy_int64 n_, npy_int64 d_) {
     return r;
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_negative(rational r) {
     rational x;
     x.n = safe_neg(r.n);
@@ -192,7 +192,7 @@ rational_negative(rational r) {
     return x;
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_add(rational x, rational y) {
     /*
      * Note that the numerator computation can never overflow int128_t,
@@ -202,25 +202,25 @@ rational_add(rational x, rational y) {
         (npy_int64)d(x)*d(y));
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_subtract(rational x, rational y) {
     /* We're safe from overflow as with + */
     return make_rational_fast((npy_int64)x.n*d(y)-(npy_int64)d(x)*y.n,
         (npy_int64)d(x)*d(y));
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_multiply(rational x, rational y) {
     /* We're safe from overflow as with + */
     return make_rational_fast((npy_int64)x.n*y.n,(npy_int64)d(x)*d(y));
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_divide(rational x, rational y) {
     return make_rational_slow((npy_int64)x.n*d(y),(npy_int64)d(x)*y.n);
 }
 
-static NPY_INLINE npy_int64
+static inline npy_int64
 rational_floor(rational x) {
     /* Always round down */
     if (x.n>=0) {
@@ -233,18 +233,18 @@ rational_floor(rational x) {
     return -((-(npy_int64)x.n+d(x)-1)/d(x));
 }
 
-static NPY_INLINE npy_int64
+static inline npy_int64
 rational_ceil(rational x) {
     return -rational_floor(rational_negative(x));
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_remainder(rational x, rational y) {
     return rational_subtract(x, rational_multiply(y,make_rational_int(
                     rational_floor(rational_divide(x,y)))));
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_abs(rational x) {
     rational y;
     y.n = safe_abs32(x.n);
@@ -252,7 +252,7 @@ rational_abs(rational x) {
     return y;
 }
 
-static NPY_INLINE npy_int64
+static inline npy_int64
 rational_rint(rational x) {
     /*
      * Round towards nearest integer, moving exact half integers towards
@@ -262,12 +262,12 @@ rational_rint(rational x) {
     return (2*(npy_int64)x.n+(x.n<0?-d_:d_))/(2*(npy_int64)d_);
 }
 
-static NPY_INLINE int
+static inline int
 rational_sign(rational x) {
     return x.n<0?-1:x.n==0?0:1;
 }
 
-static NPY_INLINE rational
+static inline rational
 rational_inverse(rational x) {
     rational y = {0};
     if (!x.n) {
@@ -286,7 +286,7 @@ rational_inverse(rational x) {
     return y;
 }
 
-static NPY_INLINE int
+static inline int
 rational_eq(rational x, rational y) {
     /*
      * Since we enforce d > 0, and store fractions in reduced form,
@@ -295,42 +295,42 @@ rational_eq(rational x, rational y) {
     return x.n==y.n && x.dmm==y.dmm;
 }
 
-static NPY_INLINE int
+static inline int
 rational_ne(rational x, rational y) {
     return !rational_eq(x,y);
 }
 
-static NPY_INLINE int
+static inline int
 rational_lt(rational x, rational y) {
     return (npy_int64)x.n*d(y) < (npy_int64)y.n*d(x);
 }
 
-static NPY_INLINE int
+static inline int
 rational_gt(rational x, rational y) {
     return rational_lt(y,x);
 }
 
-static NPY_INLINE int
+static inline int
 rational_le(rational x, rational y) {
     return !rational_lt(y,x);
 }
 
-static NPY_INLINE int
+static inline int
 rational_ge(rational x, rational y) {
     return !rational_lt(x,y);
 }
 
-static NPY_INLINE npy_int32
+static inline npy_int32
 rational_int(rational x) {
     return x.n/d(x);
 }
 
-static NPY_INLINE double
+static inline double
 rational_double(rational x) {
     return (double)x.n/d(x);
 }
 
-static NPY_INLINE int
+static inline int
 rational_nonzero(rational x) {
     return x.n!=0;
 }
@@ -367,7 +367,7 @@ typedef struct {
 
 static PyTypeObject PyRational_Type;
 
-static NPY_INLINE int
+static inline int
 PyRational_Check(PyObject* object) {
     return PyObject_IsInstance(object,(PyObject*)&PyRational_Type);
 }
@@ -753,7 +753,7 @@ npyrational_setitem(PyObject* item, void* data, void* arr) {
     return 0;
 }
 
-static NPY_INLINE void
+static inline void
 byteswap(npy_int32* x) {
     char* p = (char*)x;
     size_t i;
@@ -996,7 +996,7 @@ UNARY_UFUNC(reciprocal,rational,rational_inverse(x))
 UNARY_UFUNC(numerator,npy_int64,x.n)
 UNARY_UFUNC(denominator,npy_int64,d(x))
 
-static NPY_INLINE void
+static inline void
 rational_matrix_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
     /* pointers to data for input and output arrays */
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/_scaled_float_dtype.c b/contrib/python/numpy/py3/numpy/core/src/umath/_scaled_float_dtype.c
index 2d6ba3574e5..c26ace9f139 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/_scaled_float_dtype.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/_scaled_float_dtype.c
@@ -26,6 +26,14 @@
 #include "dispatching.h"
 
 
+/* TODO: from wrapping_array_method.c, use proper public header eventually  */
+NPY_NO_EXPORT int
+PyUFunc_AddWrappingLoop(PyObject *ufunc_obj,
+        PyArray_DTypeMeta *new_dtypes[], PyArray_DTypeMeta *wrapped_dtypes[],
+        translate_given_descrs_func *translate_given_descrs,
+        translate_loop_descrs_func *translate_loop_descrs);
+
+
 typedef struct {
     PyArray_Descr base;
     double scaling;
@@ -240,7 +248,7 @@ static PyArray_DTypeMeta PyArray_SFloatDType = {{{
     }},
     .type_num = -1,
     .scalar_type = NULL,
-    .flags = NPY_DT_PARAMETRIC,
+    .flags = NPY_DT_PARAMETRIC | NPY_DT_NUMERIC,
     .dt_slots = &sfloat_slots,
 };
 
@@ -439,7 +447,7 @@ sfloat_to_bool_resolve_descriptors(
 
 
 static int
-init_casts(void)
+sfloat_init_casts(void)
 {
     PyArray_DTypeMeta *dtypes[2] = {&PyArray_SFloatDType, &PyArray_SFloatDType};
     PyType_Slot slots[4] = {{0, NULL}};
@@ -645,13 +653,55 @@ add_sfloats_resolve_descriptors(
 }
 
 
+/*
+ * We define the hypot loop using the "PyUFunc_AddWrappingLoop" API.
+ * We use this very narrowly for mapping to the double hypot loop currently.
+ */
 static int
-add_loop(const char *ufunc_name,
-        PyArray_DTypeMeta *dtypes[3], PyObject *meth_or_promoter)
+translate_given_descrs_to_double(
+        int nin, int nout, PyArray_DTypeMeta *wrapped_dtypes[],
+        PyArray_Descr *given_descrs[], PyArray_Descr *new_descrs[])
+{
+    assert(nin == 2 && nout == 1);
+    for (int i = 0; i < 3; i++) {
+        if (given_descrs[i] == NULL) {
+            new_descrs[i] = NULL;
+        }
+        else {
+            new_descrs[i] = PyArray_DescrFromType(NPY_DOUBLE);
+        }
+    }
+    return 0;
+}
+
+
+static int
+translate_loop_descrs(
+        int nin, int nout, PyArray_DTypeMeta *new_dtypes[],
+        PyArray_Descr *given_descrs[],
+        PyArray_Descr *NPY_UNUSED(original_descrs[]),
+        PyArray_Descr *loop_descrs[])
+{
+    assert(nin == 2 && nout == 1);
+    loop_descrs[0] = sfloat_common_instance(
+            given_descrs[0], given_descrs[1]);
+    if (loop_descrs[0] == 0) {
+        return -1;
+    }
+    Py_INCREF(loop_descrs[0]);
+    loop_descrs[1] = loop_descrs[0];
+    Py_INCREF(loop_descrs[0]);
+    loop_descrs[2] = loop_descrs[0];
+    return 0;
+}
+
+
+static PyObject *
+sfloat_get_ufunc(const char *ufunc_name)
 {
     PyObject *mod = PyImport_ImportModule("numpy");
     if (mod == NULL) {
-        return -1;
+        return NULL;
     }
     PyObject *ufunc = PyObject_GetAttrString(mod, ufunc_name);
     Py_DECREF(mod);
@@ -659,6 +709,18 @@ add_loop(const char *ufunc_name,
         Py_DECREF(ufunc);
         PyErr_Format(PyExc_TypeError,
                 "numpy.%s was not a ufunc!", ufunc_name);
+        return NULL;
+    }
+    return ufunc;
+}
+
+
+static int
+sfloat_add_loop(const char *ufunc_name,
+        PyArray_DTypeMeta *dtypes[3], PyObject *meth_or_promoter)
+{
+    PyObject *ufunc = sfloat_get_ufunc(ufunc_name);
+    if (ufunc == NULL) {
         return -1;
     }
     PyObject *dtype_tup = PyArray_TupleFromItems(3, (PyObject **)dtypes, 1);
@@ -679,6 +741,24 @@ add_loop(const char *ufunc_name,
 }
 
 
+static int
+sfloat_add_wrapping_loop(const char *ufunc_name, PyArray_DTypeMeta *dtypes[3])
+{
+    PyObject *ufunc = sfloat_get_ufunc(ufunc_name);
+    if (ufunc == NULL) {
+        return -1;
+    }
+    PyArray_DTypeMeta *double_dt = PyArray_DTypeFromTypeNum(NPY_DOUBLE);
+    PyArray_DTypeMeta *wrapped_dtypes[3] = {double_dt, double_dt, double_dt};
+    int res = PyUFunc_AddWrappingLoop(
+        ufunc, dtypes, wrapped_dtypes, &translate_given_descrs_to_double,
+        &translate_loop_descrs);
+    Py_DECREF(ufunc);
+    Py_DECREF(double_dt);
+
+    return res;
+}
+
 
 /*
  * We add some very basic promoters to allow multiplying normal and scaled
@@ -706,7 +786,7 @@ promote_to_sfloat(PyUFuncObject *NPY_UNUSED(ufunc),
  * get less so with the introduction of public API).
  */
 static int
-init_ufuncs(void) {
+sfloat_init_ufuncs(void) {
     PyArray_DTypeMeta *dtypes[3] = {
             &PyArray_SFloatDType, &PyArray_SFloatDType, &PyArray_SFloatDType};
     PyType_Slot slots[3] = {{0, NULL}};
@@ -727,7 +807,7 @@ init_ufuncs(void) {
     if (bmeth == NULL) {
         return -1;
     }
-    int res = add_loop("multiply",
+    int res = sfloat_add_loop("multiply",
             bmeth->dtypes, (PyObject *)bmeth->method);
     Py_DECREF(bmeth);
     if (res < 0) {
@@ -745,13 +825,18 @@ init_ufuncs(void) {
     if (bmeth == NULL) {
         return -1;
     }
-    res = add_loop("add",
+    res = sfloat_add_loop("add",
             bmeth->dtypes, (PyObject *)bmeth->method);
     Py_DECREF(bmeth);
     if (res < 0) {
         return -1;
     }
 
+    /* N.B.: Wrapping isn't actually correct if scaling can be negative */
+    if (sfloat_add_wrapping_loop("hypot", dtypes) < 0) {
+        return -1;
+    }
+
     /*
      * Add a promoter for both directions of multiply with double.
      */
@@ -766,14 +851,14 @@ init_ufuncs(void) {
     if (promoter == NULL) {
         return -1;
     }
-    res = add_loop("multiply", promoter_dtypes, promoter);
+    res = sfloat_add_loop("multiply", promoter_dtypes, promoter);
     if (res < 0) {
         Py_DECREF(promoter);
         return -1;
     }
     promoter_dtypes[0] = double_DType;
     promoter_dtypes[1] = &PyArray_SFloatDType;
-    res = add_loop("multiply", promoter_dtypes, promoter);
+    res = sfloat_add_loop("multiply", promoter_dtypes, promoter);
     Py_DECREF(promoter);
     if (res < 0) {
         return -1;
@@ -814,11 +899,11 @@ get_sfloat_dtype(PyObject *NPY_UNUSED(mod), PyObject *NPY_UNUSED(args))
         return NULL;
     }
 
-    if (init_casts() < 0) {
+    if (sfloat_init_casts() < 0) {
         return NULL;
     }
 
-    if (init_ufuncs() < 0) {
+    if (sfloat_init_ufuncs() < 0) {
         return NULL;
     }
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c b/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c
index d16ac210d60..514e756541c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c
@@ -19,6 +19,9 @@
 #include <Python.h>
 
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#if defined(NPY_INTERNAL_BUILD)
+#undef NPY_INTERNAL_BUILD
+#endif
 #include "numpy/arrayobject.h"
 #include "numpy/ufuncobject.h"
 #include "numpy/npy_math.h"
@@ -29,6 +32,9 @@
 #include "npy_cpu_features.h"
 #include "npy_cpu_dispatch.h"
 #include "numpy/npy_cpu.h"
+#include "npy_import.h"
+#include "numpy/experimental_dtype_api.h"
+
 
 /*
  *****************************************************************************
@@ -86,7 +92,7 @@ always_error_loop(
 
 char *inner1d_signature = "(i),(i)->()";
 
-#line 84
+#line 90
 
 /*
  *  This implements the function
@@ -113,7 +119,7 @@ LONG_inner1d(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 }
 
 
-#line 84
+#line 90
 
 /*
  *  This implements the function
@@ -143,7 +149,7 @@ DOUBLE_inner1d(char **args, npy_intp const *dimensions, npy_intp const *steps, v
 
 char *innerwt_signature = "(i),(i),(i)->()";
 
-#line 118
+#line 124
 
 
 /*
@@ -172,7 +178,7 @@ LONG_innerwt(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 }
 
 
-#line 118
+#line 124
 
 
 /*
@@ -206,7 +212,7 @@ char *matrix_multiply_signature = "(m,n),(n,p)->(m,p)";
 /* for use with matrix_multiply code, but different signature */
 char *matmul_signature = "(m?,n),(n,p?)->(m?,p?)";
 
-#line 156
+#line 162
 
 /*
  *  This implements the function
@@ -267,7 +273,7 @@ FLOAT_matrix_multiply(char **args, npy_intp const *dimensions, npy_intp const *s
 }
 
 
-#line 156
+#line 162
 
 /*
  *  This implements the function
@@ -328,7 +334,7 @@ DOUBLE_matrix_multiply(char **args, npy_intp const *dimensions, npy_intp const *
 }
 
 
-#line 156
+#line 162
 
 /*
  *  This implements the function
@@ -392,7 +398,7 @@ LONG_matrix_multiply(char **args, npy_intp const *dimensions, npy_intp const *st
 
 char *cross1d_signature = "(3),(3)->(3)";
 
-#line 224
+#line 230
 
 /*
  *  This implements the cross product:
@@ -424,7 +430,7 @@ LONG_cross1d(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 }
 
 
-#line 224
+#line 230
 
 /*
  *  This implements the cross product:
@@ -459,7 +465,7 @@ DOUBLE_cross1d(char **args, npy_intp const *dimensions, npy_intp const *steps, v
 
 char *euclidean_pdist_signature = "(n,d)->(p)";
 
-#line 264
+#line 270
 
 /*
  *  This implements the function
@@ -499,7 +505,7 @@ FLOAT_euclidean_pdist(char **args, npy_intp const *dimensions, npy_intp const *s
                     ptr_this += stride_d;
                     ptr_that += stride_d;
                 }
-                *(npy_float *)data_out = npy_sqrtf(out);
+                *(npy_float *)data_out = sqrtf(out);
                 data_that += stride_n;
                 data_out += stride_p;
             }
@@ -509,7 +515,7 @@ FLOAT_euclidean_pdist(char **args, npy_intp const *dimensions, npy_intp const *s
 }
 
 
-#line 264
+#line 270
 
 /*
  *  This implements the function
@@ -549,7 +555,7 @@ DOUBLE_euclidean_pdist(char **args, npy_intp const *dimensions, npy_intp const *
                     ptr_this += stride_d;
                     ptr_that += stride_d;
                 }
-                *(npy_double *)data_out = npy_sqrt(out);
+                *(npy_double *)data_out = sqrt(out);
                 data_that += stride_n;
                 data_out += stride_p;
             }
@@ -567,7 +573,7 @@ char *cumsum_signature = "(i)->(i)";
  *        out[n] = sum_i^n in[i]
  */
 
-#line 326
+#line 332
 
 static void
 LONG_cumsum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -587,7 +593,7 @@ LONG_cumsum(char **args, npy_intp const *dimensions, npy_intp const *steps, void
 }
 
 
-#line 326
+#line 332
 
 static void
 DOUBLE_cumsum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -608,6 +614,55 @@ DOUBLE_cumsum(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
 
 
 
+static int
+INT32_negative(PyArrayMethod_Context *NPY_UNUSED(context),
+               char **args, npy_intp const *dimensions,
+               npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    npy_intp di = dimensions[0];
+    npy_intp i;
+    npy_intp is=steps[0], os=steps[1];
+    char *ip=args[0], *op=args[1];
+    for (i = 0; i < di; i++, ip += is, op += os) {
+        if (i == 3) {
+            *(int32_t *)op = - 100;
+        } else {
+            *(int32_t *)op = - *(int32_t *)ip;
+        }
+    }
+    return 0;
+}
+
+
+static int
+INT32_negative_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                           char * const*args, npy_intp const *dimensions,
+                           npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    npy_intp is1 = steps[0], isindex = steps[1];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    int32_t *indexed;
+    for(i = 0; i < n; i++, indxp += isindex) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (int32_t *)(ip1 + is1 * indx);
+        if (i == 3) {
+            *indexed = -200;
+        } else {
+            *indexed = - *indexed;
+        }
+    }
+    return 0;
+}
+
+
+
 /*  The following lines were generated using a slightly modified
     version of code_generators/generate_umath.py and adding these
     lines to defdict:
@@ -911,28 +966,28 @@ UMath_Tests_test_dispatch(PyObject *NPY_UNUSED(dummy), PyObject *NPY_UNUSED(dumm
     if (dict == NULL) {
         return NULL;
     }
-    #line 652
+    #line 707
     item = PyUnicode_FromString(highest_func);
     if (item == NULL || PyDict_SetItemString(dict, "func", item) < 0) {
         goto err;
     }
     Py_DECREF(item);
     
-#line 652
+#line 707
     item = PyUnicode_FromString(highest_var);
     if (item == NULL || PyDict_SetItemString(dict, "var", item) < 0) {
         goto err;
     }
     Py_DECREF(item);
     
-#line 652
+#line 707
     item = PyUnicode_FromString(highest_func_xb);
     if (item == NULL || PyDict_SetItemString(dict, "func_xb", item) < 0) {
         goto err;
     }
     Py_DECREF(item);
     
-#line 652
+#line 707
     item = PyUnicode_FromString(highest_var_xb);
     if (item == NULL || PyDict_SetItemString(dict, "var_xb", item) < 0) {
         goto err;
@@ -955,6 +1010,43 @@ err:
     return NULL;
 }
 
+static int
+add_INT32_negative_indexed(PyObject *module, PyObject *dict) {
+    if (import_experimental_dtype_api(__EXPERIMENTAL_DTYPE_API_VERSION) < 0) {
+        return -1;
+    }
+
+    PyObject * negative = PyUFunc_FromFuncAndData(NULL, NULL, NULL, 0, 1, 1,
+                                    PyUFunc_Zero, "indexed_negative", NULL, 0);
+    if (negative == NULL) {
+        return -1;
+    }
+    PyArray_DTypeMeta *dtypes[] = {&PyArray_Int32DType, &PyArray_Int32DType};
+
+    PyType_Slot slots[] = {
+        {NPY_METH_contiguous_indexed_loop, INT32_negative_indexed},
+        {NPY_METH_strided_loop, INT32_negative},
+        {0, NULL}
+    };
+
+    PyArrayMethod_Spec spec = {
+        .name = "negative_indexed_loop",
+        .nin = 1,
+        .nout = 1,
+        .dtypes = dtypes,
+        .slots = slots,
+        .flags = NPY_METH_NO_FLOATINGPOINT_ERRORS
+    };
+
+    if (PyUFunc_AddLoopFromSpec(negative, &spec) < 0) {
+        Py_DECREF(negative);
+        return -1;
+    }
+    PyDict_SetItemString(dict, "indexed_negative", negative);
+    Py_DECREF(negative);
+    return 0;
+}
+
 static PyMethodDef UMath_TestsMethods[] = {
     {"test_signature",  UMath_Tests_test_signature, METH_VARARGS,
      "Test signature parsing of ufunc. \n"
@@ -1017,6 +1109,14 @@ PyMODINIT_FUNC PyInit__umath_tests(void) {
                         "cannot load _umath_tests module.");
         return NULL;
     }
+
+    if (add_INT32_negative_indexed(m, d) < 0) {
+        Py_DECREF(m);
+        PyErr_Print();
+        PyErr_SetString(PyExc_RuntimeError,
+                        "cannot load _umath_tests module.");
+        return NULL;
+    }
     return m;
 }
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c.src
index 1bf459ce666..b9e192706d0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/_umath_tests.c.src
@@ -9,6 +9,9 @@
 #include <Python.h>
 
 #define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#if defined(NPY_INTERNAL_BUILD)
+#undef NPY_INTERNAL_BUILD
+#endif
 #include "numpy/arrayobject.h"
 #include "numpy/ufuncobject.h"
 #include "numpy/npy_math.h"
@@ -19,6 +22,9 @@
 #include "npy_cpu_features.h"
 #include "npy_cpu_dispatch.h"
 #include "numpy/npy_cpu.h"
+#include "npy_import.h"
+#include "numpy/experimental_dtype_api.h"
+
 
 /*
  *****************************************************************************
@@ -300,7 +306,7 @@ static void
                     ptr_this += stride_d;
                     ptr_that += stride_d;
                 }
-                *(@typ@ *)data_out = npy_@sqrt_func@(out);
+                *(@typ@ *)data_out = @sqrt_func@(out);
                 data_that += stride_n;
                 data_out += stride_p;
             }
@@ -343,6 +349,55 @@ static void
 
 /**end repeat**/
 
+static int
+INT32_negative(PyArrayMethod_Context *NPY_UNUSED(context),
+               char **args, npy_intp const *dimensions,
+               npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    npy_intp di = dimensions[0];
+    npy_intp i;
+    npy_intp is=steps[0], os=steps[1];
+    char *ip=args[0], *op=args[1];
+    for (i = 0; i < di; i++, ip += is, op += os) {
+        if (i == 3) {
+            *(int32_t *)op = - 100;
+        } else {
+            *(int32_t *)op = - *(int32_t *)ip;
+        }
+    }
+    return 0;
+}
+
+
+static int
+INT32_negative_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                           char * const*args, npy_intp const *dimensions,
+                           npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    npy_intp is1 = steps[0], isindex = steps[1];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    int32_t *indexed;
+    for(i = 0; i < n; i++, indxp += isindex) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (int32_t *)(ip1 + is1 * indx);
+        if (i == 3) {
+            *indexed = -200;
+        } else {
+            *indexed = - *indexed;
+        }
+    }
+    return 0;
+}
+
+
+
 /*  The following lines were generated using a slightly modified
     version of code_generators/generate_umath.py and adding these
     lines to defdict:
@@ -671,6 +726,43 @@ err:
     return NULL;
 }
 
+static int
+add_INT32_negative_indexed(PyObject *module, PyObject *dict) {
+    if (import_experimental_dtype_api(__EXPERIMENTAL_DTYPE_API_VERSION) < 0) {
+        return -1;
+    }
+
+    PyObject * negative = PyUFunc_FromFuncAndData(NULL, NULL, NULL, 0, 1, 1,
+                                    PyUFunc_Zero, "indexed_negative", NULL, 0);
+    if (negative == NULL) {
+        return -1;
+    }
+    PyArray_DTypeMeta *dtypes[] = {&PyArray_Int32DType, &PyArray_Int32DType};
+
+    PyType_Slot slots[] = {
+        {NPY_METH_contiguous_indexed_loop, INT32_negative_indexed},
+        {NPY_METH_strided_loop, INT32_negative},
+        {0, NULL}
+    };
+
+    PyArrayMethod_Spec spec = {
+        .name = "negative_indexed_loop",
+        .nin = 1,
+        .nout = 1,
+        .dtypes = dtypes,
+        .slots = slots,
+        .flags = NPY_METH_NO_FLOATINGPOINT_ERRORS
+    };
+
+    if (PyUFunc_AddLoopFromSpec(negative, &spec) < 0) {
+        Py_DECREF(negative);
+        return -1;
+    }
+    PyDict_SetItemString(dict, "indexed_negative", negative);
+    Py_DECREF(negative);
+    return 0;
+}
+
 static PyMethodDef UMath_TestsMethods[] = {
     {"test_signature",  UMath_Tests_test_signature, METH_VARARGS,
      "Test signature parsing of ufunc. \n"
@@ -733,5 +825,13 @@ PyMODINIT_FUNC PyInit__umath_tests(void) {
                         "cannot load _umath_tests module.");
         return NULL;
     }
+
+    if (add_INT32_negative_indexed(m, d) < 0) {
+        Py_DECREF(m);
+        PyErr_Print();
+        PyErr_SetString(PyExc_RuntimeError,
+                        "cannot load _umath_tests module.");
+        return NULL;
+    }
     return m;
 }
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/dispatching.c b/contrib/python/numpy/py3/numpy/core/src/umath/dispatching.c
index 077d56f33bc..fee77b2d6f0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/dispatching.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/dispatching.c
@@ -43,6 +43,7 @@
 #include <convert_datatype.h>
 
 #include "numpy/ndarraytypes.h"
+#include "numpy/npy_3kcompat.h"
 #include "common.h"
 
 #include "dispatching.h"
@@ -58,7 +59,7 @@
 
 
 /* forward declaration */
-static NPY_INLINE PyObject *
+static inline PyObject *
 promote_and_get_info_and_ufuncimpl(PyUFuncObject *ufunc,
         PyArrayObject *const ops[],
         PyArray_DTypeMeta *signature[],
@@ -725,7 +726,7 @@ add_and_return_legacy_wrapping_ufunc_loop(PyUFuncObject *ufunc,
  * If value-based promotion is necessary, this is handled ahead of time by
  * `promote_and_get_ufuncimpl`.
  */
-static NPY_INLINE PyObject *
+static inline PyObject *
 promote_and_get_info_and_ufuncimpl(PyUFuncObject *ufunc,
         PyArrayObject *const ops[],
         PyArray_DTypeMeta *signature[],
@@ -913,8 +914,8 @@ promote_and_get_ufuncimpl(PyUFuncObject *ufunc,
     int nin = ufunc->nin, nargs = ufunc->nargs;
 
     /*
-     * Get the actual DTypes we operate with by mixing the operand array
-     * ones with the passed signature.
+     * Get the actual DTypes we operate with by setting op_dtypes[i] from
+     * signature[i].
      */
     for (int i = 0; i < nargs; i++) {
         if (signature[i] != NULL) {
@@ -947,7 +948,7 @@ promote_and_get_ufuncimpl(PyUFuncObject *ufunc,
         int cacheable = 1;  /* unused, as we modify the original `op_dtypes` */
         if (legacy_promote_using_legacy_type_resolver(ufunc,
                 ops, signature, op_dtypes, &cacheable, NPY_FALSE) < 0) {
-            return NULL;
+            goto handle_error;
         }
     }
 
@@ -959,10 +960,7 @@ promote_and_get_ufuncimpl(PyUFuncObject *ufunc,
     npy_promotion_state = old_promotion_state;
 
     if (info == NULL) {
-        if (!PyErr_Occurred()) {
-            raise_no_loop_found_error(ufunc, (PyObject **)op_dtypes);
-        }
-        return NULL;
+        goto handle_error;
     }
 
     PyArrayMethodObject *method = (PyArrayMethodObject *)PyTuple_GET_ITEM(info, 1);
@@ -984,7 +982,7 @@ promote_and_get_ufuncimpl(PyUFuncObject *ufunc,
         /* Reset the promotion state: */
         npy_promotion_state = NPY_USE_WEAK_PROMOTION_AND_WARN;
         if (res < 0) {
-            return NULL;
+            goto handle_error;
         }
     }
 
@@ -1018,12 +1016,29 @@ promote_and_get_ufuncimpl(PyUFuncObject *ufunc,
              * If signature is forced the cache may contain an incompatible
              * loop found via promotion (signature not enforced).  Reject it.
              */
-            raise_no_loop_found_error(ufunc, (PyObject **)op_dtypes);
-            return NULL;
+            goto handle_error;
         }
     }
 
     return method;
+
+  handle_error:
+    /* We only set the "no loop found error here" */
+    if (!PyErr_Occurred()) {
+        raise_no_loop_found_error(ufunc, (PyObject **)op_dtypes);
+    }
+    /*
+     * Otherwise an error occurred, but if the error was DTypePromotionError
+     * then we chain it, because DTypePromotionError effectively means that there
+     * is no loop available.  (We failed finding a loop by using promotion.)
+     */
+    else if (PyErr_ExceptionMatches(npy_DTypePromotionError)) {
+        PyObject *err_type = NULL, *err_value = NULL, *err_traceback = NULL;
+        PyErr_Fetch(&err_type, &err_value, &err_traceback);
+        raise_no_loop_found_error(ufunc, (PyObject **)op_dtypes);
+        npy_PyErr_ChainExceptionsCause(err_type, err_value, err_traceback);
+    }
+    return NULL;
 }
 
 
@@ -1225,3 +1240,40 @@ install_logical_ufunc_promoter(PyObject *ufunc)
 
     return PyUFunc_AddLoop((PyUFuncObject *)ufunc, info, 0);
 }
+
+/*
+ * Return the PyArrayMethodObject or PyCapsule that matches a registered
+ * tuple of identical dtypes. Return a borrowed ref of the first match.
+ */
+NPY_NO_EXPORT PyObject *
+get_info_no_cast(PyUFuncObject *ufunc, PyArray_DTypeMeta *op_dtype,
+                 int ndtypes)
+{
+    PyObject *t_dtypes = PyTuple_New(ndtypes);
+    if (t_dtypes == NULL) {
+        return NULL;
+    }
+    for (int i=0; i < ndtypes; i++) {
+        PyTuple_SetItem(t_dtypes, i, (PyObject *)op_dtype);
+    }
+    PyObject *loops = ufunc->_loops;
+    Py_ssize_t length = PyList_Size(loops);
+    for (Py_ssize_t i = 0; i < length; i++) {
+        PyObject *item = PyList_GetItem(loops, i);
+        PyObject *cur_DType_tuple = PyTuple_GetItem(item, 0);
+        int cmp = PyObject_RichCompareBool(cur_DType_tuple,
+                                           t_dtypes, Py_EQ);
+        if (cmp < 0) {
+            Py_DECREF(t_dtypes);
+            return NULL;
+        }
+        if (cmp == 0) {
+            continue;
+        }
+        /* Got the match */
+        Py_DECREF(t_dtypes);
+        return PyTuple_GetItem(item, 1);
+    }
+    Py_DECREF(t_dtypes);
+    Py_RETURN_NONE;
+}
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/fast_loop_macros.h b/contrib/python/numpy/py3/numpy/core/src/umath/fast_loop_macros.h
index 430033d3147..6a8b8727093 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/fast_loop_macros.h
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/fast_loop_macros.h
@@ -12,6 +12,19 @@
 
 #include <assert.h>
 
+#include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
+
+/*
+ * largest simd vector size in bytes numpy supports
+ * it is currently a extremely large value as it is only used for memory
+ * overlap checks
+ */
+#if NPY_SIMD > 0
+    // Enough for compiler unroll
+    #define AUTOVEC_OVERLAP_SIZE NPY_SIMD_WIDTH*4
+#else
+    #define AUTOVEC_OVERLAP_SIZE 1024
+#endif
 /*
  * MAX_STEP_SIZE is used to determine if we need to use SIMD version of the ufunc.
  * Very large step size can be as slow as processing it using scalar. The
@@ -27,7 +40,7 @@
  */
 #define MAX_STEP_SIZE 2097152
 
-static NPY_INLINE npy_uintp
+static inline npy_uintp
 abs_ptrdiff(char *a, char *b)
 {
     return (a > b) ? (a - b) : (b - a);
@@ -219,11 +232,11 @@ abs_ptrdiff(char *a, char *b)
         /* condition allows compiler to optimize the generic macro */ \
         if (IS_BINARY_CONT(tin, tout)) { \
             if (abs_ptrdiff(args[2], args[0]) == 0 && \
-                    abs_ptrdiff(args[2], args[1]) >= NPY_MAX_SIMD_SIZE) { \
+                    abs_ptrdiff(args[2], args[1]) >= AUTOVEC_OVERLAP_SIZE) { \
                 BASE_BINARY_LOOP_INP(tin, tout, op) \
             } \
             else if (abs_ptrdiff(args[2], args[1]) == 0 && \
-                         abs_ptrdiff(args[2], args[0]) >= NPY_MAX_SIMD_SIZE) { \
+                         abs_ptrdiff(args[2], args[0]) >= AUTOVEC_OVERLAP_SIZE) { \
                 BASE_BINARY_LOOP_INP(tin, tout, op) \
             } \
             else { \
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/legacy_array_method.c b/contrib/python/numpy/py3/numpy/core/src/umath/legacy_array_method.c
index 56ac9659869..965a0eb8340 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/legacy_array_method.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/legacy_array_method.c
@@ -13,10 +13,13 @@
 
 #include "convert_datatype.h"
 #include "array_method.h"
+#include "array_coercion.h"
 #include "dtype_transfer.h"
 #include "legacy_array_method.h"
 #include "dtypemeta.h"
 
+#include "ufunc_object.h"
+
 
 typedef struct {
     NpyAuxData base;
@@ -91,7 +94,6 @@ generic_wrapped_legacy_loop(PyArrayMethod_Context *NPY_UNUSED(context),
     return 0;
 }
 
-
 /*
  * Signal that the old type-resolution function must be used to resolve
  * the descriptors (mainly/only used for datetimes due to the unit).
@@ -234,6 +236,97 @@ get_wrapped_legacy_ufunc_loop(PyArrayMethod_Context *context,
 }
 
 
+
+/*
+ * We can shave off a bit of time by just caching the initial and this is
+ * trivial for all internal numeric types.  (Wrapped ufuncs never use
+ * byte-swapping.)
+ */
+static int
+copy_cached_initial(
+        PyArrayMethod_Context *context, npy_bool NPY_UNUSED(reduction_is_empty),
+        char *initial)
+{
+    memcpy(initial, context->method->legacy_initial,
+           context->descriptors[0]->elsize);
+    return 1;
+}
+
+
+/*
+ * The default `get_reduction_initial` attempts to look up the identity
+ * from the calling ufunc.  This might fail, so we only call it when necessary.
+ *
+ * For internal number dtypes, we can easily cache it, so do so after the
+ * first call by overriding the function with `copy_cache_initial`.
+ * This path is not publicly available.  That could be added, and for a
+ * custom initial getter it should be static/compile time data anyway.
+ */
+static int
+get_initial_from_ufunc(
+        PyArrayMethod_Context *context, npy_bool reduction_is_empty,
+        char *initial)
+{
+    if (context->caller == NULL
+            || !PyObject_TypeCheck(context->caller, &PyUFunc_Type)) {
+        /* Impossible in NumPy 1.24;  guard in case it becomes possible. */
+        PyErr_SetString(PyExc_ValueError,
+                "getting initial failed because it can only done for legacy "
+                "ufunc loops when the ufunc is provided.");
+        return -1;
+    }
+    npy_bool reorderable;
+    PyObject *identity_obj = PyUFunc_GetDefaultIdentity(
+            (PyUFuncObject *)context->caller, &reorderable);
+    if (identity_obj == NULL) {
+        return -1;
+    }
+    if (identity_obj == Py_None) {
+        /* UFunc has no idenity (should not happen) */
+        Py_DECREF(identity_obj);
+        return 0;
+    }
+    if (PyTypeNum_ISUNSIGNED(context->descriptors[1]->type_num)
+            && PyLong_CheckExact(identity_obj)) {
+        /*
+         * This is a bit of a hack until we have truly loop specific
+         * identities.  Python -1 cannot be cast to unsigned so convert
+         * it to a NumPy scalar, but we use -1 for bitwise functions to
+         * signal all 1s.
+         * (A builtin identity would not overflow here, although we may
+         * unnecessary convert 0 and 1.)
+         */
+        Py_SETREF(identity_obj, PyObject_CallFunctionObjArgs(
+                     (PyObject *)&PyLongArrType_Type, identity_obj, NULL));
+        if (identity_obj == NULL) {
+            return -1;
+        }
+    }
+    else if (context->descriptors[0]->type_num == NPY_OBJECT
+            && !reduction_is_empty) {
+        /* Allows `sum([object()])` to work, but use 0 when empty. */
+        Py_DECREF(identity_obj);
+        return 0;
+    }
+
+    int res = PyArray_Pack(context->descriptors[0], initial, identity_obj);
+    Py_DECREF(identity_obj);
+    if (res < 0) {
+        return -1;
+    }
+
+    if (PyTypeNum_ISNUMBER(context->descriptors[0]->type_num)) {
+        /* For numbers we can cache to avoid going via Python ints */
+        memcpy(context->method->legacy_initial, initial,
+               context->descriptors[0]->elsize);
+        context->method->get_reduction_initial = &copy_cached_initial;
+    }
+
+    /* Reduction can use the initial value */
+    return 1;
+}
+
+
 /*
  * Get the unbound ArrayMethod which wraps the instances of the ufunc.
  * Note that this function stores the result on the ufunc and then only
@@ -273,6 +366,27 @@ PyArray_NewLegacyWrappingArrayMethod(PyUFuncObject *ufunc,
         flags = _NPY_METH_FORCE_CAST_INPUTS;
     }
 
+    get_reduction_initial_function *get_reduction_intial = NULL;
+    if (ufunc->nin == 2 && ufunc->nout == 1) {
+        npy_bool reorderable = NPY_FALSE;
+        PyObject *identity_obj = PyUFunc_GetDefaultIdentity(
+                ufunc, &reorderable);
+        if (identity_obj == NULL) {
+            return NULL;
+        }
+        /*
+         * TODO: For object, "reorderable" is needed(?), because otherwise
+         *       we disable multi-axis reductions `arr.sum(0, 1)`. But for
+         *       `arr = array([["a", "b"], ["c", "d"]], dtype="object")`
+         *       it isn't actually reorderable (order changes result).
+         */
+        if (reorderable) {
+            flags |= NPY_METH_IS_REORDERABLE;
+        }
+        if (identity_obj != Py_None) {
+            get_reduction_intial = &get_initial_from_ufunc;
+        }
+    }
     for (int i = 0; i < ufunc->nin+ufunc->nout; i++) {
         if (signature[i]->singleton->flags & (
                 NPY_ITEM_REFCOUNT | NPY_ITEM_IS_POINTER | NPY_NEEDS_PYAPI)) {
@@ -283,9 +397,10 @@ PyArray_NewLegacyWrappingArrayMethod(PyUFuncObject *ufunc,
         }
     }
 
-    PyType_Slot slots[3] = {
-        {NPY_METH_get_loop, &get_wrapped_legacy_ufunc_loop},
+    PyType_Slot slots[4] = {
+        {_NPY_METH_get_loop, &get_wrapped_legacy_ufunc_loop},
         {NPY_METH_resolve_descriptors, &simple_legacy_resolve_descriptors},
+        {NPY_METH_get_reduction_initial, get_reduction_intial},
         {0, NULL},
     };
     if (any_output_flexible) {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops.c
index 8a2f24d9bf6..b2e03f7d461 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops.c
@@ -23,6 +23,7 @@
 #include "numpy/npy_math.h"
 #include "numpy/halffloat.h"
 #include "lowlevel_strided_loops.h"
+#include "loops_utils.h"
 
 #include "npy_pycompat.h"
 
@@ -41,34 +42,16 @@
  */
 #define PW_BLOCKSIZE    128
 
-
-/*
- * largest simd vector size in bytes numpy supports
- * it is currently a extremely large value as it is only used for memory
- * overlap checks
- */
-#ifndef NPY_MAX_SIMD_SIZE
-#define NPY_MAX_SIMD_SIZE 1024
-#endif
-
 /** Provides the various *_LOOP macros */
 #include "fast_loop_macros.h"
 
-/*
- * include vectorized functions and dispatchers
- * this file is safe to include also for generic builds
- * platform specific instructions are either masked via the proprocessor or
- * runtime detected
- */
-#include "simd.inc"
-
 /******************************************************************************
  **                          GENERIC FLOAT LOOPS                             **
  *****************************************************************************/
 
 /* direct loops using a suitable callback */
 
-#line 65
+#line 48
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -96,7 +79,7 @@ PyUFunc_ee_e(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 }
 
 
-#line 65
+#line 48
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -124,7 +107,7 @@ PyUFunc_ff_f(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 }
 
 
-#line 65
+#line 48
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -152,7 +135,7 @@ PyUFunc_dd_d(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 }
 
 
-#line 65
+#line 48
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -182,7 +165,7 @@ PyUFunc_gg_g(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 
 
 /* indirect loops with casting */
-#line 103
+#line 86
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -209,7 +192,7 @@ PyUFunc_ee_e_As_ff_f(char **args, npy_intp const *dimensions, npy_intp const *st
 }
 
 
-#line 103
+#line 86
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -236,7 +219,7 @@ PyUFunc_ee_e_As_dd_d(char **args, npy_intp const *dimensions, npy_intp const *st
 }
 
 
-#line 103
+#line 86
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -269,7 +252,7 @@ PyUFunc_ff_f_As_dd_d(char **args, npy_intp const *dimensions, npy_intp const *st
  *****************************************************************************/
 
 /* direct loops using a suitable callback */
-#line 139
+#line 122
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -298,7 +281,7 @@ PyUFunc_FF_F(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 139
+#line 122
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -327,7 +310,7 @@ PyUFunc_DD_D(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 139
+#line 122
 
 /*UFUNC_API*/
 NPY_NO_EXPORT void
@@ -608,172 +591,6 @@ PyUFunc_On_Om(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
  *****************************************************************************
  */
 
-#line 425
-
-NPY_NO_EXPORT void
-BOOL_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if(IS_BINARY_REDUCE) {
-#ifdef NPY_HAVE_SSE2_INTRINSICS
-        /*
-         * stick with our variant for more reliable performance, only known
-         * platform which outperforms it by ~20% is an i7 with glibc 2.17
-         */
-        if (run_reduce_simd_logical_and_BOOL(args, dimensions, steps)) {
-            return;
-        }
-#else
-        /* for now only use libc on 32-bit/non-x86 */
-        if (steps[1] == 1) {
-            npy_bool * op = (npy_bool *)args[0];
-#if 1
-            /* np.all(), search for a zero (false) */
-            if (*op) {
-                *op = memchr(args[1], 0, dimensions[0]) == NULL;
-            }
-#else
-            /*
-             * np.any(), search for a non-zero (true) via comparing against
-             * zero blocks, memcmp is faster than memchr on SSE4 machines
-             * with glibc >= 2.12 and memchr can only check for equal 1
-             */
-            static const npy_bool zero[4096]; /* zero by C standard */
-            npy_uintp i, n = dimensions[0];
-
-            for (i = 0; !*op && i < n - (n % sizeof(zero)); i += sizeof(zero)) {
-                *op = memcmp(&args[1][i], zero, sizeof(zero)) != 0;
-            }
-            if (!*op && n - i > 0) {
-                *op = memcmp(&args[1][i], zero, n - i) != 0;
-            }
-#endif
-            return;
-        }
-#endif
-        else {
-            BINARY_REDUCE_LOOP(npy_bool) {
-                const npy_bool in2 = *(npy_bool *)ip2;
-                io1 = io1 && in2;
-                if (io1 == 0) {
-                    break;
-                }
-            }
-            *((npy_bool *)iop1) = io1;
-        }
-    }
-    else {
-        if (run_binary_simd_logical_and_BOOL(args, dimensions, steps)) {
-            return;
-        }
-        else {
-            BINARY_LOOP {
-                const npy_bool in1 = *(npy_bool *)ip1;
-                const npy_bool in2 = *(npy_bool *)ip2;
-                *((npy_bool *)op1) = in1 && in2;
-            }
-        }
-    }
-}
-
-#line 425
-
-NPY_NO_EXPORT void
-BOOL_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if(IS_BINARY_REDUCE) {
-#ifdef NPY_HAVE_SSE2_INTRINSICS
-        /*
-         * stick with our variant for more reliable performance, only known
-         * platform which outperforms it by ~20% is an i7 with glibc 2.17
-         */
-        if (run_reduce_simd_logical_or_BOOL(args, dimensions, steps)) {
-            return;
-        }
-#else
-        /* for now only use libc on 32-bit/non-x86 */
-        if (steps[1] == 1) {
-            npy_bool * op = (npy_bool *)args[0];
-#if 0
-            /* np.all(), search for a zero (false) */
-            if (*op) {
-                *op = memchr(args[1], 0, dimensions[0]) == NULL;
-            }
-#else
-            /*
-             * np.any(), search for a non-zero (true) via comparing against
-             * zero blocks, memcmp is faster than memchr on SSE4 machines
-             * with glibc >= 2.12 and memchr can only check for equal 1
-             */
-            static const npy_bool zero[4096]; /* zero by C standard */
-            npy_uintp i, n = dimensions[0];
-
-            for (i = 0; !*op && i < n - (n % sizeof(zero)); i += sizeof(zero)) {
-                *op = memcmp(&args[1][i], zero, sizeof(zero)) != 0;
-            }
-            if (!*op && n - i > 0) {
-                *op = memcmp(&args[1][i], zero, n - i) != 0;
-            }
-#endif
-            return;
-        }
-#endif
-        else {
-            BINARY_REDUCE_LOOP(npy_bool) {
-                const npy_bool in2 = *(npy_bool *)ip2;
-                io1 = io1 || in2;
-                if (io1 != 0) {
-                    break;
-                }
-            }
-            *((npy_bool *)iop1) = io1;
-        }
-    }
-    else {
-        if (run_binary_simd_logical_or_BOOL(args, dimensions, steps)) {
-            return;
-        }
-        else {
-            BINARY_LOOP {
-                const npy_bool in1 = *(npy_bool *)ip1;
-                const npy_bool in2 = *(npy_bool *)ip2;
-                *((npy_bool *)op1) = in1 || in2;
-            }
-        }
-    }
-}
-
-
-#line 496
-NPY_NO_EXPORT void
-BOOL_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (run_unary_simd_absolute_BOOL(args, dimensions, steps)) {
-        return;
-    }
-    else {
-        UNARY_LOOP {
-            npy_bool in1 = *(npy_bool *)ip1;
-            *((npy_bool *)op1) = in1 != 0;
-        }
-    }
-}
-
-#line 496
-NPY_NO_EXPORT void
-BOOL_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (run_unary_simd_logical_not_BOOL(args, dimensions, steps)) {
-        return;
-    }
-    else {
-        UNARY_LOOP {
-            npy_bool in1 = *(npy_bool *)ip1;
-            *((npy_bool *)op1) = in1 == 0;
-        }
-    }
-}
-
-
 NPY_NO_EXPORT void
 BOOL__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -782,55 +599,20 @@ BOOL__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-
-#line 525
-NPY_NO_EXPORT void
-BOOL_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_bool, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-
-#line 525
-NPY_NO_EXPORT void
-BOOL_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_bool, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-
-#line 525
-NPY_NO_EXPORT void
-BOOL_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_bool, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
 /*
  *****************************************************************************
  **                           INTEGER LOOPS
  *****************************************************************************
  */
 
-#line 553
+#line 426
 
 #define BYTE_floor_divide BYTE_divide
+#define BYTE_floor_divide_indexed BYTE_divide_indexed
 #define BYTE_fmax BYTE_maximum
+#define BYTE_fmax_indexed BYTE_maximum_indexed
 #define BYTE_fmin BYTE_minimum
+#define BYTE_fmin_indexed BYTE_minimum_indexed
 
 NPY_NO_EXPORT void
 BYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -839,482 +621,161 @@ BYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_byte *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-BYTE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+BYTE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_byte *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+BYTE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_byte *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+BYTE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-BYTE_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_byte, npy_byte, *out = npy_lshifthh(in1, in2));
-
-#ifdef BYTE_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_BYTE_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-BYTE_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_byte, npy_byte, *out = npy_rshifthh(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_byte, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_byte, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-BYTE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_byte *)ip1;
-        const int t2 = !!*(npy_byte *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 ^ in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_byte *)value;
     }
+    return 0;
 }
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
 
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-BYTE_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+BYTE_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP_FAST(npy_byte, npy_byte, *out = npy_lshifthh(in1, in2));
-
-#ifdef BYTE_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_BYTE_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-BYTE_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_byte, npy_byte, *out = npy_rshifthh(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_byte, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_byte, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_byte *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+BYTE_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_byte *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-BYTE_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+BYTE_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_byte *)ip1;
-        const int t2 = !!*(npy_byte *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_byte *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -1357,46 +818,14 @@ BYTE_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-BYTE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_byte, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-BYTE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_byte, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-BYTE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_byte, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define UBYTE_floor_divide UBYTE_divide
+#define UBYTE_floor_divide_indexed UBYTE_divide_indexed
 #define UBYTE_fmax UBYTE_maximum
+#define UBYTE_fmax_indexed UBYTE_maximum_indexed
 #define UBYTE_fmin UBYTE_minimum
+#define UBYTE_fmin_indexed UBYTE_minimum_indexed
 
 NPY_NO_EXPORT void
 UBYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -1405,482 +834,161 @@ UBYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_ubyte *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-UBYTE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UBYTE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_ubyte *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UBYTE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_ubyte *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UBYTE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UBYTE_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = npy_lshiftuhh(in1, in2));
-
-#ifdef UBYTE_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_UBYTE_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-UBYTE_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = npy_rshiftuhh(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ubyte, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ubyte, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UBYTE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ubyte *)ip1;
-        const int t2 = !!*(npy_ubyte *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 ^ in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_ubyte *)value;
     }
+    return 0;
 }
-#endif
 
+#line 446
 
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UBYTE_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UBYTE_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = npy_lshiftuhh(in1, in2));
-
-#ifdef UBYTE_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_UBYTE_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-UBYTE_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = npy_rshiftuhh(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ubyte, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ubyte, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_ubyte *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UBYTE_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_ubyte *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UBYTE_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UBYTE_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ubyte *)ip1;
-        const int t2 = !!*(npy_ubyte *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_ubyte *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -1923,46 +1031,14 @@ UBYTE_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-UBYTE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ubyte, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-UBYTE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ubyte, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-UBYTE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ubyte, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define SHORT_floor_divide SHORT_divide
+#define SHORT_floor_divide_indexed SHORT_divide_indexed
 #define SHORT_fmax SHORT_maximum
+#define SHORT_fmax_indexed SHORT_maximum_indexed
 #define SHORT_fmin SHORT_minimum
+#define SHORT_fmin_indexed SHORT_minimum_indexed
 
 NPY_NO_EXPORT void
 SHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -1971,482 +1047,161 @@ SHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_short *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-SHORT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+SHORT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_short, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_short *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+SHORT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-SHORT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_short, npy_short, *out = npy_lshifth(in1, in2));
-
-#ifdef SHORT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_SHORT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-SHORT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_short, npy_short, *out = npy_rshifth(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_short, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_short, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-SHORT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_short *)ip1;
-        const int t2 = !!*(npy_short *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_short *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+SHORT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_short, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 ^ in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_short *)value;
     }
+    return 0;
 }
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-SHORT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_short, npy_short, *out = npy_lshifth(in1, in2));
-
-#ifdef SHORT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_SHORT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-SHORT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_short, npy_short, *out = npy_rshifth(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_short, npy_bool, *out = in1 && in2);
-}
-#endif
 
+#line 446
 
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+SHORT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_short, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_short *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+SHORT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_short *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-SHORT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+SHORT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_short *)ip1;
-        const int t2 = !!*(npy_short *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_short *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -2489,46 +1244,14 @@ SHORT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-SHORT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_short, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-SHORT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_short, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-SHORT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_short, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define USHORT_floor_divide USHORT_divide
+#define USHORT_floor_divide_indexed USHORT_divide_indexed
 #define USHORT_fmax USHORT_maximum
+#define USHORT_fmax_indexed USHORT_maximum_indexed
 #define USHORT_fmin USHORT_minimum
+#define USHORT_fmin_indexed USHORT_minimum_indexed
 
 NPY_NO_EXPORT void
 USHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -2537,482 +1260,161 @@ USHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps
         *((npy_ushort *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-USHORT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+USHORT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_ushort *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+USHORT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-USHORT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = npy_lshiftuh(in1, in2));
-
-#ifdef USHORT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_USHORT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-USHORT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = npy_rshiftuh(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ushort, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ushort, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-USHORT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ushort *)ip1;
-        const int t2 = !!*(npy_ushort *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_ushort *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+USHORT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 ^ in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_ushort *)value;
     }
+    return 0;
 }
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-USHORT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = npy_lshiftuh(in1, in2));
-
-#ifdef USHORT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_USHORT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-USHORT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = npy_rshiftuh(in1, in2));
-}
-#endif
 
-#line 695
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+USHORT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ushort, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ushort, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_ushort *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+USHORT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_ushort *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-USHORT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+USHORT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ushort *)ip1;
-        const int t2 = !!*(npy_ushort *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_ushort *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -3055,46 +1457,14 @@ USHORT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-USHORT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ushort, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-USHORT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ushort, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-USHORT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ushort, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define INT_floor_divide INT_divide
+#define INT_floor_divide_indexed INT_divide_indexed
 #define INT_fmax INT_maximum
+#define INT_fmax_indexed INT_maximum_indexed
 #define INT_fmin INT_minimum
+#define INT_fmin_indexed INT_minimum_indexed
 
 NPY_NO_EXPORT void
 INT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -3103,482 +1473,161 @@ INT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, v
         *((npy_int *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-INT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+INT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_int *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+INT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_int *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+INT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-INT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_int, npy_int, *out = npy_lshift(in1, in2));
-
-#ifdef INT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_INT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-INT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_int, npy_int, *out = npy_rshift(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_int, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_int, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-INT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_int *)ip1;
-        const int t2 = !!*(npy_int *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 | in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_int *)value;
     }
+    return 0;
 }
-#endif
-
 
-#line 631
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+INT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_int, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-INT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_int, npy_int, *out = npy_lshift(in1, in2));
-
-#ifdef INT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_INT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-INT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_int, npy_int, *out = npy_rshift(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_int, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_int, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_int *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+INT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_int *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-INT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+INT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_int *)ip1;
-        const int t2 = !!*(npy_int *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_int *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -3621,46 +1670,14 @@ INT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-INT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_int, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-INT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_int, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-INT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_int, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define UINT_floor_divide UINT_divide
+#define UINT_floor_divide_indexed UINT_divide_indexed
 #define UINT_fmax UINT_maximum
+#define UINT_fmax_indexed UINT_maximum_indexed
 #define UINT_fmin UINT_minimum
+#define UINT_fmin_indexed UINT_minimum_indexed
 
 NPY_NO_EXPORT void
 UINT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -3669,482 +1686,161 @@ UINT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_uint *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-UINT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UINT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_uint *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UINT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_uint *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UINT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UINT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_uint, npy_uint, *out = npy_lshiftu(in1, in2));
-
-#ifdef UINT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_UINT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-UINT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_uint, npy_uint, *out = npy_rshiftu(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_uint, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_uint, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-UINT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_uint *)ip1;
-        const int t2 = !!*(npy_uint *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 | in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_uint *)value;
     }
+    return 0;
 }
-#endif
 
+#line 446
 
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UINT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UINT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_uint, npy_uint, *out = npy_lshiftu(in1, in2));
-
-#ifdef UINT_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_UINT_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-UINT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_uint, npy_uint, *out = npy_rshiftu(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_uint, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_uint, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_uint *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UINT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_uint *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-UINT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+UINT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_uint *)ip1;
-        const int t2 = !!*(npy_uint *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_uint *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -4187,46 +1883,14 @@ UINT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-UINT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_uint, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-UINT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_uint, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-UINT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_uint, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define LONG_floor_divide LONG_divide
+#define LONG_floor_divide_indexed LONG_divide_indexed
 #define LONG_fmax LONG_maximum
+#define LONG_fmax_indexed LONG_maximum_indexed
 #define LONG_fmin LONG_minimum
+#define LONG_fmin_indexed LONG_minimum_indexed
 
 NPY_NO_EXPORT void
 LONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -4235,482 +1899,161 @@ LONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_long *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-LONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_long, npy_long, *out = npy_lshiftl(in1, in2));
-
-#ifdef LONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_LONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-LONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_long, npy_long, *out = npy_rshiftl(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_long, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_long, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_long *)ip1;
-        const int t2 = !!*(npy_long *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_long *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_long *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_long, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 ^ in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_long *)value;
     }
+    return 0;
 }
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_long, npy_long, *out = npy_lshiftl(in1, in2));
-
-#ifdef LONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_LONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-LONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_long, npy_long, *out = npy_rshiftl(in1, in2));
-}
-#endif
 
-#line 695
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_long, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_long, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_long *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_long *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_long *)ip1;
-        const int t2 = !!*(npy_long *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_long *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -4753,46 +2096,14 @@ LONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-LONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_long, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-LONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_long, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-LONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_long, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define ULONG_floor_divide ULONG_divide
+#define ULONG_floor_divide_indexed ULONG_divide_indexed
 #define ULONG_fmax ULONG_maximum
+#define ULONG_fmax_indexed ULONG_maximum_indexed
 #define ULONG_fmin ULONG_minimum
+#define ULONG_fmin_indexed ULONG_minimum_indexed
 
 NPY_NO_EXPORT void
 ULONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -4801,482 +2112,161 @@ ULONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_ulong *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-ULONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_ulong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_ulong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = npy_lshiftul(in1, in2));
-
-#ifdef ULONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_ULONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-ULONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = npy_rshiftul(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulong, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulong, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ulong *)ip1;
-        const int t2 = !!*(npy_ulong *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 ^ in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_ulong *)value;
     }
+    return 0;
 }
-#endif
-
-
 
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
+#line 446
 
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = npy_lshiftul(in1, in2));
-
-#ifdef ULONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_ULONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-ULONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = npy_rshiftul(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulong, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulong, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_ulong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_ulong *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ulong *)ip1;
-        const int t2 = !!*(npy_ulong *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_ulong *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -5319,46 +2309,14 @@ ULONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-ULONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ulong, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-ULONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ulong, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-ULONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ulong, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define LONGLONG_floor_divide LONGLONG_divide
+#define LONGLONG_floor_divide_indexed LONGLONG_divide_indexed
 #define LONGLONG_fmax LONGLONG_maximum
+#define LONGLONG_fmax_indexed LONGLONG_maximum_indexed
 #define LONGLONG_fmin LONGLONG_minimum
+#define LONGLONG_fmin_indexed LONGLONG_minimum_indexed
 
 NPY_NO_EXPORT void
 LONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -5367,482 +2325,161 @@ LONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *ste
         *((npy_longlong *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-LONGLONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONGLONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = npy_lshiftll(in1, in2));
-
-#ifdef LONGLONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_LONGLONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-LONGLONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = npy_rshiftll(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONGLONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_longlong, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_longlong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONGLONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_longlong, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-LONGLONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_longlong *)ip1;
-        const int t2 = !!*(npy_longlong *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_longlong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONGLONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 ^ in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_longlong *)value;
     }
+    return 0;
 }
-#endif
-
 
+#line 446
 
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONGLONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONGLONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = npy_lshiftll(in1, in2));
-
-#ifdef LONGLONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_LONGLONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-LONGLONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = npy_rshiftll(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_longlong, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_longlong, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_longlong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONGLONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_longlong *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-LONGLONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+LONGLONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_longlong *)ip1;
-        const int t2 = !!*(npy_longlong *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_longlong *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -5885,46 +2522,14 @@ LONGLONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-LONGLONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_longlong, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-LONGLONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_longlong, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-LONGLONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_longlong, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-#line 553
+#line 426
 
 #define ULONGLONG_floor_divide ULONGLONG_divide
+#define ULONGLONG_floor_divide_indexed ULONGLONG_divide_indexed
 #define ULONGLONG_fmax ULONGLONG_maximum
+#define ULONGLONG_fmax_indexed ULONGLONG_maximum_indexed
 #define ULONGLONG_fmin ULONGLONG_minimum
+#define ULONGLONG_fmin_indexed ULONGLONG_minimum_indexed
 
 NPY_NO_EXPORT void
 ULONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -5933,482 +2538,161 @@ ULONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *st
         *((npy_ulonglong *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-ULONGLONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONGLONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = +in);
-}
-
-#line 577
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in * in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = 1.0 / in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = -in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_ulonglong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONGLONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = !in);
-}
-#endif
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_ulonglong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONGLONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 & in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONGLONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = npy_lshiftull(in1, in2));
-
-#ifdef ULONGLONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if 1
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_ULONGLONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-ULONGLONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = npy_rshiftull(in1, in2));
-}
-#endif
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
-
-#if 1
-NPY_NO_EXPORT NPY_GCC_OPT_3  void
-ULONGLONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ulonglong *)ip1;
-        const int t2 = !!*(npy_ulonglong *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
-}
-#endif
-
-
-#line 577
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in * in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = 1.0 / in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = -in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = !in);
-}
-#endif
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = ~in);
-}
-#endif
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 += in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 + in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 -= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 - in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 *= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 * in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 &= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 & in2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_ulonglong *)value;
     }
+    return 0;
 }
-#endif
 
+#line 446
 
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONGLONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 |= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 | in2);
-    }
-}
-#endif
-
-
-#line 631
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 ^= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 ^ in2);
-    }
-}
-#endif
-
-
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONGLONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = npy_lshiftull(in1, in2));
-
-#ifdef ULONGLONG_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_ULONGLONG_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-ULONGLONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = npy_rshiftull(in1, in2));
-}
-#endif
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = in1 && in2);
-}
-#endif
-
-
-#line 695
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = in1 || in2);
-}
-#endif
-
-
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = *indexed & *(npy_ulonglong *)value;
+    }
+    return 0;
+}
+
+#line 446
+
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONGLONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = *indexed | *(npy_ulonglong *)value;
+    }
+    return 0;
+}
+
+#line 446
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX2)
-NPY_NO_EXPORT NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX2 void
-ULONGLONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+ULONGLONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
 {
-    BINARY_LOOP {
-        const int t1 = !!*(npy_ulonglong *)ip1;
-        const int t2 = !!*(npy_ulonglong *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = *indexed ^ *(npy_ulonglong *)value;
     }
+    return 0;
 }
-#endif
-
 
 
 NPY_NO_EXPORT void
@@ -6451,57 +2735,9 @@ ULONGLONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 769
-NPY_NO_EXPORT void
-ULONGLONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-ULONGLONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-#line 769
-NPY_NO_EXPORT void
-ULONGLONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, (void)in; *out = NPY_TRUE);
-}
-
-
-
-
-#line 787
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-BYTE_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = (in >= 0) ? in : -in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-BYTE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
-}
 
-#line 803
+#line 518
+#line 521
 NPY_NO_EXPORT void
 BYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6512,7 +2748,7 @@ BYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *N
     }
 }
 
-#line 803
+#line 521
 NPY_NO_EXPORT void
 BYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6524,22 +2760,8 @@ BYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *N
 }
 
 
-
-#line 787
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-SHORT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = (in >= 0) ? in : -in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-SHORT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_short, npy_short, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
-}
-
-#line 803
+#line 518
+#line 521
 NPY_NO_EXPORT void
 SHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6550,7 +2772,7 @@ SHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
     }
 }
 
-#line 803
+#line 521
 NPY_NO_EXPORT void
 SHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6562,22 +2784,8 @@ SHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
 }
 
 
-
-#line 787
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-INT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = (in >= 0) ? in : -in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-INT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_int, npy_int, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
-}
-
-#line 803
+#line 518
+#line 521
 NPY_NO_EXPORT void
 INT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6588,7 +2796,7 @@ INT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NP
     }
 }
 
-#line 803
+#line 521
 NPY_NO_EXPORT void
 INT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6600,22 +2808,8 @@ INT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NP
 }
 
 
-
-#line 787
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = (in >= 0) ? in : -in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_long, npy_long, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
-}
-
-#line 803
+#line 518
+#line 521
 NPY_NO_EXPORT void
 LONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6626,7 +2820,7 @@ LONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *N
     }
 }
 
-#line 803
+#line 521
 NPY_NO_EXPORT void
 LONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6638,22 +2832,8 @@ LONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *N
 }
 
 
-
-#line 787
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONGLONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = (in >= 0) ? in : -in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-LONGLONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
-}
-
-#line 803
+#line 518
+#line 521
 NPY_NO_EXPORT void
 LONGLONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6664,7 +2844,7 @@ LONGLONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 803
+#line 521
 NPY_NO_EXPORT void
 LONGLONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6677,22 +2857,8 @@ LONGLONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 
 
 
-
-#line 821
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UBYTE_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UBYTE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in > 0 ? 1 : 0);
-}
-
-#line 837
+#line 538
+#line 541
 NPY_NO_EXPORT void
 UBYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6703,7 +2869,7 @@ UBYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
     }
 }
 
-#line 837
+#line 541
 NPY_NO_EXPORT void
 UBYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6715,22 +2881,8 @@ UBYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
 }
 
 
-
-#line 821
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-USHORT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-USHORT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in > 0 ? 1 : 0);
-}
-
-#line 837
+#line 538
+#line 541
 NPY_NO_EXPORT void
 USHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6741,7 +2893,7 @@ USHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 837
+#line 541
 NPY_NO_EXPORT void
 USHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6753,22 +2905,8 @@ USHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void
 }
 
 
-
-#line 821
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UINT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-UINT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in > 0 ? 1 : 0);
-}
-
-#line 837
+#line 538
+#line 541
 NPY_NO_EXPORT void
 UINT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6779,7 +2917,7 @@ UINT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *N
     }
 }
 
-#line 837
+#line 541
 NPY_NO_EXPORT void
 UINT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6791,22 +2929,8 @@ UINT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *N
 }
 
 
-
-#line 821
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in > 0 ? 1 : 0);
-}
-
-#line 837
+#line 538
+#line 541
 NPY_NO_EXPORT void
 ULONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6817,7 +2941,7 @@ ULONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
     }
 }
 
-#line 837
+#line 541
 NPY_NO_EXPORT void
 ULONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6829,43 +2953,222 @@ ULONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
 }
 
 
+#line 538
+#line 541
+NPY_NO_EXPORT void
+ULONGLONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        *((npy_ulonglong *)op1) = npy_gcdull(in1, in2);
+    }
+}
+
+#line 541
+NPY_NO_EXPORT void
+ULONGLONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        *((npy_ulonglong *)op1) = npy_lcmull(in1, in2);
+    }
+}
 
-#line 821
 
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONGLONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+
+
+/*
+ * NOTE: It may be nice to vectorize these, OTOH, these are still faster
+ *       than the cast we used to do.
+ */
+
+#line 563
+NPY_NO_EXPORT void
+LONGLONG_Qq_bool_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in);
+    BINARY_LOOP {
+        const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_longlong in2 = *(npy_longlong *)ip2;
+        if (in2 < 0) {
+            *(npy_bool *)op1 = 0 == in2;
+        }
+        else {
+            *(npy_bool *)op1 = in1 == (npy_ulonglong)in2;
+        }
+    }
 }
 
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-ULONGLONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in > 0 ? 1 : 0);
+    BINARY_LOOP {
+        const npy_longlong in1 = *(npy_longlong *)ip1;
+        const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        if (in1 < 0) {
+            *(npy_bool *)op1 = in1 == 0;
+        }
+        else {
+            *(npy_bool *)op1 = (npy_ulonglong)in1 == in2;
+        }
+    }
 }
 
-#line 837
+#line 563
 NPY_NO_EXPORT void
-ULONGLONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+LONGLONG_Qq_bool_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
     BINARY_LOOP {
         const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_longlong in2 = *(npy_longlong *)ip2;
+        if (in2 < 0) {
+            *(npy_bool *)op1 = 0 != in2;
+        }
+        else {
+            *(npy_bool *)op1 = in1 != (npy_ulonglong)in2;
+        }
+    }
+}
+
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_longlong in1 = *(npy_longlong *)ip1;
         const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
-        *((npy_ulonglong *)op1) = npy_gcdull(in1, in2);
+        if (in1 < 0) {
+            *(npy_bool *)op1 = in1 != 0;
+        }
+        else {
+            *(npy_bool *)op1 = (npy_ulonglong)in1 != in2;
+        }
     }
 }
 
-#line 837
+#line 563
 NPY_NO_EXPORT void
-ULONGLONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+LONGLONG_Qq_bool_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
     BINARY_LOOP {
         const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_longlong in2 = *(npy_longlong *)ip2;
+        if (in2 < 0) {
+            *(npy_bool *)op1 = 0 < in2;
+        }
+        else {
+            *(npy_bool *)op1 = in1 < (npy_ulonglong)in2;
+        }
+    }
+}
+
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_longlong in1 = *(npy_longlong *)ip1;
         const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
-        *((npy_ulonglong *)op1) = npy_lcmull(in1, in2);
+        if (in1 < 0) {
+            *(npy_bool *)op1 = in1 < 0;
+        }
+        else {
+            *(npy_bool *)op1 = (npy_ulonglong)in1 < in2;
+        }
+    }
+}
+
+#line 563
+NPY_NO_EXPORT void
+LONGLONG_Qq_bool_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_longlong in2 = *(npy_longlong *)ip2;
+        if (in2 < 0) {
+            *(npy_bool *)op1 = 0 <= in2;
+        }
+        else {
+            *(npy_bool *)op1 = in1 <= (npy_ulonglong)in2;
+        }
+    }
+}
+
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_longlong in1 = *(npy_longlong *)ip1;
+        const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        if (in1 < 0) {
+            *(npy_bool *)op1 = in1 <= 0;
+        }
+        else {
+            *(npy_bool *)op1 = (npy_ulonglong)in1 <= in2;
+        }
+    }
+}
+
+#line 563
+NPY_NO_EXPORT void
+LONGLONG_Qq_bool_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_longlong in2 = *(npy_longlong *)ip2;
+        if (in2 < 0) {
+            *(npy_bool *)op1 = 0 > in2;
+        }
+        else {
+            *(npy_bool *)op1 = in1 > (npy_ulonglong)in2;
+        }
+    }
+}
+
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_longlong in1 = *(npy_longlong *)ip1;
+        const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        if (in1 < 0) {
+            *(npy_bool *)op1 = in1 > 0;
+        }
+        else {
+            *(npy_bool *)op1 = (npy_ulonglong)in1 > in2;
+        }
+    }
+}
+
+#line 563
+NPY_NO_EXPORT void
+LONGLONG_Qq_bool_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_longlong in2 = *(npy_longlong *)ip2;
+        if (in2 < 0) {
+            *(npy_bool *)op1 = 0 >= in2;
+        }
+        else {
+            *(npy_bool *)op1 = in1 >= (npy_ulonglong)in2;
+        }
     }
 }
 
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_longlong in1 = *(npy_longlong *)ip1;
+        const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        if (in1 < 0) {
+            *(npy_bool *)op1 = in1 >= 0;
+        }
+        else {
+            *(npy_bool *)op1 = (npy_ulonglong)in1 >= in2;
+        }
+    }
+}
 
 
 
@@ -6921,7 +3224,7 @@ TIMEDELTA_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 906
+#line 651
 
 NPY_NO_EXPORT void
 DATETIME_isnat(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -6942,12 +3245,6 @@ DATETIME_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps
 }
 
 NPY_NO_EXPORT void
-DATETIME_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_bool, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-NPY_NO_EXPORT void
 DATETIME__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
@@ -6955,7 +3252,7 @@ DATETIME__ones_like(char **args, npy_intp const *dimensions, npy_intp const *ste
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 DATETIME_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6968,7 +3265,7 @@ DATETIME_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 DATETIME_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6981,7 +3278,7 @@ DATETIME_greater(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 DATETIME_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -6994,7 +3291,7 @@ DATETIME_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 DATETIME_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7007,7 +3304,7 @@ DATETIME_less(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 DATETIME_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7034,7 +3331,7 @@ DATETIME_not_equal(char **args, npy_intp const *dimensions, npy_intp const *step
 }
 
 
-#line 973
+#line 712
 NPY_NO_EXPORT void
 DATETIME_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7053,7 +3350,8 @@ DATETIME_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 973
+
+#line 712
 NPY_NO_EXPORT void
 DATETIME_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7073,7 +3371,8 @@ DATETIME_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 
-#line 996
+
+#line 736
 NPY_NO_EXPORT void
 DATETIME_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7092,7 +3391,7 @@ DATETIME_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     }
 }
 
-#line 996
+#line 736
 NPY_NO_EXPORT void
 DATETIME_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7113,7 +3412,7 @@ DATETIME_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
 
 
 
-#line 906
+#line 651
 
 NPY_NO_EXPORT void
 TIMEDELTA_isnat(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -7134,12 +3433,6 @@ TIMEDELTA_isfinite(char **args, npy_intp const *dimensions, npy_intp const *step
 }
 
 NPY_NO_EXPORT void
-TIMEDELTA_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_bool, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-NPY_NO_EXPORT void
 TIMEDELTA__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
@@ -7147,7 +3440,7 @@ TIMEDELTA__ones_like(char **args, npy_intp const *dimensions, npy_intp const *st
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 TIMEDELTA_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7160,7 +3453,7 @@ TIMEDELTA_equal(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 TIMEDELTA_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7173,7 +3466,7 @@ TIMEDELTA_greater(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 TIMEDELTA_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7186,7 +3479,7 @@ TIMEDELTA_greater_equal(char **args, npy_intp const *dimensions, npy_intp const
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 TIMEDELTA_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7199,7 +3492,7 @@ TIMEDELTA_less(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 943
+#line 682
 NPY_NO_EXPORT void
 TIMEDELTA_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7226,7 +3519,7 @@ TIMEDELTA_not_equal(char **args, npy_intp const *dimensions, npy_intp const *ste
 }
 
 
-#line 973
+#line 712
 NPY_NO_EXPORT void
 TIMEDELTA_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7245,7 +3538,8 @@ TIMEDELTA_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
-#line 973
+
+#line 712
 NPY_NO_EXPORT void
 TIMEDELTA_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7265,7 +3559,8 @@ TIMEDELTA_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps
 }
 
 
-#line 996
+
+#line 736
 NPY_NO_EXPORT void
 TIMEDELTA_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7284,7 +3579,7 @@ TIMEDELTA_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 996
+#line 736
 NPY_NO_EXPORT void
 TIMEDELTA_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7692,8 +3987,8 @@ TIMEDELTA_mm_qm_divmod(char **args, npy_intp const *dimensions, npy_intp const *
  *****************************************************************************
  */
 
-#line 1410
-#line 1414
+#line 1152
+#line 1156
 NPY_NO_EXPORT void
 FLOAT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7705,7 +4000,7 @@ FLOAT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1414
+#line 1156
 NPY_NO_EXPORT void
 FLOAT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7737,147 +4032,53 @@ FLOAT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
-#line 1449
-
-#line 1455
-
-#if 1
+#if !1
+#line 1192
 NPY_NO_EXPORT void
 FLOAT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isnan_simd_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_isnan(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_float in1 = *(npy_float *)ip1;
+        *((npy_bool *)op1) = npy_isnan(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-#line 1455
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-FLOAT_isnan_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isnan_avx512_skx_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_isnan(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-
-#line 1449
-
-#line 1455
-
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 FLOAT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isinf_simd_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_isinf(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-FLOAT_isinf_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isinf_avx512_skx_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_isinf(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_float in1 = *(npy_float *)ip1;
+        *((npy_bool *)op1) = npy_isinf(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-
-#line 1449
 
-#line 1455
-
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 FLOAT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isfinite_simd_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_isfinite(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-FLOAT_isfinite_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isfinite_avx512_skx_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_isfinite(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_float in1 = *(npy_float *)ip1;
+        *((npy_bool *)op1) = npy_isfinite(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-
-#line 1449
-
-#line 1455
 
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 FLOAT_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_signbit_simd_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_signbit(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_float in1 = *(npy_float *)ip1;
+        *((npy_bool *)op1) = npy_signbit(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-#line 1455
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-FLOAT_signbit_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_signbit_avx512_skx_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_bool *)op1) = npy_signbit(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
 #endif
 
-
-
 NPY_NO_EXPORT void
 FLOAT_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7917,6 +4118,30 @@ FLOAT_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *step
     }
 }
 
+NPY_NO_EXPORT int
+FLOAT_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = npy_floor_dividef(*indexed, *(npy_float *)value);
+    }
+    return 0;
+}
+
 NPY_NO_EXPORT void
 FLOAT_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -7955,17 +4180,6 @@ FLOAT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 NPY_NO_EXPORT void
-FLOAT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_unary_simd_negative_FLOAT(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            *((npy_float *)op1) = -in1;
-        }
-    }
-}
-
-NPY_NO_EXPORT void
 FLOAT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
     UNARY_LOOP {
@@ -8025,8 +4239,8 @@ FLOAT_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 
-#line 1410
-#line 1414
+#line 1152
+#line 1156
 NPY_NO_EXPORT void
 DOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8038,7 +4252,7 @@ DOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *step
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1414
+#line 1156
 NPY_NO_EXPORT void
 DOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8070,147 +4284,53 @@ DOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *step
     }
 }
 
-#line 1449
-
-#line 1455
-
-#if 1
+#if !1
+#line 1192
 NPY_NO_EXPORT void
 DOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isnan_simd_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_isnan(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-DOUBLE_isnan_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isnan_avx512_skx_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_isnan(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_double in1 = *(npy_double *)ip1;
+        *((npy_bool *)op1) = npy_isnan(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
 
-#line 1449
-
-#line 1455
-
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 DOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isinf_simd_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_isinf(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-DOUBLE_isinf_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isinf_avx512_skx_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_isinf(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_double in1 = *(npy_double *)ip1;
+        *((npy_bool *)op1) = npy_isinf(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-
-#line 1449
 
-#line 1455
-
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 DOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isfinite_simd_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_isfinite(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-DOUBLE_isfinite_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isfinite_avx512_skx_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_isfinite(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_double in1 = *(npy_double *)ip1;
+        *((npy_bool *)op1) = npy_isfinite(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-
-#line 1449
 
-#line 1455
-
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 DOUBLE_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_signbit_simd_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_signbit(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_double in1 = *(npy_double *)ip1;
+        *((npy_bool *)op1) = npy_signbit(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-#line 1455
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-DOUBLE_signbit_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_signbit_avx512_skx_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_bool *)op1) = npy_signbit(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
 #endif
 
-
-
 NPY_NO_EXPORT void
 DOUBLE_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8250,6 +4370,30 @@ DOUBLE_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *ste
     }
 }
 
+NPY_NO_EXPORT int
+DOUBLE_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = npy_floor_divide(*indexed, *(npy_double *)value);
+    }
+    return 0;
+}
+
 NPY_NO_EXPORT void
 DOUBLE_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8288,17 +4432,6 @@ DOUBLE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 NPY_NO_EXPORT void
-DOUBLE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_unary_simd_negative_DOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            *((npy_double *)op1) = -in1;
-        }
-    }
-}
-
-NPY_NO_EXPORT void
 DOUBLE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
     UNARY_LOOP {
@@ -8358,8 +4491,8 @@ DOUBLE_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *steps
 }
 
 
-#line 1410
-#line 1414
+#line 1152
+#line 1156
 NPY_NO_EXPORT void
 LONGDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8371,7 +4504,7 @@ LONGDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1414
+#line 1156
 NPY_NO_EXPORT void
 LONGDOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8403,147 +4536,53 @@ LONGDOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *
     }
 }
 
-#line 1449
-
-#line 1455
-
-#if 1
+#if !0
+#line 1192
 NPY_NO_EXPORT void
 LONGDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isnan_simd_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_isnan(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-LONGDOUBLE_isnan_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isnan_avx512_skx_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_isnan(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_longdouble in1 = *(npy_longdouble *)ip1;
+        *((npy_bool *)op1) = npy_isnan(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-
-#line 1449
 
-#line 1455
-
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 LONGDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isinf_simd_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_isinf(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-LONGDOUBLE_isinf_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isinf_avx512_skx_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_isinf(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_longdouble in1 = *(npy_longdouble *)ip1;
+        *((npy_bool *)op1) = npy_isinf(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
 
-
-#line 1449
-
-#line 1455
-
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 LONGDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_isfinite_simd_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_isfinite(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-#endif
-
-#line 1455
-
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-LONGDOUBLE_isfinite_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_isfinite_avx512_skx_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_isfinite(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_longdouble in1 = *(npy_longdouble *)ip1;
+        *((npy_bool *)op1) = npy_isfinite(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-
-#line 1449
-
-#line 1455
 
-#if 1
+#line 1192
 NPY_NO_EXPORT void
 LONGDOUBLE_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_signbit_simd_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_signbit(in1) != 0;
-        }
+    UNARY_LOOP {
+        const npy_longdouble in1 = *(npy_longdouble *)ip1;
+        *((npy_bool *)op1) = npy_signbit(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-
-#line 1455
 
-#if defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)
-NPY_NO_EXPORT void
-LONGDOUBLE_signbit_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_signbit_avx512_skx_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_bool *)op1) = npy_signbit(in1) != 0;
-        }
-    }
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
 #endif
 
-
-
 NPY_NO_EXPORT void
 LONGDOUBLE_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8583,6 +4622,30 @@ LONGDOUBLE_floor_divide(char **args, npy_intp const *dimensions, npy_intp const
     }
 }
 
+NPY_NO_EXPORT int
+LONGDOUBLE_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = npy_floor_dividel(*indexed, *(npy_longdouble *)value);
+    }
+    return 0;
+}
+
 NPY_NO_EXPORT void
 LONGDOUBLE_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8621,17 +4684,6 @@ LONGDOUBLE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *st
 }
 
 NPY_NO_EXPORT void
-LONGDOUBLE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_unary_simd_negative_LONGDOUBLE(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const npy_longdouble in1 = *(npy_longdouble *)ip1;
-            *((npy_longdouble *)op1) = -in1;
-        }
-    }
-}
-
-NPY_NO_EXPORT void
 LONGDOUBLE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
     UNARY_LOOP {
@@ -8698,7 +4750,7 @@ LONGDOUBLE_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *s
  *****************************************************************************
  */
 
-#line 1632
+#line 1377
 NPY_NO_EXPORT void
 LONGDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8724,7 +4776,32 @@ LONGDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 1632
+NPY_NO_EXPORT int
+LONGDOUBLE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_longdouble *)value;
+    }
+    return 0;
+}
+
+
+#line 1377
 NPY_NO_EXPORT void
 LONGDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8750,7 +4827,32 @@ LONGDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *ste
     }
 }
 
-#line 1632
+NPY_NO_EXPORT int
+LONGDOUBLE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_longdouble *)value;
+    }
+    return 0;
+}
+
+
+#line 1377
 NPY_NO_EXPORT void
 LONGDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8776,7 +4878,32 @@ LONGDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *ste
     }
 }
 
-#line 1632
+NPY_NO_EXPORT int
+LONGDOUBLE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_longdouble *)value;
+    }
+    return 0;
+}
+
+
+#line 1377
 NPY_NO_EXPORT void
 LONGDOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8802,8 +4929,33 @@ LONGDOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
+NPY_NO_EXPORT int
+LONGDOUBLE_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = *indexed / *(npy_longdouble *)value;
+    }
+    return 0;
+}
+
 
-#line 1662
+
+#line 1432
 NPY_NO_EXPORT void
 LONGDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8815,7 +4967,7 @@ LONGDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps,
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1662
+#line 1432
 NPY_NO_EXPORT void
 LONGDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8827,7 +4979,7 @@ LONGDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *st
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1662
+#line 1432
 NPY_NO_EXPORT void
 LONGDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8839,7 +4991,7 @@ LONGDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps,
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1662
+#line 1432
 NPY_NO_EXPORT void
 LONGDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8851,7 +5003,7 @@ LONGDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *s
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1662
+#line 1432
 NPY_NO_EXPORT void
 LONGDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8863,7 +5015,7 @@ LONGDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *step
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1662
+#line 1432
 NPY_NO_EXPORT void
 LONGDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8932,7 +5084,7 @@ LONGDOUBLE_ldexp(char **args, npy_intp const *dimensions, npy_intp const *steps,
  */
 
 
-#line 1736
+#line 1506
 NPY_NO_EXPORT void
 HALF_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8959,7 +5111,32 @@ HALF_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *N
     }
 }
 
-#line 1736
+NPY_NO_EXPORT int
+HALF_add_indexed(void *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        const float v = npy_half_to_float(*(npy_half *)value);
+        *indexed = npy_float_to_half(npy_half_to_float(*indexed) + v);
+    }
+    return 0;
+}
+
+#line 1506
 NPY_NO_EXPORT void
 HALF_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -8986,7 +5163,32 @@ HALF_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     }
 }
 
-#line 1736
+NPY_NO_EXPORT int
+HALF_subtract_indexed(void *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        const float v = npy_half_to_float(*(npy_half *)value);
+        *indexed = npy_float_to_half(npy_half_to_float(*indexed) - v);
+    }
+    return 0;
+}
+
+#line 1506
 NPY_NO_EXPORT void
 HALF_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9013,7 +5215,32 @@ HALF_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     }
 }
 
-#line 1736
+NPY_NO_EXPORT int
+HALF_multiply_indexed(void *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        const float v = npy_half_to_float(*(npy_half *)value);
+        *indexed = npy_float_to_half(npy_half_to_float(*indexed) * v);
+    }
+    return 0;
+}
+
+#line 1506
 NPY_NO_EXPORT void
 HALF_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9040,10 +5267,35 @@ HALF_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
+NPY_NO_EXPORT int
+HALF_divide_indexed(void *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        const float v = npy_half_to_float(*(npy_half *)value);
+        *indexed = npy_float_to_half(npy_half_to_float(*indexed) / v);
+    }
+    return 0;
+}
+
 
 #define _HALF_LOGICAL_AND(a,b) (!npy_half_iszero(a) && !npy_half_iszero(b))
 #define _HALF_LOGICAL_OR(a,b) (!npy_half_iszero(a) || !npy_half_iszero(b))
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9054,7 +5306,7 @@ HALF_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9065,7 +5317,7 @@ HALF_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9076,7 +5328,7 @@ HALF_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
     }
 }
 
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9087,7 +5339,7 @@ HALF_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9098,7 +5350,7 @@ HALF_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9109,7 +5361,7 @@ HALF_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *step
     }
 }
 
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9120,7 +5372,7 @@ HALF_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 1771
+#line 1566
 NPY_NO_EXPORT void
 HALF_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9153,7 +5405,7 @@ HALF_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 1807
+#line 1602
 NPY_NO_EXPORT void
 HALF_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9164,7 +5416,7 @@ HALF_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1807
+#line 1602
 NPY_NO_EXPORT void
 HALF_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9175,7 +5427,7 @@ HALF_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1807
+#line 1602
 NPY_NO_EXPORT void
 HALF_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9186,7 +5438,7 @@ HALF_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 1807
+#line 1602
 NPY_NO_EXPORT void
 HALF_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9227,7 +5479,7 @@ HALF_nextafter(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 1851
+#line 1646
 NPY_NO_EXPORT void
 HALF_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9240,7 +5492,33 @@ HALF_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     /* npy_half_isnan will never set floatstatus_invalid, so do not clear */
 }
 
-#line 1851
+NPY_NO_EXPORT int
+HALF_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        npy_half v = *(npy_half *)value;
+        *indexed = (npy_half_ge(*indexed, v) || npy_half_isnan(*indexed)) ? *indexed : v;
+    }
+    return 0;
+}
+
+
+#line 1646
 NPY_NO_EXPORT void
 HALF_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9253,8 +5531,34 @@ HALF_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     /* npy_half_isnan will never set floatstatus_invalid, so do not clear */
 }
 
+NPY_NO_EXPORT int
+HALF_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        npy_half v = *(npy_half *)value;
+        *indexed = (npy_half_le(*indexed, v) || npy_half_isnan(*indexed)) ? *indexed : v;
+    }
+    return 0;
+}
+
 
-#line 1868
+
+#line 1689
 NPY_NO_EXPORT void
 HALF_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9264,10 +5568,36 @@ HALF_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
         const npy_half in2 = *(npy_half *)ip2;
         *((npy_half *)op1) = (npy_half_ge(in1, in2) || npy_half_isnan(in2)) ? in1 : in2;
     }
-    /* npy_half_isnan will never set floatstatus_invalid, so do not clear */
+    /* no need to clear floatstatus_invalid */
+}
+
+NPY_NO_EXPORT int
+HALF_fmax_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for (i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        npy_half v = *(npy_half *)value;
+        *indexed = (npy_half_ge(*indexed, v) || npy_half_isnan(v)) ? *indexed: v;
+    }
+    return 0;
 }
 
-#line 1868
+
+#line 1689
 NPY_NO_EXPORT void
 HALF_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9277,10 +5607,36 @@ HALF_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *
         const npy_half in2 = *(npy_half *)ip2;
         *((npy_half *)op1) = (npy_half_le(in1, in2) || npy_half_isnan(in2)) ? in1 : in2;
     }
-    /* npy_half_isnan will never set floatstatus_invalid, so do not clear */
+    /* no need to clear floatstatus_invalid */
+}
+
+NPY_NO_EXPORT int
+HALF_fmin_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for (i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        npy_half v = *(npy_half *)value;
+        *indexed = (npy_half_le(*indexed, v) || npy_half_isnan(v)) ? *indexed: v;
+    }
+    return 0;
 }
 
 
+
 NPY_NO_EXPORT void
 HALF_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9297,6 +5653,32 @@ HALF_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
+NPY_NO_EXPORT int
+HALF_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        float v = npy_half_to_float(*(npy_half *)value);
+        float div = npy_floor_dividef(npy_half_to_float(*indexed), v);
+        *indexed = npy_float_to_half(div);
+    }
+    return 0;
+}
+
 NPY_NO_EXPORT void
 HALF_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9356,12 +5738,6 @@ HALF_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-HALF_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_half, npy_half, *out = in&0x7fffu);
-}
-
 NPY_NO_EXPORT void
 HALF_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9470,11 +5846,11 @@ HALF_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *steps,
 #define CEQ(xr,xi,yr,yi) (xr == yr && xi == yi)
 #define CNE(xr,xi,yr,yi) (xr != yr || xi != yi)
 
-#line 2078
+#line 1945
 
 #if !1
 // CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
-#line 2087
+#line 1954
 NPY_NO_EXPORT void
 CFLOAT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9502,7 +5878,32 @@ CFLOAT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 2087
+NPY_NO_EXPORT int CFLOAT_add_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        const npy_float b_r = ((npy_float *)value)[0];
+        const npy_float b_i = ((npy_float *)value)[1];
+        indexed[0] += b_r;
+        indexed[1] += b_i;
+    }
+    return 0;
+}
+
+#line 1954
 NPY_NO_EXPORT void
 CFLOAT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9530,6 +5931,31 @@ CFLOAT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
+NPY_NO_EXPORT int CFLOAT_subtract_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        const npy_float b_r = ((npy_float *)value)[0];
+        const npy_float b_i = ((npy_float *)value)[1];
+        indexed[0] -= b_r;
+        indexed[1] -= b_i;
+    }
+    return 0;
+}
+
 
 NPY_NO_EXPORT void
 CFLOAT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -9543,6 +5969,33 @@ CFLOAT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps,
         ((npy_float *)op1)[1] = in1r*in2i + in1i*in2r;
     }
 }
+
+NPY_NO_EXPORT int CFLOAT_multiply_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        const npy_float a_r = indexed[0];
+        const npy_float a_i = indexed[1];
+        const npy_float b_r = ((npy_float *)value)[0];
+        const npy_float b_i = ((npy_float *)value)[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    }
+    return 0;
+}
 #endif // !SIMD
 
 NPY_NO_EXPORT void
@@ -9578,7 +6031,7 @@ CFLOAT_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
 }
 
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CFLOAT_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9591,7 +6044,7 @@ CFLOAT_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CFLOAT_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9604,7 +6057,7 @@ CFLOAT_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *st
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CFLOAT_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9617,7 +6070,7 @@ CFLOAT_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CFLOAT_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9630,7 +6083,7 @@ CFLOAT_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CFLOAT_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9643,7 +6096,7 @@ CFLOAT_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CFLOAT_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9657,7 +6110,7 @@ CFLOAT_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 
-#line 2184
+#line 2103
 NPY_NO_EXPORT void
 CFLOAT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9670,7 +6123,7 @@ CFLOAT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *step
     }
 }
 
-#line 2184
+#line 2103
 NPY_NO_EXPORT void
 CFLOAT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9708,7 +6161,7 @@ CFLOAT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *step
     }
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CFLOAT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9720,7 +6173,7 @@ CFLOAT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CFLOAT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9732,7 +6185,7 @@ CFLOAT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CFLOAT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9745,6 +6198,8 @@ CFLOAT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 
+#if !1
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 CFLOAT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -9755,6 +6210,7 @@ CFLOAT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
         ((npy_float *)op1)[1] = in1r*in1i + in1i*in1r;
     }
 }
+#endif
 
 NPY_NO_EXPORT void
 CFLOAT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -9785,6 +6241,8 @@ CFLOAT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
+#if !1
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 CFLOAT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     UNARY_LOOP {
@@ -9804,35 +6262,6 @@ CFLOAT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_float *)op1) = npy_hypotf(in1r, in1i);
     }
 }
-
-#if 1 && defined(HAVE_ATTRIBUTE_TARGET_AVX512F)
-#line 2303
-NPY_NO_EXPORT void
-CFLOAT_conjugate_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_conjugate_CFLOAT(args, dimensions, steps)) {
-        CFLOAT_conjugate(args, dimensions, steps, func);
-    }
-}
-
-#line 2303
-NPY_NO_EXPORT void
-CFLOAT_square_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_square_CFLOAT(args, dimensions, steps)) {
-        CFLOAT_square(args, dimensions, steps, func);
-    }
-}
-
-#line 2303
-NPY_NO_EXPORT void
-CFLOAT_absolute_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_absolute_CFLOAT(args, dimensions, steps)) {
-        CFLOAT_absolute(args, dimensions, steps, func);
-    }
-}
-
 #endif
 
 NPY_NO_EXPORT void
@@ -9859,7 +6288,7 @@ CFLOAT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 2341
+#line 2251
 NPY_NO_EXPORT void
 CFLOAT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9878,7 +6307,7 @@ CFLOAT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2341
+#line 2251
 NPY_NO_EXPORT void
 CFLOAT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9898,7 +6327,7 @@ CFLOAT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, v
 }
 
 
-#line 2364
+#line 2274
 NPY_NO_EXPORT void
 CFLOAT_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9919,7 +6348,7 @@ CFLOAT_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2364
+#line 2274
 NPY_NO_EXPORT void
 CFLOAT_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9942,11 +6371,11 @@ CFLOAT_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void
 
 
 
-#line 2078
+#line 1945
 
 #if !1
 // CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
-#line 2087
+#line 1954
 NPY_NO_EXPORT void
 CDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -9974,7 +6403,32 @@ CDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 2087
+NPY_NO_EXPORT int CDOUBLE_add_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        const npy_double b_r = ((npy_double *)value)[0];
+        const npy_double b_i = ((npy_double *)value)[1];
+        indexed[0] += b_r;
+        indexed[1] += b_i;
+    }
+    return 0;
+}
+
+#line 1954
 NPY_NO_EXPORT void
 CDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10002,6 +6456,31 @@ CDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
+NPY_NO_EXPORT int CDOUBLE_subtract_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        const npy_double b_r = ((npy_double *)value)[0];
+        const npy_double b_i = ((npy_double *)value)[1];
+        indexed[0] -= b_r;
+        indexed[1] -= b_i;
+    }
+    return 0;
+}
+
 
 NPY_NO_EXPORT void
 CDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -10015,6 +6494,33 @@ CDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps,
         ((npy_double *)op1)[1] = in1r*in2i + in1i*in2r;
     }
 }
+
+NPY_NO_EXPORT int CDOUBLE_multiply_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        const npy_double a_r = indexed[0];
+        const npy_double a_i = indexed[1];
+        const npy_double b_r = ((npy_double *)value)[0];
+        const npy_double b_i = ((npy_double *)value)[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    }
+    return 0;
+}
 #endif // !SIMD
 
 NPY_NO_EXPORT void
@@ -10050,7 +6556,7 @@ CDOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, v
 }
 
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10063,7 +6569,7 @@ CDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10076,7 +6582,7 @@ CDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *s
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10089,7 +6595,7 @@ CDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10102,7 +6608,7 @@ CDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *step
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10115,7 +6621,7 @@ CDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10129,7 +6635,7 @@ CDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps
 }
 
 
-#line 2184
+#line 2103
 NPY_NO_EXPORT void
 CDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10142,7 +6648,7 @@ CDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *ste
     }
 }
 
-#line 2184
+#line 2103
 NPY_NO_EXPORT void
 CDOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10180,7 +6686,7 @@ CDOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *ste
     }
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10192,7 +6698,7 @@ CDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10204,7 +6710,7 @@ CDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10217,6 +6723,8 @@ CDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 
+#if !1
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 CDOUBLE_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -10227,6 +6735,7 @@ CDOUBLE_square(char **args, npy_intp const *dimensions, npy_intp const *steps, v
         ((npy_double *)op1)[1] = in1r*in1i + in1i*in1r;
     }
 }
+#endif
 
 NPY_NO_EXPORT void
 CDOUBLE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -10257,6 +6766,8 @@ CDOUBLE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *step
     }
 }
 
+#if !1
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 CDOUBLE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     UNARY_LOOP {
@@ -10276,35 +6787,6 @@ CDOUBLE_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps,
         *((npy_double *)op1) = npy_hypot(in1r, in1i);
     }
 }
-
-#if 1 && defined(HAVE_ATTRIBUTE_TARGET_AVX512F)
-#line 2303
-NPY_NO_EXPORT void
-CDOUBLE_conjugate_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_conjugate_CDOUBLE(args, dimensions, steps)) {
-        CDOUBLE_conjugate(args, dimensions, steps, func);
-    }
-}
-
-#line 2303
-NPY_NO_EXPORT void
-CDOUBLE_square_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_square_CDOUBLE(args, dimensions, steps)) {
-        CDOUBLE_square(args, dimensions, steps, func);
-    }
-}
-
-#line 2303
-NPY_NO_EXPORT void
-CDOUBLE_absolute_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_absolute_CDOUBLE(args, dimensions, steps)) {
-        CDOUBLE_absolute(args, dimensions, steps, func);
-    }
-}
-
 #endif
 
 NPY_NO_EXPORT void
@@ -10331,7 +6813,7 @@ CDOUBLE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 2341
+#line 2251
 NPY_NO_EXPORT void
 CDOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10350,7 +6832,7 @@ CDOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps,
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2341
+#line 2251
 NPY_NO_EXPORT void
 CDOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10370,7 +6852,7 @@ CDOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 
-#line 2364
+#line 2274
 NPY_NO_EXPORT void
 CDOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10391,7 +6873,7 @@ CDOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2364
+#line 2274
 NPY_NO_EXPORT void
 CDOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10414,11 +6896,11 @@ CDOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 
 
 
-#line 2078
+#line 1945
 
 #if !0
 // CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
-#line 2087
+#line 1954
 NPY_NO_EXPORT void
 CLONGDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10446,7 +6928,32 @@ CLONGDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 2087
+NPY_NO_EXPORT int CLONGDOUBLE_add_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        const npy_longdouble b_r = ((npy_longdouble *)value)[0];
+        const npy_longdouble b_i = ((npy_longdouble *)value)[1];
+        indexed[0] += b_r;
+        indexed[1] += b_i;
+    }
+    return 0;
+}
+
+#line 1954
 NPY_NO_EXPORT void
 CLONGDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10474,6 +6981,31 @@ CLONGDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *st
     }
 }
 
+NPY_NO_EXPORT int CLONGDOUBLE_subtract_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        const npy_longdouble b_r = ((npy_longdouble *)value)[0];
+        const npy_longdouble b_i = ((npy_longdouble *)value)[1];
+        indexed[0] -= b_r;
+        indexed[1] -= b_i;
+    }
+    return 0;
+}
+
 
 NPY_NO_EXPORT void
 CLONGDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -10487,6 +7019,33 @@ CLONGDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *st
         ((npy_longdouble *)op1)[1] = in1r*in2i + in1i*in2r;
     }
 }
+
+NPY_NO_EXPORT int CLONGDOUBLE_multiply_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        const npy_longdouble a_r = indexed[0];
+        const npy_longdouble a_i = indexed[1];
+        const npy_longdouble b_r = ((npy_longdouble *)value)[0];
+        const npy_longdouble b_i = ((npy_longdouble *)value)[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    }
+    return 0;
+}
 #endif // !SIMD
 
 NPY_NO_EXPORT void
@@ -10522,7 +7081,7 @@ CLONGDOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *step
 }
 
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CLONGDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10535,7 +7094,7 @@ CLONGDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *ste
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CLONGDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10548,7 +7107,7 @@ CLONGDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp cons
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CLONGDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10561,7 +7120,7 @@ CLONGDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CLONGDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10574,7 +7133,7 @@ CLONGDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CLONGDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10587,7 +7146,7 @@ CLONGDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
-#line 2166
+#line 2085
 NPY_NO_EXPORT void
 CLONGDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10601,7 +7160,7 @@ CLONGDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *s
 }
 
 
-#line 2184
+#line 2103
 NPY_NO_EXPORT void
 CLONGDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10614,7 +7173,7 @@ CLONGDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const
     }
 }
 
-#line 2184
+#line 2103
 NPY_NO_EXPORT void
 CLONGDOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10652,7 +7211,7 @@ CLONGDOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const
     }
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CLONGDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10664,7 +7223,7 @@ CLONGDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CLONGDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10676,7 +7235,7 @@ CLONGDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2226
+#line 2145
 NPY_NO_EXPORT void
 CLONGDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10689,6 +7248,8 @@ CLONGDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *st
 }
 
 
+#if !0
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 CLONGDOUBLE_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -10699,6 +7260,7 @@ CLONGDOUBLE_square(char **args, npy_intp const *dimensions, npy_intp const *step
         ((npy_longdouble *)op1)[1] = in1r*in1i + in1i*in1r;
     }
 }
+#endif
 
 NPY_NO_EXPORT void
 CLONGDOUBLE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -10729,6 +7291,8 @@ CLONGDOUBLE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *
     }
 }
 
+#if !0
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 CLONGDOUBLE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     UNARY_LOOP {
@@ -10748,35 +7312,6 @@ CLONGDOUBLE_absolute(char **args, npy_intp const *dimensions, npy_intp const *st
         *((npy_longdouble *)op1) = npy_hypotl(in1r, in1i);
     }
 }
-
-#if 0 && defined(HAVE_ATTRIBUTE_TARGET_AVX512F)
-#line 2303
-NPY_NO_EXPORT void
-CLONGDOUBLE_conjugate_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_conjugate_CLONGDOUBLE(args, dimensions, steps)) {
-        CLONGDOUBLE_conjugate(args, dimensions, steps, func);
-    }
-}
-
-#line 2303
-NPY_NO_EXPORT void
-CLONGDOUBLE_square_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_square_CLONGDOUBLE(args, dimensions, steps)) {
-        CLONGDOUBLE_square(args, dimensions, steps, func);
-    }
-}
-
-#line 2303
-NPY_NO_EXPORT void
-CLONGDOUBLE_absolute_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_absolute_CLONGDOUBLE(args, dimensions, steps)) {
-        CLONGDOUBLE_absolute(args, dimensions, steps, func);
-    }
-}
-
 #endif
 
 NPY_NO_EXPORT void
@@ -10803,7 +7338,7 @@ CLONGDOUBLE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 2341
+#line 2251
 NPY_NO_EXPORT void
 CLONGDOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10822,7 +7357,7 @@ CLONGDOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *ste
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2341
+#line 2251
 NPY_NO_EXPORT void
 CLONGDOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10842,7 +7377,7 @@ CLONGDOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *ste
 }
 
 
-#line 2364
+#line 2274
 NPY_NO_EXPORT void
 CLONGDOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10863,7 +7398,7 @@ CLONGDOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps,
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
 
-#line 2364
+#line 2274
 NPY_NO_EXPORT void
 CLONGDOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -10900,9 +7435,9 @@ CLONGDOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps,
  *****************************************************************************
  */
 
-#line 2405
+#line 2315
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -10937,7 +7472,7 @@ OBJECT_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
     }
 }
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_OO_O_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -10973,9 +7508,9 @@ OBJECT_OO_O_equal(char **args, npy_intp const *dimensions, npy_intp const *steps
 }
 
 
-#line 2405
+#line 2315
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11010,7 +7545,7 @@ OBJECT_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_OO_O_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11046,9 +7581,9 @@ OBJECT_OO_O_not_equal(char **args, npy_intp const *dimensions, npy_intp const *s
 }
 
 
-#line 2405
+#line 2315
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11083,7 +7618,7 @@ OBJECT_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_OO_O_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11119,9 +7654,9 @@ OBJECT_OO_O_greater(char **args, npy_intp const *dimensions, npy_intp const *ste
 }
 
 
-#line 2405
+#line 2315
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11156,7 +7691,7 @@ OBJECT_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *st
     }
 }
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_OO_O_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11192,9 +7727,9 @@ OBJECT_OO_O_greater_equal(char **args, npy_intp const *dimensions, npy_intp cons
 }
 
 
-#line 2405
+#line 2315
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11229,7 +7764,7 @@ OBJECT_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
 }
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_OO_O_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11265,9 +7800,9 @@ OBJECT_OO_O_less(char **args, npy_intp const *dimensions, npy_intp const *steps,
 }
 
 
-#line 2405
+#line 2315
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
@@ -11302,7 +7837,7 @@ OBJECT_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
-#line 2410
+#line 2320
 NPY_NO_EXPORT void
 OBJECT_OO_O_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     BINARY_LOOP {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops.c.src
index fe5aa937474..e69a27a9ea7 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops.c.src
@@ -13,6 +13,7 @@
 #include "numpy/npy_math.h"
 #include "numpy/halffloat.h"
 #include "lowlevel_strided_loops.h"
+#include "loops_utils.h"
 
 #include "npy_pycompat.h"
 
@@ -31,27 +32,9 @@
  */
 #define PW_BLOCKSIZE    128
 
-
-/*
- * largest simd vector size in bytes numpy supports
- * it is currently a extremely large value as it is only used for memory
- * overlap checks
- */
-#ifndef NPY_MAX_SIMD_SIZE
-#define NPY_MAX_SIMD_SIZE 1024
-#endif
-
 /** Provides the various *_LOOP macros */
 #include "fast_loop_macros.h"
 
-/*
- * include vectorized functions and dispatchers
- * this file is safe to include also for generic builds
- * platform specific instructions are either masked via the proprocessor or
- * runtime detected
- */
-#include "simd.inc"
-
 /******************************************************************************
  **                          GENERIC FLOAT LOOPS                             **
  *****************************************************************************/
@@ -416,98 +399,6 @@ PyUFunc_On_Om(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
  *****************************************************************************
  */
 
-/**begin repeat
- * #kind = logical_and, logical_or#
- * #OP =  &&, ||#
- * #SC =  ==, !=#
- * #and = 1, 0#
- **/
-
-NPY_NO_EXPORT void
-BOOL_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if(IS_BINARY_REDUCE) {
-#ifdef NPY_HAVE_SSE2_INTRINSICS
-        /*
-         * stick with our variant for more reliable performance, only known
-         * platform which outperforms it by ~20% is an i7 with glibc 2.17
-         */
-        if (run_reduce_simd_@kind@_BOOL(args, dimensions, steps)) {
-            return;
-        }
-#else
-        /* for now only use libc on 32-bit/non-x86 */
-        if (steps[1] == 1) {
-            npy_bool * op = (npy_bool *)args[0];
-#if @and@
-            /* np.all(), search for a zero (false) */
-            if (*op) {
-                *op = memchr(args[1], 0, dimensions[0]) == NULL;
-            }
-#else
-            /*
-             * np.any(), search for a non-zero (true) via comparing against
-             * zero blocks, memcmp is faster than memchr on SSE4 machines
-             * with glibc >= 2.12 and memchr can only check for equal 1
-             */
-            static const npy_bool zero[4096]; /* zero by C standard */
-            npy_uintp i, n = dimensions[0];
-
-            for (i = 0; !*op && i < n - (n % sizeof(zero)); i += sizeof(zero)) {
-                *op = memcmp(&args[1][i], zero, sizeof(zero)) != 0;
-            }
-            if (!*op && n - i > 0) {
-                *op = memcmp(&args[1][i], zero, n - i) != 0;
-            }
-#endif
-            return;
-        }
-#endif
-        else {
-            BINARY_REDUCE_LOOP(npy_bool) {
-                const npy_bool in2 = *(npy_bool *)ip2;
-                io1 = io1 @OP@ in2;
-                if (io1 @SC@ 0) {
-                    break;
-                }
-            }
-            *((npy_bool *)iop1) = io1;
-        }
-    }
-    else {
-        if (run_binary_simd_@kind@_BOOL(args, dimensions, steps)) {
-            return;
-        }
-        else {
-            BINARY_LOOP {
-                const npy_bool in1 = *(npy_bool *)ip1;
-                const npy_bool in2 = *(npy_bool *)ip2;
-                *((npy_bool *)op1) = in1 @OP@ in2;
-            }
-        }
-    }
-}
-/**end repeat**/
-
-/**begin repeat
- * #kind = absolute, logical_not#
- * #OP =  !=, ==#
- **/
-NPY_NO_EXPORT void
-BOOL_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (run_unary_simd_@kind@_BOOL(args, dimensions, steps)) {
-        return;
-    }
-    else {
-        UNARY_LOOP {
-            npy_bool in1 = *(npy_bool *)ip1;
-            *((npy_bool *)op1) = in1 @OP@ 0;
-        }
-    }
-}
-/**end repeat**/
-
 NPY_NO_EXPORT void
 BOOL__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -516,24 +407,6 @@ BOOL__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
     }
 }
 
-
-/**begin repeat
- * #kind = isnan, isinf, isfinite#
- * #func = npy_isnan, npy_isinf, npy_isfinite#
- * #val = NPY_FALSE, NPY_FALSE, NPY_TRUE#
- **/
-NPY_NO_EXPORT void
-BOOL_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(npy_bool, npy_bool, (void)in; *out = @val@);
-}
-
-/**end repeat**/
-
 /*
  *****************************************************************************
  **                           INTEGER LOOPS
@@ -552,8 +425,11 @@ BOOL_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void
  */
 
 #define @TYPE@_floor_divide @TYPE@_divide
+#define @TYPE@_floor_divide_indexed @TYPE@_divide_indexed
 #define @TYPE@_fmax @TYPE@_maximum
+#define @TYPE@_fmax_indexed @TYPE@_maximum_indexed
 #define @TYPE@_fmin @TYPE@_minimum
+#define @TYPE@_fmin_indexed @TYPE@_minimum_indexed
 
 NPY_NO_EXPORT void
 @TYPE@__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -562,163 +438,35 @@ NPY_NO_EXPORT void
         *((@type@ *)op1) = 1;
     }
 }
-
-NPY_NO_EXPORT void
-@TYPE@_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = +in);
-}
-
 /**begin repeat1
- * #isa = , _avx2#
- * #CHK = 1, defined(HAVE_ATTRIBUTE_TARGET_AVX2)#
- * #ATTR = , NPY_GCC_TARGET_AVX2#
- */
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_square@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = in * in);
-}
-#endif
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_reciprocal@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = 1.0 / in);
-}
-#endif
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_conjugate@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = in);
-}
-#endif
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_negative@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = -in);
-}
-#endif
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_logical_not@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, npy_bool, *out = !in);
-}
-#endif
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_invert@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = ~in);
-}
-#endif
-
-/**begin repeat2
  * Arithmetic
  * #kind = add, subtract, multiply, bitwise_and, bitwise_or, bitwise_xor#
  * #OP = +, -, *, &, |, ^#
  */
 
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_@kind@@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-        BINARY_REDUCE_LOOP_FAST(@type@, io1 @OP@= in2);
-    }
-    else {
-        BINARY_LOOP_FAST(@type@, @type@, *out = in1 @OP@ in2);
-    }
-}
-#endif
-
-/**end repeat2**/
-
-/*
- * Arithmetic bit shift operations.
- *
- * Intel hardware masks bit shift values, so large shifts wrap around
- * and can produce surprising results. The special handling ensures that
- * behavior is independent of compiler or hardware.
- * TODO: We could implement consistent behavior for negative shifts,
- *       which is undefined in C.
- */
-
-#define INT_left_shift_needs_clear_floatstatus
-#define UINT_left_shift_needs_clear_floatstatus
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-@TYPE@_left_shift@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                  void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(@type@, @type@, *out = npy_lshift@c@(in1, in2));
-
-#ifdef @TYPE@_left_shift_needs_clear_floatstatus
-    // For some reason, our macOS CI sets an "invalid" flag here, but only
-    // for some types.
-    npy_clear_floatstatus_barrier((char*)dimensions);
-#endif
-}
-#endif
-
-#undef INT_left_shift_needs_clear_floatstatus
-#undef UINT_left_shift_needs_clear_floatstatus
-
-#if @CHK@
-NPY_NO_EXPORT
-#ifndef NPY_DO_NOT_OPTIMIZE_@TYPE@_right_shift
-NPY_GCC_OPT_3
-#endif
-void
-@TYPE@_right_shift@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps,
-                   void *NPY_UNUSED(func))
-{
-    BINARY_LOOP_FAST(@type@, @type@, *out = npy_rshift@c@(in1, in2));
-}
-#endif
-
-/**begin repeat2
- * #kind = logical_and, logical_or#
- * #OP = &&, ||#
- */
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_@kind@@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * gcc vectorization of this is not good (PR60575) but manual integer
-     * vectorization is too tedious to be worthwhile
-     */
-    BINARY_LOOP_FAST(@type@, npy_bool, *out = in1 @OP@ in2);
-}
-#endif
-
-/**end repeat2**/
-
-#if @CHK@
-NPY_NO_EXPORT NPY_GCC_OPT_3 @ATTR@ void
-@TYPE@_logical_xor@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    BINARY_LOOP {
-        const int t1 = !!*(@type@ *)ip1;
-        const int t2 = !!*(@type@ *)ip2;
-        *((npy_bool *)op1) = (t1 != t2);
-    }
+NPY_NO_EXPORT NPY_GCC_OPT_3 int
+@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+                      char **args, npy_intp const *dimensions, npy_intp const *steps,
+                      void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @type@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@type@ *)(ip1 + is1 * indx);
+        *indexed = *indexed @OP@ *(@type@ *)value;
+    }
+    return 0;
 }
-#endif
-
 /**end repeat1**/
 
 NPY_NO_EXPORT void
@@ -760,23 +508,6 @@ NPY_NO_EXPORT void
         *((@type@ *) op1) = out;
     }
 }
-
-/**begin repeat1
- * #kind = isnan, isinf, isfinite#
- * #func = npy_isnan, npy_isinf, npy_isfinite#
- * #val = NPY_FALSE, NPY_FALSE, NPY_TRUE#
- **/
-NPY_NO_EXPORT void
-@TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    /*
-     * The (void)in; suppresses an unused variable warning raised by gcc and allows
-     * us to re-use this macro even though we do not depend on in
-     */
-    UNARY_LOOP_FAST(@type@, npy_bool, (void)in; *out = @val@);
-}
-/**end repeat1**/
-
 /**end repeat**/
 
 /**begin repeat
@@ -784,19 +515,6 @@ NPY_NO_EXPORT void
  * #type = npy_byte, npy_short, npy_int, npy_long, npy_longlong#
  * #c    = ,,,l,ll#
  */
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-@TYPE@_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = (in >= 0) ? in : -in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-@TYPE@_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
-}
-
 /**begin repeat1
  * #kind = gcd, lcm#
  **/
@@ -810,7 +528,6 @@ NPY_NO_EXPORT void
     }
 }
 /**end repeat1**/
-
 /**end repeat**/
 
 /**begin repeat
@@ -818,19 +535,6 @@ NPY_NO_EXPORT void
  * #type = npy_ubyte, npy_ushort, npy_uint, npy_ulong, npy_ulonglong#
  * #c    = u,u,u,ul,ull#
  */
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-@TYPE@_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = in);
-}
-
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-@TYPE@_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(@type@, @type@, *out = in > 0 ? 1 : 0);
-}
-
 /**begin repeat1
  * #kind = gcd, lcm#
  **/
@@ -844,9 +548,50 @@ NPY_NO_EXPORT void
     }
 }
 /**end repeat1**/
+/**end repeat**/
+
 
+/*
+ * NOTE: It may be nice to vectorize these, OTOH, these are still faster
+ *       than the cast we used to do.
+ */
+
+/**begin repeat
+ * #kind = equal, not_equal, less, less_equal, greater, greater_equal#
+ * #OP = ==, !=, <, <=, >, >=#
+ */
+NPY_NO_EXPORT void
+LONGLONG_Qq_bool_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        const npy_longlong in2 = *(npy_longlong *)ip2;
+        if (in2 < 0) {
+            *(npy_bool *)op1 = 0 @OP@ in2;
+        }
+        else {
+            *(npy_bool *)op1 = in1 @OP@ (npy_ulonglong)in2;
+        }
+    }
+}
+
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP {
+        const npy_longlong in1 = *(npy_longlong *)ip1;
+        const npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        if (in1 < 0) {
+            *(npy_bool *)op1 = in1 @OP@ 0;
+        }
+        else {
+            *(npy_bool *)op1 = (npy_ulonglong)in1 @OP@ in2;
+        }
+    }
+}
 /**end repeat**/
 
+
 /*
  *****************************************************************************
  **                           DATETIME LOOPS                                **
@@ -923,12 +668,6 @@ NPY_NO_EXPORT void
 }
 
 NPY_NO_EXPORT void
-@TYPE@_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_bool, npy_bool, (void)in; *out = NPY_FALSE);
-}
-
-NPY_NO_EXPORT void
 @TYPE@__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
@@ -987,6 +726,7 @@ NPY_NO_EXPORT void
         }
     }
 }
+
 /**end repeat1**/
 
 /**begin repeat1
@@ -1406,6 +1146,8 @@ TIMEDELTA_mm_qm_divmod(char **args, npy_intp const *dimensions, npy_intp const *
  *  #TYPE = FLOAT, DOUBLE, LONGDOUBLE#
  *  #c = f, , l#
  *  #C = F, , L#
+ *  #fd = 1, 1, 0#
+ *  #VCHK = 1, 1, 0#
  */
 /**begin repeat1
  * #kind = logical_and, logical_or#
@@ -1442,32 +1184,22 @@ NPY_NO_EXPORT void
     }
 }
 
+#if !@fd@
 /**begin repeat1
  * #kind = isnan, isinf, isfinite, signbit#
  * #func = npy_isnan, npy_isinf, npy_isfinite, npy_signbit#
  **/
-
-/**begin repeat2
- * #ISA  = , _avx512_skx#
- * #isa  = simd, avx512_skx#
- * #CHK  = 1, defined(HAVE_ATTRIBUTE_TARGET_AVX512_SKX)#
- **/
-
-#if @CHK@
 NPY_NO_EXPORT void
-@TYPE@_@kind@@ISA@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+@TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_@kind@_@isa@_@TYPE@(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const @type@ in1 = *(@type@ *)ip1;
-            *((npy_bool *)op1) = @func@(in1) != 0;
-        }
+    UNARY_LOOP {
+        const @type@ in1 = *(@type@ *)ip1;
+        *((npy_bool *)op1) = @func@(in1) != 0;
     }
     npy_clear_floatstatus_barrier((char*)dimensions);
 }
-#endif
-/**end repeat2**/
 /**end repeat1**/
+#endif
 
 NPY_NO_EXPORT void
 @TYPE@_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
@@ -1508,6 +1240,30 @@ NPY_NO_EXPORT void
     }
 }
 
+NPY_NO_EXPORT int
+@TYPE@_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @type@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@type@ *)(ip1 + is1 * indx);
+        *indexed = npy_floor_divide@c@(*indexed, *(@type@ *)value);
+    }
+    return 0;
+}
+
 NPY_NO_EXPORT void
 @TYPE@_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -1546,17 +1302,6 @@ NPY_NO_EXPORT void
 }
 
 NPY_NO_EXPORT void
-@TYPE@_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_unary_simd_negative_@TYPE@(args, dimensions, steps)) {
-        UNARY_LOOP {
-            const @type@ in1 = *(@type@ *)ip1;
-            *((@type@ *)op1) = -in1;
-        }
-    }
-}
-
-NPY_NO_EXPORT void
 @TYPE@_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
     UNARY_LOOP {
@@ -1653,6 +1398,31 @@ LONGDOUBLE_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps
         }
     }
 }
+
+NPY_NO_EXPORT int
+LONGDOUBLE_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = *indexed @OP@ *(npy_longdouble *)value;
+    }
+    return 0;
+}
+
 /**end repeat**/
 
 /**begin repeat
@@ -1758,6 +1528,31 @@ HALF_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void
         }
     }
 }
+
+NPY_NO_EXPORT int
+HALF_@kind@_indexed(void *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        const float v = npy_half_to_float(*(npy_half *)value);
+        *indexed = npy_float_to_half(npy_half_to_float(*indexed) @OP@ v);
+    }
+    return 0;
+}
 /**end repeat**/
 
 #define _HALF_LOGICAL_AND(a,b) (!npy_half_iszero(a) && !npy_half_iszero(b))
@@ -1859,6 +1654,32 @@ HALF_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void
     }
     /* npy_half_isnan will never set floatstatus_invalid, so do not clear */
 }
+
+NPY_NO_EXPORT int
+HALF_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        npy_half v = *(npy_half *)value;
+        *indexed = (@OP@(*indexed, v) || npy_half_isnan(*indexed)) ? *indexed : v;
+    }
+    return 0;
+}
+
 /**end repeat**/
 
 /**begin repeat
@@ -1874,8 +1695,34 @@ HALF_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void
         const npy_half in2 = *(npy_half *)ip2;
         *((npy_half *)op1) = (@OP@(in1, in2) || npy_half_isnan(in2)) ? in1 : in2;
     }
-    /* npy_half_isnan will never set floatstatus_invalid, so do not clear */
+    /* no need to clear floatstatus_invalid */
+}
+
+NPY_NO_EXPORT int
+HALF_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for (i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        npy_half v = *(npy_half *)value;
+        *indexed = (@OP@(*indexed, v) || npy_half_isnan(v)) ? *indexed: v;
+    }
+    return 0;
 }
+
 /**end repeat**/
 
 NPY_NO_EXPORT void
@@ -1894,6 +1741,32 @@ HALF_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps
     }
 }
 
+NPY_NO_EXPORT int
+HALF_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context),
+         char **args, npy_intp const *dimensions, npy_intp const *steps,
+         void *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_half *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_half *)(ip1 + is1 * indx);
+        float v = npy_half_to_float(*(npy_half *)value);
+        float div = npy_floor_dividef(npy_half_to_float(*indexed), v);
+        *indexed = npy_float_to_half(div);
+    }
+    return 0;
+}
+
 NPY_NO_EXPORT void
 HALF_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -1953,12 +1826,6 @@ HALF_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, v
     }
 }
 
-NPY_NO_EXPORT NPY_GCC_OPT_3 void
-HALF_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    UNARY_LOOP_FAST(npy_half, npy_half, *out = in&0x7fffu);
-}
-
 NPY_NO_EXPORT void
 HALF_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
@@ -2110,6 +1977,31 @@ NPY_NO_EXPORT void
         }
     }
 }
+
+NPY_NO_EXPORT int @TYPE@_@kind@_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @ftype@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@ftype@ *)(ip1 + is1 * indx);
+        const @ftype@ b_r = ((@ftype@ *)value)[0];
+        const @ftype@ b_i = ((@ftype@ *)value)[1];
+        indexed[0] @OP@= b_r;
+        indexed[1] @OP@= b_i;
+    }
+    return 0;
+}
 /**end repeat1**/
 
 NPY_NO_EXPORT void
@@ -2124,6 +2016,33 @@ NPY_NO_EXPORT void
         ((@ftype@ *)op1)[1] = in1r*in2i + in1i*in2r;
     }
 }
+
+NPY_NO_EXPORT int @TYPE@_multiply_indexed
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @ftype@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@ftype@ *)(ip1 + is1 * indx);
+        const @ftype@ a_r = indexed[0];
+        const @ftype@ a_i = indexed[1];
+        const @ftype@ b_r = ((@ftype@ *)value)[0];
+        const @ftype@ b_i = ((@ftype@ *)value)[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    }
+    return 0;
+}
 #endif // !SIMD
 
 NPY_NO_EXPORT void
@@ -2235,6 +2154,8 @@ NPY_NO_EXPORT void
 }
 /**end repeat1**/
 
+#if !@SIMD@
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 @TYPE@_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2245,6 +2166,7 @@ NPY_NO_EXPORT void
         ((@ftype@ *)op1)[1] = in1r*in1i + in1i*in1r;
     }
 }
+#endif
 
 NPY_NO_EXPORT void
 @TYPE@_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
@@ -2275,6 +2197,8 @@ NPY_NO_EXPORT void
     }
 }
 
+#if !@SIMD@
+// CFLOAT & CDOUBLE defined by 'loops_arithm_fp.dispatch.c.src'
 NPY_NO_EXPORT void
 @TYPE@_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)) {
     UNARY_LOOP {
@@ -2294,20 +2218,6 @@ NPY_NO_EXPORT void
         *((@ftype@ *)op1) = npy_hypot@c@(in1r, in1i);
     }
 }
-
-#if @SIMD@ && defined(HAVE_ATTRIBUTE_TARGET_AVX512F)
-/**begin repeat1
- * arithmetic
- * #kind = conjugate, square, absolute#
- */
-NPY_NO_EXPORT void
-@TYPE@_@kind@_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *func)
-{
-    if (!run_unary_avx512f_@kind@_@TYPE@(args, dimensions, steps)) {
-        @TYPE@_@kind@(args, dimensions, steps, func);
-    }
-}
-/**end repeat1**/
 #endif
 
 NPY_NO_EXPORT void
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops.h
index 11f3dc253c1..6df7c4c634e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops.h
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops.h
@@ -20,86 +20,115 @@
     #define NPY_NO_EXPORT NPY_VISIBILITY_HIDDEN
 #endif
 
-#define BOOL_invert BOOL_logical_not
-#define BOOL_add BOOL_logical_or
-#define BOOL_bitwise_and BOOL_logical_and
-#define BOOL_bitwise_or BOOL_logical_or
-#define BOOL_logical_xor BOOL_not_equal
-#define BOOL_bitwise_xor BOOL_logical_xor
-#define BOOL_multiply BOOL_logical_and
-#define BOOL_maximum BOOL_logical_or
-#define BOOL_minimum BOOL_logical_and
-#define BOOL_fmax BOOL_maximum
-#define BOOL_fmin BOOL_minimum
-
 /*
  *****************************************************************************
  **                             BOOLEAN LOOPS                               **
  *****************************************************************************
  */
 
+/*
+ * Following functions are defined by umath generator
+ * to enable runtime dispatching without the need
+ * to redefine them within dsipatch-able sources.
+ */
+// #define BOOL_invert BOOL_logical_not
+// #define BOOL_add BOOL_logical_or
+// #define BOOL_bitwise_and BOOL_logical_and
+// #define BOOL_bitwise_or BOOL_logical_or
+// #define BOOL_logical_xor BOOL_not_equal
+// #define BOOL_bitwise_xor BOOL_logical_xor
+// #define BOOL_multiply BOOL_logical_and
+// #define BOOL_maximum BOOL_logical_or
+// #define BOOL_minimum BOOL_logical_and
+// #define BOOL_fmax BOOL_maximum
+// #define BOOL_fmin BOOL_minimum
+
+typedef struct PyArrayMethod_Context_tag PyArrayMethod_Context;
+typedef struct NpyAuxData_tag NpyAuxData;
+
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_comparison.dispatch.h"
 #endif
 
-#line 38
+#line 46
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 38
+#line 46
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 38
+#line 46
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 38
+#line 46
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 38
+#line 46
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 38
+#line 46
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 45
-NPY_NO_EXPORT void
-BOOL_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_logical.dispatch.h"
+#endif
 
-#line 45
-NPY_NO_EXPORT void
-BOOL_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 57
+ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_logical_and,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 45
-NPY_NO_EXPORT void
-BOOL_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 57
+ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_logical_or,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 45
-NPY_NO_EXPORT void
-BOOL_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 57
+ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_logical_not,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 57
+ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_absolute,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
 NPY_NO_EXPORT void
 BOOL__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 55
+#line 67
 NPY_NO_EXPORT void
 BOOL_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 55
+#line 67
 NPY_NO_EXPORT void
 BOOL_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 55
+#line 67
 NPY_NO_EXPORT void
 BOOL_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
+#line 78
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+#line 78
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+#line 78
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+
 /*
  *****************************************************************************
  **                           INTEGER LOOPS
@@ -110,187 +139,237 @@ BOOL_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
     #include "loops_arithmetic.dispatch.h"
 #endif
 
-#line 73
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+UBYTE_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+USHORT_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+UINT_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+ULONG_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+ULONGLONG_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+BYTE_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+SHORT_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+INT_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 73
+NPY_NO_EXPORT int
+LONG_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
+#line 96
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+NPY_NO_EXPORT int
+LONGLONG_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
+
 
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_modulo.dispatch.h"
 #endif
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 85
-#line 88
+#line 113
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_divmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_fmod,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 88
+#line 116
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_remainder,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
@@ -300,1287 +379,1599 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_remainder,
     #include "loops_comparison.dispatch.h"
 #endif
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 101
-#line 104
+#line 129
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_not_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_greater,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_greater_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_less,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 104
+#line 132
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_less_equal,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
 
-#line 112
 
-#line 118
-
-#define BYTE_floor_divide BYTE_divide
-#define BYTE_fmax BYTE_maximum
-#define BYTE_fmin BYTE_minimum
-
-NPY_NO_EXPORT void
-BYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 132
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-BYTE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-BYTE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 132
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-BYTE_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-BYTE_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-BYTE_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 179
-NPY_NO_EXPORT void
-BYTE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 179
-NPY_NO_EXPORT void
-BYTE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-NPY_NO_EXPORT void
-BYTE_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-BYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 201
-NPY_NO_EXPORT void
-BYTE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 201
-NPY_NO_EXPORT void
-BYTE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 201
-NPY_NO_EXPORT void
-BYTE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 118
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#define UBYTE_floor_divide UBYTE_divide
-#define UBYTE_fmax UBYTE_maximum
-#define UBYTE_fmin UBYTE_minimum
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 132
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-UBYTE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-UBYTE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-UBYTE_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-UBYTE_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-UBYTE_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 179
-NPY_NO_EXPORT void
-UBYTE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 179
-NPY_NO_EXPORT void
-UBYTE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-UBYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 201
-NPY_NO_EXPORT void
-UBYTE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 201
-NPY_NO_EXPORT void
-UBYTE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 201
-NPY_NO_EXPORT void
-UBYTE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 112
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 118
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#define SHORT_floor_divide SHORT_divide
-#define SHORT_fmax SHORT_maximum
-#define SHORT_fmin SHORT_minimum
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 132
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-SHORT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-SHORT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 132
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-NPY_NO_EXPORT void
-SHORT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 145
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_invert,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_logical_not,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_conjugate,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_reciprocal,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_square,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_add,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_subtract,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_multiply,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_bitwise_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_bitwise_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_bitwise_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_left_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_right_shift,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_logical_and,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 157
-NPY_NO_EXPORT void
-SHORT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_logical_or,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_logical_xor,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_isnan,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-SHORT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_isfinite,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 166
-NPY_NO_EXPORT void
-SHORT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+#line 152
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_sign,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-NPY_NO_EXPORT void
-SHORT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 160
+#line 165
+#define BYTE_floor_divide BYTE_divide
+#define BYTE_floor_divide_indexed BYTE_divide_indexed
+#define BYTE_fmax BYTE_maximum
+#define BYTE_fmin BYTE_minimum
+#define BYTE_fmax_indexed BYTE_maximum_indexed
+#define BYTE_fmin_indexed BYTE_minimum_indexed
 
-#line 179
 NPY_NO_EXPORT void
-SHORT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+BYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 179
 NPY_NO_EXPORT void
-SHORT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+BYTE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+BYTE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-SHORT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-SHORT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+BYTE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-SHORT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-SHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+BYTE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-SHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-SHORT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+BYTE_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-SHORT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-SHORT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+BYTE_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
+#line 184
+NPY_NO_EXPORT int
+BYTE_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 118
 
-#define USHORT_floor_divide USHORT_divide
-#define USHORT_fmax USHORT_maximum
-#define USHORT_fmin USHORT_minimum
+#line 184
+NPY_NO_EXPORT int
+BYTE_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-NPY_NO_EXPORT void
-USHORT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+BYTE_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 132
 
-NPY_NO_EXPORT void
-USHORT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
+#line 193
 NPY_NO_EXPORT void
-USHORT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+BYTE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+NPY_NO_EXPORT int
+BYTE_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 193
 NPY_NO_EXPORT void
-USHORT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+BYTE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+NPY_NO_EXPORT int
+BYTE_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-USHORT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+BYTE_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+BYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+BYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-#line 157
-NPY_NO_EXPORT void
-USHORT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 165
+#define UBYTE_floor_divide UBYTE_divide
+#define UBYTE_floor_divide_indexed UBYTE_divide_indexed
+#define UBYTE_fmax UBYTE_maximum
+#define UBYTE_fmin UBYTE_minimum
+#define UBYTE_fmax_indexed UBYTE_maximum_indexed
+#define UBYTE_fmin_indexed UBYTE_minimum_indexed
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+UBYTE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+UBYTE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+UBYTE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-USHORT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+UBYTE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-USHORT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+UBYTE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-USHORT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+UBYTE_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
+#line 184
+NPY_NO_EXPORT int
+UBYTE_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
+#line 184
+NPY_NO_EXPORT int
+UBYTE_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-NPY_NO_EXPORT void
-USHORT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 184
+NPY_NO_EXPORT int
+UBYTE_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+UBYTE_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
+#line 193
 NPY_NO_EXPORT void
-USHORT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+UBYTE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-USHORT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+NPY_NO_EXPORT int
+UBYTE_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 157
+#line 193
 NPY_NO_EXPORT void
-USHORT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+UBYTE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+UBYTE_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-USHORT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+UBYTE_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+UBYTE_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
 NPY_NO_EXPORT void
-USHORT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+UBYTE_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-#line 157
-NPY_NO_EXPORT void
-USHORT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
+#line 160
+#line 165
+#define SHORT_floor_divide SHORT_divide
+#define SHORT_floor_divide_indexed SHORT_divide_indexed
+#define SHORT_fmax SHORT_maximum
+#define SHORT_fmin SHORT_minimum
+#define SHORT_fmax_indexed SHORT_maximum_indexed
+#define SHORT_fmin_indexed SHORT_minimum_indexed
 
-#line 166
 NPY_NO_EXPORT void
-USHORT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+SHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 166
 NPY_NO_EXPORT void
-USHORT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+SHORT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+SHORT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-NPY_NO_EXPORT void
-USHORT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+SHORT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 179
-NPY_NO_EXPORT void
-USHORT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+SHORT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 179
-NPY_NO_EXPORT void
-USHORT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+SHORT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+SHORT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+SHORT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-USHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-USHORT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+SHORT_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-USHORT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-USHORT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+SHORT_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
 
+#line 193
+NPY_NO_EXPORT void
+SHORT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+SHORT_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 112
-
-#line 118
-
-#define INT_floor_divide INT_divide
-#define INT_fmax INT_maximum
-#define INT_fmin INT_minimum
 
+#line 193
 NPY_NO_EXPORT void
-INT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+SHORT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-INT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+NPY_NO_EXPORT int
+SHORT_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 132
 
-NPY_NO_EXPORT void
-INT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
-INT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+SHORT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-INT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+SHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-INT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+SHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-NPY_NO_EXPORT void
-INT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-INT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 165
+#define USHORT_floor_divide USHORT_divide
+#define USHORT_floor_divide_indexed USHORT_divide_indexed
+#define USHORT_fmax USHORT_maximum
+#define USHORT_fmin USHORT_minimum
+#define USHORT_fmax_indexed USHORT_maximum_indexed
+#define USHORT_fmin_indexed USHORT_minimum_indexed
 
-#line 157
 NPY_NO_EXPORT void
-INT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+USHORT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 157
 NPY_NO_EXPORT void
-INT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+USHORT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+USHORT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+USHORT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+USHORT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+USHORT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+USHORT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+USHORT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+USHORT_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-INT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+USHORT_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
+
+#line 193
 NPY_NO_EXPORT void
-INT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+USHORT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+USHORT_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
+#line 193
 NPY_NO_EXPORT void
-INT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+USHORT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
+NPY_NO_EXPORT int
+USHORT_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-INT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-NPY_NO_EXPORT void
-INT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
-INT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+USHORT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-INT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+USHORT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-INT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+USHORT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-NPY_NO_EXPORT void
-INT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 160
+#line 165
+#define INT_floor_divide INT_divide
+#define INT_floor_divide_indexed INT_divide_indexed
+#define INT_fmax INT_maximum
+#define INT_fmin INT_minimum
+#define INT_fmax_indexed INT_maximum_indexed
+#define INT_fmin_indexed INT_minimum_indexed
 
-#line 157
 NPY_NO_EXPORT void
-INT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+INT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 157
 NPY_NO_EXPORT void
-INT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+INT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+INT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+INT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+INT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+INT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+INT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-INT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+INT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-INT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+INT_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-INT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+INT_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
 
+#line 193
 NPY_NO_EXPORT void
-INT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+INT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+INT_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 179
-NPY_NO_EXPORT void
-INT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 179
+#line 193
 NPY_NO_EXPORT void
 INT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+INT_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-INT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-INT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-INT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+INT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 INT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 INT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-#line 201
-NPY_NO_EXPORT void
-INT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-INT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-INT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-#line 118
 
+#line 165
 #define UINT_floor_divide UINT_divide
+#define UINT_floor_divide_indexed UINT_divide_indexed
 #define UINT_fmax UINT_maximum
 #define UINT_fmin UINT_minimum
+#define UINT_fmax_indexed UINT_maximum_indexed
+#define UINT_fmin_indexed UINT_minimum_indexed
 
 NPY_NO_EXPORT void
 UINT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
@@ -1588,204 +1979,92 @@ UINT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
 NPY_NO_EXPORT void
 UINT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
-
-NPY_NO_EXPORT void
-UINT_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-UINT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-UINT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-UINT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-UINT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-UINT_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 157
-NPY_NO_EXPORT void
-UINT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+#line 184
+NPY_NO_EXPORT int
+UINT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-UINT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+UINT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-UINT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+UINT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
+#line 184
+NPY_NO_EXPORT int
+UINT_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
+#line 184
+NPY_NO_EXPORT int
+UINT_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-NPY_NO_EXPORT void
-UINT_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 184
+NPY_NO_EXPORT int
+UINT_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+UINT_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+UINT_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-UINT_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
+#line 193
 NPY_NO_EXPORT void
-UINT_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-UINT_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-#line 166
-NPY_NO_EXPORT void
-UINT_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 166
-NPY_NO_EXPORT void
-UINT_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-NPY_NO_EXPORT void
-UINT_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+UINT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+UINT_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 179
-NPY_NO_EXPORT void
-UINT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 179
+#line 193
 NPY_NO_EXPORT void
 UINT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+UINT_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-UINT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-UINT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+UINT_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 UINT_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 UINT_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-UINT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-UINT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-UINT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
+/**end repeat2**/
 
 
-#line 112
-
-#line 118
 
+#line 160
+#line 165
 #define LONG_floor_divide LONG_divide
+#define LONG_floor_divide_indexed LONG_divide_indexed
 #define LONG_fmax LONG_maximum
 #define LONG_fmin LONG_minimum
+#define LONG_fmax_indexed LONG_maximum_indexed
+#define LONG_fmin_indexed LONG_minimum_indexed
 
 NPY_NO_EXPORT void
 LONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
@@ -1793,200 +2072,90 @@ LONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
 NPY_NO_EXPORT void
 LONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
-
-NPY_NO_EXPORT void
-LONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-LONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-LONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 157
-NPY_NO_EXPORT void
-LONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+LONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
+#line 184
+NPY_NO_EXPORT int
+LONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 166
-NPY_NO_EXPORT void
-LONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+LONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 166
-NPY_NO_EXPORT void
-LONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+LONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-NPY_NO_EXPORT void
-LONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+LONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 132
 
-NPY_NO_EXPORT void
-LONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 184
+NPY_NO_EXPORT int
+LONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-NPY_NO_EXPORT void
-LONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+LONG_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 157
-NPY_NO_EXPORT void
-LONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-#line 166
-NPY_NO_EXPORT void
-LONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 166
-NPY_NO_EXPORT void
-LONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+LONG_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
 
+#line 193
 NPY_NO_EXPORT void
-LONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONG_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 179
-NPY_NO_EXPORT void
-LONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 179
+#line 193
 NPY_NO_EXPORT void
 LONG_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONG_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-LONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 LONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 LONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-#line 201
-NPY_NO_EXPORT void
-LONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-LONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-LONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-#line 118
 
+#line 165
 #define ULONG_floor_divide ULONG_divide
+#define ULONG_floor_divide_indexed ULONG_divide_indexed
 #define ULONG_fmax ULONG_maximum
 #define ULONG_fmin ULONG_minimum
+#define ULONG_fmax_indexed ULONG_maximum_indexed
+#define ULONG_fmin_indexed ULONG_minimum_indexed
 
 NPY_NO_EXPORT void
 ULONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
@@ -1994,204 +2163,92 @@ ULONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps,
 NPY_NO_EXPORT void
 ULONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
-
-NPY_NO_EXPORT void
-ULONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-ULONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-ULONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-ULONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-ULONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-ULONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
+#line 184
+NPY_NO_EXPORT int
+ULONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 166
-NPY_NO_EXPORT void
-ULONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+ULONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 166
-NPY_NO_EXPORT void
-ULONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+ULONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-NPY_NO_EXPORT void
-ULONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+ULONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 132
 
-NPY_NO_EXPORT void
-ULONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-ULONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-ULONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-ULONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-ULONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-ULONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+ULONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-ULONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-ULONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+ULONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-ULONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+ULONG_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-ULONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+ULONG_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
 
+#line 193
 NPY_NO_EXPORT void
-ULONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+ULONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+ULONG_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 179
-NPY_NO_EXPORT void
-ULONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 179
+#line 193
 NPY_NO_EXPORT void
 ULONG_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+ULONG_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-ULONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+ULONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 ULONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 ULONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-#line 201
-NPY_NO_EXPORT void
-ULONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-ULONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-ULONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-
-
-#line 112
-
-#line 118
 
+#line 160
+#line 165
 #define LONGLONG_floor_divide LONGLONG_divide
+#define LONGLONG_floor_divide_indexed LONGLONG_divide_indexed
 #define LONGLONG_fmax LONGLONG_maximum
 #define LONGLONG_fmin LONGLONG_minimum
+#define LONGLONG_fmax_indexed LONGLONG_maximum_indexed
+#define LONGLONG_fmin_indexed LONGLONG_minimum_indexed
 
 NPY_NO_EXPORT void
 LONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
@@ -2199,200 +2256,90 @@ LONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *ste
 NPY_NO_EXPORT void
 LONGLONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
-
-NPY_NO_EXPORT void
-LONGLONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-LONGLONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-LONGLONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONGLONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONGLONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONGLONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-#line 166
-NPY_NO_EXPORT void
-LONGLONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 166
-NPY_NO_EXPORT void
-LONGLONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-NPY_NO_EXPORT void
-LONGLONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 132
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONGLONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-LONGLONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-LONGLONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONGLONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONGLONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-LONGLONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-LONGLONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-LONGLONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 166
-NPY_NO_EXPORT void
-LONGLONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+LONGLONG_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
 
+#line 193
 NPY_NO_EXPORT void
-LONGLONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONGLONG_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 179
-NPY_NO_EXPORT void
-LONGLONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 179
+#line 193
 NPY_NO_EXPORT void
 LONGLONG_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONGLONG_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONGLONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-LONGLONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-LONGLONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 LONGLONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 LONGLONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-#line 201
-NPY_NO_EXPORT void
-LONGLONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-LONGLONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 201
-NPY_NO_EXPORT void
-LONGLONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-#line 118
 
+#line 165
 #define ULONGLONG_floor_divide ULONGLONG_divide
+#define ULONGLONG_floor_divide_indexed ULONGLONG_divide_indexed
 #define ULONGLONG_fmax ULONGLONG_maximum
 #define ULONGLONG_fmin ULONGLONG_minimum
+#define ULONGLONG_fmax_indexed ULONGLONG_maximum_indexed
+#define ULONGLONG_fmin_indexed ULONGLONG_minimum_indexed
 
 NPY_NO_EXPORT void
 ULONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
@@ -2400,194 +2347,190 @@ ULONGLONG__ones_like(char **args, npy_intp const *dimensions, npy_intp const *st
 NPY_NO_EXPORT void
 ULONGLONG_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 132
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_square(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-NPY_NO_EXPORT void
-ULONGLONG_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_invert(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_bitwise_and_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-ULONGLONG_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_bitwise_or_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-ULONGLONG_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_bitwise_xor_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-ULONGLONG_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_left_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-ULONGLONG_bitwise_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 184
+NPY_NO_EXPORT int
+ULONGLONG_right_shift_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-ULONGLONG_bitwise_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
+#line 193
 NPY_NO_EXPORT void
-ULONGLONG_bitwise_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+ULONGLONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 157
-NPY_NO_EXPORT void
-ULONGLONG_left_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+NPY_NO_EXPORT int
+ULONGLONG_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
 
-#line 157
+#line 193
 NPY_NO_EXPORT void
-ULONGLONG_right_shift(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+ULONGLONG_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+ULONGLONG_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 166
-NPY_NO_EXPORT void
-ULONGLONG_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 166
 NPY_NO_EXPORT void
-ULONGLONG_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+ULONGLONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT void
+ULONGLONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-ULONGLONG_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+ULONGLONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
-#line 132
 
-NPY_NO_EXPORT void
-ULONGLONG_square_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-NPY_NO_EXPORT void
-ULONGLONG_reciprocal_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
+#line 218
 NPY_NO_EXPORT void
-ULONGLONG_conjugate_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+LONGLONG_Qq_bool_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 NPY_NO_EXPORT void
-ULONGLONG_negative_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_qQ_bool_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_logical_not_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 218
 NPY_NO_EXPORT void
-ULONGLONG_invert_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 157
+LONGLONG_Qq_bool_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 NPY_NO_EXPORT void
-ULONGLONG_add_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_qQ_bool_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
+#line 218
 NPY_NO_EXPORT void
-ULONGLONG_subtract_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
+LONGLONG_Qq_bool_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 NPY_NO_EXPORT void
-ULONGLONG_multiply_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_qQ_bool_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
+#line 218
 NPY_NO_EXPORT void
-ULONGLONG_bitwise_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
+LONGLONG_Qq_bool_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 NPY_NO_EXPORT void
-ULONGLONG_bitwise_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_qQ_bool_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
+#line 218
 NPY_NO_EXPORT void
-ULONGLONG_bitwise_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 157
+LONGLONG_Qq_bool_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 NPY_NO_EXPORT void
-ULONGLONG_left_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_qQ_bool_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 157
+#line 218
+NPY_NO_EXPORT void
+LONGLONG_Qq_bool_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 NPY_NO_EXPORT void
-ULONGLONG_right_shift_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+LONGLONG_qQ_bool_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
 
-#line 166
-NPY_NO_EXPORT void
-ULONGLONG_logical_and_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary.dispatch.h"
+#endif
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 166
-NPY_NO_EXPORT void
-ULONGLONG_logical_or_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-NPY_NO_EXPORT void
-ULONGLONG_logical_xor_avx2(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 179
-NPY_NO_EXPORT void
-ULONGLONG_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 179
-NPY_NO_EXPORT void
-ULONGLONG_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-NPY_NO_EXPORT void
-ULONGLONG_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-NPY_NO_EXPORT void
-ULONGLONG_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-ULONGLONG_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-NPY_NO_EXPORT void
-ULONGLONG_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-ULONGLONG_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 201
-NPY_NO_EXPORT void
-ULONGLONG_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 201
-NPY_NO_EXPORT void
-ULONGLONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
+#line 233
+#line 236
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
 
@@ -2600,126 +2543,220 @@ ULONGLONG_isfinite(char **args, npy_intp const *dimensions, npy_intp const *step
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_unary_fp.dispatch.h"
 #endif
-#line 220
-#line 223
+#line 253
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_rint,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_floor,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_trunc,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_ceil,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_sqrt,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_absolute,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_square,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_reciprocal,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 220
-#line 223
+#line 253
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_rint,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_floor,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_trunc,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_ceil,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_sqrt,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_absolute,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_square,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 223
+#line 256
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_reciprocal,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
 
 #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary_fp_le.dispatch.h"
+#endif
+#line 267
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_isnan,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_isinf,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_isfinite,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_signbit,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+#line 267
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_isnan,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_isinf,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_isfinite,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 270
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_signbit,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary.dispatch.h"
+#endif
+#line 281
+#line 284
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+#line 281
+#line 284
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+#line 281
+#line 284
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGDOUBLE_negative,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+
+#ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_arithm_fp.dispatch.h"
 #endif
-#line 234
-#line 239
+#line 295
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_add,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 239
+NPY_NO_EXPORT int
+FLOAT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_subtract,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 239
+NPY_NO_EXPORT int
+FLOAT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_multiply,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 239
+NPY_NO_EXPORT int
+FLOAT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+NPY_NO_EXPORT int
+FLOAT_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
 
-#line 234
-#line 239
+#line 295
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_add,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 239
+NPY_NO_EXPORT int
+DOUBLE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_subtract,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 239
+NPY_NO_EXPORT int
+DOUBLE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_multiply,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
-#line 239
+NPY_NO_EXPORT int
+DOUBLE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 299
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
+NPY_NO_EXPORT int
+DOUBLE_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
 
 
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_hyperbolic.dispatch.h"
 #endif
-#line 250
-#line 253
+#line 314
+#line 317
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_tanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 250
-#line 253
+#line 314
+#line 317
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_tanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
@@ -2730,196 +2767,196 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_tanh,
     #include "loops_umath_fp.dispatch.h"
 #endif
 
-#line 266
-#line 269
+#line 330
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_tanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_exp2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_log2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_log10,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_expm1,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_log1p,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_cbrt,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_tan,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_arcsin,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_arccos,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_arctan,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_sinh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_cosh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_arcsinh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_arccosh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_arctanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
 
-#line 266
-#line 269
+#line 330
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_tanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_exp2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_log2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_log10,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_expm1,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_log1p,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_cbrt,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_tan,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arcsin,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arccos,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arctan,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_sinh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_cosh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arcsinh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arccosh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 269
+#line 333
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arctanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
@@ -2927,162 +2964,149 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arctanh,
 
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_sin,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_cos,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_tan,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_exp,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_exp2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_log,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_log2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_log10,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_expm1,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_log1p,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_cbrt,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_arcsin,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_arccos,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_arctan,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_sinh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_cosh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_tanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_arcsinh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_arccosh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 279
+#line 343
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_arctanh,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
 
-#line 288
-
-NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_sin,
-    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
-
-
-#line 288
-
-NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_cos,
-    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
-
-
-
-#line 297
-#line 300
+#line 352
+#line 355
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_power,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 300
+#line 355
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_arctan2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
 
-#line 297
-#line 300
+#line 352
+#line 355
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_power,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 
 
-#line 300
+#line 355
 
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arctan2,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
@@ -3090,87 +3114,77 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_arctan2,
 
 
 
-#line 310
-#line 313
-NPY_NO_EXPORT void
-FLOAT_maximum_avx512f(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 313
-NPY_NO_EXPORT void
-FLOAT_minimum_avx512f(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-#line 310
-#line 313
-NPY_NO_EXPORT void
-DOUBLE_maximum_avx512f(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 313
-NPY_NO_EXPORT void
-DOUBLE_minimum_avx512f(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-
-
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_trigonometric.dispatch.h"
 #endif
-#line 325
+
+#line 369
+#line 372
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_sin, (
     char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 325
+#line 372
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_cos, (
     char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
 
+#line 369
+#line 372
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_sin, (
+    char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
+))
+
+#line 372
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_cos, (
+    char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
+))
+
+
+
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_exponent_log.dispatch.h"
 #endif
-#line 336
-#line 339
+#line 384
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_exp, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 339
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_log, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 339
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_frexp, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 339
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_ldexp, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
 
-#line 336
-#line 339
+#line 384
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_exp, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 339
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_log, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 339
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_frexp, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 339
+#line 387
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_ldexp, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
@@ -3180,95 +3194,112 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_ldexp, (
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_comparison.dispatch.h"
 #endif
-#line 351
-#line 354
+#line 399
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_not_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_less, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_less_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_greater, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_greater_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
 
-#line 351
-#line 354
+#line 399
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_not_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_less, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_less_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_greater, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
-#line 354
+#line 402
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_greater_equal, (
   char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
 
 
 
-#line 366
+#line 416
 
-#line 372
+#line 422
 NPY_NO_EXPORT void
 HALF_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+HALF_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 HALF_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+HALF_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 HALF_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+HALF_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 HALF_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+HALF_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+/**end repeat1**/
 
-#line 380
+#line 435
 NPY_NO_EXPORT void
 HALF_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 380
+#line 435
 NPY_NO_EXPORT void
 HALF_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3279,105 +3310,103 @@ HALF_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps,
 NPY_NO_EXPORT void
 HALF_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 HALF_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-HALF_isnan_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 HALF_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-HALF_isinf_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 HALF_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-HALF_isfinite_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 HALF_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-HALF_signbit_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !0
 NPY_NO_EXPORT void
 HALF_copysign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-HALF_copysign_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !0
 NPY_NO_EXPORT void
 HALF_nextafter(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-HALF_nextafter_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !0
 NPY_NO_EXPORT void
 HALF_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 398
-NPY_NO_EXPORT void
-HALF_spacing_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+#endif
+/**end repeat2**/
 
 
-#line 407
+#line 461
 NPY_NO_EXPORT void
 HALF_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 407
+NPY_NO_EXPORT int
+HALF_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 461
 NPY_NO_EXPORT void
 HALF_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+HALF_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 
-#line 415
+
+#line 472
 NPY_NO_EXPORT void
 HALF_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 415
+NPY_NO_EXPORT int
+HALF_fmax_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 472
 NPY_NO_EXPORT void
 HALF_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+HALF_fmin_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
 
 NPY_NO_EXPORT void
 HALF_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+HALF_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 NPY_NO_EXPORT void
 HALF_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3399,8 +3428,10 @@ HALF_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps, v
 NPY_NO_EXPORT void
 HALF_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#if 1
 NPY_NO_EXPORT void
 HALF_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
 
 NPY_NO_EXPORT void
 HALF_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -3420,30 +3451,47 @@ HALF_ldexp(char **args, npy_intp const *dimensions, npy_intp const *steps, void
 NPY_NO_EXPORT void
 HALF_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 366
+#line 416
 
-#line 372
+#line 422
 NPY_NO_EXPORT void
 FLOAT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+FLOAT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 FLOAT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+FLOAT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 FLOAT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+FLOAT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 FLOAT_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+FLOAT_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 380
+
+/**end repeat1**/
+
+#line 435
 NPY_NO_EXPORT void
 FLOAT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 380
+#line 435
 NPY_NO_EXPORT void
 FLOAT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3454,105 +3502,103 @@ FLOAT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *steps
 NPY_NO_EXPORT void
 FLOAT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 FLOAT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-FLOAT_isnan_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 FLOAT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-FLOAT_isinf_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 FLOAT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-FLOAT_isfinite_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 FLOAT_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-FLOAT_signbit_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !0
 NPY_NO_EXPORT void
 FLOAT_copysign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-FLOAT_copysign_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !0
 NPY_NO_EXPORT void
 FLOAT_nextafter(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-FLOAT_nextafter_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !0
 NPY_NO_EXPORT void
 FLOAT_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 398
-NPY_NO_EXPORT void
-FLOAT_spacing_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+#endif
+/**end repeat2**/
 
 
-#line 407
+#line 461
 NPY_NO_EXPORT void
 FLOAT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 407
+NPY_NO_EXPORT int
+FLOAT_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 461
 NPY_NO_EXPORT void
 FLOAT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+FLOAT_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 
-#line 415
+
+#line 472
 NPY_NO_EXPORT void
 FLOAT_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 415
+NPY_NO_EXPORT int
+FLOAT_fmax_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 472
 NPY_NO_EXPORT void
 FLOAT_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+FLOAT_fmin_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
 
 NPY_NO_EXPORT void
 FLOAT_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+FLOAT_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 NPY_NO_EXPORT void
 FLOAT_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3574,8 +3620,10 @@ FLOAT_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps,
 NPY_NO_EXPORT void
 FLOAT_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#if 0
 NPY_NO_EXPORT void
 FLOAT_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
 
 NPY_NO_EXPORT void
 FLOAT_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -3595,30 +3643,47 @@ FLOAT_ldexp(char **args, npy_intp const *dimensions, npy_intp const *steps, void
 NPY_NO_EXPORT void
 FLOAT_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 366
+#line 416
 
-#line 372
+#line 422
 NPY_NO_EXPORT void
 DOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+DOUBLE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 DOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+DOUBLE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 DOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+DOUBLE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 DOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+DOUBLE_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 
-#line 380
+/**end repeat1**/
+
+#line 435
 NPY_NO_EXPORT void
 DOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 380
+#line 435
 NPY_NO_EXPORT void
 DOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3629,105 +3694,103 @@ DOUBLE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *step
 NPY_NO_EXPORT void
 DOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 DOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-DOUBLE_isnan_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 DOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-DOUBLE_isinf_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 DOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-DOUBLE_isfinite_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !1
 NPY_NO_EXPORT void
 DOUBLE_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-DOUBLE_signbit_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !0
 NPY_NO_EXPORT void
 DOUBLE_copysign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-DOUBLE_copysign_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !0
 NPY_NO_EXPORT void
 DOUBLE_nextafter(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-DOUBLE_nextafter_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !1 || !0
 NPY_NO_EXPORT void
 DOUBLE_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 398
-NPY_NO_EXPORT void
-DOUBLE_spacing_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+#endif
+/**end repeat2**/
 
 
-#line 407
+#line 461
 NPY_NO_EXPORT void
 DOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 407
+NPY_NO_EXPORT int
+DOUBLE_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 461
 NPY_NO_EXPORT void
 DOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+DOUBLE_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
 
-#line 415
+#line 472
 NPY_NO_EXPORT void
 DOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 415
+NPY_NO_EXPORT int
+DOUBLE_fmax_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 472
 NPY_NO_EXPORT void
 DOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+DOUBLE_fmin_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
 
 NPY_NO_EXPORT void
 DOUBLE_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+DOUBLE_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 NPY_NO_EXPORT void
 DOUBLE_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3749,8 +3812,10 @@ DOUBLE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *steps,
 NPY_NO_EXPORT void
 DOUBLE_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#if 0
 NPY_NO_EXPORT void
 DOUBLE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
 
 NPY_NO_EXPORT void
 DOUBLE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -3770,30 +3835,47 @@ DOUBLE_ldexp(char **args, npy_intp const *dimensions, npy_intp const *steps, voi
 NPY_NO_EXPORT void
 DOUBLE_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 366
+#line 416
 
-#line 372
+#line 422
 NPY_NO_EXPORT void
 LONGDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+LONGDOUBLE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 LONGDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+LONGDOUBLE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 LONGDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 372
+NPY_NO_EXPORT int
+LONGDOUBLE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 422
 NPY_NO_EXPORT void
 LONGDOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONGDOUBLE_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 
-#line 380
+/**end repeat1**/
+
+#line 435
 NPY_NO_EXPORT void
 LONGDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 380
+#line 435
 NPY_NO_EXPORT void
 LONGDOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3804,105 +3886,103 @@ LONGDOUBLE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *
 NPY_NO_EXPORT void
 LONGDOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 LONGDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-LONGDOUBLE_isnan_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 LONGDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-LONGDOUBLE_isinf_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 LONGDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-LONGDOUBLE_isfinite_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !1
 NPY_NO_EXPORT void
 LONGDOUBLE_signbit(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-LONGDOUBLE_signbit_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !0
 NPY_NO_EXPORT void
 LONGDOUBLE_copysign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-LONGDOUBLE_copysign_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !0
 NPY_NO_EXPORT void
 LONGDOUBLE_nextafter(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
+/**end repeat2**/
 
-#line 398
-NPY_NO_EXPORT void
-LONGDOUBLE_nextafter_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-
-#line 394
+#line 450
 
-#line 398
+#if !0 || !0
 NPY_NO_EXPORT void
 LONGDOUBLE_spacing(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-#line 398
-NPY_NO_EXPORT void
-LONGDOUBLE_spacing_avx512_skx(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
+#endif
+/**end repeat2**/
 
 
-#line 407
+#line 461
 NPY_NO_EXPORT void
 LONGDOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 407
+NPY_NO_EXPORT int
+LONGDOUBLE_maximum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 461
 NPY_NO_EXPORT void
 LONGDOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONGDOUBLE_minimum_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-#line 415
+
+
+#line 472
 NPY_NO_EXPORT void
 LONGDOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 415
+NPY_NO_EXPORT int
+LONGDOUBLE_fmax_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
+#line 472
 NPY_NO_EXPORT void
 LONGDOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONGDOUBLE_fmin_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+
 
 NPY_NO_EXPORT void
 LONGDOUBLE_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+LONGDOUBLE_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 NPY_NO_EXPORT void
 LONGDOUBLE_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -3924,8 +4004,10 @@ LONGDOUBLE_conjugate(char **args, npy_intp const *dimensions, npy_intp const *st
 NPY_NO_EXPORT void
 LONGDOUBLE_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#if 0
 NPY_NO_EXPORT void
 LONGDOUBLE_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
 
 NPY_NO_EXPORT void
 LONGDOUBLE_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -3946,59 +4028,68 @@ NPY_NO_EXPORT void
 LONGDOUBLE_ldexp_long(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 468
-#line 471
+#line 534
+#line 537
 NPY_NO_EXPORT void
 HALF_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 HALF_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 HALF_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 HALF_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 HALF_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 HALF_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 468
-#line 471
+#line 534
+#line 537
 NPY_NO_EXPORT void
 LONGDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 LONGDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 LONGDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 LONGDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 LONGDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 471
+#line 537
 NPY_NO_EXPORT void
 LONGDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
 
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
+#line 548
+#line 551
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_absolute,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+
 /*
  *****************************************************************************
  **                           COMPLEX LOOPS                                 **
@@ -4007,33 +4098,65 @@ LONGDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_arithm_fp.dispatch.h"
 #endif
-#line 487
-#line 490
+#line 566
+#line 569
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CFLOAT_add,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 490
+#line 569
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CFLOAT_subtract,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 490
+#line 569
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CFLOAT_multiply,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
+#line 569
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CFLOAT_conjugate,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 487
-#line 490
+#line 569
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CFLOAT_square,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+#line 566
+#line 569
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CDOUBLE_add,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 490
+#line 569
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CDOUBLE_subtract,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 490
+#line 569
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CDOUBLE_multiply,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
+#line 569
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CDOUBLE_conjugate,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+#line 569
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CDOUBLE_square,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary_complex.dispatch.h"
+#endif
+#line 580
+#line 583
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CFLOAT_absolute,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
+
+#line 580
+#line 583
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CDOUBLE_absolute,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+
 
 
 #define CGE(xr,xi,yr,yi) (xr > yr || (xr == yr && xi >= yi));
@@ -4043,54 +4166,63 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void CDOUBLE_multiply,
 #define CEQ(xr,xi,yr,yi) (xr == yr && xi == yi);
 #define CNE(xr,xi,yr,yi) (xr != yr || xi != yi);
 
-#line 508
+#line 601
 
-#line 513
+#line 606
 NPY_NO_EXPORT void
 CFLOAT_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 513
+NPY_NO_EXPORT int
+CFLOAT_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+#line 606
 NPY_NO_EXPORT void
 CFLOAT_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 513
+NPY_NO_EXPORT int
+CFLOAT_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+#line 606
 NPY_NO_EXPORT void
 CFLOAT_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+CFLOAT_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 
 NPY_NO_EXPORT void
 CFLOAT_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CFLOAT_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CFLOAT_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CFLOAT_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CFLOAT_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CFLOAT_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CFLOAT_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 533
+#line 629
 NPY_NO_EXPORT void
 CFLOAT_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 533
+#line 629
 NPY_NO_EXPORT void
 CFLOAT_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4100,15 +4232,15 @@ CFLOAT_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *step
 
 NPY_NO_EXPORT void
 CFLOAT_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CFLOAT_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CFLOAT_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CFLOAT_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4119,8 +4251,6 @@ CFLOAT_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *steps
 NPY_NO_EXPORT void
 CFLOAT__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 560
-
 NPY_NO_EXPORT void
 CFLOAT_conjugate(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
 
@@ -4130,91 +4260,88 @@ CFLOAT_absolute(char **args, const npy_intp *dimensions, const npy_intp *steps,
 NPY_NO_EXPORT void
 CFLOAT_square(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
 
-#line 560
-
-NPY_NO_EXPORT void
-CFLOAT_conjugate_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-CFLOAT_absolute_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-CFLOAT_square_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
-
-
 NPY_NO_EXPORT void
 CFLOAT__arg(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 CFLOAT_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 581
+#line 672
 NPY_NO_EXPORT void
 CFLOAT_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 581
+#line 672
 NPY_NO_EXPORT void
 CFLOAT_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 589
+#line 680
 NPY_NO_EXPORT void
 CFLOAT_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 589
+#line 680
 NPY_NO_EXPORT void
 CFLOAT_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
 
-#line 508
+#line 601
 
-#line 513
+#line 606
 NPY_NO_EXPORT void
 CDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 513
+NPY_NO_EXPORT int
+CDOUBLE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+#line 606
 NPY_NO_EXPORT void
 CDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 513
+NPY_NO_EXPORT int
+CDOUBLE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+#line 606
 NPY_NO_EXPORT void
 CDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+CDOUBLE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 
 NPY_NO_EXPORT void
 CDOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 533
+#line 629
 NPY_NO_EXPORT void
 CDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 533
+#line 629
 NPY_NO_EXPORT void
 CDOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4224,15 +4351,15 @@ CDOUBLE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const *ste
 
 NPY_NO_EXPORT void
 CDOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4243,8 +4370,6 @@ CDOUBLE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *step
 NPY_NO_EXPORT void
 CDOUBLE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 560
-
 NPY_NO_EXPORT void
 CDOUBLE_conjugate(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
 
@@ -4254,91 +4379,88 @@ CDOUBLE_absolute(char **args, const npy_intp *dimensions, const npy_intp *steps,
 NPY_NO_EXPORT void
 CDOUBLE_square(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
 
-#line 560
-
-NPY_NO_EXPORT void
-CDOUBLE_conjugate_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-CDOUBLE_absolute_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-CDOUBLE_square_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
-
-
 NPY_NO_EXPORT void
 CDOUBLE__arg(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 CDOUBLE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 581
+#line 672
 NPY_NO_EXPORT void
 CDOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 581
+#line 672
 NPY_NO_EXPORT void
 CDOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 589
+#line 680
 NPY_NO_EXPORT void
 CDOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 589
+#line 680
 NPY_NO_EXPORT void
 CDOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
 
-#line 508
+#line 601
 
-#line 513
+#line 606
 NPY_NO_EXPORT void
 CLONGDOUBLE_add(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 513
+NPY_NO_EXPORT int
+CLONGDOUBLE_add_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+#line 606
 NPY_NO_EXPORT void
 CLONGDOUBLE_subtract(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 513
+NPY_NO_EXPORT int
+CLONGDOUBLE_subtract_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+#line 606
 NPY_NO_EXPORT void
 CLONGDOUBLE_multiply(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+CLONGDOUBLE_multiply_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 
 NPY_NO_EXPORT void
 CLONGDOUBLE_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CLONGDOUBLE_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CLONGDOUBLE_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CLONGDOUBLE_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CLONGDOUBLE_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CLONGDOUBLE_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 524
+#line 620
 NPY_NO_EXPORT void
 CLONGDOUBLE_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 533
+#line 629
 NPY_NO_EXPORT void
 CLONGDOUBLE_logical_and(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 533
+#line 629
 NPY_NO_EXPORT void
 CLONGDOUBLE_logical_or(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4348,15 +4470,15 @@ CLONGDOUBLE_logical_xor(char **args, npy_intp const *dimensions, npy_intp const
 
 NPY_NO_EXPORT void
 CLONGDOUBLE_logical_not(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CLONGDOUBLE_isnan(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CLONGDOUBLE_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 547
+#line 643
 NPY_NO_EXPORT void
 CLONGDOUBLE_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4367,8 +4489,6 @@ CLONGDOUBLE_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *
 NPY_NO_EXPORT void
 CLONGDOUBLE__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 560
-
 NPY_NO_EXPORT void
 CLONGDOUBLE_conjugate(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
 
@@ -4378,38 +4498,26 @@ CLONGDOUBLE_absolute(char **args, const npy_intp *dimensions, const npy_intp *st
 NPY_NO_EXPORT void
 CLONGDOUBLE_square(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
 
-#line 560
-
-NPY_NO_EXPORT void
-CLONGDOUBLE_conjugate_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-CLONGDOUBLE_absolute_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-CLONGDOUBLE_square_avx512f(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
-
-
 NPY_NO_EXPORT void
 CLONGDOUBLE__arg(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 CLONGDOUBLE_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 581
+#line 672
 NPY_NO_EXPORT void
 CLONGDOUBLE_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 581
+#line 672
 NPY_NO_EXPORT void
 CLONGDOUBLE_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 589
+#line 680
 NPY_NO_EXPORT void
 CLONGDOUBLE_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 589
+#line 680
 NPY_NO_EXPORT void
 CLONGDOUBLE_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4441,7 +4549,7 @@ TIMEDELTA_absolute(char **args, npy_intp const *dimensions, npy_intp const *step
 NPY_NO_EXPORT void
 TIMEDELTA_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 623
+#line 714
 
 NPY_NO_EXPORT void
 DATETIME_isnat(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -4449,58 +4557,55 @@ DATETIME_isnat(char **args, npy_intp const *dimensions, npy_intp const *steps, v
 NPY_NO_EXPORT void
 DATETIME_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-DATETIME_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
 #define DATETIME_isnan DATETIME_isnat
 
 NPY_NO_EXPORT void
 DATETIME__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 DATETIME_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 DATETIME_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 DATETIME_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 DATETIME_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 DATETIME_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 DATETIME_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 DATETIME_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 DATETIME_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 DATETIME_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 DATETIME_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
 
-#line 623
+#line 714
 
 NPY_NO_EXPORT void
 TIMEDELTA_isnat(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -4508,52 +4613,49 @@ TIMEDELTA_isnat(char **args, npy_intp const *dimensions, npy_intp const *steps,
 NPY_NO_EXPORT void
 TIMEDELTA_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-TIMEDELTA_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
 #define TIMEDELTA_isnan TIMEDELTA_isnat
 
 NPY_NO_EXPORT void
 TIMEDELTA__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 TIMEDELTA_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 TIMEDELTA_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 TIMEDELTA_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 TIMEDELTA_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 TIMEDELTA_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 642
+#line 730
 NPY_NO_EXPORT void
 TIMEDELTA_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 TIMEDELTA_maximum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 TIMEDELTA_minimum(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 TIMEDELTA_fmin(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 649
+#line 737
 NPY_NO_EXPORT void
 TIMEDELTA_fmax(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4613,68 +4715,85 @@ TIMEDELTA_mm_qm_divmod(char **args, npy_intp const *dimensions, npy_intp const *
 #define TIMEDELTA_md_m_floor_divide TIMEDELTA_md_m_divide
 /* #define TIMEDELTA_mm_d_floor_divide TIMEDELTA_mm_d_divide */
 
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
+#line 803
+#line 806
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void TIMEDELTA_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+
+#line 803
+#line 806
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DATETIME_isinf,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+
+
 /*
  *****************************************************************************
  **                            OBJECT LOOPS                                 **
  *****************************************************************************
  */
 
-#line 718
-#line 721
+#line 821
+#line 824
 NPY_NO_EXPORT void
 OBJECT_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 721
+#line 824
 NPY_NO_EXPORT void
 OBJECT_OO_O_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 718
-#line 721
+#line 821
+#line 824
 NPY_NO_EXPORT void
 OBJECT_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 721
+#line 824
 NPY_NO_EXPORT void
 OBJECT_OO_O_not_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 718
-#line 721
+#line 821
+#line 824
 NPY_NO_EXPORT void
 OBJECT_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 721
+#line 824
 NPY_NO_EXPORT void
 OBJECT_OO_O_greater(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 718
-#line 721
+#line 821
+#line 824
 NPY_NO_EXPORT void
 OBJECT_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 721
+#line 824
 NPY_NO_EXPORT void
 OBJECT_OO_O_greater_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 718
-#line 721
+#line 821
+#line 824
 NPY_NO_EXPORT void
 OBJECT_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 721
+#line 824
 NPY_NO_EXPORT void
 OBJECT_OO_O_less(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 
-#line 718
-#line 721
+#line 821
+#line 824
 NPY_NO_EXPORT void
 OBJECT_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-#line 721
+#line 824
 NPY_NO_EXPORT void
 OBJECT_OO_O_less_equal(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -4698,102 +4817,102 @@ PyUFunc_OOO_O(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
 
 //---------- Integers ----------
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BYTE_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UBYTE_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void SHORT_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void USHORT_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void INT_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void UINT_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONG_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONG_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGLONG_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 748
-#line 751
+#line 851
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 751
+#line 854
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void ULONGLONG_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
@@ -4801,56 +4920,56 @@ PyUFunc_OOO_O(char **args, npy_intp const *dimensions, npy_intp const *steps, vo
 
 //---------- Float ----------
 
- #line 761
-#line 764
+ #line 864
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_fmax,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_fmin,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 761
-#line 764
+#line 864
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_fmax,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_fmin,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
 
-#line 761
-#line 764
+#line 864
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGDOUBLE_maximum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGDOUBLE_minimum,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGDOUBLE_fmax,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
-#line 764
+#line 867
  NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void LONGDOUBLE_fmin,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops.h.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops.h.src
index 424e204c189..cce73aff850 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops.h.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops.h.src
@@ -10,24 +10,32 @@
     #define NPY_NO_EXPORT NPY_VISIBILITY_HIDDEN
 #endif
 
-#define BOOL_invert BOOL_logical_not
-#define BOOL_add BOOL_logical_or
-#define BOOL_bitwise_and BOOL_logical_and
-#define BOOL_bitwise_or BOOL_logical_or
-#define BOOL_logical_xor BOOL_not_equal
-#define BOOL_bitwise_xor BOOL_logical_xor
-#define BOOL_multiply BOOL_logical_and
-#define BOOL_maximum BOOL_logical_or
-#define BOOL_minimum BOOL_logical_and
-#define BOOL_fmax BOOL_maximum
-#define BOOL_fmin BOOL_minimum
-
 /*
  *****************************************************************************
  **                             BOOLEAN LOOPS                               **
  *****************************************************************************
  */
 
+/*
+ * Following functions are defined by umath generator
+ * to enable runtime dispatching without the need
+ * to redefine them within dsipatch-able sources.
+ */
+// #define BOOL_invert BOOL_logical_not
+// #define BOOL_add BOOL_logical_or
+// #define BOOL_bitwise_and BOOL_logical_and
+// #define BOOL_bitwise_or BOOL_logical_or
+// #define BOOL_logical_xor BOOL_not_equal
+// #define BOOL_bitwise_xor BOOL_logical_xor
+// #define BOOL_multiply BOOL_logical_and
+// #define BOOL_maximum BOOL_logical_or
+// #define BOOL_minimum BOOL_logical_and
+// #define BOOL_fmax BOOL_maximum
+// #define BOOL_fmin BOOL_minimum
+
+typedef struct PyArrayMethod_Context_tag PyArrayMethod_Context;
+typedef struct NpyAuxData_tag NpyAuxData;
+
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_comparison.dispatch.h"
 #endif
@@ -39,11 +47,15 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_@kind@,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
 /**end repeat**/
 
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_logical.dispatch.h"
+#endif
+
 /**begin repeat
- * #kind = logical_and, logical_or, absolute, logical_not#
- **/
-NPY_NO_EXPORT void
-BOOL_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+ * #kind = logical_and, logical_or, logical_not, absolute#
+ */
+ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_@kind@,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
 /**end repeat**/
 
 NPY_NO_EXPORT void
@@ -56,6 +68,17 @@ NPY_NO_EXPORT void
 BOOL_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 /**end repeat**/
 
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
+/**begin repeat
+ * #kind = isnan, isinf, isfinite#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void BOOL_@kind@,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+/**end repeat**/
+
 /*
  *****************************************************************************
  **                           INTEGER LOOPS
@@ -72,6 +95,11 @@ BOOL_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void
  */
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_divide,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+NPY_NO_EXPORT int
+@TYPE@_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat3**/
 /**end repeat**/
 
 #ifndef NPY_DISABLE_OPTIMIZATION
@@ -106,19 +134,40 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
 /**end repeat1**/
 /**end repeat**/
 
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
 /**begin repeat
- * #TYPE = BYTE, SHORT, INT, LONG, LONGLONG#
+ * #TYPE = UBYTE, USHORT, UINT, ULONG, ULONGLONG,
+           BYTE,  SHORT,  INT,  LONG,  LONGLONG#
  */
+/**begin repeat1
+ * #kind = invert, logical_not, conjugate, reciprocal, square, add,
+ *         subtract, multiply, bitwise_and, bitwise_or, bitwise_xor,
+ *         left_shift, right_shift, logical_and, logical_or,
+ *         logical_xor, isnan, isinf, isfinite,
+ *         absolute, sign#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+/**end repeat1**/
+/**end repeat**/
 
+/**begin repeat
+ * #TYPE = BYTE, SHORT, INT, LONG, LONGLONG#
+ */
 /**begin repeat1
  * both signed and unsigned integer types
  * #s = , u#
  * #S = , U#
  */
-
 #define @S@@TYPE@_floor_divide @S@@TYPE@_divide
+#define @S@@TYPE@_floor_divide_indexed @S@@TYPE@_divide_indexed
 #define @S@@TYPE@_fmax @S@@TYPE@_maximum
 #define @S@@TYPE@_fmin @S@@TYPE@_minimum
+#define @S@@TYPE@_fmax_indexed @S@@TYPE@_maximum_indexed
+#define @S@@TYPE@_fmin_indexed @S@@TYPE@_minimum_indexed
 
 NPY_NO_EXPORT void
 @S@@TYPE@__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
@@ -127,85 +176,69 @@ NPY_NO_EXPORT void
 @S@@TYPE@_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 /**begin repeat2
- * #isa = , _avx2#
- */
-
-NPY_NO_EXPORT void
-@S@@TYPE@_square@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-@S@@TYPE@_reciprocal@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
-
-NPY_NO_EXPORT void
-@S@@TYPE@_conjugate@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-@S@@TYPE@_negative@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-@S@@TYPE@_logical_not@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-NPY_NO_EXPORT void
-@S@@TYPE@_invert@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-/**begin repeat3
  * Arithmetic
  * #kind = add, subtract, multiply, bitwise_and, bitwise_or, bitwise_xor,
  *          left_shift, right_shift#
  * #OP = +, -,*, &, |, ^, <<, >>#
  */
-NPY_NO_EXPORT void
-@S@@TYPE@_@kind@@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-/**end repeat3**/
-
-/**begin repeat3
- * #kind = logical_and, logical_or#
- * #OP = &&, ||#
- */
-NPY_NO_EXPORT void
-@S@@TYPE@_@kind@@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-/**end repeat3**/
+NPY_NO_EXPORT int
+@S@@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args,
+                         npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-@S@@TYPE@_logical_xor@isa@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 /**end repeat2**/
 
 /**begin repeat2
  * #kind = maximum, minimum#
- * #OP =  >, <#
  **/
 NPY_NO_EXPORT void
 @S@@TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-/**end repeat2**/
 
-NPY_NO_EXPORT void
-@S@@TYPE@_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+NPY_NO_EXPORT int
+@S@@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-@S@@TYPE@_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+/**end repeat2**/
 
 NPY_NO_EXPORT void
-@S@@TYPE@_sign(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+@S@@TYPE@_power(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 @S@@TYPE@_gcd(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
 @S@@TYPE@_lcm(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-/**begin repeat2
- * #kind = isnan, isinf, isfinite#
- **/
-NPY_NO_EXPORT void
-@S@@TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 /**end repeat2**/
 
 /**end repeat1**/
+/**end repeat**/
+
+/**begin repeat
+ * #kind = equal, not_equal, less, less_equal, greater, greater_equal#
+ * #OP = ==, !=, <, <=, >, >=#
+ */
+NPY_NO_EXPORT void
+LONGLONG_Qq_bool_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+NPY_NO_EXPORT void
+LONGLONG_qQ_bool_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
 /**end repeat**/
 
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary.dispatch.h"
+#endif
+/**begin repeat
+ * #TYPE = UBYTE, USHORT, UINT, ULONG, ULONGLONG,
+ *         BYTE,  SHORT,  INT,  LONG,  LONGLONG#
+ */
+/**begin repeat1
+ * #kind = negative#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+/**end repeat1**/
+/**end repeat**/
+
+
 /*
  *****************************************************************************
  **                             FLOAT LOOPS                                 **
@@ -226,6 +259,34 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
 /**end repeat**/
 
 #ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary_fp_le.dispatch.h"
+#endif
+/**begin repeat
+ *  #TYPE = FLOAT, DOUBLE#
+ */
+/**begin repeat1
+ * #kind = isnan, isinf, isfinite, signbit#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+/**end repeat1**/
+/**end repeat**/
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary.dispatch.h"
+#endif
+/**begin repeat
+ *  #TYPE = FLOAT, DOUBLE, LONGDOUBLE#
+ */
+/**begin repeat1
+ * #kind = negative#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+/**end repeat1**/
+/**end repeat**/
+
+#ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_arithm_fp.dispatch.h"
 #endif
 /**begin repeat
@@ -234,10 +295,13 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
 /**begin repeat1
  * Arithmetic
  * # kind = add, subtract, multiply, divide#
- * # OP = +, -, *, /#
  */
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
     (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+
+NPY_NO_EXPORT int
+@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 /**end repeat1**/
 /**end repeat**/
 
@@ -283,15 +347,6 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void HALF_@func@,
 /**end repeat**/
 
 /**begin repeat
- * #func = sin, cos#
- */
-
-NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_@func@,
-    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
-
-/**end repeat**/
-
-/**begin repeat
  *  #TYPE = FLOAT, DOUBLE#
  */
 /**begin repeat1
@@ -304,27 +359,20 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@func@,
 /**end repeat1**/
 /**end repeat**/
 
-/**begin repeat
- *  #TYPE = FLOAT, DOUBLE#
- */
-/**begin repeat1
- * #func = maximum, minimum#
- */
-NPY_NO_EXPORT void
-@TYPE@_@func@_avx512f(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
-/**end repeat1**/
-/**end repeat**/
-
 #ifndef NPY_DISABLE_OPTIMIZATION
     #include "loops_trigonometric.dispatch.h"
 #endif
+
 /**begin repeat
+ *  #TYPE = FLOAT, DOUBLE#
+ */
+/**begin repeat1
  *  #func = sin, cos#
  */
-NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void FLOAT_@func@, (
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@func@, (
     char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)
 ))
+/**end repeat1**/
 /**end repeat**/
 
 #ifndef NPY_DISABLE_OPTIMIZATION
@@ -362,6 +410,8 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@, (
  *  #TYPE = HALF, FLOAT, DOUBLE, LONGDOUBLE#
  *  #c = f, f, , l#
  *  #C = F, F, , L#
+ *  #half = 1, 0, 0, 0#
+ *  #fd = 0, 1, 1, 0#
  */
 
 /**begin repeat1
@@ -371,6 +421,11 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@, (
  */
 NPY_NO_EXPORT void
 @TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+
+NPY_NO_EXPORT int
+@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
+/**end repeat1**/
 /**end repeat1**/
 
 /**begin repeat1
@@ -390,35 +445,44 @@ NPY_NO_EXPORT void
 /**begin repeat1
  * #kind = isnan, isinf, isfinite, signbit, copysign, nextafter, spacing#
  * #func = npy_isnan, npy_isinf, npy_isfinite, npy_signbit, npy_copysign, nextafter, spacing#
+ * #dispatched = 1, 1, 1, 1, 0, 0, 0#
  **/
 
-/**begin repeat2
- * #ISA  = , _avx512_skx#
- **/
+#if !@fd@ || !@dispatched@
 NPY_NO_EXPORT void
-@TYPE@_@kind@@ISA@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+@TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
 /**end repeat2**/
 /**end repeat1**/
 
 /**begin repeat1
  * #kind = maximum, minimum#
- * #OP =  >=, <=#
  **/
 NPY_NO_EXPORT void
 @TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+
+NPY_NO_EXPORT int
+@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 /**end repeat1**/
 
 /**begin repeat1
  * #kind = fmax, fmin#
- * #OP =  >=, <=#
  **/
 NPY_NO_EXPORT void
 @TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+
+NPY_NO_EXPORT int
+@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 /**end repeat1**/
 
 NPY_NO_EXPORT void
 @TYPE@_floor_divide(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+NPY_NO_EXPORT int
+@TYPE@_floor_divide_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
+
 NPY_NO_EXPORT void
 @TYPE@_remainder(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
@@ -440,8 +504,10 @@ NPY_NO_EXPORT void
 NPY_NO_EXPORT void
 @TYPE@_absolute(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
+#if @half@
 NPY_NO_EXPORT void
 @TYPE@_negative(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+#endif
 
 NPY_NO_EXPORT void
 @TYPE@_positive(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -473,6 +539,19 @@ NPY_NO_EXPORT void
 /**end repeat1**/
 /**end repeat**/
 
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
+/**begin repeat
+ * #TYPE = HALF#
+ */
+/**begin repeat1
+ * #kind = absolute#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+/**end repeat1**/
+/**end repeat**/
 /*
  *****************************************************************************
  **                           COMPLEX LOOPS                                 **
@@ -485,7 +564,21 @@ NPY_NO_EXPORT void
  * #TYPE = CFLOAT, CDOUBLE#
  */
 /**begin repeat1
- * #kind = add, subtract, multiply#
+ * #kind = add, subtract, multiply, conjugate, square#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+/**end repeat1**/
+/**end repeat**/
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary_complex.dispatch.h"
+#endif
+/**begin repeat
+ * #TYPE = CFLOAT, CDOUBLE#
+ */
+/**begin repeat1
+ * #kind = absolute#
  */
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
@@ -512,6 +605,9 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
  */
 NPY_NO_EXPORT void
 C@TYPE@_@kind@(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
+
+NPY_NO_EXPORT int
+C@TYPE@_@kind@_indexed(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func));
 /**end repeat1**/
 
 NPY_NO_EXPORT void
@@ -554,19 +650,14 @@ C@TYPE@_reciprocal(char **args, npy_intp const *dimensions, npy_intp const *step
 NPY_NO_EXPORT void
 C@TYPE@__ones_like(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data));
 
-/**begin repeat1
- * #isa = , _avx512f#
- */
-
 NPY_NO_EXPORT void
-C@TYPE@_conjugate@isa@(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
+C@TYPE@_conjugate(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-C@TYPE@_absolute@isa@(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
+C@TYPE@_absolute(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func));
 
 NPY_NO_EXPORT void
-C@TYPE@_square@isa@(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
-/**end repeat1**/
+C@TYPE@_square(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 C@TYPE@__arg(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
@@ -627,9 +718,6 @@ NPY_NO_EXPORT void
 NPY_NO_EXPORT void
 @TYPE@_isfinite(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
 
-NPY_NO_EXPORT void
-@TYPE@_isinf(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func));
-
 #define @TYPE@_isnan @TYPE@_isnat
 
 NPY_NO_EXPORT void
@@ -705,6 +793,21 @@ TIMEDELTA_mm_qm_divmod(char **args, npy_intp const *dimensions, npy_intp const *
 #define TIMEDELTA_md_m_floor_divide TIMEDELTA_md_m_divide
 /* #define TIMEDELTA_mm_d_floor_divide TIMEDELTA_mm_d_divide */
 
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_autovec.dispatch.h"
+#endif
+/**begin repeat
+ * #TYPE = TIMEDELTA, DATETIME#
+ */
+/**begin repeat1
+ * #kind = isinf#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func)))
+/**end repeat1**/
+/**end repeat**/
+
 /*
  *****************************************************************************
  **                            OBJECT LOOPS                                 **
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c
index edd0453b570..e9adb7c1f5b 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c
@@ -10,7 +10,9 @@
 #line 1
 /*@targets
  ** $maxopt baseline
- ** sse2 avx2 avx512f
+ ** sse2 (avx2 fma3)
+ ** neon asimd
+ ** vsx2 vsx3
  ** vx vxe
  **/
 #define _UMATHMODULE
@@ -24,4453 +26,3475 @@
 // Provides the various *_LOOP macros
 #include "fast_loop_macros.h"
 
-// TODO: replace raw SIMD with NPYV
+/**
+ * TODO:
+ *  - Improve the implementation of SIMD complex absolute,
+ *    current one kinda slow and it can be optimized by
+ *    at least avoiding the division and keep sqrt.
+ *  - Vectorize reductions
+ *  - Add support for ASIMD/VCMLA through universal intrinics.
+ */
+
 //###############################################################################
 //## Real Single/Double precision
 //###############################################################################
 /********************************************************************************
- ** Defining the SIMD kernels
+ ** Defining ufunc inner functions
  ********************************************************************************/
-#ifdef NPY_HAVE_SSE2
-#line 42
-#line 48
-static void
-sse2_binary_add_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] + ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 c = _mm512_add_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 b = _mm512_load_ps(&ip2[i]);
-                __m512 c = _mm512_add_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_add_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_add_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 c = _mm512_add_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 b = _mm512_loadu_ps(&ip2[i]);
-                __m512 c = _mm512_add_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] + ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 c = _mm256_add_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 b = _mm256_load_ps(&ip2[i]);
-                __m256 c = _mm256_add_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_add_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_add_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 c = _mm256_add_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 b = _mm256_loadu_ps(&ip2[i]);
-                __m256 c = _mm256_add_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] + ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 c = _mm_add_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 b = _mm_load_ps(&ip2[i]);
-                __m128 c = _mm_add_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_add_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_add_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 c = _mm_add_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 b = _mm_loadu_ps(&ip2[i]);
-                __m128 c = _mm_add_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[i];
-    }
-}
 
-static void
-sse2_binary_scalar1_add_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 a = _mm512_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] + ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_add_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_add_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 a = _mm256_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] + ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_add_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_add_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 a = _mm_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] + ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_add_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_add_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] + ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar2_add_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 b = _mm512_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] + ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 c = _mm512_add_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 c = _mm512_add_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 b = _mm256_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] + ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 c = _mm256_add_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 c = _mm256_add_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 b = _mm_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] + ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 c = _mm_add_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 c = _mm_add_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[0];
-    }
-}
-
-
-#line 48
-static void
-sse2_binary_subtract_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] - ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 c = _mm512_sub_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 b = _mm512_load_ps(&ip2[i]);
-                __m512 c = _mm512_sub_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_sub_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_sub_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 c = _mm512_sub_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 b = _mm512_loadu_ps(&ip2[i]);
-                __m512 c = _mm512_sub_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] - ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 c = _mm256_sub_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 b = _mm256_load_ps(&ip2[i]);
-                __m256 c = _mm256_sub_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_sub_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_sub_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 c = _mm256_sub_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 b = _mm256_loadu_ps(&ip2[i]);
-                __m256 c = _mm256_sub_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
+/*
+ * clang has a bug that's present at -O1 or greater.  When partially loading a
+ * vector register for a divide operation, the remaining elements are set
+ * to 1 to avoid divide-by-zero.  The partial load is paired with a partial
+ * store after the divide operation.  clang notices that the entire register
+ * is not needed for the store and optimizes out the fill of 1 to the remaining
+ * elements.  This causes either a divide-by-zero or 0/0 with invalid exception
+ * that we were trying to avoid by filling.
+ *
+ * Using a dummy variable marked 'volatile' convinces clang not to ignore
+ * the explicit fill of remaining elements.  If `-ftrapping-math` is
+ * supported, then it'll also avoid the bug.  `-ftrapping-math` is supported
+ * on Apple clang v12+ for x86_64.  It is not currently supported for arm64.
+ * `-ftrapping-math` is set by default of Numpy builds in
+ * numpy/distutils/ccompiler.py.
+ *
+ * Note: Apple clang and clang upstream have different versions that overlap
+ */
+#if defined(__clang__)
+    #if defined(__apple_build_version__)
+    // Apple Clang
+        #if __apple_build_version__ < 12000000
+        // Apple Clang before v12
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64)
+        // Apple Clang after v12, targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 0
+        #else
+        // Apple Clang after v12, not targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #endif
+    #else
+    // Clang, not Apple Clang
+        #if __clang_major__ < 10
+        // Clang before v10
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #elif defined(_MSC_VER)
+        // clang-cl has the same bug
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64)
+        // Clang v10+, targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 0
+        #else
+        // Clang v10+, not targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #endif
+    #endif
 #else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] - ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 c = _mm_sub_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 b = _mm_load_ps(&ip2[i]);
-                __m128 c = _mm_sub_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_sub_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_sub_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 c = _mm_sub_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 b = _mm_loadu_ps(&ip2[i]);
-                __m128 c = _mm_sub_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
+// Not a Clang compiler
+#define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 0
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[i];
-    }
-}
 
-static void
-sse2_binary_scalar1_subtract_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
+#line 96
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 a = _mm512_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] - ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_sub_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_sub_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 a = _mm256_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] - ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_sub_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_sub_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 a = _mm_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] - ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_sub_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 1
+        *((npy_float*)src0) += FLOAT_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_float acc = *((npy_float*)src0);
+        if (ssrc1 == sizeof(npy_float)) {
+            for (; len > 0; --len, src1 += sizeof(npy_float)) {
+                acc += *(npy_float *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc += *(npy_float *)src1;
+            }
+        }
+        *((npy_float*)src0) = acc;
+    #endif
+        return;
     }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_sub_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
+#if NPY_SIMD_F32
+    if (len > npyv_nlanes_f32*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f32;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_float) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_add_f32(a0, b0);
+                npyv_f32 r1 = npyv_add_f32(a1, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_add_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_add_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_float) && sdst == ssrc1) {
+            npyv_f32 a = npyv_setall_f32(*((npy_float*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_add_f32(a, b0);
+                npyv_f32 r1 = npyv_add_f32(a, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_add_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_add_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_float) && sdst == ssrc0) {
+            npyv_f32 b = npyv_setall_f32(*((npy_float*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 r0 = npyv_add_f32(a0, b);
+                npyv_f32 r1 = npyv_add_f32(a1, b);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+            #endif
+                npyv_f32 r = npyv_add_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 r = npyv_add_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] - ip2[i];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_float a = *((npy_float*)src0);
+        const npy_float b = *((npy_float*)src1);
+        *((npy_float*)dst) = a + b;
     }
 }
 
-static void
-sse2_binary_scalar2_subtract_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_add_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 b = _mm512_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] - ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 c = _mm512_sub_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 c = _mm512_sub_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 b = _mm256_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] - ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 c = _mm256_sub_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 c = _mm256_sub_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 b = _mm_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] - ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 c = _mm_sub_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 c = _mm_sub_ps(a, b);
-            _mm_store_ps(&op[i], c);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_float *)value;
     }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[0];
-    }
+    return 0;
 }
 
 
-#line 48
-static void
-sse2_binary_multiply_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] * ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 c = _mm512_mul_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 b = _mm512_load_ps(&ip2[i]);
-                __m512 c = _mm512_mul_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_mul_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_mul_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 c = _mm512_mul_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 b = _mm512_loadu_ps(&ip2[i]);
-                __m512 c = _mm512_mul_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] * ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 c = _mm256_mul_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 b = _mm256_load_ps(&ip2[i]);
-                __m256 c = _mm256_mul_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_mul_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_mul_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 c = _mm256_mul_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 b = _mm256_loadu_ps(&ip2[i]);
-                __m256 c = _mm256_mul_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] * ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 c = _mm_mul_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 b = _mm_load_ps(&ip2[i]);
-                __m128 c = _mm_mul_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_mul_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_mul_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 c = _mm_mul_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 b = _mm_loadu_ps(&ip2[i]);
-                __m128 c = _mm_mul_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar1_multiply_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 a = _mm512_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] * ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_mul_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_mul_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 a = _mm256_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] * ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_mul_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_mul_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 a = _mm_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] * ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_mul_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 0
+        *((npy_float*)src0) -= FLOAT_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_float acc = *((npy_float*)src0);
+        if (ssrc1 == sizeof(npy_float)) {
+            for (; len > 0; --len, src1 += sizeof(npy_float)) {
+                acc -= *(npy_float *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc -= *(npy_float *)src1;
+            }
+        }
+        *((npy_float*)src0) = acc;
+    #endif
+        return;
     }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_mul_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
+#if NPY_SIMD_F32
+    if (len > npyv_nlanes_f32*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f32;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_float) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_sub_f32(a0, b0);
+                npyv_f32 r1 = npyv_sub_f32(a1, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_sub_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_sub_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_float) && sdst == ssrc1) {
+            npyv_f32 a = npyv_setall_f32(*((npy_float*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_sub_f32(a, b0);
+                npyv_f32 r1 = npyv_sub_f32(a, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_sub_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_sub_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_float) && sdst == ssrc0) {
+            npyv_f32 b = npyv_setall_f32(*((npy_float*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 r0 = npyv_sub_f32(a0, b);
+                npyv_f32 r1 = npyv_sub_f32(a1, b);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+            #endif
+                npyv_f32 r = npyv_sub_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 r = npyv_sub_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] * ip2[i];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_float a = *((npy_float*)src0);
+        const npy_float b = *((npy_float*)src1);
+        *((npy_float*)dst) = a - b;
     }
 }
 
-static void
-sse2_binary_scalar2_multiply_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_subtract_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 b = _mm512_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] * ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 c = _mm512_mul_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 c = _mm512_mul_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 b = _mm256_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] * ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 c = _mm256_mul_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 c = _mm256_mul_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 b = _mm_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] * ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 c = _mm_mul_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 c = _mm_mul_ps(a, b);
-            _mm_store_ps(&op[i], c);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_float *)value;
     }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[0];
-    }
+    return 0;
 }
 
 
-#line 48
-static void
-sse2_binary_divide_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] / ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 c = _mm512_div_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_load_ps(&ip1[i]);
-                __m512 b = _mm512_load_ps(&ip2[i]);
-                __m512 c = _mm512_div_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_div_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_div_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 c = _mm512_div_ps(a, a);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m512 a = _mm512_loadu_ps(&ip1[i]);
-                __m512 b = _mm512_loadu_ps(&ip2[i]);
-                __m512 c = _mm512_div_ps(a, b);
-                _mm512_store_ps(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] / ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 c = _mm256_div_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_load_ps(&ip1[i]);
-                __m256 b = _mm256_load_ps(&ip2[i]);
-                __m256 c = _mm256_div_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_div_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_div_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 c = _mm256_div_ps(a, a);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m256 a = _mm256_loadu_ps(&ip1[i]);
-                __m256 b = _mm256_loadu_ps(&ip2[i]);
-                __m256 c = _mm256_div_ps(a, b);
-                _mm256_store_ps(&op[i], c);
-            }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] / ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 c = _mm_div_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_load_ps(&ip1[i]);
-                __m128 b = _mm_load_ps(&ip2[i]);
-                __m128 c = _mm_div_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_div_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_div_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 c = _mm_div_ps(a, a);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_float, vector_size_bytes) {
-                __m128 a = _mm_loadu_ps(&ip1[i]);
-                __m128 b = _mm_loadu_ps(&ip2[i]);
-                __m128 c = _mm_div_ps(a, b);
-                _mm_store_ps(&op[i], c);
-            }
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar1_divide_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 a = _mm512_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] / ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_load_ps(&ip2[i]);
-            __m512 c = _mm512_div_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 b = _mm512_loadu_ps(&ip2[i]);
-            __m512 c = _mm512_div_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 a = _mm256_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] / ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_load_ps(&ip2[i]);
-            __m256 c = _mm256_div_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 b = _mm256_loadu_ps(&ip2[i]);
-            __m256 c = _mm256_div_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 a = _mm_set1_ps(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[0] / ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_load_ps(&ip2[i]);
-            __m128 c = _mm_div_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 b = _mm_loadu_ps(&ip2[i]);
-            __m128 c = _mm_div_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] / ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar2_divide_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512 b = _mm512_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] / ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_load_ps(&ip1[i]);
-            __m512 c = _mm512_div_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m512 a = _mm512_loadu_ps(&ip1[i]);
-            __m512 c = _mm512_div_ps(a, b);
-            _mm512_store_ps(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256 b = _mm256_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] / ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_load_ps(&ip1[i]);
-            __m256 c = _mm256_div_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m256 a = _mm256_loadu_ps(&ip1[i]);
-            __m256 c = _mm256_div_ps(a, b);
-            _mm256_store_ps(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128 b = _mm_set1_ps(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, vector_size_bytes)
-        op[i] = ip1[i] / ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_load_ps(&ip1[i]);
-            __m128 c = _mm_div_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 0
+        *((npy_float*)src0) *= FLOAT_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_float acc = *((npy_float*)src0);
+        if (ssrc1 == sizeof(npy_float)) {
+            for (; len > 0; --len, src1 += sizeof(npy_float)) {
+                acc *= *(npy_float *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc *= *(npy_float *)src1;
+            }
+        }
+        *((npy_float*)src0) = acc;
+    #endif
+        return;
     }
-    else {
-        LOOP_BLOCKED(npy_float, vector_size_bytes) {
-            __m128 a = _mm_loadu_ps(&ip1[i]);
-            __m128 c = _mm_div_ps(a, b);
-            _mm_store_ps(&op[i], c);
-        }
+#if NPY_SIMD_F32
+    if (len > npyv_nlanes_f32*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f32;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_float) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_mul_f32(a0, b0);
+                npyv_f32 r1 = npyv_mul_f32(a1, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_mul_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_mul_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_float) && sdst == ssrc1) {
+            npyv_f32 a = npyv_setall_f32(*((npy_float*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_mul_f32(a, b0);
+                npyv_f32 r1 = npyv_mul_f32(a, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0 || 1
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_mul_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_mul_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_float) && sdst == ssrc0) {
+            npyv_f32 b = npyv_setall_f32(*((npy_float*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 r0 = npyv_mul_f32(a0, b);
+                npyv_f32 r1 = npyv_mul_f32(a1, b);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0 || 1
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+            #endif
+                npyv_f32 r = npyv_mul_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 r = npyv_mul_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[0];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_float a = *((npy_float*)src0);
+        const npy_float b = *((npy_float*)src1);
+        *((npy_float*)dst) = a * b;
     }
 }
 
-
-
-#line 42
-#line 48
-static void
-sse2_binary_add_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_multiply_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] + ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d c = _mm512_add_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d b = _mm512_load_pd(&ip2[i]);
-                __m512d c = _mm512_add_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_add_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_add_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d c = _mm512_add_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d b = _mm512_loadu_pd(&ip2[i]);
-                __m512d c = _mm512_add_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] + ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d c = _mm256_add_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d b = _mm256_load_pd(&ip2[i]);
-                __m256d c = _mm256_add_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_add_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_add_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d c = _mm256_add_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d b = _mm256_loadu_pd(&ip2[i]);
-                __m256d c = _mm256_add_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] + ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d c = _mm_add_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d b = _mm_load_pd(&ip2[i]);
-                __m128d c = _mm_add_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_add_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_add_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d c = _mm_add_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d b = _mm_loadu_pd(&ip2[i]);
-                __m128d c = _mm_add_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_float *)value;
     }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[i];
-    }
+    return 0;
 }
 
-static void
-sse2_binary_scalar1_add_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d a = _mm512_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] + ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_add_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_add_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-
 
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d a = _mm256_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] + ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_add_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_add_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d a = _mm_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] + ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_add_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_add_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] + ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar2_add_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_divide)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d b = _mm512_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] + ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d c = _mm512_add_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d c = _mm512_add_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d b = _mm256_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] + ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d c = _mm256_add_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d c = _mm256_add_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d b = _mm_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] + ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d c = _mm_add_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 0
+        *((npy_float*)src0) /= FLOAT_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_float acc = *((npy_float*)src0);
+        if (ssrc1 == sizeof(npy_float)) {
+            for (; len > 0; --len, src1 += sizeof(npy_float)) {
+                acc /= *(npy_float *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc /= *(npy_float *)src1;
+            }
+        }
+        *((npy_float*)src0) = acc;
+    #endif
+        return;
     }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d c = _mm_add_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+#if NPY_SIMD_F32
+    if (len > npyv_nlanes_f32*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f32;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_float) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_div_f32(a0, b0);
+                npyv_f32 r1 = npyv_div_f32(a1, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 1
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_div_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_div_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_float) && sdst == ssrc1) {
+            npyv_f32 a = npyv_setall_f32(*((npy_float*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f32 b0 = npyv_load_f32((const npy_float*)src1);
+                npyv_f32 b1 = npyv_load_f32((const npy_float*)(src1 + vstep));
+                npyv_f32 r0 = npyv_div_f32(a, b0);
+                npyv_f32 r1 = npyv_div_f32(a, b1);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 1 || 0
+                npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+            #else
+                npyv_f32 b = npyv_load_tillz_f32((const npy_float*)src1, len);
+            #endif
+                npyv_f32 r = npyv_div_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 b = npyv_load_till_f32((const npy_float*)src1, len, 1.0f);
+                npyv_f32 r = npyv_div_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_float) && sdst == ssrc0) {
+            npyv_f32 b = npyv_setall_f32(*((npy_float*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32((const npy_float*)src0);
+                npyv_f32 a1 = npyv_load_f32((const npy_float*)(src0 + vstep));
+                npyv_f32 r0 = npyv_div_f32(a0, b);
+                npyv_f32 r1 = npyv_div_f32(a1, b);
+                npyv_store_f32((npy_float*)dst, r0);
+                npyv_store_f32((npy_float*)(dst + vstep), r1);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 1 || 0
+                npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+            #else
+                npyv_f32 a = npyv_load_tillz_f32((const npy_float*)src0, len);
+            #endif
+                npyv_f32 r = npyv_div_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f32 a = npyv_load_till_f32((const npy_float*)src0, len, 1.0f);
+                npyv_f32 r = npyv_div_f32(a, b);
+                npyv_store_till_f32((npy_float*)dst, len, r);
+            }
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[0];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_float a = *((npy_float*)src0);
+        const npy_float b = *((npy_float*)src1);
+        *((npy_float*)dst) = a / b;
     }
 }
 
-
-#line 48
-static void
-sse2_binary_subtract_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] - ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d c = _mm512_sub_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d b = _mm512_load_pd(&ip2[i]);
-                __m512d c = _mm512_sub_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_sub_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_sub_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d c = _mm512_sub_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d b = _mm512_loadu_pd(&ip2[i]);
-                __m512d c = _mm512_sub_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] - ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d c = _mm256_sub_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d b = _mm256_load_pd(&ip2[i]);
-                __m256d c = _mm256_sub_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_sub_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_sub_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d c = _mm256_sub_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d b = _mm256_loadu_pd(&ip2[i]);
-                __m256d c = _mm256_sub_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] - ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d c = _mm_sub_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d b = _mm_load_pd(&ip2[i]);
-                __m128d c = _mm_sub_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_sub_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_sub_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d c = _mm_sub_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d b = _mm_loadu_pd(&ip2[i]);
-                __m128d c = _mm_sub_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = *indexed / *(npy_float *)value;
     }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[i];
-    }
+    return 0;
 }
 
-static void
-sse2_binary_scalar1_subtract_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d a = _mm512_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] - ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_sub_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_sub_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
 
 
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d a = _mm256_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] - ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_sub_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_sub_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d a = _mm_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] - ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_sub_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_sub_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] - ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar2_subtract_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+#line 96
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d b = _mm512_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] - ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d c = _mm512_sub_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d c = _mm512_sub_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d b = _mm256_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] - ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d c = _mm256_sub_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d c = _mm256_sub_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d b = _mm_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] - ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d c = _mm_sub_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 1
+        *((npy_double*)src0) += DOUBLE_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_double acc = *((npy_double*)src0);
+        if (ssrc1 == sizeof(npy_double)) {
+            for (; len > 0; --len, src1 += sizeof(npy_double)) {
+                acc += *(npy_double *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc += *(npy_double *)src1;
+            }
+        }
+        *((npy_double*)src0) = acc;
+    #endif
+        return;
     }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d c = _mm_sub_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+#if NPY_SIMD_F64
+    if (len > npyv_nlanes_f64*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f64;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_double) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_add_f64(a0, b0);
+                npyv_f64 r1 = npyv_add_f64(a1, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_add_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_add_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_double) && sdst == ssrc1) {
+            npyv_f64 a = npyv_setall_f64(*((npy_double*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_add_f64(a, b0);
+                npyv_f64 r1 = npyv_add_f64(a, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_add_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_add_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_double) && sdst == ssrc0) {
+            npyv_f64 b = npyv_setall_f64(*((npy_double*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 r0 = npyv_add_f64(a0, b);
+                npyv_f64 r1 = npyv_add_f64(a1, b);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+            #endif
+                npyv_f64 r = npyv_add_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 r = npyv_add_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[0];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_double a = *((npy_double*)src0);
+        const npy_double b = *((npy_double*)src1);
+        *((npy_double*)dst) = a + b;
     }
 }
 
-
-#line 48
-static void
-sse2_binary_multiply_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_add_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] * ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d c = _mm512_mul_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d b = _mm512_load_pd(&ip2[i]);
-                __m512d c = _mm512_mul_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_mul_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_mul_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d c = _mm512_mul_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d b = _mm512_loadu_pd(&ip2[i]);
-                __m512d c = _mm512_mul_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] * ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d c = _mm256_mul_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d b = _mm256_load_pd(&ip2[i]);
-                __m256d c = _mm256_mul_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_mul_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_mul_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d c = _mm256_mul_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d b = _mm256_loadu_pd(&ip2[i]);
-                __m256d c = _mm256_mul_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] * ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d c = _mm_mul_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d b = _mm_load_pd(&ip2[i]);
-                __m128d c = _mm_mul_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_mul_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_mul_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d c = _mm_mul_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d b = _mm_loadu_pd(&ip2[i]);
-                __m128d c = _mm_mul_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = *indexed + *(npy_double *)value;
     }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[i];
-    }
+    return 0;
 }
 
-static void
-sse2_binary_scalar1_multiply_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d a = _mm512_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] * ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_mul_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_mul_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-
 
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d a = _mm256_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] * ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_mul_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_mul_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d a = _mm_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] * ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_mul_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_mul_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] * ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar2_multiply_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d b = _mm512_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] * ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d c = _mm512_mul_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d c = _mm512_mul_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d b = _mm256_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] * ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d c = _mm256_mul_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d c = _mm256_mul_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d b = _mm_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] * ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d c = _mm_mul_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 0
+        *((npy_double*)src0) -= DOUBLE_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_double acc = *((npy_double*)src0);
+        if (ssrc1 == sizeof(npy_double)) {
+            for (; len > 0; --len, src1 += sizeof(npy_double)) {
+                acc -= *(npy_double *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc -= *(npy_double *)src1;
+            }
+        }
+        *((npy_double*)src0) = acc;
+    #endif
+        return;
     }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d c = _mm_mul_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+#if NPY_SIMD_F64
+    if (len > npyv_nlanes_f64*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f64;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_double) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_sub_f64(a0, b0);
+                npyv_f64 r1 = npyv_sub_f64(a1, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_sub_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_sub_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_double) && sdst == ssrc1) {
+            npyv_f64 a = npyv_setall_f64(*((npy_double*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_sub_f64(a, b0);
+                npyv_f64 r1 = npyv_sub_f64(a, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_sub_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_sub_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_double) && sdst == ssrc0) {
+            npyv_f64 b = npyv_setall_f64(*((npy_double*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 r0 = npyv_sub_f64(a0, b);
+                npyv_f64 r1 = npyv_sub_f64(a1, b);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0 || 0
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+            #endif
+                npyv_f64 r = npyv_sub_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 r = npyv_sub_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (0 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[0];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_double a = *((npy_double*)src0);
+        const npy_double b = *((npy_double*)src1);
+        *((npy_double*)dst) = a - b;
     }
 }
 
-
-#line 48
-static void
-sse2_binary_divide_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_subtract_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] / ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d c = _mm512_div_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_load_pd(&ip1[i]);
-                __m512d b = _mm512_load_pd(&ip2[i]);
-                __m512d c = _mm512_div_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_div_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_div_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d c = _mm512_div_pd(a, a);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m512d a = _mm512_loadu_pd(&ip1[i]);
-                __m512d b = _mm512_loadu_pd(&ip2[i]);
-                __m512d c = _mm512_div_pd(a, b);
-                _mm512_store_pd(&op[i], c);
-            }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] / ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d c = _mm256_div_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_load_pd(&ip1[i]);
-                __m256d b = _mm256_load_pd(&ip2[i]);
-                __m256d c = _mm256_div_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_div_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_div_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d c = _mm256_div_pd(a, a);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m256d a = _mm256_loadu_pd(&ip1[i]);
-                __m256d b = _mm256_loadu_pd(&ip2[i]);
-                __m256d c = _mm256_div_pd(a, b);
-                _mm256_store_pd(&op[i], c);
-            }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] / ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d c = _mm_div_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_load_pd(&ip1[i]);
-                __m128d b = _mm_load_pd(&ip2[i]);
-                __m128d c = _mm_div_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_div_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_div_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d c = _mm_div_pd(a, a);
-                _mm_store_pd(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(npy_double, vector_size_bytes) {
-                __m128d a = _mm_loadu_pd(&ip1[i]);
-                __m128d b = _mm_loadu_pd(&ip2[i]);
-                __m128d c = _mm_div_pd(a, b);
-                _mm_store_pd(&op[i], c);
-            }
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = *indexed - *(npy_double *)value;
     }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[i];
-    }
+    return 0;
 }
 
-static void
-sse2_binary_scalar1_divide_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d a = _mm512_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] / ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_load_pd(&ip2[i]);
-            __m512d c = _mm512_div_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d b = _mm512_loadu_pd(&ip2[i]);
-            __m512d c = _mm512_div_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d a = _mm256_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] / ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_load_pd(&ip2[i]);
-            __m256d c = _mm256_div_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d b = _mm256_loadu_pd(&ip2[i]);
-            __m256d c = _mm256_div_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d a = _mm_set1_pd(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[0] / ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_load_pd(&ip2[i]);
-            __m128d c = _mm_div_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d b = _mm_loadu_pd(&ip2[i]);
-            __m128d c = _mm_div_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] / ip2[i];
-    }
-}
 
-static void
-sse2_binary_scalar2_divide_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const __m512d b = _mm512_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] / ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_load_pd(&ip1[i]);
-            __m512d c = _mm512_div_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m512d a = _mm512_loadu_pd(&ip1[i]);
-            __m512d c = _mm512_div_pd(a, b);
-            _mm512_store_pd(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const __m256d b = _mm256_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] / ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_load_pd(&ip1[i]);
-            __m256d c = _mm256_div_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m256d a = _mm256_loadu_pd(&ip1[i]);
-            __m256d c = _mm256_div_pd(a, b);
-            _mm256_store_pd(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const __m128d b = _mm_set1_pd(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, vector_size_bytes)
-        op[i] = ip1[i] / ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_load_pd(&ip1[i]);
-            __m128d c = _mm_div_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 0
+        *((npy_double*)src0) *= DOUBLE_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_double acc = *((npy_double*)src0);
+        if (ssrc1 == sizeof(npy_double)) {
+            for (; len > 0; --len, src1 += sizeof(npy_double)) {
+                acc *= *(npy_double *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc *= *(npy_double *)src1;
+            }
+        }
+        *((npy_double*)src0) = acc;
+    #endif
+        return;
     }
-    else {
-        LOOP_BLOCKED(npy_double, vector_size_bytes) {
-            __m128d a = _mm_loadu_pd(&ip1[i]);
-            __m128d c = _mm_div_pd(a, b);
-            _mm_store_pd(&op[i], c);
-        }
+#if NPY_SIMD_F64
+    if (len > npyv_nlanes_f64*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f64;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_double) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_mul_f64(a0, b0);
+                npyv_f64 r1 = npyv_mul_f64(a1, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_mul_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_mul_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if 0 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_double) && sdst == ssrc1) {
+            npyv_f64 a = npyv_setall_f64(*((npy_double*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_mul_f64(a, b0);
+                npyv_f64 r1 = npyv_mul_f64(a, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0 || 1
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_mul_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_mul_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_double) && sdst == ssrc0) {
+            npyv_f64 b = npyv_setall_f64(*((npy_double*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 r0 = npyv_mul_f64(a0, b);
+                npyv_f64 r1 = npyv_mul_f64(a1, b);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0 || 1
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+            #endif
+                npyv_f64 r = npyv_mul_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 r = npyv_mul_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (0 || 1) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[0];
-    }
-}
-
-
-
-
-#else // NPY_HAVE_SSE2
-
-#line 370
-#if NPY_SIMD_F32
-#line 377
-
-static void
-simd_binary_add_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] + ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 c = npyv_add_f32(a, a);
-            npyv_store_f32(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 b = npyv_load_f32(&ip2[i]);
-            npyv_f32 c = npyv_add_f32(a, b);
-            npyv_store_f32(&op[i], c);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar1_add_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    const npyv_f32 v1 = npyv_setall_f32(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] + ip2[i];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v2 = npyv_load_f32(&ip2[i]);
-        npyv_f32 v3 = npyv_add_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] + ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar2_add_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    const npyv_f32 v2 = npyv_setall_f32(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] + ip2[0];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v1 = npyv_load_f32(&ip1[i]);
-        npyv_f32 v3 = npyv_add_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[0];
-    }
-}
-
-#line 377
-
-static void
-simd_binary_subtract_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] - ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 c = npyv_sub_f32(a, a);
-            npyv_store_f32(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 b = npyv_load_f32(&ip2[i]);
-            npyv_f32 c = npyv_sub_f32(a, b);
-            npyv_store_f32(&op[i], c);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar1_subtract_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    const npyv_f32 v1 = npyv_setall_f32(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] - ip2[i];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v2 = npyv_load_f32(&ip2[i]);
-        npyv_f32 v3 = npyv_sub_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] - ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar2_subtract_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    const npyv_f32 v2 = npyv_setall_f32(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] - ip2[0];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v1 = npyv_load_f32(&ip1[i]);
-        npyv_f32 v3 = npyv_sub_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[0];
-    }
-}
-
-#line 377
-
-static void
-simd_binary_multiply_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] * ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 c = npyv_mul_f32(a, a);
-            npyv_store_f32(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 b = npyv_load_f32(&ip2[i]);
-            npyv_f32 c = npyv_mul_f32(a, b);
-            npyv_store_f32(&op[i], c);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar1_multiply_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    const npyv_f32 v1 = npyv_setall_f32(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] * ip2[i];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v2 = npyv_load_f32(&ip2[i]);
-        npyv_f32 v3 = npyv_mul_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] * ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar2_multiply_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    const npyv_f32 v2 = npyv_setall_f32(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] * ip2[0];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v1 = npyv_load_f32(&ip1[i]);
-        npyv_f32 v3 = npyv_mul_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[0];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_double a = *((npy_double*)src0);
+        const npy_double b = *((npy_double*)src1);
+        *((npy_double*)dst) = a * b;
     }
 }
 
-#line 377
-
-static void
-simd_binary_divide_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_multiply_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] / ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 c = npyv_div_f32(a, a);
-            npyv_store_f32(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-            npyv_f32 a = npyv_load_f32(&ip1[i]);
-            npyv_f32 b = npyv_load_f32(&ip2[i]);
-            npyv_f32 c = npyv_div_f32(a, b);
-            npyv_store_f32(&op[i], c);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = *indexed * *(npy_double *)value;
     }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[i];
-    }
+    return 0;
 }
 
-static void
-simd_binary_scalar1_divide_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
-{
-    const npyv_f32 v1 = npyv_setall_f32(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] / ip2[i];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v2 = npyv_load_f32(&ip2[i]);
-        npyv_f32 v3 = npyv_div_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] / ip2[i];
-    }
-}
 
-static void
-simd_binary_scalar2_divide_FLOAT(npy_float * op, npy_float * ip1, npy_float * ip2, npy_intp n)
+#line 105
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_divide)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    const npyv_f32 v2 = npyv_setall_f32(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] / ip2[0];
-    }
-    LOOP_BLOCKED(npy_float, NPY_SIMD_WIDTH) {
-        npyv_f32 v1 = npyv_load_f32(&ip1[i]);
-        npyv_f32 v3 = npyv_div_f32(v1, v2);
-        npyv_store_f32(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[0];
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if 0
+        *((npy_double*)src0) /= DOUBLE_pairwise_sum(src1, len, ssrc1);
+    #else
+        npy_double acc = *((npy_double*)src0);
+        if (ssrc1 == sizeof(npy_double)) {
+            for (; len > 0; --len, src1 += sizeof(npy_double)) {
+                acc /= *(npy_double *)src1;
+            }
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc /= *(npy_double *)src1;
+            }
+        }
+        *((npy_double*)src0) = acc;
+    #endif
+        return;
     }
-}
-
-#endif /* NPY_SIMD_F32 */
-
-#line 370
 #if NPY_SIMD_F64
-#line 377
-
-static void
-simd_binary_add_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] + ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 c = npyv_add_f64(a, a);
-            npyv_store_f64(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 b = npyv_load_f64(&ip2[i]);
-            npyv_f64 c = npyv_add_f64(a, b);
-            npyv_store_f64(&op[i], c);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar1_add_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    const npyv_f64 v1 = npyv_setall_f64(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] + ip2[i];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v2 = npyv_load_f64(&ip2[i]);
-        npyv_f64 v3 = npyv_add_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] + ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar2_add_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    const npyv_f64 v2 = npyv_setall_f64(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] + ip2[0];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v1 = npyv_load_f64(&ip1[i]);
-        npyv_f64 v3 = npyv_add_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
+    if (len > npyv_nlanes_f64*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_f64;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(npy_double) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_div_f64(a0, b0);
+                npyv_f64 r1 = npyv_div_f64(a1, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if 1
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_div_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_div_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if 1 && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(npy_double) && sdst == ssrc1) {
+            npyv_f64 a = npyv_setall_f64(*((npy_double*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_f64 b0 = npyv_load_f64((const npy_double*)src1);
+                npyv_f64 b1 = npyv_load_f64((const npy_double*)(src1 + vstep));
+                npyv_f64 r0 = npyv_div_f64(a, b0);
+                npyv_f64 r1 = npyv_div_f64(a, b1);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 1 || 0
+                npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+            #else
+                npyv_f64 b = npyv_load_tillz_f64((const npy_double*)src1, len);
+            #endif
+                npyv_f64 r = npyv_div_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 b = npyv_load_till_f64((const npy_double*)src1, len, 1.0);
+                npyv_f64 r = npyv_div_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(npy_double) && sdst == ssrc0) {
+            npyv_f64 b = npyv_setall_f64(*((npy_double*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64((const npy_double*)src0);
+                npyv_f64 a1 = npyv_load_f64((const npy_double*)(src0 + vstep));
+                npyv_f64 r0 = npyv_div_f64(a0, b);
+                npyv_f64 r1 = npyv_div_f64(a1, b);
+                npyv_store_f64((npy_double*)dst, r0);
+                npyv_store_f64((npy_double*)(dst + vstep), r1);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 1 || 0
+                npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+            #else
+                npyv_f64 a = npyv_load_tillz_f64((const npy_double*)src0, len);
+            #endif
+                npyv_f64 r = npyv_div_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_f64 a = npyv_load_till_f64((const npy_double*)src0, len, 1.0);
+                npyv_f64 r = npyv_div_f64(a, b);
+                npyv_store_till_f64((npy_double*)dst, len, r);
+            }
+        #endif // #if (1 || 0) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
+        }
+        npyv_cleanup();
+        return;
     }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] + ip2[0];
+loop_scalar:
+#endif
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const npy_double a = *((npy_double*)src0);
+        const npy_double b = *((npy_double*)src1);
+        *((npy_double*)dst) = a / b;
     }
 }
 
-#line 377
-
-static void
-simd_binary_subtract_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] - ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 c = npyv_sub_f64(a, a);
-            npyv_store_f64(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 b = npyv_load_f64(&ip2[i]);
-            npyv_f64 c = npyv_sub_f64(a, b);
-            npyv_store_f64(&op[i], c);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = *indexed / *(npy_double *)value;
     }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[i];
-    }
+    return 0;
 }
 
-static void
-simd_binary_scalar1_subtract_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    const npyv_f64 v1 = npyv_setall_f64(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] - ip2[i];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v2 = npyv_load_f64(&ip2[i]);
-        npyv_f64 v3 = npyv_sub_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] - ip2[i];
-    }
-}
 
-static void
-simd_binary_scalar2_subtract_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    const npyv_f64 v2 = npyv_setall_f64(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] - ip2[0];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v1 = npyv_load_f64(&ip1[i]);
-        npyv_f64 v3 = npyv_sub_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] - ip2[0];
-    }
-}
 
-#line 377
 
-static void
-simd_binary_multiply_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] * ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 c = npyv_mul_f64(a, a);
-            npyv_store_f64(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 b = npyv_load_f64(&ip2[i]);
-            npyv_f64 c = npyv_mul_f64(a, b);
-            npyv_store_f64(&op[i], c);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[i];
-    }
-}
-
-static void
-simd_binary_scalar1_multiply_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    const npyv_f64 v1 = npyv_setall_f64(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] * ip2[i];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v2 = npyv_load_f64(&ip2[i]);
-        npyv_f64 v3 = npyv_mul_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] * ip2[i];
-    }
-}
+#undef WORKAROUND_CLANG_PARTIAL_LOAD_BUG
 
-static void
-simd_binary_scalar2_multiply_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    const npyv_f64 v2 = npyv_setall_f64(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] * ip2[0];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v1 = npyv_load_f64(&ip1[i]);
-        npyv_f64 v3 = npyv_mul_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] * ip2[0];
-    }
-}
-
-#line 377
-
-static void
-simd_binary_divide_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] / ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 c = npyv_div_f64(a, a);
-            npyv_store_f64(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-            npyv_f64 a = npyv_load_f64(&ip1[i]);
-            npyv_f64 b = npyv_load_f64(&ip2[i]);
-            npyv_f64 c = npyv_div_f64(a, b);
-            npyv_store_f64(&op[i], c);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[i];
-    }
-}
+//###############################################################################
+//## Complex Single/Double precision
+//###############################################################################
 
-static void
-simd_binary_scalar1_divide_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
-{
-    const npyv_f64 v1 = npyv_setall_f64(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] / ip2[i];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v2 = npyv_load_f64(&ip2[i]);
-        npyv_f64 v3 = npyv_div_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] / ip2[i];
-    }
-}
+/********************************************************************************
+ ** op intrinics
+ ********************************************************************************/
 
-static void
-simd_binary_scalar2_divide_DOUBLE(npy_double * op, npy_double * ip1, npy_double * ip2, npy_intp n)
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_f32x2 simd_set2_f32(const float *a)
 {
-    const npyv_f64 v2 = npyv_setall_f64(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] / ip2[0];
-    }
-    LOOP_BLOCKED(npy_double, NPY_SIMD_WIDTH) {
-        npyv_f64 v1 = npyv_load_f64(&ip1[i]);
-        npyv_f64 v3 = npyv_div_f64(v1, v2);
-        npyv_store_f64(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] / ip2[0];
-    }
+    npyv_f32 fill = npyv_reinterpret_f32_u64(npyv_setall_u64(*(npy_uint64*)a));
+    npyv_f32x2 r;
+    r.val[0] = fill;
+    r.val[1] = fill;
+    return r;
 }
 
-#endif /* NPY_SIMD_F64 */
-
-#endif // NPY_HAVE_SSE2
-
-#line 450
-#line 454
-static NPY_INLINE int
-run_binary_simd_add_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
+NPY_FINLINE npyv_f32
+simd_cconjugate_f32(npyv_f32 x)
 {
-#if 1 && defined NPY_HAVE_SSE2
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
+#if NPY_SIMD_BIGENDIAN
+    const npyv_f32 mask = npyv_reinterpret_f32_u64(npyv_setall_u64(0x80000000));
 #else
-    const npy_uintp vector_size_bytes = 32;
+    const npyv_f32 mask = npyv_reinterpret_f32_u64(npyv_setall_u64(0x8000000000000000ULL));
 #endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar1_add_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar2_add_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_add_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F32
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_add_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_add_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_add_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
+    return npyv_xor_f32(x, mask);
 }
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_subtract_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
+NPY_FINLINE npyv_f32
+simd_cmul_f32(npyv_f32 a, npyv_f32 b)
 {
-#if 1 && defined NPY_HAVE_SSE2
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar1_subtract_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar2_subtract_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_subtract_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F32
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_subtract_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_subtract_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_subtract_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
+    npyv_f32 b_rev = npyv_permi128_f32(b, 1, 0, 3, 2);
+    npyv_f32 a_re = npyv_permi128_f32(a, 0, 0, 2, 2);
+    npyv_f32 a_im = npyv_permi128_f32(a, 1, 1, 3, 3);
+    // a_im * b_im, a_im * b_re
+    npyv_f32 ab_iiir = npyv_mul_f32(a_im, b_rev);
+    return npyv_muladdsub_f32(a_re, b, ab_iiir);
 }
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_multiply_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar1_multiply_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar2_multiply_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_multiply_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F32
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_multiply_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_multiply_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_multiply_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
+NPY_FINLINE npyv_f32
+simd_csquare_f32(npyv_f32 x)
+{ return simd_cmul_f32(x, x); }
 #endif
-    return 0;
-}
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_divide_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
+#if NPY_SIMD_F64
+
+NPY_FINLINE npyv_f64x2 simd_set2_f64(const double *a)
 {
-#if 1 && defined NPY_HAVE_SSE2
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar1_divide_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_scalar2_divide_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), vector_size_bytes)) {
-        sse2_binary_divide_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F32
-    npy_float * ip1 = (npy_float *)args[0];
-    npy_float * ip2 = (npy_float *)args[1];
-    npy_float * op = (npy_float *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_divide_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_divide_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_float), NPY_SIMD_WIDTH)) {
-        simd_binary_divide_FLOAT(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
+    npyv_f64 r = npyv_setall_f64(a[0]);
+    npyv_f64 i = npyv_setall_f64(a[1]);
+    return npyv_zip_f64(r, i);
 }
 
-
-#line 450
-#line 454
-static NPY_INLINE int
-run_binary_simd_add_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
+NPY_FINLINE npyv_f64
+simd_cconjugate_f64(npyv_f64 x)
 {
-#if 1 && defined NPY_HAVE_SSE2
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar1_add_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar2_add_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_add_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F64
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_add_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_add_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_add_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
+    const npyv_f64 mask = npyv_reinterpret_f64_u64(npyv_set_u64(
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL
+    ));
+    return npyv_xor_f64(x, mask);
 }
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_subtract_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
+NPY_FINLINE npyv_f64
+simd_cmul_f64(npyv_f64 a, npyv_f64 b)
 {
-#if 1 && defined NPY_HAVE_SSE2
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar1_subtract_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar2_subtract_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_subtract_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F64
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_subtract_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_subtract_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_subtract_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
+    npyv_f64 b_rev = npyv_permi128_f64(b, 1, 0);
+    npyv_f64 a_re = npyv_permi128_f64(a, 0, 0);
+    npyv_f64 a_im = npyv_permi128_f64(a, 1, 1);
+    // a_im * b_im, a_im * b_re
+    npyv_f64 ab_iiir = npyv_mul_f64(a_im, b_rev);
+    return npyv_muladdsub_f64(a_re, b, ab_iiir);
 }
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_multiply_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
+NPY_FINLINE npyv_f64
+simd_csquare_f64(npyv_f64 x)
+{ return simd_cmul_f64(x, x); }
 #endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar1_multiply_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar2_multiply_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_multiply_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F64
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_multiply_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_multiply_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_multiply_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
-}
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_divide_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
+#line 381
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_f32
+simd_cabsolute_f32(npyv_f32 re, npyv_f32 im)
 {
-#if 1 && defined NPY_HAVE_SSE2
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar1_divide_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_scalar2_divide_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), vector_size_bytes)) {
-        sse2_binary_divide_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif NPY_SIMD_F64
-    npy_double * ip1 = (npy_double *)args[0];
-    npy_double * ip2 = (npy_double *)args[1];
-    npy_double * op = (npy_double *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_divide_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_divide_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_double), NPY_SIMD_WIDTH)) {
-        simd_binary_divide_DOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
-}
+    const npyv_f32 inf = npyv_setall_f32(NPY_INFINITYF);
+    const npyv_f32 nan = npyv_setall_f32(NPY_NANF);
 
+    re = npyv_abs_f32(re);
+    im = npyv_abs_f32(im);
+    /*
+     * If real or imag = INF, then convert it to inf + j*inf
+     * Handles: inf + j*nan, nan + j*inf
+     */
+    npyv_b32 re_infmask = npyv_cmpeq_f32(re, inf);
+    npyv_b32 im_infmask = npyv_cmpeq_f32(im, inf);
+    im = npyv_select_f32(re_infmask, inf, im);
+    re = npyv_select_f32(im_infmask, inf, re);
+    /*
+     * If real or imag = NAN, then convert it to nan + j*nan
+     * Handles: x + j*nan, nan + j*x
+     */
+    npyv_b32 re_nnanmask = npyv_notnan_f32(re);
+    npyv_b32 im_nnanmask = npyv_notnan_f32(im);
+    im = npyv_select_f32(re_nnanmask, im, nan);
+    re = npyv_select_f32(im_nnanmask, re, nan);
+
+    npyv_f32 larger  = npyv_max_f32(re, im);
+    npyv_f32 smaller = npyv_min_f32(im, re);
+    /*
+     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
+     */
+    npyv_b32 zeromask = npyv_cmpeq_f32(larger, npyv_zero_f32());
+    npyv_b32 infmask = npyv_cmpeq_f32(smaller, inf);
+    npyv_b32 div_mask = npyv_not_b32(npyv_or_b32(zeromask, infmask));
 
-#line 450
-#line 454
-static NPY_INLINE int
-run_binary_simd_add_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar1_add_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar2_add_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_add_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif 0
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_add_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_add_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_add_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
+    npyv_f32 ratio = npyv_ifdivz_f32(div_mask, smaller, larger);
+    npyv_f32 hypot = npyv_sqrt_f32(
+        npyv_muladd_f32(ratio, ratio, npyv_setall_f32(1.0f)
+    ));
+    return npyv_mul_f32(hypot, larger);
 }
+#endif // VECTOR
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_subtract_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
+#line 381
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_f64
+simd_cabsolute_f64(npyv_f64 re, npyv_f64 im)
 {
-#if 0 && defined NPY_HAVE_SSE2
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar1_subtract_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar2_subtract_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_subtract_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif 0
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_subtract_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_subtract_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_subtract_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
-}
+    const npyv_f64 inf = npyv_setall_f64(NPY_INFINITY);
+    const npyv_f64 nan = npyv_setall_f64(NPY_NAN);
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_multiply_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar1_multiply_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar2_multiply_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_multiply_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif 0
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_multiply_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_multiply_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_multiply_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
-}
+    re = npyv_abs_f64(re);
+    im = npyv_abs_f64(im);
+    /*
+     * If real or imag = INF, then convert it to inf + j*inf
+     * Handles: inf + j*nan, nan + j*inf
+     */
+    npyv_b64 re_infmask = npyv_cmpeq_f64(re, inf);
+    npyv_b64 im_infmask = npyv_cmpeq_f64(im, inf);
+    im = npyv_select_f64(re_infmask, inf, im);
+    re = npyv_select_f64(im_infmask, inf, re);
+    /*
+     * If real or imag = NAN, then convert it to nan + j*nan
+     * Handles: x + j*nan, nan + j*x
+     */
+    npyv_b64 re_nnanmask = npyv_notnan_f64(re);
+    npyv_b64 im_nnanmask = npyv_notnan_f64(im);
+    im = npyv_select_f64(re_nnanmask, im, nan);
+    re = npyv_select_f64(im_nnanmask, re, nan);
+
+    npyv_f64 larger  = npyv_max_f64(re, im);
+    npyv_f64 smaller = npyv_min_f64(im, re);
+    /*
+     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
+     */
+    npyv_b64 zeromask = npyv_cmpeq_f64(larger, npyv_zero_f64());
+    npyv_b64 infmask = npyv_cmpeq_f64(smaller, inf);
+    npyv_b64 div_mask = npyv_not_b64(npyv_or_b64(zeromask, infmask));
 
-#line 454
-static NPY_INLINE int
-run_binary_simd_divide_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
-#else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar1_divide_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_scalar2_divide_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), vector_size_bytes)) {
-        sse2_binary_divide_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#elif 0
-    npy_longdouble * ip1 = (npy_longdouble *)args[0];
-    npy_longdouble * ip2 = (npy_longdouble *)args[1];
-    npy_longdouble * op = (npy_longdouble *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_divide_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_divide_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(npy_longdouble), NPY_SIMD_WIDTH)) {
-        simd_binary_divide_LONGDOUBLE(op, ip1, ip2, n);
-        return 1;
-    }
-#endif
-    return 0;
+    npyv_f64 ratio = npyv_ifdivz_f64(div_mask, smaller, larger);
+    npyv_f64 hypot = npyv_sqrt_f64(
+        npyv_muladd_f64(ratio, ratio, npyv_setall_f64(1.0)
+    ));
+    return npyv_mul_f64(hypot, larger);
 }
-
+#endif // VECTOR
 
 
 /********************************************************************************
  ** Defining ufunc inner functions
  ********************************************************************************/
-#line 518
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_add)
+#line 437
+#line 445
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_add)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
+    npy_intp len = dimensions[0];
+    char *b_src0 = args[0], *b_src1 = args[1], *b_dst = args[2];
+    npy_intp b_ssrc0 = steps[0], b_ssrc1 = steps[1], b_sdst = steps[2];
 #if 1
-        npy_float * iop1 = (npy_float *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 += FLOAT_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_float) {
-            io1 += *(npy_float *)ip2;
-        }
-        *((npy_float *)iop1) = io1;
-#endif
-    }
-    else if (!run_binary_simd_add_FLOAT(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            const npy_float in2 = *(npy_float *)ip2;
-            *((npy_float *)op1) = in1 + in2;
-        }
+    // reduce
+    if (b_ssrc0 == 0 && b_ssrc0 == b_sdst && b_src0 == b_dst &&
+        b_ssrc1 % (sizeof(npy_float)*2) == 0
+    ) {
+        npy_float *rl_im = (npy_float *)b_src0;
+        npy_float rr, ri;
+        CFLOAT_pairwise_sum(&rr, &ri, b_src1, len * 2, b_ssrc1 / 2);
+        rl_im[0] += rr;
+        rl_im[1] += ri;
+        return;
     }
-}
-
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_subtract)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-#if 0
-        npy_float * iop1 = (npy_float *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 -= FLOAT_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_float) {
-            io1 -= *(npy_float *)ip2;
-        }
-        *((npy_float *)iop1) = io1;
 #endif
-    }
-    else if (!run_binary_simd_subtract_FLOAT(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            const npy_float in2 = *(npy_float *)ip2;
-            *((npy_float *)op1) = in1 - in2;
-        }
-    }
-}
-
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_multiply)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-#if 0
-        npy_float * iop1 = (npy_float *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 *= FLOAT_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_float) {
-            io1 *= *(npy_float *)ip2;
+#if NPY_SIMD_F32
+    if (is_mem_overlap(b_src0, b_ssrc0, b_dst, b_sdst, len) ||
+        is_mem_overlap(b_src1, b_ssrc1, b_dst, b_sdst, len) ||
+        b_sdst  % sizeof(npy_float) != 0 || b_sdst == 0 ||
+        b_ssrc0 % sizeof(npy_float) != 0 ||
+        b_ssrc1 % sizeof(npy_float) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_float *src0 = (npy_float*)b_src0;
+    const npy_float *src1 = (npy_float*)b_src1;
+          npy_float *dst  = (npy_float*)b_dst;
+
+    const npy_intp ssrc0 = b_ssrc0 / sizeof(npy_float);
+    const npy_intp ssrc1 = b_ssrc1 / sizeof(npy_float);
+    const npy_intp sdst  = b_sdst / sizeof(npy_float);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    const int loadable0 = npyv_loadable_stride_s64(ssrc0);
+    const int loadable1 = npyv_loadable_stride_s64(ssrc1);
+    const int storable = npyv_storable_stride_s64(sdst);
+
+    // lots**lots of specializations, to squeeze out max performance
+    // contig
+    if (ssrc0 == 2 && ssrc0 == ssrc1 && ssrc0 == sdst) {
+        for (; len >= vstep; len -= vstep, src0 += wstep, src1 += wstep, dst += wstep) {
+            npyv_f32 a0 = npyv_load_f32(src0);
+            npyv_f32 a1 = npyv_load_f32(src0 + vstep);
+            npyv_f32 b0 = npyv_load_f32(src1);
+            npyv_f32 b1 = npyv_load_f32(src1 + vstep);
+            npyv_f32 r0 = npyv_add_f32(a0, b0);
+            npyv_f32 r1 = npyv_add_f32(a1, b1);
+            npyv_store_f32(dst, r0);
+            npyv_store_f32(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            npyv_f32 a = npyv_load2_tillz_f32(src0, len);
+            npyv_f32 b = npyv_load2_tillz_f32(src1, len);
+            npyv_f32 r = npyv_add_f32(a, b);
+            npyv_store2_till_f32(dst, len, r);
+        }
+    }
+    // scalar 0
+    else if (ssrc0 == 0) {
+        npyv_f32x2 a = simd_set2_f32(src0);
+        // contig
+        if (ssrc1 == 2 && sdst == ssrc1) {
+            for (; len >= vstep; len -= vstep, src1 += wstep, dst += wstep) {
+                npyv_f32 b0 = npyv_load_f32(src1);
+                npyv_f32 b1 = npyv_load_f32(src1 + vstep);
+                npyv_f32 r0 = npyv_add_f32(a.val[0], b0);
+                npyv_f32 r1 = npyv_add_f32(a.val[1], b1);
+                npyv_store_f32(dst, r0);
+                npyv_store_f32(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f32 b = npyv_load2_till_f32(src1, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 b = npyv_load2_tillz_f32(src1, len);
+            #endif
+                npyv_f32 r = npyv_add_f32(a.val[0], b);
+                npyv_store2_till_f32(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable1 && storable) {
+            for (; len >= vstep; len -= vstep, src1 += ssrc1*vstep, dst += sdst*vstep) {
+                npyv_f32 b0 = npyv_loadn2_f32(src1, ssrc1);
+                npyv_f32 b1 = npyv_loadn2_f32(src1 + ssrc1*hstep, ssrc1);
+                npyv_f32 r0 = npyv_add_f32(a.val[0], b0);
+                npyv_f32 r1 = npyv_add_f32(a.val[1], b1);
+                npyv_storen2_f32(dst, sdst, r0);
+                npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += ssrc1*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f32 b = npyv_loadn2_till_f32(src1, ssrc1, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 b = npyv_loadn2_tillz_f32(src1, ssrc1, len);
+            #endif
+                npyv_f32 r = npyv_add_f32(a.val[0], b);
+                npyv_storen2_till_f32(dst, sdst, len, r);
+            }
         }
-        *((npy_float *)iop1) = io1;
-#endif
-    }
-    else if (!run_binary_simd_multiply_FLOAT(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            const npy_float in2 = *(npy_float *)ip2;
-            *((npy_float *)op1) = in1 * in2;
+        else {
+            goto loop_scalar;
+        }
+    }
+    // scalar 1
+    else if (ssrc1 == 0) {
+        npyv_f32x2 b = simd_set2_f32(src1);
+        if (ssrc0 == 2 && sdst == ssrc0) {
+            for (; len >= vstep; len -= vstep, src0 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32(src0);
+                npyv_f32 a1 = npyv_load_f32(src0 + vstep);
+                npyv_f32 r0 = npyv_add_f32(a0, b.val[0]);
+                npyv_f32 r1 = npyv_add_f32(a1, b.val[1]);
+                npyv_store_f32(dst, r0);
+                npyv_store_f32(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0
+                npyv_f32 a = npyv_load2_till_f32(src0, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 a = npyv_load2_tillz_f32(src0, len);
+            #endif
+                npyv_f32 r = npyv_add_f32(a, b.val[0]);
+                npyv_store2_till_f32(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable0 && storable) {
+            for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep, dst += sdst*vstep) {
+                npyv_f32 a0 = npyv_loadn2_f32(src0, ssrc0);
+                npyv_f32 a1 = npyv_loadn2_f32(src0 + ssrc0*hstep, ssrc0);
+                npyv_f32 r0 = npyv_add_f32(a0, b.val[0]);
+                npyv_f32 r1 = npyv_add_f32(a1, b.val[1]);
+                npyv_storen2_f32(dst, sdst, r0);
+                npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += ssrc0*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f32 a = npyv_loadn2_till_f32(src0, ssrc0, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 a = npyv_loadn2_tillz_f32(src0, ssrc0, len);
+            #endif
+                npyv_f32 r = npyv_add_f32(a, b.val[0]);
+                npyv_storen2_till_f32(dst, sdst, len, r);
+            }
         }
+        else {
+            goto loop_scalar;
+        }
+    }
+    #if 0
+    // non-contig
+    else if (loadable0 && loadable1 && storable) {
+        for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep,
+                            src1 += ssrc1*vstep, dst += sdst*vstep
+        ) {
+            npyv_f32 a0 = npyv_loadn2_f32(src0, ssrc0);
+            npyv_f32 a1 = npyv_loadn2_f32(src0 + ssrc0*hstep, ssrc0);
+            npyv_f32 b0 = npyv_loadn2_f32(src1, ssrc1);
+            npyv_f32 b1 = npyv_loadn2_f32(src1 + ssrc1*hstep, ssrc1);
+            npyv_f32 r0 = npyv_add_f32(a0, b0);
+            npyv_f32 r1 = npyv_add_f32(a1, b1);
+            npyv_storen2_f32(dst, sdst, r0);
+            npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += ssrc0*hstep,
+                       src1 += ssrc1*hstep, dst += sdst*hstep
+        ) {
+        #if 0
+            npyv_f32 a = npyv_loadn2_till_f32(src0, ssrc0, len, 1.0f, 1.0f);
+            npyv_f32 b = npyv_loadn2_till_f32(src1, ssrc1, len, 1.0f, 1.0f);
+        #else
+            npyv_f32 a = npyv_loadn2_tillz_f32(src0, ssrc0, len);
+            npyv_f32 b = npyv_loadn2_tillz_f32(src1, ssrc1, len);
+        #endif
+            npyv_f32 r = npyv_add_f32(a, b);
+            npyv_storen2_till_f32(dst, sdst, len, r);
+        }
+    }
+    #endif
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src0 += b_ssrc0, b_src1 += b_ssrc1, b_dst += b_sdst) {
+        const npy_float a_r = ((npy_float *)b_src0)[0];
+        const npy_float a_i = ((npy_float *)b_src0)[1];
+        const npy_float b_r = ((npy_float *)b_src1)[0];
+        const npy_float b_i = ((npy_float *)b_src1)[1];
+    #if 0
+        ((npy_float *)b_dst)[0] = a_r*b_r - a_i*b_i;
+        ((npy_float *)b_dst)[1] = a_r*b_i + a_i*b_r;
+    #else
+        ((npy_float *)b_dst)[0] = a_r + b_r;
+        ((npy_float *)b_dst)[1] = a_i + b_i;
+    #endif
+    }
+}
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(CFLOAT_add_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        const npy_float b_r = ((npy_float *)value)[0];
+        const npy_float b_i = ((npy_float *)value)[1];
+    #if 0
+        const npy_float a_r = indexed[0];
+        const npy_float a_i = indexed[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    #else
+        indexed[0] += b_r;
+        indexed[1] += b_i;
+    #endif
     }
+    return 0;
 }
 
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_divide)
+#line 445
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_subtract)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
+    npy_intp len = dimensions[0];
+    char *b_src0 = args[0], *b_src1 = args[1], *b_dst = args[2];
+    npy_intp b_ssrc0 = steps[0], b_ssrc1 = steps[1], b_sdst = steps[2];
 #if 0
-        npy_float * iop1 = (npy_float *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 /= FLOAT_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_float) {
-            io1 /= *(npy_float *)ip2;
-        }
-        *((npy_float *)iop1) = io1;
-#endif
-    }
-    else if (!run_binary_simd_divide_FLOAT(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_float in1 = *(npy_float *)ip1;
-            const npy_float in2 = *(npy_float *)ip2;
-            *((npy_float *)op1) = in1 / in2;
-        }
+    // reduce
+    if (b_ssrc0 == 0 && b_ssrc0 == b_sdst && b_src0 == b_dst &&
+        b_ssrc1 % (sizeof(npy_float)*2) == 0
+    ) {
+        npy_float *rl_im = (npy_float *)b_src0;
+        npy_float rr, ri;
+        CFLOAT_pairwise_sum(&rr, &ri, b_src1, len * 2, b_ssrc1 / 2);
+        rl_im[0] -= rr;
+        rl_im[1] -= ri;
+        return;
     }
-}
-
-
-#line 518
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_add)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-#if 1
-        npy_double * iop1 = (npy_double *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 += DOUBLE_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_double) {
-            io1 += *(npy_double *)ip2;
-        }
-        *((npy_double *)iop1) = io1;
 #endif
-    }
-    else if (!run_binary_simd_add_DOUBLE(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            const npy_double in2 = *(npy_double *)ip2;
-            *((npy_double *)op1) = in1 + in2;
-        }
-    }
-}
-
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_subtract)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-#if 0
-        npy_double * iop1 = (npy_double *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 -= DOUBLE_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_double) {
-            io1 -= *(npy_double *)ip2;
+#if NPY_SIMD_F32
+    if (is_mem_overlap(b_src0, b_ssrc0, b_dst, b_sdst, len) ||
+        is_mem_overlap(b_src1, b_ssrc1, b_dst, b_sdst, len) ||
+        b_sdst  % sizeof(npy_float) != 0 || b_sdst == 0 ||
+        b_ssrc0 % sizeof(npy_float) != 0 ||
+        b_ssrc1 % sizeof(npy_float) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_float *src0 = (npy_float*)b_src0;
+    const npy_float *src1 = (npy_float*)b_src1;
+          npy_float *dst  = (npy_float*)b_dst;
+
+    const npy_intp ssrc0 = b_ssrc0 / sizeof(npy_float);
+    const npy_intp ssrc1 = b_ssrc1 / sizeof(npy_float);
+    const npy_intp sdst  = b_sdst / sizeof(npy_float);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    const int loadable0 = npyv_loadable_stride_s64(ssrc0);
+    const int loadable1 = npyv_loadable_stride_s64(ssrc1);
+    const int storable = npyv_storable_stride_s64(sdst);
+
+    // lots**lots of specializations, to squeeze out max performance
+    // contig
+    if (ssrc0 == 2 && ssrc0 == ssrc1 && ssrc0 == sdst) {
+        for (; len >= vstep; len -= vstep, src0 += wstep, src1 += wstep, dst += wstep) {
+            npyv_f32 a0 = npyv_load_f32(src0);
+            npyv_f32 a1 = npyv_load_f32(src0 + vstep);
+            npyv_f32 b0 = npyv_load_f32(src1);
+            npyv_f32 b1 = npyv_load_f32(src1 + vstep);
+            npyv_f32 r0 = npyv_sub_f32(a0, b0);
+            npyv_f32 r1 = npyv_sub_f32(a1, b1);
+            npyv_store_f32(dst, r0);
+            npyv_store_f32(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            npyv_f32 a = npyv_load2_tillz_f32(src0, len);
+            npyv_f32 b = npyv_load2_tillz_f32(src1, len);
+            npyv_f32 r = npyv_sub_f32(a, b);
+            npyv_store2_till_f32(dst, len, r);
+        }
+    }
+    // scalar 0
+    else if (ssrc0 == 0) {
+        npyv_f32x2 a = simd_set2_f32(src0);
+        // contig
+        if (ssrc1 == 2 && sdst == ssrc1) {
+            for (; len >= vstep; len -= vstep, src1 += wstep, dst += wstep) {
+                npyv_f32 b0 = npyv_load_f32(src1);
+                npyv_f32 b1 = npyv_load_f32(src1 + vstep);
+                npyv_f32 r0 = npyv_sub_f32(a.val[0], b0);
+                npyv_f32 r1 = npyv_sub_f32(a.val[1], b1);
+                npyv_store_f32(dst, r0);
+                npyv_store_f32(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f32 b = npyv_load2_till_f32(src1, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 b = npyv_load2_tillz_f32(src1, len);
+            #endif
+                npyv_f32 r = npyv_sub_f32(a.val[0], b);
+                npyv_store2_till_f32(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable1 && storable) {
+            for (; len >= vstep; len -= vstep, src1 += ssrc1*vstep, dst += sdst*vstep) {
+                npyv_f32 b0 = npyv_loadn2_f32(src1, ssrc1);
+                npyv_f32 b1 = npyv_loadn2_f32(src1 + ssrc1*hstep, ssrc1);
+                npyv_f32 r0 = npyv_sub_f32(a.val[0], b0);
+                npyv_f32 r1 = npyv_sub_f32(a.val[1], b1);
+                npyv_storen2_f32(dst, sdst, r0);
+                npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += ssrc1*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f32 b = npyv_loadn2_till_f32(src1, ssrc1, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 b = npyv_loadn2_tillz_f32(src1, ssrc1, len);
+            #endif
+                npyv_f32 r = npyv_sub_f32(a.val[0], b);
+                npyv_storen2_till_f32(dst, sdst, len, r);
+            }
         }
-        *((npy_double *)iop1) = io1;
-#endif
-    }
-    else if (!run_binary_simd_subtract_DOUBLE(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            const npy_double in2 = *(npy_double *)ip2;
-            *((npy_double *)op1) = in1 - in2;
+        else {
+            goto loop_scalar;
+        }
+    }
+    // scalar 1
+    else if (ssrc1 == 0) {
+        npyv_f32x2 b = simd_set2_f32(src1);
+        if (ssrc0 == 2 && sdst == ssrc0) {
+            for (; len >= vstep; len -= vstep, src0 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32(src0);
+                npyv_f32 a1 = npyv_load_f32(src0 + vstep);
+                npyv_f32 r0 = npyv_sub_f32(a0, b.val[0]);
+                npyv_f32 r1 = npyv_sub_f32(a1, b.val[1]);
+                npyv_store_f32(dst, r0);
+                npyv_store_f32(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0
+                npyv_f32 a = npyv_load2_till_f32(src0, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 a = npyv_load2_tillz_f32(src0, len);
+            #endif
+                npyv_f32 r = npyv_sub_f32(a, b.val[0]);
+                npyv_store2_till_f32(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable0 && storable) {
+            for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep, dst += sdst*vstep) {
+                npyv_f32 a0 = npyv_loadn2_f32(src0, ssrc0);
+                npyv_f32 a1 = npyv_loadn2_f32(src0 + ssrc0*hstep, ssrc0);
+                npyv_f32 r0 = npyv_sub_f32(a0, b.val[0]);
+                npyv_f32 r1 = npyv_sub_f32(a1, b.val[1]);
+                npyv_storen2_f32(dst, sdst, r0);
+                npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += ssrc0*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f32 a = npyv_loadn2_till_f32(src0, ssrc0, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 a = npyv_loadn2_tillz_f32(src0, ssrc0, len);
+            #endif
+                npyv_f32 r = npyv_sub_f32(a, b.val[0]);
+                npyv_storen2_till_f32(dst, sdst, len, r);
+            }
         }
+        else {
+            goto loop_scalar;
+        }
+    }
+    #if 0
+    // non-contig
+    else if (loadable0 && loadable1 && storable) {
+        for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep,
+                            src1 += ssrc1*vstep, dst += sdst*vstep
+        ) {
+            npyv_f32 a0 = npyv_loadn2_f32(src0, ssrc0);
+            npyv_f32 a1 = npyv_loadn2_f32(src0 + ssrc0*hstep, ssrc0);
+            npyv_f32 b0 = npyv_loadn2_f32(src1, ssrc1);
+            npyv_f32 b1 = npyv_loadn2_f32(src1 + ssrc1*hstep, ssrc1);
+            npyv_f32 r0 = npyv_sub_f32(a0, b0);
+            npyv_f32 r1 = npyv_sub_f32(a1, b1);
+            npyv_storen2_f32(dst, sdst, r0);
+            npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += ssrc0*hstep,
+                       src1 += ssrc1*hstep, dst += sdst*hstep
+        ) {
+        #if 0
+            npyv_f32 a = npyv_loadn2_till_f32(src0, ssrc0, len, 1.0f, 1.0f);
+            npyv_f32 b = npyv_loadn2_till_f32(src1, ssrc1, len, 1.0f, 1.0f);
+        #else
+            npyv_f32 a = npyv_loadn2_tillz_f32(src0, ssrc0, len);
+            npyv_f32 b = npyv_loadn2_tillz_f32(src1, ssrc1, len);
+        #endif
+            npyv_f32 r = npyv_sub_f32(a, b);
+            npyv_storen2_till_f32(dst, sdst, len, r);
+        }
+    }
+    #endif
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src0 += b_ssrc0, b_src1 += b_ssrc1, b_dst += b_sdst) {
+        const npy_float a_r = ((npy_float *)b_src0)[0];
+        const npy_float a_i = ((npy_float *)b_src0)[1];
+        const npy_float b_r = ((npy_float *)b_src1)[0];
+        const npy_float b_i = ((npy_float *)b_src1)[1];
+    #if 0
+        ((npy_float *)b_dst)[0] = a_r*b_r - a_i*b_i;
+        ((npy_float *)b_dst)[1] = a_r*b_i + a_i*b_r;
+    #else
+        ((npy_float *)b_dst)[0] = a_r - b_r;
+        ((npy_float *)b_dst)[1] = a_i - b_i;
+    #endif
+    }
+}
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(CFLOAT_subtract_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        const npy_float b_r = ((npy_float *)value)[0];
+        const npy_float b_i = ((npy_float *)value)[1];
+    #if 0
+        const npy_float a_r = indexed[0];
+        const npy_float a_i = indexed[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    #else
+        indexed[0] -= b_r;
+        indexed[1] -= b_i;
+    #endif
     }
+    return 0;
 }
 
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_multiply)
+#line 445
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_multiply)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (IS_BINARY_REDUCE) {
+    npy_intp len = dimensions[0];
+    char *b_src0 = args[0], *b_src1 = args[1], *b_dst = args[2];
+    npy_intp b_ssrc0 = steps[0], b_ssrc1 = steps[1], b_sdst = steps[2];
 #if 0
-        npy_double * iop1 = (npy_double *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 *= DOUBLE_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_double) {
-            io1 *= *(npy_double *)ip2;
-        }
-        *((npy_double *)iop1) = io1;
-#endif
-    }
-    else if (!run_binary_simd_multiply_DOUBLE(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            const npy_double in2 = *(npy_double *)ip2;
-            *((npy_double *)op1) = in1 * in2;
-        }
+    // reduce
+    if (b_ssrc0 == 0 && b_ssrc0 == b_sdst && b_src0 == b_dst &&
+        b_ssrc1 % (sizeof(npy_float)*2) == 0
+    ) {
+        npy_float *rl_im = (npy_float *)b_src0;
+        npy_float rr, ri;
+        CFLOAT_pairwise_sum(&rr, &ri, b_src1, len * 2, b_ssrc1 / 2);
+        rl_im[0] *= rr;
+        rl_im[1] *= ri;
+        return;
     }
-}
-
-#line 524
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_divide)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (IS_BINARY_REDUCE) {
-#if 0
-        npy_double * iop1 = (npy_double *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 /= DOUBLE_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(npy_double) {
-            io1 /= *(npy_double *)ip2;
-        }
-        *((npy_double *)iop1) = io1;
 #endif
-    }
-    else if (!run_binary_simd_divide_DOUBLE(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_double in1 = *(npy_double *)ip1;
-            const npy_double in2 = *(npy_double *)ip2;
-            *((npy_double *)op1) = in1 / in2;
+#if NPY_SIMD_F32
+    if (is_mem_overlap(b_src0, b_ssrc0, b_dst, b_sdst, len) ||
+        is_mem_overlap(b_src1, b_ssrc1, b_dst, b_sdst, len) ||
+        b_sdst  % sizeof(npy_float) != 0 || b_sdst == 0 ||
+        b_ssrc0 % sizeof(npy_float) != 0 ||
+        b_ssrc1 % sizeof(npy_float) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_float *src0 = (npy_float*)b_src0;
+    const npy_float *src1 = (npy_float*)b_src1;
+          npy_float *dst  = (npy_float*)b_dst;
+
+    const npy_intp ssrc0 = b_ssrc0 / sizeof(npy_float);
+    const npy_intp ssrc1 = b_ssrc1 / sizeof(npy_float);
+    const npy_intp sdst  = b_sdst / sizeof(npy_float);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    const int loadable0 = npyv_loadable_stride_s64(ssrc0);
+    const int loadable1 = npyv_loadable_stride_s64(ssrc1);
+    const int storable = npyv_storable_stride_s64(sdst);
+
+    // lots**lots of specializations, to squeeze out max performance
+    // contig
+    if (ssrc0 == 2 && ssrc0 == ssrc1 && ssrc0 == sdst) {
+        for (; len >= vstep; len -= vstep, src0 += wstep, src1 += wstep, dst += wstep) {
+            npyv_f32 a0 = npyv_load_f32(src0);
+            npyv_f32 a1 = npyv_load_f32(src0 + vstep);
+            npyv_f32 b0 = npyv_load_f32(src1);
+            npyv_f32 b1 = npyv_load_f32(src1 + vstep);
+            npyv_f32 r0 = simd_cmul_f32(a0, b0);
+            npyv_f32 r1 = simd_cmul_f32(a1, b1);
+            npyv_store_f32(dst, r0);
+            npyv_store_f32(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            npyv_f32 a = npyv_load2_tillz_f32(src0, len);
+            npyv_f32 b = npyv_load2_tillz_f32(src1, len);
+            npyv_f32 r = simd_cmul_f32(a, b);
+            npyv_store2_till_f32(dst, len, r);
+        }
+    }
+    // scalar 0
+    else if (ssrc0 == 0) {
+        npyv_f32x2 a = simd_set2_f32(src0);
+        // contig
+        if (ssrc1 == 2 && sdst == ssrc1) {
+            for (; len >= vstep; len -= vstep, src1 += wstep, dst += wstep) {
+                npyv_f32 b0 = npyv_load_f32(src1);
+                npyv_f32 b1 = npyv_load_f32(src1 + vstep);
+                npyv_f32 r0 = simd_cmul_f32(a.val[0], b0);
+                npyv_f32 r1 = simd_cmul_f32(a.val[1], b1);
+                npyv_store_f32(dst, r0);
+                npyv_store_f32(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 1
+                npyv_f32 b = npyv_load2_till_f32(src1, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 b = npyv_load2_tillz_f32(src1, len);
+            #endif
+                npyv_f32 r = simd_cmul_f32(a.val[0], b);
+                npyv_store2_till_f32(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable1 && storable) {
+            for (; len >= vstep; len -= vstep, src1 += ssrc1*vstep, dst += sdst*vstep) {
+                npyv_f32 b0 = npyv_loadn2_f32(src1, ssrc1);
+                npyv_f32 b1 = npyv_loadn2_f32(src1 + ssrc1*hstep, ssrc1);
+                npyv_f32 r0 = simd_cmul_f32(a.val[0], b0);
+                npyv_f32 r1 = simd_cmul_f32(a.val[1], b1);
+                npyv_storen2_f32(dst, sdst, r0);
+                npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += ssrc1*hstep, dst += sdst*hstep) {
+            #if 1
+                npyv_f32 b = npyv_loadn2_till_f32(src1, ssrc1, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 b = npyv_loadn2_tillz_f32(src1, ssrc1, len);
+            #endif
+                npyv_f32 r = simd_cmul_f32(a.val[0], b);
+                npyv_storen2_till_f32(dst, sdst, len, r);
+            }
         }
+        else {
+            goto loop_scalar;
+        }
+    }
+    // scalar 1
+    else if (ssrc1 == 0) {
+        npyv_f32x2 b = simd_set2_f32(src1);
+        if (ssrc0 == 2 && sdst == ssrc0) {
+            for (; len >= vstep; len -= vstep, src0 += wstep, dst += wstep) {
+                npyv_f32 a0 = npyv_load_f32(src0);
+                npyv_f32 a1 = npyv_load_f32(src0 + vstep);
+                npyv_f32 r0 = simd_cmul_f32(a0, b.val[0]);
+                npyv_f32 r1 = simd_cmul_f32(a1, b.val[1]);
+                npyv_store_f32(dst, r0);
+                npyv_store_f32(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 1
+                npyv_f32 a = npyv_load2_till_f32(src0, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 a = npyv_load2_tillz_f32(src0, len);
+            #endif
+                npyv_f32 r = simd_cmul_f32(a, b.val[0]);
+                npyv_store2_till_f32(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable0 && storable) {
+            for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep, dst += sdst*vstep) {
+                npyv_f32 a0 = npyv_loadn2_f32(src0, ssrc0);
+                npyv_f32 a1 = npyv_loadn2_f32(src0 + ssrc0*hstep, ssrc0);
+                npyv_f32 r0 = simd_cmul_f32(a0, b.val[0]);
+                npyv_f32 r1 = simd_cmul_f32(a1, b.val[1]);
+                npyv_storen2_f32(dst, sdst, r0);
+                npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += ssrc0*hstep, dst += sdst*hstep) {
+            #if 1
+                npyv_f32 a = npyv_loadn2_till_f32(src0, ssrc0, len, 1.0f, 1.0f);
+            #else
+                npyv_f32 a = npyv_loadn2_tillz_f32(src0, ssrc0, len);
+            #endif
+                npyv_f32 r = simd_cmul_f32(a, b.val[0]);
+                npyv_storen2_till_f32(dst, sdst, len, r);
+            }
+        }
+        else {
+            goto loop_scalar;
+        }
+    }
+    #if 1
+    // non-contig
+    else if (loadable0 && loadable1 && storable) {
+        for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep,
+                            src1 += ssrc1*vstep, dst += sdst*vstep
+        ) {
+            npyv_f32 a0 = npyv_loadn2_f32(src0, ssrc0);
+            npyv_f32 a1 = npyv_loadn2_f32(src0 + ssrc0*hstep, ssrc0);
+            npyv_f32 b0 = npyv_loadn2_f32(src1, ssrc1);
+            npyv_f32 b1 = npyv_loadn2_f32(src1 + ssrc1*hstep, ssrc1);
+            npyv_f32 r0 = simd_cmul_f32(a0, b0);
+            npyv_f32 r1 = simd_cmul_f32(a1, b1);
+            npyv_storen2_f32(dst, sdst, r0);
+            npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += ssrc0*hstep,
+                       src1 += ssrc1*hstep, dst += sdst*hstep
+        ) {
+        #if 1
+            npyv_f32 a = npyv_loadn2_till_f32(src0, ssrc0, len, 1.0f, 1.0f);
+            npyv_f32 b = npyv_loadn2_till_f32(src1, ssrc1, len, 1.0f, 1.0f);
+        #else
+            npyv_f32 a = npyv_loadn2_tillz_f32(src0, ssrc0, len);
+            npyv_f32 b = npyv_loadn2_tillz_f32(src1, ssrc1, len);
+        #endif
+            npyv_f32 r = simd_cmul_f32(a, b);
+            npyv_storen2_till_f32(dst, sdst, len, r);
+        }
+    }
+    #endif
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src0 += b_ssrc0, b_src1 += b_ssrc1, b_dst += b_sdst) {
+        const npy_float a_r = ((npy_float *)b_src0)[0];
+        const npy_float a_i = ((npy_float *)b_src0)[1];
+        const npy_float b_r = ((npy_float *)b_src1)[0];
+        const npy_float b_i = ((npy_float *)b_src1)[1];
+    #if 1
+        ((npy_float *)b_dst)[0] = a_r*b_r - a_i*b_i;
+        ((npy_float *)b_dst)[1] = a_r*b_i + a_i*b_r;
+    #else
+        ((npy_float *)b_dst)[0] = a_r * b_r;
+        ((npy_float *)b_dst)[1] = a_i * b_i;
+    #endif
+    }
+}
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(CFLOAT_multiply_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        const npy_float b_r = ((npy_float *)value)[0];
+        const npy_float b_i = ((npy_float *)value)[1];
+    #if 1
+        const npy_float a_r = indexed[0];
+        const npy_float a_i = indexed[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    #else
+        indexed[0] *= b_r;
+        indexed[1] *= b_i;
+    #endif
     }
-}
-
-
-
-//###############################################################################
-//## Complex Single/Double precision
-//###############################################################################
-/********************************************************************************
- ** Defining the SIMD kernels
- ********************************************************************************/
-#if !defined(_MSC_VER) && defined(NPY_HAVE_AVX512F)
-    /**
-     * For somehow MSVC commit aggressive optimization lead
-     * to raises 'RuntimeWarning: invalid value encountered in multiply'
-     *
-     * the issue mainly caused by '_mm512_maskz_loadu_ps', we need to
-     * investigate about it while moving to NPYV.
-     */
-    #define AVX512F_NOMSVC
-#endif
-
-#ifdef AVX512F_NOMSVC
-NPY_FINLINE __mmask16
-avx512_get_full_load_mask_ps(void)
-{
-    return 0xFFFF;
-}
-
-NPY_FINLINE __mmask8
-avx512_get_full_load_mask_pd(void)
-{
-    return 0xFF;
-}
-NPY_FINLINE __m512
-avx512_masked_load_ps(__mmask16 mask, npy_float* addr)
-{
-    return _mm512_maskz_loadu_ps(mask, (__m512 *)addr);
-}
-
-NPY_FINLINE __m512d
-avx512_masked_load_pd(__mmask8 mask, npy_double* addr)
-{
-    return _mm512_maskz_loadu_pd(mask, (__m512d *)addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_get_partial_load_mask_ps(const npy_int num_elem, const npy_int total_elem)
-{
-    return (0x0001 << num_elem) - 0x0001;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem)
-{
-    return (0x01 << num_elem) - 0x01;
-}
-#line 617
-NPY_FINLINE __m512
-avx512_hadd_ps(const __m512 x)
-{
-    return _mm512_add_ps(x, _mm512_permute_ps(x, 0xb1));
-}
-
-NPY_FINLINE __m512
-avx512_hsub_ps(const __m512 x)
-{
-    return _mm512_sub_ps(x, _mm512_permute_ps(x, 0xb1));
-}
-NPY_FINLINE __m512
-avx512_cmul_ps(__m512 x1, __m512 x2)
-{
-    // x1 = r1, i1
-    // x2 = r2, i2
-    __m512 x3  = _mm512_permute_ps(x2, 0xb1);   // i2, r2
-    __m512 x12 = _mm512_mul_ps(x1, x2);            // r1*r2, i1*i2
-    __m512 x13 = _mm512_mul_ps(x1, x3);            // r1*i2, r2*i1
-    __m512 outreal = avx512_hsub_ps(x12);          // r1*r2 - i1*i2, r1*r2 - i1*i2
-    __m512 outimg  = avx512_hadd_ps(x13);          // r1*i2 + i1*r2, r1*i2 + i1*r2
-    return _mm512_mask_blend_ps(0xAAAA, outreal, outimg);
-}
-
-#line 617
-NPY_FINLINE __m512d
-avx512_hadd_pd(const __m512d x)
-{
-    return _mm512_add_pd(x, _mm512_permute_pd(x, 0x55));
-}
-
-NPY_FINLINE __m512d
-avx512_hsub_pd(const __m512d x)
-{
-    return _mm512_sub_pd(x, _mm512_permute_pd(x, 0x55));
-}
-NPY_FINLINE __m512d
-avx512_cmul_pd(__m512d x1, __m512d x2)
-{
-    // x1 = r1, i1
-    // x2 = r2, i2
-    __m512d x3  = _mm512_permute_pd(x2, 0x55);   // i2, r2
-    __m512d x12 = _mm512_mul_pd(x1, x2);            // r1*r2, i1*i2
-    __m512d x13 = _mm512_mul_pd(x1, x3);            // r1*i2, r2*i1
-    __m512d outreal = avx512_hsub_pd(x12);          // r1*r2 - i1*i2, r1*r2 - i1*i2
-    __m512d outimg  = avx512_hadd_pd(x13);          // r1*i2 + i1*r2, r1*i2 + i1*r2
-    return _mm512_mask_blend_pd(0xAA, outreal, outimg);
-}
-
-#endif
-
-#line 657
-#line 661
-#if defined AVX512F_NOMSVC
-static NPY_INLINE void
-AVX512F_add_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-    const npy_intp array_size = dimensions[0];
-    npy_intp num_remaining_elements = 2*array_size;
-    npy_float* ip1 = (npy_float*) args[0];
-    npy_float* ip2 = (npy_float*) args[1];
-    npy_float* op  = (npy_float*) args[2];
-
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1, x2;
-        x1 = avx512_masked_load_ps(load_mask, ip1);
-        x2 = avx512_masked_load_ps(load_mask, ip2);
-
-        __m512 out = _mm512_add_ps(x1, x2);
-
-        _mm512_mask_storeu_ps(op, load_mask, out);
-
-        ip1 += 16;
-        ip2 += 16;
-        op += 16;
-        num_remaining_elements -= 16;
-    }
-}
-#endif // AVX512F_NOMSVC
-
-#line 661
-#if defined AVX512F_NOMSVC
-static NPY_INLINE void
-AVX512F_subtract_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-    const npy_intp array_size = dimensions[0];
-    npy_intp num_remaining_elements = 2*array_size;
-    npy_float* ip1 = (npy_float*) args[0];
-    npy_float* ip2 = (npy_float*) args[1];
-    npy_float* op  = (npy_float*) args[2];
-
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1, x2;
-        x1 = avx512_masked_load_ps(load_mask, ip1);
-        x2 = avx512_masked_load_ps(load_mask, ip2);
-
-        __m512 out = _mm512_sub_ps(x1, x2);
-
-        _mm512_mask_storeu_ps(op, load_mask, out);
-
-        ip1 += 16;
-        ip2 += 16;
-        op += 16;
-        num_remaining_elements -= 16;
-    }
-}
-#endif // AVX512F_NOMSVC
-
-#line 661
-#if defined AVX512F_NOMSVC
-static NPY_INLINE void
-AVX512F_multiply_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-    const npy_intp array_size = dimensions[0];
-    npy_intp num_remaining_elements = 2*array_size;
-    npy_float* ip1 = (npy_float*) args[0];
-    npy_float* ip2 = (npy_float*) args[1];
-    npy_float* op  = (npy_float*) args[2];
-
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1, x2;
-        x1 = avx512_masked_load_ps(load_mask, ip1);
-        x2 = avx512_masked_load_ps(load_mask, ip2);
-
-        __m512 out = avx512_cmul_ps(x1, x2);
-
-        _mm512_mask_storeu_ps(op, load_mask, out);
-
-        ip1 += 16;
-        ip2 += 16;
-        op += 16;
-        num_remaining_elements -= 16;
-    }
-}
-#endif // AVX512F_NOMSVC
-
-
-#line 657
-#line 661
-#if defined AVX512F_NOMSVC
-static NPY_INLINE void
-AVX512F_add_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-    const npy_intp array_size = dimensions[0];
-    npy_intp num_remaining_elements = 2*array_size;
-    npy_double* ip1 = (npy_double*) args[0];
-    npy_double* ip2 = (npy_double*) args[1];
-    npy_double* op  = (npy_double*) args[2];
-
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1, x2;
-        x1 = avx512_masked_load_pd(load_mask, ip1);
-        x2 = avx512_masked_load_pd(load_mask, ip2);
-
-        __m512d out = _mm512_add_pd(x1, x2);
-
-        _mm512_mask_storeu_pd(op, load_mask, out);
-
-        ip1 += 8;
-        ip2 += 8;
-        op += 8;
-        num_remaining_elements -= 8;
-    }
-}
-#endif // AVX512F_NOMSVC
-
-#line 661
-#if defined AVX512F_NOMSVC
-static NPY_INLINE void
-AVX512F_subtract_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-    const npy_intp array_size = dimensions[0];
-    npy_intp num_remaining_elements = 2*array_size;
-    npy_double* ip1 = (npy_double*) args[0];
-    npy_double* ip2 = (npy_double*) args[1];
-    npy_double* op  = (npy_double*) args[2];
-
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1, x2;
-        x1 = avx512_masked_load_pd(load_mask, ip1);
-        x2 = avx512_masked_load_pd(load_mask, ip2);
-
-        __m512d out = _mm512_sub_pd(x1, x2);
-
-        _mm512_mask_storeu_pd(op, load_mask, out);
-
-        ip1 += 8;
-        ip2 += 8;
-        op += 8;
-        num_remaining_elements -= 8;
-    }
-}
-#endif // AVX512F_NOMSVC
-
-#line 661
-#if defined AVX512F_NOMSVC
-static NPY_INLINE void
-AVX512F_multiply_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-    const npy_intp array_size = dimensions[0];
-    npy_intp num_remaining_elements = 2*array_size;
-    npy_double* ip1 = (npy_double*) args[0];
-    npy_double* ip2 = (npy_double*) args[1];
-    npy_double* op  = (npy_double*) args[2];
-
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1, x2;
-        x1 = avx512_masked_load_pd(load_mask, ip1);
-        x2 = avx512_masked_load_pd(load_mask, ip2);
-
-        __m512d out = avx512_cmul_pd(x1, x2);
-
-        _mm512_mask_storeu_pd(op, load_mask, out);
-
-        ip1 += 8;
-        ip2 += 8;
-        op += 8;
-        num_remaining_elements -= 8;
-    }
-}
-#endif // AVX512F_NOMSVC
-
-
-
-#line 701
-#line 704
-static NPY_INLINE int
-run_binary_avx512f_add_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined AVX512F_NOMSVC
-    if (IS_BINARY_STRIDE_ONE(8, 64)) {
-        AVX512F_add_CFLOAT(args, dimensions, steps);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-#line 704
-static NPY_INLINE int
-run_binary_avx512f_subtract_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined AVX512F_NOMSVC
-    if (IS_BINARY_STRIDE_ONE(8, 64)) {
-        AVX512F_subtract_CFLOAT(args, dimensions, steps);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-#line 704
-static NPY_INLINE int
-run_binary_avx512f_multiply_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined AVX512F_NOMSVC
-    if (IS_BINARY_STRIDE_ONE(8, 64)) {
-        AVX512F_multiply_CFLOAT(args, dimensions, steps);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-#line 701
-#line 704
-static NPY_INLINE int
-run_binary_avx512f_add_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined AVX512F_NOMSVC
-    if (IS_BINARY_STRIDE_ONE(16, 64)) {
-        AVX512F_add_CDOUBLE(args, dimensions, steps);
-        return 1;
-    }
-    else
-        return 0;
-#endif
     return 0;
 }
 
-#line 704
-static NPY_INLINE int
-run_binary_avx512f_subtract_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined AVX512F_NOMSVC
-    if (IS_BINARY_STRIDE_ONE(16, 64)) {
-        AVX512F_subtract_CDOUBLE(args, dimensions, steps);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
 
-#line 704
-static NPY_INLINE int
-run_binary_avx512f_multiply_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
+#line 691
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#if defined AVX512F_NOMSVC
-    if (IS_BINARY_STRIDE_ONE(16, 64)) {
-        AVX512F_multiply_CDOUBLE(args, dimensions, steps);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-
-/********************************************************************************
- ** Defining ufunc inner functions
- ********************************************************************************/
-#line 730
-#line 736
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_add)
+    npy_intp len = dimensions[0];
+    char *b_src = args[0], *b_dst = args[1];
+    npy_intp b_ssrc = steps[0], b_sdst = steps[1];
+#if NPY_SIMD_F32
+    if (is_mem_overlap(b_src, b_ssrc, b_dst, b_sdst, len) ||
+        b_sdst % sizeof(npy_float) != 0 ||
+        b_ssrc % sizeof(npy_float) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_float *src  = (npy_float*)b_src;
+          npy_float *dst  = (npy_float*)b_dst;
+    const npy_intp ssrc = b_ssrc / sizeof(npy_float);
+    const npy_intp sdst = b_sdst / sizeof(npy_float);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    if (ssrc == 2 && ssrc == sdst) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += wstep) {
+            npyv_f32 a0 = npyv_load_f32(src);
+            npyv_f32 a1 = npyv_load_f32(src + vstep);
+            npyv_f32 r0 = simd_cconjugate_f32(a0);
+            npyv_f32 r1 = simd_cconjugate_f32(a1);
+            npyv_store_f32(dst, r0);
+            npyv_store_f32(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += vstep) {
+            npyv_f32 a = npyv_load2_tillz_f32(src, len);
+            npyv_f32 r = simd_cconjugate_f32(a);
+            npyv_store2_till_f32(dst, len, r);
+        }
+    }
+    else if (ssrc == 2 && npyv_storable_stride_s64(sdst)) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += sdst*vstep) {
+            npyv_f32 a0 = npyv_load_f32(src);
+            npyv_f32 a1 = npyv_load_f32(src + vstep);
+            npyv_f32 r0 = simd_cconjugate_f32(a0);
+            npyv_f32 r1 = simd_cconjugate_f32(a1);
+            npyv_storen2_f32(dst, sdst, r0);
+            npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += sdst*hstep) {
+            npyv_f32 a = npyv_load2_tillz_f32(src, len);
+            npyv_f32 r = simd_cconjugate_f32(a);
+            npyv_storen2_till_f32(dst, sdst, len, r);
+        }
+    }
+    else if (sdst == 2 && npyv_loadable_stride_s64(ssrc)) {
+        for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += wstep) {
+            npyv_f32 a0 = npyv_loadn2_f32(src, ssrc);
+            npyv_f32 a1 = npyv_loadn2_f32(src + ssrc*hstep, ssrc);
+            npyv_f32 r0 = simd_cconjugate_f32(a0);
+            npyv_f32 r1 = simd_cconjugate_f32(a1);
+            npyv_store_f32(dst, r0);
+            npyv_store_f32(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += ssrc*hstep, dst += vstep) {
+            npyv_f32 a = npyv_loadn2_tillz_f32((npy_float*)src, ssrc, len);
+            npyv_f32 r = simd_cconjugate_f32(a);
+            npyv_store2_till_f32(dst, len, r);
+        }
+    }
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src += b_ssrc, b_dst += b_sdst) {
+        const npy_float rl = ((npy_float *)b_src)[0];
+        const npy_float im = ((npy_float *)b_src)[1];
+    #if 0
+        ((npy_float *)b_dst)[0] = rl*rl - im*im;
+        ((npy_float *)b_dst)[1] = rl*im + im*rl;
+    #else
+        ((npy_float *)b_dst)[0] = rl;
+        ((npy_float *)b_dst)[1] = -im;
+    #endif
+    }
+}
+
+#line 691
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_square)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    // Parenthesis around 1 tells clang dead code is intentional
-    if (IS_BINARY_REDUCE && (1)) {
-        npy_intp n = dimensions[0];
-        npy_float * or = ((npy_float *)args[0]);
-        npy_float * oi = ((npy_float *)args[0]) + 1;
-        npy_float rr, ri;
-
-        CFLOAT_pairwise_sum(&rr, &ri, args[1], n * 2, steps[1] / 2);
-        *or += rr;
-        *oi += ri;
-        return;
-    }
-    if (!run_binary_avx512f_add_CFLOAT(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_float in1r = ((npy_float *)ip1)[0];
-            const npy_float in1i = ((npy_float *)ip1)[1];
-            const npy_float in2r = ((npy_float *)ip2)[0];
-            const npy_float in2i = ((npy_float *)ip2)[1];
-            ((npy_float *)op1)[0] = in1r + in2r;
-            ((npy_float *)op1)[1] = in1i + in2i;
-        }
-    }
-}
-
-#line 736
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_subtract)
+    npy_intp len = dimensions[0];
+    char *b_src = args[0], *b_dst = args[1];
+    npy_intp b_ssrc = steps[0], b_sdst = steps[1];
+#if NPY_SIMD_F32
+    if (is_mem_overlap(b_src, b_ssrc, b_dst, b_sdst, len) ||
+        b_sdst % sizeof(npy_float) != 0 ||
+        b_ssrc % sizeof(npy_float) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_float *src  = (npy_float*)b_src;
+          npy_float *dst  = (npy_float*)b_dst;
+    const npy_intp ssrc = b_ssrc / sizeof(npy_float);
+    const npy_intp sdst = b_sdst / sizeof(npy_float);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    if (ssrc == 2 && ssrc == sdst) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += wstep) {
+            npyv_f32 a0 = npyv_load_f32(src);
+            npyv_f32 a1 = npyv_load_f32(src + vstep);
+            npyv_f32 r0 = simd_csquare_f32(a0);
+            npyv_f32 r1 = simd_csquare_f32(a1);
+            npyv_store_f32(dst, r0);
+            npyv_store_f32(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += vstep) {
+            npyv_f32 a = npyv_load2_tillz_f32(src, len);
+            npyv_f32 r = simd_csquare_f32(a);
+            npyv_store2_till_f32(dst, len, r);
+        }
+    }
+    else if (ssrc == 2 && npyv_storable_stride_s64(sdst)) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += sdst*vstep) {
+            npyv_f32 a0 = npyv_load_f32(src);
+            npyv_f32 a1 = npyv_load_f32(src + vstep);
+            npyv_f32 r0 = simd_csquare_f32(a0);
+            npyv_f32 r1 = simd_csquare_f32(a1);
+            npyv_storen2_f32(dst, sdst, r0);
+            npyv_storen2_f32(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += sdst*hstep) {
+            npyv_f32 a = npyv_load2_tillz_f32(src, len);
+            npyv_f32 r = simd_csquare_f32(a);
+            npyv_storen2_till_f32(dst, sdst, len, r);
+        }
+    }
+    else if (sdst == 2 && npyv_loadable_stride_s64(ssrc)) {
+        for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += wstep) {
+            npyv_f32 a0 = npyv_loadn2_f32(src, ssrc);
+            npyv_f32 a1 = npyv_loadn2_f32(src + ssrc*hstep, ssrc);
+            npyv_f32 r0 = simd_csquare_f32(a0);
+            npyv_f32 r1 = simd_csquare_f32(a1);
+            npyv_store_f32(dst, r0);
+            npyv_store_f32(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += ssrc*hstep, dst += vstep) {
+            npyv_f32 a = npyv_loadn2_tillz_f32((npy_float*)src, ssrc, len);
+            npyv_f32 r = simd_csquare_f32(a);
+            npyv_store2_till_f32(dst, len, r);
+        }
+    }
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src += b_ssrc, b_dst += b_sdst) {
+        const npy_float rl = ((npy_float *)b_src)[0];
+        const npy_float im = ((npy_float *)b_src)[1];
+    #if 1
+        ((npy_float *)b_dst)[0] = rl*rl - im*im;
+        ((npy_float *)b_dst)[1] = rl*im + im*rl;
+    #else
+        ((npy_float *)b_dst)[0] = rl;
+        ((npy_float *)b_dst)[1] = -im;
+    #endif
+    }
+}
+
+
+#line 437
+#line 445
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_add)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    // Parenthesis around 0 tells clang dead code is intentional
-    if (IS_BINARY_REDUCE && (0)) {
-        npy_intp n = dimensions[0];
-        npy_float * or = ((npy_float *)args[0]);
-        npy_float * oi = ((npy_float *)args[0]) + 1;
-        npy_float rr, ri;
-
-        CFLOAT_pairwise_sum(&rr, &ri, args[1], n * 2, steps[1] / 2);
-        *or -= rr;
-        *oi -= ri;
+    npy_intp len = dimensions[0];
+    char *b_src0 = args[0], *b_src1 = args[1], *b_dst = args[2];
+    npy_intp b_ssrc0 = steps[0], b_ssrc1 = steps[1], b_sdst = steps[2];
+#if 1
+    // reduce
+    if (b_ssrc0 == 0 && b_ssrc0 == b_sdst && b_src0 == b_dst &&
+        b_ssrc1 % (sizeof(npy_double)*2) == 0
+    ) {
+        npy_double *rl_im = (npy_double *)b_src0;
+        npy_double rr, ri;
+        CDOUBLE_pairwise_sum(&rr, &ri, b_src1, len * 2, b_ssrc1 / 2);
+        rl_im[0] += rr;
+        rl_im[1] += ri;
         return;
     }
-    if (!run_binary_avx512f_subtract_CFLOAT(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_float in1r = ((npy_float *)ip1)[0];
-            const npy_float in1i = ((npy_float *)ip1)[1];
-            const npy_float in2r = ((npy_float *)ip2)[0];
-            const npy_float in2i = ((npy_float *)ip2)[1];
-            ((npy_float *)op1)[0] = in1r - in2r;
-            ((npy_float *)op1)[1] = in1i - in2i;
+#endif
+#if NPY_SIMD_F64
+    if (is_mem_overlap(b_src0, b_ssrc0, b_dst, b_sdst, len) ||
+        is_mem_overlap(b_src1, b_ssrc1, b_dst, b_sdst, len) ||
+        b_sdst  % sizeof(npy_double) != 0 || b_sdst == 0 ||
+        b_ssrc0 % sizeof(npy_double) != 0 ||
+        b_ssrc1 % sizeof(npy_double) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_double *src0 = (npy_double*)b_src0;
+    const npy_double *src1 = (npy_double*)b_src1;
+          npy_double *dst  = (npy_double*)b_dst;
+
+    const npy_intp ssrc0 = b_ssrc0 / sizeof(npy_double);
+    const npy_intp ssrc1 = b_ssrc1 / sizeof(npy_double);
+    const npy_intp sdst  = b_sdst / sizeof(npy_double);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    const int loadable0 = npyv_loadable_stride_s64(ssrc0);
+    const int loadable1 = npyv_loadable_stride_s64(ssrc1);
+    const int storable = npyv_storable_stride_s64(sdst);
+
+    // lots**lots of specializations, to squeeze out max performance
+    // contig
+    if (ssrc0 == 2 && ssrc0 == ssrc1 && ssrc0 == sdst) {
+        for (; len >= vstep; len -= vstep, src0 += wstep, src1 += wstep, dst += wstep) {
+            npyv_f64 a0 = npyv_load_f64(src0);
+            npyv_f64 a1 = npyv_load_f64(src0 + vstep);
+            npyv_f64 b0 = npyv_load_f64(src1);
+            npyv_f64 b1 = npyv_load_f64(src1 + vstep);
+            npyv_f64 r0 = npyv_add_f64(a0, b0);
+            npyv_f64 r1 = npyv_add_f64(a1, b1);
+            npyv_store_f64(dst, r0);
+            npyv_store_f64(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            npyv_f64 a = npyv_load2_tillz_f64(src0, len);
+            npyv_f64 b = npyv_load2_tillz_f64(src1, len);
+            npyv_f64 r = npyv_add_f64(a, b);
+            npyv_store2_till_f64(dst, len, r);
+        }
+    }
+    // scalar 0
+    else if (ssrc0 == 0) {
+        npyv_f64x2 a = simd_set2_f64(src0);
+        // contig
+        if (ssrc1 == 2 && sdst == ssrc1) {
+            for (; len >= vstep; len -= vstep, src1 += wstep, dst += wstep) {
+                npyv_f64 b0 = npyv_load_f64(src1);
+                npyv_f64 b1 = npyv_load_f64(src1 + vstep);
+                npyv_f64 r0 = npyv_add_f64(a.val[0], b0);
+                npyv_f64 r1 = npyv_add_f64(a.val[1], b1);
+                npyv_store_f64(dst, r0);
+                npyv_store_f64(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f64 b = npyv_load2_till_f64(src1, len, 1.0, 1.0);
+            #else
+                npyv_f64 b = npyv_load2_tillz_f64(src1, len);
+            #endif
+                npyv_f64 r = npyv_add_f64(a.val[0], b);
+                npyv_store2_till_f64(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable1 && storable) {
+            for (; len >= vstep; len -= vstep, src1 += ssrc1*vstep, dst += sdst*vstep) {
+                npyv_f64 b0 = npyv_loadn2_f64(src1, ssrc1);
+                npyv_f64 b1 = npyv_loadn2_f64(src1 + ssrc1*hstep, ssrc1);
+                npyv_f64 r0 = npyv_add_f64(a.val[0], b0);
+                npyv_f64 r1 = npyv_add_f64(a.val[1], b1);
+                npyv_storen2_f64(dst, sdst, r0);
+                npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += ssrc1*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f64 b = npyv_loadn2_till_f64(src1, ssrc1, len, 1.0, 1.0);
+            #else
+                npyv_f64 b = npyv_loadn2_tillz_f64(src1, ssrc1, len);
+            #endif
+                npyv_f64 r = npyv_add_f64(a.val[0], b);
+                npyv_storen2_till_f64(dst, sdst, len, r);
+            }
         }
-    }
-}
-
-
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_multiply)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-    if (!run_binary_avx512f_multiply_CFLOAT(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_float in1r = ((npy_float *)ip1)[0];
-            const npy_float in1i = ((npy_float *)ip1)[1];
-            const npy_float in2r = ((npy_float *)ip2)[0];
-            const npy_float in2i = ((npy_float *)ip2)[1];
-            ((npy_float *)op1)[0] = in1r*in2r - in1i*in2i;
-            ((npy_float *)op1)[1] = in1r*in2i + in1i*in2r;
+        else {
+            goto loop_scalar;
+        }
+    }
+    // scalar 1
+    else if (ssrc1 == 0) {
+        npyv_f64x2 b = simd_set2_f64(src1);
+        if (ssrc0 == 2 && sdst == ssrc0) {
+            for (; len >= vstep; len -= vstep, src0 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64(src0);
+                npyv_f64 a1 = npyv_load_f64(src0 + vstep);
+                npyv_f64 r0 = npyv_add_f64(a0, b.val[0]);
+                npyv_f64 r1 = npyv_add_f64(a1, b.val[1]);
+                npyv_store_f64(dst, r0);
+                npyv_store_f64(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0
+                npyv_f64 a = npyv_load2_till_f64(src0, len, 1.0, 1.0);
+            #else
+                npyv_f64 a = npyv_load2_tillz_f64(src0, len);
+            #endif
+                npyv_f64 r = npyv_add_f64(a, b.val[0]);
+                npyv_store2_till_f64(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable0 && storable) {
+            for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep, dst += sdst*vstep) {
+                npyv_f64 a0 = npyv_loadn2_f64(src0, ssrc0);
+                npyv_f64 a1 = npyv_loadn2_f64(src0 + ssrc0*hstep, ssrc0);
+                npyv_f64 r0 = npyv_add_f64(a0, b.val[0]);
+                npyv_f64 r1 = npyv_add_f64(a1, b.val[1]);
+                npyv_storen2_f64(dst, sdst, r0);
+                npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += ssrc0*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f64 a = npyv_loadn2_till_f64(src0, ssrc0, len, 1.0, 1.0);
+            #else
+                npyv_f64 a = npyv_loadn2_tillz_f64(src0, ssrc0, len);
+            #endif
+                npyv_f64 r = npyv_add_f64(a, b.val[0]);
+                npyv_storen2_till_f64(dst, sdst, len, r);
+            }
         }
+        else {
+            goto loop_scalar;
+        }
+    }
+    #if 0
+    // non-contig
+    else if (loadable0 && loadable1 && storable) {
+        for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep,
+                            src1 += ssrc1*vstep, dst += sdst*vstep
+        ) {
+            npyv_f64 a0 = npyv_loadn2_f64(src0, ssrc0);
+            npyv_f64 a1 = npyv_loadn2_f64(src0 + ssrc0*hstep, ssrc0);
+            npyv_f64 b0 = npyv_loadn2_f64(src1, ssrc1);
+            npyv_f64 b1 = npyv_loadn2_f64(src1 + ssrc1*hstep, ssrc1);
+            npyv_f64 r0 = npyv_add_f64(a0, b0);
+            npyv_f64 r1 = npyv_add_f64(a1, b1);
+            npyv_storen2_f64(dst, sdst, r0);
+            npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += ssrc0*hstep,
+                       src1 += ssrc1*hstep, dst += sdst*hstep
+        ) {
+        #if 0
+            npyv_f64 a = npyv_loadn2_till_f64(src0, ssrc0, len, 1.0, 1.0);
+            npyv_f64 b = npyv_loadn2_till_f64(src1, ssrc1, len, 1.0, 1.0);
+        #else
+            npyv_f64 a = npyv_loadn2_tillz_f64(src0, ssrc0, len);
+            npyv_f64 b = npyv_loadn2_tillz_f64(src1, ssrc1, len);
+        #endif
+            npyv_f64 r = npyv_add_f64(a, b);
+            npyv_storen2_till_f64(dst, sdst, len, r);
+        }
+    }
+    #endif
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src0 += b_ssrc0, b_src1 += b_ssrc1, b_dst += b_sdst) {
+        const npy_double a_r = ((npy_double *)b_src0)[0];
+        const npy_double a_i = ((npy_double *)b_src0)[1];
+        const npy_double b_r = ((npy_double *)b_src1)[0];
+        const npy_double b_i = ((npy_double *)b_src1)[1];
+    #if 0
+        ((npy_double *)b_dst)[0] = a_r*b_r - a_i*b_i;
+        ((npy_double *)b_dst)[1] = a_r*b_i + a_i*b_r;
+    #else
+        ((npy_double *)b_dst)[0] = a_r + b_r;
+        ((npy_double *)b_dst)[1] = a_i + b_i;
+    #endif
+    }
+}
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(CDOUBLE_add_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        const npy_double b_r = ((npy_double *)value)[0];
+        const npy_double b_i = ((npy_double *)value)[1];
+    #if 0
+        const npy_double a_r = indexed[0];
+        const npy_double a_i = indexed[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    #else
+        indexed[0] += b_r;
+        indexed[1] += b_i;
+    #endif
     }
+    return 0;
 }
 
-#line 730
-#line 736
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_add)
+#line 445
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_subtract)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    // Parenthesis around 1 tells clang dead code is intentional
-    if (IS_BINARY_REDUCE && (1)) {
-        npy_intp n = dimensions[0];
-        npy_double * or = ((npy_double *)args[0]);
-        npy_double * oi = ((npy_double *)args[0]) + 1;
+    npy_intp len = dimensions[0];
+    char *b_src0 = args[0], *b_src1 = args[1], *b_dst = args[2];
+    npy_intp b_ssrc0 = steps[0], b_ssrc1 = steps[1], b_sdst = steps[2];
+#if 0
+    // reduce
+    if (b_ssrc0 == 0 && b_ssrc0 == b_sdst && b_src0 == b_dst &&
+        b_ssrc1 % (sizeof(npy_double)*2) == 0
+    ) {
+        npy_double *rl_im = (npy_double *)b_src0;
         npy_double rr, ri;
-
-        CDOUBLE_pairwise_sum(&rr, &ri, args[1], n * 2, steps[1] / 2);
-        *or += rr;
-        *oi += ri;
+        CDOUBLE_pairwise_sum(&rr, &ri, b_src1, len * 2, b_ssrc1 / 2);
+        rl_im[0] -= rr;
+        rl_im[1] -= ri;
         return;
     }
-    if (!run_binary_avx512f_add_CDOUBLE(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_double in1r = ((npy_double *)ip1)[0];
-            const npy_double in1i = ((npy_double *)ip1)[1];
-            const npy_double in2r = ((npy_double *)ip2)[0];
-            const npy_double in2i = ((npy_double *)ip2)[1];
-            ((npy_double *)op1)[0] = in1r + in2r;
-            ((npy_double *)op1)[1] = in1i + in2i;
+#endif
+#if NPY_SIMD_F64
+    if (is_mem_overlap(b_src0, b_ssrc0, b_dst, b_sdst, len) ||
+        is_mem_overlap(b_src1, b_ssrc1, b_dst, b_sdst, len) ||
+        b_sdst  % sizeof(npy_double) != 0 || b_sdst == 0 ||
+        b_ssrc0 % sizeof(npy_double) != 0 ||
+        b_ssrc1 % sizeof(npy_double) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_double *src0 = (npy_double*)b_src0;
+    const npy_double *src1 = (npy_double*)b_src1;
+          npy_double *dst  = (npy_double*)b_dst;
+
+    const npy_intp ssrc0 = b_ssrc0 / sizeof(npy_double);
+    const npy_intp ssrc1 = b_ssrc1 / sizeof(npy_double);
+    const npy_intp sdst  = b_sdst / sizeof(npy_double);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    const int loadable0 = npyv_loadable_stride_s64(ssrc0);
+    const int loadable1 = npyv_loadable_stride_s64(ssrc1);
+    const int storable = npyv_storable_stride_s64(sdst);
+
+    // lots**lots of specializations, to squeeze out max performance
+    // contig
+    if (ssrc0 == 2 && ssrc0 == ssrc1 && ssrc0 == sdst) {
+        for (; len >= vstep; len -= vstep, src0 += wstep, src1 += wstep, dst += wstep) {
+            npyv_f64 a0 = npyv_load_f64(src0);
+            npyv_f64 a1 = npyv_load_f64(src0 + vstep);
+            npyv_f64 b0 = npyv_load_f64(src1);
+            npyv_f64 b1 = npyv_load_f64(src1 + vstep);
+            npyv_f64 r0 = npyv_sub_f64(a0, b0);
+            npyv_f64 r1 = npyv_sub_f64(a1, b1);
+            npyv_store_f64(dst, r0);
+            npyv_store_f64(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            npyv_f64 a = npyv_load2_tillz_f64(src0, len);
+            npyv_f64 b = npyv_load2_tillz_f64(src1, len);
+            npyv_f64 r = npyv_sub_f64(a, b);
+            npyv_store2_till_f64(dst, len, r);
+        }
+    }
+    // scalar 0
+    else if (ssrc0 == 0) {
+        npyv_f64x2 a = simd_set2_f64(src0);
+        // contig
+        if (ssrc1 == 2 && sdst == ssrc1) {
+            for (; len >= vstep; len -= vstep, src1 += wstep, dst += wstep) {
+                npyv_f64 b0 = npyv_load_f64(src1);
+                npyv_f64 b1 = npyv_load_f64(src1 + vstep);
+                npyv_f64 r0 = npyv_sub_f64(a.val[0], b0);
+                npyv_f64 r1 = npyv_sub_f64(a.val[1], b1);
+                npyv_store_f64(dst, r0);
+                npyv_store_f64(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 0
+                npyv_f64 b = npyv_load2_till_f64(src1, len, 1.0, 1.0);
+            #else
+                npyv_f64 b = npyv_load2_tillz_f64(src1, len);
+            #endif
+                npyv_f64 r = npyv_sub_f64(a.val[0], b);
+                npyv_store2_till_f64(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable1 && storable) {
+            for (; len >= vstep; len -= vstep, src1 += ssrc1*vstep, dst += sdst*vstep) {
+                npyv_f64 b0 = npyv_loadn2_f64(src1, ssrc1);
+                npyv_f64 b1 = npyv_loadn2_f64(src1 + ssrc1*hstep, ssrc1);
+                npyv_f64 r0 = npyv_sub_f64(a.val[0], b0);
+                npyv_f64 r1 = npyv_sub_f64(a.val[1], b1);
+                npyv_storen2_f64(dst, sdst, r0);
+                npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += ssrc1*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f64 b = npyv_loadn2_till_f64(src1, ssrc1, len, 1.0, 1.0);
+            #else
+                npyv_f64 b = npyv_loadn2_tillz_f64(src1, ssrc1, len);
+            #endif
+                npyv_f64 r = npyv_sub_f64(a.val[0], b);
+                npyv_storen2_till_f64(dst, sdst, len, r);
+            }
         }
+        else {
+            goto loop_scalar;
+        }
+    }
+    // scalar 1
+    else if (ssrc1 == 0) {
+        npyv_f64x2 b = simd_set2_f64(src1);
+        if (ssrc0 == 2 && sdst == ssrc0) {
+            for (; len >= vstep; len -= vstep, src0 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64(src0);
+                npyv_f64 a1 = npyv_load_f64(src0 + vstep);
+                npyv_f64 r0 = npyv_sub_f64(a0, b.val[0]);
+                npyv_f64 r1 = npyv_sub_f64(a1, b.val[1]);
+                npyv_store_f64(dst, r0);
+                npyv_store_f64(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 0
+                npyv_f64 a = npyv_load2_till_f64(src0, len, 1.0, 1.0);
+            #else
+                npyv_f64 a = npyv_load2_tillz_f64(src0, len);
+            #endif
+                npyv_f64 r = npyv_sub_f64(a, b.val[0]);
+                npyv_store2_till_f64(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable0 && storable) {
+            for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep, dst += sdst*vstep) {
+                npyv_f64 a0 = npyv_loadn2_f64(src0, ssrc0);
+                npyv_f64 a1 = npyv_loadn2_f64(src0 + ssrc0*hstep, ssrc0);
+                npyv_f64 r0 = npyv_sub_f64(a0, b.val[0]);
+                npyv_f64 r1 = npyv_sub_f64(a1, b.val[1]);
+                npyv_storen2_f64(dst, sdst, r0);
+                npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += ssrc0*hstep, dst += sdst*hstep) {
+            #if 0
+                npyv_f64 a = npyv_loadn2_till_f64(src0, ssrc0, len, 1.0, 1.0);
+            #else
+                npyv_f64 a = npyv_loadn2_tillz_f64(src0, ssrc0, len);
+            #endif
+                npyv_f64 r = npyv_sub_f64(a, b.val[0]);
+                npyv_storen2_till_f64(dst, sdst, len, r);
+            }
+        }
+        else {
+            goto loop_scalar;
+        }
+    }
+    #if 0
+    // non-contig
+    else if (loadable0 && loadable1 && storable) {
+        for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep,
+                            src1 += ssrc1*vstep, dst += sdst*vstep
+        ) {
+            npyv_f64 a0 = npyv_loadn2_f64(src0, ssrc0);
+            npyv_f64 a1 = npyv_loadn2_f64(src0 + ssrc0*hstep, ssrc0);
+            npyv_f64 b0 = npyv_loadn2_f64(src1, ssrc1);
+            npyv_f64 b1 = npyv_loadn2_f64(src1 + ssrc1*hstep, ssrc1);
+            npyv_f64 r0 = npyv_sub_f64(a0, b0);
+            npyv_f64 r1 = npyv_sub_f64(a1, b1);
+            npyv_storen2_f64(dst, sdst, r0);
+            npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += ssrc0*hstep,
+                       src1 += ssrc1*hstep, dst += sdst*hstep
+        ) {
+        #if 0
+            npyv_f64 a = npyv_loadn2_till_f64(src0, ssrc0, len, 1.0, 1.0);
+            npyv_f64 b = npyv_loadn2_till_f64(src1, ssrc1, len, 1.0, 1.0);
+        #else
+            npyv_f64 a = npyv_loadn2_tillz_f64(src0, ssrc0, len);
+            npyv_f64 b = npyv_loadn2_tillz_f64(src1, ssrc1, len);
+        #endif
+            npyv_f64 r = npyv_sub_f64(a, b);
+            npyv_storen2_till_f64(dst, sdst, len, r);
+        }
+    }
+    #endif
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src0 += b_ssrc0, b_src1 += b_ssrc1, b_dst += b_sdst) {
+        const npy_double a_r = ((npy_double *)b_src0)[0];
+        const npy_double a_i = ((npy_double *)b_src0)[1];
+        const npy_double b_r = ((npy_double *)b_src1)[0];
+        const npy_double b_i = ((npy_double *)b_src1)[1];
+    #if 0
+        ((npy_double *)b_dst)[0] = a_r*b_r - a_i*b_i;
+        ((npy_double *)b_dst)[1] = a_r*b_i + a_i*b_r;
+    #else
+        ((npy_double *)b_dst)[0] = a_r - b_r;
+        ((npy_double *)b_dst)[1] = a_i - b_i;
+    #endif
+    }
+}
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(CDOUBLE_subtract_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        const npy_double b_r = ((npy_double *)value)[0];
+        const npy_double b_i = ((npy_double *)value)[1];
+    #if 0
+        const npy_double a_r = indexed[0];
+        const npy_double a_i = indexed[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    #else
+        indexed[0] -= b_r;
+        indexed[1] -= b_i;
+    #endif
     }
+    return 0;
 }
 
-#line 736
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_subtract)
+#line 445
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_multiply)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    // Parenthesis around 0 tells clang dead code is intentional
-    if (IS_BINARY_REDUCE && (0)) {
-        npy_intp n = dimensions[0];
-        npy_double * or = ((npy_double *)args[0]);
-        npy_double * oi = ((npy_double *)args[0]) + 1;
+    npy_intp len = dimensions[0];
+    char *b_src0 = args[0], *b_src1 = args[1], *b_dst = args[2];
+    npy_intp b_ssrc0 = steps[0], b_ssrc1 = steps[1], b_sdst = steps[2];
+#if 0
+    // reduce
+    if (b_ssrc0 == 0 && b_ssrc0 == b_sdst && b_src0 == b_dst &&
+        b_ssrc1 % (sizeof(npy_double)*2) == 0
+    ) {
+        npy_double *rl_im = (npy_double *)b_src0;
         npy_double rr, ri;
-
-        CDOUBLE_pairwise_sum(&rr, &ri, args[1], n * 2, steps[1] / 2);
-        *or -= rr;
-        *oi -= ri;
+        CDOUBLE_pairwise_sum(&rr, &ri, b_src1, len * 2, b_ssrc1 / 2);
+        rl_im[0] *= rr;
+        rl_im[1] *= ri;
         return;
     }
-    if (!run_binary_avx512f_subtract_CDOUBLE(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_double in1r = ((npy_double *)ip1)[0];
-            const npy_double in1i = ((npy_double *)ip1)[1];
-            const npy_double in2r = ((npy_double *)ip2)[0];
-            const npy_double in2i = ((npy_double *)ip2)[1];
-            ((npy_double *)op1)[0] = in1r - in2r;
-            ((npy_double *)op1)[1] = in1i - in2i;
+#endif
+#if NPY_SIMD_F64
+    if (is_mem_overlap(b_src0, b_ssrc0, b_dst, b_sdst, len) ||
+        is_mem_overlap(b_src1, b_ssrc1, b_dst, b_sdst, len) ||
+        b_sdst  % sizeof(npy_double) != 0 || b_sdst == 0 ||
+        b_ssrc0 % sizeof(npy_double) != 0 ||
+        b_ssrc1 % sizeof(npy_double) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_double *src0 = (npy_double*)b_src0;
+    const npy_double *src1 = (npy_double*)b_src1;
+          npy_double *dst  = (npy_double*)b_dst;
+
+    const npy_intp ssrc0 = b_ssrc0 / sizeof(npy_double);
+    const npy_intp ssrc1 = b_ssrc1 / sizeof(npy_double);
+    const npy_intp sdst  = b_sdst / sizeof(npy_double);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    const int loadable0 = npyv_loadable_stride_s64(ssrc0);
+    const int loadable1 = npyv_loadable_stride_s64(ssrc1);
+    const int storable = npyv_storable_stride_s64(sdst);
+
+    // lots**lots of specializations, to squeeze out max performance
+    // contig
+    if (ssrc0 == 2 && ssrc0 == ssrc1 && ssrc0 == sdst) {
+        for (; len >= vstep; len -= vstep, src0 += wstep, src1 += wstep, dst += wstep) {
+            npyv_f64 a0 = npyv_load_f64(src0);
+            npyv_f64 a1 = npyv_load_f64(src0 + vstep);
+            npyv_f64 b0 = npyv_load_f64(src1);
+            npyv_f64 b1 = npyv_load_f64(src1 + vstep);
+            npyv_f64 r0 = simd_cmul_f64(a0, b0);
+            npyv_f64 r1 = simd_cmul_f64(a1, b1);
+            npyv_store_f64(dst, r0);
+            npyv_store_f64(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            npyv_f64 a = npyv_load2_tillz_f64(src0, len);
+            npyv_f64 b = npyv_load2_tillz_f64(src1, len);
+            npyv_f64 r = simd_cmul_f64(a, b);
+            npyv_store2_till_f64(dst, len, r);
+        }
+    }
+    // scalar 0
+    else if (ssrc0 == 0) {
+        npyv_f64x2 a = simd_set2_f64(src0);
+        // contig
+        if (ssrc1 == 2 && sdst == ssrc1) {
+            for (; len >= vstep; len -= vstep, src1 += wstep, dst += wstep) {
+                npyv_f64 b0 = npyv_load_f64(src1);
+                npyv_f64 b1 = npyv_load_f64(src1 + vstep);
+                npyv_f64 r0 = simd_cmul_f64(a.val[0], b0);
+                npyv_f64 r1 = simd_cmul_f64(a.val[1], b1);
+                npyv_store_f64(dst, r0);
+                npyv_store_f64(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += vstep, dst += vstep) {
+            #if 1
+                npyv_f64 b = npyv_load2_till_f64(src1, len, 1.0, 1.0);
+            #else
+                npyv_f64 b = npyv_load2_tillz_f64(src1, len);
+            #endif
+                npyv_f64 r = simd_cmul_f64(a.val[0], b);
+                npyv_store2_till_f64(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable1 && storable) {
+            for (; len >= vstep; len -= vstep, src1 += ssrc1*vstep, dst += sdst*vstep) {
+                npyv_f64 b0 = npyv_loadn2_f64(src1, ssrc1);
+                npyv_f64 b1 = npyv_loadn2_f64(src1 + ssrc1*hstep, ssrc1);
+                npyv_f64 r0 = simd_cmul_f64(a.val[0], b0);
+                npyv_f64 r1 = simd_cmul_f64(a.val[1], b1);
+                npyv_storen2_f64(dst, sdst, r0);
+                npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += ssrc1*hstep, dst += sdst*hstep) {
+            #if 1
+                npyv_f64 b = npyv_loadn2_till_f64(src1, ssrc1, len, 1.0, 1.0);
+            #else
+                npyv_f64 b = npyv_loadn2_tillz_f64(src1, ssrc1, len);
+            #endif
+                npyv_f64 r = simd_cmul_f64(a.val[0], b);
+                npyv_storen2_till_f64(dst, sdst, len, r);
+            }
+        }
+        else {
+            goto loop_scalar;
+        }
+    }
+    // scalar 1
+    else if (ssrc1 == 0) {
+        npyv_f64x2 b = simd_set2_f64(src1);
+        if (ssrc0 == 2 && sdst == ssrc0) {
+            for (; len >= vstep; len -= vstep, src0 += wstep, dst += wstep) {
+                npyv_f64 a0 = npyv_load_f64(src0);
+                npyv_f64 a1 = npyv_load_f64(src0 + vstep);
+                npyv_f64 r0 = simd_cmul_f64(a0, b.val[0]);
+                npyv_f64 r1 = simd_cmul_f64(a1, b.val[1]);
+                npyv_store_f64(dst, r0);
+                npyv_store_f64(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += vstep, dst += vstep) {
+            #if 1
+                npyv_f64 a = npyv_load2_till_f64(src0, len, 1.0, 1.0);
+            #else
+                npyv_f64 a = npyv_load2_tillz_f64(src0, len);
+            #endif
+                npyv_f64 r = simd_cmul_f64(a, b.val[0]);
+                npyv_store2_till_f64(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable0 && storable) {
+            for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep, dst += sdst*vstep) {
+                npyv_f64 a0 = npyv_loadn2_f64(src0, ssrc0);
+                npyv_f64 a1 = npyv_loadn2_f64(src0 + ssrc0*hstep, ssrc0);
+                npyv_f64 r0 = simd_cmul_f64(a0, b.val[0]);
+                npyv_f64 r1 = simd_cmul_f64(a1, b.val[1]);
+                npyv_storen2_f64(dst, sdst, r0);
+                npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += ssrc0*hstep, dst += sdst*hstep) {
+            #if 1
+                npyv_f64 a = npyv_loadn2_till_f64(src0, ssrc0, len, 1.0, 1.0);
+            #else
+                npyv_f64 a = npyv_loadn2_tillz_f64(src0, ssrc0, len);
+            #endif
+                npyv_f64 r = simd_cmul_f64(a, b.val[0]);
+                npyv_storen2_till_f64(dst, sdst, len, r);
+            }
         }
+        else {
+            goto loop_scalar;
+        }
+    }
+    #if 1
+    // non-contig
+    else if (loadable0 && loadable1 && storable) {
+        for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep,
+                            src1 += ssrc1*vstep, dst += sdst*vstep
+        ) {
+            npyv_f64 a0 = npyv_loadn2_f64(src0, ssrc0);
+            npyv_f64 a1 = npyv_loadn2_f64(src0 + ssrc0*hstep, ssrc0);
+            npyv_f64 b0 = npyv_loadn2_f64(src1, ssrc1);
+            npyv_f64 b1 = npyv_loadn2_f64(src1 + ssrc1*hstep, ssrc1);
+            npyv_f64 r0 = simd_cmul_f64(a0, b0);
+            npyv_f64 r1 = simd_cmul_f64(a1, b1);
+            npyv_storen2_f64(dst, sdst, r0);
+            npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += ssrc0*hstep,
+                       src1 += ssrc1*hstep, dst += sdst*hstep
+        ) {
+        #if 1
+            npyv_f64 a = npyv_loadn2_till_f64(src0, ssrc0, len, 1.0, 1.0);
+            npyv_f64 b = npyv_loadn2_till_f64(src1, ssrc1, len, 1.0, 1.0);
+        #else
+            npyv_f64 a = npyv_loadn2_tillz_f64(src0, ssrc0, len);
+            npyv_f64 b = npyv_loadn2_tillz_f64(src1, ssrc1, len);
+        #endif
+            npyv_f64 r = simd_cmul_f64(a, b);
+            npyv_storen2_till_f64(dst, sdst, len, r);
+        }
+    }
+    #endif
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src0 += b_ssrc0, b_src1 += b_ssrc1, b_dst += b_sdst) {
+        const npy_double a_r = ((npy_double *)b_src0)[0];
+        const npy_double a_i = ((npy_double *)b_src0)[1];
+        const npy_double b_r = ((npy_double *)b_src1)[0];
+        const npy_double b_i = ((npy_double *)b_src1)[1];
+    #if 1
+        ((npy_double *)b_dst)[0] = a_r*b_r - a_i*b_i;
+        ((npy_double *)b_dst)[1] = a_r*b_i + a_i*b_r;
+    #else
+        ((npy_double *)b_dst)[0] = a_r * b_r;
+        ((npy_double *)b_dst)[1] = a_i * b_i;
+    #endif
+    }
+}
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(CDOUBLE_multiply_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        const npy_double b_r = ((npy_double *)value)[0];
+        const npy_double b_i = ((npy_double *)value)[1];
+    #if 1
+        const npy_double a_r = indexed[0];
+        const npy_double a_i = indexed[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    #else
+        indexed[0] *= b_r;
+        indexed[1] *= b_i;
+    #endif
     }
+    return 0;
 }
 
 
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_multiply)
+#line 691
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_conjugate)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_binary_avx512f_multiply_CDOUBLE(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const npy_double in1r = ((npy_double *)ip1)[0];
-            const npy_double in1i = ((npy_double *)ip1)[1];
-            const npy_double in2r = ((npy_double *)ip2)[0];
-            const npy_double in2i = ((npy_double *)ip2)[1];
-            ((npy_double *)op1)[0] = in1r*in2r - in1i*in2i;
-            ((npy_double *)op1)[1] = in1r*in2i + in1i*in2r;
-        }
+    npy_intp len = dimensions[0];
+    char *b_src = args[0], *b_dst = args[1];
+    npy_intp b_ssrc = steps[0], b_sdst = steps[1];
+#if NPY_SIMD_F64
+    if (is_mem_overlap(b_src, b_ssrc, b_dst, b_sdst, len) ||
+        b_sdst % sizeof(npy_double) != 0 ||
+        b_ssrc % sizeof(npy_double) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_double *src  = (npy_double*)b_src;
+          npy_double *dst  = (npy_double*)b_dst;
+    const npy_intp ssrc = b_ssrc / sizeof(npy_double);
+    const npy_intp sdst = b_sdst / sizeof(npy_double);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    if (ssrc == 2 && ssrc == sdst) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += wstep) {
+            npyv_f64 a0 = npyv_load_f64(src);
+            npyv_f64 a1 = npyv_load_f64(src + vstep);
+            npyv_f64 r0 = simd_cconjugate_f64(a0);
+            npyv_f64 r1 = simd_cconjugate_f64(a1);
+            npyv_store_f64(dst, r0);
+            npyv_store_f64(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += vstep) {
+            npyv_f64 a = npyv_load2_tillz_f64(src, len);
+            npyv_f64 r = simd_cconjugate_f64(a);
+            npyv_store2_till_f64(dst, len, r);
+        }
+    }
+    else if (ssrc == 2 && npyv_storable_stride_s64(sdst)) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += sdst*vstep) {
+            npyv_f64 a0 = npyv_load_f64(src);
+            npyv_f64 a1 = npyv_load_f64(src + vstep);
+            npyv_f64 r0 = simd_cconjugate_f64(a0);
+            npyv_f64 r1 = simd_cconjugate_f64(a1);
+            npyv_storen2_f64(dst, sdst, r0);
+            npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += sdst*hstep) {
+            npyv_f64 a = npyv_load2_tillz_f64(src, len);
+            npyv_f64 r = simd_cconjugate_f64(a);
+            npyv_storen2_till_f64(dst, sdst, len, r);
+        }
+    }
+    else if (sdst == 2 && npyv_loadable_stride_s64(ssrc)) {
+        for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += wstep) {
+            npyv_f64 a0 = npyv_loadn2_f64(src, ssrc);
+            npyv_f64 a1 = npyv_loadn2_f64(src + ssrc*hstep, ssrc);
+            npyv_f64 r0 = simd_cconjugate_f64(a0);
+            npyv_f64 r1 = simd_cconjugate_f64(a1);
+            npyv_store_f64(dst, r0);
+            npyv_store_f64(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += ssrc*hstep, dst += vstep) {
+            npyv_f64 a = npyv_loadn2_tillz_f64((npy_double*)src, ssrc, len);
+            npyv_f64 r = simd_cconjugate_f64(a);
+            npyv_store2_till_f64(dst, len, r);
+        }
+    }
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src += b_ssrc, b_dst += b_sdst) {
+        const npy_double rl = ((npy_double *)b_src)[0];
+        const npy_double im = ((npy_double *)b_src)[1];
+    #if 0
+        ((npy_double *)b_dst)[0] = rl*rl - im*im;
+        ((npy_double *)b_dst)[1] = rl*im + im*rl;
+    #else
+        ((npy_double *)b_dst)[0] = rl;
+        ((npy_double *)b_dst)[1] = -im;
+    #endif
+    }
+}
+
+#line 691
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    npy_intp len = dimensions[0];
+    char *b_src = args[0], *b_dst = args[1];
+    npy_intp b_ssrc = steps[0], b_sdst = steps[1];
+#if NPY_SIMD_F64
+    if (is_mem_overlap(b_src, b_ssrc, b_dst, b_sdst, len) ||
+        b_sdst % sizeof(npy_double) != 0 ||
+        b_ssrc % sizeof(npy_double) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const npy_double *src  = (npy_double*)b_src;
+          npy_double *dst  = (npy_double*)b_dst;
+    const npy_intp ssrc = b_ssrc / sizeof(npy_double);
+    const npy_intp sdst = b_sdst / sizeof(npy_double);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    if (ssrc == 2 && ssrc == sdst) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += wstep) {
+            npyv_f64 a0 = npyv_load_f64(src);
+            npyv_f64 a1 = npyv_load_f64(src + vstep);
+            npyv_f64 r0 = simd_csquare_f64(a0);
+            npyv_f64 r1 = simd_csquare_f64(a1);
+            npyv_store_f64(dst, r0);
+            npyv_store_f64(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += vstep) {
+            npyv_f64 a = npyv_load2_tillz_f64(src, len);
+            npyv_f64 r = simd_csquare_f64(a);
+            npyv_store2_till_f64(dst, len, r);
+        }
+    }
+    else if (ssrc == 2 && npyv_storable_stride_s64(sdst)) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += sdst*vstep) {
+            npyv_f64 a0 = npyv_load_f64(src);
+            npyv_f64 a1 = npyv_load_f64(src + vstep);
+            npyv_f64 r0 = simd_csquare_f64(a0);
+            npyv_f64 r1 = simd_csquare_f64(a1);
+            npyv_storen2_f64(dst, sdst, r0);
+            npyv_storen2_f64(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += sdst*hstep) {
+            npyv_f64 a = npyv_load2_tillz_f64(src, len);
+            npyv_f64 r = simd_csquare_f64(a);
+            npyv_storen2_till_f64(dst, sdst, len, r);
+        }
+    }
+    else if (sdst == 2 && npyv_loadable_stride_s64(ssrc)) {
+        for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += wstep) {
+            npyv_f64 a0 = npyv_loadn2_f64(src, ssrc);
+            npyv_f64 a1 = npyv_loadn2_f64(src + ssrc*hstep, ssrc);
+            npyv_f64 r0 = simd_csquare_f64(a0);
+            npyv_f64 r1 = simd_csquare_f64(a1);
+            npyv_store_f64(dst, r0);
+            npyv_store_f64(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += ssrc*hstep, dst += vstep) {
+            npyv_f64 a = npyv_loadn2_tillz_f64((npy_double*)src, ssrc, len);
+            npyv_f64 r = simd_csquare_f64(a);
+            npyv_store2_till_f64(dst, len, r);
+        }
+    }
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src += b_ssrc, b_dst += b_sdst) {
+        const npy_double rl = ((npy_double *)b_src)[0];
+        const npy_double im = ((npy_double *)b_src)[1];
+    #if 1
+        ((npy_double *)b_dst)[0] = rl*rl - im*im;
+        ((npy_double *)b_dst)[1] = rl*im + im*rl;
+    #else
+        ((npy_double *)b_dst)[0] = rl;
+        ((npy_double *)b_dst)[1] = -im;
+    #endif
     }
 }
 
 
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src
index bf8142880aa..7ba3981e811 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.c.src
@@ -1,6 +1,8 @@
 /*@targets
  ** $maxopt baseline
- ** sse2 avx2 avx512f
+ ** sse2 (avx2 fma3)
+ ** neon asimd
+ ** vsx2 vsx3
  ** vx vxe
  **/
 #define _UMATHMODULE
@@ -14,707 +16,410 @@
 // Provides the various *_LOOP macros
 #include "fast_loop_macros.h"
 
-// TODO: replace raw SIMD with NPYV
+/**
+ * TODO:
+ *  - Improve the implementation of SIMD complex absolute,
+ *    current one kinda slow and it can be optimized by
+ *    at least avoiding the division and keep sqrt.
+ *  - Vectorize reductions
+ *  - Add support for ASIMD/VCMLA through universal intrinics.
+ */
+
 //###############################################################################
 //## Real Single/Double precision
 //###############################################################################
 /********************************************************************************
- ** Defining the SIMD kernels
+ ** Defining ufunc inner functions
  ********************************************************************************/
-#ifdef NPY_HAVE_SSE2
+
+/*
+ * clang has a bug that's present at -O1 or greater.  When partially loading a
+ * vector register for a divide operation, the remaining elements are set
+ * to 1 to avoid divide-by-zero.  The partial load is paired with a partial
+ * store after the divide operation.  clang notices that the entire register
+ * is not needed for the store and optimizes out the fill of 1 to the remaining
+ * elements.  This causes either a divide-by-zero or 0/0 with invalid exception
+ * that we were trying to avoid by filling.
+ *
+ * Using a dummy variable marked 'volatile' convinces clang not to ignore
+ * the explicit fill of remaining elements.  If `-ftrapping-math` is
+ * supported, then it'll also avoid the bug.  `-ftrapping-math` is supported
+ * on Apple clang v12+ for x86_64.  It is not currently supported for arm64.
+ * `-ftrapping-math` is set by default of Numpy builds in
+ * numpy/distutils/ccompiler.py.
+ *
+ * Note: Apple clang and clang upstream have different versions that overlap
+ */
+#if defined(__clang__)
+    #if defined(__apple_build_version__)
+    // Apple Clang
+        #if __apple_build_version__ < 12000000
+        // Apple Clang before v12
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64)
+        // Apple Clang after v12, targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 0
+        #else
+        // Apple Clang after v12, not targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #endif
+    #else
+    // Clang, not Apple Clang
+        #if __clang_major__ < 10
+        // Clang before v10
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #elif defined(_MSC_VER)
+        // clang-cl has the same bug
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64)
+        // Clang v10+, targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 0
+        #else
+        // Clang v10+, not targeting i386 or x86_64
+        #define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 1
+        #endif
+    #endif
+#else
+// Not a Clang compiler
+#define WORKAROUND_CLANG_PARTIAL_LOAD_BUG 0
+#endif
+
 /**begin repeat
+ * Float types
  *  #type = npy_float, npy_double#
  *  #TYPE = FLOAT, DOUBLE#
- *  #scalarf = npy_sqrtf, npy_sqrt#
+ *  #sfx  = f32, f64#
  *  #c = f, #
- *  #vtype = __m128, __m128d#
- *  #vtype256 = __m256, __m256d#
- *  #vtype512 = __m512, __m512d#
- *  #vpre = _mm, _mm#
- *  #vpre256 = _mm256, _mm256#
- *  #vpre512 = _mm512, _mm512#
- *  #vsuf = ps, pd#
- *  #vsufs = ss, sd#
- *  #nan = NPY_NANF, NPY_NAN#
- *  #double = 0, 1#
- *  #cast = _mm_castps_si128, _mm_castpd_si128#
+ *  #C = F, #
+ *  #VECTOR = NPY_SIMD_F32, NPY_SIMD_F64#
  */
 /**begin repeat1
-* Arithmetic
-* # kind = add, subtract, multiply, divide#
-* # OP = +, -, *, /#
-* # VOP = add, sub, mul, div#
-*/
-static void
-sse2_binary_@kind@_@TYPE@(@type@ * op, @type@ * ip1, @type@ * ip2, npy_intp n)
+ * Arithmetic
+ * # kind = add, subtract, multiply, divide#
+ * # intrin = add, sub, mul, div#
+ * # OP = +, -, *, /#
+ * # PW = 1, 0, 0, 0#
+ * # is_div = 0*3, 1#
+ * # is_mul = 0*2, 1, 0#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[i] @OP@ ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) && npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype512@ a = @vpre512@_load_@vsuf@(&ip1[i]);
-                @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, a);
-                @vpre512@_store_@vsuf@(&op[i], c);
+    npy_intp len = dimensions[0];
+    char *src0 = args[0], *src1 = args[1], *dst = args[2];
+    npy_intp ssrc0 = steps[0], ssrc1 = steps[1], sdst = steps[2];
+    // reduce
+    if (ssrc0 == 0 && ssrc0 == sdst && src0 == dst) {
+    #if @PW@
+        *((@type@*)src0) @OP@= @TYPE@_pairwise_sum(src1, len, ssrc1);
+    #else
+        @type@ acc = *((@type@*)src0);
+        if (ssrc1 == sizeof(@type@)) {
+            for (; len > 0; --len, src1 += sizeof(@type@)) {
+                acc @OP@= *(@type@ *)src1;
             }
-        }
-        else {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype512@ a = @vpre512@_load_@vsuf@(&ip1[i]);
-                @vtype512@ b = @vpre512@_load_@vsuf@(&ip2[i]);
-                @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-                @vpre512@_store_@vsuf@(&op[i], c);
+        } else {
+            for (; len > 0; --len, src1 += ssrc1) {
+                acc @OP@= *(@type@ *)src1;
             }
         }
+        *((@type@*)src0) = acc;
+    #endif
+        return;
     }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype512@ a = @vpre512@_load_@vsuf@(&ip1[i]);
-            @vtype512@ b = @vpre512@_loadu_@vsuf@(&ip2[i]);
-            @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-            @vpre512@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype512@ a = @vpre512@_loadu_@vsuf@(&ip1[i]);
-            @vtype512@ b = @vpre512@_load_@vsuf@(&ip2[i]);
-            @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-            @vpre512@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype512@ a = @vpre512@_loadu_@vsuf@(&ip1[i]);
-                @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, a);
-                @vpre512@_store_@vsuf@(&op[i], c);
-            }
-        }
-        else {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype512@ a = @vpre512@_loadu_@vsuf@(&ip1[i]);
-                @vtype512@ b = @vpre512@_loadu_@vsuf@(&ip2[i]);
-                @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-                @vpre512@_store_@vsuf@(&op[i], c);
+#if @VECTOR@
+    if (len > npyv_nlanes_@sfx@*2 &&
+        !is_mem_overlap(src0, ssrc0, dst, sdst, len) &&
+        !is_mem_overlap(src1, ssrc1, dst, sdst, len)
+    ) {
+        const int vstep = npyv_nlanes_u8;
+        const int wstep = vstep * 2;
+        const int hstep = npyv_nlanes_@sfx@;
+        const int lstep = hstep * 2;
+        // lots of specializations, to squeeze out max performance
+        if (ssrc0 == sizeof(@type@) && ssrc0 == ssrc1 && ssrc0 == sdst) {
+            for (; len >= lstep; len -= lstep, src0 += wstep, src1 += wstep, dst += wstep) {
+                npyv_@sfx@ a0 = npyv_load_@sfx@((const @type@*)src0);
+                npyv_@sfx@ a1 = npyv_load_@sfx@((const @type@*)(src0 + vstep));
+                npyv_@sfx@ b0 = npyv_load_@sfx@((const @type@*)src1);
+                npyv_@sfx@ b1 = npyv_load_@sfx@((const @type@*)(src1 + vstep));
+                npyv_@sfx@ r0 = npyv_@intrin@_@sfx@(a0, b0);
+                npyv_@sfx@ r1 = npyv_@intrin@_@sfx@(a1, b1);
+                npyv_store_@sfx@((@type@*)dst, r0);
+                npyv_store_@sfx@((@type@*)(dst + vstep), r1);
             }
-        }
-    }
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[i] @OP@ ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype256@ a = @vpre256@_load_@vsuf@(&ip1[i]);
-                @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, a);
-                @vpre256@_store_@vsuf@(&op[i], c);
+        #if @is_div@ && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if @is_div@ && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            #if @is_div@
+                npyv_@sfx@ a = npyv_load_till_@sfx@((const @type@*)src0, len, 1.0@c@);
+                npyv_@sfx@ b = npyv_load_till_@sfx@((const @type@*)src1, len, 1.0@c@);
+            #else
+                npyv_@sfx@ a = npyv_load_tillz_@sfx@((const @type@*)src0, len);
+                npyv_@sfx@ b = npyv_load_tillz_@sfx@((const @type@*)src1, len);
+            #endif
+                npyv_@sfx@ r = npyv_@intrin@_@sfx@(a, b);
+                npyv_store_till_@sfx@((@type@*)dst, len, r);
             }
-        }
-        else {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype256@ a = @vpre256@_load_@vsuf@(&ip1[i]);
-                @vtype256@ b = @vpre256@_load_@vsuf@(&ip2[i]);
-                @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-                @vpre256@_store_@vsuf@(&op[i], c);
+        #if @is_div@ && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for divide and working around clang partial load bug
+            if(len > 0){
+                npyv_@sfx@ a = npyv_load_till_@sfx@((const @type@*)src0, len, 1.0@c@);
+                volatile npyv_@sfx@ b = npyv_load_till_@sfx@((const @type@*)src1, len, 1.0@c@);
+                npyv_@sfx@ r = npyv_@intrin@_@sfx@(a, b);
+                npyv_store_till_@sfx@((@type@*)dst, len, r);
             }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype256@ a = @vpre256@_load_@vsuf@(&ip1[i]);
-            @vtype256@ b = @vpre256@_loadu_@vsuf@(&ip2[i]);
-            @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-            @vpre256@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype256@ a = @vpre256@_loadu_@vsuf@(&ip1[i]);
-            @vtype256@ b = @vpre256@_load_@vsuf@(&ip2[i]);
-            @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-            @vpre256@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype256@ a = @vpre256@_loadu_@vsuf@(&ip1[i]);
-                @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, a);
-                @vpre256@_store_@vsuf@(&op[i], c);
+        #endif // #if @is_div@ && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc0 == 0 && ssrc1 == sizeof(@type@) && sdst == ssrc1) {
+            npyv_@sfx@ a = npyv_setall_@sfx@(*((@type@*)src0));
+            for (; len >= lstep; len -= lstep, src1 += wstep, dst += wstep) {
+                npyv_@sfx@ b0 = npyv_load_@sfx@((const @type@*)src1);
+                npyv_@sfx@ b1 = npyv_load_@sfx@((const @type@*)(src1 + vstep));
+                npyv_@sfx@ r0 = npyv_@intrin@_@sfx@(a, b0);
+                npyv_@sfx@ r1 = npyv_@intrin@_@sfx@(a, b1);
+                npyv_store_@sfx@((@type@*)dst, r0);
+                npyv_store_@sfx@((@type@*)(dst + vstep), r1);
             }
-        }
-        else {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype256@ a = @vpre256@_loadu_@vsuf@(&ip1[i]);
-                @vtype256@ b = @vpre256@_loadu_@vsuf@(&ip2[i]);
-                @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-                @vpre256@_store_@vsuf@(&op[i], c);
+        #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src1 += vstep, dst += vstep) {
+            #if @is_div@ || @is_mul@
+                npyv_@sfx@ b = npyv_load_till_@sfx@((const @type@*)src1, len, 1.0@c@);
+            #else
+                npyv_@sfx@ b = npyv_load_tillz_@sfx@((const @type@*)src1, len);
+            #endif
+                npyv_@sfx@ r = npyv_@intrin@_@sfx@(a, b);
+                npyv_store_till_@sfx@((@type@*)dst, len, r);
             }
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[i] @OP@ ip2[i];
-    /* lots of specializations, to squeeze out max performance */
-    if (npy_is_aligned(&ip1[i], vector_size_bytes) &&
-            npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype@ a = @vpre@_load_@vsuf@(&ip1[i]);
-                @vtype@ c = @vpre@_@VOP@_@vsuf@(a, a);
-                @vpre@_store_@vsuf@(&op[i], c);
+        #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_@sfx@ b = npyv_load_till_@sfx@((const @type@*)src1, len, 1.0@c@);
+                npyv_@sfx@ r = npyv_@intrin@_@sfx@(a, b);
+                npyv_store_till_@sfx@((@type@*)dst, len, r);
             }
-        }
-        else {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype@ a = @vpre@_load_@vsuf@(&ip1[i]);
-                @vtype@ b = @vpre@_load_@vsuf@(&ip2[i]);
-                @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-                @vpre@_store_@vsuf@(&op[i], c);
+        #endif // #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        }
+        else if (ssrc1 == 0 && ssrc0 == sizeof(@type@) && sdst == ssrc0) {
+            npyv_@sfx@ b = npyv_setall_@sfx@(*((@type@*)src1));
+            for (; len >= lstep; len -= lstep, src0 += wstep, dst += wstep) {
+                npyv_@sfx@ a0 = npyv_load_@sfx@((const @type@*)src0);
+                npyv_@sfx@ a1 = npyv_load_@sfx@((const @type@*)(src0 + vstep));
+                npyv_@sfx@ r0 = npyv_@intrin@_@sfx@(a0, b);
+                npyv_@sfx@ r1 = npyv_@intrin@_@sfx@(a1, b);
+                npyv_store_@sfx@((@type@*)dst, r0);
+                npyv_store_@sfx@((@type@*)(dst + vstep), r1);
             }
-        }
-    }
-    else if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype@ a = @vpre@_load_@vsuf@(&ip1[i]);
-            @vtype@ b = @vpre@_loadu_@vsuf@(&ip2[i]);
-            @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-            @vpre@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype@ a = @vpre@_loadu_@vsuf@(&ip1[i]);
-            @vtype@ b = @vpre@_load_@vsuf@(&ip2[i]);
-            @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-            @vpre@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        if (ip1 == ip2) {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype@ a = @vpre@_loadu_@vsuf@(&ip1[i]);
-                @vtype@ c = @vpre@_@VOP@_@vsuf@(a, a);
-                @vpre@_store_@vsuf@(&op[i], c);
+        #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            const int vstop = hstep - 1;
+        #else
+            const int vstop = 0;
+        #endif // #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            for (; len > vstop; len -= hstep, src0 += vstep, dst += vstep) {
+            #if @is_div@ || @is_mul@
+                npyv_@sfx@ a = npyv_load_till_@sfx@((const @type@*)src0, len, 1.0@c@);
+            #else
+                npyv_@sfx@ a = npyv_load_tillz_@sfx@((const @type@*)src0, len);
+            #endif
+                npyv_@sfx@ r = npyv_@intrin@_@sfx@(a, b);
+                npyv_store_till_@sfx@((@type@*)dst, len, r);
             }
-        }
-        else {
-            LOOP_BLOCKED(@type@, vector_size_bytes) {
-                @vtype@ a = @vpre@_loadu_@vsuf@(&ip1[i]);
-                @vtype@ b = @vpre@_loadu_@vsuf@(&ip2[i]);
-                @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-                @vpre@_store_@vsuf@(&op[i], c);
+        #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+            // last partial iteration for multiply / divide and working around clang partial load bug
+            if(len > 0){
+                volatile npyv_@sfx@ a = npyv_load_till_@sfx@((const @type@*)src0, len, 1.0@c@);
+                npyv_@sfx@ r = npyv_@intrin@_@sfx@(a, b);
+                npyv_store_till_@sfx@((@type@*)dst, len, r);
             }
+        #endif // #if (@is_div@ || @is_mul@) && WORKAROUND_CLANG_PARTIAL_LOAD_BUG
+        } else {
+            goto loop_scalar;
         }
+        npyv_cleanup();
+        return;
     }
+loop_scalar:
 #endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] @OP@ ip2[i];
-    }
-}
-
-static void
-sse2_binary_scalar1_@kind@_@TYPE@(@type@ * op, @type@ * ip1, @type@ * ip2, npy_intp n)
-{
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const @vtype512@ a = @vpre512@_set1_@vsuf@(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[0] @OP@ ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype512@ b = @vpre512@_load_@vsuf@(&ip2[i]);
-            @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-            @vpre512@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype512@ b = @vpre512@_loadu_@vsuf@(&ip2[i]);
-            @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-            @vpre512@_store_@vsuf@(&op[i], c);
-        }
-    }
-
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const @vtype256@ a = @vpre256@_set1_@vsuf@(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[0] @OP@ ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype256@ b = @vpre256@_load_@vsuf@(&ip2[i]);
-            @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-            @vpre256@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype256@ b = @vpre256@_loadu_@vsuf@(&ip2[i]);
-            @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-            @vpre256@_store_@vsuf@(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const @vtype@ a = @vpre@_set1_@vsuf@(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[0] @OP@ ip2[i];
-    if (npy_is_aligned(&ip2[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype@ b = @vpre@_load_@vsuf@(&ip2[i]);
-            @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-            @vpre@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype@ b = @vpre@_loadu_@vsuf@(&ip2[i]);
-            @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-            @vpre@_store_@vsuf@(&op[i], c);
-        }
-    }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] @OP@ ip2[i];
+    for (; len > 0; --len, src0 += ssrc0, src1 += ssrc1, dst += sdst) {
+        const @type@ a = *((@type@*)src0);
+        const @type@ b = *((@type@*)src1);
+        *((@type@*)dst) = a @OP@ b;
     }
 }
 
-static void
-sse2_binary_scalar2_@kind@_@TYPE@(@type@ * op, @type@ * ip1, @type@ * ip2, npy_intp n)
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
 {
-#ifdef NPY_HAVE_AVX512F
-    const npy_intp vector_size_bytes = 64;
-    const @vtype512@ b = @vpre512@_set1_@vsuf@(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[i] @OP@ ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype512@ a = @vpre512@_load_@vsuf@(&ip1[i]);
-            @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-            @vpre512@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype512@ a = @vpre512@_loadu_@vsuf@(&ip1[i]);
-            @vtype512@ c = @vpre512@_@VOP@_@vsuf@(a, b);
-            @vpre512@_store_@vsuf@(&op[i], c);
-        }
-    }
-
-#elif defined NPY_HAVE_AVX2
-    const npy_intp vector_size_bytes = 32;
-    const @vtype256@ b = @vpre256@_set1_@vsuf@(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[i] @OP@ ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype256@ a = @vpre256@_load_@vsuf@(&ip1[i]);
-            @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-            @vpre256@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype256@ a = @vpre256@_loadu_@vsuf@(&ip1[i]);
-            @vtype256@ c = @vpre256@_@VOP@_@vsuf@(a, b);
-            @vpre256@_store_@vsuf@(&op[i], c);
-        }
-    }
-#else
-    const npy_intp vector_size_bytes = 16;
-    const @vtype@ b = @vpre@_set1_@vsuf@(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, vector_size_bytes)
-        op[i] = ip1[i] @OP@ ip2[0];
-    if (npy_is_aligned(&ip1[i], vector_size_bytes)) {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype@ a = @vpre@_load_@vsuf@(&ip1[i]);
-            @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-            @vpre@_store_@vsuf@(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, vector_size_bytes) {
-            @vtype@ a = @vpre@_loadu_@vsuf@(&ip1[i]);
-            @vtype@ c = @vpre@_@VOP@_@vsuf@(a, b);
-            @vpre@_store_@vsuf@(&op[i], c);
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @type@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
         }
+        indexed = (@type@ *)(ip1 + is1 * indx);
+        *indexed = *indexed @OP@ *(@type@ *)value;
     }
-#endif
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] @OP@ ip2[0];
-    }
+    return 0;
 }
 
 /**end repeat1**/
 /**end repeat**/
 
-#else // NPY_HAVE_SSE2
+#undef WORKAROUND_CLANG_PARTIAL_LOAD_BUG
 
-/**begin repeat
- *  #type = npy_float, npy_double#
- *  #TYPE = FLOAT, DOUBLE#
- *  #sfx = f32, f64#
- *  #CHK = _F32, _F64#
- */
-#if NPY_SIMD@CHK@
-/**begin repeat1
-* Arithmetic
-* # kind = add, subtract, multiply, divide#
-* # OP = +, -, *, /#
-* # VOP = add, sub, mul, div#
-*/
-
-static void
-simd_binary_@kind@_@TYPE@(@type@ * op, @type@ * ip1, @type@ * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] @OP@ ip2[i];
-    }
-    /* lots of specializations, to squeeze out max performance */
-    if (ip1 == ip2) {
-        LOOP_BLOCKED(@type@, NPY_SIMD_WIDTH) {
-            npyv_@sfx@ a = npyv_load_@sfx@(&ip1[i]);
-            npyv_@sfx@ c = npyv_@VOP@_@sfx@(a, a);
-            npyv_store_@sfx@(&op[i], c);
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, NPY_SIMD_WIDTH) {
-            npyv_@sfx@ a = npyv_load_@sfx@(&ip1[i]);
-            npyv_@sfx@ b = npyv_load_@sfx@(&ip2[i]);
-            npyv_@sfx@ c = npyv_@VOP@_@sfx@(a, b);
-            npyv_store_@sfx@(&op[i], c);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] @OP@ ip2[i];
-    }
-}
+//###############################################################################
+//## Complex Single/Double precision
+//###############################################################################
 
-static void
-simd_binary_scalar1_@kind@_@TYPE@(@type@ * op, @type@ * ip1, @type@ * ip2, npy_intp n)
-{
-    const npyv_@sfx@ v1 = npyv_setall_@sfx@(ip1[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, NPY_SIMD_WIDTH) {
-        op[i] = ip1[0] @OP@ ip2[i];
-    }
-    LOOP_BLOCKED(@type@, NPY_SIMD_WIDTH) {
-        npyv_@sfx@ v2 = npyv_load_@sfx@(&ip2[i]);
-        npyv_@sfx@ v3 = npyv_@VOP@_@sfx@(v1, v2);
-        npyv_store_@sfx@(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[0] @OP@ ip2[i];
-    }
-}
+/********************************************************************************
+ ** op intrinics
+ ********************************************************************************/
 
-static void
-simd_binary_scalar2_@kind@_@TYPE@(@type@ * op, @type@ * ip1, @type@ * ip2, npy_intp n)
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_f32x2 simd_set2_f32(const float *a)
 {
-    const npyv_@sfx@ v2 = npyv_setall_@sfx@(ip2[0]);
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, NPY_SIMD_WIDTH) {
-        op[i] = ip1[i] @OP@ ip2[0];
-    }
-    LOOP_BLOCKED(@type@, NPY_SIMD_WIDTH) {
-        npyv_@sfx@ v1 = npyv_load_@sfx@(&ip1[i]);
-        npyv_@sfx@ v3 = npyv_@VOP@_@sfx@(v1, v2);
-        npyv_store_@sfx@(&op[i], v3);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = ip1[i] @OP@ ip2[0];
-    }
+    npyv_f32 fill = npyv_reinterpret_f32_u64(npyv_setall_u64(*(npy_uint64*)a));
+    npyv_f32x2 r;
+    r.val[0] = fill;
+    r.val[1] = fill;
+    return r;
 }
-/**end repeat1**/
-#endif /* NPY_SIMD@CHK@ */
-/**end repeat**/
-#endif // NPY_HAVE_SSE2
 
-/**begin repeat
- * Float types
- *  #type = npy_float, npy_double, npy_longdouble#
- *  #TYPE = FLOAT, DOUBLE, LONGDOUBLE#
- *  #vector = 1, 1, 0#
- *  #VECTOR = NPY_SIMD_F32, NPY_SIMD_F64, 0 #
- */
-/**begin repeat1
- * Arithmetic
- * # kind = add, subtract, multiply, divide#
- */
-static NPY_INLINE int
-run_binary_simd_@kind@_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *steps)
+NPY_FINLINE npyv_f32
+simd_cconjugate_f32(npyv_f32 x)
 {
-#if @vector@ && defined NPY_HAVE_SSE2
-    @type@ * ip1 = (@type@ *)args[0];
-    @type@ * ip2 = (@type@ *)args[1];
-    @type@ * op = (@type@ *)args[2];
-    npy_intp n = dimensions[0];
-#if defined NPY_HAVE_AVX512F
-    const npy_uintp vector_size_bytes = 64;
-#elif defined NPY_HAVE_AVX2
-    const npy_uintp vector_size_bytes = 32;
+#if NPY_SIMD_BIGENDIAN
+    const npyv_f32 mask = npyv_reinterpret_f32_u64(npyv_setall_u64(0x80000000));
 #else
-    const npy_uintp vector_size_bytes = 32;
-#endif
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(@type@), vector_size_bytes)) {
-        sse2_binary_scalar1_@kind@_@TYPE@(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(@type@), vector_size_bytes)) {
-        sse2_binary_scalar2_@kind@_@TYPE@(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(@type@), vector_size_bytes)) {
-        sse2_binary_@kind@_@TYPE@(op, ip1, ip2, n);
-        return 1;
-    }
-#elif @VECTOR@
-    @type@ * ip1 = (@type@ *)args[0];
-    @type@ * ip2 = (@type@ *)args[1];
-    @type@ * op = (@type@ *)args[2];
-    npy_intp n = dimensions[0];
-    /* argument one scalar */
-    if (IS_BLOCKABLE_BINARY_SCALAR1(sizeof(@type@), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar1_@kind@_@TYPE@(op, ip1, ip2, n);
-        return 1;
-    }
-    /* argument two scalar */
-    else if (IS_BLOCKABLE_BINARY_SCALAR2(sizeof(@type@), NPY_SIMD_WIDTH)) {
-        simd_binary_scalar2_@kind@_@TYPE@(op, ip1, ip2, n);
-        return 1;
-    }
-    else if (IS_BLOCKABLE_BINARY(sizeof(@type@), NPY_SIMD_WIDTH)) {
-        simd_binary_@kind@_@TYPE@(op, ip1, ip2, n);
-        return 1;
-    }
+    const npyv_f32 mask = npyv_reinterpret_f32_u64(npyv_setall_u64(0x8000000000000000ULL));
 #endif
-    return 0;
+    return npyv_xor_f32(x, mask);
 }
-/**end repeat1**/
-/**end repeat**/
 
-/********************************************************************************
- ** Defining ufunc inner functions
- ********************************************************************************/
-/**begin repeat
- * Float types
- *  #type = npy_float, npy_double#
- *  #TYPE = FLOAT, DOUBLE#
- *  #c = f, #
- *  #C = F, #
- */
-/**begin repeat1
- * Arithmetic
- * # kind = add, subtract, multiply, divide#
- * # OP = +, -, *, /#
- * # PW = 1, 0, 0, 0#
- */
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+NPY_FINLINE npyv_f32
+simd_cmul_f32(npyv_f32 a, npyv_f32 b)
 {
-    if (IS_BINARY_REDUCE) {
-#if @PW@
-        @type@ * iop1 = (@type@ *)args[0];
-        npy_intp n = dimensions[0];
-
-        *iop1 @OP@= @TYPE@_pairwise_sum(args[1], n, steps[1]);
-#else
-        BINARY_REDUCE_LOOP(@type@) {
-            io1 @OP@= *(@type@ *)ip2;
-        }
-        *((@type@ *)iop1) = io1;
-#endif
-    }
-    else if (!run_binary_simd_@kind@_@TYPE@(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const @type@ in1 = *(@type@ *)ip1;
-            const @type@ in2 = *(@type@ *)ip2;
-            *((@type@ *)op1) = in1 @OP@ in2;
-        }
-    }
+    npyv_f32 b_rev = npyv_permi128_f32(b, 1, 0, 3, 2);
+    npyv_f32 a_re = npyv_permi128_f32(a, 0, 0, 2, 2);
+    npyv_f32 a_im = npyv_permi128_f32(a, 1, 1, 3, 3);
+    // a_im * b_im, a_im * b_re
+    npyv_f32 ab_iiir = npyv_mul_f32(a_im, b_rev);
+    return npyv_muladdsub_f32(a_re, b, ab_iiir);
 }
-/**end repeat1**/
-/**end repeat**/
 
-//###############################################################################
-//## Complex Single/Double precision
-//###############################################################################
-/********************************************************************************
- ** Defining the SIMD kernels
- ********************************************************************************/
-#if !defined(_MSC_VER) && defined(NPY_HAVE_AVX512F)
-    /**
-     * For somehow MSVC commit aggressive optimization lead
-     * to raises 'RuntimeWarning: invalid value encountered in multiply'
-     *
-     * the issue mainly caused by '_mm512_maskz_loadu_ps', we need to
-     * investigate about it while moving to NPYV.
-     */
-    #define AVX512F_NOMSVC
+NPY_FINLINE npyv_f32
+simd_csquare_f32(npyv_f32 x)
+{ return simd_cmul_f32(x, x); }
 #endif
 
-#ifdef AVX512F_NOMSVC
-NPY_FINLINE __mmask16
-avx512_get_full_load_mask_ps(void)
-{
-    return 0xFFFF;
-}
-
-NPY_FINLINE __mmask8
-avx512_get_full_load_mask_pd(void)
-{
-    return 0xFF;
-}
-NPY_FINLINE __m512
-avx512_masked_load_ps(__mmask16 mask, npy_float* addr)
-{
-    return _mm512_maskz_loadu_ps(mask, (__m512 *)addr);
-}
+#if NPY_SIMD_F64
 
-NPY_FINLINE __m512d
-avx512_masked_load_pd(__mmask8 mask, npy_double* addr)
+NPY_FINLINE npyv_f64x2 simd_set2_f64(const double *a)
 {
-    return _mm512_maskz_loadu_pd(mask, (__m512d *)addr);
+    npyv_f64 r = npyv_setall_f64(a[0]);
+    npyv_f64 i = npyv_setall_f64(a[1]);
+    return npyv_zip_f64(r, i);
 }
 
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_get_partial_load_mask_ps(const npy_int num_elem, const npy_int total_elem)
+NPY_FINLINE npyv_f64
+simd_cconjugate_f64(npyv_f64 x)
 {
-    return (0x0001 << num_elem) - 0x0001;
+    const npyv_f64 mask = npyv_reinterpret_f64_u64(npyv_set_u64(
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL,
+       0, 0x8000000000000000ULL, 0, 0x8000000000000000ULL
+    ));
+    return npyv_xor_f64(x, mask);
 }
 
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem)
-{
-    return (0x01 << num_elem) - 0x01;
-}
-/**begin repeat
- *  #vsub  = ps, pd#
- *  #type= npy_float, npy_double#
- *  #epi_vsub  = epi32, epi64#
- *  #vtype = __m512, __m512d#
- *  #mask = __mmask16, __mmask8#
- *  #and_const = 0x7fffffff, 0x7fffffffffffffffLL#
- *  #neg_mask = 0x80000000, 0x8000000000000000#
- *  #perm_ = 0xb1, 0x55#
- *  #cmpx_img_mask = 0xAAAA, 0xAA#
- *  #cmpx_re_mask = 0x5555, 0x55#
- *  #INF = NPY_INFINITYF, NPY_INFINITY#
- *  #NAN = NPY_NANF, NPY_NAN#
- */
-NPY_FINLINE @vtype@
-avx512_hadd_@vsub@(const @vtype@ x)
+NPY_FINLINE npyv_f64
+simd_cmul_f64(npyv_f64 a, npyv_f64 b)
 {
-    return _mm512_add_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@));
+    npyv_f64 b_rev = npyv_permi128_f64(b, 1, 0);
+    npyv_f64 a_re = npyv_permi128_f64(a, 0, 0);
+    npyv_f64 a_im = npyv_permi128_f64(a, 1, 1);
+    // a_im * b_im, a_im * b_re
+    npyv_f64 ab_iiir = npyv_mul_f64(a_im, b_rev);
+    return npyv_muladdsub_f64(a_re, b, ab_iiir);
 }
 
-NPY_FINLINE @vtype@
-avx512_hsub_@vsub@(const @vtype@ x)
-{
-    return _mm512_sub_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@));
-}
-NPY_FINLINE @vtype@
-avx512_cmul_@vsub@(@vtype@ x1, @vtype@ x2)
-{
-    // x1 = r1, i1
-    // x2 = r2, i2
-    @vtype@ x3  = _mm512_permute_@vsub@(x2, @perm_@);   // i2, r2
-    @vtype@ x12 = _mm512_mul_@vsub@(x1, x2);            // r1*r2, i1*i2
-    @vtype@ x13 = _mm512_mul_@vsub@(x1, x3);            // r1*i2, r2*i1
-    @vtype@ outreal = avx512_hsub_@vsub@(x12);          // r1*r2 - i1*i2, r1*r2 - i1*i2
-    @vtype@ outimg  = avx512_hadd_@vsub@(x13);          // r1*i2 + i1*r2, r1*i2 + i1*r2
-    return _mm512_mask_blend_@vsub@(@cmpx_img_mask@, outreal, outimg);
-}
-/**end repeat**/
+NPY_FINLINE npyv_f64
+simd_csquare_f64(npyv_f64 x)
+{ return simd_cmul_f64(x, x); }
 #endif
 
 /**begin repeat
- * #TYPE = CFLOAT, CDOUBLE#
  * #type = npy_float, npy_double#
- * #num_lanes = 16, 8#
- * #vsuffix = ps, pd#
- * #epi_vsub  = epi32, epi64#
- * #mask = __mmask16, __mmask8#
- * #vtype = __m512, __m512d#
- * #scale = 4, 8#
- * #vindextype = __m512i, __m256i#
- * #vindexload = _mm512_loadu_si512, _mm256_loadu_si256#
- * #storemask = 0xFF, 0xF#
- * #IS_FLOAT = 1, 0#
- */
-/**begin repeat1
- *  #func = add, subtract, multiply#
- *  #vectorf = _mm512_add, _mm512_sub, avx512_cmul#
- */
-#if defined AVX512F_NOMSVC
-static NPY_INLINE void
-AVX512F_@func@_@TYPE@(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-    const npy_intp array_size = dimensions[0];
-    npy_intp num_remaining_elements = 2*array_size;
-    @type@* ip1 = (@type@*) args[0];
-    @type@* ip2 = (@type@*) args[1];
-    @type@* op  = (@type@*) args[2];
-
-    @mask@ load_mask = avx512_get_full_load_mask_@vsuffix@();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < @num_lanes@) {
-            load_mask = avx512_get_partial_load_mask_@vsuffix@(
-                                    num_remaining_elements, @num_lanes@);
-        }
-        @vtype@ x1, x2;
-        x1 = avx512_masked_load_@vsuffix@(load_mask, ip1);
-        x2 = avx512_masked_load_@vsuffix@(load_mask, ip2);
-
-        @vtype@ out = @vectorf@_@vsuffix@(x1, x2);
-
-        _mm512_mask_storeu_@vsuffix@(op, load_mask, out);
-
-        ip1 += @num_lanes@;
-        ip2 += @num_lanes@;
-        op += @num_lanes@;
-        num_remaining_elements -= @num_lanes@;
-    }
-}
-#endif // AVX512F_NOMSVC
-/**end repeat1**/
-/**end repeat**/
-
-/**begin repeat
- * #TYPE = CFLOAT, CDOUBLE#
- * #type= npy_float, npy_double#
- * #esize = 8, 16#
- */
-/**begin repeat1
- *  #func = add, subtract, multiply#
+ * #sfx = f32, f64#
+ * #bsfx = b32, b64#
+ * #usfx = b32, u64#
+ * #VECTOR = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #is_double = 0, 1#
+ * #c = f, #
+ * #INF = NPY_INFINITYF, NPY_INFINITY#
+ * #NAN = NPY_NANF, NPY_NAN#
  */
-static NPY_INLINE int
-run_binary_avx512f_@func@_@TYPE@(char **args, const npy_intp *dimensions, const npy_intp *steps)
+#if @VECTOR@
+NPY_FINLINE npyv_@sfx@
+simd_cabsolute_@sfx@(npyv_@sfx@ re, npyv_@sfx@ im)
 {
-#if defined AVX512F_NOMSVC
-    if (IS_BINARY_STRIDE_ONE(@esize@, 64)) {
-        AVX512F_@func@_@TYPE@(args, dimensions, steps);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
+    const npyv_@sfx@ inf = npyv_setall_@sfx@(@INF@);
+    const npyv_@sfx@ nan = npyv_setall_@sfx@(@NAN@);
+
+    re = npyv_abs_@sfx@(re);
+    im = npyv_abs_@sfx@(im);
+    /*
+     * If real or imag = INF, then convert it to inf + j*inf
+     * Handles: inf + j*nan, nan + j*inf
+     */
+    npyv_@bsfx@ re_infmask = npyv_cmpeq_@sfx@(re, inf);
+    npyv_@bsfx@ im_infmask = npyv_cmpeq_@sfx@(im, inf);
+    im = npyv_select_@sfx@(re_infmask, inf, im);
+    re = npyv_select_@sfx@(im_infmask, inf, re);
+    /*
+     * If real or imag = NAN, then convert it to nan + j*nan
+     * Handles: x + j*nan, nan + j*x
+     */
+    npyv_@bsfx@ re_nnanmask = npyv_notnan_@sfx@(re);
+    npyv_@bsfx@ im_nnanmask = npyv_notnan_@sfx@(im);
+    im = npyv_select_@sfx@(re_nnanmask, im, nan);
+    re = npyv_select_@sfx@(im_nnanmask, re, nan);
+
+    npyv_@sfx@ larger  = npyv_max_@sfx@(re, im);
+    npyv_@sfx@ smaller = npyv_min_@sfx@(im, re);
+    /*
+     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
+     */
+    npyv_@bsfx@ zeromask = npyv_cmpeq_@sfx@(larger, npyv_zero_@sfx@());
+    npyv_@bsfx@ infmask = npyv_cmpeq_@sfx@(smaller, inf);
+    npyv_@bsfx@ div_mask = npyv_not_@bsfx@(npyv_or_@bsfx@(zeromask, infmask));
+
+    npyv_@sfx@ ratio = npyv_ifdivz_@sfx@(div_mask, smaller, larger);
+    npyv_@sfx@ hypot = npyv_sqrt_@sfx@(
+        npyv_muladd_@sfx@(ratio, ratio, npyv_setall_@sfx@(1.0@c@)
+    ));
+    return npyv_mul_@sfx@(hypot, larger);
 }
-/**end repeat1**/
+#endif // VECTOR
 /**end repeat**/
 
 /********************************************************************************
@@ -724,55 +429,350 @@ run_binary_avx512f_@func@_@TYPE@(char **args, const npy_intp *dimensions, const
  * complex types
  * #TYPE = CFLOAT, CDOUBLE#
  * #ftype = npy_float, npy_double#
+ * #VECTOR = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #sfx = f32, f64#
  * #c = f, #
  * #C = F, #
  */
 /**begin repeat1
  * arithmetic
- * #kind = add, subtract#
- * #OP = +, -#
- * #PW = 1, 0#
+ * #kind = add, subtract, multiply#
+ * #vectorf = npyv_add, npyv_sub, simd_cmul#
+ * #OP = +, -, *#
+ * #PW = 1, 0, 0#
+ * #is_mul = 0*2, 1#
  */
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    // Parenthesis around @PW@ tells clang dead code is intentional
-    if (IS_BINARY_REDUCE && (@PW@)) {
-        npy_intp n = dimensions[0];
-        @ftype@ * or = ((@ftype@ *)args[0]);
-        @ftype@ * oi = ((@ftype@ *)args[0]) + 1;
+    npy_intp len = dimensions[0];
+    char *b_src0 = args[0], *b_src1 = args[1], *b_dst = args[2];
+    npy_intp b_ssrc0 = steps[0], b_ssrc1 = steps[1], b_sdst = steps[2];
+#if @PW@
+    // reduce
+    if (b_ssrc0 == 0 && b_ssrc0 == b_sdst && b_src0 == b_dst &&
+        b_ssrc1 % (sizeof(@ftype@)*2) == 0
+    ) {
+        @ftype@ *rl_im = (@ftype@ *)b_src0;
         @ftype@ rr, ri;
-
-        @TYPE@_pairwise_sum(&rr, &ri, args[1], n * 2, steps[1] / 2);
-        *or @OP@= rr;
-        *oi @OP@= ri;
+        @TYPE@_pairwise_sum(&rr, &ri, b_src1, len * 2, b_ssrc1 / 2);
+        rl_im[0] @OP@= rr;
+        rl_im[1] @OP@= ri;
         return;
     }
-    if (!run_binary_avx512f_@kind@_@TYPE@(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const @ftype@ in1r = ((@ftype@ *)ip1)[0];
-            const @ftype@ in1i = ((@ftype@ *)ip1)[1];
-            const @ftype@ in2r = ((@ftype@ *)ip2)[0];
-            const @ftype@ in2i = ((@ftype@ *)ip2)[1];
-            ((@ftype@ *)op1)[0] = in1r @OP@ in2r;
-            ((@ftype@ *)op1)[1] = in1i @OP@ in2i;
+#endif
+#if @VECTOR@
+    if (is_mem_overlap(b_src0, b_ssrc0, b_dst, b_sdst, len) ||
+        is_mem_overlap(b_src1, b_ssrc1, b_dst, b_sdst, len) ||
+        b_sdst  % sizeof(@ftype@) != 0 || b_sdst == 0 ||
+        b_ssrc0 % sizeof(@ftype@) != 0 ||
+        b_ssrc1 % sizeof(@ftype@) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const @ftype@ *src0 = (@ftype@*)b_src0;
+    const @ftype@ *src1 = (@ftype@*)b_src1;
+          @ftype@ *dst  = (@ftype@*)b_dst;
+
+    const npy_intp ssrc0 = b_ssrc0 / sizeof(@ftype@);
+    const npy_intp ssrc1 = b_ssrc1 / sizeof(@ftype@);
+    const npy_intp sdst  = b_sdst / sizeof(@ftype@);
+
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    const int loadable0 = npyv_loadable_stride_s64(ssrc0);
+    const int loadable1 = npyv_loadable_stride_s64(ssrc1);
+    const int storable = npyv_storable_stride_s64(sdst);
+
+    // lots**lots of specializations, to squeeze out max performance
+    // contig
+    if (ssrc0 == 2 && ssrc0 == ssrc1 && ssrc0 == sdst) {
+        for (; len >= vstep; len -= vstep, src0 += wstep, src1 += wstep, dst += wstep) {
+            npyv_@sfx@ a0 = npyv_load_@sfx@(src0);
+            npyv_@sfx@ a1 = npyv_load_@sfx@(src0 + vstep);
+            npyv_@sfx@ b0 = npyv_load_@sfx@(src1);
+            npyv_@sfx@ b1 = npyv_load_@sfx@(src1 + vstep);
+            npyv_@sfx@ r0 = @vectorf@_@sfx@(a0, b0);
+            npyv_@sfx@ r1 = @vectorf@_@sfx@(a1, b1);
+            npyv_store_@sfx@(dst, r0);
+            npyv_store_@sfx@(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += vstep, src1 += vstep, dst += vstep) {
+            npyv_@sfx@ a = npyv_load2_tillz_@sfx@(src0, len);
+            npyv_@sfx@ b = npyv_load2_tillz_@sfx@(src1, len);
+            npyv_@sfx@ r = @vectorf@_@sfx@(a, b);
+            npyv_store2_till_@sfx@(dst, len, r);
+        }
+    }
+    // scalar 0
+    else if (ssrc0 == 0) {
+        npyv_@sfx@x2 a = simd_set2_@sfx@(src0);
+        // contig
+        if (ssrc1 == 2 && sdst == ssrc1) {
+            for (; len >= vstep; len -= vstep, src1 += wstep, dst += wstep) {
+                npyv_@sfx@ b0 = npyv_load_@sfx@(src1);
+                npyv_@sfx@ b1 = npyv_load_@sfx@(src1 + vstep);
+                npyv_@sfx@ r0 = @vectorf@_@sfx@(a.val[0], b0);
+                npyv_@sfx@ r1 = @vectorf@_@sfx@(a.val[1], b1);
+                npyv_store_@sfx@(dst, r0);
+                npyv_store_@sfx@(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += vstep, dst += vstep) {
+            #if @is_mul@
+                npyv_@sfx@ b = npyv_load2_till_@sfx@(src1, len, 1.0@c@, 1.0@c@);
+            #else
+                npyv_@sfx@ b = npyv_load2_tillz_@sfx@(src1, len);
+            #endif
+                npyv_@sfx@ r = @vectorf@_@sfx@(a.val[0], b);
+                npyv_store2_till_@sfx@(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable1 && storable) {
+            for (; len >= vstep; len -= vstep, src1 += ssrc1*vstep, dst += sdst*vstep) {
+                npyv_@sfx@ b0 = npyv_loadn2_@sfx@(src1, ssrc1);
+                npyv_@sfx@ b1 = npyv_loadn2_@sfx@(src1 + ssrc1*hstep, ssrc1);
+                npyv_@sfx@ r0 = @vectorf@_@sfx@(a.val[0], b0);
+                npyv_@sfx@ r1 = @vectorf@_@sfx@(a.val[1], b1);
+                npyv_storen2_@sfx@(dst, sdst, r0);
+                npyv_storen2_@sfx@(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src1 += ssrc1*hstep, dst += sdst*hstep) {
+            #if @is_mul@
+                npyv_@sfx@ b = npyv_loadn2_till_@sfx@(src1, ssrc1, len, 1.0@c@, 1.0@c@);
+            #else
+                npyv_@sfx@ b = npyv_loadn2_tillz_@sfx@(src1, ssrc1, len);
+            #endif
+                npyv_@sfx@ r = @vectorf@_@sfx@(a.val[0], b);
+                npyv_storen2_till_@sfx@(dst, sdst, len, r);
+            }
+        }
+        else {
+            goto loop_scalar;
+        }
+    }
+    // scalar 1
+    else if (ssrc1 == 0) {
+        npyv_@sfx@x2 b = simd_set2_@sfx@(src1);
+        if (ssrc0 == 2 && sdst == ssrc0) {
+            for (; len >= vstep; len -= vstep, src0 += wstep, dst += wstep) {
+                npyv_@sfx@ a0 = npyv_load_@sfx@(src0);
+                npyv_@sfx@ a1 = npyv_load_@sfx@(src0 + vstep);
+                npyv_@sfx@ r0 = @vectorf@_@sfx@(a0, b.val[0]);
+                npyv_@sfx@ r1 = @vectorf@_@sfx@(a1, b.val[1]);
+                npyv_store_@sfx@(dst, r0);
+                npyv_store_@sfx@(dst + vstep, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += vstep, dst += vstep) {
+            #if @is_mul@
+                npyv_@sfx@ a = npyv_load2_till_@sfx@(src0, len, 1.0@c@, 1.0@c@);
+            #else
+                npyv_@sfx@ a = npyv_load2_tillz_@sfx@(src0, len);
+            #endif
+                npyv_@sfx@ r = @vectorf@_@sfx@(a, b.val[0]);
+                npyv_store2_till_@sfx@(dst, len, r);
+            }
+        }
+        // non-contig
+        else if (loadable0 && storable) {
+            for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep, dst += sdst*vstep) {
+                npyv_@sfx@ a0 = npyv_loadn2_@sfx@(src0, ssrc0);
+                npyv_@sfx@ a1 = npyv_loadn2_@sfx@(src0 + ssrc0*hstep, ssrc0);
+                npyv_@sfx@ r0 = @vectorf@_@sfx@(a0, b.val[0]);
+                npyv_@sfx@ r1 = @vectorf@_@sfx@(a1, b.val[1]);
+                npyv_storen2_@sfx@(dst, sdst, r0);
+                npyv_storen2_@sfx@(dst + sdst*hstep, sdst, r1);
+            }
+            for (; len > 0; len -= hstep, src0 += ssrc0*hstep, dst += sdst*hstep) {
+            #if @is_mul@
+                npyv_@sfx@ a = npyv_loadn2_till_@sfx@(src0, ssrc0, len, 1.0@c@, 1.0@c@);
+            #else
+                npyv_@sfx@ a = npyv_loadn2_tillz_@sfx@(src0, ssrc0, len);
+            #endif
+                npyv_@sfx@ r = @vectorf@_@sfx@(a, b.val[0]);
+                npyv_storen2_till_@sfx@(dst, sdst, len, r);
+            }
         }
+        else {
+            goto loop_scalar;
+        }
+    }
+    #if @is_mul@
+    // non-contig
+    else if (loadable0 && loadable1 && storable) {
+        for (; len >= vstep; len -= vstep, src0 += ssrc0*vstep,
+                            src1 += ssrc1*vstep, dst += sdst*vstep
+        ) {
+            npyv_@sfx@ a0 = npyv_loadn2_@sfx@(src0, ssrc0);
+            npyv_@sfx@ a1 = npyv_loadn2_@sfx@(src0 + ssrc0*hstep, ssrc0);
+            npyv_@sfx@ b0 = npyv_loadn2_@sfx@(src1, ssrc1);
+            npyv_@sfx@ b1 = npyv_loadn2_@sfx@(src1 + ssrc1*hstep, ssrc1);
+            npyv_@sfx@ r0 = @vectorf@_@sfx@(a0, b0);
+            npyv_@sfx@ r1 = @vectorf@_@sfx@(a1, b1);
+            npyv_storen2_@sfx@(dst, sdst, r0);
+            npyv_storen2_@sfx@(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src0 += ssrc0*hstep,
+                       src1 += ssrc1*hstep, dst += sdst*hstep
+        ) {
+        #if @is_mul@
+            npyv_@sfx@ a = npyv_loadn2_till_@sfx@(src0, ssrc0, len, 1.0@c@, 1.0@c@);
+            npyv_@sfx@ b = npyv_loadn2_till_@sfx@(src1, ssrc1, len, 1.0@c@, 1.0@c@);
+        #else
+            npyv_@sfx@ a = npyv_loadn2_tillz_@sfx@(src0, ssrc0, len);
+            npyv_@sfx@ b = npyv_loadn2_tillz_@sfx@(src1, ssrc1, len);
+        #endif
+            npyv_@sfx@ r = @vectorf@_@sfx@(a, b);
+            npyv_storen2_till_@sfx@(dst, sdst, len, r);
+        }
+    }
+    #endif
+    else {
+        goto loop_scalar;
     }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src0 += b_ssrc0, b_src1 += b_ssrc1, b_dst += b_sdst) {
+        const @ftype@ a_r = ((@ftype@ *)b_src0)[0];
+        const @ftype@ a_i = ((@ftype@ *)b_src0)[1];
+        const @ftype@ b_r = ((@ftype@ *)b_src1)[0];
+        const @ftype@ b_i = ((@ftype@ *)b_src1)[1];
+    #if @is_mul@
+        ((@ftype@ *)b_dst)[0] = a_r*b_r - a_i*b_i;
+        ((@ftype@ *)b_dst)[1] = a_r*b_i + a_i*b_r;
+    #else
+        ((@ftype@ *)b_dst)[0] = a_r @OP@ b_r;
+        ((@ftype@ *)b_dst)[1] = a_i @OP@ b_i;
+    #endif
+    }
+}
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @ftype@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@ftype@ *)(ip1 + is1 * indx);
+        const @ftype@ b_r = ((@ftype@ *)value)[0];
+        const @ftype@ b_i = ((@ftype@ *)value)[1];
+    #if @is_mul@
+        const @ftype@ a_r = indexed[0];
+        const @ftype@ a_i = indexed[1];
+        indexed[0] = a_r*b_r - a_i*b_i;
+        indexed[1] = a_r*b_i + a_i*b_r;
+    #else
+        indexed[0] @OP@= b_r;
+        indexed[1] @OP@= b_i;
+    #endif
+    }
+    return 0;
 }
 /**end repeat1**/
 
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_multiply)
+/**begin repeat1
+ *  #kind = conjugate, square#
+ *  #is_square = 0, 1#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
 {
-    if (!run_binary_avx512f_multiply_@TYPE@(args, dimensions, steps)) {
-        BINARY_LOOP {
-            const @ftype@ in1r = ((@ftype@ *)ip1)[0];
-            const @ftype@ in1i = ((@ftype@ *)ip1)[1];
-            const @ftype@ in2r = ((@ftype@ *)ip2)[0];
-            const @ftype@ in2i = ((@ftype@ *)ip2)[1];
-            ((@ftype@ *)op1)[0] = in1r*in2r - in1i*in2i;
-            ((@ftype@ *)op1)[1] = in1r*in2i + in1i*in2r;
+    npy_intp len = dimensions[0];
+    char *b_src = args[0], *b_dst = args[1];
+    npy_intp b_ssrc = steps[0], b_sdst = steps[1];
+#if @VECTOR@
+    if (is_mem_overlap(b_src, b_ssrc, b_dst, b_sdst, len) ||
+        b_sdst % sizeof(@ftype@) != 0 ||
+        b_ssrc % sizeof(@ftype@) != 0
+    ) {
+        goto loop_scalar;
+    }
+    const @ftype@ *src  = (@ftype@*)b_src;
+          @ftype@ *dst  = (@ftype@*)b_dst;
+    const npy_intp ssrc = b_ssrc / sizeof(@ftype@);
+    const npy_intp sdst = b_sdst / sizeof(@ftype@);
+
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * 2;
+    const int hstep = vstep / 2;
+
+    if (ssrc == 2 && ssrc == sdst) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += wstep) {
+            npyv_@sfx@ a0 = npyv_load_@sfx@(src);
+            npyv_@sfx@ a1 = npyv_load_@sfx@(src + vstep);
+            npyv_@sfx@ r0 = simd_c@kind@_@sfx@(a0);
+            npyv_@sfx@ r1 = simd_c@kind@_@sfx@(a1);
+            npyv_store_@sfx@(dst, r0);
+            npyv_store_@sfx@(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += vstep) {
+            npyv_@sfx@ a = npyv_load2_tillz_@sfx@(src, len);
+            npyv_@sfx@ r = simd_c@kind@_@sfx@(a);
+            npyv_store2_till_@sfx@(dst, len, r);
+        }
+    }
+    else if (ssrc == 2 && npyv_storable_stride_s64(sdst)) {
+        for (; len >= vstep; len -= vstep, src += wstep, dst += sdst*vstep) {
+            npyv_@sfx@ a0 = npyv_load_@sfx@(src);
+            npyv_@sfx@ a1 = npyv_load_@sfx@(src + vstep);
+            npyv_@sfx@ r0 = simd_c@kind@_@sfx@(a0);
+            npyv_@sfx@ r1 = simd_c@kind@_@sfx@(a1);
+            npyv_storen2_@sfx@(dst, sdst, r0);
+            npyv_storen2_@sfx@(dst + sdst*hstep, sdst, r1);
+        }
+        for (; len > 0; len -= hstep, src += vstep, dst += sdst*hstep) {
+            npyv_@sfx@ a = npyv_load2_tillz_@sfx@(src, len);
+            npyv_@sfx@ r = simd_c@kind@_@sfx@(a);
+            npyv_storen2_till_@sfx@(dst, sdst, len, r);
+        }
+    }
+    else if (sdst == 2 && npyv_loadable_stride_s64(ssrc)) {
+        for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += wstep) {
+            npyv_@sfx@ a0 = npyv_loadn2_@sfx@(src, ssrc);
+            npyv_@sfx@ a1 = npyv_loadn2_@sfx@(src + ssrc*hstep, ssrc);
+            npyv_@sfx@ r0 = simd_c@kind@_@sfx@(a0);
+            npyv_@sfx@ r1 = simd_c@kind@_@sfx@(a1);
+            npyv_store_@sfx@(dst, r0);
+            npyv_store_@sfx@(dst + vstep, r1);
+        }
+        for (; len > 0; len -= hstep, src += ssrc*hstep, dst += vstep) {
+            npyv_@sfx@ a = npyv_loadn2_tillz_@sfx@((@ftype@*)src, ssrc, len);
+            npyv_@sfx@ r = simd_c@kind@_@sfx@(a);
+            npyv_store2_till_@sfx@(dst, len, r);
         }
     }
+    else {
+        goto loop_scalar;
+    }
+    npyv_cleanup();
+    return;
+loop_scalar:
+#endif
+    for (; len > 0; --len, b_src += b_ssrc, b_dst += b_sdst) {
+        const @ftype@ rl = ((@ftype@ *)b_src)[0];
+        const @ftype@ im = ((@ftype@ *)b_src)[1];
+    #if @is_square@
+        ((@ftype@ *)b_dst)[0] = rl*rl - im*im;
+        ((@ftype@ *)b_dst)[1] = rl*im + im*rl;
+    #else
+        ((@ftype@ *)b_dst)[0] = rl;
+        ((@ftype@ *)b_dst)[1] = -im;
+    #endif
+    }
 }
+/**end repeat1**/
 /**end repeat**/
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.fma3.avx2.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.fma3.avx2.c
new file mode 100644
index 00000000000..96b2cdc2593
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.fma3.avx2.c
@@ -0,0 +1,18 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT FMA3__AVX2
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#define NPY__CPU_TARGET_POPCNT
+#define NPY__CPU_TARGET_SSE42
+#define NPY__CPU_TARGET_AVX
+#define NPY__CPU_TARGET_F16C
+#define NPY__CPU_TARGET_FMA3
+#define NPY__CPU_TARGET_AVX2
+#include "numpy/core/src/umath/loops_arithm_fp.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.h
index 9d4f5be1c3b..8fccadf008f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.h
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.h
@@ -1,4 +1,4 @@
-// cache_hash:56451526
+// cache_hash:4149988827
 /**
  * AUTOGENERATED DON'T EDIT
  * Please make changes to the code generator (distutils/ccompiler_opt.py)
@@ -12,8 +12,7 @@
 	NPY__CPU_DISPATCH_EXPAND_(CB(__VA_ARGS__))
 #if defined(__x86_64__) || defined(_M_X64)
 #define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
-	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX512F)), AVX512F, __VA_ARGS__)) \
-	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX)&&CHK(F16C)&&CHK(AVX2)), AVX2, __VA_ARGS__))
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX)&&CHK(F16C)&&CHK(FMA3)&&CHK(AVX2)), FMA3__AVX2, __VA_ARGS__))
 #elif defined(__aarch64__)
 #define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c
index fbb63d3e8b4..25fae7f711c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c
@@ -48,7 +48,7 @@
 
 #if NPY_SIMD
 #line 45
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src   = (npyv_lanetype_s8 *) args[0];
@@ -109,7 +109,7 @@ simd_divide_by_scalar_contig_s8(char **args, npy_intp len)
 }
 
 #line 45
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src   = (npyv_lanetype_s16 *) args[0];
@@ -170,7 +170,7 @@ simd_divide_by_scalar_contig_s16(char **args, npy_intp len)
 }
 
 #line 45
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src   = (npyv_lanetype_s32 *) args[0];
@@ -231,7 +231,7 @@ simd_divide_by_scalar_contig_s32(char **args, npy_intp len)
 }
 
 #line 45
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src   = (npyv_lanetype_s64 *) args[0];
@@ -293,7 +293,7 @@ simd_divide_by_scalar_contig_s64(char **args, npy_intp len)
 
 
 #line 111
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src   = (npyv_lanetype_u8 *) args[0];
@@ -316,7 +316,7 @@ simd_divide_by_scalar_contig_u8(char **args, npy_intp len)
 }
 
 #line 111
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src   = (npyv_lanetype_u16 *) args[0];
@@ -339,7 +339,7 @@ simd_divide_by_scalar_contig_u16(char **args, npy_intp len)
 }
 
 #line 111
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src   = (npyv_lanetype_u32 *) args[0];
@@ -362,7 +362,7 @@ simd_divide_by_scalar_contig_u32(char **args, npy_intp len)
 }
 
 #line 111
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src   = (npyv_lanetype_u64 *) args[0];
@@ -519,7 +519,7 @@ vsx4_div_s16(npyv_s16 a, npyv_s16 b)
 
 
 #line 210
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src1 = (npyv_lanetype_u8 *) args[0];
@@ -553,7 +553,7 @@ vsx4_simd_divide_contig_u8(char **args, npy_intp len)
 }
 
 #line 210
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src1 = (npyv_lanetype_u16 *) args[0];
@@ -587,7 +587,7 @@ vsx4_simd_divide_contig_u16(char **args, npy_intp len)
 }
 
 #line 210
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src1 = (npyv_lanetype_u32 *) args[0];
@@ -621,7 +621,7 @@ vsx4_simd_divide_contig_u32(char **args, npy_intp len)
 }
 
 #line 210
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src1 = (npyv_lanetype_u64 *) args[0];
@@ -656,7 +656,7 @@ vsx4_simd_divide_contig_u64(char **args, npy_intp len)
 
 
 #line 249
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src1 = (npyv_lanetype_s8 *) args[0];
@@ -725,7 +725,7 @@ vsx4_simd_divide_contig_s8(char **args, npy_intp len)
 }
 
 #line 249
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src1 = (npyv_lanetype_s16 *) args[0];
@@ -794,7 +794,7 @@ vsx4_simd_divide_contig_s16(char **args, npy_intp len)
 }
 
 #line 249
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src1 = (npyv_lanetype_s32 *) args[0];
@@ -863,7 +863,7 @@ vsx4_simd_divide_contig_s32(char **args, npy_intp len)
 }
 
 #line 249
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src1 = (npyv_lanetype_s64 *) args[0];
@@ -1019,6 +1019,29 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_divide)
     }
 }
 
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(BYTE_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = floor_div_BYTE(*indexed, *(npy_byte *)value);
+    }
+    return 0;
+}
+
+
 #line 329
 #undef TO_SIMD_SFX
 #if 0
@@ -1100,6 +1123,29 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_divide)
     }
 }
 
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(SHORT_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = floor_div_SHORT(*indexed, *(npy_short *)value);
+    }
+    return 0;
+}
+
+
 #line 329
 #undef TO_SIMD_SFX
 #if 0
@@ -1181,6 +1227,29 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_divide)
     }
 }
 
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(INT_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = floor_div_INT(*indexed, *(npy_int *)value);
+    }
+    return 0;
+}
+
+
 #line 329
 #undef TO_SIMD_SFX
 #if 0
@@ -1262,6 +1331,29 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_divide)
     }
 }
 
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONG_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = floor_div_LONG(*indexed, *(npy_long *)value);
+    }
+    return 0;
+}
+
+
 #line 329
 #undef TO_SIMD_SFX
 #if 0
@@ -1343,23 +1435,46 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_divide)
     }
 }
 
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGLONG_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = floor_div_LONGLONG(*indexed, *(npy_longlong *)value);
+    }
+    return 0;
+}
+
 
-#line 406
+
+#line 429
 #undef TO_SIMD_SFX
 #if 0
-#line 411
+#line 434
 #elif NPY_BITSOF_BYTE == 8
     #define TO_SIMD_SFX(X) X##_u8
 
-#line 411
+#line 434
 #elif NPY_BITSOF_BYTE == 16
     #define TO_SIMD_SFX(X) X##_u16
 
-#line 411
+#line 434
 #elif NPY_BITSOF_BYTE == 32
     #define TO_SIMD_SFX(X) X##_u32
 
-#line 411
+#line 434
 #elif NPY_BITSOF_BYTE == 64
     #define TO_SIMD_SFX(X) X##_u64
 
@@ -1416,22 +1531,51 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_divide)
     }
 }
 
-#line 406
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UBYTE_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        npy_ubyte in2 = *(npy_ubyte *)value;
+        if (NPY_UNLIKELY(in2 == 0)) {
+            npy_set_floatstatus_divbyzero();
+            *indexed = 0;
+        } else {
+            *indexed = *indexed / in2;
+        }
+    }
+    return 0;
+}
+
+
+#line 429
 #undef TO_SIMD_SFX
 #if 0
-#line 411
+#line 434
 #elif NPY_BITSOF_SHORT == 8
     #define TO_SIMD_SFX(X) X##_u8
 
-#line 411
+#line 434
 #elif NPY_BITSOF_SHORT == 16
     #define TO_SIMD_SFX(X) X##_u16
 
-#line 411
+#line 434
 #elif NPY_BITSOF_SHORT == 32
     #define TO_SIMD_SFX(X) X##_u32
 
-#line 411
+#line 434
 #elif NPY_BITSOF_SHORT == 64
     #define TO_SIMD_SFX(X) X##_u64
 
@@ -1488,22 +1632,51 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_divide)
     }
 }
 
-#line 406
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(USHORT_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        npy_ushort in2 = *(npy_ushort *)value;
+        if (NPY_UNLIKELY(in2 == 0)) {
+            npy_set_floatstatus_divbyzero();
+            *indexed = 0;
+        } else {
+            *indexed = *indexed / in2;
+        }
+    }
+    return 0;
+}
+
+
+#line 429
 #undef TO_SIMD_SFX
 #if 0
-#line 411
+#line 434
 #elif NPY_BITSOF_INT == 8
     #define TO_SIMD_SFX(X) X##_u8
 
-#line 411
+#line 434
 #elif NPY_BITSOF_INT == 16
     #define TO_SIMD_SFX(X) X##_u16
 
-#line 411
+#line 434
 #elif NPY_BITSOF_INT == 32
     #define TO_SIMD_SFX(X) X##_u32
 
-#line 411
+#line 434
 #elif NPY_BITSOF_INT == 64
     #define TO_SIMD_SFX(X) X##_u64
 
@@ -1560,22 +1733,51 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_divide)
     }
 }
 
-#line 406
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UINT_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        npy_uint in2 = *(npy_uint *)value;
+        if (NPY_UNLIKELY(in2 == 0)) {
+            npy_set_floatstatus_divbyzero();
+            *indexed = 0;
+        } else {
+            *indexed = *indexed / in2;
+        }
+    }
+    return 0;
+}
+
+
+#line 429
 #undef TO_SIMD_SFX
 #if 0
-#line 411
+#line 434
 #elif NPY_BITSOF_LONG == 8
     #define TO_SIMD_SFX(X) X##_u8
 
-#line 411
+#line 434
 #elif NPY_BITSOF_LONG == 16
     #define TO_SIMD_SFX(X) X##_u16
 
-#line 411
+#line 434
 #elif NPY_BITSOF_LONG == 32
     #define TO_SIMD_SFX(X) X##_u32
 
-#line 411
+#line 434
 #elif NPY_BITSOF_LONG == 64
     #define TO_SIMD_SFX(X) X##_u64
 
@@ -1632,22 +1834,51 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_divide)
     }
 }
 
-#line 406
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONG_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        npy_ulong in2 = *(npy_ulong *)value;
+        if (NPY_UNLIKELY(in2 == 0)) {
+            npy_set_floatstatus_divbyzero();
+            *indexed = 0;
+        } else {
+            *indexed = *indexed / in2;
+        }
+    }
+    return 0;
+}
+
+
+#line 429
 #undef TO_SIMD_SFX
 #if 0
-#line 411
+#line 434
 #elif NPY_BITSOF_LONGLONG == 8
     #define TO_SIMD_SFX(X) X##_u8
 
-#line 411
+#line 434
 #elif NPY_BITSOF_LONGLONG == 16
     #define TO_SIMD_SFX(X) X##_u16
 
-#line 411
+#line 434
 #elif NPY_BITSOF_LONGLONG == 32
     #define TO_SIMD_SFX(X) X##_u32
 
-#line 411
+#line 434
 #elif NPY_BITSOF_LONGLONG == 64
     #define TO_SIMD_SFX(X) X##_u64
 
@@ -1704,4 +1935,33 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_divide)
     }
 }
 
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONGLONG_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        npy_ulonglong in2 = *(npy_ulonglong *)value;
+        if (NPY_UNLIKELY(in2 == 0)) {
+            npy_set_floatstatus_divbyzero();
+            *indexed = 0;
+        } else {
+            *indexed = *indexed / in2;
+        }
+    }
+    return 0;
+}
+
+
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c.src
index 5b5f13ad189..e07bb79808a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithmetic.dispatch.c.src
@@ -42,7 +42,7 @@
  * #sfx    = s8, s16, s32, s64#
  * #len    = 8,  16,  32,  64#
  */
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src   = (npyv_lanetype_@sfx@ *) args[0];
@@ -108,7 +108,7 @@ simd_divide_by_scalar_contig_@sfx@(char **args, npy_intp len)
  * #sfx    = u8, u16, u32, u64#
  * #len    = 8,  16,  32,  64#
  */
-static NPY_INLINE void
+static inline void
 simd_divide_by_scalar_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src   = (npyv_lanetype_@sfx@ *) args[0];
@@ -207,7 +207,7 @@ vsx4_div_@t@16(npyv_@t@16 a, npyv_@t@16 b)
  * #sfx = u8, u16, u32, u64#
  * #len = 8,  16,  32,  64#
  */
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src1 = (npyv_lanetype_@sfx@ *) args[0];
@@ -246,7 +246,7 @@ vsx4_simd_divide_contig_@sfx@(char **args, npy_intp len)
  * #sfx = s8, s16, s32, s64#
  * #len = 8,  16,  32,  64#
  */
-static NPY_INLINE void
+static inline void
 vsx4_simd_divide_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src1 = (npyv_lanetype_@sfx@ *) args[0];
@@ -395,6 +395,29 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_divide)
         }
     }
 }
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(@TYPE@_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @type@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@type@ *)(ip1 + is1 * indx);
+        *indexed = floor_div_@TYPE@(*indexed, *(@type@ *)value);
+    }
+    return 0;
+}
+
 /**end repeat**/
 
 /**begin repeat
@@ -463,4 +486,33 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_divide)
         }
     }
 }
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(@TYPE@_divide_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char * const*args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp shape = steps[3];
+    npy_intp n = dimensions[0];
+    npy_intp i;
+    @type@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@type@ *)(ip1 + is1 * indx);
+        @type@ in2 = *(@type@ *)value;
+        if (NPY_UNLIKELY(in2 == 0)) {
+            npy_set_floatstatus_divbyzero();
+            *indexed = 0;
+        } else {
+            *indexed = *indexed / in2;
+        }
+    }
+    return 0;
+}
+
 /**end repeat**/
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.avx2.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.avx2.c
index 4e446ae18c6..d94ca1f3215 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.avx2.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.avx2.c
@@ -14,4 +14,4 @@
 #define NPY__CPU_TARGET_AVX
 #define NPY__CPU_TARGET_F16C
 #define NPY__CPU_TARGET_AVX2
-#include "numpy/core/src/umath/loops_arithm_fp.dispatch.c"
+#include "numpy/core/src/umath/loops_autovec.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.c
new file mode 100644
index 00000000000..309f234ba7b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.c
@@ -0,0 +1,2113 @@
+#line 1 "numpy/core/src/umath/loops_autovec.dispatch.c.src"
+
+/*
+ *****************************************************************************
+ **       This file was autogenerated from a template  DO NOT EDIT!!!!      **
+ **       Changes should be made to the original source (.src) file         **
+ *****************************************************************************
+ */
+
+#line 1
+/*@targets
+ ** $maxopt $autovec baseline
+ ** sse2 avx2
+ ** neon
+ ** vsx2
+ ** vx
+ **/
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/*
+ *****************************************************************************
+ **                           INTEGER LOOPS
+ *****************************************************************************
+ */
+/*
+ * Arithmetic bit shift operations.
+ *
+ * Intel hardware masks bit shift values, so large shifts wrap around
+ * and can produce surprising results. The special handling ensures that
+ * behavior is independent of compiler or hardware.
+ * TODO: We could implement consistent behavior for negative shifts,
+ *       which is undefined in C.
+ */
+#define INT_left_shift_needs_clear_floatstatus
+#define UINT_left_shift_needs_clear_floatstatus
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_byte, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_byte, npy_byte, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_byte, npy_byte, *out = npy_lshifthh(in1, in2));
+#ifdef BYTE_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_BYTE_right_shift
+    BINARY_LOOP_FAST(npy_byte, npy_byte, *out = npy_rshifthh(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_byte in1 = *(npy_byte *)ip1;
+        npy_byte in2 = *(npy_byte *)ip2;
+        *(npy_byte *)op1 = npy_rshifthh(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = npy_lshiftuhh(in1, in2));
+#ifdef UBYTE_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_UBYTE_right_shift
+    BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = npy_rshiftuhh(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_ubyte in1 = *(npy_ubyte *)ip1;
+        npy_ubyte in2 = *(npy_ubyte *)ip2;
+        *(npy_ubyte *)op1 = npy_rshiftuhh(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_short, npy_short, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_short, npy_short, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_short, npy_short, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_short, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_short, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_short, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_short, npy_short, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_short, npy_short, *out = npy_lshifth(in1, in2));
+#ifdef SHORT_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_SHORT_right_shift
+    BINARY_LOOP_FAST(npy_short, npy_short, *out = npy_rshifth(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_short in1 = *(npy_short *)ip1;
+        npy_short in2 = *(npy_short *)ip2;
+        *(npy_short *)op1 = npy_rshifth(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = npy_lshiftuh(in1, in2));
+#ifdef USHORT_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_USHORT_right_shift
+    BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = npy_rshiftuh(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_ushort in1 = *(npy_ushort *)ip1;
+        npy_ushort in2 = *(npy_ushort *)ip2;
+        *(npy_ushort *)op1 = npy_rshiftuh(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_int, npy_int, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_int, npy_int, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_int, npy_int, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_int, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_int, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_int, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_int, npy_int, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_int, npy_int, *out = npy_lshift(in1, in2));
+#ifdef INT_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_INT_right_shift
+    BINARY_LOOP_FAST(npy_int, npy_int, *out = npy_rshift(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_int in1 = *(npy_int *)ip1;
+        npy_int in2 = *(npy_int *)ip2;
+        *(npy_int *)op1 = npy_rshift(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_uint, npy_uint, *out = npy_lshiftu(in1, in2));
+#ifdef UINT_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_UINT_right_shift
+    BINARY_LOOP_FAST(npy_uint, npy_uint, *out = npy_rshiftu(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_uint in1 = *(npy_uint *)ip1;
+        npy_uint in2 = *(npy_uint *)ip2;
+        *(npy_uint *)op1 = npy_rshiftu(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_long, npy_long, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_long, npy_long, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_long, npy_long, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_long, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_long, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_long, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_long, npy_long, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_long, npy_long, *out = npy_lshiftl(in1, in2));
+#ifdef LONG_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_LONG_right_shift
+    BINARY_LOOP_FAST(npy_long, npy_long, *out = npy_rshiftl(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_long in1 = *(npy_long *)ip1;
+        npy_long in2 = *(npy_long *)ip2;
+        *(npy_long *)op1 = npy_rshiftl(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = npy_lshiftul(in1, in2));
+#ifdef ULONG_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_ULONG_right_shift
+    BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = npy_rshiftul(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_ulong in1 = *(npy_ulong *)ip1;
+        npy_ulong in2 = *(npy_ulong *)ip2;
+        *(npy_ulong *)op1 = npy_rshiftul(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_longlong, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = npy_lshiftll(in1, in2));
+#ifdef LONGLONG_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_LONGLONG_right_shift
+    BINARY_LOOP_FAST(npy_longlong, npy_longlong, *out = npy_rshiftll(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_longlong in1 = *(npy_longlong *)ip1;
+        npy_longlong in2 = *(npy_longlong *)ip2;
+        *(npy_longlong *)op1 = npy_rshiftll(in1, in2);
+    }
+#endif
+}
+
+#line 45
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = 1.0 / in);
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_add)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 += in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 + in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_subtract)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 -= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 - in2);
+    }
+}
+
+#line 69
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_multiply)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 *= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 * in2);
+    }
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = npy_lshiftull(in1, in2));
+#ifdef ULONGLONG_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_ULONGLONG_right_shift
+    BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = npy_rshiftull(in1, in2));
+#else
+    BINARY_LOOP {
+        npy_ulonglong in1 = *(npy_ulonglong *)ip1;
+        npy_ulonglong in2 = *(npy_ulonglong *)ip2;
+        *(npy_ulonglong *)op1 = npy_rshiftull(in1, in2);
+    }
+#endif
+}
+
+
+/*
+ *****************************************************************************
+ **                         UNSIGNED INTEGER LOOPS
+ *****************************************************************************
+ */
+#line 118
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in > 0 ? 1 : 0);
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 &= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 & in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 |= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 | in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ubyte, io1 ^= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in1 ^ in2);
+    }
+}
+
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ubyte, npy_bool, *out = in1 && in2);
+}
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ubyte, npy_bool, *out = in1 || in2);
+}
+
+
+NPY_FINLINE npy_bool UBYTE_logical_xor_(npy_ubyte in1, npy_ubyte in2)
+{ return (!!in1) != (!!in2); }
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ubyte, npy_bool, *out = UBYTE_logical_xor_(in1, in2));
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(UBYTE_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ubyte, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(UBYTE_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ubyte, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(UBYTE_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ubyte, npy_bool, (void)in; *out = NPY_TRUE);
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_bool, *out = !in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ubyte, npy_ubyte, *out = ~in);
+}
+
+#line 118
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in > 0 ? 1 : 0);
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 &= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 & in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 |= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 | in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ushort, io1 ^= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in1 ^ in2);
+    }
+}
+
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ushort, npy_bool, *out = in1 && in2);
+}
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ushort, npy_bool, *out = in1 || in2);
+}
+
+
+NPY_FINLINE npy_bool USHORT_logical_xor_(npy_ushort in1, npy_ushort in2)
+{ return (!!in1) != (!!in2); }
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ushort, npy_bool, *out = USHORT_logical_xor_(in1, in2));
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(USHORT_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ushort, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(USHORT_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ushort, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(USHORT_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ushort, npy_bool, (void)in; *out = NPY_TRUE);
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_bool, *out = !in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ushort, npy_ushort, *out = ~in);
+}
+
+#line 118
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in > 0 ? 1 : 0);
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 &= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 & in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 |= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 | in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_uint, io1 ^= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_uint, npy_uint, *out = in1 ^ in2);
+    }
+}
+
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_uint, npy_bool, *out = in1 && in2);
+}
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_uint, npy_bool, *out = in1 || in2);
+}
+
+
+NPY_FINLINE npy_bool UINT_logical_xor_(npy_uint in1, npy_uint in2)
+{ return (!!in1) != (!!in2); }
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_uint, npy_bool, *out = UINT_logical_xor_(in1, in2));
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(UINT_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_uint, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(UINT_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_uint, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(UINT_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_uint, npy_bool, (void)in; *out = NPY_TRUE);
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_bool, *out = !in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_uint, npy_uint, *out = ~in);
+}
+
+#line 118
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in > 0 ? 1 : 0);
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 &= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 & in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 |= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 | in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulong, io1 ^= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in1 ^ in2);
+    }
+}
+
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ulong, npy_bool, *out = in1 && in2);
+}
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ulong, npy_bool, *out = in1 || in2);
+}
+
+
+NPY_FINLINE npy_bool ULONG_logical_xor_(npy_ulong in1, npy_ulong in2)
+{ return (!!in1) != (!!in2); }
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ulong, npy_bool, *out = ULONG_logical_xor_(in1, in2));
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(ULONG_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ulong, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(ULONG_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ulong, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(ULONG_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ulong, npy_bool, (void)in; *out = NPY_TRUE);
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_bool, *out = !in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulong, npy_ulong, *out = ~in);
+}
+
+#line 118
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in > 0 ? 1 : 0);
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 &= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 & in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 |= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 | in2);
+    }
+}
+
+#line 135
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(npy_ulonglong, io1 ^= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in1 ^ in2);
+    }
+}
+
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = in1 && in2);
+}
+
+#line 151
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = in1 || in2);
+}
+
+
+NPY_FINLINE npy_bool ULONGLONG_logical_xor_(npy_ulonglong in1, npy_ulonglong in2)
+{ return (!!in1) != (!!in2); }
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = ULONGLONG_logical_xor_(in1, in2));
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(ULONGLONG_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(ULONGLONG_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, (void)in; *out = NPY_FALSE);
+}
+
+#line 176
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(ULONGLONG_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, (void)in; *out = NPY_TRUE);
+}
+
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_bool, *out = !in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_ulonglong, npy_ulonglong, *out = ~in);
+}
+
+
+/*
+ *****************************************************************************
+ **                         SIGNED! INTEGER LOOPS
+ *****************************************************************************
+ */
+
+#line 217
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = (in >= 0) ? in : -in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_byte, npy_byte, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_conjugate)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_invert)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_isnan)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_isinf)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_isfinite)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_logical_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_logical_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_logical_xor)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_logical_not)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_bitwise_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_bitwise_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_bitwise_xor)(args, dimensions, steps, func);
+}
+
+
+#line 217
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_short, npy_short, *out = (in >= 0) ? in : -in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_short, npy_short, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_conjugate)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_invert)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_isnan)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_isinf)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_isfinite)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_logical_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_logical_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_logical_xor)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_logical_not)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_bitwise_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_bitwise_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(USHORT_bitwise_xor)(args, dimensions, steps, func);
+}
+
+
+#line 217
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_int, npy_int, *out = (in >= 0) ? in : -in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_int, npy_int, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_conjugate)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_invert)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_isnan)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_isinf)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_isfinite)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_logical_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_logical_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_logical_xor)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_logical_not)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_bitwise_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_bitwise_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UINT_bitwise_xor)(args, dimensions, steps, func);
+}
+
+
+#line 217
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_long, npy_long, *out = (in >= 0) ? in : -in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_long, npy_long, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_conjugate)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_invert)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_isnan)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_isinf)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_isfinite)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_logical_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_logical_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_logical_xor)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_logical_not)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_bitwise_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_bitwise_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONG_bitwise_xor)(args, dimensions, steps, func);
+}
+
+
+#line 217
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = (in >= 0) ? in : -in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_longlong, npy_longlong, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_conjugate)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_invert)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_isnan)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_isinf)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_isfinite)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_xor)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_logical_not)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_bitwise_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_bitwise_and)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_bitwise_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_bitwise_or)(args, dimensions, steps, func);
+}
+
+#line 235
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_bitwise_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_bitwise_xor)(args, dimensions, steps, func);
+}
+
+
+
+/*
+ *****************************************************************************
+ **                             BOOLEAN LOOPS                               **
+ *****************************************************************************
+ */
+#line 253
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_isnan)(args, dimensions, steps, func);
+}
+
+#line 253
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_isinf)(args, dimensions, steps, func);
+}
+
+#line 253
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_isfinite)(args, dimensions, steps, func);
+}
+
+
+/*
+ *****************************************************************************
+ **                          HALF-FLOAT LOOPS                               **
+ *****************************************************************************
+ */
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_half, npy_half, *out = in&0x7fffu);
+}
+
+/*
+ *****************************************************************************
+ **                           DATETIME LOOPS                                **
+ *****************************************************************************
+ */
+
+#line 282
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DATETIME_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_isinf)(args, dimensions, steps, func);
+}
+
+#line 282
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(TIMEDELTA_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_isinf)(args, dimensions, steps, func);
+}
+
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.c.src
new file mode 100644
index 00000000000..bdbfa0f869d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.c.src
@@ -0,0 +1,287 @@
+/*@targets
+ ** $maxopt $autovec baseline
+ ** sse2 avx2
+ ** neon
+ ** vsx2
+ ** vx
+ **/
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/*
+ *****************************************************************************
+ **                           INTEGER LOOPS
+ *****************************************************************************
+ */
+/*
+ * Arithmetic bit shift operations.
+ *
+ * Intel hardware masks bit shift values, so large shifts wrap around
+ * and can produce surprising results. The special handling ensures that
+ * behavior is independent of compiler or hardware.
+ * TODO: We could implement consistent behavior for negative shifts,
+ *       which is undefined in C.
+ */
+#define INT_left_shift_needs_clear_floatstatus
+#define UINT_left_shift_needs_clear_floatstatus
+
+/**begin repeat
+ * #TYPE = BYTE, UBYTE, SHORT, USHORT, INT, UINT,
+ *         LONG, ULONG, LONGLONG, ULONGLONG#
+ * #type = npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, npy_uint,
+ *         npy_long, npy_ulong, npy_longlong, npy_ulonglong#
+ * #ftype = npy_float, npy_float, npy_float, npy_float, npy_double, npy_double,
+ *          npy_double, npy_double, npy_double, npy_double#
+ * #SIGNED = 1, 0, 1, 0, 1, 0, 1, 0, 1, 0#
+ * #c = hh,uhh,h,uh,,u,l,ul,ll,ull#
+ */
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_positive)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = +in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_square)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = in * in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_reciprocal)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = 1.0 / in);
+}
+
+/**begin repeat1
+ * Arithmetic
+ * #kind = add, subtract, multiply#
+ * #OP = +, -, *#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(@type@, io1 @OP@= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(@type@, @type@, *out = in1 @OP@ in2);
+    }
+}
+/**end repeat1**/
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_left_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps,
+                  void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(@type@, @type@, *out = npy_lshift@c@(in1, in2));
+#ifdef @TYPE@_left_shift_needs_clear_floatstatus
+    // For some reason, our macOS CI sets an "invalid" flag here, but only
+    // for some types.
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_right_shift)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#ifndef NPY_DO_NOT_OPTIMIZE_@TYPE@_right_shift
+    BINARY_LOOP_FAST(@type@, @type@, *out = npy_rshift@c@(in1, in2));
+#else
+    BINARY_LOOP {
+        @type@ in1 = *(@type@ *)ip1;
+        @type@ in2 = *(@type@ *)ip2;
+        *(@type@ *)op1 = npy_rshift@c@(in1, in2);
+    }
+#endif
+}
+/**end repeat**/
+
+/*
+ *****************************************************************************
+ **                         UNSIGNED INTEGER LOOPS
+ *****************************************************************************
+ */
+/**begin repeat
+ * #TYPE = UBYTE, USHORT, UINT, ULONG, ULONGLONG#
+ * #type = npy_ubyte, npy_ushort, npy_uint, npy_ulong, npy_ulonglong#
+ * #c    = u,u,u,ul,ull#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = in > 0 ? 1 : 0);
+}
+
+/**begin repeat1
+ * Arithmetic
+ * #kind = bitwise_and, bitwise_or, bitwise_xor#
+ * #OP = &, |, ^#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (IS_BINARY_REDUCE) {
+        BINARY_REDUCE_LOOP_FAST(@type@, io1 @OP@= in2);
+    }
+    else {
+        BINARY_LOOP_FAST(@type@, @type@, *out = in1 @OP@ in2);
+    }
+}
+/**end repeat1**/
+
+/**begin repeat1
+ * #kind = logical_and, logical_or#
+ * #OP = &&, ||#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * gcc vectorization of this is not good (PR60575) but manual integer
+     * vectorization is too tedious to be worthwhile
+     */
+    BINARY_LOOP_FAST(@type@, npy_bool, *out = in1 @OP@ in2);
+}
+/**end repeat1**/
+
+NPY_FINLINE npy_bool @TYPE@_logical_xor_(@type@ in1, @type@ in2)
+{ return (!!in1) != (!!in2); }
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_logical_xor)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    BINARY_LOOP_FAST(@type@, npy_bool, *out = @TYPE@_logical_xor_(in1, in2));
+}
+
+/**begin repeat1
+ * #kind = isnan, isinf, isfinite#
+ * #func = npy_isnan, npy_isinf, npy_isfinite#
+ * #val = NPY_FALSE, NPY_FALSE, NPY_TRUE#
+ **/
+NPY_NO_EXPORT void  NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    /*
+     * The (void)in; suppresses an unused variable warning raised by gcc and allows
+     * us to re-use this macro even though we do not depend on in
+     */
+    UNARY_LOOP_FAST(@type@, npy_bool, (void)in; *out = @val@);
+}
+/**end repeat1**/
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_conjugate)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, npy_bool, *out = !in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_invert)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = ~in);
+}
+/**end repeat**/
+
+/*
+ *****************************************************************************
+ **                         SIGNED! INTEGER LOOPS
+ *****************************************************************************
+ */
+
+/**begin repeat
+ * #TYPE = BYTE, SHORT, INT, LONG, LONGLONG#
+ * #type = npy_byte, npy_short, npy_int, npy_long, npy_longlong#
+ * #c    = ,,,l,ll#
+ */
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = (in >= 0) ? in : -in);
+}
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_sign)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(@type@, @type@, *out = in > 0 ? 1 : (in < 0 ? -1 : 0));
+}
+
+/**begin repeat1
+ * #kind = conjugate, invert, isnan, isinf, isfinite,
+ *         logical_and, logical_or, logical_xor, logical_not,
+ *         bitwise_and, bitwise_or, bitwise_xor#
+ **/
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(U@TYPE@_@kind@)(args, dimensions, steps, func);
+}
+/**end repeat1**/
+/**end repeat**/
+
+/*
+ *****************************************************************************
+ **                             BOOLEAN LOOPS                               **
+ *****************************************************************************
+ */
+/**begin repeat
+ * #kind = isnan, isinf, isfinite#
+ * #func = npy_isnan, npy_isinf, npy_isfinite#
+ * #val = NPY_FALSE, NPY_FALSE, NPY_TRUE#
+ **/
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(UBYTE_@kind@)(args, dimensions, steps, func);
+}
+/**end repeat**/
+
+/*
+ *****************************************************************************
+ **                          HALF-FLOAT LOOPS                               **
+ *****************************************************************************
+ */
+
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    UNARY_LOOP_FAST(npy_half, npy_half, *out = in&0x7fffu);
+}
+
+/*
+ *****************************************************************************
+ **                           DATETIME LOOPS                                **
+ *****************************************************************************
+ */
+
+/**begin repeat
+ * #type = npy_datetime, npy_timedelta#
+ * #TYPE = DATETIME, TIMEDELTA#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *func)
+{
+    NPY_CPU_DISPATCH_CURFX(ULONGLONG_isinf)(args, dimensions, steps, func);
+}
+/**end repeat**/
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.h
new file mode 100644
index 00000000000..d88120cccbe
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_autovec.dispatch.h
@@ -0,0 +1,19 @@
+// cache_hash:2830050559
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator (distutils/ccompiler_opt.py)
+ */
+#ifndef NPY__CPU_DISPATCH_EXPAND_
+    #define NPY__CPU_DISPATCH_EXPAND_(X) X
+#endif
+#undef NPY__CPU_DISPATCH_BASELINE_CALL
+#undef NPY__CPU_DISPATCH_CALL
+#define NPY__CPU_DISPATCH_BASELINE_CALL(CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB(__VA_ARGS__))
+#if defined(__x86_64__) || defined(_M_X64)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX)&&CHK(F16C)&&CHK(AVX2)), AVX2, __VA_ARGS__))
+#elif defined(__aarch64__)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c
index 8c479643d16..ac4b92bd94a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c
@@ -7434,7 +7434,7 @@ static void simd_binary_scalar2_less_equal_b8(char **args, npy_intp len)
 #line 304
 #line 310
 #if !((1 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_b8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7473,7 +7473,7 @@ run_binary_simd_equal_b8(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 1) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_b8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7512,7 +7512,7 @@ run_binary_simd_not_equal_b8(char **args, npy_intp const *dimensions, npy_intp c
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_b8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7551,7 +7551,7 @@ run_binary_simd_less_b8(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_b8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7592,7 +7592,7 @@ run_binary_simd_less_equal_b8(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_u8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7631,7 +7631,7 @@ run_binary_simd_equal_u8(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 1) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_u8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7670,7 +7670,7 @@ run_binary_simd_not_equal_u8(char **args, npy_intp const *dimensions, npy_intp c
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_u8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7709,7 +7709,7 @@ run_binary_simd_less_u8(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_u8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7750,7 +7750,7 @@ run_binary_simd_less_equal_u8(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_s8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7789,7 +7789,7 @@ run_binary_simd_equal_s8(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 1) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_s8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7828,7 +7828,7 @@ run_binary_simd_not_equal_s8(char **args, npy_intp const *dimensions, npy_intp c
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_s8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7867,7 +7867,7 @@ run_binary_simd_less_s8(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_s8(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7908,7 +7908,7 @@ run_binary_simd_less_equal_s8(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_u16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7947,7 +7947,7 @@ run_binary_simd_equal_u16(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_u16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -7986,7 +7986,7 @@ run_binary_simd_not_equal_u16(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_u16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8025,7 +8025,7 @@ run_binary_simd_less_u16(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_u16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8066,7 +8066,7 @@ run_binary_simd_less_equal_u16(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_s16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8105,7 +8105,7 @@ run_binary_simd_equal_s16(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_s16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8144,7 +8144,7 @@ run_binary_simd_not_equal_s16(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_s16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8183,7 +8183,7 @@ run_binary_simd_less_s16(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_s16(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8224,7 +8224,7 @@ run_binary_simd_less_equal_s16(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_u32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8263,7 +8263,7 @@ run_binary_simd_equal_u32(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_u32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8302,7 +8302,7 @@ run_binary_simd_not_equal_u32(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_u32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8341,7 +8341,7 @@ run_binary_simd_less_u32(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_u32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8382,7 +8382,7 @@ run_binary_simd_less_equal_u32(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_s32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8421,7 +8421,7 @@ run_binary_simd_equal_s32(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_s32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8460,7 +8460,7 @@ run_binary_simd_not_equal_s32(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_s32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8499,7 +8499,7 @@ run_binary_simd_less_s32(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_s32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8540,7 +8540,7 @@ run_binary_simd_less_equal_s32(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_u64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8579,7 +8579,7 @@ run_binary_simd_equal_u64(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_u64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8618,7 +8618,7 @@ run_binary_simd_not_equal_u64(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_u64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8657,7 +8657,7 @@ run_binary_simd_less_u64(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_u64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8698,7 +8698,7 @@ run_binary_simd_less_equal_u64(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_s64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8737,7 +8737,7 @@ run_binary_simd_equal_s64(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_s64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8776,7 +8776,7 @@ run_binary_simd_not_equal_s64(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_s64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8815,7 +8815,7 @@ run_binary_simd_less_s64(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 1)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_s64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD
@@ -8856,7 +8856,7 @@ run_binary_simd_less_equal_s64(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_f32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F32
@@ -8895,7 +8895,7 @@ run_binary_simd_equal_f32(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_f32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F32
@@ -8934,7 +8934,7 @@ run_binary_simd_not_equal_f32(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_f32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F32
@@ -8973,7 +8973,7 @@ run_binary_simd_less_f32(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_f32(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F32
@@ -9014,7 +9014,7 @@ run_binary_simd_less_equal_f32(char **args, npy_intp const *dimensions, npy_intp
 #line 304
 #line 310
 #if !((1 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_equal_f64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F64
@@ -9053,7 +9053,7 @@ run_binary_simd_equal_f64(char **args, npy_intp const *dimensions, npy_intp cons
 
 #line 310
 #if !((0 || 1) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_not_equal_f64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F64
@@ -9092,7 +9092,7 @@ run_binary_simd_not_equal_f64(char **args, npy_intp const *dimensions, npy_intp
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_f64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F64
@@ -9131,7 +9131,7 @@ run_binary_simd_less_f64(char **args, npy_intp const *dimensions, npy_intp const
 
 #line 310
 #if !((0 || 0) && 0)
-static NPY_INLINE void
+static inline void
 run_binary_simd_less_equal_f64(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if NPY_SIMD_F64
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c.src
index 4a8eac4b927..7510808714a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_comparison.dispatch.c.src
@@ -308,7 +308,7 @@ static void simd_binary_scalar2_@kind@_b8(char **args, npy_intp len)
  * #OP = ==, !=, <, <=#
  */
 #if !((@eq@ || @neq@) && @signed@)
-static NPY_INLINE void
+static inline void
 run_binary_simd_@kind@_@sfx@(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
 #if @VECTOR@
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c
index b7c068d750e..5e9827a14cf 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c
@@ -249,7 +249,7 @@ fma_scalef_ps(__m256 poly, __m256 quadrant)
 
 #ifdef SIMD_AVX512F
 
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
+NPY_FINLINE __mmask16
 avx512_get_full_load_mask_ps(void)
 {
     return 0xFFFF;
@@ -1488,7 +1488,7 @@ AVX512F_log_DOUBLE(npy_double * op,
 
 #ifdef SIMD_AVX512_SKX
 #line 1149
-static NPY_INLINE void
+static inline void
 AVX512_SKX_ldexp_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
     const npy_intp stride_ip1 = steps[0]/(npy_intp)sizeof(npy_float);
@@ -1557,7 +1557,7 @@ AVX512_SKX_ldexp_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *
     }
 }
 
-static NPY_INLINE void
+static inline void
 AVX512_SKX_frexp_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
     const npy_intp stride_ip1 = steps[0]/(npy_intp)sizeof(npy_float);
@@ -1635,7 +1635,7 @@ AVX512_SKX_frexp_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *
 }
 
 #line 1149
-static NPY_INLINE void
+static inline void
 AVX512_SKX_ldexp_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
     const npy_intp stride_ip1 = steps[0]/(npy_intp)sizeof(npy_double);
@@ -1704,7 +1704,7 @@ AVX512_SKX_ldexp_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const
     }
 }
 
-static NPY_INLINE void
+static inline void
 AVX512_SKX_frexp_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
     const npy_intp stride_ip1 = steps[0]/(npy_intp)sizeof(npy_double);
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c.src
index 8f123a48b2f..1fac3c150c7 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_exponent_log.dispatch.c.src
@@ -239,7 +239,7 @@ fma_scalef_ps(__m256 poly, __m256 quadrant)
 
 #ifdef SIMD_AVX512F
 
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
+NPY_FINLINE __mmask16
 avx512_get_full_load_mask_ps(void)
 {
     return 0xFFFF;
@@ -1146,7 +1146,7 @@ AVX512F_log_DOUBLE(npy_double * op,
  * #vtype2_scatter = _mm512_mask_i32scatter_epi32, _mm256_mask_i32scatter_epi32#
  * #setzero = _mm512_setzero_epi32, _mm256_setzero_si256#
  */
-static NPY_INLINE void
+static inline void
 AVX512_SKX_ldexp_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
     const npy_intp stride_ip1 = steps[0]/(npy_intp)sizeof(@type@);
@@ -1215,7 +1215,7 @@ AVX512_SKX_ldexp_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const
     }
 }
 
-static NPY_INLINE void
+static inline void
 AVX512_SKX_frexp_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *steps)
 {
     const npy_intp stride_ip1 = steps[0]/(npy_intp)sizeof(@type@);
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.avx2.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.avx2.c
new file mode 100644
index 00000000000..9f5b3f17367
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.avx2.c
@@ -0,0 +1,17 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT AVX2
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#define NPY__CPU_TARGET_POPCNT
+#define NPY__CPU_TARGET_SSE42
+#define NPY__CPU_TARGET_AVX
+#define NPY__CPU_TARGET_F16C
+#define NPY__CPU_TARGET_AVX2
+#include "numpy/core/src/umath/loops_logical.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.avx512_skx.cpp b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.avx512_skx.c
index 844b06a8477..5987a2dbde9 100644
--- a/contrib/python/numpy/py3/numpy/core/src/npysort/x86-qsort.dispatch.avx512_skx.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.avx512_skx.c
@@ -18,4 +18,4 @@
 #define NPY__CPU_TARGET_AVX512F
 #define NPY__CPU_TARGET_AVX512CD
 #define NPY__CPU_TARGET_AVX512_SKX
-#include "numpy/core/src/npysort/x86-qsort.dispatch.cpp"
+#include "numpy/core/src/umath/loops_logical.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.c
new file mode 100644
index 00000000000..85448f5c2c3
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.c
@@ -0,0 +1,1194 @@
+#line 1 "numpy/core/src/umath/loops_logical.dispatch.c.src"
+
+/*
+ *****************************************************************************
+ **       This file was autogenerated from a template  DO NOT EDIT!!!!      **
+ **       Changes should be made to the original source (.src) file         **
+ *****************************************************************************
+ */
+
+#line 1
+/*@targets
+ ** $maxopt baseline
+ ** neon asimd
+ ** sse2 avx2 avx512_skx
+ ** vsx2
+ ** vx
+ **/
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/*******************************************************************************
+ ** Defining the SIMD kernels
+ ******************************************************************************/
+
+#if NPY_SIMD
+/*
+ * convert any bit set to boolean true so vectorized and normal operations are
+ * consistent, should not be required if bool is used correctly everywhere but
+ * you never know
+ */
+NPY_FINLINE npyv_u8 byte_to_true(npyv_u8 v)
+{
+    const npyv_u8 zero = npyv_zero_u8();
+    const npyv_u8 truemask = npyv_setall_u8(1 == 1);
+    // cmpeq(v, 0) turns 0x00 -> 0xff and non-zero -> 0x00
+    npyv_u8 tmp = npyv_cvt_u8_b8(npyv_cmpeq_u8(v, zero));
+    // tmp is filled with 0xff/0x00, negate and mask to boolean true
+    return npyv_andc_u8(truemask, tmp);
+}
+/*
+ * convert mask vector (0xff/0x00) to boolean true.  similar to byte_to_true(),
+ * but we've already got a mask and can skip negation.
+ */
+NPY_FINLINE npyv_u8 mask_to_true(npyv_b8 v)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1 == 1);
+    return npyv_and_u8(truemask, npyv_cvt_u8_b8(v));
+}
+/*
+ * For logical_and, we have to be careful to handle non-bool inputs where
+ * bits of each operand might not overlap. Example: a = 0x01, b = 0x80
+ * Both evaluate to boolean true, however, a & b is false.  Return value
+ * should be consistent with byte_to_true().
+ */
+NPY_FINLINE npyv_u8 simd_logical_and_u8(npyv_u8 a, npyv_u8 b)
+{
+    const npyv_u8 zero = npyv_zero_u8();
+    const npyv_u8 truemask = npyv_setall_u8(1 == 1);
+    npyv_b8 ma = npyv_cmpeq_u8(a, zero);
+    npyv_b8 mb = npyv_cmpeq_u8(b, zero);
+    npyv_u8 r = npyv_cvt_u8_b8(npyv_or_b8(ma, mb));
+    return npyv_andc_u8(truemask, r);
+}
+/*
+ * We don't really need the following, but it simplifies the templating code
+ * below since it is paired with simd_logical_and_u8() above.
+ */
+NPY_FINLINE npyv_u8 simd_logical_or_u8(npyv_u8 a, npyv_u8 b)
+{
+    npyv_u8 r = npyv_or_u8(a, b);
+    return byte_to_true(r);
+}
+
+
+#line 82
+static void
+simd_binary_logical_and_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2, npy_intp len)
+{
+    #define UNROLL 16
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip1 += wstep, ip2 += wstep, op += wstep) {
+        #line 95
+        #if UNROLL > 0
+        npyv_u8 a0 = npyv_load_u8(ip1 + vstep * 0);
+        npyv_u8 b0 = npyv_load_u8(ip2 + vstep * 0);
+        npyv_u8 r0 = simd_logical_and_u8(a0, b0);
+        npyv_store_u8(op + vstep * 0, r0);
+        #endif
+        
+#line 95
+        #if UNROLL > 1
+        npyv_u8 a1 = npyv_load_u8(ip1 + vstep * 1);
+        npyv_u8 b1 = npyv_load_u8(ip2 + vstep * 1);
+        npyv_u8 r1 = simd_logical_and_u8(a1, b1);
+        npyv_store_u8(op + vstep * 1, r1);
+        #endif
+        
+#line 95
+        #if UNROLL > 2
+        npyv_u8 a2 = npyv_load_u8(ip1 + vstep * 2);
+        npyv_u8 b2 = npyv_load_u8(ip2 + vstep * 2);
+        npyv_u8 r2 = simd_logical_and_u8(a2, b2);
+        npyv_store_u8(op + vstep * 2, r2);
+        #endif
+        
+#line 95
+        #if UNROLL > 3
+        npyv_u8 a3 = npyv_load_u8(ip1 + vstep * 3);
+        npyv_u8 b3 = npyv_load_u8(ip2 + vstep * 3);
+        npyv_u8 r3 = simd_logical_and_u8(a3, b3);
+        npyv_store_u8(op + vstep * 3, r3);
+        #endif
+        
+#line 95
+        #if UNROLL > 4
+        npyv_u8 a4 = npyv_load_u8(ip1 + vstep * 4);
+        npyv_u8 b4 = npyv_load_u8(ip2 + vstep * 4);
+        npyv_u8 r4 = simd_logical_and_u8(a4, b4);
+        npyv_store_u8(op + vstep * 4, r4);
+        #endif
+        
+#line 95
+        #if UNROLL > 5
+        npyv_u8 a5 = npyv_load_u8(ip1 + vstep * 5);
+        npyv_u8 b5 = npyv_load_u8(ip2 + vstep * 5);
+        npyv_u8 r5 = simd_logical_and_u8(a5, b5);
+        npyv_store_u8(op + vstep * 5, r5);
+        #endif
+        
+#line 95
+        #if UNROLL > 6
+        npyv_u8 a6 = npyv_load_u8(ip1 + vstep * 6);
+        npyv_u8 b6 = npyv_load_u8(ip2 + vstep * 6);
+        npyv_u8 r6 = simd_logical_and_u8(a6, b6);
+        npyv_store_u8(op + vstep * 6, r6);
+        #endif
+        
+#line 95
+        #if UNROLL > 7
+        npyv_u8 a7 = npyv_load_u8(ip1 + vstep * 7);
+        npyv_u8 b7 = npyv_load_u8(ip2 + vstep * 7);
+        npyv_u8 r7 = simd_logical_and_u8(a7, b7);
+        npyv_store_u8(op + vstep * 7, r7);
+        #endif
+        
+#line 95
+        #if UNROLL > 8
+        npyv_u8 a8 = npyv_load_u8(ip1 + vstep * 8);
+        npyv_u8 b8 = npyv_load_u8(ip2 + vstep * 8);
+        npyv_u8 r8 = simd_logical_and_u8(a8, b8);
+        npyv_store_u8(op + vstep * 8, r8);
+        #endif
+        
+#line 95
+        #if UNROLL > 9
+        npyv_u8 a9 = npyv_load_u8(ip1 + vstep * 9);
+        npyv_u8 b9 = npyv_load_u8(ip2 + vstep * 9);
+        npyv_u8 r9 = simd_logical_and_u8(a9, b9);
+        npyv_store_u8(op + vstep * 9, r9);
+        #endif
+        
+#line 95
+        #if UNROLL > 10
+        npyv_u8 a10 = npyv_load_u8(ip1 + vstep * 10);
+        npyv_u8 b10 = npyv_load_u8(ip2 + vstep * 10);
+        npyv_u8 r10 = simd_logical_and_u8(a10, b10);
+        npyv_store_u8(op + vstep * 10, r10);
+        #endif
+        
+#line 95
+        #if UNROLL > 11
+        npyv_u8 a11 = npyv_load_u8(ip1 + vstep * 11);
+        npyv_u8 b11 = npyv_load_u8(ip2 + vstep * 11);
+        npyv_u8 r11 = simd_logical_and_u8(a11, b11);
+        npyv_store_u8(op + vstep * 11, r11);
+        #endif
+        
+#line 95
+        #if UNROLL > 12
+        npyv_u8 a12 = npyv_load_u8(ip1 + vstep * 12);
+        npyv_u8 b12 = npyv_load_u8(ip2 + vstep * 12);
+        npyv_u8 r12 = simd_logical_and_u8(a12, b12);
+        npyv_store_u8(op + vstep * 12, r12);
+        #endif
+        
+#line 95
+        #if UNROLL > 13
+        npyv_u8 a13 = npyv_load_u8(ip1 + vstep * 13);
+        npyv_u8 b13 = npyv_load_u8(ip2 + vstep * 13);
+        npyv_u8 r13 = simd_logical_and_u8(a13, b13);
+        npyv_store_u8(op + vstep * 13, r13);
+        #endif
+        
+#line 95
+        #if UNROLL > 14
+        npyv_u8 a14 = npyv_load_u8(ip1 + vstep * 14);
+        npyv_u8 b14 = npyv_load_u8(ip2 + vstep * 14);
+        npyv_u8 r14 = simd_logical_and_u8(a14, b14);
+        npyv_store_u8(op + vstep * 14, r14);
+        #endif
+        
+#line 95
+        #if UNROLL > 15
+        npyv_u8 a15 = npyv_load_u8(ip1 + vstep * 15);
+        npyv_u8 b15 = npyv_load_u8(ip2 + vstep * 15);
+        npyv_u8 r15 = simd_logical_and_u8(a15, b15);
+        npyv_store_u8(op + vstep * 15, r15);
+        #endif
+        
+    }
+    #undef UNROLL
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip1 += vstep, ip2 += vstep, op += vstep) {
+        npyv_u8 a = npyv_load_u8(ip1);
+        npyv_u8 b = npyv_load_u8(ip2);
+        npyv_u8 r = simd_logical_and_u8(a, b);
+        npyv_store_u8(op, r);
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; len--, ip1++, ip2++, op++) {
+        *op = *ip1 && *ip2;
+    }
+}
+
+static void
+simd_reduce_logical_and_BOOL(npy_bool * op, npy_bool * ip, npy_intp len)
+{
+    #define UNROLL 8
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip += wstep) {
+    #if defined(NPY_HAVE_SSE2)
+        NPY_PREFETCH(ip + wstep, 0, 3);
+    #endif
+        npyv_u8 v0 = npyv_load_u8(ip + vstep * 0);
+        npyv_u8 v1 = npyv_load_u8(ip + vstep * 1);
+        npyv_u8 v2 = npyv_load_u8(ip + vstep * 2);
+        npyv_u8 v3 = npyv_load_u8(ip + vstep * 3);
+        npyv_u8 v4 = npyv_load_u8(ip + vstep * 4);
+        npyv_u8 v5 = npyv_load_u8(ip + vstep * 5);
+        npyv_u8 v6 = npyv_load_u8(ip + vstep * 6);
+        npyv_u8 v7 = npyv_load_u8(ip + vstep * 7);
+
+        npyv_u8 m01 = npyv_min_u8(v0, v1);
+        npyv_u8 m23 = npyv_min_u8(v2, v3);
+        npyv_u8 m45 = npyv_min_u8(v4, v5);
+        npyv_u8 m67 = npyv_min_u8(v6, v7);
+
+        npyv_u8 m0123 = npyv_min_u8(m01, m23);
+        npyv_u8 m4567 = npyv_min_u8(m45, m67);
+
+        npyv_u8 mv = npyv_min_u8(m0123, m4567);
+
+        if(npyv_all_u8(mv) == 0){
+            *op = !1;
+            return;
+        }
+    }
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip += vstep) {
+        npyv_u8 v0 = npyv_load_u8(ip);
+        if(npyv_all_u8(v0) == 0){
+            *op = !1;
+            return;
+        }
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ++ip) {
+        *op = *op && *ip;
+        if (*op == 0) {
+            return;
+        }
+    }
+#undef UNROLL
+}
+
+#line 82
+static void
+simd_binary_logical_or_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2, npy_intp len)
+{
+    #define UNROLL 16
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip1 += wstep, ip2 += wstep, op += wstep) {
+        #line 95
+        #if UNROLL > 0
+        npyv_u8 a0 = npyv_load_u8(ip1 + vstep * 0);
+        npyv_u8 b0 = npyv_load_u8(ip2 + vstep * 0);
+        npyv_u8 r0 = simd_logical_or_u8(a0, b0);
+        npyv_store_u8(op + vstep * 0, r0);
+        #endif
+        
+#line 95
+        #if UNROLL > 1
+        npyv_u8 a1 = npyv_load_u8(ip1 + vstep * 1);
+        npyv_u8 b1 = npyv_load_u8(ip2 + vstep * 1);
+        npyv_u8 r1 = simd_logical_or_u8(a1, b1);
+        npyv_store_u8(op + vstep * 1, r1);
+        #endif
+        
+#line 95
+        #if UNROLL > 2
+        npyv_u8 a2 = npyv_load_u8(ip1 + vstep * 2);
+        npyv_u8 b2 = npyv_load_u8(ip2 + vstep * 2);
+        npyv_u8 r2 = simd_logical_or_u8(a2, b2);
+        npyv_store_u8(op + vstep * 2, r2);
+        #endif
+        
+#line 95
+        #if UNROLL > 3
+        npyv_u8 a3 = npyv_load_u8(ip1 + vstep * 3);
+        npyv_u8 b3 = npyv_load_u8(ip2 + vstep * 3);
+        npyv_u8 r3 = simd_logical_or_u8(a3, b3);
+        npyv_store_u8(op + vstep * 3, r3);
+        #endif
+        
+#line 95
+        #if UNROLL > 4
+        npyv_u8 a4 = npyv_load_u8(ip1 + vstep * 4);
+        npyv_u8 b4 = npyv_load_u8(ip2 + vstep * 4);
+        npyv_u8 r4 = simd_logical_or_u8(a4, b4);
+        npyv_store_u8(op + vstep * 4, r4);
+        #endif
+        
+#line 95
+        #if UNROLL > 5
+        npyv_u8 a5 = npyv_load_u8(ip1 + vstep * 5);
+        npyv_u8 b5 = npyv_load_u8(ip2 + vstep * 5);
+        npyv_u8 r5 = simd_logical_or_u8(a5, b5);
+        npyv_store_u8(op + vstep * 5, r5);
+        #endif
+        
+#line 95
+        #if UNROLL > 6
+        npyv_u8 a6 = npyv_load_u8(ip1 + vstep * 6);
+        npyv_u8 b6 = npyv_load_u8(ip2 + vstep * 6);
+        npyv_u8 r6 = simd_logical_or_u8(a6, b6);
+        npyv_store_u8(op + vstep * 6, r6);
+        #endif
+        
+#line 95
+        #if UNROLL > 7
+        npyv_u8 a7 = npyv_load_u8(ip1 + vstep * 7);
+        npyv_u8 b7 = npyv_load_u8(ip2 + vstep * 7);
+        npyv_u8 r7 = simd_logical_or_u8(a7, b7);
+        npyv_store_u8(op + vstep * 7, r7);
+        #endif
+        
+#line 95
+        #if UNROLL > 8
+        npyv_u8 a8 = npyv_load_u8(ip1 + vstep * 8);
+        npyv_u8 b8 = npyv_load_u8(ip2 + vstep * 8);
+        npyv_u8 r8 = simd_logical_or_u8(a8, b8);
+        npyv_store_u8(op + vstep * 8, r8);
+        #endif
+        
+#line 95
+        #if UNROLL > 9
+        npyv_u8 a9 = npyv_load_u8(ip1 + vstep * 9);
+        npyv_u8 b9 = npyv_load_u8(ip2 + vstep * 9);
+        npyv_u8 r9 = simd_logical_or_u8(a9, b9);
+        npyv_store_u8(op + vstep * 9, r9);
+        #endif
+        
+#line 95
+        #if UNROLL > 10
+        npyv_u8 a10 = npyv_load_u8(ip1 + vstep * 10);
+        npyv_u8 b10 = npyv_load_u8(ip2 + vstep * 10);
+        npyv_u8 r10 = simd_logical_or_u8(a10, b10);
+        npyv_store_u8(op + vstep * 10, r10);
+        #endif
+        
+#line 95
+        #if UNROLL > 11
+        npyv_u8 a11 = npyv_load_u8(ip1 + vstep * 11);
+        npyv_u8 b11 = npyv_load_u8(ip2 + vstep * 11);
+        npyv_u8 r11 = simd_logical_or_u8(a11, b11);
+        npyv_store_u8(op + vstep * 11, r11);
+        #endif
+        
+#line 95
+        #if UNROLL > 12
+        npyv_u8 a12 = npyv_load_u8(ip1 + vstep * 12);
+        npyv_u8 b12 = npyv_load_u8(ip2 + vstep * 12);
+        npyv_u8 r12 = simd_logical_or_u8(a12, b12);
+        npyv_store_u8(op + vstep * 12, r12);
+        #endif
+        
+#line 95
+        #if UNROLL > 13
+        npyv_u8 a13 = npyv_load_u8(ip1 + vstep * 13);
+        npyv_u8 b13 = npyv_load_u8(ip2 + vstep * 13);
+        npyv_u8 r13 = simd_logical_or_u8(a13, b13);
+        npyv_store_u8(op + vstep * 13, r13);
+        #endif
+        
+#line 95
+        #if UNROLL > 14
+        npyv_u8 a14 = npyv_load_u8(ip1 + vstep * 14);
+        npyv_u8 b14 = npyv_load_u8(ip2 + vstep * 14);
+        npyv_u8 r14 = simd_logical_or_u8(a14, b14);
+        npyv_store_u8(op + vstep * 14, r14);
+        #endif
+        
+#line 95
+        #if UNROLL > 15
+        npyv_u8 a15 = npyv_load_u8(ip1 + vstep * 15);
+        npyv_u8 b15 = npyv_load_u8(ip2 + vstep * 15);
+        npyv_u8 r15 = simd_logical_or_u8(a15, b15);
+        npyv_store_u8(op + vstep * 15, r15);
+        #endif
+        
+    }
+    #undef UNROLL
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip1 += vstep, ip2 += vstep, op += vstep) {
+        npyv_u8 a = npyv_load_u8(ip1);
+        npyv_u8 b = npyv_load_u8(ip2);
+        npyv_u8 r = simd_logical_or_u8(a, b);
+        npyv_store_u8(op, r);
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; len--, ip1++, ip2++, op++) {
+        *op = *ip1 || *ip2;
+    }
+}
+
+static void
+simd_reduce_logical_or_BOOL(npy_bool * op, npy_bool * ip, npy_intp len)
+{
+    #define UNROLL 8
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip += wstep) {
+    #if defined(NPY_HAVE_SSE2)
+        NPY_PREFETCH(ip + wstep, 0, 3);
+    #endif
+        npyv_u8 v0 = npyv_load_u8(ip + vstep * 0);
+        npyv_u8 v1 = npyv_load_u8(ip + vstep * 1);
+        npyv_u8 v2 = npyv_load_u8(ip + vstep * 2);
+        npyv_u8 v3 = npyv_load_u8(ip + vstep * 3);
+        npyv_u8 v4 = npyv_load_u8(ip + vstep * 4);
+        npyv_u8 v5 = npyv_load_u8(ip + vstep * 5);
+        npyv_u8 v6 = npyv_load_u8(ip + vstep * 6);
+        npyv_u8 v7 = npyv_load_u8(ip + vstep * 7);
+
+        npyv_u8 m01 = npyv_max_u8(v0, v1);
+        npyv_u8 m23 = npyv_max_u8(v2, v3);
+        npyv_u8 m45 = npyv_max_u8(v4, v5);
+        npyv_u8 m67 = npyv_max_u8(v6, v7);
+
+        npyv_u8 m0123 = npyv_max_u8(m01, m23);
+        npyv_u8 m4567 = npyv_max_u8(m45, m67);
+
+        npyv_u8 mv = npyv_max_u8(m0123, m4567);
+
+        if(npyv_any_u8(mv) != 0){
+            *op = !0;
+            return;
+        }
+    }
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip += vstep) {
+        npyv_u8 v0 = npyv_load_u8(ip);
+        if(npyv_any_u8(v0) != 0){
+            *op = !0;
+            return;
+        }
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ++ip) {
+        *op = *op || *ip;
+        if (*op != 0) {
+            return;
+        }
+    }
+#undef UNROLL
+}
+
+
+#line 182
+static void
+simd_logical_not_BOOL(npy_bool * op, npy_bool * ip, npy_intp len)
+{
+    #define UNROLL 16
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    #if 1
+    const npyv_u8 zero = npyv_zero_u8();
+    #endif
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+        #line 199
+        #if UNROLL > 0
+        npyv_u8 v0 = npyv_load_u8(ip + vstep * 0);
+#if 1
+        npyv_u8 r0 = mask_to_true(npyv_cmpeq_u8(v0, zero));
+#else
+        npyv_u8 r0 = byte_to_true(v0);
+#endif
+        npyv_store_u8(op + vstep * 0, r0);
+        #endif
+        
+#line 199
+        #if UNROLL > 1
+        npyv_u8 v1 = npyv_load_u8(ip + vstep * 1);
+#if 1
+        npyv_u8 r1 = mask_to_true(npyv_cmpeq_u8(v1, zero));
+#else
+        npyv_u8 r1 = byte_to_true(v1);
+#endif
+        npyv_store_u8(op + vstep * 1, r1);
+        #endif
+        
+#line 199
+        #if UNROLL > 2
+        npyv_u8 v2 = npyv_load_u8(ip + vstep * 2);
+#if 1
+        npyv_u8 r2 = mask_to_true(npyv_cmpeq_u8(v2, zero));
+#else
+        npyv_u8 r2 = byte_to_true(v2);
+#endif
+        npyv_store_u8(op + vstep * 2, r2);
+        #endif
+        
+#line 199
+        #if UNROLL > 3
+        npyv_u8 v3 = npyv_load_u8(ip + vstep * 3);
+#if 1
+        npyv_u8 r3 = mask_to_true(npyv_cmpeq_u8(v3, zero));
+#else
+        npyv_u8 r3 = byte_to_true(v3);
+#endif
+        npyv_store_u8(op + vstep * 3, r3);
+        #endif
+        
+#line 199
+        #if UNROLL > 4
+        npyv_u8 v4 = npyv_load_u8(ip + vstep * 4);
+#if 1
+        npyv_u8 r4 = mask_to_true(npyv_cmpeq_u8(v4, zero));
+#else
+        npyv_u8 r4 = byte_to_true(v4);
+#endif
+        npyv_store_u8(op + vstep * 4, r4);
+        #endif
+        
+#line 199
+        #if UNROLL > 5
+        npyv_u8 v5 = npyv_load_u8(ip + vstep * 5);
+#if 1
+        npyv_u8 r5 = mask_to_true(npyv_cmpeq_u8(v5, zero));
+#else
+        npyv_u8 r5 = byte_to_true(v5);
+#endif
+        npyv_store_u8(op + vstep * 5, r5);
+        #endif
+        
+#line 199
+        #if UNROLL > 6
+        npyv_u8 v6 = npyv_load_u8(ip + vstep * 6);
+#if 1
+        npyv_u8 r6 = mask_to_true(npyv_cmpeq_u8(v6, zero));
+#else
+        npyv_u8 r6 = byte_to_true(v6);
+#endif
+        npyv_store_u8(op + vstep * 6, r6);
+        #endif
+        
+#line 199
+        #if UNROLL > 7
+        npyv_u8 v7 = npyv_load_u8(ip + vstep * 7);
+#if 1
+        npyv_u8 r7 = mask_to_true(npyv_cmpeq_u8(v7, zero));
+#else
+        npyv_u8 r7 = byte_to_true(v7);
+#endif
+        npyv_store_u8(op + vstep * 7, r7);
+        #endif
+        
+#line 199
+        #if UNROLL > 8
+        npyv_u8 v8 = npyv_load_u8(ip + vstep * 8);
+#if 1
+        npyv_u8 r8 = mask_to_true(npyv_cmpeq_u8(v8, zero));
+#else
+        npyv_u8 r8 = byte_to_true(v8);
+#endif
+        npyv_store_u8(op + vstep * 8, r8);
+        #endif
+        
+#line 199
+        #if UNROLL > 9
+        npyv_u8 v9 = npyv_load_u8(ip + vstep * 9);
+#if 1
+        npyv_u8 r9 = mask_to_true(npyv_cmpeq_u8(v9, zero));
+#else
+        npyv_u8 r9 = byte_to_true(v9);
+#endif
+        npyv_store_u8(op + vstep * 9, r9);
+        #endif
+        
+#line 199
+        #if UNROLL > 10
+        npyv_u8 v10 = npyv_load_u8(ip + vstep * 10);
+#if 1
+        npyv_u8 r10 = mask_to_true(npyv_cmpeq_u8(v10, zero));
+#else
+        npyv_u8 r10 = byte_to_true(v10);
+#endif
+        npyv_store_u8(op + vstep * 10, r10);
+        #endif
+        
+#line 199
+        #if UNROLL > 11
+        npyv_u8 v11 = npyv_load_u8(ip + vstep * 11);
+#if 1
+        npyv_u8 r11 = mask_to_true(npyv_cmpeq_u8(v11, zero));
+#else
+        npyv_u8 r11 = byte_to_true(v11);
+#endif
+        npyv_store_u8(op + vstep * 11, r11);
+        #endif
+        
+#line 199
+        #if UNROLL > 12
+        npyv_u8 v12 = npyv_load_u8(ip + vstep * 12);
+#if 1
+        npyv_u8 r12 = mask_to_true(npyv_cmpeq_u8(v12, zero));
+#else
+        npyv_u8 r12 = byte_to_true(v12);
+#endif
+        npyv_store_u8(op + vstep * 12, r12);
+        #endif
+        
+#line 199
+        #if UNROLL > 13
+        npyv_u8 v13 = npyv_load_u8(ip + vstep * 13);
+#if 1
+        npyv_u8 r13 = mask_to_true(npyv_cmpeq_u8(v13, zero));
+#else
+        npyv_u8 r13 = byte_to_true(v13);
+#endif
+        npyv_store_u8(op + vstep * 13, r13);
+        #endif
+        
+#line 199
+        #if UNROLL > 14
+        npyv_u8 v14 = npyv_load_u8(ip + vstep * 14);
+#if 1
+        npyv_u8 r14 = mask_to_true(npyv_cmpeq_u8(v14, zero));
+#else
+        npyv_u8 r14 = byte_to_true(v14);
+#endif
+        npyv_store_u8(op + vstep * 14, r14);
+        #endif
+        
+#line 199
+        #if UNROLL > 15
+        npyv_u8 v15 = npyv_load_u8(ip + vstep * 15);
+#if 1
+        npyv_u8 r15 = mask_to_true(npyv_cmpeq_u8(v15, zero));
+#else
+        npyv_u8 r15 = byte_to_true(v15);
+#endif
+        npyv_store_u8(op + vstep * 15, r15);
+        #endif
+        
+    }
+    #undef UNROLL
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += vstep) {
+        npyv_u8 v = npyv_load_u8(ip);
+#if 1
+        npyv_u8 r = mask_to_true(npyv_cmpeq_u8(v, zero));
+#else
+        npyv_u8 r = byte_to_true(v);
+#endif
+        npyv_store_u8(op, r);
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = (*ip == 0);
+    }
+}
+
+#line 182
+static void
+simd_absolute_BOOL(npy_bool * op, npy_bool * ip, npy_intp len)
+{
+    #define UNROLL 16
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    #if 0
+    const npyv_u8 zero = npyv_zero_u8();
+    #endif
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+        #line 199
+        #if UNROLL > 0
+        npyv_u8 v0 = npyv_load_u8(ip + vstep * 0);
+#if 0
+        npyv_u8 r0 = mask_to_true(npyv_cmpeq_u8(v0, zero));
+#else
+        npyv_u8 r0 = byte_to_true(v0);
+#endif
+        npyv_store_u8(op + vstep * 0, r0);
+        #endif
+        
+#line 199
+        #if UNROLL > 1
+        npyv_u8 v1 = npyv_load_u8(ip + vstep * 1);
+#if 0
+        npyv_u8 r1 = mask_to_true(npyv_cmpeq_u8(v1, zero));
+#else
+        npyv_u8 r1 = byte_to_true(v1);
+#endif
+        npyv_store_u8(op + vstep * 1, r1);
+        #endif
+        
+#line 199
+        #if UNROLL > 2
+        npyv_u8 v2 = npyv_load_u8(ip + vstep * 2);
+#if 0
+        npyv_u8 r2 = mask_to_true(npyv_cmpeq_u8(v2, zero));
+#else
+        npyv_u8 r2 = byte_to_true(v2);
+#endif
+        npyv_store_u8(op + vstep * 2, r2);
+        #endif
+        
+#line 199
+        #if UNROLL > 3
+        npyv_u8 v3 = npyv_load_u8(ip + vstep * 3);
+#if 0
+        npyv_u8 r3 = mask_to_true(npyv_cmpeq_u8(v3, zero));
+#else
+        npyv_u8 r3 = byte_to_true(v3);
+#endif
+        npyv_store_u8(op + vstep * 3, r3);
+        #endif
+        
+#line 199
+        #if UNROLL > 4
+        npyv_u8 v4 = npyv_load_u8(ip + vstep * 4);
+#if 0
+        npyv_u8 r4 = mask_to_true(npyv_cmpeq_u8(v4, zero));
+#else
+        npyv_u8 r4 = byte_to_true(v4);
+#endif
+        npyv_store_u8(op + vstep * 4, r4);
+        #endif
+        
+#line 199
+        #if UNROLL > 5
+        npyv_u8 v5 = npyv_load_u8(ip + vstep * 5);
+#if 0
+        npyv_u8 r5 = mask_to_true(npyv_cmpeq_u8(v5, zero));
+#else
+        npyv_u8 r5 = byte_to_true(v5);
+#endif
+        npyv_store_u8(op + vstep * 5, r5);
+        #endif
+        
+#line 199
+        #if UNROLL > 6
+        npyv_u8 v6 = npyv_load_u8(ip + vstep * 6);
+#if 0
+        npyv_u8 r6 = mask_to_true(npyv_cmpeq_u8(v6, zero));
+#else
+        npyv_u8 r6 = byte_to_true(v6);
+#endif
+        npyv_store_u8(op + vstep * 6, r6);
+        #endif
+        
+#line 199
+        #if UNROLL > 7
+        npyv_u8 v7 = npyv_load_u8(ip + vstep * 7);
+#if 0
+        npyv_u8 r7 = mask_to_true(npyv_cmpeq_u8(v7, zero));
+#else
+        npyv_u8 r7 = byte_to_true(v7);
+#endif
+        npyv_store_u8(op + vstep * 7, r7);
+        #endif
+        
+#line 199
+        #if UNROLL > 8
+        npyv_u8 v8 = npyv_load_u8(ip + vstep * 8);
+#if 0
+        npyv_u8 r8 = mask_to_true(npyv_cmpeq_u8(v8, zero));
+#else
+        npyv_u8 r8 = byte_to_true(v8);
+#endif
+        npyv_store_u8(op + vstep * 8, r8);
+        #endif
+        
+#line 199
+        #if UNROLL > 9
+        npyv_u8 v9 = npyv_load_u8(ip + vstep * 9);
+#if 0
+        npyv_u8 r9 = mask_to_true(npyv_cmpeq_u8(v9, zero));
+#else
+        npyv_u8 r9 = byte_to_true(v9);
+#endif
+        npyv_store_u8(op + vstep * 9, r9);
+        #endif
+        
+#line 199
+        #if UNROLL > 10
+        npyv_u8 v10 = npyv_load_u8(ip + vstep * 10);
+#if 0
+        npyv_u8 r10 = mask_to_true(npyv_cmpeq_u8(v10, zero));
+#else
+        npyv_u8 r10 = byte_to_true(v10);
+#endif
+        npyv_store_u8(op + vstep * 10, r10);
+        #endif
+        
+#line 199
+        #if UNROLL > 11
+        npyv_u8 v11 = npyv_load_u8(ip + vstep * 11);
+#if 0
+        npyv_u8 r11 = mask_to_true(npyv_cmpeq_u8(v11, zero));
+#else
+        npyv_u8 r11 = byte_to_true(v11);
+#endif
+        npyv_store_u8(op + vstep * 11, r11);
+        #endif
+        
+#line 199
+        #if UNROLL > 12
+        npyv_u8 v12 = npyv_load_u8(ip + vstep * 12);
+#if 0
+        npyv_u8 r12 = mask_to_true(npyv_cmpeq_u8(v12, zero));
+#else
+        npyv_u8 r12 = byte_to_true(v12);
+#endif
+        npyv_store_u8(op + vstep * 12, r12);
+        #endif
+        
+#line 199
+        #if UNROLL > 13
+        npyv_u8 v13 = npyv_load_u8(ip + vstep * 13);
+#if 0
+        npyv_u8 r13 = mask_to_true(npyv_cmpeq_u8(v13, zero));
+#else
+        npyv_u8 r13 = byte_to_true(v13);
+#endif
+        npyv_store_u8(op + vstep * 13, r13);
+        #endif
+        
+#line 199
+        #if UNROLL > 14
+        npyv_u8 v14 = npyv_load_u8(ip + vstep * 14);
+#if 0
+        npyv_u8 r14 = mask_to_true(npyv_cmpeq_u8(v14, zero));
+#else
+        npyv_u8 r14 = byte_to_true(v14);
+#endif
+        npyv_store_u8(op + vstep * 14, r14);
+        #endif
+        
+#line 199
+        #if UNROLL > 15
+        npyv_u8 v15 = npyv_load_u8(ip + vstep * 15);
+#if 0
+        npyv_u8 r15 = mask_to_true(npyv_cmpeq_u8(v15, zero));
+#else
+        npyv_u8 r15 = byte_to_true(v15);
+#endif
+        npyv_store_u8(op + vstep * 15, r15);
+        #endif
+        
+    }
+    #undef UNROLL
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += vstep) {
+        npyv_u8 v = npyv_load_u8(ip);
+#if 0
+        npyv_u8 r = mask_to_true(npyv_cmpeq_u8(v, zero));
+#else
+        npyv_u8 r = byte_to_true(v);
+#endif
+        npyv_store_u8(op, r);
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = (*ip != 0);
+    }
+}
+
+
+#endif // NPY_SIMD
+
+/*******************************************************************************
+ ** Defining ufunc inner functions
+ ******************************************************************************/
+
+#line 239
+static NPY_INLINE int
+run_binary_simd_logical_or_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_BINARY(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_binary_logical_or_BOOL((npy_bool*)args[2], (npy_bool*)args[0],
+                               (npy_bool*)args[1], dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+
+
+static NPY_INLINE int
+run_reduce_simd_logical_or_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_REDUCE(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_reduce_logical_or_BOOL((npy_bool*)args[0], (npy_bool*)args[1],
+                                dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+
+#line 239
+static NPY_INLINE int
+run_binary_simd_logical_and_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_BINARY(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_binary_logical_and_BOOL((npy_bool*)args[2], (npy_bool*)args[0],
+                               (npy_bool*)args[1], dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+
+
+static NPY_INLINE int
+run_reduce_simd_logical_and_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_REDUCE(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_reduce_logical_and_BOOL((npy_bool*)args[0], (npy_bool*)args[1],
+                                dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+
+
+#line 272
+static NPY_INLINE int
+run_unary_simd_logical_not_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_UNARY(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_logical_not_BOOL((npy_bool*)args[1], (npy_bool*)args[0], dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+
+#line 272
+static NPY_INLINE int
+run_unary_simd_absolute_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_UNARY(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_absolute_BOOL((npy_bool*)args[1], (npy_bool*)args[0], dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+
+
+
+#line 293
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_logical_and)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if(IS_BINARY_REDUCE) {
+#if NPY_SIMD
+        /*
+         * stick with our variant for more reliable performance, only known
+         * platform which outperforms it by ~20% is an i7 with glibc 2.17
+         */
+        if (run_reduce_simd_logical_and_BOOL(args, dimensions, steps)) {
+            return;
+        }
+#else
+        /* for now only use libc on 32-bit/non-x86 */
+        if (steps[1] == 1) {
+            npy_bool * op = (npy_bool *)args[0];
+#if 1
+            /* np.all(), search for a zero (false) */
+            if (*op) {
+                *op = memchr(args[1], 0, dimensions[0]) == NULL;
+            }
+#else
+            /*
+             * np.any(), search for a non-zero (true) via comparing against
+             * zero blocks, memcmp is faster than memchr on SSE4 machines
+             * with glibc >= 2.12 and memchr can only check for equal 1
+             */
+            static const npy_bool zero[4096]; /* zero by C standard */
+            npy_uintp i, n = dimensions[0];
+
+            for (i = 0; !*op && i < n - (n % sizeof(zero)); i += sizeof(zero)) {
+                *op = memcmp(&args[1][i], zero, sizeof(zero)) != 0;
+            }
+            if (!*op && n - i > 0) {
+                *op = memcmp(&args[1][i], zero, n - i) != 0;
+            }
+#endif
+            return;
+        }
+#endif
+        else {
+            BINARY_REDUCE_LOOP(npy_bool) {
+                const npy_bool in2 = *(npy_bool *)ip2;
+                io1 = io1 && in2;
+                if (io1 == 0) {
+                    break;
+                }
+            }
+            *((npy_bool *)iop1) = io1;
+        }
+    }
+    else {
+        if (run_binary_simd_logical_and_BOOL(args, dimensions, steps)) {
+            return;
+        }
+        else {
+            BINARY_LOOP {
+                const npy_bool in1 = *(npy_bool *)ip1;
+                const npy_bool in2 = *(npy_bool *)ip2;
+                *((npy_bool *)op1) = in1 && in2;
+            }
+        }
+    }
+}
+
+#line 293
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_logical_or)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if(IS_BINARY_REDUCE) {
+#if NPY_SIMD
+        /*
+         * stick with our variant for more reliable performance, only known
+         * platform which outperforms it by ~20% is an i7 with glibc 2.17
+         */
+        if (run_reduce_simd_logical_or_BOOL(args, dimensions, steps)) {
+            return;
+        }
+#else
+        /* for now only use libc on 32-bit/non-x86 */
+        if (steps[1] == 1) {
+            npy_bool * op = (npy_bool *)args[0];
+#if 0
+            /* np.all(), search for a zero (false) */
+            if (*op) {
+                *op = memchr(args[1], 0, dimensions[0]) == NULL;
+            }
+#else
+            /*
+             * np.any(), search for a non-zero (true) via comparing against
+             * zero blocks, memcmp is faster than memchr on SSE4 machines
+             * with glibc >= 2.12 and memchr can only check for equal 1
+             */
+            static const npy_bool zero[4096]; /* zero by C standard */
+            npy_uintp i, n = dimensions[0];
+
+            for (i = 0; !*op && i < n - (n % sizeof(zero)); i += sizeof(zero)) {
+                *op = memcmp(&args[1][i], zero, sizeof(zero)) != 0;
+            }
+            if (!*op && n - i > 0) {
+                *op = memcmp(&args[1][i], zero, n - i) != 0;
+            }
+#endif
+            return;
+        }
+#endif
+        else {
+            BINARY_REDUCE_LOOP(npy_bool) {
+                const npy_bool in2 = *(npy_bool *)ip2;
+                io1 = io1 || in2;
+                if (io1 != 0) {
+                    break;
+                }
+            }
+            *((npy_bool *)iop1) = io1;
+        }
+    }
+    else {
+        if (run_binary_simd_logical_or_BOOL(args, dimensions, steps)) {
+            return;
+        }
+        else {
+            BINARY_LOOP {
+                const npy_bool in1 = *(npy_bool *)ip1;
+                const npy_bool in2 = *(npy_bool *)ip2;
+                *((npy_bool *)op1) = in1 || in2;
+            }
+        }
+    }
+}
+
+
+#line 363
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_logical_not)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (run_unary_simd_logical_not_BOOL(args, dimensions, steps)) {
+        return;
+    }
+    else {
+        UNARY_LOOP {
+            npy_bool in1 = *(npy_bool *)ip1;
+            *((npy_bool *)op1) = in1 == 0;
+        }
+    }
+}
+
+#line 363
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (run_unary_simd_absolute_BOOL(args, dimensions, steps)) {
+        return;
+    }
+    else {
+        UNARY_LOOP {
+            npy_bool in1 = *(npy_bool *)ip1;
+            *((npy_bool *)op1) = in1 != 0;
+        }
+    }
+}
+
+
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.c.src
new file mode 100644
index 00000000000..c07525be402
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.c.src
@@ -0,0 +1,377 @@
+/*@targets
+ ** $maxopt baseline
+ ** neon asimd
+ ** sse2 avx2 avx512_skx
+ ** vsx2
+ ** vx
+ **/
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/*******************************************************************************
+ ** Defining the SIMD kernels
+ ******************************************************************************/
+
+#if NPY_SIMD
+/*
+ * convert any bit set to boolean true so vectorized and normal operations are
+ * consistent, should not be required if bool is used correctly everywhere but
+ * you never know
+ */
+NPY_FINLINE npyv_u8 byte_to_true(npyv_u8 v)
+{
+    const npyv_u8 zero = npyv_zero_u8();
+    const npyv_u8 truemask = npyv_setall_u8(1 == 1);
+    // cmpeq(v, 0) turns 0x00 -> 0xff and non-zero -> 0x00
+    npyv_u8 tmp = npyv_cvt_u8_b8(npyv_cmpeq_u8(v, zero));
+    // tmp is filled with 0xff/0x00, negate and mask to boolean true
+    return npyv_andc_u8(truemask, tmp);
+}
+/*
+ * convert mask vector (0xff/0x00) to boolean true.  similar to byte_to_true(),
+ * but we've already got a mask and can skip negation.
+ */
+NPY_FINLINE npyv_u8 mask_to_true(npyv_b8 v)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1 == 1);
+    return npyv_and_u8(truemask, npyv_cvt_u8_b8(v));
+}
+/*
+ * For logical_and, we have to be careful to handle non-bool inputs where
+ * bits of each operand might not overlap. Example: a = 0x01, b = 0x80
+ * Both evaluate to boolean true, however, a & b is false.  Return value
+ * should be consistent with byte_to_true().
+ */
+NPY_FINLINE npyv_u8 simd_logical_and_u8(npyv_u8 a, npyv_u8 b)
+{
+    const npyv_u8 zero = npyv_zero_u8();
+    const npyv_u8 truemask = npyv_setall_u8(1 == 1);
+    npyv_b8 ma = npyv_cmpeq_u8(a, zero);
+    npyv_b8 mb = npyv_cmpeq_u8(b, zero);
+    npyv_u8 r = npyv_cvt_u8_b8(npyv_or_b8(ma, mb));
+    return npyv_andc_u8(truemask, r);
+}
+/*
+ * We don't really need the following, but it simplifies the templating code
+ * below since it is paired with simd_logical_and_u8() above.
+ */
+NPY_FINLINE npyv_u8 simd_logical_or_u8(npyv_u8 a, npyv_u8 b)
+{
+    npyv_u8 r = npyv_or_u8(a, b);
+    return byte_to_true(r);
+}
+
+
+/**begin repeat
+ * #kind = logical_and, logical_or#
+ * #and  = 1, 0#
+ * #scalar_op = &&, ||#
+ * #intrin = and, or#
+ * #reduce = min, max#
+ * #scalar_cmp = ==, !=#
+ * #anyall = all, any#
+ */
+static void
+simd_binary_@kind@_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2, npy_intp len)
+{
+    #define UNROLL 16
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip1 += wstep, ip2 += wstep, op += wstep) {
+        /**begin repeat1
+         * #unroll = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
+         */
+        #if UNROLL > @unroll@
+        npyv_u8 a@unroll@ = npyv_load_u8(ip1 + vstep * @unroll@);
+        npyv_u8 b@unroll@ = npyv_load_u8(ip2 + vstep * @unroll@);
+        npyv_u8 r@unroll@ = simd_logical_@intrin@_u8(a@unroll@, b@unroll@);
+        npyv_store_u8(op + vstep * @unroll@, r@unroll@);
+        #endif
+        /**end repeat1**/
+    }
+    #undef UNROLL
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip1 += vstep, ip2 += vstep, op += vstep) {
+        npyv_u8 a = npyv_load_u8(ip1);
+        npyv_u8 b = npyv_load_u8(ip2);
+        npyv_u8 r = simd_logical_@intrin@_u8(a, b);
+        npyv_store_u8(op, r);
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; len--, ip1++, ip2++, op++) {
+        *op = *ip1 @scalar_op@ *ip2;
+    }
+}
+
+static void
+simd_reduce_@kind@_BOOL(npy_bool * op, npy_bool * ip, npy_intp len)
+{
+    #define UNROLL 8
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip += wstep) {
+    #if defined(NPY_HAVE_SSE2)
+        NPY_PREFETCH(ip + wstep, 0, 3);
+    #endif
+        npyv_u8 v0 = npyv_load_u8(ip + vstep * 0);
+        npyv_u8 v1 = npyv_load_u8(ip + vstep * 1);
+        npyv_u8 v2 = npyv_load_u8(ip + vstep * 2);
+        npyv_u8 v3 = npyv_load_u8(ip + vstep * 3);
+        npyv_u8 v4 = npyv_load_u8(ip + vstep * 4);
+        npyv_u8 v5 = npyv_load_u8(ip + vstep * 5);
+        npyv_u8 v6 = npyv_load_u8(ip + vstep * 6);
+        npyv_u8 v7 = npyv_load_u8(ip + vstep * 7);
+
+        npyv_u8 m01 = npyv_@reduce@_u8(v0, v1);
+        npyv_u8 m23 = npyv_@reduce@_u8(v2, v3);
+        npyv_u8 m45 = npyv_@reduce@_u8(v4, v5);
+        npyv_u8 m67 = npyv_@reduce@_u8(v6, v7);
+
+        npyv_u8 m0123 = npyv_@reduce@_u8(m01, m23);
+        npyv_u8 m4567 = npyv_@reduce@_u8(m45, m67);
+
+        npyv_u8 mv = npyv_@reduce@_u8(m0123, m4567);
+
+        if(npyv_@anyall@_u8(mv) @scalar_cmp@ 0){
+            *op = !@and@;
+            return;
+        }
+    }
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip += vstep) {
+        npyv_u8 v0 = npyv_load_u8(ip);
+        if(npyv_@anyall@_u8(v0) @scalar_cmp@ 0){
+            *op = !@and@;
+            return;
+        }
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ++ip) {
+        *op = *op @scalar_op@ *ip;
+        if (*op @scalar_cmp@ 0) {
+            return;
+        }
+    }
+#undef UNROLL
+}
+/**end repeat**/ 
+
+/**begin repeat
+ * #kind = logical_not, absolute#
+ * #op = ==, !=#
+ * #not = 1, 0#
+ */
+static void
+simd_@kind@_BOOL(npy_bool * op, npy_bool * ip, npy_intp len)
+{
+    #define UNROLL 16
+
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    #if @not@
+    const npyv_u8 zero = npyv_zero_u8();
+    #endif
+
+    // Unrolled vectors loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+        /**begin repeat1
+         * #unroll = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
+         */
+        #if UNROLL > @unroll@
+        npyv_u8 v@unroll@ = npyv_load_u8(ip + vstep * @unroll@);
+#if @not@
+        npyv_u8 r@unroll@ = mask_to_true(npyv_cmpeq_u8(v@unroll@, zero));
+#else
+        npyv_u8 r@unroll@ = byte_to_true(v@unroll@);
+#endif
+        npyv_store_u8(op + vstep * @unroll@, r@unroll@);
+        #endif
+        /**end repeat1**/
+    }
+    #undef UNROLL
+
+    // Single vectors loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += vstep) {
+        npyv_u8 v = npyv_load_u8(ip);
+#if @not@
+        npyv_u8 r = mask_to_true(npyv_cmpeq_u8(v, zero));
+#else
+        npyv_u8 r = byte_to_true(v);
+#endif
+        npyv_store_u8(op, r);
+    }
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = (*ip @op@ 0);
+    }
+}
+/**end repeat**/
+
+#endif // NPY_SIMD
+
+/*******************************************************************************
+ ** Defining ufunc inner functions
+ ******************************************************************************/
+
+/**begin repeat
+ * # kind = logical_or, logical_and#
+ */
+static NPY_INLINE int
+run_binary_simd_@kind@_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_BINARY(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_binary_@kind@_BOOL((npy_bool*)args[2], (npy_bool*)args[0],
+                               (npy_bool*)args[1], dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+
+
+static NPY_INLINE int
+run_reduce_simd_@kind@_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_REDUCE(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_reduce_@kind@_BOOL((npy_bool*)args[0], (npy_bool*)args[1],
+                                dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+/**end repeat**/
+
+/**begin repeat
+ * #kind = logical_not, absolute#
+ */
+static NPY_INLINE int
+run_unary_simd_@kind@_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
+{
+#if NPY_SIMD
+    if (sizeof(npy_bool) == 1 &&
+            IS_BLOCKABLE_UNARY(sizeof(npy_bool), NPY_SIMD_WIDTH)) {
+        simd_@kind@_BOOL((npy_bool*)args[1], (npy_bool*)args[0], dimensions[0]);
+        return 1;
+    }
+#endif
+    return 0;
+}
+/**end repeat**/
+
+
+/**begin repeat
+ * #kind = logical_and, logical_or#
+ * #OP =  &&, ||#
+ * #SC =  ==, !=#
+ * #and = 1, 0#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if(IS_BINARY_REDUCE) {
+#if NPY_SIMD
+        /*
+         * stick with our variant for more reliable performance, only known
+         * platform which outperforms it by ~20% is an i7 with glibc 2.17
+         */
+        if (run_reduce_simd_@kind@_BOOL(args, dimensions, steps)) {
+            return;
+        }
+#else
+        /* for now only use libc on 32-bit/non-x86 */
+        if (steps[1] == 1) {
+            npy_bool * op = (npy_bool *)args[0];
+#if @and@
+            /* np.all(), search for a zero (false) */
+            if (*op) {
+                *op = memchr(args[1], 0, dimensions[0]) == NULL;
+            }
+#else
+            /*
+             * np.any(), search for a non-zero (true) via comparing against
+             * zero blocks, memcmp is faster than memchr on SSE4 machines
+             * with glibc >= 2.12 and memchr can only check for equal 1
+             */
+            static const npy_bool zero[4096]; /* zero by C standard */
+            npy_uintp i, n = dimensions[0];
+
+            for (i = 0; !*op && i < n - (n % sizeof(zero)); i += sizeof(zero)) {
+                *op = memcmp(&args[1][i], zero, sizeof(zero)) != 0;
+            }
+            if (!*op && n - i > 0) {
+                *op = memcmp(&args[1][i], zero, n - i) != 0;
+            }
+#endif
+            return;
+        }
+#endif
+        else {
+            BINARY_REDUCE_LOOP(npy_bool) {
+                const npy_bool in2 = *(npy_bool *)ip2;
+                io1 = io1 @OP@ in2;
+                if (io1 @SC@ 0) {
+                    break;
+                }
+            }
+            *((npy_bool *)iop1) = io1;
+        }
+    }
+    else {
+        if (run_binary_simd_@kind@_BOOL(args, dimensions, steps)) {
+            return;
+        }
+        else {
+            BINARY_LOOP {
+                const npy_bool in1 = *(npy_bool *)ip1;
+                const npy_bool in2 = *(npy_bool *)ip2;
+                *((npy_bool *)op1) = in1 @OP@ in2;
+            }
+        }
+    }
+}
+/**end repeat**/
+
+/**begin repeat
+ * #kind = logical_not, absolute#
+ * #OP = ==, !=#
+ **/
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BOOL_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    if (run_unary_simd_@kind@_BOOL(args, dimensions, steps)) {
+        return;
+    }
+    else {
+        UNARY_LOOP {
+            npy_bool in1 = *(npy_bool *)ip1;
+            *((npy_bool *)op1) = in1 @OP@ 0;
+        }
+    }
+}
+/**end repeat**/
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.h
new file mode 100644
index 00000000000..b9dbdcb1891
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_logical.dispatch.h
@@ -0,0 +1,20 @@
+// cache_hash:2841248447
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator (distutils/ccompiler_opt.py)
+ */
+#ifndef NPY__CPU_DISPATCH_EXPAND_
+    #define NPY__CPU_DISPATCH_EXPAND_(X) X
+#endif
+#undef NPY__CPU_DISPATCH_BASELINE_CALL
+#undef NPY__CPU_DISPATCH_CALL
+#define NPY__CPU_DISPATCH_BASELINE_CALL(CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB(__VA_ARGS__))
+#if defined(__x86_64__) || defined(_M_X64)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX512_SKX)), AVX512_SKX, __VA_ARGS__)) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX)&&CHK(F16C)&&CHK(AVX2)), AVX2, __VA_ARGS__))
+#elif defined(__aarch64__)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c
index 54d082445c2..ad8c1ef3978 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c
@@ -6961,6 +6961,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UBYTE_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ubyte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -7113,6 +7136,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UBYTE_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ubyte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -7265,6 +7311,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UBYTE_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ubyte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -7417,6 +7486,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UBYTE_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ubyte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ubyte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ubyte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -7639,6 +7731,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(USHORT_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ushort *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -7791,6 +7906,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(USHORT_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ushort *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -7943,6 +8081,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(USHORT_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ushort *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -8095,6 +8256,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(USHORT_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ushort *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ushort *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ushort *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -8317,6 +8501,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UINT_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_uint *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -8469,6 +8676,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UINT_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_uint *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -8621,6 +8851,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UINT_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_uint *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -8773,6 +9026,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(UINT_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_uint *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_uint *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_uint *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -8995,6 +9271,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONG_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -9147,6 +9446,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONG_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -9299,6 +9621,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONG_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -9451,6 +9796,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONG_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -9673,6 +10041,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONGLONG_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulonglong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -9825,6 +10216,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONGLONG_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulonglong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -9977,6 +10391,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONGLONG_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulonglong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -10129,6 +10566,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(ULONGLONG_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_ulonglong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_ulonglong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_ulonglong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -10351,6 +10811,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(BYTE_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_byte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -10503,6 +10986,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(BYTE_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_byte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -10655,6 +11161,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(BYTE_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_byte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -10807,6 +11336,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(BYTE_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_byte *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_byte *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_byte *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -11029,6 +11581,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(SHORT_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_short *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -11181,6 +11756,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(SHORT_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_short *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -11333,6 +11931,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(SHORT_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_short *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -11485,6 +12106,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(SHORT_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_short *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_short *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_short *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -11707,6 +12351,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(INT_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_int *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -11859,6 +12526,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(INT_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_int *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -12011,6 +12701,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(INT_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_int *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -12163,6 +12876,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(INT_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_int *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_int *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_int *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -12385,6 +13121,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONG_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_long *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -12537,6 +13296,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONG_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_long *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -12689,6 +13471,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONG_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_long *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -12841,6 +13646,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONG_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_long *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_long *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_long *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -13063,6 +13891,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGLONG_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longlong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -13215,6 +14066,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGLONG_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longlong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -13367,6 +14241,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGLONG_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longlong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -13519,6 +14416,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGLONG_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longlong *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longlong *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longlong *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -13741,6 +14661,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_float *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -13893,6 +14836,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_float *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -14045,6 +15011,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_float *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -14197,6 +15186,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(FLOAT_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_float *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_float *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_float *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -14419,6 +15431,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_double *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -14571,6 +15606,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_double *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -14723,6 +15781,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_double *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -14875,6 +15956,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(DOUBLE_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_double *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_double *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_double *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -15097,6 +16201,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGDOUBLE_maximum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longdouble *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -15249,6 +16376,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGDOUBLE_minimum_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longdouble *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -15401,6 +16551,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGDOUBLE_fmax_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longdouble *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
@@ -15553,6 +16726,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(LONGDOUBLE_fmin_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    npy_longdouble *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (npy_longdouble *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(npy_longdouble *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c.src
index 237c8e933b4..236e2e2eb76 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_minmax.dispatch.c.src
@@ -451,6 +451,29 @@ clear_fp:
 #endif
 }
 
+
+NPY_NO_EXPORT int NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@_indexed)
+(PyArrayMethod_Context *NPY_UNUSED(context), char *const *args, npy_intp const *dimensions, npy_intp const *steps, NpyAuxData *NPY_UNUSED(func))
+{
+    char *ip1 = args[0];
+    char *indxp = args[1];
+    char *value = args[2];
+    npy_intp is1 = steps[0], isindex = steps[1], isb = steps[2];
+    npy_intp n = dimensions[0];
+    npy_intp shape = steps[3];
+    npy_intp i;
+    @type@ *indexed;
+    for(i = 0; i < n; i++, indxp += isindex, value += isb) {
+        npy_intp indx = *(npy_intp *)indxp;
+        if (indx < 0) {
+            indx += shape;
+        }
+        indexed = (@type@ *)(ip1 + is1 * indx);
+        *indexed = SCALAR_OP(*indexed, *(@type@ *)value);
+    }
+    return 0;
+}
+
 #undef SCALAR_OP
 
 #endif // !fp_only || (is_fp && fp_only)
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c
index 24211b1c1cd..f7f47f8edcc 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c
@@ -399,7 +399,7 @@ vsx4_divisor_s64(const npyv_s64 vscalar)
 
 #line 170
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src1 = (npyv_lanetype_u8 *) args[0];
@@ -467,7 +467,7 @@ vsx4_simd_fmod_contig_u8(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src1  = (npyv_lanetype_u8 *) args[0];
@@ -509,7 +509,7 @@ vsx4_simd_fmod_by_scalar_contig_u8(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src1 = (npyv_lanetype_u8 *) args[0];
@@ -577,7 +577,7 @@ vsx4_simd_remainder_contig_u8(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src1  = (npyv_lanetype_u8 *) args[0];
@@ -619,7 +619,7 @@ vsx4_simd_remainder_by_scalar_contig_u8(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src1 = (npyv_lanetype_u8 *) args[0];
@@ -687,7 +687,7 @@ vsx4_simd_divmod_contig_u8(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_u8(char **args, npy_intp len)
 {
     npyv_lanetype_u8 *src1  = (npyv_lanetype_u8 *) args[0];
@@ -731,7 +731,7 @@ vsx4_simd_divmod_by_scalar_contig_u8(char **args, npy_intp len)
 
 #line 170
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src1 = (npyv_lanetype_u16 *) args[0];
@@ -799,7 +799,7 @@ vsx4_simd_fmod_contig_u16(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src1  = (npyv_lanetype_u16 *) args[0];
@@ -841,7 +841,7 @@ vsx4_simd_fmod_by_scalar_contig_u16(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src1 = (npyv_lanetype_u16 *) args[0];
@@ -909,7 +909,7 @@ vsx4_simd_remainder_contig_u16(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src1  = (npyv_lanetype_u16 *) args[0];
@@ -951,7 +951,7 @@ vsx4_simd_remainder_by_scalar_contig_u16(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src1 = (npyv_lanetype_u16 *) args[0];
@@ -1019,7 +1019,7 @@ vsx4_simd_divmod_contig_u16(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_u16(char **args, npy_intp len)
 {
     npyv_lanetype_u16 *src1  = (npyv_lanetype_u16 *) args[0];
@@ -1063,7 +1063,7 @@ vsx4_simd_divmod_by_scalar_contig_u16(char **args, npy_intp len)
 
 #line 170
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src1 = (npyv_lanetype_u32 *) args[0];
@@ -1131,7 +1131,7 @@ vsx4_simd_fmod_contig_u32(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src1  = (npyv_lanetype_u32 *) args[0];
@@ -1173,7 +1173,7 @@ vsx4_simd_fmod_by_scalar_contig_u32(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src1 = (npyv_lanetype_u32 *) args[0];
@@ -1241,7 +1241,7 @@ vsx4_simd_remainder_contig_u32(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src1  = (npyv_lanetype_u32 *) args[0];
@@ -1283,7 +1283,7 @@ vsx4_simd_remainder_by_scalar_contig_u32(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src1 = (npyv_lanetype_u32 *) args[0];
@@ -1351,7 +1351,7 @@ vsx4_simd_divmod_contig_u32(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_u32(char **args, npy_intp len)
 {
     npyv_lanetype_u32 *src1  = (npyv_lanetype_u32 *) args[0];
@@ -1395,7 +1395,7 @@ vsx4_simd_divmod_by_scalar_contig_u32(char **args, npy_intp len)
 
 #line 170
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src1 = (npyv_lanetype_u64 *) args[0];
@@ -1463,7 +1463,7 @@ vsx4_simd_fmod_contig_u64(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src1  = (npyv_lanetype_u64 *) args[0];
@@ -1505,7 +1505,7 @@ vsx4_simd_fmod_by_scalar_contig_u64(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src1 = (npyv_lanetype_u64 *) args[0];
@@ -1573,7 +1573,7 @@ vsx4_simd_remainder_contig_u64(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src1  = (npyv_lanetype_u64 *) args[0];
@@ -1615,7 +1615,7 @@ vsx4_simd_remainder_by_scalar_contig_u64(char **args, npy_intp len)
 }
 
 #line 174
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src1 = (npyv_lanetype_u64 *) args[0];
@@ -1683,7 +1683,7 @@ vsx4_simd_divmod_contig_u64(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_u64(char **args, npy_intp len)
 {
     npyv_lanetype_u64 *src1  = (npyv_lanetype_u64 *) args[0];
@@ -1728,7 +1728,7 @@ vsx4_simd_divmod_by_scalar_contig_u64(char **args, npy_intp len)
 
 #line 291
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src1 = (npyv_lanetype_s8 *) args[0];
@@ -1846,7 +1846,7 @@ vsx4_simd_fmod_contig_s8(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src1  = (npyv_lanetype_s8 *) args[0];
@@ -1941,7 +1941,7 @@ vsx4_simd_fmod_by_scalar_contig_s8(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src1 = (npyv_lanetype_s8 *) args[0];
@@ -2059,7 +2059,7 @@ vsx4_simd_remainder_contig_s8(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src1  = (npyv_lanetype_s8 *) args[0];
@@ -2154,7 +2154,7 @@ vsx4_simd_remainder_by_scalar_contig_s8(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src1 = (npyv_lanetype_s8 *) args[0];
@@ -2272,7 +2272,7 @@ vsx4_simd_divmod_contig_s8(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_s8(char **args, npy_intp len)
 {
     npyv_lanetype_s8 *src1  = (npyv_lanetype_s8 *) args[0];
@@ -2369,7 +2369,7 @@ vsx4_simd_divmod_by_scalar_contig_s8(char **args, npy_intp len)
 
 #line 291
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src1 = (npyv_lanetype_s16 *) args[0];
@@ -2487,7 +2487,7 @@ vsx4_simd_fmod_contig_s16(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src1  = (npyv_lanetype_s16 *) args[0];
@@ -2582,7 +2582,7 @@ vsx4_simd_fmod_by_scalar_contig_s16(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src1 = (npyv_lanetype_s16 *) args[0];
@@ -2700,7 +2700,7 @@ vsx4_simd_remainder_contig_s16(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src1  = (npyv_lanetype_s16 *) args[0];
@@ -2795,7 +2795,7 @@ vsx4_simd_remainder_by_scalar_contig_s16(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src1 = (npyv_lanetype_s16 *) args[0];
@@ -2913,7 +2913,7 @@ vsx4_simd_divmod_contig_s16(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_s16(char **args, npy_intp len)
 {
     npyv_lanetype_s16 *src1  = (npyv_lanetype_s16 *) args[0];
@@ -3010,7 +3010,7 @@ vsx4_simd_divmod_by_scalar_contig_s16(char **args, npy_intp len)
 
 #line 291
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src1 = (npyv_lanetype_s32 *) args[0];
@@ -3128,7 +3128,7 @@ vsx4_simd_fmod_contig_s32(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src1  = (npyv_lanetype_s32 *) args[0];
@@ -3223,7 +3223,7 @@ vsx4_simd_fmod_by_scalar_contig_s32(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src1 = (npyv_lanetype_s32 *) args[0];
@@ -3341,7 +3341,7 @@ vsx4_simd_remainder_contig_s32(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src1  = (npyv_lanetype_s32 *) args[0];
@@ -3436,7 +3436,7 @@ vsx4_simd_remainder_by_scalar_contig_s32(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src1 = (npyv_lanetype_s32 *) args[0];
@@ -3554,7 +3554,7 @@ vsx4_simd_divmod_contig_s32(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_s32(char **args, npy_intp len)
 {
     npyv_lanetype_s32 *src1  = (npyv_lanetype_s32 *) args[0];
@@ -3651,7 +3651,7 @@ vsx4_simd_divmod_by_scalar_contig_s32(char **args, npy_intp len)
 
 #line 291
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src1 = (npyv_lanetype_s64 *) args[0];
@@ -3769,7 +3769,7 @@ vsx4_simd_fmod_contig_s64(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_fmod_by_scalar_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src1  = (npyv_lanetype_s64 *) args[0];
@@ -3864,7 +3864,7 @@ vsx4_simd_fmod_by_scalar_contig_s64(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src1 = (npyv_lanetype_s64 *) args[0];
@@ -3982,7 +3982,7 @@ vsx4_simd_remainder_contig_s64(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_remainder_by_scalar_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src1  = (npyv_lanetype_s64 *) args[0];
@@ -4077,7 +4077,7 @@ vsx4_simd_remainder_by_scalar_contig_s64(char **args, npy_intp len)
 }
 
 #line 295
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src1 = (npyv_lanetype_s64 *) args[0];
@@ -4195,7 +4195,7 @@ vsx4_simd_divmod_contig_s64(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_divmod_by_scalar_contig_s64(char **args, npy_intp len)
 {
     npyv_lanetype_s64 *src1  = (npyv_lanetype_s64 *) args[0];
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c.src
index 53b7da2892b..25edffb1e2c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_modulo.dispatch.c.src
@@ -171,7 +171,7 @@ vsx4_divisor_@sfx@(const npyv_@sfx@ vscalar)
  * #func = fmod, remainder, divmod#
  * #id = 0, 1, 2#
  */
-static NPY_INLINE void
+static inline void
 vsx4_simd_@func@_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src1 = (npyv_lanetype_@sfx@ *) args[0];
@@ -239,7 +239,7 @@ vsx4_simd_@func@_contig_@sfx@(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_@func@_by_scalar_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src1  = (npyv_lanetype_@sfx@ *) args[0];
@@ -292,7 +292,7 @@ vsx4_simd_@func@_by_scalar_contig_@sfx@(char **args, npy_intp len)
  * #func = fmod, remainder, divmod#
  * #id = 0, 1, 2#
  */
-static NPY_INLINE void
+static inline void
 vsx4_simd_@func@_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src1 = (npyv_lanetype_@sfx@ *) args[0];
@@ -410,7 +410,7 @@ vsx4_simd_@func@_contig_@sfx@(char **args, npy_intp len)
     npyv_cleanup();
 }
 
-static NPY_INLINE void
+static inline void
 vsx4_simd_@func@_by_scalar_contig_@sfx@(char **args, npy_intp len)
 {
     npyv_lanetype_@sfx@ *src1  = (npyv_lanetype_@sfx@ *) args[0];
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c
index 86db298a6cf..9d9bc64a160 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c
@@ -19,12 +19,35 @@
 #include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
 #include "loops_utils.h"
 #include "loops.h"
+#include "fast_loop_macros.h"
 /*
  * TODO:
  * - use vectorized version of Payne-Hanek style reduction for large elements or
  *   when there's no native FUSED support instead of fallback to libc
  */
-#if NPY_SIMD_F32 && NPY_SIMD_FMA3 // native support
+#if NPY_SIMD_FMA3  // native support
+#line 23
+#if NPY_SIMD_F64
+/*
+ * Vectorized Cody-Waite range reduction technique
+ * Performs the reduction step x* = x - y*C in three steps:
+ * 1) x* = x - y*c1
+ * 2) x* = x - y*c2
+ * 3) x* = x - y*c3
+ * c1, c2 are exact floating points, c3 = C - c1 - c2 simulates higher precision
+ */
+NPY_FINLINE npyv_f64
+simd_range_reduction_f64(npyv_f64 x, npyv_f64 y, npyv_f64 c1, npyv_f64 c2, npyv_f64 c3)
+{
+    npyv_f64 reduced_x = npyv_muladd_f64(y, c1, x);
+    reduced_x = npyv_muladd_f64(y, c2, reduced_x);
+    reduced_x = npyv_muladd_f64(y, c3, reduced_x);
+    return reduced_x;
+}
+#endif
+
+#line 23
+#if NPY_SIMD_F32
 /*
  * Vectorized Cody-Waite range reduction technique
  * Performs the reduction step x* = x - y*C in three steps:
@@ -41,6 +64,245 @@ simd_range_reduction_f32(npyv_f32 x, npyv_f32 y, npyv_f32 c1, npyv_f32 c2, npyv_
     reduced_x = npyv_muladd_f32(y, c3, reduced_x);
     return reduced_x;
 }
+#endif
+
+
+#if NPY_SIMD_F64
+#line 47
+#if defined(NPY_OS_WIN32) || defined(NPY_OS_CYGWIN)
+NPY_FINLINE npyv_f64
+#else
+NPY_NOINLINE npyv_f64
+#endif
+simd_cos_scalar_f64(npyv_f64 out, npy_uint64 cmp_bits)
+{
+    // MSVC doesn't compile with direct vector access, so we copy it here
+    // as we have no npyv_get_lane/npyv_set_lane intrinsics
+    npy_double NPY_DECL_ALIGNED(NPY_SIMD_WIDTH) out_copy[npyv_nlanes_f64];
+    npyv_storea_f64(out_copy, out);
+
+    for (unsigned i = 0; i < npyv_nlanes_f64; ++i) {
+        if (cmp_bits & (1 << i)) {
+            out_copy[i] = npy_cos(out_copy[i]);
+        }
+    }
+
+    return npyv_loada_f64(out_copy);
+}
+
+#line 47
+#if defined(NPY_OS_WIN32) || defined(NPY_OS_CYGWIN)
+NPY_FINLINE npyv_f64
+#else
+NPY_NOINLINE npyv_f64
+#endif
+simd_sin_scalar_f64(npyv_f64 out, npy_uint64 cmp_bits)
+{
+    // MSVC doesn't compile with direct vector access, so we copy it here
+    // as we have no npyv_get_lane/npyv_set_lane intrinsics
+    npy_double NPY_DECL_ALIGNED(NPY_SIMD_WIDTH) out_copy[npyv_nlanes_f64];
+    npyv_storea_f64(out_copy, out);
+
+    for (unsigned i = 0; i < npyv_nlanes_f64; ++i) {
+        if (cmp_bits & (1 << i)) {
+            out_copy[i] = npy_sin(out_copy[i]);
+        }
+    }
+
+    return npyv_loada_f64(out_copy);
+}
+
+
+/*
+ * Approximate sine algorithm for x \in [-pi/2, pi/2]
+ * worst-case error is 3.5 ulp.
+ * abs error: 0x1.be222a58p-53 in [-pi/2, pi/2].
+ */
+NPY_FINLINE npyv_f64
+simd_approx_sine_poly_f64(npyv_f64 r)
+{
+    const npyv_f64 poly1 = npyv_setall_f64(-0x1.9f4a9c8b21dc9p-41);
+    const npyv_f64 poly2 = npyv_setall_f64(0x1.60e88a10163f2p-33);
+    const npyv_f64 poly3 = npyv_setall_f64(-0x1.ae6361b7254e7p-26);
+    const npyv_f64 poly4 = npyv_setall_f64(0x1.71de382e8d62bp-19);
+    const npyv_f64 poly5 = npyv_setall_f64(-0x1.a01a019aeb4ffp-13);
+    const npyv_f64 poly6 = npyv_setall_f64(0x1.111111110b25ep-7);
+    const npyv_f64 poly7 = npyv_setall_f64(-0x1.55555555554c3p-3);
+
+    npyv_f64 r2 = npyv_mul_f64(r, r);
+    npyv_f64 y = npyv_muladd_f64(poly1, r2, poly2);
+    y = npyv_muladd_f64(y, r2, poly3);
+    y = npyv_muladd_f64(y, r2, poly4);
+    y = npyv_muladd_f64(y, r2, poly5);
+    y = npyv_muladd_f64(y, r2, poly6);
+    y = npyv_muladd_f64(y, r2, poly7);
+    y = npyv_muladd_f64(npyv_mul_f64(y, r2), r, r);
+
+    return y;
+}
+
+/* r = |x| - n*pi  (range reduction into -pi/2 .. pi/2).  */
+NPY_FINLINE npyv_f64
+simd_range_reduction_pi2(npyv_f64 r, npyv_f64 n) {
+    const npyv_f64 pi1 = npyv_setall_f64(-0x1.921fb54442d18p+1);
+    const npyv_f64 pi2 = npyv_setall_f64(-0x1.1a62633145c06p-53);
+    const npyv_f64 pi3 = npyv_setall_f64(-0x1.c1cd129024e09p-106);
+
+    return simd_range_reduction_f64(r, n, pi1, pi2, pi3);
+}
+
+NPY_FINLINE npyv_b64 simd_sin_range_check_f64(npyv_u64 ir) {
+    const npyv_u64 tiny_bound = npyv_setall_u64(0x202); /* top12 (asuint64 (0x1p-509)).  */
+    const npyv_u64 simd_thresh = npyv_setall_u64(0x214); /* top12 (asuint64 (RangeVal)) - SIMD_TINY_BOUND.  */
+
+    return npyv_cmpge_u64(npyv_sub_u64(npyv_shri_u64(ir, 52), tiny_bound), simd_thresh);
+}
+
+NPY_FINLINE npyv_b64 simd_cos_range_check_f64(npyv_u64 ir) {
+    const npyv_f64 range_val = npyv_setall_f64(0x1p23);
+
+    return npyv_cmpge_u64(ir, npyv_reinterpret_u64_f64(range_val));
+}
+
+NPY_FINLINE npyv_f64
+simd_cos_poly_f64(npyv_f64 r, npyv_u64 ir, npyv_u64 sign)
+{
+    const npyv_f64 inv_pi = npyv_setall_f64(0x1.45f306dc9c883p-2);
+    const npyv_f64 half_pi = npyv_setall_f64(0x1.921fb54442d18p+0);
+    const npyv_f64 shift = npyv_setall_f64(0x1.8p52);
+
+    /* n = rint((|x|+pi/2)/pi) - 0.5.  */
+    npyv_f64 n = npyv_muladd_f64(inv_pi, npyv_add_f64(r, half_pi), shift);
+    npyv_u64 odd = npyv_shli_u64(npyv_reinterpret_u64_f64(n), 63);
+    n = npyv_sub_f64(n, shift);
+    n = npyv_sub_f64(n, npyv_setall_f64(0.5));
+
+    /* r = |x| - n*pi  (range reduction into -pi/2 .. pi/2).  */
+    r = simd_range_reduction_pi2(r, n);
+
+    /* sin(r) poly approx.  */
+    npyv_f64 y = simd_approx_sine_poly_f64(r);
+
+    /* sign.  */
+    return npyv_reinterpret_f64_u64(npyv_xor_u64(npyv_reinterpret_u64_f64(y), odd));
+}
+
+NPY_FINLINE npyv_f64
+simd_sin_poly_f64(npyv_f64 r, npyv_u64 ir, npyv_u64 sign)
+{
+    const npyv_f64 inv_pi = npyv_setall_f64(0x1.45f306dc9c883p-2);
+    const npyv_f64 shift = npyv_setall_f64(0x1.8p52);
+
+    /* n = rint(|x|/pi).  */
+    npyv_f64 n = npyv_muladd_f64(inv_pi, r, shift);
+    npyv_u64 odd = npyv_shli_u64(npyv_reinterpret_u64_f64(n), 63);
+    n = npyv_sub_f64(n, shift);
+
+    /* r = |x| - n*pi  (range reduction into -pi/2 .. pi/2).  */
+    r = simd_range_reduction_pi2(r, n);
+
+    /* sin(r) poly approx.  */
+    npyv_f64 y = simd_approx_sine_poly_f64(r);
+
+    /* sign.  */
+    return npyv_reinterpret_f64_u64(npyv_xor_u64(npyv_xor_u64(npyv_reinterpret_u64_f64(y), sign), odd));
+}
+
+#line 167
+NPY_FINLINE void
+simd_cos_f64(const double *src, npy_intp ssrc, double *dst, npy_intp sdst, npy_intp len)
+{
+    const npyv_u64 abs_mask = npyv_setall_u64(0x7fffffffffffffff);
+    const int vstep = npyv_nlanes_f64;
+
+    npyv_f64 out = npyv_zero_f64();
+    npyv_f64 x_in;
+
+    for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+        if (ssrc == 1) {
+            x_in = npyv_load_tillz_f64(src, len);
+        } else {
+            x_in = npyv_loadn_tillz_f64(src, ssrc, len);
+        }
+
+        npyv_u64 ir = npyv_and_u64(npyv_reinterpret_u64_f64(x_in), abs_mask);
+        npyv_f64 r = npyv_reinterpret_f64_u64(ir);
+        npyv_u64 sign = npyv_and_u64(npyv_reinterpret_u64_f64(x_in), npyv_not_u64(abs_mask));
+
+        npyv_b64 cmp = simd_cos_range_check_f64(ir);
+        /* If fenv exceptions are to be triggered correctly, set any special lanes
+        to 1 (which is neutral w.r.t. fenv). These lanes will be fixed by
+        scalar loop later.  */
+        r = npyv_select_f64(cmp, npyv_setall_f64(1.0), r);
+
+        // Some in range, at least one calculation is useful
+        if (!npyv_all_b64(cmp)) {
+            out = simd_cos_poly_f64(r, ir, sign);
+        }
+
+        if (npyv_any_b64(cmp)) {
+            out = npyv_select_f64(cmp, x_in, out);
+            out = simd_cos_scalar_f64(out, npyv_tobits_b64(cmp));
+        }
+
+        if (sdst == 1) {
+            npyv_store_till_f64(dst, len, out);
+        } else {
+            npyv_storen_till_f64(dst, sdst, len, out);
+        }
+    }
+    npyv_cleanup();
+}
+
+#line 167
+NPY_FINLINE void
+simd_sin_f64(const double *src, npy_intp ssrc, double *dst, npy_intp sdst, npy_intp len)
+{
+    const npyv_u64 abs_mask = npyv_setall_u64(0x7fffffffffffffff);
+    const int vstep = npyv_nlanes_f64;
+
+    npyv_f64 out = npyv_zero_f64();
+    npyv_f64 x_in;
+
+    for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+        if (ssrc == 1) {
+            x_in = npyv_load_tillz_f64(src, len);
+        } else {
+            x_in = npyv_loadn_tillz_f64(src, ssrc, len);
+        }
+
+        npyv_u64 ir = npyv_and_u64(npyv_reinterpret_u64_f64(x_in), abs_mask);
+        npyv_f64 r = npyv_reinterpret_f64_u64(ir);
+        npyv_u64 sign = npyv_and_u64(npyv_reinterpret_u64_f64(x_in), npyv_not_u64(abs_mask));
+
+        npyv_b64 cmp = simd_sin_range_check_f64(ir);
+        /* If fenv exceptions are to be triggered correctly, set any special lanes
+        to 1 (which is neutral w.r.t. fenv). These lanes will be fixed by
+        scalar loop later.  */
+        r = npyv_select_f64(cmp, npyv_setall_f64(1.0), r);
+
+        // Some in range, at least one calculation is useful
+        if (!npyv_all_b64(cmp)) {
+            out = simd_sin_poly_f64(r, ir, sign);
+        }
+
+        if (npyv_any_b64(cmp)) {
+            out = npyv_select_f64(cmp, x_in, out);
+            out = simd_sin_scalar_f64(out, npyv_tobits_b64(cmp));
+        }
+
+        if (sdst == 1) {
+            npyv_store_till_f64(dst, len, out);
+        } else {
+            npyv_storen_till_f64(dst, sdst, len, out);
+        }
+    }
+    npyv_cleanup();
+}
+
+#endif // NPY_SIMD_F64
+
+#if NPY_SIMD_F32
 /*
  * Approximate cosine algorithm for x \in [-PI/4, PI/4]
  * Maximum ULP across all 32-bit floats = 0.875
@@ -183,7 +445,7 @@ simd_sincos_f32(const float *src, npy_intp ssrc, float *dst, npy_intp sdst,
                 npyv_storen_till_f32(dst, sdst, len, cos);
             }
         }
-        if (simd_maski != ((1 << vstep) - 1)) {
+        if (simd_maski != (npy_uint64)((1 << vstep) - 1)) {
             float NPY_DECL_ALIGNED(NPY_SIMD_WIDTH) ip_fback[npyv_nlanes_f32];
             npyv_storea_f32(ip_fback, x_in);
 
@@ -208,51 +470,91 @@ simd_sincos_f32(const float *src, npy_intp ssrc, float *dst, npy_intp sdst,
     }
     npyv_cleanup();
 }
-#endif // NPY_SIMD_FMA3
+#endif // NPY_SIMD_FP32
+#endif // NYP_SIMD_FMA3
 
-#line 207
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_cos)
+#line 388
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_cos)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
-    const float *src = (float*)args[0];
-          float *dst = (float*)args[1];
+    /* Disable SIMD code and revert to libm: see
+     * https://mail.python.org/archives/list/numpy-discussion@python.org/thread/C6EYZZSR4EWGVKHAZXLE7IBILRMNVK7L/
+     * for detailed discussion on this*/
+//#if NPY_SIMD_F64 && NPY_SIMD_FMA3
+#if 0
+    const double *src = (double*)args[0];
+          double *dst = (double*)args[1];
+    const int lsize = sizeof(src[0]);
+    const npy_intp ssrc = steps[0] / lsize;
+    const npy_intp sdst = steps[1] / lsize;
+    npy_intp len = dimensions[0];
+    assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
+
+    if (is_mem_overlap(src, steps[0], dst, steps[1], len) ||
+        !npyv_loadable_stride_f64(ssrc) || !npyv_storable_stride_f64(sdst)
+    ) {
+        for (; len > 0; --len, src += ssrc, dst += sdst) {
+            simd_cos_f64(src, 1, dst, 1, 1);
+        }
+    } else {
+        simd_cos_f64(src, ssrc, dst, sdst, len);
+    }
+#else
+    UNARY_LOOP {
+        const npy_double in1 = *(npy_double *)ip1;
+        *(npy_double *)op1 = npy_cos(in1);
+    }
+#endif
+}
 
+#line 388
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_sin)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    /* Disable SIMD code and revert to libm: see
+     * https://mail.python.org/archives/list/numpy-discussion@python.org/thread/C6EYZZSR4EWGVKHAZXLE7IBILRMNVK7L/
+     * for detailed discussion on this*/
+//#if NPY_SIMD_F64 && NPY_SIMD_FMA3
+#if 0
+    const double *src = (double*)args[0];
+          double *dst = (double*)args[1];
     const int lsize = sizeof(src[0]);
     const npy_intp ssrc = steps[0] / lsize;
     const npy_intp sdst = steps[1] / lsize;
     npy_intp len = dimensions[0];
     assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
-#if NPY_SIMD_F32 && NPY_SIMD_FMA3
+
     if (is_mem_overlap(src, steps[0], dst, steps[1], len) ||
-        !npyv_loadable_stride_f32(ssrc) || !npyv_storable_stride_f32(sdst)
+        !npyv_loadable_stride_f64(ssrc) || !npyv_storable_stride_f64(sdst)
     ) {
         for (; len > 0; --len, src += ssrc, dst += sdst) {
-            simd_sincos_f32(src, 1, dst, 1, 1, SIMD_COMPUTE_COS);
+            simd_sin_f64(src, 1, dst, 1, 1);
         }
     } else {
-        simd_sincos_f32(src, ssrc, dst, sdst, len, SIMD_COMPUTE_COS);
+        simd_sin_f64(src, ssrc, dst, sdst, len);
     }
 #else
-    for (; len > 0; --len, src += ssrc, dst += sdst) {
-        const float src0 = *src;
-        *dst = npy_cosf(src0);
+    UNARY_LOOP {
+        const npy_double in1 = *(npy_double *)ip1;
+        *(npy_double *)op1 = npy_sin(in1);
     }
 #endif
 }
 
-#line 207
+
+#line 426
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_sin)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
-    const float *src = (float*)args[0];
-          float *dst = (float*)args[1];
+#if NPY_SIMD_F32 && NPY_SIMD_FMA3
+    const npy_float *src = (npy_float*)args[0];
+          npy_float *dst = (npy_float*)args[1];
 
     const int lsize = sizeof(src[0]);
     const npy_intp ssrc = steps[0] / lsize;
     const npy_intp sdst = steps[1] / lsize;
     npy_intp len = dimensions[0];
     assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
-#if NPY_SIMD_F32 && NPY_SIMD_FMA3
     if (is_mem_overlap(src, steps[0], dst, steps[1], len) ||
         !npyv_loadable_stride_f32(ssrc) || !npyv_storable_stride_f32(sdst)
     ) {
@@ -263,9 +565,39 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_sin)
         simd_sincos_f32(src, ssrc, dst, sdst, len, SIMD_COMPUTE_SIN);
     }
 #else
-    for (; len > 0; --len, src += ssrc, dst += sdst) {
-        const float src0 = *src;
-        *dst = npy_sinf(src0);
+    UNARY_LOOP {
+        const npy_float in1 = *(npy_float *)ip1;
+        *(npy_float *)op1 = npy_sinf(in1);
+    }
+#endif
+}
+
+#line 426
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_cos)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+#if NPY_SIMD_F32 && NPY_SIMD_FMA3
+    const npy_float *src = (npy_float*)args[0];
+          npy_float *dst = (npy_float*)args[1];
+
+    const int lsize = sizeof(src[0]);
+    const npy_intp ssrc = steps[0] / lsize;
+    const npy_intp sdst = steps[1] / lsize;
+    npy_intp len = dimensions[0];
+    assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
+    if (is_mem_overlap(src, steps[0], dst, steps[1], len) ||
+        !npyv_loadable_stride_f32(ssrc) || !npyv_storable_stride_f32(sdst)
+    ) {
+        for (; len > 0; --len, src += ssrc, dst += sdst) {
+            simd_sincos_f32(src, 1, dst, 1, 1, SIMD_COMPUTE_COS);
+        }
+    } else {
+        simd_sincos_f32(src, ssrc, dst, sdst, len, SIMD_COMPUTE_COS);
+    }
+#else
+    UNARY_LOOP {
+        const npy_float in1 = *(npy_float *)ip1;
+        *(npy_float *)op1 = npy_cosf(in1);
     }
 #endif
 }
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c.src
index 9f9978e6682..f07cb70f397 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_trigonometric.dispatch.c.src
@@ -9,12 +9,18 @@
 #include "simd/simd.h"
 #include "loops_utils.h"
 #include "loops.h"
+#include "fast_loop_macros.h"
 /*
  * TODO:
  * - use vectorized version of Payne-Hanek style reduction for large elements or
  *   when there's no native FUSED support instead of fallback to libc
  */
-#if NPY_SIMD_F32 && NPY_SIMD_FMA3 // native support
+#if NPY_SIMD_FMA3  // native support
+/**begin repeat
+ *  #check = F64, F32#
+ *  #sfx  = f64, f32#
+ */
+#if NPY_SIMD_@check@
 /*
  * Vectorized Cody-Waite range reduction technique
  * Performs the reduction step x* = x - y*C in three steps:
@@ -23,14 +29,189 @@
  * 3) x* = x - y*c3
  * c1, c2 are exact floating points, c3 = C - c1 - c2 simulates higher precision
  */
-NPY_FINLINE npyv_f32
-simd_range_reduction_f32(npyv_f32 x, npyv_f32 y, npyv_f32 c1, npyv_f32 c2, npyv_f32 c3)
+NPY_FINLINE npyv_@sfx@
+simd_range_reduction_@sfx@(npyv_@sfx@ x, npyv_@sfx@ y, npyv_@sfx@ c1, npyv_@sfx@ c2, npyv_@sfx@ c3)
 {
-    npyv_f32 reduced_x = npyv_muladd_f32(y, c1, x);
-    reduced_x = npyv_muladd_f32(y, c2, reduced_x);
-    reduced_x = npyv_muladd_f32(y, c3, reduced_x);
+    npyv_@sfx@ reduced_x = npyv_muladd_@sfx@(y, c1, x);
+    reduced_x = npyv_muladd_@sfx@(y, c2, reduced_x);
+    reduced_x = npyv_muladd_@sfx@(y, c3, reduced_x);
     return reduced_x;
 }
+#endif
+/**end repeat**/
+
+#if NPY_SIMD_F64
+/**begin repeat
+ *  #op = cos, sin#
+ */
+#if defined(NPY_OS_WIN32) || defined(NPY_OS_CYGWIN)
+NPY_FINLINE npyv_f64
+#else
+NPY_NOINLINE npyv_f64
+#endif
+simd_@op@_scalar_f64(npyv_f64 out, npy_uint64 cmp_bits)
+{
+    // MSVC doesn't compile with direct vector access, so we copy it here
+    // as we have no npyv_get_lane/npyv_set_lane intrinsics
+    npy_double NPY_DECL_ALIGNED(NPY_SIMD_WIDTH) out_copy[npyv_nlanes_f64];
+    npyv_storea_f64(out_copy, out);
+
+    for (unsigned i = 0; i < npyv_nlanes_f64; ++i) {
+        if (cmp_bits & (1 << i)) {
+            out_copy[i] = npy_@op@(out_copy[i]);
+        }
+    }
+
+    return npyv_loada_f64(out_copy);
+}
+/**end repeat**/
+
+/*
+ * Approximate sine algorithm for x \in [-pi/2, pi/2]
+ * worst-case error is 3.5 ulp.
+ * abs error: 0x1.be222a58p-53 in [-pi/2, pi/2].
+ */
+NPY_FINLINE npyv_f64
+simd_approx_sine_poly_f64(npyv_f64 r)
+{
+    const npyv_f64 poly1 = npyv_setall_f64(-0x1.9f4a9c8b21dc9p-41);
+    const npyv_f64 poly2 = npyv_setall_f64(0x1.60e88a10163f2p-33);
+    const npyv_f64 poly3 = npyv_setall_f64(-0x1.ae6361b7254e7p-26);
+    const npyv_f64 poly4 = npyv_setall_f64(0x1.71de382e8d62bp-19);
+    const npyv_f64 poly5 = npyv_setall_f64(-0x1.a01a019aeb4ffp-13);
+    const npyv_f64 poly6 = npyv_setall_f64(0x1.111111110b25ep-7);
+    const npyv_f64 poly7 = npyv_setall_f64(-0x1.55555555554c3p-3);
+
+    npyv_f64 r2 = npyv_mul_f64(r, r);
+    npyv_f64 y = npyv_muladd_f64(poly1, r2, poly2);
+    y = npyv_muladd_f64(y, r2, poly3);
+    y = npyv_muladd_f64(y, r2, poly4);
+    y = npyv_muladd_f64(y, r2, poly5);
+    y = npyv_muladd_f64(y, r2, poly6);
+    y = npyv_muladd_f64(y, r2, poly7);
+    y = npyv_muladd_f64(npyv_mul_f64(y, r2), r, r);
+
+    return y;
+}
+
+/* r = |x| - n*pi  (range reduction into -pi/2 .. pi/2).  */
+NPY_FINLINE npyv_f64
+simd_range_reduction_pi2(npyv_f64 r, npyv_f64 n) {
+    const npyv_f64 pi1 = npyv_setall_f64(-0x1.921fb54442d18p+1);
+    const npyv_f64 pi2 = npyv_setall_f64(-0x1.1a62633145c06p-53);
+    const npyv_f64 pi3 = npyv_setall_f64(-0x1.c1cd129024e09p-106);
+
+    return simd_range_reduction_f64(r, n, pi1, pi2, pi3);
+}
+
+NPY_FINLINE npyv_b64 simd_sin_range_check_f64(npyv_u64 ir) {
+    const npyv_u64 tiny_bound = npyv_setall_u64(0x202); /* top12 (asuint64 (0x1p-509)).  */
+    const npyv_u64 simd_thresh = npyv_setall_u64(0x214); /* top12 (asuint64 (RangeVal)) - SIMD_TINY_BOUND.  */
+
+    return npyv_cmpge_u64(npyv_sub_u64(npyv_shri_u64(ir, 52), tiny_bound), simd_thresh);
+}
+
+NPY_FINLINE npyv_b64 simd_cos_range_check_f64(npyv_u64 ir) {
+    const npyv_f64 range_val = npyv_setall_f64(0x1p23);
+
+    return npyv_cmpge_u64(ir, npyv_reinterpret_u64_f64(range_val));
+}
+
+NPY_FINLINE npyv_f64
+simd_cos_poly_f64(npyv_f64 r, npyv_u64 ir, npyv_u64 sign)
+{
+    const npyv_f64 inv_pi = npyv_setall_f64(0x1.45f306dc9c883p-2);
+    const npyv_f64 half_pi = npyv_setall_f64(0x1.921fb54442d18p+0);
+    const npyv_f64 shift = npyv_setall_f64(0x1.8p52);
+
+    /* n = rint((|x|+pi/2)/pi) - 0.5.  */
+    npyv_f64 n = npyv_muladd_f64(inv_pi, npyv_add_f64(r, half_pi), shift);
+    npyv_u64 odd = npyv_shli_u64(npyv_reinterpret_u64_f64(n), 63);
+    n = npyv_sub_f64(n, shift);
+    n = npyv_sub_f64(n, npyv_setall_f64(0.5));
+
+    /* r = |x| - n*pi  (range reduction into -pi/2 .. pi/2).  */
+    r = simd_range_reduction_pi2(r, n);
+
+    /* sin(r) poly approx.  */
+    npyv_f64 y = simd_approx_sine_poly_f64(r);
+
+    /* sign.  */
+    return npyv_reinterpret_f64_u64(npyv_xor_u64(npyv_reinterpret_u64_f64(y), odd));
+}
+
+NPY_FINLINE npyv_f64
+simd_sin_poly_f64(npyv_f64 r, npyv_u64 ir, npyv_u64 sign)
+{
+    const npyv_f64 inv_pi = npyv_setall_f64(0x1.45f306dc9c883p-2);
+    const npyv_f64 shift = npyv_setall_f64(0x1.8p52);
+
+    /* n = rint(|x|/pi).  */
+    npyv_f64 n = npyv_muladd_f64(inv_pi, r, shift);
+    npyv_u64 odd = npyv_shli_u64(npyv_reinterpret_u64_f64(n), 63);
+    n = npyv_sub_f64(n, shift);
+
+    /* r = |x| - n*pi  (range reduction into -pi/2 .. pi/2).  */
+    r = simd_range_reduction_pi2(r, n);
+
+    /* sin(r) poly approx.  */
+    npyv_f64 y = simd_approx_sine_poly_f64(r);
+
+    /* sign.  */
+    return npyv_reinterpret_f64_u64(npyv_xor_u64(npyv_xor_u64(npyv_reinterpret_u64_f64(y), sign), odd));
+}
+
+/**begin repeat
+ *  #op = cos, sin#
+ */
+NPY_FINLINE void
+simd_@op@_f64(const double *src, npy_intp ssrc, double *dst, npy_intp sdst, npy_intp len)
+{
+    const npyv_u64 abs_mask = npyv_setall_u64(0x7fffffffffffffff);
+    const int vstep = npyv_nlanes_f64;
+
+    npyv_f64 out = npyv_zero_f64();
+    npyv_f64 x_in;
+
+    for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+        if (ssrc == 1) {
+            x_in = npyv_load_tillz_f64(src, len);
+        } else {
+            x_in = npyv_loadn_tillz_f64(src, ssrc, len);
+        }
+
+        npyv_u64 ir = npyv_and_u64(npyv_reinterpret_u64_f64(x_in), abs_mask);
+        npyv_f64 r = npyv_reinterpret_f64_u64(ir);
+        npyv_u64 sign = npyv_and_u64(npyv_reinterpret_u64_f64(x_in), npyv_not_u64(abs_mask));
+
+        npyv_b64 cmp = simd_@op@_range_check_f64(ir);
+        /* If fenv exceptions are to be triggered correctly, set any special lanes
+        to 1 (which is neutral w.r.t. fenv). These lanes will be fixed by
+        scalar loop later.  */
+        r = npyv_select_f64(cmp, npyv_setall_f64(1.0), r);
+
+        // Some in range, at least one calculation is useful
+        if (!npyv_all_b64(cmp)) {
+            out = simd_@op@_poly_f64(r, ir, sign);
+        }
+
+        if (npyv_any_b64(cmp)) {
+            out = npyv_select_f64(cmp, x_in, out);
+            out = simd_@op@_scalar_f64(out, npyv_tobits_b64(cmp));
+        }
+
+        if (sdst == 1) {
+            npyv_store_till_f64(dst, len, out);
+        } else {
+            npyv_storen_till_f64(dst, sdst, len, out);
+        }
+    }
+    npyv_cleanup();
+}
+/**end repeat**/
+#endif // NPY_SIMD_F64
+
+#if NPY_SIMD_F32
 /*
  * Approximate cosine algorithm for x \in [-PI/4, PI/4]
  * Maximum ULP across all 32-bit floats = 0.875
@@ -173,7 +354,7 @@ simd_sincos_f32(const float *src, npy_intp ssrc, float *dst, npy_intp sdst,
                 npyv_storen_till_f32(dst, sdst, len, cos);
             }
         }
-        if (simd_maski != ((1 << vstep) - 1)) {
+        if (simd_maski != (npy_uint64)((1 << vstep) - 1)) {
             float NPY_DECL_ALIGNED(NPY_SIMD_WIDTH) ip_fback[npyv_nlanes_f32];
             npyv_storea_f32(ip_fback, x_in);
 
@@ -198,24 +379,62 @@ simd_sincos_f32(const float *src, npy_intp ssrc, float *dst, npy_intp sdst,
     }
     npyv_cleanup();
 }
-#endif // NPY_SIMD_FMA3
+#endif // NPY_SIMD_FP32
+#endif // NYP_SIMD_FMA3
 
 /**begin repeat
  *  #func = cos, sin#
- *  #enum = SIMD_COMPUTE_COS, SIMD_COMPUTE_SIN#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_@func@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
+{
+    /* Disable SIMD code and revert to libm: see
+     * https://mail.python.org/archives/list/numpy-discussion@python.org/thread/C6EYZZSR4EWGVKHAZXLE7IBILRMNVK7L/
+     * for detailed discussion on this*/
+//#if NPY_SIMD_F64 && NPY_SIMD_FMA3
+#if 0
+    const double *src = (double*)args[0];
+          double *dst = (double*)args[1];
+    const int lsize = sizeof(src[0]);
+    const npy_intp ssrc = steps[0] / lsize;
+    const npy_intp sdst = steps[1] / lsize;
+    npy_intp len = dimensions[0];
+    assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
+
+    if (is_mem_overlap(src, steps[0], dst, steps[1], len) ||
+        !npyv_loadable_stride_f64(ssrc) || !npyv_storable_stride_f64(sdst)
+    ) {
+        for (; len > 0; --len, src += ssrc, dst += sdst) {
+            simd_@func@_f64(src, 1, dst, 1, 1);
+        }
+    } else {
+        simd_@func@_f64(src, ssrc, dst, sdst, len);
+    }
+#else
+    UNARY_LOOP {
+        const npy_double in1 = *(npy_double *)ip1;
+        *(npy_double *)op1 = npy_@func@(in1);
+    }
+#endif
+}
+/**end repeat**/
+
+/**begin repeat
+ *  #func = sin, cos#
+ *  #enum = SIMD_COMPUTE_SIN, SIMD_COMPUTE_COS#
  */
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_@func@)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
-    const float *src = (float*)args[0];
-          float *dst = (float*)args[1];
+#if NPY_SIMD_F32 && NPY_SIMD_FMA3
+    const npy_float *src = (npy_float*)args[0];
+          npy_float *dst = (npy_float*)args[1];
 
     const int lsize = sizeof(src[0]);
     const npy_intp ssrc = steps[0] / lsize;
     const npy_intp sdst = steps[1] / lsize;
     npy_intp len = dimensions[0];
     assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
-#if NPY_SIMD_F32 && NPY_SIMD_FMA3
     if (is_mem_overlap(src, steps[0], dst, steps[1], len) ||
         !npyv_loadable_stride_f32(ssrc) || !npyv_storable_stride_f32(sdst)
     ) {
@@ -226,9 +445,9 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_@func@)
         simd_sincos_f32(src, ssrc, dst, sdst, len, @enum@);
     }
 #else
-    for (; len > 0; --len, src += ssrc, dst += sdst) {
-        const float src0 = *src;
-        *dst = npy_@func@f(src0);
+    UNARY_LOOP {
+        const npy_float in1 = *(npy_float *)ip1;
+        *(npy_float *)op1 = npy_@func@f(in1);
     }
 #endif
 }
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c
index f187e89798c..42db9e03956 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c
@@ -1194,55 +1194,8 @@ simd_atanh_f64(const npyv_lanetype_f64 *src, npy_intp ssrc,
 
 
 
-#line 65
-static void
-simd_sin_f64(const double *src, npy_intp ssrc,
-                      double *dst, npy_intp sdst, npy_intp len)
-{
-    const int vstep = npyv_nlanes_f64;
-    for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
-        npyv_f64 x;
-        if (ssrc == 1) {
-            x = npyv_load_tillz_f64(src, len);
-        } else {
-            x = npyv_loadn_tillz_f64(src, ssrc, len);
-        }
-        npyv_f64 out = __svml_sin8(x);
-        if (sdst == 1) {
-            npyv_store_till_f64(dst, len, out);
-        } else {
-            npyv_storen_till_f64(dst, sdst, len, out);
-        }
-    }
-    npyv_cleanup();
-}
-
-#line 65
-static void
-simd_cos_f64(const double *src, npy_intp ssrc,
-                      double *dst, npy_intp sdst, npy_intp len)
-{
-    const int vstep = npyv_nlanes_f64;
-    for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
-        npyv_f64 x;
-        if (ssrc == 1) {
-            x = npyv_load_tillz_f64(src, len);
-        } else {
-            x = npyv_loadn_tillz_f64(src, ssrc, len);
-        }
-        npyv_f64 out = __svml_cos8(x);
-        if (sdst == 1) {
-            npyv_store_till_f64(dst, len, out);
-        } else {
-            npyv_storen_till_f64(dst, sdst, len, out);
-        }
-    }
-    npyv_cleanup();
-}
-
-
-#line 92
-#line 95
+#line 66
+#line 69
 
 static void
 simd_pow_f32(const npyv_lanetype_f32 *src1, npy_intp ssrc1,
@@ -1274,7 +1227,7 @@ simd_pow_f32(const npyv_lanetype_f32 *src1, npy_intp ssrc1,
     }
 }
 
-#line 95
+#line 69
 
 static void
 simd_atan2_f32(const npyv_lanetype_f32 *src1, npy_intp ssrc1,
@@ -1307,8 +1260,8 @@ simd_atan2_f32(const npyv_lanetype_f32 *src1, npy_intp ssrc1,
 }
 
 
-#line 92
-#line 95
+#line 66
+#line 69
 
 static void
 simd_pow_f64(const npyv_lanetype_f64 *src1, npy_intp ssrc1,
@@ -1340,7 +1293,7 @@ simd_pow_f64(const npyv_lanetype_f64 *src1, npy_intp ssrc1,
     }
 }
 
-#line 95
+#line 69
 
 static void
 simd_atan2_f64(const npyv_lanetype_f64 *src1, npy_intp ssrc1,
@@ -1393,7 +1346,7 @@ NPY_FINLINE void npyvh_store_till_f16(npy_half *ptr, npy_uintp nlane, npyvh_f16
     _mm256_mask_storeu_epi16(ptr, mask, data);
 }
 
-#line 151
+#line 125
 static void
 avx512_sin_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1419,7 +1372,7 @@ avx512_sin_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_cos_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1445,7 +1398,7 @@ avx512_cos_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_tan_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1471,7 +1424,7 @@ avx512_tan_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_exp_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1497,7 +1450,7 @@ avx512_exp_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_exp2_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1523,7 +1476,7 @@ avx512_exp2_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_expm1_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1549,7 +1502,7 @@ avx512_expm1_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_log_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1575,7 +1528,7 @@ avx512_log_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_log2_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1601,7 +1554,7 @@ avx512_log2_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_log10_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1627,7 +1580,7 @@ avx512_log10_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_log1p_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1653,7 +1606,7 @@ avx512_log1p_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_cbrt_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1679,7 +1632,7 @@ avx512_cbrt_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_asin_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1705,7 +1658,7 @@ avx512_asin_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_acos_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1731,7 +1684,7 @@ avx512_acos_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_atan_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1757,7 +1710,7 @@ avx512_atan_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_sinh_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1783,7 +1736,7 @@ avx512_sinh_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_cosh_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1809,7 +1762,7 @@ avx512_cosh_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_tanh_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1835,7 +1788,7 @@ avx512_tanh_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_asinh_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1861,7 +1814,7 @@ avx512_asinh_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_acosh_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1887,7 +1840,7 @@ avx512_acosh_f16(const npy_half *src, npy_half *dst, npy_intp len)
     npyv_cleanup();
 }
 
-#line 151
+#line 125
 static void
 avx512_atanh_f16(const npy_half *src, npy_half *dst, npy_intp len)
 {
@@ -1915,7 +1868,7 @@ avx512_atanh_f16(const npy_half *src, npy_half *dst, npy_intp len)
 
 #endif
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_sin)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -1939,7 +1892,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_sin)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_cos)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -1963,7 +1916,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_cos)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_tan)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -1987,7 +1940,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_tan)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_exp)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2011,7 +1964,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_exp)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_exp2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2035,7 +1988,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_exp2)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_expm1)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2059,7 +2012,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_expm1)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2083,7 +2036,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2107,7 +2060,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log2)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log10)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2131,7 +2084,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log10)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log1p)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2155,7 +2108,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_log1p)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_cbrt)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2179,7 +2132,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_cbrt)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arcsin)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2203,7 +2156,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arcsin)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arccos)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2227,7 +2180,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arccos)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arctan)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2251,7 +2204,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arctan)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_sinh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2275,7 +2228,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_sinh)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_cosh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2299,7 +2252,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_cosh)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_tanh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2323,7 +2276,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_tanh)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arcsinh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2347,7 +2300,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arcsinh)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arccosh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2371,7 +2324,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arccosh)
     }
 }
 
-#line 182
+#line 156
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arctanh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2396,8 +2349,8 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(HALF_arctanh)
 }
 
 
-#line 212
-#line 216
+#line 186
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_exp2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2422,7 +2375,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_exp2)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_log2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2447,7 +2400,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_log2)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_log10)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2472,7 +2425,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_log10)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_expm1)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2497,7 +2450,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_expm1)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_log1p)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2522,7 +2475,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_log1p)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_cbrt)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2547,7 +2500,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_cbrt)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_tan)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2572,7 +2525,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_tan)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arcsin)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2597,7 +2550,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arcsin)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arccos)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2622,7 +2575,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arccos)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arctan)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2647,7 +2600,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arctan)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_sinh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2672,7 +2625,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_sinh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_cosh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2697,7 +2650,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_cosh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arcsinh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2722,7 +2675,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arcsinh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arccosh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2747,7 +2700,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arccosh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arctanh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2773,8 +2726,8 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arctanh)
 }
 
 
-#line 212
-#line 216
+#line 186
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_exp2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2799,7 +2752,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_exp2)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_log2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2824,7 +2777,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_log2)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_log10)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2849,7 +2802,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_log10)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_expm1)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2874,7 +2827,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_expm1)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_log1p)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2899,7 +2852,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_log1p)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_cbrt)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2924,7 +2877,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_cbrt)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_tan)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2949,7 +2902,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_tan)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arcsin)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2974,7 +2927,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arcsin)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arccos)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -2999,7 +2952,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arccos)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arctan)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3024,7 +2977,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arctan)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_sinh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3049,7 +3002,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_sinh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_cosh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3074,7 +3027,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_cosh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arcsinh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3099,7 +3052,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arcsinh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arccosh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3124,7 +3077,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arccosh)
     }
 }
 
-#line 216
+#line 190
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arctanh)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3151,8 +3104,8 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arctanh)
 
 
 
-#line 248
-#line 252
+#line 222
+#line 226
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_power)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3180,7 +3133,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_power)
     }
 }
 
-#line 252
+#line 226
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arctan2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3209,8 +3162,8 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_arctan2)
 }
 
 
-#line 248
-#line 252
+#line 222
+#line 226
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_power)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3238,7 +3191,7 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_power)
     }
 }
 
-#line 252
+#line 226
 NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arctan2)
 (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
 {
@@ -3268,54 +3221,3 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_arctan2)
 
 
 
-#line 284
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_sin)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML)
-    const double *src = (double*)args[0];
-          double *dst = (double*)args[1];
-    const int lsize = sizeof(src[0]);
-    const npy_intp ssrc = steps[0] / lsize;
-    const npy_intp sdst = steps[1] / lsize;
-    const npy_intp len = dimensions[0];
-    assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
-    if (!is_mem_overlap(src, steps[0], dst, steps[1], len) &&
-        npyv_loadable_stride_f64(ssrc) &&
-        npyv_storable_stride_f64(sdst)) {
-        simd_sin_f64(src, ssrc, dst, sdst, len);
-        return;
-    }
-#endif
-    UNARY_LOOP {
-        const npy_double in1 = *(npy_double *)ip1;
-        *(npy_double *)op1 = npy_sin(in1);
-    }
-}
-
-#line 284
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_cos)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML)
-    const double *src = (double*)args[0];
-          double *dst = (double*)args[1];
-    const int lsize = sizeof(src[0]);
-    const npy_intp ssrc = steps[0] / lsize;
-    const npy_intp sdst = steps[1] / lsize;
-    const npy_intp len = dimensions[0];
-    assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
-    if (!is_mem_overlap(src, steps[0], dst, steps[1], len) &&
-        npyv_loadable_stride_f64(ssrc) &&
-        npyv_storable_stride_f64(sdst)) {
-        simd_cos_f64(src, ssrc, dst, sdst, len);
-        return;
-    }
-#endif
-    UNARY_LOOP {
-        const npy_double in1 = *(npy_double *)ip1;
-        *(npy_double *)op1 = npy_cos(in1);
-    }
-}
-
-
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c.src
index d6703bfb94f..89999e87995 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_umath_fp.dispatch.c.src
@@ -60,32 +60,6 @@ simd_@func@_@sfx@(const npyv_lanetype_@sfx@ *src, npy_intp ssrc,
 /**end repeat**/
 
 /**begin repeat
- * #func = sin, cos#
- */
-static void
-simd_@func@_f64(const double *src, npy_intp ssrc,
-                      double *dst, npy_intp sdst, npy_intp len)
-{
-    const int vstep = npyv_nlanes_f64;
-    for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
-        npyv_f64 x;
-        if (ssrc == 1) {
-            x = npyv_load_tillz_f64(src, len);
-        } else {
-            x = npyv_loadn_tillz_f64(src, ssrc, len);
-        }
-        npyv_f64 out = __svml_@func@8(x);
-        if (sdst == 1) {
-            npyv_store_till_f64(dst, len, out);
-        } else {
-            npyv_storen_till_f64(dst, sdst, len, out);
-        }
-    }
-    npyv_cleanup();
-}
-/**end repeat**/
-
-/**begin repeat
  * #sfx = f32, f64#
  * #func_suffix = f16, 8#
  */
@@ -277,31 +251,3 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@func@)
 }
 /**end repeat1**/
 /**end repeat**/
-
-/**begin repeat
- *  #func = sin, cos#
- */
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_@func@)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data))
-{
-#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML)
-    const double *src = (double*)args[0];
-          double *dst = (double*)args[1];
-    const int lsize = sizeof(src[0]);
-    const npy_intp ssrc = steps[0] / lsize;
-    const npy_intp sdst = steps[1] / lsize;
-    const npy_intp len = dimensions[0];
-    assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0));
-    if (!is_mem_overlap(src, steps[0], dst, steps[1], len) &&
-        npyv_loadable_stride_f64(ssrc) &&
-        npyv_storable_stride_f64(sdst)) {
-        simd_@func@_f64(src, ssrc, dst, sdst, len);
-        return;
-    }
-#endif
-    UNARY_LOOP {
-        const npy_double in1 = *(npy_double *)ip1;
-        *(npy_double *)op1 = npy_@func@(in1);
-    }
-}
-/**end repeat**/
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.avx2.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.avx2.c
new file mode 100644
index 00000000000..7e8a7043586
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.avx2.c
@@ -0,0 +1,17 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT AVX2
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#define NPY__CPU_TARGET_POPCNT
+#define NPY__CPU_TARGET_SSE42
+#define NPY__CPU_TARGET_AVX
+#define NPY__CPU_TARGET_F16C
+#define NPY__CPU_TARGET_AVX2
+#include "numpy/core/src/umath/loops_unary.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.avx512_skx.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.avx512_skx.c
new file mode 100644
index 00000000000..d741e4a6910
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.avx512_skx.c
@@ -0,0 +1,21 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT AVX512_SKX
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#define NPY__CPU_TARGET_POPCNT
+#define NPY__CPU_TARGET_SSE42
+#define NPY__CPU_TARGET_AVX
+#define NPY__CPU_TARGET_F16C
+#define NPY__CPU_TARGET_FMA3
+#define NPY__CPU_TARGET_AVX2
+#define NPY__CPU_TARGET_AVX512F
+#define NPY__CPU_TARGET_AVX512CD
+#define NPY__CPU_TARGET_AVX512_SKX
+#include "numpy/core/src/umath/loops_unary.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.c
new file mode 100644
index 00000000000..3ea2747d9e4
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.c
@@ -0,0 +1,8952 @@
+#line 1 "numpy/core/src/umath/loops_unary.dispatch.c.src"
+
+/*
+ *****************************************************************************
+ **       This file was autogenerated from a template  DO NOT EDIT!!!!      **
+ **       Changes should be made to the original source (.src) file         **
+ *****************************************************************************
+ */
+
+#line 1
+/*@targets
+ ** $maxopt baseline
+ ** neon asimd
+ ** sse2 avx2 avx512_skx
+ ** vsx2
+ ** vx vxe
+ **/
+
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "numpy/npy_math.h"
+#include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/*******************************************************************************
+ ** Scalar ops
+ ******************************************************************************/
+#define scalar_negative(X) (-X)
+
+/*******************************************************************************
+ ** extra SIMD intrinsics
+ ******************************************************************************/
+
+#if NPY_SIMD
+
+#line 36
+static NPY_INLINE npyv_s8
+npyv_negative_s8(npyv_s8 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 8 < 64)
+    return npyv_reinterpret_s8_s8(vnegq_s8(npyv_reinterpret_s8_s8(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_s8 m1 = npyv_setall_s8((npyv_lanetype_s8)-1);
+    return npyv_sub_s8(npyv_xor_s8(v, m1), m1);
+#endif
+}
+
+#line 36
+static NPY_INLINE npyv_u8
+npyv_negative_u8(npyv_u8 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 8 < 64)
+    return npyv_reinterpret_u8_s8(vnegq_s8(npyv_reinterpret_s8_u8(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_u8 m1 = npyv_setall_u8((npyv_lanetype_u8)-1);
+    return npyv_sub_u8(npyv_xor_u8(v, m1), m1);
+#endif
+}
+
+#line 36
+static NPY_INLINE npyv_s16
+npyv_negative_s16(npyv_s16 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 16 < 64)
+    return npyv_reinterpret_s16_s16(vnegq_s16(npyv_reinterpret_s16_s16(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_s16 m1 = npyv_setall_s16((npyv_lanetype_s16)-1);
+    return npyv_sub_s16(npyv_xor_s16(v, m1), m1);
+#endif
+}
+
+#line 36
+static NPY_INLINE npyv_u16
+npyv_negative_u16(npyv_u16 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 16 < 64)
+    return npyv_reinterpret_u16_s16(vnegq_s16(npyv_reinterpret_s16_u16(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_u16 m1 = npyv_setall_u16((npyv_lanetype_u16)-1);
+    return npyv_sub_u16(npyv_xor_u16(v, m1), m1);
+#endif
+}
+
+#line 36
+static NPY_INLINE npyv_s32
+npyv_negative_s32(npyv_s32 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 32 < 64)
+    return npyv_reinterpret_s32_s32(vnegq_s32(npyv_reinterpret_s32_s32(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_s32 m1 = npyv_setall_s32((npyv_lanetype_s32)-1);
+    return npyv_sub_s32(npyv_xor_s32(v, m1), m1);
+#endif
+}
+
+#line 36
+static NPY_INLINE npyv_u32
+npyv_negative_u32(npyv_u32 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 32 < 64)
+    return npyv_reinterpret_u32_s32(vnegq_s32(npyv_reinterpret_s32_u32(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_u32 m1 = npyv_setall_u32((npyv_lanetype_u32)-1);
+    return npyv_sub_u32(npyv_xor_u32(v, m1), m1);
+#endif
+}
+
+#line 36
+static NPY_INLINE npyv_s64
+npyv_negative_s64(npyv_s64 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 64 < 64)
+    return npyv_reinterpret_s64_s64(vnegq_s64(npyv_reinterpret_s64_s64(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_s64 m1 = npyv_setall_s64((npyv_lanetype_s64)-1);
+    return npyv_sub_s64(npyv_xor_s64(v, m1), m1);
+#endif
+}
+
+#line 36
+static NPY_INLINE npyv_u64
+npyv_negative_u64(npyv_u64 v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || 64 < 64)
+    return npyv_reinterpret_u64_s64(vnegq_s64(npyv_reinterpret_s64_u64(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_u64 m1 = npyv_setall_u64((npyv_lanetype_u64)-1);
+    return npyv_sub_u64(npyv_xor_u64(v, m1), m1);
+#endif
+}
+
+
+#line 54
+#if NPY_SIMD_F32
+static NPY_INLINE npyv_f32
+npyv_negative_f32(npyv_f32 v)
+{
+#if defined(NPY_HAVE_NEON)
+    return vnegq_f32(v);
+#else
+    // (v ^ signmask)
+    const npyv_f32 signmask = npyv_setall_f32(-0.f);
+    return npyv_xor_f32(v, signmask);
+#endif
+}
+#endif // NPY_SIMD_F32
+
+#line 54
+#if NPY_SIMD_F64
+static NPY_INLINE npyv_f64
+npyv_negative_f64(npyv_f64 v)
+{
+#if defined(NPY_HAVE_NEON)
+    return vnegq_f64(v);
+#else
+    // (v ^ signmask)
+    const npyv_f64 signmask = npyv_setall_f64(-0.);
+    return npyv_xor_f64(v, signmask);
+#endif
+}
+#endif // NPY_SIMD_F64
+
+
+#endif // NPY_SIMD
+
+/********************************************************************************
+ ** Defining the SIMD kernels
+ ********************************************************************************/
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_s8(const npyv_lanetype_s8 *ip,
+                             npyv_lanetype_s8 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_s8 v_0 = npyv_load_s8(ip + 0 * vstep);
+        npyv_s8 r_0 = npyv_negative_s8(v_0);
+        npyv_store_s8(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_s8 v_1 = npyv_load_s8(ip + 1 * vstep);
+        npyv_s8 r_1 = npyv_negative_s8(v_1);
+        npyv_store_s8(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_s8 v_2 = npyv_load_s8(ip + 2 * vstep);
+        npyv_s8 r_2 = npyv_negative_s8(v_2);
+        npyv_store_s8(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_s8 v_3 = npyv_load_s8(ip + 3 * vstep);
+        npyv_s8 r_3 = npyv_negative_s8(v_3);
+        npyv_store_s8(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_s8 v_4 = npyv_load_s8(ip + 4 * vstep);
+        npyv_s8 r_4 = npyv_negative_s8(v_4);
+        npyv_store_s8(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_s8 v_5 = npyv_load_s8(ip + 5 * vstep);
+        npyv_s8 r_5 = npyv_negative_s8(v_5);
+        npyv_store_s8(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_s8 v_6 = npyv_load_s8(ip + 6 * vstep);
+        npyv_s8 r_6 = npyv_negative_s8(v_6);
+        npyv_store_s8(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_s8 v_7 = npyv_load_s8(ip + 7 * vstep);
+        npyv_s8 r_7 = npyv_negative_s8(v_7);
+        npyv_store_s8(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_s8 v_8 = npyv_load_s8(ip + 8 * vstep);
+        npyv_s8 r_8 = npyv_negative_s8(v_8);
+        npyv_store_s8(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_s8 v_9 = npyv_load_s8(ip + 9 * vstep);
+        npyv_s8 r_9 = npyv_negative_s8(v_9);
+        npyv_store_s8(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_s8 v_10 = npyv_load_s8(ip + 10 * vstep);
+        npyv_s8 r_10 = npyv_negative_s8(v_10);
+        npyv_store_s8(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_s8 v_11 = npyv_load_s8(ip + 11 * vstep);
+        npyv_s8 r_11 = npyv_negative_s8(v_11);
+        npyv_store_s8(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_s8 v_12 = npyv_load_s8(ip + 12 * vstep);
+        npyv_s8 r_12 = npyv_negative_s8(v_12);
+        npyv_store_s8(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_s8 v_13 = npyv_load_s8(ip + 13 * vstep);
+        npyv_s8 r_13 = npyv_negative_s8(v_13);
+        npyv_store_s8(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_s8 v_14 = npyv_load_s8(ip + 14 * vstep);
+        npyv_s8 r_14 = npyv_negative_s8(v_14);
+        npyv_store_s8(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_s8 v_15 = npyv_load_s8(ip + 15 * vstep);
+        npyv_s8 r_15 = npyv_negative_s8(v_15);
+        npyv_store_s8(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_s8 v = npyv_load_s8(ip);
+        npyv_s8 r = npyv_negative_s8(v);
+        npyv_store_s8(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 0
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_s8(const npyv_lanetype_s8 *ip,
+                             npyv_lanetype_s8 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_s8 v_0 = npyv_load_s8(ip + 0 * vstep);
+        npyv_s8 r_0 = npyv_negative_s8(v_0);
+        npyv_storen_s8(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_s8 v_1 = npyv_load_s8(ip + 1 * vstep);
+        npyv_s8 r_1 = npyv_negative_s8(v_1);
+        npyv_storen_s8(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_s8 v_2 = npyv_load_s8(ip + 2 * vstep);
+        npyv_s8 r_2 = npyv_negative_s8(v_2);
+        npyv_storen_s8(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_s8 v_3 = npyv_load_s8(ip + 3 * vstep);
+        npyv_s8 r_3 = npyv_negative_s8(v_3);
+        npyv_storen_s8(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_s8 v_4 = npyv_load_s8(ip + 4 * vstep);
+        npyv_s8 r_4 = npyv_negative_s8(v_4);
+        npyv_storen_s8(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_s8 v_5 = npyv_load_s8(ip + 5 * vstep);
+        npyv_s8 r_5 = npyv_negative_s8(v_5);
+        npyv_storen_s8(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_s8 v_6 = npyv_load_s8(ip + 6 * vstep);
+        npyv_s8 r_6 = npyv_negative_s8(v_6);
+        npyv_storen_s8(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_s8 v_7 = npyv_load_s8(ip + 7 * vstep);
+        npyv_s8 r_7 = npyv_negative_s8(v_7);
+        npyv_storen_s8(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_s8 v_8 = npyv_load_s8(ip + 8 * vstep);
+        npyv_s8 r_8 = npyv_negative_s8(v_8);
+        npyv_storen_s8(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_s8 v_9 = npyv_load_s8(ip + 9 * vstep);
+        npyv_s8 r_9 = npyv_negative_s8(v_9);
+        npyv_storen_s8(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_s8 v_10 = npyv_load_s8(ip + 10 * vstep);
+        npyv_s8 r_10 = npyv_negative_s8(v_10);
+        npyv_storen_s8(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_s8 v_11 = npyv_load_s8(ip + 11 * vstep);
+        npyv_s8 r_11 = npyv_negative_s8(v_11);
+        npyv_storen_s8(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_s8 v_12 = npyv_load_s8(ip + 12 * vstep);
+        npyv_s8 r_12 = npyv_negative_s8(v_12);
+        npyv_storen_s8(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_s8 v_13 = npyv_load_s8(ip + 13 * vstep);
+        npyv_s8 r_13 = npyv_negative_s8(v_13);
+        npyv_storen_s8(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_s8 v_14 = npyv_load_s8(ip + 14 * vstep);
+        npyv_s8 r_14 = npyv_negative_s8(v_14);
+        npyv_storen_s8(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_s8 v_15 = npyv_load_s8(ip + 15 * vstep);
+        npyv_s8 r_15 = npyv_negative_s8(v_15);
+        npyv_storen_s8(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_s8 v = npyv_load_s8(ip);
+        npyv_s8 r = npyv_negative_s8(v);
+        npyv_storen_s8(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_s8(const npyv_lanetype_s8 *ip, npy_intp istride,
+                             npyv_lanetype_s8 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_s8 v_0 = npyv_loadn_s8(ip + 0 * vstep * istride, istride);
+        npyv_s8 r_0 = npyv_negative_s8(v_0);
+        npyv_store_s8(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_s8 v_1 = npyv_loadn_s8(ip + 1 * vstep * istride, istride);
+        npyv_s8 r_1 = npyv_negative_s8(v_1);
+        npyv_store_s8(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_s8 v_2 = npyv_loadn_s8(ip + 2 * vstep * istride, istride);
+        npyv_s8 r_2 = npyv_negative_s8(v_2);
+        npyv_store_s8(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_s8 v_3 = npyv_loadn_s8(ip + 3 * vstep * istride, istride);
+        npyv_s8 r_3 = npyv_negative_s8(v_3);
+        npyv_store_s8(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_s8 v_4 = npyv_loadn_s8(ip + 4 * vstep * istride, istride);
+        npyv_s8 r_4 = npyv_negative_s8(v_4);
+        npyv_store_s8(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_s8 v_5 = npyv_loadn_s8(ip + 5 * vstep * istride, istride);
+        npyv_s8 r_5 = npyv_negative_s8(v_5);
+        npyv_store_s8(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_s8 v_6 = npyv_loadn_s8(ip + 6 * vstep * istride, istride);
+        npyv_s8 r_6 = npyv_negative_s8(v_6);
+        npyv_store_s8(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_s8 v_7 = npyv_loadn_s8(ip + 7 * vstep * istride, istride);
+        npyv_s8 r_7 = npyv_negative_s8(v_7);
+        npyv_store_s8(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_s8 v_8 = npyv_loadn_s8(ip + 8 * vstep * istride, istride);
+        npyv_s8 r_8 = npyv_negative_s8(v_8);
+        npyv_store_s8(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_s8 v_9 = npyv_loadn_s8(ip + 9 * vstep * istride, istride);
+        npyv_s8 r_9 = npyv_negative_s8(v_9);
+        npyv_store_s8(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_s8 v_10 = npyv_loadn_s8(ip + 10 * vstep * istride, istride);
+        npyv_s8 r_10 = npyv_negative_s8(v_10);
+        npyv_store_s8(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_s8 v_11 = npyv_loadn_s8(ip + 11 * vstep * istride, istride);
+        npyv_s8 r_11 = npyv_negative_s8(v_11);
+        npyv_store_s8(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_s8 v_12 = npyv_loadn_s8(ip + 12 * vstep * istride, istride);
+        npyv_s8 r_12 = npyv_negative_s8(v_12);
+        npyv_store_s8(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_s8 v_13 = npyv_loadn_s8(ip + 13 * vstep * istride, istride);
+        npyv_s8 r_13 = npyv_negative_s8(v_13);
+        npyv_store_s8(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_s8 v_14 = npyv_loadn_s8(ip + 14 * vstep * istride, istride);
+        npyv_s8 r_14 = npyv_negative_s8(v_14);
+        npyv_store_s8(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_s8 v_15 = npyv_loadn_s8(ip + 15 * vstep * istride, istride);
+        npyv_s8 r_15 = npyv_negative_s8(v_15);
+        npyv_store_s8(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_s8 v = npyv_loadn_s8(ip, istride);
+        npyv_s8 r = npyv_negative_s8(v);
+        npyv_store_s8(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_s8(const npyv_lanetype_s8 *ip, npy_intp istride,
+                             npyv_lanetype_s8 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_s8 v_0 = npyv_loadn_s8(ip + 0 * vstep * istride, istride);
+        npyv_s8 r_0 = npyv_negative_s8(v_0);
+        npyv_storen_s8(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_s8 v_1 = npyv_loadn_s8(ip + 1 * vstep * istride, istride);
+        npyv_s8 r_1 = npyv_negative_s8(v_1);
+        npyv_storen_s8(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_s8 v_2 = npyv_loadn_s8(ip + 2 * vstep * istride, istride);
+        npyv_s8 r_2 = npyv_negative_s8(v_2);
+        npyv_storen_s8(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_s8 v_3 = npyv_loadn_s8(ip + 3 * vstep * istride, istride);
+        npyv_s8 r_3 = npyv_negative_s8(v_3);
+        npyv_storen_s8(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_s8 v_4 = npyv_loadn_s8(ip + 4 * vstep * istride, istride);
+        npyv_s8 r_4 = npyv_negative_s8(v_4);
+        npyv_storen_s8(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_s8 v_5 = npyv_loadn_s8(ip + 5 * vstep * istride, istride);
+        npyv_s8 r_5 = npyv_negative_s8(v_5);
+        npyv_storen_s8(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_s8 v_6 = npyv_loadn_s8(ip + 6 * vstep * istride, istride);
+        npyv_s8 r_6 = npyv_negative_s8(v_6);
+        npyv_storen_s8(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_s8 v_7 = npyv_loadn_s8(ip + 7 * vstep * istride, istride);
+        npyv_s8 r_7 = npyv_negative_s8(v_7);
+        npyv_storen_s8(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_s8 v_8 = npyv_loadn_s8(ip + 8 * vstep * istride, istride);
+        npyv_s8 r_8 = npyv_negative_s8(v_8);
+        npyv_storen_s8(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_s8 v_9 = npyv_loadn_s8(ip + 9 * vstep * istride, istride);
+        npyv_s8 r_9 = npyv_negative_s8(v_9);
+        npyv_storen_s8(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_s8 v_10 = npyv_loadn_s8(ip + 10 * vstep * istride, istride);
+        npyv_s8 r_10 = npyv_negative_s8(v_10);
+        npyv_storen_s8(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_s8 v_11 = npyv_loadn_s8(ip + 11 * vstep * istride, istride);
+        npyv_s8 r_11 = npyv_negative_s8(v_11);
+        npyv_storen_s8(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_s8 v_12 = npyv_loadn_s8(ip + 12 * vstep * istride, istride);
+        npyv_s8 r_12 = npyv_negative_s8(v_12);
+        npyv_storen_s8(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_s8 v_13 = npyv_loadn_s8(ip + 13 * vstep * istride, istride);
+        npyv_s8 r_13 = npyv_negative_s8(v_13);
+        npyv_storen_s8(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_s8 v_14 = npyv_loadn_s8(ip + 14 * vstep * istride, istride);
+        npyv_s8 r_14 = npyv_negative_s8(v_14);
+        npyv_storen_s8(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_s8 v_15 = npyv_loadn_s8(ip + 15 * vstep * istride, istride);
+        npyv_s8 r_15 = npyv_negative_s8(v_15);
+        npyv_storen_s8(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_s8 v = npyv_loadn_s8(ip, istride);
+        npyv_s8 r = npyv_negative_s8(v);
+        npyv_storen_s8(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 0
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_u8(const npyv_lanetype_u8 *ip,
+                             npyv_lanetype_u8 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_u8 v_0 = npyv_load_u8(ip + 0 * vstep);
+        npyv_u8 r_0 = npyv_negative_u8(v_0);
+        npyv_store_u8(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_u8 v_1 = npyv_load_u8(ip + 1 * vstep);
+        npyv_u8 r_1 = npyv_negative_u8(v_1);
+        npyv_store_u8(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_u8 v_2 = npyv_load_u8(ip + 2 * vstep);
+        npyv_u8 r_2 = npyv_negative_u8(v_2);
+        npyv_store_u8(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_u8 v_3 = npyv_load_u8(ip + 3 * vstep);
+        npyv_u8 r_3 = npyv_negative_u8(v_3);
+        npyv_store_u8(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_u8 v_4 = npyv_load_u8(ip + 4 * vstep);
+        npyv_u8 r_4 = npyv_negative_u8(v_4);
+        npyv_store_u8(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_u8 v_5 = npyv_load_u8(ip + 5 * vstep);
+        npyv_u8 r_5 = npyv_negative_u8(v_5);
+        npyv_store_u8(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_u8 v_6 = npyv_load_u8(ip + 6 * vstep);
+        npyv_u8 r_6 = npyv_negative_u8(v_6);
+        npyv_store_u8(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_u8 v_7 = npyv_load_u8(ip + 7 * vstep);
+        npyv_u8 r_7 = npyv_negative_u8(v_7);
+        npyv_store_u8(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_u8 v_8 = npyv_load_u8(ip + 8 * vstep);
+        npyv_u8 r_8 = npyv_negative_u8(v_8);
+        npyv_store_u8(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_u8 v_9 = npyv_load_u8(ip + 9 * vstep);
+        npyv_u8 r_9 = npyv_negative_u8(v_9);
+        npyv_store_u8(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_u8 v_10 = npyv_load_u8(ip + 10 * vstep);
+        npyv_u8 r_10 = npyv_negative_u8(v_10);
+        npyv_store_u8(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_u8 v_11 = npyv_load_u8(ip + 11 * vstep);
+        npyv_u8 r_11 = npyv_negative_u8(v_11);
+        npyv_store_u8(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_u8 v_12 = npyv_load_u8(ip + 12 * vstep);
+        npyv_u8 r_12 = npyv_negative_u8(v_12);
+        npyv_store_u8(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_u8 v_13 = npyv_load_u8(ip + 13 * vstep);
+        npyv_u8 r_13 = npyv_negative_u8(v_13);
+        npyv_store_u8(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_u8 v_14 = npyv_load_u8(ip + 14 * vstep);
+        npyv_u8 r_14 = npyv_negative_u8(v_14);
+        npyv_store_u8(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_u8 v_15 = npyv_load_u8(ip + 15 * vstep);
+        npyv_u8 r_15 = npyv_negative_u8(v_15);
+        npyv_store_u8(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_u8 v = npyv_load_u8(ip);
+        npyv_u8 r = npyv_negative_u8(v);
+        npyv_store_u8(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 0
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_u8(const npyv_lanetype_u8 *ip,
+                             npyv_lanetype_u8 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_u8 v_0 = npyv_load_u8(ip + 0 * vstep);
+        npyv_u8 r_0 = npyv_negative_u8(v_0);
+        npyv_storen_u8(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_u8 v_1 = npyv_load_u8(ip + 1 * vstep);
+        npyv_u8 r_1 = npyv_negative_u8(v_1);
+        npyv_storen_u8(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_u8 v_2 = npyv_load_u8(ip + 2 * vstep);
+        npyv_u8 r_2 = npyv_negative_u8(v_2);
+        npyv_storen_u8(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_u8 v_3 = npyv_load_u8(ip + 3 * vstep);
+        npyv_u8 r_3 = npyv_negative_u8(v_3);
+        npyv_storen_u8(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_u8 v_4 = npyv_load_u8(ip + 4 * vstep);
+        npyv_u8 r_4 = npyv_negative_u8(v_4);
+        npyv_storen_u8(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_u8 v_5 = npyv_load_u8(ip + 5 * vstep);
+        npyv_u8 r_5 = npyv_negative_u8(v_5);
+        npyv_storen_u8(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_u8 v_6 = npyv_load_u8(ip + 6 * vstep);
+        npyv_u8 r_6 = npyv_negative_u8(v_6);
+        npyv_storen_u8(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_u8 v_7 = npyv_load_u8(ip + 7 * vstep);
+        npyv_u8 r_7 = npyv_negative_u8(v_7);
+        npyv_storen_u8(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_u8 v_8 = npyv_load_u8(ip + 8 * vstep);
+        npyv_u8 r_8 = npyv_negative_u8(v_8);
+        npyv_storen_u8(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_u8 v_9 = npyv_load_u8(ip + 9 * vstep);
+        npyv_u8 r_9 = npyv_negative_u8(v_9);
+        npyv_storen_u8(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_u8 v_10 = npyv_load_u8(ip + 10 * vstep);
+        npyv_u8 r_10 = npyv_negative_u8(v_10);
+        npyv_storen_u8(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_u8 v_11 = npyv_load_u8(ip + 11 * vstep);
+        npyv_u8 r_11 = npyv_negative_u8(v_11);
+        npyv_storen_u8(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_u8 v_12 = npyv_load_u8(ip + 12 * vstep);
+        npyv_u8 r_12 = npyv_negative_u8(v_12);
+        npyv_storen_u8(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_u8 v_13 = npyv_load_u8(ip + 13 * vstep);
+        npyv_u8 r_13 = npyv_negative_u8(v_13);
+        npyv_storen_u8(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_u8 v_14 = npyv_load_u8(ip + 14 * vstep);
+        npyv_u8 r_14 = npyv_negative_u8(v_14);
+        npyv_storen_u8(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_u8 v_15 = npyv_load_u8(ip + 15 * vstep);
+        npyv_u8 r_15 = npyv_negative_u8(v_15);
+        npyv_storen_u8(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_u8 v = npyv_load_u8(ip);
+        npyv_u8 r = npyv_negative_u8(v);
+        npyv_storen_u8(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_u8(const npyv_lanetype_u8 *ip, npy_intp istride,
+                             npyv_lanetype_u8 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_u8 v_0 = npyv_loadn_u8(ip + 0 * vstep * istride, istride);
+        npyv_u8 r_0 = npyv_negative_u8(v_0);
+        npyv_store_u8(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_u8 v_1 = npyv_loadn_u8(ip + 1 * vstep * istride, istride);
+        npyv_u8 r_1 = npyv_negative_u8(v_1);
+        npyv_store_u8(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_u8 v_2 = npyv_loadn_u8(ip + 2 * vstep * istride, istride);
+        npyv_u8 r_2 = npyv_negative_u8(v_2);
+        npyv_store_u8(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_u8 v_3 = npyv_loadn_u8(ip + 3 * vstep * istride, istride);
+        npyv_u8 r_3 = npyv_negative_u8(v_3);
+        npyv_store_u8(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_u8 v_4 = npyv_loadn_u8(ip + 4 * vstep * istride, istride);
+        npyv_u8 r_4 = npyv_negative_u8(v_4);
+        npyv_store_u8(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_u8 v_5 = npyv_loadn_u8(ip + 5 * vstep * istride, istride);
+        npyv_u8 r_5 = npyv_negative_u8(v_5);
+        npyv_store_u8(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_u8 v_6 = npyv_loadn_u8(ip + 6 * vstep * istride, istride);
+        npyv_u8 r_6 = npyv_negative_u8(v_6);
+        npyv_store_u8(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_u8 v_7 = npyv_loadn_u8(ip + 7 * vstep * istride, istride);
+        npyv_u8 r_7 = npyv_negative_u8(v_7);
+        npyv_store_u8(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_u8 v_8 = npyv_loadn_u8(ip + 8 * vstep * istride, istride);
+        npyv_u8 r_8 = npyv_negative_u8(v_8);
+        npyv_store_u8(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_u8 v_9 = npyv_loadn_u8(ip + 9 * vstep * istride, istride);
+        npyv_u8 r_9 = npyv_negative_u8(v_9);
+        npyv_store_u8(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_u8 v_10 = npyv_loadn_u8(ip + 10 * vstep * istride, istride);
+        npyv_u8 r_10 = npyv_negative_u8(v_10);
+        npyv_store_u8(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_u8 v_11 = npyv_loadn_u8(ip + 11 * vstep * istride, istride);
+        npyv_u8 r_11 = npyv_negative_u8(v_11);
+        npyv_store_u8(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_u8 v_12 = npyv_loadn_u8(ip + 12 * vstep * istride, istride);
+        npyv_u8 r_12 = npyv_negative_u8(v_12);
+        npyv_store_u8(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_u8 v_13 = npyv_loadn_u8(ip + 13 * vstep * istride, istride);
+        npyv_u8 r_13 = npyv_negative_u8(v_13);
+        npyv_store_u8(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_u8 v_14 = npyv_loadn_u8(ip + 14 * vstep * istride, istride);
+        npyv_u8 r_14 = npyv_negative_u8(v_14);
+        npyv_store_u8(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_u8 v_15 = npyv_loadn_u8(ip + 15 * vstep * istride, istride);
+        npyv_u8 r_15 = npyv_negative_u8(v_15);
+        npyv_store_u8(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_u8 v = npyv_loadn_u8(ip, istride);
+        npyv_u8 r = npyv_negative_u8(v);
+        npyv_store_u8(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_u8(const npyv_lanetype_u8 *ip, npy_intp istride,
+                             npyv_lanetype_u8 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u8;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_u8 v_0 = npyv_loadn_u8(ip + 0 * vstep * istride, istride);
+        npyv_u8 r_0 = npyv_negative_u8(v_0);
+        npyv_storen_u8(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_u8 v_1 = npyv_loadn_u8(ip + 1 * vstep * istride, istride);
+        npyv_u8 r_1 = npyv_negative_u8(v_1);
+        npyv_storen_u8(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_u8 v_2 = npyv_loadn_u8(ip + 2 * vstep * istride, istride);
+        npyv_u8 r_2 = npyv_negative_u8(v_2);
+        npyv_storen_u8(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_u8 v_3 = npyv_loadn_u8(ip + 3 * vstep * istride, istride);
+        npyv_u8 r_3 = npyv_negative_u8(v_3);
+        npyv_storen_u8(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_u8 v_4 = npyv_loadn_u8(ip + 4 * vstep * istride, istride);
+        npyv_u8 r_4 = npyv_negative_u8(v_4);
+        npyv_storen_u8(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_u8 v_5 = npyv_loadn_u8(ip + 5 * vstep * istride, istride);
+        npyv_u8 r_5 = npyv_negative_u8(v_5);
+        npyv_storen_u8(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_u8 v_6 = npyv_loadn_u8(ip + 6 * vstep * istride, istride);
+        npyv_u8 r_6 = npyv_negative_u8(v_6);
+        npyv_storen_u8(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_u8 v_7 = npyv_loadn_u8(ip + 7 * vstep * istride, istride);
+        npyv_u8 r_7 = npyv_negative_u8(v_7);
+        npyv_storen_u8(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_u8 v_8 = npyv_loadn_u8(ip + 8 * vstep * istride, istride);
+        npyv_u8 r_8 = npyv_negative_u8(v_8);
+        npyv_storen_u8(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_u8 v_9 = npyv_loadn_u8(ip + 9 * vstep * istride, istride);
+        npyv_u8 r_9 = npyv_negative_u8(v_9);
+        npyv_storen_u8(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_u8 v_10 = npyv_loadn_u8(ip + 10 * vstep * istride, istride);
+        npyv_u8 r_10 = npyv_negative_u8(v_10);
+        npyv_storen_u8(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_u8 v_11 = npyv_loadn_u8(ip + 11 * vstep * istride, istride);
+        npyv_u8 r_11 = npyv_negative_u8(v_11);
+        npyv_storen_u8(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_u8 v_12 = npyv_loadn_u8(ip + 12 * vstep * istride, istride);
+        npyv_u8 r_12 = npyv_negative_u8(v_12);
+        npyv_storen_u8(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_u8 v_13 = npyv_loadn_u8(ip + 13 * vstep * istride, istride);
+        npyv_u8 r_13 = npyv_negative_u8(v_13);
+        npyv_storen_u8(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_u8 v_14 = npyv_loadn_u8(ip + 14 * vstep * istride, istride);
+        npyv_u8 r_14 = npyv_negative_u8(v_14);
+        npyv_storen_u8(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_u8 v_15 = npyv_loadn_u8(ip + 15 * vstep * istride, istride);
+        npyv_u8 r_15 = npyv_negative_u8(v_15);
+        npyv_storen_u8(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_u8 v = npyv_loadn_u8(ip, istride);
+        npyv_u8 r = npyv_negative_u8(v);
+        npyv_storen_u8(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 0
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_s16(const npyv_lanetype_s16 *ip,
+                             npyv_lanetype_s16 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_s16 v_0 = npyv_load_s16(ip + 0 * vstep);
+        npyv_s16 r_0 = npyv_negative_s16(v_0);
+        npyv_store_s16(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_s16 v_1 = npyv_load_s16(ip + 1 * vstep);
+        npyv_s16 r_1 = npyv_negative_s16(v_1);
+        npyv_store_s16(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_s16 v_2 = npyv_load_s16(ip + 2 * vstep);
+        npyv_s16 r_2 = npyv_negative_s16(v_2);
+        npyv_store_s16(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_s16 v_3 = npyv_load_s16(ip + 3 * vstep);
+        npyv_s16 r_3 = npyv_negative_s16(v_3);
+        npyv_store_s16(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_s16 v_4 = npyv_load_s16(ip + 4 * vstep);
+        npyv_s16 r_4 = npyv_negative_s16(v_4);
+        npyv_store_s16(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_s16 v_5 = npyv_load_s16(ip + 5 * vstep);
+        npyv_s16 r_5 = npyv_negative_s16(v_5);
+        npyv_store_s16(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_s16 v_6 = npyv_load_s16(ip + 6 * vstep);
+        npyv_s16 r_6 = npyv_negative_s16(v_6);
+        npyv_store_s16(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_s16 v_7 = npyv_load_s16(ip + 7 * vstep);
+        npyv_s16 r_7 = npyv_negative_s16(v_7);
+        npyv_store_s16(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_s16 v_8 = npyv_load_s16(ip + 8 * vstep);
+        npyv_s16 r_8 = npyv_negative_s16(v_8);
+        npyv_store_s16(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_s16 v_9 = npyv_load_s16(ip + 9 * vstep);
+        npyv_s16 r_9 = npyv_negative_s16(v_9);
+        npyv_store_s16(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_s16 v_10 = npyv_load_s16(ip + 10 * vstep);
+        npyv_s16 r_10 = npyv_negative_s16(v_10);
+        npyv_store_s16(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_s16 v_11 = npyv_load_s16(ip + 11 * vstep);
+        npyv_s16 r_11 = npyv_negative_s16(v_11);
+        npyv_store_s16(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_s16 v_12 = npyv_load_s16(ip + 12 * vstep);
+        npyv_s16 r_12 = npyv_negative_s16(v_12);
+        npyv_store_s16(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_s16 v_13 = npyv_load_s16(ip + 13 * vstep);
+        npyv_s16 r_13 = npyv_negative_s16(v_13);
+        npyv_store_s16(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_s16 v_14 = npyv_load_s16(ip + 14 * vstep);
+        npyv_s16 r_14 = npyv_negative_s16(v_14);
+        npyv_store_s16(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_s16 v_15 = npyv_load_s16(ip + 15 * vstep);
+        npyv_s16 r_15 = npyv_negative_s16(v_15);
+        npyv_store_s16(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_s16 v = npyv_load_s16(ip);
+        npyv_s16 r = npyv_negative_s16(v);
+        npyv_store_s16(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 0
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_s16(const npyv_lanetype_s16 *ip,
+                             npyv_lanetype_s16 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_s16 v_0 = npyv_load_s16(ip + 0 * vstep);
+        npyv_s16 r_0 = npyv_negative_s16(v_0);
+        npyv_storen_s16(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_s16 v_1 = npyv_load_s16(ip + 1 * vstep);
+        npyv_s16 r_1 = npyv_negative_s16(v_1);
+        npyv_storen_s16(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_s16 v_2 = npyv_load_s16(ip + 2 * vstep);
+        npyv_s16 r_2 = npyv_negative_s16(v_2);
+        npyv_storen_s16(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_s16 v_3 = npyv_load_s16(ip + 3 * vstep);
+        npyv_s16 r_3 = npyv_negative_s16(v_3);
+        npyv_storen_s16(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_s16 v_4 = npyv_load_s16(ip + 4 * vstep);
+        npyv_s16 r_4 = npyv_negative_s16(v_4);
+        npyv_storen_s16(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_s16 v_5 = npyv_load_s16(ip + 5 * vstep);
+        npyv_s16 r_5 = npyv_negative_s16(v_5);
+        npyv_storen_s16(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_s16 v_6 = npyv_load_s16(ip + 6 * vstep);
+        npyv_s16 r_6 = npyv_negative_s16(v_6);
+        npyv_storen_s16(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_s16 v_7 = npyv_load_s16(ip + 7 * vstep);
+        npyv_s16 r_7 = npyv_negative_s16(v_7);
+        npyv_storen_s16(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_s16 v_8 = npyv_load_s16(ip + 8 * vstep);
+        npyv_s16 r_8 = npyv_negative_s16(v_8);
+        npyv_storen_s16(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_s16 v_9 = npyv_load_s16(ip + 9 * vstep);
+        npyv_s16 r_9 = npyv_negative_s16(v_9);
+        npyv_storen_s16(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_s16 v_10 = npyv_load_s16(ip + 10 * vstep);
+        npyv_s16 r_10 = npyv_negative_s16(v_10);
+        npyv_storen_s16(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_s16 v_11 = npyv_load_s16(ip + 11 * vstep);
+        npyv_s16 r_11 = npyv_negative_s16(v_11);
+        npyv_storen_s16(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_s16 v_12 = npyv_load_s16(ip + 12 * vstep);
+        npyv_s16 r_12 = npyv_negative_s16(v_12);
+        npyv_storen_s16(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_s16 v_13 = npyv_load_s16(ip + 13 * vstep);
+        npyv_s16 r_13 = npyv_negative_s16(v_13);
+        npyv_storen_s16(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_s16 v_14 = npyv_load_s16(ip + 14 * vstep);
+        npyv_s16 r_14 = npyv_negative_s16(v_14);
+        npyv_storen_s16(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_s16 v_15 = npyv_load_s16(ip + 15 * vstep);
+        npyv_s16 r_15 = npyv_negative_s16(v_15);
+        npyv_storen_s16(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_s16 v = npyv_load_s16(ip);
+        npyv_s16 r = npyv_negative_s16(v);
+        npyv_storen_s16(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_s16(const npyv_lanetype_s16 *ip, npy_intp istride,
+                             npyv_lanetype_s16 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_s16 v_0 = npyv_loadn_s16(ip + 0 * vstep * istride, istride);
+        npyv_s16 r_0 = npyv_negative_s16(v_0);
+        npyv_store_s16(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_s16 v_1 = npyv_loadn_s16(ip + 1 * vstep * istride, istride);
+        npyv_s16 r_1 = npyv_negative_s16(v_1);
+        npyv_store_s16(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_s16 v_2 = npyv_loadn_s16(ip + 2 * vstep * istride, istride);
+        npyv_s16 r_2 = npyv_negative_s16(v_2);
+        npyv_store_s16(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_s16 v_3 = npyv_loadn_s16(ip + 3 * vstep * istride, istride);
+        npyv_s16 r_3 = npyv_negative_s16(v_3);
+        npyv_store_s16(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_s16 v_4 = npyv_loadn_s16(ip + 4 * vstep * istride, istride);
+        npyv_s16 r_4 = npyv_negative_s16(v_4);
+        npyv_store_s16(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_s16 v_5 = npyv_loadn_s16(ip + 5 * vstep * istride, istride);
+        npyv_s16 r_5 = npyv_negative_s16(v_5);
+        npyv_store_s16(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_s16 v_6 = npyv_loadn_s16(ip + 6 * vstep * istride, istride);
+        npyv_s16 r_6 = npyv_negative_s16(v_6);
+        npyv_store_s16(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_s16 v_7 = npyv_loadn_s16(ip + 7 * vstep * istride, istride);
+        npyv_s16 r_7 = npyv_negative_s16(v_7);
+        npyv_store_s16(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_s16 v_8 = npyv_loadn_s16(ip + 8 * vstep * istride, istride);
+        npyv_s16 r_8 = npyv_negative_s16(v_8);
+        npyv_store_s16(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_s16 v_9 = npyv_loadn_s16(ip + 9 * vstep * istride, istride);
+        npyv_s16 r_9 = npyv_negative_s16(v_9);
+        npyv_store_s16(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_s16 v_10 = npyv_loadn_s16(ip + 10 * vstep * istride, istride);
+        npyv_s16 r_10 = npyv_negative_s16(v_10);
+        npyv_store_s16(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_s16 v_11 = npyv_loadn_s16(ip + 11 * vstep * istride, istride);
+        npyv_s16 r_11 = npyv_negative_s16(v_11);
+        npyv_store_s16(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_s16 v_12 = npyv_loadn_s16(ip + 12 * vstep * istride, istride);
+        npyv_s16 r_12 = npyv_negative_s16(v_12);
+        npyv_store_s16(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_s16 v_13 = npyv_loadn_s16(ip + 13 * vstep * istride, istride);
+        npyv_s16 r_13 = npyv_negative_s16(v_13);
+        npyv_store_s16(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_s16 v_14 = npyv_loadn_s16(ip + 14 * vstep * istride, istride);
+        npyv_s16 r_14 = npyv_negative_s16(v_14);
+        npyv_store_s16(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_s16 v_15 = npyv_loadn_s16(ip + 15 * vstep * istride, istride);
+        npyv_s16 r_15 = npyv_negative_s16(v_15);
+        npyv_store_s16(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_s16 v = npyv_loadn_s16(ip, istride);
+        npyv_s16 r = npyv_negative_s16(v);
+        npyv_store_s16(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_s16(const npyv_lanetype_s16 *ip, npy_intp istride,
+                             npyv_lanetype_s16 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_s16 v_0 = npyv_loadn_s16(ip + 0 * vstep * istride, istride);
+        npyv_s16 r_0 = npyv_negative_s16(v_0);
+        npyv_storen_s16(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_s16 v_1 = npyv_loadn_s16(ip + 1 * vstep * istride, istride);
+        npyv_s16 r_1 = npyv_negative_s16(v_1);
+        npyv_storen_s16(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_s16 v_2 = npyv_loadn_s16(ip + 2 * vstep * istride, istride);
+        npyv_s16 r_2 = npyv_negative_s16(v_2);
+        npyv_storen_s16(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_s16 v_3 = npyv_loadn_s16(ip + 3 * vstep * istride, istride);
+        npyv_s16 r_3 = npyv_negative_s16(v_3);
+        npyv_storen_s16(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_s16 v_4 = npyv_loadn_s16(ip + 4 * vstep * istride, istride);
+        npyv_s16 r_4 = npyv_negative_s16(v_4);
+        npyv_storen_s16(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_s16 v_5 = npyv_loadn_s16(ip + 5 * vstep * istride, istride);
+        npyv_s16 r_5 = npyv_negative_s16(v_5);
+        npyv_storen_s16(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_s16 v_6 = npyv_loadn_s16(ip + 6 * vstep * istride, istride);
+        npyv_s16 r_6 = npyv_negative_s16(v_6);
+        npyv_storen_s16(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_s16 v_7 = npyv_loadn_s16(ip + 7 * vstep * istride, istride);
+        npyv_s16 r_7 = npyv_negative_s16(v_7);
+        npyv_storen_s16(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_s16 v_8 = npyv_loadn_s16(ip + 8 * vstep * istride, istride);
+        npyv_s16 r_8 = npyv_negative_s16(v_8);
+        npyv_storen_s16(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_s16 v_9 = npyv_loadn_s16(ip + 9 * vstep * istride, istride);
+        npyv_s16 r_9 = npyv_negative_s16(v_9);
+        npyv_storen_s16(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_s16 v_10 = npyv_loadn_s16(ip + 10 * vstep * istride, istride);
+        npyv_s16 r_10 = npyv_negative_s16(v_10);
+        npyv_storen_s16(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_s16 v_11 = npyv_loadn_s16(ip + 11 * vstep * istride, istride);
+        npyv_s16 r_11 = npyv_negative_s16(v_11);
+        npyv_storen_s16(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_s16 v_12 = npyv_loadn_s16(ip + 12 * vstep * istride, istride);
+        npyv_s16 r_12 = npyv_negative_s16(v_12);
+        npyv_storen_s16(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_s16 v_13 = npyv_loadn_s16(ip + 13 * vstep * istride, istride);
+        npyv_s16 r_13 = npyv_negative_s16(v_13);
+        npyv_storen_s16(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_s16 v_14 = npyv_loadn_s16(ip + 14 * vstep * istride, istride);
+        npyv_s16 r_14 = npyv_negative_s16(v_14);
+        npyv_storen_s16(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_s16 v_15 = npyv_loadn_s16(ip + 15 * vstep * istride, istride);
+        npyv_s16 r_15 = npyv_negative_s16(v_15);
+        npyv_storen_s16(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_s16 v = npyv_loadn_s16(ip, istride);
+        npyv_s16 r = npyv_negative_s16(v);
+        npyv_storen_s16(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 0
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_u16(const npyv_lanetype_u16 *ip,
+                             npyv_lanetype_u16 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_u16 v_0 = npyv_load_u16(ip + 0 * vstep);
+        npyv_u16 r_0 = npyv_negative_u16(v_0);
+        npyv_store_u16(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_u16 v_1 = npyv_load_u16(ip + 1 * vstep);
+        npyv_u16 r_1 = npyv_negative_u16(v_1);
+        npyv_store_u16(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_u16 v_2 = npyv_load_u16(ip + 2 * vstep);
+        npyv_u16 r_2 = npyv_negative_u16(v_2);
+        npyv_store_u16(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_u16 v_3 = npyv_load_u16(ip + 3 * vstep);
+        npyv_u16 r_3 = npyv_negative_u16(v_3);
+        npyv_store_u16(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_u16 v_4 = npyv_load_u16(ip + 4 * vstep);
+        npyv_u16 r_4 = npyv_negative_u16(v_4);
+        npyv_store_u16(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_u16 v_5 = npyv_load_u16(ip + 5 * vstep);
+        npyv_u16 r_5 = npyv_negative_u16(v_5);
+        npyv_store_u16(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_u16 v_6 = npyv_load_u16(ip + 6 * vstep);
+        npyv_u16 r_6 = npyv_negative_u16(v_6);
+        npyv_store_u16(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_u16 v_7 = npyv_load_u16(ip + 7 * vstep);
+        npyv_u16 r_7 = npyv_negative_u16(v_7);
+        npyv_store_u16(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_u16 v_8 = npyv_load_u16(ip + 8 * vstep);
+        npyv_u16 r_8 = npyv_negative_u16(v_8);
+        npyv_store_u16(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_u16 v_9 = npyv_load_u16(ip + 9 * vstep);
+        npyv_u16 r_9 = npyv_negative_u16(v_9);
+        npyv_store_u16(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_u16 v_10 = npyv_load_u16(ip + 10 * vstep);
+        npyv_u16 r_10 = npyv_negative_u16(v_10);
+        npyv_store_u16(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_u16 v_11 = npyv_load_u16(ip + 11 * vstep);
+        npyv_u16 r_11 = npyv_negative_u16(v_11);
+        npyv_store_u16(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_u16 v_12 = npyv_load_u16(ip + 12 * vstep);
+        npyv_u16 r_12 = npyv_negative_u16(v_12);
+        npyv_store_u16(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_u16 v_13 = npyv_load_u16(ip + 13 * vstep);
+        npyv_u16 r_13 = npyv_negative_u16(v_13);
+        npyv_store_u16(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_u16 v_14 = npyv_load_u16(ip + 14 * vstep);
+        npyv_u16 r_14 = npyv_negative_u16(v_14);
+        npyv_store_u16(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_u16 v_15 = npyv_load_u16(ip + 15 * vstep);
+        npyv_u16 r_15 = npyv_negative_u16(v_15);
+        npyv_store_u16(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_u16 v = npyv_load_u16(ip);
+        npyv_u16 r = npyv_negative_u16(v);
+        npyv_store_u16(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 0
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_u16(const npyv_lanetype_u16 *ip,
+                             npyv_lanetype_u16 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_u16 v_0 = npyv_load_u16(ip + 0 * vstep);
+        npyv_u16 r_0 = npyv_negative_u16(v_0);
+        npyv_storen_u16(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_u16 v_1 = npyv_load_u16(ip + 1 * vstep);
+        npyv_u16 r_1 = npyv_negative_u16(v_1);
+        npyv_storen_u16(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_u16 v_2 = npyv_load_u16(ip + 2 * vstep);
+        npyv_u16 r_2 = npyv_negative_u16(v_2);
+        npyv_storen_u16(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_u16 v_3 = npyv_load_u16(ip + 3 * vstep);
+        npyv_u16 r_3 = npyv_negative_u16(v_3);
+        npyv_storen_u16(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_u16 v_4 = npyv_load_u16(ip + 4 * vstep);
+        npyv_u16 r_4 = npyv_negative_u16(v_4);
+        npyv_storen_u16(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_u16 v_5 = npyv_load_u16(ip + 5 * vstep);
+        npyv_u16 r_5 = npyv_negative_u16(v_5);
+        npyv_storen_u16(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_u16 v_6 = npyv_load_u16(ip + 6 * vstep);
+        npyv_u16 r_6 = npyv_negative_u16(v_6);
+        npyv_storen_u16(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_u16 v_7 = npyv_load_u16(ip + 7 * vstep);
+        npyv_u16 r_7 = npyv_negative_u16(v_7);
+        npyv_storen_u16(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_u16 v_8 = npyv_load_u16(ip + 8 * vstep);
+        npyv_u16 r_8 = npyv_negative_u16(v_8);
+        npyv_storen_u16(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_u16 v_9 = npyv_load_u16(ip + 9 * vstep);
+        npyv_u16 r_9 = npyv_negative_u16(v_9);
+        npyv_storen_u16(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_u16 v_10 = npyv_load_u16(ip + 10 * vstep);
+        npyv_u16 r_10 = npyv_negative_u16(v_10);
+        npyv_storen_u16(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_u16 v_11 = npyv_load_u16(ip + 11 * vstep);
+        npyv_u16 r_11 = npyv_negative_u16(v_11);
+        npyv_storen_u16(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_u16 v_12 = npyv_load_u16(ip + 12 * vstep);
+        npyv_u16 r_12 = npyv_negative_u16(v_12);
+        npyv_storen_u16(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_u16 v_13 = npyv_load_u16(ip + 13 * vstep);
+        npyv_u16 r_13 = npyv_negative_u16(v_13);
+        npyv_storen_u16(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_u16 v_14 = npyv_load_u16(ip + 14 * vstep);
+        npyv_u16 r_14 = npyv_negative_u16(v_14);
+        npyv_storen_u16(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_u16 v_15 = npyv_load_u16(ip + 15 * vstep);
+        npyv_u16 r_15 = npyv_negative_u16(v_15);
+        npyv_storen_u16(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_u16 v = npyv_load_u16(ip);
+        npyv_u16 r = npyv_negative_u16(v);
+        npyv_storen_u16(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_u16(const npyv_lanetype_u16 *ip, npy_intp istride,
+                             npyv_lanetype_u16 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_u16 v_0 = npyv_loadn_u16(ip + 0 * vstep * istride, istride);
+        npyv_u16 r_0 = npyv_negative_u16(v_0);
+        npyv_store_u16(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_u16 v_1 = npyv_loadn_u16(ip + 1 * vstep * istride, istride);
+        npyv_u16 r_1 = npyv_negative_u16(v_1);
+        npyv_store_u16(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_u16 v_2 = npyv_loadn_u16(ip + 2 * vstep * istride, istride);
+        npyv_u16 r_2 = npyv_negative_u16(v_2);
+        npyv_store_u16(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_u16 v_3 = npyv_loadn_u16(ip + 3 * vstep * istride, istride);
+        npyv_u16 r_3 = npyv_negative_u16(v_3);
+        npyv_store_u16(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_u16 v_4 = npyv_loadn_u16(ip + 4 * vstep * istride, istride);
+        npyv_u16 r_4 = npyv_negative_u16(v_4);
+        npyv_store_u16(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_u16 v_5 = npyv_loadn_u16(ip + 5 * vstep * istride, istride);
+        npyv_u16 r_5 = npyv_negative_u16(v_5);
+        npyv_store_u16(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_u16 v_6 = npyv_loadn_u16(ip + 6 * vstep * istride, istride);
+        npyv_u16 r_6 = npyv_negative_u16(v_6);
+        npyv_store_u16(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_u16 v_7 = npyv_loadn_u16(ip + 7 * vstep * istride, istride);
+        npyv_u16 r_7 = npyv_negative_u16(v_7);
+        npyv_store_u16(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_u16 v_8 = npyv_loadn_u16(ip + 8 * vstep * istride, istride);
+        npyv_u16 r_8 = npyv_negative_u16(v_8);
+        npyv_store_u16(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_u16 v_9 = npyv_loadn_u16(ip + 9 * vstep * istride, istride);
+        npyv_u16 r_9 = npyv_negative_u16(v_9);
+        npyv_store_u16(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_u16 v_10 = npyv_loadn_u16(ip + 10 * vstep * istride, istride);
+        npyv_u16 r_10 = npyv_negative_u16(v_10);
+        npyv_store_u16(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_u16 v_11 = npyv_loadn_u16(ip + 11 * vstep * istride, istride);
+        npyv_u16 r_11 = npyv_negative_u16(v_11);
+        npyv_store_u16(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_u16 v_12 = npyv_loadn_u16(ip + 12 * vstep * istride, istride);
+        npyv_u16 r_12 = npyv_negative_u16(v_12);
+        npyv_store_u16(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_u16 v_13 = npyv_loadn_u16(ip + 13 * vstep * istride, istride);
+        npyv_u16 r_13 = npyv_negative_u16(v_13);
+        npyv_store_u16(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_u16 v_14 = npyv_loadn_u16(ip + 14 * vstep * istride, istride);
+        npyv_u16 r_14 = npyv_negative_u16(v_14);
+        npyv_store_u16(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_u16 v_15 = npyv_loadn_u16(ip + 15 * vstep * istride, istride);
+        npyv_u16 r_15 = npyv_negative_u16(v_15);
+        npyv_store_u16(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_u16 v = npyv_loadn_u16(ip, istride);
+        npyv_u16 r = npyv_negative_u16(v);
+        npyv_store_u16(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_u16(const npyv_lanetype_u16 *ip, npy_intp istride,
+                             npyv_lanetype_u16 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u16;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_u16 v_0 = npyv_loadn_u16(ip + 0 * vstep * istride, istride);
+        npyv_u16 r_0 = npyv_negative_u16(v_0);
+        npyv_storen_u16(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_u16 v_1 = npyv_loadn_u16(ip + 1 * vstep * istride, istride);
+        npyv_u16 r_1 = npyv_negative_u16(v_1);
+        npyv_storen_u16(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_u16 v_2 = npyv_loadn_u16(ip + 2 * vstep * istride, istride);
+        npyv_u16 r_2 = npyv_negative_u16(v_2);
+        npyv_storen_u16(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_u16 v_3 = npyv_loadn_u16(ip + 3 * vstep * istride, istride);
+        npyv_u16 r_3 = npyv_negative_u16(v_3);
+        npyv_storen_u16(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_u16 v_4 = npyv_loadn_u16(ip + 4 * vstep * istride, istride);
+        npyv_u16 r_4 = npyv_negative_u16(v_4);
+        npyv_storen_u16(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_u16 v_5 = npyv_loadn_u16(ip + 5 * vstep * istride, istride);
+        npyv_u16 r_5 = npyv_negative_u16(v_5);
+        npyv_storen_u16(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_u16 v_6 = npyv_loadn_u16(ip + 6 * vstep * istride, istride);
+        npyv_u16 r_6 = npyv_negative_u16(v_6);
+        npyv_storen_u16(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_u16 v_7 = npyv_loadn_u16(ip + 7 * vstep * istride, istride);
+        npyv_u16 r_7 = npyv_negative_u16(v_7);
+        npyv_storen_u16(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_u16 v_8 = npyv_loadn_u16(ip + 8 * vstep * istride, istride);
+        npyv_u16 r_8 = npyv_negative_u16(v_8);
+        npyv_storen_u16(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_u16 v_9 = npyv_loadn_u16(ip + 9 * vstep * istride, istride);
+        npyv_u16 r_9 = npyv_negative_u16(v_9);
+        npyv_storen_u16(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_u16 v_10 = npyv_loadn_u16(ip + 10 * vstep * istride, istride);
+        npyv_u16 r_10 = npyv_negative_u16(v_10);
+        npyv_storen_u16(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_u16 v_11 = npyv_loadn_u16(ip + 11 * vstep * istride, istride);
+        npyv_u16 r_11 = npyv_negative_u16(v_11);
+        npyv_storen_u16(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_u16 v_12 = npyv_loadn_u16(ip + 12 * vstep * istride, istride);
+        npyv_u16 r_12 = npyv_negative_u16(v_12);
+        npyv_storen_u16(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_u16 v_13 = npyv_loadn_u16(ip + 13 * vstep * istride, istride);
+        npyv_u16 r_13 = npyv_negative_u16(v_13);
+        npyv_storen_u16(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_u16 v_14 = npyv_loadn_u16(ip + 14 * vstep * istride, istride);
+        npyv_u16 r_14 = npyv_negative_u16(v_14);
+        npyv_storen_u16(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_u16 v_15 = npyv_loadn_u16(ip + 15 * vstep * istride, istride);
+        npyv_u16 r_15 = npyv_negative_u16(v_15);
+        npyv_storen_u16(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_u16 v = npyv_loadn_u16(ip, istride);
+        npyv_u16 r = npyv_negative_u16(v);
+        npyv_storen_u16(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 0
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_s32(const npyv_lanetype_s32 *ip,
+                             npyv_lanetype_s32 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_s32 v_0 = npyv_load_s32(ip + 0 * vstep);
+        npyv_s32 r_0 = npyv_negative_s32(v_0);
+        npyv_store_s32(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_s32 v_1 = npyv_load_s32(ip + 1 * vstep);
+        npyv_s32 r_1 = npyv_negative_s32(v_1);
+        npyv_store_s32(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_s32 v_2 = npyv_load_s32(ip + 2 * vstep);
+        npyv_s32 r_2 = npyv_negative_s32(v_2);
+        npyv_store_s32(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_s32 v_3 = npyv_load_s32(ip + 3 * vstep);
+        npyv_s32 r_3 = npyv_negative_s32(v_3);
+        npyv_store_s32(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_s32 v_4 = npyv_load_s32(ip + 4 * vstep);
+        npyv_s32 r_4 = npyv_negative_s32(v_4);
+        npyv_store_s32(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_s32 v_5 = npyv_load_s32(ip + 5 * vstep);
+        npyv_s32 r_5 = npyv_negative_s32(v_5);
+        npyv_store_s32(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_s32 v_6 = npyv_load_s32(ip + 6 * vstep);
+        npyv_s32 r_6 = npyv_negative_s32(v_6);
+        npyv_store_s32(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_s32 v_7 = npyv_load_s32(ip + 7 * vstep);
+        npyv_s32 r_7 = npyv_negative_s32(v_7);
+        npyv_store_s32(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_s32 v_8 = npyv_load_s32(ip + 8 * vstep);
+        npyv_s32 r_8 = npyv_negative_s32(v_8);
+        npyv_store_s32(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_s32 v_9 = npyv_load_s32(ip + 9 * vstep);
+        npyv_s32 r_9 = npyv_negative_s32(v_9);
+        npyv_store_s32(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_s32 v_10 = npyv_load_s32(ip + 10 * vstep);
+        npyv_s32 r_10 = npyv_negative_s32(v_10);
+        npyv_store_s32(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_s32 v_11 = npyv_load_s32(ip + 11 * vstep);
+        npyv_s32 r_11 = npyv_negative_s32(v_11);
+        npyv_store_s32(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_s32 v_12 = npyv_load_s32(ip + 12 * vstep);
+        npyv_s32 r_12 = npyv_negative_s32(v_12);
+        npyv_store_s32(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_s32 v_13 = npyv_load_s32(ip + 13 * vstep);
+        npyv_s32 r_13 = npyv_negative_s32(v_13);
+        npyv_store_s32(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_s32 v_14 = npyv_load_s32(ip + 14 * vstep);
+        npyv_s32 r_14 = npyv_negative_s32(v_14);
+        npyv_store_s32(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_s32 v_15 = npyv_load_s32(ip + 15 * vstep);
+        npyv_s32 r_15 = npyv_negative_s32(v_15);
+        npyv_store_s32(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_s32 v = npyv_load_s32(ip);
+        npyv_s32 r = npyv_negative_s32(v);
+        npyv_store_s32(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 1
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_s32(const npyv_lanetype_s32 *ip,
+                             npyv_lanetype_s32 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_s32 v_0 = npyv_load_s32(ip + 0 * vstep);
+        npyv_s32 r_0 = npyv_negative_s32(v_0);
+        npyv_storen_s32(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_s32 v_1 = npyv_load_s32(ip + 1 * vstep);
+        npyv_s32 r_1 = npyv_negative_s32(v_1);
+        npyv_storen_s32(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_s32 v_2 = npyv_load_s32(ip + 2 * vstep);
+        npyv_s32 r_2 = npyv_negative_s32(v_2);
+        npyv_storen_s32(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_s32 v_3 = npyv_load_s32(ip + 3 * vstep);
+        npyv_s32 r_3 = npyv_negative_s32(v_3);
+        npyv_storen_s32(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_s32 v_4 = npyv_load_s32(ip + 4 * vstep);
+        npyv_s32 r_4 = npyv_negative_s32(v_4);
+        npyv_storen_s32(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_s32 v_5 = npyv_load_s32(ip + 5 * vstep);
+        npyv_s32 r_5 = npyv_negative_s32(v_5);
+        npyv_storen_s32(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_s32 v_6 = npyv_load_s32(ip + 6 * vstep);
+        npyv_s32 r_6 = npyv_negative_s32(v_6);
+        npyv_storen_s32(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_s32 v_7 = npyv_load_s32(ip + 7 * vstep);
+        npyv_s32 r_7 = npyv_negative_s32(v_7);
+        npyv_storen_s32(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_s32 v_8 = npyv_load_s32(ip + 8 * vstep);
+        npyv_s32 r_8 = npyv_negative_s32(v_8);
+        npyv_storen_s32(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_s32 v_9 = npyv_load_s32(ip + 9 * vstep);
+        npyv_s32 r_9 = npyv_negative_s32(v_9);
+        npyv_storen_s32(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_s32 v_10 = npyv_load_s32(ip + 10 * vstep);
+        npyv_s32 r_10 = npyv_negative_s32(v_10);
+        npyv_storen_s32(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_s32 v_11 = npyv_load_s32(ip + 11 * vstep);
+        npyv_s32 r_11 = npyv_negative_s32(v_11);
+        npyv_storen_s32(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_s32 v_12 = npyv_load_s32(ip + 12 * vstep);
+        npyv_s32 r_12 = npyv_negative_s32(v_12);
+        npyv_storen_s32(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_s32 v_13 = npyv_load_s32(ip + 13 * vstep);
+        npyv_s32 r_13 = npyv_negative_s32(v_13);
+        npyv_storen_s32(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_s32 v_14 = npyv_load_s32(ip + 14 * vstep);
+        npyv_s32 r_14 = npyv_negative_s32(v_14);
+        npyv_storen_s32(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_s32 v_15 = npyv_load_s32(ip + 15 * vstep);
+        npyv_s32 r_15 = npyv_negative_s32(v_15);
+        npyv_storen_s32(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_s32 v = npyv_load_s32(ip);
+        npyv_s32 r = npyv_negative_s32(v);
+        npyv_storen_s32(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_s32(const npyv_lanetype_s32 *ip, npy_intp istride,
+                             npyv_lanetype_s32 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_s32 v_0 = npyv_loadn_s32(ip + 0 * vstep * istride, istride);
+        npyv_s32 r_0 = npyv_negative_s32(v_0);
+        npyv_store_s32(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_s32 v_1 = npyv_loadn_s32(ip + 1 * vstep * istride, istride);
+        npyv_s32 r_1 = npyv_negative_s32(v_1);
+        npyv_store_s32(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_s32 v_2 = npyv_loadn_s32(ip + 2 * vstep * istride, istride);
+        npyv_s32 r_2 = npyv_negative_s32(v_2);
+        npyv_store_s32(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_s32 v_3 = npyv_loadn_s32(ip + 3 * vstep * istride, istride);
+        npyv_s32 r_3 = npyv_negative_s32(v_3);
+        npyv_store_s32(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_s32 v_4 = npyv_loadn_s32(ip + 4 * vstep * istride, istride);
+        npyv_s32 r_4 = npyv_negative_s32(v_4);
+        npyv_store_s32(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_s32 v_5 = npyv_loadn_s32(ip + 5 * vstep * istride, istride);
+        npyv_s32 r_5 = npyv_negative_s32(v_5);
+        npyv_store_s32(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_s32 v_6 = npyv_loadn_s32(ip + 6 * vstep * istride, istride);
+        npyv_s32 r_6 = npyv_negative_s32(v_6);
+        npyv_store_s32(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_s32 v_7 = npyv_loadn_s32(ip + 7 * vstep * istride, istride);
+        npyv_s32 r_7 = npyv_negative_s32(v_7);
+        npyv_store_s32(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_s32 v_8 = npyv_loadn_s32(ip + 8 * vstep * istride, istride);
+        npyv_s32 r_8 = npyv_negative_s32(v_8);
+        npyv_store_s32(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_s32 v_9 = npyv_loadn_s32(ip + 9 * vstep * istride, istride);
+        npyv_s32 r_9 = npyv_negative_s32(v_9);
+        npyv_store_s32(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_s32 v_10 = npyv_loadn_s32(ip + 10 * vstep * istride, istride);
+        npyv_s32 r_10 = npyv_negative_s32(v_10);
+        npyv_store_s32(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_s32 v_11 = npyv_loadn_s32(ip + 11 * vstep * istride, istride);
+        npyv_s32 r_11 = npyv_negative_s32(v_11);
+        npyv_store_s32(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_s32 v_12 = npyv_loadn_s32(ip + 12 * vstep * istride, istride);
+        npyv_s32 r_12 = npyv_negative_s32(v_12);
+        npyv_store_s32(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_s32 v_13 = npyv_loadn_s32(ip + 13 * vstep * istride, istride);
+        npyv_s32 r_13 = npyv_negative_s32(v_13);
+        npyv_store_s32(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_s32 v_14 = npyv_loadn_s32(ip + 14 * vstep * istride, istride);
+        npyv_s32 r_14 = npyv_negative_s32(v_14);
+        npyv_store_s32(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_s32 v_15 = npyv_loadn_s32(ip + 15 * vstep * istride, istride);
+        npyv_s32 r_15 = npyv_negative_s32(v_15);
+        npyv_store_s32(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_s32 v = npyv_loadn_s32(ip, istride);
+        npyv_s32 r = npyv_negative_s32(v);
+        npyv_store_s32(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_s32(const npyv_lanetype_s32 *ip, npy_intp istride,
+                             npyv_lanetype_s32 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_s32 v_0 = npyv_loadn_s32(ip + 0 * vstep * istride, istride);
+        npyv_s32 r_0 = npyv_negative_s32(v_0);
+        npyv_storen_s32(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_s32 v_1 = npyv_loadn_s32(ip + 1 * vstep * istride, istride);
+        npyv_s32 r_1 = npyv_negative_s32(v_1);
+        npyv_storen_s32(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_s32 v_2 = npyv_loadn_s32(ip + 2 * vstep * istride, istride);
+        npyv_s32 r_2 = npyv_negative_s32(v_2);
+        npyv_storen_s32(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_s32 v_3 = npyv_loadn_s32(ip + 3 * vstep * istride, istride);
+        npyv_s32 r_3 = npyv_negative_s32(v_3);
+        npyv_storen_s32(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_s32 v_4 = npyv_loadn_s32(ip + 4 * vstep * istride, istride);
+        npyv_s32 r_4 = npyv_negative_s32(v_4);
+        npyv_storen_s32(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_s32 v_5 = npyv_loadn_s32(ip + 5 * vstep * istride, istride);
+        npyv_s32 r_5 = npyv_negative_s32(v_5);
+        npyv_storen_s32(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_s32 v_6 = npyv_loadn_s32(ip + 6 * vstep * istride, istride);
+        npyv_s32 r_6 = npyv_negative_s32(v_6);
+        npyv_storen_s32(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_s32 v_7 = npyv_loadn_s32(ip + 7 * vstep * istride, istride);
+        npyv_s32 r_7 = npyv_negative_s32(v_7);
+        npyv_storen_s32(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_s32 v_8 = npyv_loadn_s32(ip + 8 * vstep * istride, istride);
+        npyv_s32 r_8 = npyv_negative_s32(v_8);
+        npyv_storen_s32(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_s32 v_9 = npyv_loadn_s32(ip + 9 * vstep * istride, istride);
+        npyv_s32 r_9 = npyv_negative_s32(v_9);
+        npyv_storen_s32(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_s32 v_10 = npyv_loadn_s32(ip + 10 * vstep * istride, istride);
+        npyv_s32 r_10 = npyv_negative_s32(v_10);
+        npyv_storen_s32(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_s32 v_11 = npyv_loadn_s32(ip + 11 * vstep * istride, istride);
+        npyv_s32 r_11 = npyv_negative_s32(v_11);
+        npyv_storen_s32(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_s32 v_12 = npyv_loadn_s32(ip + 12 * vstep * istride, istride);
+        npyv_s32 r_12 = npyv_negative_s32(v_12);
+        npyv_storen_s32(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_s32 v_13 = npyv_loadn_s32(ip + 13 * vstep * istride, istride);
+        npyv_s32 r_13 = npyv_negative_s32(v_13);
+        npyv_storen_s32(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_s32 v_14 = npyv_loadn_s32(ip + 14 * vstep * istride, istride);
+        npyv_s32 r_14 = npyv_negative_s32(v_14);
+        npyv_storen_s32(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_s32 v_15 = npyv_loadn_s32(ip + 15 * vstep * istride, istride);
+        npyv_s32 r_15 = npyv_negative_s32(v_15);
+        npyv_storen_s32(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_s32 v = npyv_loadn_s32(ip, istride);
+        npyv_s32 r = npyv_negative_s32(v);
+        npyv_storen_s32(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 1
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_u32(const npyv_lanetype_u32 *ip,
+                             npyv_lanetype_u32 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_u32 v_0 = npyv_load_u32(ip + 0 * vstep);
+        npyv_u32 r_0 = npyv_negative_u32(v_0);
+        npyv_store_u32(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_u32 v_1 = npyv_load_u32(ip + 1 * vstep);
+        npyv_u32 r_1 = npyv_negative_u32(v_1);
+        npyv_store_u32(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_u32 v_2 = npyv_load_u32(ip + 2 * vstep);
+        npyv_u32 r_2 = npyv_negative_u32(v_2);
+        npyv_store_u32(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_u32 v_3 = npyv_load_u32(ip + 3 * vstep);
+        npyv_u32 r_3 = npyv_negative_u32(v_3);
+        npyv_store_u32(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_u32 v_4 = npyv_load_u32(ip + 4 * vstep);
+        npyv_u32 r_4 = npyv_negative_u32(v_4);
+        npyv_store_u32(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_u32 v_5 = npyv_load_u32(ip + 5 * vstep);
+        npyv_u32 r_5 = npyv_negative_u32(v_5);
+        npyv_store_u32(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_u32 v_6 = npyv_load_u32(ip + 6 * vstep);
+        npyv_u32 r_6 = npyv_negative_u32(v_6);
+        npyv_store_u32(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_u32 v_7 = npyv_load_u32(ip + 7 * vstep);
+        npyv_u32 r_7 = npyv_negative_u32(v_7);
+        npyv_store_u32(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_u32 v_8 = npyv_load_u32(ip + 8 * vstep);
+        npyv_u32 r_8 = npyv_negative_u32(v_8);
+        npyv_store_u32(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_u32 v_9 = npyv_load_u32(ip + 9 * vstep);
+        npyv_u32 r_9 = npyv_negative_u32(v_9);
+        npyv_store_u32(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_u32 v_10 = npyv_load_u32(ip + 10 * vstep);
+        npyv_u32 r_10 = npyv_negative_u32(v_10);
+        npyv_store_u32(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_u32 v_11 = npyv_load_u32(ip + 11 * vstep);
+        npyv_u32 r_11 = npyv_negative_u32(v_11);
+        npyv_store_u32(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_u32 v_12 = npyv_load_u32(ip + 12 * vstep);
+        npyv_u32 r_12 = npyv_negative_u32(v_12);
+        npyv_store_u32(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_u32 v_13 = npyv_load_u32(ip + 13 * vstep);
+        npyv_u32 r_13 = npyv_negative_u32(v_13);
+        npyv_store_u32(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_u32 v_14 = npyv_load_u32(ip + 14 * vstep);
+        npyv_u32 r_14 = npyv_negative_u32(v_14);
+        npyv_store_u32(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_u32 v_15 = npyv_load_u32(ip + 15 * vstep);
+        npyv_u32 r_15 = npyv_negative_u32(v_15);
+        npyv_store_u32(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_u32 v = npyv_load_u32(ip);
+        npyv_u32 r = npyv_negative_u32(v);
+        npyv_store_u32(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 1
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_u32(const npyv_lanetype_u32 *ip,
+                             npyv_lanetype_u32 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_u32 v_0 = npyv_load_u32(ip + 0 * vstep);
+        npyv_u32 r_0 = npyv_negative_u32(v_0);
+        npyv_storen_u32(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_u32 v_1 = npyv_load_u32(ip + 1 * vstep);
+        npyv_u32 r_1 = npyv_negative_u32(v_1);
+        npyv_storen_u32(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_u32 v_2 = npyv_load_u32(ip + 2 * vstep);
+        npyv_u32 r_2 = npyv_negative_u32(v_2);
+        npyv_storen_u32(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_u32 v_3 = npyv_load_u32(ip + 3 * vstep);
+        npyv_u32 r_3 = npyv_negative_u32(v_3);
+        npyv_storen_u32(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_u32 v_4 = npyv_load_u32(ip + 4 * vstep);
+        npyv_u32 r_4 = npyv_negative_u32(v_4);
+        npyv_storen_u32(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_u32 v_5 = npyv_load_u32(ip + 5 * vstep);
+        npyv_u32 r_5 = npyv_negative_u32(v_5);
+        npyv_storen_u32(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_u32 v_6 = npyv_load_u32(ip + 6 * vstep);
+        npyv_u32 r_6 = npyv_negative_u32(v_6);
+        npyv_storen_u32(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_u32 v_7 = npyv_load_u32(ip + 7 * vstep);
+        npyv_u32 r_7 = npyv_negative_u32(v_7);
+        npyv_storen_u32(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_u32 v_8 = npyv_load_u32(ip + 8 * vstep);
+        npyv_u32 r_8 = npyv_negative_u32(v_8);
+        npyv_storen_u32(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_u32 v_9 = npyv_load_u32(ip + 9 * vstep);
+        npyv_u32 r_9 = npyv_negative_u32(v_9);
+        npyv_storen_u32(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_u32 v_10 = npyv_load_u32(ip + 10 * vstep);
+        npyv_u32 r_10 = npyv_negative_u32(v_10);
+        npyv_storen_u32(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_u32 v_11 = npyv_load_u32(ip + 11 * vstep);
+        npyv_u32 r_11 = npyv_negative_u32(v_11);
+        npyv_storen_u32(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_u32 v_12 = npyv_load_u32(ip + 12 * vstep);
+        npyv_u32 r_12 = npyv_negative_u32(v_12);
+        npyv_storen_u32(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_u32 v_13 = npyv_load_u32(ip + 13 * vstep);
+        npyv_u32 r_13 = npyv_negative_u32(v_13);
+        npyv_storen_u32(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_u32 v_14 = npyv_load_u32(ip + 14 * vstep);
+        npyv_u32 r_14 = npyv_negative_u32(v_14);
+        npyv_storen_u32(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_u32 v_15 = npyv_load_u32(ip + 15 * vstep);
+        npyv_u32 r_15 = npyv_negative_u32(v_15);
+        npyv_storen_u32(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_u32 v = npyv_load_u32(ip);
+        npyv_u32 r = npyv_negative_u32(v);
+        npyv_storen_u32(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_u32(const npyv_lanetype_u32 *ip, npy_intp istride,
+                             npyv_lanetype_u32 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_u32 v_0 = npyv_loadn_u32(ip + 0 * vstep * istride, istride);
+        npyv_u32 r_0 = npyv_negative_u32(v_0);
+        npyv_store_u32(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_u32 v_1 = npyv_loadn_u32(ip + 1 * vstep * istride, istride);
+        npyv_u32 r_1 = npyv_negative_u32(v_1);
+        npyv_store_u32(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_u32 v_2 = npyv_loadn_u32(ip + 2 * vstep * istride, istride);
+        npyv_u32 r_2 = npyv_negative_u32(v_2);
+        npyv_store_u32(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_u32 v_3 = npyv_loadn_u32(ip + 3 * vstep * istride, istride);
+        npyv_u32 r_3 = npyv_negative_u32(v_3);
+        npyv_store_u32(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_u32 v_4 = npyv_loadn_u32(ip + 4 * vstep * istride, istride);
+        npyv_u32 r_4 = npyv_negative_u32(v_4);
+        npyv_store_u32(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_u32 v_5 = npyv_loadn_u32(ip + 5 * vstep * istride, istride);
+        npyv_u32 r_5 = npyv_negative_u32(v_5);
+        npyv_store_u32(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_u32 v_6 = npyv_loadn_u32(ip + 6 * vstep * istride, istride);
+        npyv_u32 r_6 = npyv_negative_u32(v_6);
+        npyv_store_u32(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_u32 v_7 = npyv_loadn_u32(ip + 7 * vstep * istride, istride);
+        npyv_u32 r_7 = npyv_negative_u32(v_7);
+        npyv_store_u32(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_u32 v_8 = npyv_loadn_u32(ip + 8 * vstep * istride, istride);
+        npyv_u32 r_8 = npyv_negative_u32(v_8);
+        npyv_store_u32(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_u32 v_9 = npyv_loadn_u32(ip + 9 * vstep * istride, istride);
+        npyv_u32 r_9 = npyv_negative_u32(v_9);
+        npyv_store_u32(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_u32 v_10 = npyv_loadn_u32(ip + 10 * vstep * istride, istride);
+        npyv_u32 r_10 = npyv_negative_u32(v_10);
+        npyv_store_u32(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_u32 v_11 = npyv_loadn_u32(ip + 11 * vstep * istride, istride);
+        npyv_u32 r_11 = npyv_negative_u32(v_11);
+        npyv_store_u32(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_u32 v_12 = npyv_loadn_u32(ip + 12 * vstep * istride, istride);
+        npyv_u32 r_12 = npyv_negative_u32(v_12);
+        npyv_store_u32(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_u32 v_13 = npyv_loadn_u32(ip + 13 * vstep * istride, istride);
+        npyv_u32 r_13 = npyv_negative_u32(v_13);
+        npyv_store_u32(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_u32 v_14 = npyv_loadn_u32(ip + 14 * vstep * istride, istride);
+        npyv_u32 r_14 = npyv_negative_u32(v_14);
+        npyv_store_u32(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_u32 v_15 = npyv_loadn_u32(ip + 15 * vstep * istride, istride);
+        npyv_u32 r_15 = npyv_negative_u32(v_15);
+        npyv_store_u32(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_u32 v = npyv_loadn_u32(ip, istride);
+        npyv_u32 r = npyv_negative_u32(v);
+        npyv_store_u32(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_u32(const npyv_lanetype_u32 *ip, npy_intp istride,
+                             npyv_lanetype_u32 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_u32 v_0 = npyv_loadn_u32(ip + 0 * vstep * istride, istride);
+        npyv_u32 r_0 = npyv_negative_u32(v_0);
+        npyv_storen_u32(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_u32 v_1 = npyv_loadn_u32(ip + 1 * vstep * istride, istride);
+        npyv_u32 r_1 = npyv_negative_u32(v_1);
+        npyv_storen_u32(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_u32 v_2 = npyv_loadn_u32(ip + 2 * vstep * istride, istride);
+        npyv_u32 r_2 = npyv_negative_u32(v_2);
+        npyv_storen_u32(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_u32 v_3 = npyv_loadn_u32(ip + 3 * vstep * istride, istride);
+        npyv_u32 r_3 = npyv_negative_u32(v_3);
+        npyv_storen_u32(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_u32 v_4 = npyv_loadn_u32(ip + 4 * vstep * istride, istride);
+        npyv_u32 r_4 = npyv_negative_u32(v_4);
+        npyv_storen_u32(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_u32 v_5 = npyv_loadn_u32(ip + 5 * vstep * istride, istride);
+        npyv_u32 r_5 = npyv_negative_u32(v_5);
+        npyv_storen_u32(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_u32 v_6 = npyv_loadn_u32(ip + 6 * vstep * istride, istride);
+        npyv_u32 r_6 = npyv_negative_u32(v_6);
+        npyv_storen_u32(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_u32 v_7 = npyv_loadn_u32(ip + 7 * vstep * istride, istride);
+        npyv_u32 r_7 = npyv_negative_u32(v_7);
+        npyv_storen_u32(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_u32 v_8 = npyv_loadn_u32(ip + 8 * vstep * istride, istride);
+        npyv_u32 r_8 = npyv_negative_u32(v_8);
+        npyv_storen_u32(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_u32 v_9 = npyv_loadn_u32(ip + 9 * vstep * istride, istride);
+        npyv_u32 r_9 = npyv_negative_u32(v_9);
+        npyv_storen_u32(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_u32 v_10 = npyv_loadn_u32(ip + 10 * vstep * istride, istride);
+        npyv_u32 r_10 = npyv_negative_u32(v_10);
+        npyv_storen_u32(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_u32 v_11 = npyv_loadn_u32(ip + 11 * vstep * istride, istride);
+        npyv_u32 r_11 = npyv_negative_u32(v_11);
+        npyv_storen_u32(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_u32 v_12 = npyv_loadn_u32(ip + 12 * vstep * istride, istride);
+        npyv_u32 r_12 = npyv_negative_u32(v_12);
+        npyv_storen_u32(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_u32 v_13 = npyv_loadn_u32(ip + 13 * vstep * istride, istride);
+        npyv_u32 r_13 = npyv_negative_u32(v_13);
+        npyv_storen_u32(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_u32 v_14 = npyv_loadn_u32(ip + 14 * vstep * istride, istride);
+        npyv_u32 r_14 = npyv_negative_u32(v_14);
+        npyv_storen_u32(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_u32 v_15 = npyv_loadn_u32(ip + 15 * vstep * istride, istride);
+        npyv_u32 r_15 = npyv_negative_u32(v_15);
+        npyv_storen_u32(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_u32 v = npyv_loadn_u32(ip, istride);
+        npyv_u32 r = npyv_negative_u32(v);
+        npyv_storen_u32(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 1
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_s64(const npyv_lanetype_s64 *ip,
+                             npyv_lanetype_s64 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_s64 v_0 = npyv_load_s64(ip + 0 * vstep);
+        npyv_s64 r_0 = npyv_negative_s64(v_0);
+        npyv_store_s64(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_s64 v_1 = npyv_load_s64(ip + 1 * vstep);
+        npyv_s64 r_1 = npyv_negative_s64(v_1);
+        npyv_store_s64(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_s64 v_2 = npyv_load_s64(ip + 2 * vstep);
+        npyv_s64 r_2 = npyv_negative_s64(v_2);
+        npyv_store_s64(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_s64 v_3 = npyv_load_s64(ip + 3 * vstep);
+        npyv_s64 r_3 = npyv_negative_s64(v_3);
+        npyv_store_s64(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_s64 v_4 = npyv_load_s64(ip + 4 * vstep);
+        npyv_s64 r_4 = npyv_negative_s64(v_4);
+        npyv_store_s64(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_s64 v_5 = npyv_load_s64(ip + 5 * vstep);
+        npyv_s64 r_5 = npyv_negative_s64(v_5);
+        npyv_store_s64(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_s64 v_6 = npyv_load_s64(ip + 6 * vstep);
+        npyv_s64 r_6 = npyv_negative_s64(v_6);
+        npyv_store_s64(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_s64 v_7 = npyv_load_s64(ip + 7 * vstep);
+        npyv_s64 r_7 = npyv_negative_s64(v_7);
+        npyv_store_s64(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_s64 v_8 = npyv_load_s64(ip + 8 * vstep);
+        npyv_s64 r_8 = npyv_negative_s64(v_8);
+        npyv_store_s64(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_s64 v_9 = npyv_load_s64(ip + 9 * vstep);
+        npyv_s64 r_9 = npyv_negative_s64(v_9);
+        npyv_store_s64(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_s64 v_10 = npyv_load_s64(ip + 10 * vstep);
+        npyv_s64 r_10 = npyv_negative_s64(v_10);
+        npyv_store_s64(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_s64 v_11 = npyv_load_s64(ip + 11 * vstep);
+        npyv_s64 r_11 = npyv_negative_s64(v_11);
+        npyv_store_s64(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_s64 v_12 = npyv_load_s64(ip + 12 * vstep);
+        npyv_s64 r_12 = npyv_negative_s64(v_12);
+        npyv_store_s64(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_s64 v_13 = npyv_load_s64(ip + 13 * vstep);
+        npyv_s64 r_13 = npyv_negative_s64(v_13);
+        npyv_store_s64(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_s64 v_14 = npyv_load_s64(ip + 14 * vstep);
+        npyv_s64 r_14 = npyv_negative_s64(v_14);
+        npyv_store_s64(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_s64 v_15 = npyv_load_s64(ip + 15 * vstep);
+        npyv_s64 r_15 = npyv_negative_s64(v_15);
+        npyv_store_s64(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_s64 v = npyv_load_s64(ip);
+        npyv_s64 r = npyv_negative_s64(v);
+        npyv_store_s64(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 1
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_s64(const npyv_lanetype_s64 *ip,
+                             npyv_lanetype_s64 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_s64 v_0 = npyv_load_s64(ip + 0 * vstep);
+        npyv_s64 r_0 = npyv_negative_s64(v_0);
+        npyv_storen_s64(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_s64 v_1 = npyv_load_s64(ip + 1 * vstep);
+        npyv_s64 r_1 = npyv_negative_s64(v_1);
+        npyv_storen_s64(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_s64 v_2 = npyv_load_s64(ip + 2 * vstep);
+        npyv_s64 r_2 = npyv_negative_s64(v_2);
+        npyv_storen_s64(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_s64 v_3 = npyv_load_s64(ip + 3 * vstep);
+        npyv_s64 r_3 = npyv_negative_s64(v_3);
+        npyv_storen_s64(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_s64 v_4 = npyv_load_s64(ip + 4 * vstep);
+        npyv_s64 r_4 = npyv_negative_s64(v_4);
+        npyv_storen_s64(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_s64 v_5 = npyv_load_s64(ip + 5 * vstep);
+        npyv_s64 r_5 = npyv_negative_s64(v_5);
+        npyv_storen_s64(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_s64 v_6 = npyv_load_s64(ip + 6 * vstep);
+        npyv_s64 r_6 = npyv_negative_s64(v_6);
+        npyv_storen_s64(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_s64 v_7 = npyv_load_s64(ip + 7 * vstep);
+        npyv_s64 r_7 = npyv_negative_s64(v_7);
+        npyv_storen_s64(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_s64 v_8 = npyv_load_s64(ip + 8 * vstep);
+        npyv_s64 r_8 = npyv_negative_s64(v_8);
+        npyv_storen_s64(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_s64 v_9 = npyv_load_s64(ip + 9 * vstep);
+        npyv_s64 r_9 = npyv_negative_s64(v_9);
+        npyv_storen_s64(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_s64 v_10 = npyv_load_s64(ip + 10 * vstep);
+        npyv_s64 r_10 = npyv_negative_s64(v_10);
+        npyv_storen_s64(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_s64 v_11 = npyv_load_s64(ip + 11 * vstep);
+        npyv_s64 r_11 = npyv_negative_s64(v_11);
+        npyv_storen_s64(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_s64 v_12 = npyv_load_s64(ip + 12 * vstep);
+        npyv_s64 r_12 = npyv_negative_s64(v_12);
+        npyv_storen_s64(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_s64 v_13 = npyv_load_s64(ip + 13 * vstep);
+        npyv_s64 r_13 = npyv_negative_s64(v_13);
+        npyv_storen_s64(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_s64 v_14 = npyv_load_s64(ip + 14 * vstep);
+        npyv_s64 r_14 = npyv_negative_s64(v_14);
+        npyv_storen_s64(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_s64 v_15 = npyv_load_s64(ip + 15 * vstep);
+        npyv_s64 r_15 = npyv_negative_s64(v_15);
+        npyv_storen_s64(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_s64 v = npyv_load_s64(ip);
+        npyv_s64 r = npyv_negative_s64(v);
+        npyv_storen_s64(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_s64(const npyv_lanetype_s64 *ip, npy_intp istride,
+                             npyv_lanetype_s64 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_s64 v_0 = npyv_loadn_s64(ip + 0 * vstep * istride, istride);
+        npyv_s64 r_0 = npyv_negative_s64(v_0);
+        npyv_store_s64(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_s64 v_1 = npyv_loadn_s64(ip + 1 * vstep * istride, istride);
+        npyv_s64 r_1 = npyv_negative_s64(v_1);
+        npyv_store_s64(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_s64 v_2 = npyv_loadn_s64(ip + 2 * vstep * istride, istride);
+        npyv_s64 r_2 = npyv_negative_s64(v_2);
+        npyv_store_s64(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_s64 v_3 = npyv_loadn_s64(ip + 3 * vstep * istride, istride);
+        npyv_s64 r_3 = npyv_negative_s64(v_3);
+        npyv_store_s64(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_s64 v_4 = npyv_loadn_s64(ip + 4 * vstep * istride, istride);
+        npyv_s64 r_4 = npyv_negative_s64(v_4);
+        npyv_store_s64(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_s64 v_5 = npyv_loadn_s64(ip + 5 * vstep * istride, istride);
+        npyv_s64 r_5 = npyv_negative_s64(v_5);
+        npyv_store_s64(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_s64 v_6 = npyv_loadn_s64(ip + 6 * vstep * istride, istride);
+        npyv_s64 r_6 = npyv_negative_s64(v_6);
+        npyv_store_s64(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_s64 v_7 = npyv_loadn_s64(ip + 7 * vstep * istride, istride);
+        npyv_s64 r_7 = npyv_negative_s64(v_7);
+        npyv_store_s64(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_s64 v_8 = npyv_loadn_s64(ip + 8 * vstep * istride, istride);
+        npyv_s64 r_8 = npyv_negative_s64(v_8);
+        npyv_store_s64(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_s64 v_9 = npyv_loadn_s64(ip + 9 * vstep * istride, istride);
+        npyv_s64 r_9 = npyv_negative_s64(v_9);
+        npyv_store_s64(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_s64 v_10 = npyv_loadn_s64(ip + 10 * vstep * istride, istride);
+        npyv_s64 r_10 = npyv_negative_s64(v_10);
+        npyv_store_s64(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_s64 v_11 = npyv_loadn_s64(ip + 11 * vstep * istride, istride);
+        npyv_s64 r_11 = npyv_negative_s64(v_11);
+        npyv_store_s64(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_s64 v_12 = npyv_loadn_s64(ip + 12 * vstep * istride, istride);
+        npyv_s64 r_12 = npyv_negative_s64(v_12);
+        npyv_store_s64(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_s64 v_13 = npyv_loadn_s64(ip + 13 * vstep * istride, istride);
+        npyv_s64 r_13 = npyv_negative_s64(v_13);
+        npyv_store_s64(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_s64 v_14 = npyv_loadn_s64(ip + 14 * vstep * istride, istride);
+        npyv_s64 r_14 = npyv_negative_s64(v_14);
+        npyv_store_s64(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_s64 v_15 = npyv_loadn_s64(ip + 15 * vstep * istride, istride);
+        npyv_s64 r_15 = npyv_negative_s64(v_15);
+        npyv_store_s64(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_s64 v = npyv_loadn_s64(ip, istride);
+        npyv_s64 r = npyv_negative_s64(v);
+        npyv_store_s64(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_s64(const npyv_lanetype_s64 *ip, npy_intp istride,
+                             npyv_lanetype_s64 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_s64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_s64 v_0 = npyv_loadn_s64(ip + 0 * vstep * istride, istride);
+        npyv_s64 r_0 = npyv_negative_s64(v_0);
+        npyv_storen_s64(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_s64 v_1 = npyv_loadn_s64(ip + 1 * vstep * istride, istride);
+        npyv_s64 r_1 = npyv_negative_s64(v_1);
+        npyv_storen_s64(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_s64 v_2 = npyv_loadn_s64(ip + 2 * vstep * istride, istride);
+        npyv_s64 r_2 = npyv_negative_s64(v_2);
+        npyv_storen_s64(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_s64 v_3 = npyv_loadn_s64(ip + 3 * vstep * istride, istride);
+        npyv_s64 r_3 = npyv_negative_s64(v_3);
+        npyv_storen_s64(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_s64 v_4 = npyv_loadn_s64(ip + 4 * vstep * istride, istride);
+        npyv_s64 r_4 = npyv_negative_s64(v_4);
+        npyv_storen_s64(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_s64 v_5 = npyv_loadn_s64(ip + 5 * vstep * istride, istride);
+        npyv_s64 r_5 = npyv_negative_s64(v_5);
+        npyv_storen_s64(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_s64 v_6 = npyv_loadn_s64(ip + 6 * vstep * istride, istride);
+        npyv_s64 r_6 = npyv_negative_s64(v_6);
+        npyv_storen_s64(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_s64 v_7 = npyv_loadn_s64(ip + 7 * vstep * istride, istride);
+        npyv_s64 r_7 = npyv_negative_s64(v_7);
+        npyv_storen_s64(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_s64 v_8 = npyv_loadn_s64(ip + 8 * vstep * istride, istride);
+        npyv_s64 r_8 = npyv_negative_s64(v_8);
+        npyv_storen_s64(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_s64 v_9 = npyv_loadn_s64(ip + 9 * vstep * istride, istride);
+        npyv_s64 r_9 = npyv_negative_s64(v_9);
+        npyv_storen_s64(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_s64 v_10 = npyv_loadn_s64(ip + 10 * vstep * istride, istride);
+        npyv_s64 r_10 = npyv_negative_s64(v_10);
+        npyv_storen_s64(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_s64 v_11 = npyv_loadn_s64(ip + 11 * vstep * istride, istride);
+        npyv_s64 r_11 = npyv_negative_s64(v_11);
+        npyv_storen_s64(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_s64 v_12 = npyv_loadn_s64(ip + 12 * vstep * istride, istride);
+        npyv_s64 r_12 = npyv_negative_s64(v_12);
+        npyv_storen_s64(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_s64 v_13 = npyv_loadn_s64(ip + 13 * vstep * istride, istride);
+        npyv_s64 r_13 = npyv_negative_s64(v_13);
+        npyv_storen_s64(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_s64 v_14 = npyv_loadn_s64(ip + 14 * vstep * istride, istride);
+        npyv_s64 r_14 = npyv_negative_s64(v_14);
+        npyv_storen_s64(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_s64 v_15 = npyv_loadn_s64(ip + 15 * vstep * istride, istride);
+        npyv_s64 r_15 = npyv_negative_s64(v_15);
+        npyv_storen_s64(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_s64 v = npyv_loadn_s64(ip, istride);
+        npyv_s64 r = npyv_negative_s64(v);
+        npyv_storen_s64(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 1
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_u64(const npyv_lanetype_u64 *ip,
+                             npyv_lanetype_u64 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_u64 v_0 = npyv_load_u64(ip + 0 * vstep);
+        npyv_u64 r_0 = npyv_negative_u64(v_0);
+        npyv_store_u64(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_u64 v_1 = npyv_load_u64(ip + 1 * vstep);
+        npyv_u64 r_1 = npyv_negative_u64(v_1);
+        npyv_store_u64(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_u64 v_2 = npyv_load_u64(ip + 2 * vstep);
+        npyv_u64 r_2 = npyv_negative_u64(v_2);
+        npyv_store_u64(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_u64 v_3 = npyv_load_u64(ip + 3 * vstep);
+        npyv_u64 r_3 = npyv_negative_u64(v_3);
+        npyv_store_u64(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_u64 v_4 = npyv_load_u64(ip + 4 * vstep);
+        npyv_u64 r_4 = npyv_negative_u64(v_4);
+        npyv_store_u64(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_u64 v_5 = npyv_load_u64(ip + 5 * vstep);
+        npyv_u64 r_5 = npyv_negative_u64(v_5);
+        npyv_store_u64(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_u64 v_6 = npyv_load_u64(ip + 6 * vstep);
+        npyv_u64 r_6 = npyv_negative_u64(v_6);
+        npyv_store_u64(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_u64 v_7 = npyv_load_u64(ip + 7 * vstep);
+        npyv_u64 r_7 = npyv_negative_u64(v_7);
+        npyv_store_u64(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_u64 v_8 = npyv_load_u64(ip + 8 * vstep);
+        npyv_u64 r_8 = npyv_negative_u64(v_8);
+        npyv_store_u64(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_u64 v_9 = npyv_load_u64(ip + 9 * vstep);
+        npyv_u64 r_9 = npyv_negative_u64(v_9);
+        npyv_store_u64(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_u64 v_10 = npyv_load_u64(ip + 10 * vstep);
+        npyv_u64 r_10 = npyv_negative_u64(v_10);
+        npyv_store_u64(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_u64 v_11 = npyv_load_u64(ip + 11 * vstep);
+        npyv_u64 r_11 = npyv_negative_u64(v_11);
+        npyv_store_u64(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_u64 v_12 = npyv_load_u64(ip + 12 * vstep);
+        npyv_u64 r_12 = npyv_negative_u64(v_12);
+        npyv_store_u64(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_u64 v_13 = npyv_load_u64(ip + 13 * vstep);
+        npyv_u64 r_13 = npyv_negative_u64(v_13);
+        npyv_store_u64(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_u64 v_14 = npyv_load_u64(ip + 14 * vstep);
+        npyv_u64 r_14 = npyv_negative_u64(v_14);
+        npyv_store_u64(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_u64 v_15 = npyv_load_u64(ip + 15 * vstep);
+        npyv_u64 r_15 = npyv_negative_u64(v_15);
+        npyv_store_u64(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_u64 v = npyv_load_u64(ip);
+        npyv_u64 r = npyv_negative_u64(v);
+        npyv_store_u64(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 1
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_u64(const npyv_lanetype_u64 *ip,
+                             npyv_lanetype_u64 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_u64 v_0 = npyv_load_u64(ip + 0 * vstep);
+        npyv_u64 r_0 = npyv_negative_u64(v_0);
+        npyv_storen_u64(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_u64 v_1 = npyv_load_u64(ip + 1 * vstep);
+        npyv_u64 r_1 = npyv_negative_u64(v_1);
+        npyv_storen_u64(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_u64 v_2 = npyv_load_u64(ip + 2 * vstep);
+        npyv_u64 r_2 = npyv_negative_u64(v_2);
+        npyv_storen_u64(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_u64 v_3 = npyv_load_u64(ip + 3 * vstep);
+        npyv_u64 r_3 = npyv_negative_u64(v_3);
+        npyv_storen_u64(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_u64 v_4 = npyv_load_u64(ip + 4 * vstep);
+        npyv_u64 r_4 = npyv_negative_u64(v_4);
+        npyv_storen_u64(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_u64 v_5 = npyv_load_u64(ip + 5 * vstep);
+        npyv_u64 r_5 = npyv_negative_u64(v_5);
+        npyv_storen_u64(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_u64 v_6 = npyv_load_u64(ip + 6 * vstep);
+        npyv_u64 r_6 = npyv_negative_u64(v_6);
+        npyv_storen_u64(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_u64 v_7 = npyv_load_u64(ip + 7 * vstep);
+        npyv_u64 r_7 = npyv_negative_u64(v_7);
+        npyv_storen_u64(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_u64 v_8 = npyv_load_u64(ip + 8 * vstep);
+        npyv_u64 r_8 = npyv_negative_u64(v_8);
+        npyv_storen_u64(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_u64 v_9 = npyv_load_u64(ip + 9 * vstep);
+        npyv_u64 r_9 = npyv_negative_u64(v_9);
+        npyv_storen_u64(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_u64 v_10 = npyv_load_u64(ip + 10 * vstep);
+        npyv_u64 r_10 = npyv_negative_u64(v_10);
+        npyv_storen_u64(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_u64 v_11 = npyv_load_u64(ip + 11 * vstep);
+        npyv_u64 r_11 = npyv_negative_u64(v_11);
+        npyv_storen_u64(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_u64 v_12 = npyv_load_u64(ip + 12 * vstep);
+        npyv_u64 r_12 = npyv_negative_u64(v_12);
+        npyv_storen_u64(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_u64 v_13 = npyv_load_u64(ip + 13 * vstep);
+        npyv_u64 r_13 = npyv_negative_u64(v_13);
+        npyv_storen_u64(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_u64 v_14 = npyv_load_u64(ip + 14 * vstep);
+        npyv_u64 r_14 = npyv_negative_u64(v_14);
+        npyv_storen_u64(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_u64 v_15 = npyv_load_u64(ip + 15 * vstep);
+        npyv_u64 r_15 = npyv_negative_u64(v_15);
+        npyv_storen_u64(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_u64 v = npyv_load_u64(ip);
+        npyv_u64 r = npyv_negative_u64(v);
+        npyv_storen_u64(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_u64(const npyv_lanetype_u64 *ip, npy_intp istride,
+                             npyv_lanetype_u64 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_u64 v_0 = npyv_loadn_u64(ip + 0 * vstep * istride, istride);
+        npyv_u64 r_0 = npyv_negative_u64(v_0);
+        npyv_store_u64(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_u64 v_1 = npyv_loadn_u64(ip + 1 * vstep * istride, istride);
+        npyv_u64 r_1 = npyv_negative_u64(v_1);
+        npyv_store_u64(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_u64 v_2 = npyv_loadn_u64(ip + 2 * vstep * istride, istride);
+        npyv_u64 r_2 = npyv_negative_u64(v_2);
+        npyv_store_u64(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_u64 v_3 = npyv_loadn_u64(ip + 3 * vstep * istride, istride);
+        npyv_u64 r_3 = npyv_negative_u64(v_3);
+        npyv_store_u64(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_u64 v_4 = npyv_loadn_u64(ip + 4 * vstep * istride, istride);
+        npyv_u64 r_4 = npyv_negative_u64(v_4);
+        npyv_store_u64(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_u64 v_5 = npyv_loadn_u64(ip + 5 * vstep * istride, istride);
+        npyv_u64 r_5 = npyv_negative_u64(v_5);
+        npyv_store_u64(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_u64 v_6 = npyv_loadn_u64(ip + 6 * vstep * istride, istride);
+        npyv_u64 r_6 = npyv_negative_u64(v_6);
+        npyv_store_u64(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_u64 v_7 = npyv_loadn_u64(ip + 7 * vstep * istride, istride);
+        npyv_u64 r_7 = npyv_negative_u64(v_7);
+        npyv_store_u64(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_u64 v_8 = npyv_loadn_u64(ip + 8 * vstep * istride, istride);
+        npyv_u64 r_8 = npyv_negative_u64(v_8);
+        npyv_store_u64(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_u64 v_9 = npyv_loadn_u64(ip + 9 * vstep * istride, istride);
+        npyv_u64 r_9 = npyv_negative_u64(v_9);
+        npyv_store_u64(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_u64 v_10 = npyv_loadn_u64(ip + 10 * vstep * istride, istride);
+        npyv_u64 r_10 = npyv_negative_u64(v_10);
+        npyv_store_u64(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_u64 v_11 = npyv_loadn_u64(ip + 11 * vstep * istride, istride);
+        npyv_u64 r_11 = npyv_negative_u64(v_11);
+        npyv_store_u64(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_u64 v_12 = npyv_loadn_u64(ip + 12 * vstep * istride, istride);
+        npyv_u64 r_12 = npyv_negative_u64(v_12);
+        npyv_store_u64(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_u64 v_13 = npyv_loadn_u64(ip + 13 * vstep * istride, istride);
+        npyv_u64 r_13 = npyv_negative_u64(v_13);
+        npyv_store_u64(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_u64 v_14 = npyv_loadn_u64(ip + 14 * vstep * istride, istride);
+        npyv_u64 r_14 = npyv_negative_u64(v_14);
+        npyv_store_u64(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_u64 v_15 = npyv_loadn_u64(ip + 15 * vstep * istride, istride);
+        npyv_u64 r_15 = npyv_negative_u64(v_15);
+        npyv_store_u64(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_u64 v = npyv_loadn_u64(ip, istride);
+        npyv_u64 r = npyv_negative_u64(v);
+        npyv_store_u64(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_u64(const npyv_lanetype_u64 *ip, npy_intp istride,
+                             npyv_lanetype_u64 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_u64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_u64 v_0 = npyv_loadn_u64(ip + 0 * vstep * istride, istride);
+        npyv_u64 r_0 = npyv_negative_u64(v_0);
+        npyv_storen_u64(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_u64 v_1 = npyv_loadn_u64(ip + 1 * vstep * istride, istride);
+        npyv_u64 r_1 = npyv_negative_u64(v_1);
+        npyv_storen_u64(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_u64 v_2 = npyv_loadn_u64(ip + 2 * vstep * istride, istride);
+        npyv_u64 r_2 = npyv_negative_u64(v_2);
+        npyv_storen_u64(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_u64 v_3 = npyv_loadn_u64(ip + 3 * vstep * istride, istride);
+        npyv_u64 r_3 = npyv_negative_u64(v_3);
+        npyv_storen_u64(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_u64 v_4 = npyv_loadn_u64(ip + 4 * vstep * istride, istride);
+        npyv_u64 r_4 = npyv_negative_u64(v_4);
+        npyv_storen_u64(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_u64 v_5 = npyv_loadn_u64(ip + 5 * vstep * istride, istride);
+        npyv_u64 r_5 = npyv_negative_u64(v_5);
+        npyv_storen_u64(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_u64 v_6 = npyv_loadn_u64(ip + 6 * vstep * istride, istride);
+        npyv_u64 r_6 = npyv_negative_u64(v_6);
+        npyv_storen_u64(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_u64 v_7 = npyv_loadn_u64(ip + 7 * vstep * istride, istride);
+        npyv_u64 r_7 = npyv_negative_u64(v_7);
+        npyv_storen_u64(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_u64 v_8 = npyv_loadn_u64(ip + 8 * vstep * istride, istride);
+        npyv_u64 r_8 = npyv_negative_u64(v_8);
+        npyv_storen_u64(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_u64 v_9 = npyv_loadn_u64(ip + 9 * vstep * istride, istride);
+        npyv_u64 r_9 = npyv_negative_u64(v_9);
+        npyv_storen_u64(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_u64 v_10 = npyv_loadn_u64(ip + 10 * vstep * istride, istride);
+        npyv_u64 r_10 = npyv_negative_u64(v_10);
+        npyv_storen_u64(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_u64 v_11 = npyv_loadn_u64(ip + 11 * vstep * istride, istride);
+        npyv_u64 r_11 = npyv_negative_u64(v_11);
+        npyv_storen_u64(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_u64 v_12 = npyv_loadn_u64(ip + 12 * vstep * istride, istride);
+        npyv_u64 r_12 = npyv_negative_u64(v_12);
+        npyv_storen_u64(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_u64 v_13 = npyv_loadn_u64(ip + 13 * vstep * istride, istride);
+        npyv_u64 r_13 = npyv_negative_u64(v_13);
+        npyv_storen_u64(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_u64 v_14 = npyv_loadn_u64(ip + 14 * vstep * istride, istride);
+        npyv_u64 r_14 = npyv_negative_u64(v_14);
+        npyv_storen_u64(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_u64 v_15 = npyv_loadn_u64(ip + 15 * vstep * istride, istride);
+        npyv_u64 r_15 = npyv_negative_u64(v_15);
+        npyv_storen_u64(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_u64 v = npyv_loadn_u64(ip, istride);
+        npyv_u64 r = npyv_negative_u64(v);
+        npyv_storen_u64(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 1
+#undef UNROLL
+#endif // NPY_SIMD
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD_F32
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_f32(const npyv_lanetype_f32 *ip,
+                             npyv_lanetype_f32 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_f32 v_0 = npyv_load_f32(ip + 0 * vstep);
+        npyv_f32 r_0 = npyv_negative_f32(v_0);
+        npyv_store_f32(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_f32 v_1 = npyv_load_f32(ip + 1 * vstep);
+        npyv_f32 r_1 = npyv_negative_f32(v_1);
+        npyv_store_f32(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_f32 v_2 = npyv_load_f32(ip + 2 * vstep);
+        npyv_f32 r_2 = npyv_negative_f32(v_2);
+        npyv_store_f32(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_f32 v_3 = npyv_load_f32(ip + 3 * vstep);
+        npyv_f32 r_3 = npyv_negative_f32(v_3);
+        npyv_store_f32(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_f32 v_4 = npyv_load_f32(ip + 4 * vstep);
+        npyv_f32 r_4 = npyv_negative_f32(v_4);
+        npyv_store_f32(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_f32 v_5 = npyv_load_f32(ip + 5 * vstep);
+        npyv_f32 r_5 = npyv_negative_f32(v_5);
+        npyv_store_f32(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_f32 v_6 = npyv_load_f32(ip + 6 * vstep);
+        npyv_f32 r_6 = npyv_negative_f32(v_6);
+        npyv_store_f32(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_f32 v_7 = npyv_load_f32(ip + 7 * vstep);
+        npyv_f32 r_7 = npyv_negative_f32(v_7);
+        npyv_store_f32(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_f32 v_8 = npyv_load_f32(ip + 8 * vstep);
+        npyv_f32 r_8 = npyv_negative_f32(v_8);
+        npyv_store_f32(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_f32 v_9 = npyv_load_f32(ip + 9 * vstep);
+        npyv_f32 r_9 = npyv_negative_f32(v_9);
+        npyv_store_f32(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_f32 v_10 = npyv_load_f32(ip + 10 * vstep);
+        npyv_f32 r_10 = npyv_negative_f32(v_10);
+        npyv_store_f32(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_f32 v_11 = npyv_load_f32(ip + 11 * vstep);
+        npyv_f32 r_11 = npyv_negative_f32(v_11);
+        npyv_store_f32(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_f32 v_12 = npyv_load_f32(ip + 12 * vstep);
+        npyv_f32 r_12 = npyv_negative_f32(v_12);
+        npyv_store_f32(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_f32 v_13 = npyv_load_f32(ip + 13 * vstep);
+        npyv_f32 r_13 = npyv_negative_f32(v_13);
+        npyv_store_f32(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_f32 v_14 = npyv_load_f32(ip + 14 * vstep);
+        npyv_f32 r_14 = npyv_negative_f32(v_14);
+        npyv_store_f32(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_f32 v_15 = npyv_load_f32(ip + 15 * vstep);
+        npyv_f32 r_15 = npyv_negative_f32(v_15);
+        npyv_store_f32(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_f32 v = npyv_load_f32(ip);
+        npyv_f32 r = npyv_negative_f32(v);
+        npyv_store_f32(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 1
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_f32(const npyv_lanetype_f32 *ip,
+                             npyv_lanetype_f32 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_f32 v_0 = npyv_load_f32(ip + 0 * vstep);
+        npyv_f32 r_0 = npyv_negative_f32(v_0);
+        npyv_storen_f32(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_f32 v_1 = npyv_load_f32(ip + 1 * vstep);
+        npyv_f32 r_1 = npyv_negative_f32(v_1);
+        npyv_storen_f32(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_f32 v_2 = npyv_load_f32(ip + 2 * vstep);
+        npyv_f32 r_2 = npyv_negative_f32(v_2);
+        npyv_storen_f32(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_f32 v_3 = npyv_load_f32(ip + 3 * vstep);
+        npyv_f32 r_3 = npyv_negative_f32(v_3);
+        npyv_storen_f32(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_f32 v_4 = npyv_load_f32(ip + 4 * vstep);
+        npyv_f32 r_4 = npyv_negative_f32(v_4);
+        npyv_storen_f32(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_f32 v_5 = npyv_load_f32(ip + 5 * vstep);
+        npyv_f32 r_5 = npyv_negative_f32(v_5);
+        npyv_storen_f32(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_f32 v_6 = npyv_load_f32(ip + 6 * vstep);
+        npyv_f32 r_6 = npyv_negative_f32(v_6);
+        npyv_storen_f32(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_f32 v_7 = npyv_load_f32(ip + 7 * vstep);
+        npyv_f32 r_7 = npyv_negative_f32(v_7);
+        npyv_storen_f32(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_f32 v_8 = npyv_load_f32(ip + 8 * vstep);
+        npyv_f32 r_8 = npyv_negative_f32(v_8);
+        npyv_storen_f32(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_f32 v_9 = npyv_load_f32(ip + 9 * vstep);
+        npyv_f32 r_9 = npyv_negative_f32(v_9);
+        npyv_storen_f32(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_f32 v_10 = npyv_load_f32(ip + 10 * vstep);
+        npyv_f32 r_10 = npyv_negative_f32(v_10);
+        npyv_storen_f32(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_f32 v_11 = npyv_load_f32(ip + 11 * vstep);
+        npyv_f32 r_11 = npyv_negative_f32(v_11);
+        npyv_storen_f32(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_f32 v_12 = npyv_load_f32(ip + 12 * vstep);
+        npyv_f32 r_12 = npyv_negative_f32(v_12);
+        npyv_storen_f32(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_f32 v_13 = npyv_load_f32(ip + 13 * vstep);
+        npyv_f32 r_13 = npyv_negative_f32(v_13);
+        npyv_storen_f32(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_f32 v_14 = npyv_load_f32(ip + 14 * vstep);
+        npyv_f32 r_14 = npyv_negative_f32(v_14);
+        npyv_storen_f32(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_f32 v_15 = npyv_load_f32(ip + 15 * vstep);
+        npyv_f32 r_15 = npyv_negative_f32(v_15);
+        npyv_storen_f32(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_f32 v = npyv_load_f32(ip);
+        npyv_f32 r = npyv_negative_f32(v);
+        npyv_storen_f32(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_f32(const npyv_lanetype_f32 *ip, npy_intp istride,
+                             npyv_lanetype_f32 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_f32 v_0 = npyv_loadn_f32(ip + 0 * vstep * istride, istride);
+        npyv_f32 r_0 = npyv_negative_f32(v_0);
+        npyv_store_f32(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_f32 v_1 = npyv_loadn_f32(ip + 1 * vstep * istride, istride);
+        npyv_f32 r_1 = npyv_negative_f32(v_1);
+        npyv_store_f32(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_f32 v_2 = npyv_loadn_f32(ip + 2 * vstep * istride, istride);
+        npyv_f32 r_2 = npyv_negative_f32(v_2);
+        npyv_store_f32(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_f32 v_3 = npyv_loadn_f32(ip + 3 * vstep * istride, istride);
+        npyv_f32 r_3 = npyv_negative_f32(v_3);
+        npyv_store_f32(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_f32 v_4 = npyv_loadn_f32(ip + 4 * vstep * istride, istride);
+        npyv_f32 r_4 = npyv_negative_f32(v_4);
+        npyv_store_f32(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_f32 v_5 = npyv_loadn_f32(ip + 5 * vstep * istride, istride);
+        npyv_f32 r_5 = npyv_negative_f32(v_5);
+        npyv_store_f32(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_f32 v_6 = npyv_loadn_f32(ip + 6 * vstep * istride, istride);
+        npyv_f32 r_6 = npyv_negative_f32(v_6);
+        npyv_store_f32(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_f32 v_7 = npyv_loadn_f32(ip + 7 * vstep * istride, istride);
+        npyv_f32 r_7 = npyv_negative_f32(v_7);
+        npyv_store_f32(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_f32 v_8 = npyv_loadn_f32(ip + 8 * vstep * istride, istride);
+        npyv_f32 r_8 = npyv_negative_f32(v_8);
+        npyv_store_f32(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_f32 v_9 = npyv_loadn_f32(ip + 9 * vstep * istride, istride);
+        npyv_f32 r_9 = npyv_negative_f32(v_9);
+        npyv_store_f32(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_f32 v_10 = npyv_loadn_f32(ip + 10 * vstep * istride, istride);
+        npyv_f32 r_10 = npyv_negative_f32(v_10);
+        npyv_store_f32(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_f32 v_11 = npyv_loadn_f32(ip + 11 * vstep * istride, istride);
+        npyv_f32 r_11 = npyv_negative_f32(v_11);
+        npyv_store_f32(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_f32 v_12 = npyv_loadn_f32(ip + 12 * vstep * istride, istride);
+        npyv_f32 r_12 = npyv_negative_f32(v_12);
+        npyv_store_f32(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_f32 v_13 = npyv_loadn_f32(ip + 13 * vstep * istride, istride);
+        npyv_f32 r_13 = npyv_negative_f32(v_13);
+        npyv_store_f32(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_f32 v_14 = npyv_loadn_f32(ip + 14 * vstep * istride, istride);
+        npyv_f32 r_14 = npyv_negative_f32(v_14);
+        npyv_store_f32(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_f32 v_15 = npyv_loadn_f32(ip + 15 * vstep * istride, istride);
+        npyv_f32 r_15 = npyv_negative_f32(v_15);
+        npyv_store_f32(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+        npyv_f32 r = npyv_negative_f32(v);
+        npyv_store_f32(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_f32(const npyv_lanetype_f32 *ip, npy_intp istride,
+                             npyv_lanetype_f32 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_f32 v_0 = npyv_loadn_f32(ip + 0 * vstep * istride, istride);
+        npyv_f32 r_0 = npyv_negative_f32(v_0);
+        npyv_storen_f32(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_f32 v_1 = npyv_loadn_f32(ip + 1 * vstep * istride, istride);
+        npyv_f32 r_1 = npyv_negative_f32(v_1);
+        npyv_storen_f32(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_f32 v_2 = npyv_loadn_f32(ip + 2 * vstep * istride, istride);
+        npyv_f32 r_2 = npyv_negative_f32(v_2);
+        npyv_storen_f32(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_f32 v_3 = npyv_loadn_f32(ip + 3 * vstep * istride, istride);
+        npyv_f32 r_3 = npyv_negative_f32(v_3);
+        npyv_storen_f32(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_f32 v_4 = npyv_loadn_f32(ip + 4 * vstep * istride, istride);
+        npyv_f32 r_4 = npyv_negative_f32(v_4);
+        npyv_storen_f32(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_f32 v_5 = npyv_loadn_f32(ip + 5 * vstep * istride, istride);
+        npyv_f32 r_5 = npyv_negative_f32(v_5);
+        npyv_storen_f32(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_f32 v_6 = npyv_loadn_f32(ip + 6 * vstep * istride, istride);
+        npyv_f32 r_6 = npyv_negative_f32(v_6);
+        npyv_storen_f32(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_f32 v_7 = npyv_loadn_f32(ip + 7 * vstep * istride, istride);
+        npyv_f32 r_7 = npyv_negative_f32(v_7);
+        npyv_storen_f32(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_f32 v_8 = npyv_loadn_f32(ip + 8 * vstep * istride, istride);
+        npyv_f32 r_8 = npyv_negative_f32(v_8);
+        npyv_storen_f32(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_f32 v_9 = npyv_loadn_f32(ip + 9 * vstep * istride, istride);
+        npyv_f32 r_9 = npyv_negative_f32(v_9);
+        npyv_storen_f32(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_f32 v_10 = npyv_loadn_f32(ip + 10 * vstep * istride, istride);
+        npyv_f32 r_10 = npyv_negative_f32(v_10);
+        npyv_storen_f32(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_f32 v_11 = npyv_loadn_f32(ip + 11 * vstep * istride, istride);
+        npyv_f32 r_11 = npyv_negative_f32(v_11);
+        npyv_storen_f32(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_f32 v_12 = npyv_loadn_f32(ip + 12 * vstep * istride, istride);
+        npyv_f32 r_12 = npyv_negative_f32(v_12);
+        npyv_storen_f32(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_f32 v_13 = npyv_loadn_f32(ip + 13 * vstep * istride, istride);
+        npyv_f32 r_13 = npyv_negative_f32(v_13);
+        npyv_storen_f32(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_f32 v_14 = npyv_loadn_f32(ip + 14 * vstep * istride, istride);
+        npyv_f32 r_14 = npyv_negative_f32(v_14);
+        npyv_storen_f32(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_f32 v_15 = npyv_loadn_f32(ip + 15 * vstep * istride, istride);
+        npyv_f32 r_15 = npyv_negative_f32(v_15);
+        npyv_storen_f32(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+        npyv_f32 r = npyv_negative_f32(v);
+        npyv_storen_f32(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 1
+#undef UNROLL
+#endif // NPY_SIMD_F32
+/*end repeat1**/
+
+#line 80
+#line 85
+#if NPY_SIMD_F64
+#if 4 < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && 4 > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL 4
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_negative_f64(const npyv_lanetype_f64 *ip,
+                             npyv_lanetype_f64 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    #line 108
+    #if UNROLL > 0
+        npyv_f64 v_0 = npyv_load_f64(ip + 0 * vstep);
+        npyv_f64 r_0 = npyv_negative_f64(v_0);
+        npyv_store_f64(op + 0 * vstep, r_0);
+    #endif
+    
+#line 108
+    #if UNROLL > 1
+        npyv_f64 v_1 = npyv_load_f64(ip + 1 * vstep);
+        npyv_f64 r_1 = npyv_negative_f64(v_1);
+        npyv_store_f64(op + 1 * vstep, r_1);
+    #endif
+    
+#line 108
+    #if UNROLL > 2
+        npyv_f64 v_2 = npyv_load_f64(ip + 2 * vstep);
+        npyv_f64 r_2 = npyv_negative_f64(v_2);
+        npyv_store_f64(op + 2 * vstep, r_2);
+    #endif
+    
+#line 108
+    #if UNROLL > 3
+        npyv_f64 v_3 = npyv_load_f64(ip + 3 * vstep);
+        npyv_f64 r_3 = npyv_negative_f64(v_3);
+        npyv_store_f64(op + 3 * vstep, r_3);
+    #endif
+    
+#line 108
+    #if UNROLL > 4
+        npyv_f64 v_4 = npyv_load_f64(ip + 4 * vstep);
+        npyv_f64 r_4 = npyv_negative_f64(v_4);
+        npyv_store_f64(op + 4 * vstep, r_4);
+    #endif
+    
+#line 108
+    #if UNROLL > 5
+        npyv_f64 v_5 = npyv_load_f64(ip + 5 * vstep);
+        npyv_f64 r_5 = npyv_negative_f64(v_5);
+        npyv_store_f64(op + 5 * vstep, r_5);
+    #endif
+    
+#line 108
+    #if UNROLL > 6
+        npyv_f64 v_6 = npyv_load_f64(ip + 6 * vstep);
+        npyv_f64 r_6 = npyv_negative_f64(v_6);
+        npyv_store_f64(op + 6 * vstep, r_6);
+    #endif
+    
+#line 108
+    #if UNROLL > 7
+        npyv_f64 v_7 = npyv_load_f64(ip + 7 * vstep);
+        npyv_f64 r_7 = npyv_negative_f64(v_7);
+        npyv_store_f64(op + 7 * vstep, r_7);
+    #endif
+    
+#line 108
+    #if UNROLL > 8
+        npyv_f64 v_8 = npyv_load_f64(ip + 8 * vstep);
+        npyv_f64 r_8 = npyv_negative_f64(v_8);
+        npyv_store_f64(op + 8 * vstep, r_8);
+    #endif
+    
+#line 108
+    #if UNROLL > 9
+        npyv_f64 v_9 = npyv_load_f64(ip + 9 * vstep);
+        npyv_f64 r_9 = npyv_negative_f64(v_9);
+        npyv_store_f64(op + 9 * vstep, r_9);
+    #endif
+    
+#line 108
+    #if UNROLL > 10
+        npyv_f64 v_10 = npyv_load_f64(ip + 10 * vstep);
+        npyv_f64 r_10 = npyv_negative_f64(v_10);
+        npyv_store_f64(op + 10 * vstep, r_10);
+    #endif
+    
+#line 108
+    #if UNROLL > 11
+        npyv_f64 v_11 = npyv_load_f64(ip + 11 * vstep);
+        npyv_f64 r_11 = npyv_negative_f64(v_11);
+        npyv_store_f64(op + 11 * vstep, r_11);
+    #endif
+    
+#line 108
+    #if UNROLL > 12
+        npyv_f64 v_12 = npyv_load_f64(ip + 12 * vstep);
+        npyv_f64 r_12 = npyv_negative_f64(v_12);
+        npyv_store_f64(op + 12 * vstep, r_12);
+    #endif
+    
+#line 108
+    #if UNROLL > 13
+        npyv_f64 v_13 = npyv_load_f64(ip + 13 * vstep);
+        npyv_f64 r_13 = npyv_negative_f64(v_13);
+        npyv_store_f64(op + 13 * vstep, r_13);
+    #endif
+    
+#line 108
+    #if UNROLL > 14
+        npyv_f64 v_14 = npyv_load_f64(ip + 14 * vstep);
+        npyv_f64 r_14 = npyv_negative_f64(v_14);
+        npyv_store_f64(op + 14 * vstep, r_14);
+    #endif
+    
+#line 108
+    #if UNROLL > 15
+        npyv_f64 v_15 = npyv_load_f64(ip + 15 * vstep);
+        npyv_f64 r_15 = npyv_negative_f64(v_15);
+        npyv_store_f64(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_f64 v = npyv_load_f64(ip);
+        npyv_f64 r = npyv_negative_f64(v);
+        npyv_store_f64(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+
+#if 1
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_negative_f64(const npyv_lanetype_f64 *ip,
+                             npyv_lanetype_f64 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    #line 142
+    #if UNROLL > 0
+        npyv_f64 v_0 = npyv_load_f64(ip + 0 * vstep);
+        npyv_f64 r_0 = npyv_negative_f64(v_0);
+        npyv_storen_f64(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 142
+    #if UNROLL > 1
+        npyv_f64 v_1 = npyv_load_f64(ip + 1 * vstep);
+        npyv_f64 r_1 = npyv_negative_f64(v_1);
+        npyv_storen_f64(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 142
+    #if UNROLL > 2
+        npyv_f64 v_2 = npyv_load_f64(ip + 2 * vstep);
+        npyv_f64 r_2 = npyv_negative_f64(v_2);
+        npyv_storen_f64(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 142
+    #if UNROLL > 3
+        npyv_f64 v_3 = npyv_load_f64(ip + 3 * vstep);
+        npyv_f64 r_3 = npyv_negative_f64(v_3);
+        npyv_storen_f64(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 142
+    #if UNROLL > 4
+        npyv_f64 v_4 = npyv_load_f64(ip + 4 * vstep);
+        npyv_f64 r_4 = npyv_negative_f64(v_4);
+        npyv_storen_f64(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 142
+    #if UNROLL > 5
+        npyv_f64 v_5 = npyv_load_f64(ip + 5 * vstep);
+        npyv_f64 r_5 = npyv_negative_f64(v_5);
+        npyv_storen_f64(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 142
+    #if UNROLL > 6
+        npyv_f64 v_6 = npyv_load_f64(ip + 6 * vstep);
+        npyv_f64 r_6 = npyv_negative_f64(v_6);
+        npyv_storen_f64(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 142
+    #if UNROLL > 7
+        npyv_f64 v_7 = npyv_load_f64(ip + 7 * vstep);
+        npyv_f64 r_7 = npyv_negative_f64(v_7);
+        npyv_storen_f64(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 142
+    #if UNROLL > 8
+        npyv_f64 v_8 = npyv_load_f64(ip + 8 * vstep);
+        npyv_f64 r_8 = npyv_negative_f64(v_8);
+        npyv_storen_f64(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 142
+    #if UNROLL > 9
+        npyv_f64 v_9 = npyv_load_f64(ip + 9 * vstep);
+        npyv_f64 r_9 = npyv_negative_f64(v_9);
+        npyv_storen_f64(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 142
+    #if UNROLL > 10
+        npyv_f64 v_10 = npyv_load_f64(ip + 10 * vstep);
+        npyv_f64 r_10 = npyv_negative_f64(v_10);
+        npyv_storen_f64(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 142
+    #if UNROLL > 11
+        npyv_f64 v_11 = npyv_load_f64(ip + 11 * vstep);
+        npyv_f64 r_11 = npyv_negative_f64(v_11);
+        npyv_storen_f64(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 142
+    #if UNROLL > 12
+        npyv_f64 v_12 = npyv_load_f64(ip + 12 * vstep);
+        npyv_f64 r_12 = npyv_negative_f64(v_12);
+        npyv_storen_f64(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 142
+    #if UNROLL > 13
+        npyv_f64 v_13 = npyv_load_f64(ip + 13 * vstep);
+        npyv_f64 r_13 = npyv_negative_f64(v_13);
+        npyv_storen_f64(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 142
+    #if UNROLL > 14
+        npyv_f64 v_14 = npyv_load_f64(ip + 14 * vstep);
+        npyv_f64 r_14 = npyv_negative_f64(v_14);
+        npyv_storen_f64(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 142
+    #if UNROLL > 15
+        npyv_f64 v_15 = npyv_load_f64(ip + 15 * vstep);
+        npyv_f64 r_15 = npyv_negative_f64(v_15);
+        npyv_storen_f64(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_f64 v = npyv_load_f64(ip);
+        npyv_f64 r = npyv_negative_f64(v);
+        npyv_storen_f64(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_negative_f64(const npyv_lanetype_f64 *ip, npy_intp istride,
+                             npyv_lanetype_f64 *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    #line 174
+    #if UNROLL > 0
+        npyv_f64 v_0 = npyv_loadn_f64(ip + 0 * vstep * istride, istride);
+        npyv_f64 r_0 = npyv_negative_f64(v_0);
+        npyv_store_f64(op + 0 * vstep, r_0);
+    #endif
+    
+#line 174
+    #if UNROLL > 1
+        npyv_f64 v_1 = npyv_loadn_f64(ip + 1 * vstep * istride, istride);
+        npyv_f64 r_1 = npyv_negative_f64(v_1);
+        npyv_store_f64(op + 1 * vstep, r_1);
+    #endif
+    
+#line 174
+    #if UNROLL > 2
+        npyv_f64 v_2 = npyv_loadn_f64(ip + 2 * vstep * istride, istride);
+        npyv_f64 r_2 = npyv_negative_f64(v_2);
+        npyv_store_f64(op + 2 * vstep, r_2);
+    #endif
+    
+#line 174
+    #if UNROLL > 3
+        npyv_f64 v_3 = npyv_loadn_f64(ip + 3 * vstep * istride, istride);
+        npyv_f64 r_3 = npyv_negative_f64(v_3);
+        npyv_store_f64(op + 3 * vstep, r_3);
+    #endif
+    
+#line 174
+    #if UNROLL > 4
+        npyv_f64 v_4 = npyv_loadn_f64(ip + 4 * vstep * istride, istride);
+        npyv_f64 r_4 = npyv_negative_f64(v_4);
+        npyv_store_f64(op + 4 * vstep, r_4);
+    #endif
+    
+#line 174
+    #if UNROLL > 5
+        npyv_f64 v_5 = npyv_loadn_f64(ip + 5 * vstep * istride, istride);
+        npyv_f64 r_5 = npyv_negative_f64(v_5);
+        npyv_store_f64(op + 5 * vstep, r_5);
+    #endif
+    
+#line 174
+    #if UNROLL > 6
+        npyv_f64 v_6 = npyv_loadn_f64(ip + 6 * vstep * istride, istride);
+        npyv_f64 r_6 = npyv_negative_f64(v_6);
+        npyv_store_f64(op + 6 * vstep, r_6);
+    #endif
+    
+#line 174
+    #if UNROLL > 7
+        npyv_f64 v_7 = npyv_loadn_f64(ip + 7 * vstep * istride, istride);
+        npyv_f64 r_7 = npyv_negative_f64(v_7);
+        npyv_store_f64(op + 7 * vstep, r_7);
+    #endif
+    
+#line 174
+    #if UNROLL > 8
+        npyv_f64 v_8 = npyv_loadn_f64(ip + 8 * vstep * istride, istride);
+        npyv_f64 r_8 = npyv_negative_f64(v_8);
+        npyv_store_f64(op + 8 * vstep, r_8);
+    #endif
+    
+#line 174
+    #if UNROLL > 9
+        npyv_f64 v_9 = npyv_loadn_f64(ip + 9 * vstep * istride, istride);
+        npyv_f64 r_9 = npyv_negative_f64(v_9);
+        npyv_store_f64(op + 9 * vstep, r_9);
+    #endif
+    
+#line 174
+    #if UNROLL > 10
+        npyv_f64 v_10 = npyv_loadn_f64(ip + 10 * vstep * istride, istride);
+        npyv_f64 r_10 = npyv_negative_f64(v_10);
+        npyv_store_f64(op + 10 * vstep, r_10);
+    #endif
+    
+#line 174
+    #if UNROLL > 11
+        npyv_f64 v_11 = npyv_loadn_f64(ip + 11 * vstep * istride, istride);
+        npyv_f64 r_11 = npyv_negative_f64(v_11);
+        npyv_store_f64(op + 11 * vstep, r_11);
+    #endif
+    
+#line 174
+    #if UNROLL > 12
+        npyv_f64 v_12 = npyv_loadn_f64(ip + 12 * vstep * istride, istride);
+        npyv_f64 r_12 = npyv_negative_f64(v_12);
+        npyv_store_f64(op + 12 * vstep, r_12);
+    #endif
+    
+#line 174
+    #if UNROLL > 13
+        npyv_f64 v_13 = npyv_loadn_f64(ip + 13 * vstep * istride, istride);
+        npyv_f64 r_13 = npyv_negative_f64(v_13);
+        npyv_store_f64(op + 13 * vstep, r_13);
+    #endif
+    
+#line 174
+    #if UNROLL > 14
+        npyv_f64 v_14 = npyv_loadn_f64(ip + 14 * vstep * istride, istride);
+        npyv_f64 r_14 = npyv_negative_f64(v_14);
+        npyv_store_f64(op + 14 * vstep, r_14);
+    #endif
+    
+#line 174
+    #if UNROLL > 15
+        npyv_f64 v_15 = npyv_loadn_f64(ip + 15 * vstep * istride, istride);
+        npyv_f64 r_15 = npyv_negative_f64(v_15);
+        npyv_store_f64(op + 15 * vstep, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+        npyv_f64 r = npyv_negative_f64(v);
+        npyv_store_f64(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_negative(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_negative_f64(const npyv_lanetype_f64 *ip, npy_intp istride,
+                             npyv_lanetype_f64 *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    #line 211
+    #if UNROLL > 0
+        npyv_f64 v_0 = npyv_loadn_f64(ip + 0 * vstep * istride, istride);
+        npyv_f64 r_0 = npyv_negative_f64(v_0);
+        npyv_storen_f64(op + 0 * vstep * ostride, ostride, r_0);
+    #endif
+    
+#line 211
+    #if UNROLL > 1
+        npyv_f64 v_1 = npyv_loadn_f64(ip + 1 * vstep * istride, istride);
+        npyv_f64 r_1 = npyv_negative_f64(v_1);
+        npyv_storen_f64(op + 1 * vstep * ostride, ostride, r_1);
+    #endif
+    
+#line 211
+    #if UNROLL > 2
+        npyv_f64 v_2 = npyv_loadn_f64(ip + 2 * vstep * istride, istride);
+        npyv_f64 r_2 = npyv_negative_f64(v_2);
+        npyv_storen_f64(op + 2 * vstep * ostride, ostride, r_2);
+    #endif
+    
+#line 211
+    #if UNROLL > 3
+        npyv_f64 v_3 = npyv_loadn_f64(ip + 3 * vstep * istride, istride);
+        npyv_f64 r_3 = npyv_negative_f64(v_3);
+        npyv_storen_f64(op + 3 * vstep * ostride, ostride, r_3);
+    #endif
+    
+#line 211
+    #if UNROLL > 4
+        npyv_f64 v_4 = npyv_loadn_f64(ip + 4 * vstep * istride, istride);
+        npyv_f64 r_4 = npyv_negative_f64(v_4);
+        npyv_storen_f64(op + 4 * vstep * ostride, ostride, r_4);
+    #endif
+    
+#line 211
+    #if UNROLL > 5
+        npyv_f64 v_5 = npyv_loadn_f64(ip + 5 * vstep * istride, istride);
+        npyv_f64 r_5 = npyv_negative_f64(v_5);
+        npyv_storen_f64(op + 5 * vstep * ostride, ostride, r_5);
+    #endif
+    
+#line 211
+    #if UNROLL > 6
+        npyv_f64 v_6 = npyv_loadn_f64(ip + 6 * vstep * istride, istride);
+        npyv_f64 r_6 = npyv_negative_f64(v_6);
+        npyv_storen_f64(op + 6 * vstep * ostride, ostride, r_6);
+    #endif
+    
+#line 211
+    #if UNROLL > 7
+        npyv_f64 v_7 = npyv_loadn_f64(ip + 7 * vstep * istride, istride);
+        npyv_f64 r_7 = npyv_negative_f64(v_7);
+        npyv_storen_f64(op + 7 * vstep * ostride, ostride, r_7);
+    #endif
+    
+#line 211
+    #if UNROLL > 8
+        npyv_f64 v_8 = npyv_loadn_f64(ip + 8 * vstep * istride, istride);
+        npyv_f64 r_8 = npyv_negative_f64(v_8);
+        npyv_storen_f64(op + 8 * vstep * ostride, ostride, r_8);
+    #endif
+    
+#line 211
+    #if UNROLL > 9
+        npyv_f64 v_9 = npyv_loadn_f64(ip + 9 * vstep * istride, istride);
+        npyv_f64 r_9 = npyv_negative_f64(v_9);
+        npyv_storen_f64(op + 9 * vstep * ostride, ostride, r_9);
+    #endif
+    
+#line 211
+    #if UNROLL > 10
+        npyv_f64 v_10 = npyv_loadn_f64(ip + 10 * vstep * istride, istride);
+        npyv_f64 r_10 = npyv_negative_f64(v_10);
+        npyv_storen_f64(op + 10 * vstep * ostride, ostride, r_10);
+    #endif
+    
+#line 211
+    #if UNROLL > 11
+        npyv_f64 v_11 = npyv_loadn_f64(ip + 11 * vstep * istride, istride);
+        npyv_f64 r_11 = npyv_negative_f64(v_11);
+        npyv_storen_f64(op + 11 * vstep * ostride, ostride, r_11);
+    #endif
+    
+#line 211
+    #if UNROLL > 12
+        npyv_f64 v_12 = npyv_loadn_f64(ip + 12 * vstep * istride, istride);
+        npyv_f64 r_12 = npyv_negative_f64(v_12);
+        npyv_storen_f64(op + 12 * vstep * ostride, ostride, r_12);
+    #endif
+    
+#line 211
+    #if UNROLL > 13
+        npyv_f64 v_13 = npyv_loadn_f64(ip + 13 * vstep * istride, istride);
+        npyv_f64 r_13 = npyv_negative_f64(v_13);
+        npyv_storen_f64(op + 13 * vstep * ostride, ostride, r_13);
+    #endif
+    
+#line 211
+    #if UNROLL > 14
+        npyv_f64 v_14 = npyv_loadn_f64(ip + 14 * vstep * istride, istride);
+        npyv_f64 r_14 = npyv_negative_f64(v_14);
+        npyv_storen_f64(op + 14 * vstep * ostride, ostride, r_14);
+    #endif
+    
+#line 211
+    #if UNROLL > 15
+        npyv_f64 v_15 = npyv_loadn_f64(ip + 15 * vstep * istride, istride);
+        npyv_f64 r_15 = npyv_negative_f64(v_15);
+        npyv_storen_f64(op + 15 * vstep * ostride, ostride, r_15);
+    #endif
+    
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+        npyv_f64 r = npyv_negative_f64(v);
+        npyv_storen_f64(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_negative(*ip);
+    }
+}
+#endif // 1
+#undef UNROLL
+#endif // NPY_SIMD_F64
+/*end repeat1**/
+
+
+/********************************************************************************
+ ** Defining ufunc inner functions
+ ********************************************************************************/
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UBYTE_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_ubyte, npy_ubyte)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 0
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 0
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_ubyte in_0 = *((const npy_ubyte *)(ip + 0 * istep));
+        *((npy_ubyte *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_ubyte in_1 = *((const npy_ubyte *)(ip + 1 * istep));
+        *((npy_ubyte *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_ubyte in_2 = *((const npy_ubyte *)(ip + 2 * istep));
+        *((npy_ubyte *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_ubyte in_3 = *((const npy_ubyte *)(ip + 3 * istep));
+        *((npy_ubyte *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_ubyte in_4 = *((const npy_ubyte *)(ip + 4 * istep));
+        *((npy_ubyte *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_ubyte in_5 = *((const npy_ubyte *)(ip + 5 * istep));
+        *((npy_ubyte *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_ubyte in_6 = *((const npy_ubyte *)(ip + 6 * istep));
+        *((npy_ubyte *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_ubyte in_7 = *((const npy_ubyte *)(ip + 7 * istep));
+        *((npy_ubyte *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_ubyte in_8 = *((const npy_ubyte *)(ip + 8 * istep));
+        *((npy_ubyte *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_ubyte in_9 = *((const npy_ubyte *)(ip + 9 * istep));
+        *((npy_ubyte *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_ubyte in_10 = *((const npy_ubyte *)(ip + 10 * istep));
+        *((npy_ubyte *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_ubyte in_11 = *((const npy_ubyte *)(ip + 11 * istep));
+        *((npy_ubyte *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_ubyte in_12 = *((const npy_ubyte *)(ip + 12 * istep));
+        *((npy_ubyte *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_ubyte in_13 = *((const npy_ubyte *)(ip + 13 * istep));
+        *((npy_ubyte *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_ubyte in_14 = *((const npy_ubyte *)(ip + 14 * istep));
+        *((npy_ubyte *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_ubyte in_15 = *((const npy_ubyte *)(ip + 15 * istep));
+        *((npy_ubyte *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_ubyte *)op) = scalar_negative(*(const npy_ubyte *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(USHORT_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_ushort, npy_ushort)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 0
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 0
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_ushort in_0 = *((const npy_ushort *)(ip + 0 * istep));
+        *((npy_ushort *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_ushort in_1 = *((const npy_ushort *)(ip + 1 * istep));
+        *((npy_ushort *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_ushort in_2 = *((const npy_ushort *)(ip + 2 * istep));
+        *((npy_ushort *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_ushort in_3 = *((const npy_ushort *)(ip + 3 * istep));
+        *((npy_ushort *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_ushort in_4 = *((const npy_ushort *)(ip + 4 * istep));
+        *((npy_ushort *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_ushort in_5 = *((const npy_ushort *)(ip + 5 * istep));
+        *((npy_ushort *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_ushort in_6 = *((const npy_ushort *)(ip + 6 * istep));
+        *((npy_ushort *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_ushort in_7 = *((const npy_ushort *)(ip + 7 * istep));
+        *((npy_ushort *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_ushort in_8 = *((const npy_ushort *)(ip + 8 * istep));
+        *((npy_ushort *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_ushort in_9 = *((const npy_ushort *)(ip + 9 * istep));
+        *((npy_ushort *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_ushort in_10 = *((const npy_ushort *)(ip + 10 * istep));
+        *((npy_ushort *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_ushort in_11 = *((const npy_ushort *)(ip + 11 * istep));
+        *((npy_ushort *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_ushort in_12 = *((const npy_ushort *)(ip + 12 * istep));
+        *((npy_ushort *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_ushort in_13 = *((const npy_ushort *)(ip + 13 * istep));
+        *((npy_ushort *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_ushort in_14 = *((const npy_ushort *)(ip + 14 * istep));
+        *((npy_ushort *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_ushort in_15 = *((const npy_ushort *)(ip + 15 * istep));
+        *((npy_ushort *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_ushort *)op) = scalar_negative(*(const npy_ushort *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(UINT_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_uint, npy_uint)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_uint in_0 = *((const npy_uint *)(ip + 0 * istep));
+        *((npy_uint *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_uint in_1 = *((const npy_uint *)(ip + 1 * istep));
+        *((npy_uint *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_uint in_2 = *((const npy_uint *)(ip + 2 * istep));
+        *((npy_uint *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_uint in_3 = *((const npy_uint *)(ip + 3 * istep));
+        *((npy_uint *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_uint in_4 = *((const npy_uint *)(ip + 4 * istep));
+        *((npy_uint *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_uint in_5 = *((const npy_uint *)(ip + 5 * istep));
+        *((npy_uint *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_uint in_6 = *((const npy_uint *)(ip + 6 * istep));
+        *((npy_uint *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_uint in_7 = *((const npy_uint *)(ip + 7 * istep));
+        *((npy_uint *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_uint in_8 = *((const npy_uint *)(ip + 8 * istep));
+        *((npy_uint *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_uint in_9 = *((const npy_uint *)(ip + 9 * istep));
+        *((npy_uint *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_uint in_10 = *((const npy_uint *)(ip + 10 * istep));
+        *((npy_uint *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_uint in_11 = *((const npy_uint *)(ip + 11 * istep));
+        *((npy_uint *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_uint in_12 = *((const npy_uint *)(ip + 12 * istep));
+        *((npy_uint *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_uint in_13 = *((const npy_uint *)(ip + 13 * istep));
+        *((npy_uint *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_uint in_14 = *((const npy_uint *)(ip + 14 * istep));
+        *((npy_uint *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_uint in_15 = *((const npy_uint *)(ip + 15 * istep));
+        *((npy_uint *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_uint *)op) = scalar_negative(*(const npy_uint *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONG_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_ulong, npy_ulong)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_ulong in_0 = *((const npy_ulong *)(ip + 0 * istep));
+        *((npy_ulong *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_ulong in_1 = *((const npy_ulong *)(ip + 1 * istep));
+        *((npy_ulong *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_ulong in_2 = *((const npy_ulong *)(ip + 2 * istep));
+        *((npy_ulong *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_ulong in_3 = *((const npy_ulong *)(ip + 3 * istep));
+        *((npy_ulong *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_ulong in_4 = *((const npy_ulong *)(ip + 4 * istep));
+        *((npy_ulong *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_ulong in_5 = *((const npy_ulong *)(ip + 5 * istep));
+        *((npy_ulong *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_ulong in_6 = *((const npy_ulong *)(ip + 6 * istep));
+        *((npy_ulong *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_ulong in_7 = *((const npy_ulong *)(ip + 7 * istep));
+        *((npy_ulong *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_ulong in_8 = *((const npy_ulong *)(ip + 8 * istep));
+        *((npy_ulong *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_ulong in_9 = *((const npy_ulong *)(ip + 9 * istep));
+        *((npy_ulong *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_ulong in_10 = *((const npy_ulong *)(ip + 10 * istep));
+        *((npy_ulong *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_ulong in_11 = *((const npy_ulong *)(ip + 11 * istep));
+        *((npy_ulong *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_ulong in_12 = *((const npy_ulong *)(ip + 12 * istep));
+        *((npy_ulong *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_ulong in_13 = *((const npy_ulong *)(ip + 13 * istep));
+        *((npy_ulong *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_ulong in_14 = *((const npy_ulong *)(ip + 14 * istep));
+        *((npy_ulong *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_ulong in_15 = *((const npy_ulong *)(ip + 15 * istep));
+        *((npy_ulong *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_ulong *)op) = scalar_negative(*(const npy_ulong *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 1
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(ULONGLONG_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_ulonglong, npy_ulonglong)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_ulonglong in_0 = *((const npy_ulonglong *)(ip + 0 * istep));
+        *((npy_ulonglong *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_ulonglong in_1 = *((const npy_ulonglong *)(ip + 1 * istep));
+        *((npy_ulonglong *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_ulonglong in_2 = *((const npy_ulonglong *)(ip + 2 * istep));
+        *((npy_ulonglong *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_ulonglong in_3 = *((const npy_ulonglong *)(ip + 3 * istep));
+        *((npy_ulonglong *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_ulonglong in_4 = *((const npy_ulonglong *)(ip + 4 * istep));
+        *((npy_ulonglong *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_ulonglong in_5 = *((const npy_ulonglong *)(ip + 5 * istep));
+        *((npy_ulonglong *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_ulonglong in_6 = *((const npy_ulonglong *)(ip + 6 * istep));
+        *((npy_ulonglong *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_ulonglong in_7 = *((const npy_ulonglong *)(ip + 7 * istep));
+        *((npy_ulonglong *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_ulonglong in_8 = *((const npy_ulonglong *)(ip + 8 * istep));
+        *((npy_ulonglong *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_ulonglong in_9 = *((const npy_ulonglong *)(ip + 9 * istep));
+        *((npy_ulonglong *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_ulonglong in_10 = *((const npy_ulonglong *)(ip + 10 * istep));
+        *((npy_ulonglong *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_ulonglong in_11 = *((const npy_ulonglong *)(ip + 11 * istep));
+        *((npy_ulonglong *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_ulonglong in_12 = *((const npy_ulonglong *)(ip + 12 * istep));
+        *((npy_ulonglong *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_ulonglong in_13 = *((const npy_ulonglong *)(ip + 13 * istep));
+        *((npy_ulonglong *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_ulonglong in_14 = *((const npy_ulonglong *)(ip + 14 * istep));
+        *((npy_ulonglong *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_ulonglong in_15 = *((const npy_ulonglong *)(ip + 15 * istep));
+        *((npy_ulonglong *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_ulonglong *)op) = scalar_negative(*(const npy_ulonglong *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_BYTE == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_BYTE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_BYTE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(BYTE_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_byte, npy_byte)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 0
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 0
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_byte in_0 = *((const npy_byte *)(ip + 0 * istep));
+        *((npy_byte *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_byte in_1 = *((const npy_byte *)(ip + 1 * istep));
+        *((npy_byte *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_byte in_2 = *((const npy_byte *)(ip + 2 * istep));
+        *((npy_byte *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_byte in_3 = *((const npy_byte *)(ip + 3 * istep));
+        *((npy_byte *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_byte in_4 = *((const npy_byte *)(ip + 4 * istep));
+        *((npy_byte *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_byte in_5 = *((const npy_byte *)(ip + 5 * istep));
+        *((npy_byte *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_byte in_6 = *((const npy_byte *)(ip + 6 * istep));
+        *((npy_byte *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_byte in_7 = *((const npy_byte *)(ip + 7 * istep));
+        *((npy_byte *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_byte in_8 = *((const npy_byte *)(ip + 8 * istep));
+        *((npy_byte *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_byte in_9 = *((const npy_byte *)(ip + 9 * istep));
+        *((npy_byte *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_byte in_10 = *((const npy_byte *)(ip + 10 * istep));
+        *((npy_byte *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_byte in_11 = *((const npy_byte *)(ip + 11 * istep));
+        *((npy_byte *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_byte in_12 = *((const npy_byte *)(ip + 12 * istep));
+        *((npy_byte *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_byte in_13 = *((const npy_byte *)(ip + 13 * istep));
+        *((npy_byte *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_byte in_14 = *((const npy_byte *)(ip + 14 * istep));
+        *((npy_byte *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_byte in_15 = *((const npy_byte *)(ip + 15 * istep));
+        *((npy_byte *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_byte *)op) = scalar_negative(*(const npy_byte *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_SHORT == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_SHORT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_SHORT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(SHORT_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_short, npy_short)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 0
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 0
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_short in_0 = *((const npy_short *)(ip + 0 * istep));
+        *((npy_short *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_short in_1 = *((const npy_short *)(ip + 1 * istep));
+        *((npy_short *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_short in_2 = *((const npy_short *)(ip + 2 * istep));
+        *((npy_short *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_short in_3 = *((const npy_short *)(ip + 3 * istep));
+        *((npy_short *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_short in_4 = *((const npy_short *)(ip + 4 * istep));
+        *((npy_short *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_short in_5 = *((const npy_short *)(ip + 5 * istep));
+        *((npy_short *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_short in_6 = *((const npy_short *)(ip + 6 * istep));
+        *((npy_short *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_short in_7 = *((const npy_short *)(ip + 7 * istep));
+        *((npy_short *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_short in_8 = *((const npy_short *)(ip + 8 * istep));
+        *((npy_short *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_short in_9 = *((const npy_short *)(ip + 9 * istep));
+        *((npy_short *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_short in_10 = *((const npy_short *)(ip + 10 * istep));
+        *((npy_short *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_short in_11 = *((const npy_short *)(ip + 11 * istep));
+        *((npy_short *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_short in_12 = *((const npy_short *)(ip + 12 * istep));
+        *((npy_short *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_short in_13 = *((const npy_short *)(ip + 13 * istep));
+        *((npy_short *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_short in_14 = *((const npy_short *)(ip + 14 * istep));
+        *((npy_short *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_short in_15 = *((const npy_short *)(ip + 15 * istep));
+        *((npy_short *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_short *)op) = scalar_negative(*(const npy_short *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_INT == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_INT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_INT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(INT_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_int, npy_int)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_int in_0 = *((const npy_int *)(ip + 0 * istep));
+        *((npy_int *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_int in_1 = *((const npy_int *)(ip + 1 * istep));
+        *((npy_int *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_int in_2 = *((const npy_int *)(ip + 2 * istep));
+        *((npy_int *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_int in_3 = *((const npy_int *)(ip + 3 * istep));
+        *((npy_int *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_int in_4 = *((const npy_int *)(ip + 4 * istep));
+        *((npy_int *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_int in_5 = *((const npy_int *)(ip + 5 * istep));
+        *((npy_int *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_int in_6 = *((const npy_int *)(ip + 6 * istep));
+        *((npy_int *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_int in_7 = *((const npy_int *)(ip + 7 * istep));
+        *((npy_int *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_int in_8 = *((const npy_int *)(ip + 8 * istep));
+        *((npy_int *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_int in_9 = *((const npy_int *)(ip + 9 * istep));
+        *((npy_int *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_int in_10 = *((const npy_int *)(ip + 10 * istep));
+        *((npy_int *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_int in_11 = *((const npy_int *)(ip + 11 * istep));
+        *((npy_int *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_int in_12 = *((const npy_int *)(ip + 12 * istep));
+        *((npy_int *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_int in_13 = *((const npy_int *)(ip + 13 * istep));
+        *((npy_int *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_int in_14 = *((const npy_int *)(ip + 14 * istep));
+        *((npy_int *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_int in_15 = *((const npy_int *)(ip + 15 * istep));
+        *((npy_int *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_int *)op) = scalar_negative(*(const npy_int *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONG == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_LONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONG_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_long, npy_long)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_long in_0 = *((const npy_long *)(ip + 0 * istep));
+        *((npy_long *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_long in_1 = *((const npy_long *)(ip + 1 * istep));
+        *((npy_long *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_long in_2 = *((const npy_long *)(ip + 2 * istep));
+        *((npy_long *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_long in_3 = *((const npy_long *)(ip + 3 * istep));
+        *((npy_long *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_long in_4 = *((const npy_long *)(ip + 4 * istep));
+        *((npy_long *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_long in_5 = *((const npy_long *)(ip + 5 * istep));
+        *((npy_long *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_long in_6 = *((const npy_long *)(ip + 6 * istep));
+        *((npy_long *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_long in_7 = *((const npy_long *)(ip + 7 * istep));
+        *((npy_long *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_long in_8 = *((const npy_long *)(ip + 8 * istep));
+        *((npy_long *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_long in_9 = *((const npy_long *)(ip + 9 * istep));
+        *((npy_long *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_long in_10 = *((const npy_long *)(ip + 10 * istep));
+        *((npy_long *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_long in_11 = *((const npy_long *)(ip + 11 * istep));
+        *((npy_long *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_long in_12 = *((const npy_long *)(ip + 12 * istep));
+        *((npy_long *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_long in_13 = *((const npy_long *)(ip + 13 * istep));
+        *((npy_long *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_long in_14 = *((const npy_long *)(ip + 14 * istep));
+        *((npy_long *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_long in_15 = *((const npy_long *)(ip + 15 * istep));
+        *((npy_long *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_long *)op) = scalar_negative(*(const npy_long *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 8
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 16
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 32
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGLONG == 64
+    #if 0
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_LONGLONG == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGLONG == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGLONG_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_longlong, npy_longlong)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_longlong in_0 = *((const npy_longlong *)(ip + 0 * istep));
+        *((npy_longlong *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_longlong in_1 = *((const npy_longlong *)(ip + 1 * istep));
+        *((npy_longlong *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_longlong in_2 = *((const npy_longlong *)(ip + 2 * istep));
+        *((npy_longlong *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_longlong in_3 = *((const npy_longlong *)(ip + 3 * istep));
+        *((npy_longlong *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_longlong in_4 = *((const npy_longlong *)(ip + 4 * istep));
+        *((npy_longlong *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_longlong in_5 = *((const npy_longlong *)(ip + 5 * istep));
+        *((npy_longlong *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_longlong in_6 = *((const npy_longlong *)(ip + 6 * istep));
+        *((npy_longlong *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_longlong in_7 = *((const npy_longlong *)(ip + 7 * istep));
+        *((npy_longlong *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_longlong in_8 = *((const npy_longlong *)(ip + 8 * istep));
+        *((npy_longlong *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_longlong in_9 = *((const npy_longlong *)(ip + 9 * istep));
+        *((npy_longlong *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_longlong in_10 = *((const npy_longlong *)(ip + 10 * istep));
+        *((npy_longlong *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_longlong in_11 = *((const npy_longlong *)(ip + 11 * istep));
+        *((npy_longlong *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_longlong in_12 = *((const npy_longlong *)(ip + 12 * istep));
+        *((npy_longlong *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_longlong in_13 = *((const npy_longlong *)(ip + 13 * istep));
+        *((npy_longlong *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_longlong in_14 = *((const npy_longlong *)(ip + 14 * istep));
+        *((npy_longlong *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_longlong in_15 = *((const npy_longlong *)(ip + 15 * istep));
+        *((npy_longlong *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_longlong *)op) = scalar_negative(*(const npy_longlong *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 0
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_FLOAT == 8
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_FLOAT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_FLOAT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_FLOAT == 16
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_FLOAT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_FLOAT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_FLOAT == 32
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_FLOAT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_FLOAT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_FLOAT == 64
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_FLOAT == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_FLOAT == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_float, npy_float)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_float in_0 = *((const npy_float *)(ip + 0 * istep));
+        *((npy_float *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_float in_1 = *((const npy_float *)(ip + 1 * istep));
+        *((npy_float *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_float in_2 = *((const npy_float *)(ip + 2 * istep));
+        *((npy_float *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_float in_3 = *((const npy_float *)(ip + 3 * istep));
+        *((npy_float *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_float in_4 = *((const npy_float *)(ip + 4 * istep));
+        *((npy_float *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_float in_5 = *((const npy_float *)(ip + 5 * istep));
+        *((npy_float *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_float in_6 = *((const npy_float *)(ip + 6 * istep));
+        *((npy_float *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_float in_7 = *((const npy_float *)(ip + 7 * istep));
+        *((npy_float *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_float in_8 = *((const npy_float *)(ip + 8 * istep));
+        *((npy_float *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_float in_9 = *((const npy_float *)(ip + 9 * istep));
+        *((npy_float *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_float in_10 = *((const npy_float *)(ip + 10 * istep));
+        *((npy_float *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_float in_11 = *((const npy_float *)(ip + 11 * istep));
+        *((npy_float *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_float in_12 = *((const npy_float *)(ip + 12 * istep));
+        *((npy_float *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_float in_13 = *((const npy_float *)(ip + 13 * istep));
+        *((npy_float *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_float in_14 = *((const npy_float *)(ip + 14 * istep));
+        *((npy_float *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_float in_15 = *((const npy_float *)(ip + 15 * istep));
+        *((npy_float *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_float *)op) = scalar_negative(*(const npy_float *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 1
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_DOUBLE == 8
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_DOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_DOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_DOUBLE == 16
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_DOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_DOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_DOUBLE == 32
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_DOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_DOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_DOUBLE == 64
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_DOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_DOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_double, npy_double)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_double in_0 = *((const npy_double *)(ip + 0 * istep));
+        *((npy_double *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_double in_1 = *((const npy_double *)(ip + 1 * istep));
+        *((npy_double *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_double in_2 = *((const npy_double *)(ip + 2 * istep));
+        *((npy_double *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_double in_3 = *((const npy_double *)(ip + 3 * istep));
+        *((npy_double *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_double in_4 = *((const npy_double *)(ip + 4 * istep));
+        *((npy_double *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_double in_5 = *((const npy_double *)(ip + 5 * istep));
+        *((npy_double *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_double in_6 = *((const npy_double *)(ip + 6 * istep));
+        *((npy_double *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_double in_7 = *((const npy_double *)(ip + 7 * istep));
+        *((npy_double *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_double in_8 = *((const npy_double *)(ip + 8 * istep));
+        *((npy_double *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_double in_9 = *((const npy_double *)(ip + 9 * istep));
+        *((npy_double *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_double in_10 = *((const npy_double *)(ip + 10 * istep));
+        *((npy_double *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_double in_11 = *((const npy_double *)(ip + 11 * istep));
+        *((npy_double *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_double in_12 = *((const npy_double *)(ip + 12 * istep));
+        *((npy_double *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_double in_13 = *((const npy_double *)(ip + 13 * istep));
+        *((npy_double *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_double in_14 = *((const npy_double *)(ip + 14 * istep));
+        *((npy_double *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_double in_15 = *((const npy_double *)(ip + 15 * istep));
+        *((npy_double *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_double *)op) = scalar_negative(*(const npy_double *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 1
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+#line 254
+#undef TO_SIMD_SFX
+#if 0
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGDOUBLE == 8
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f8
+        #if NPY_BITSOF_LONGDOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGDOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u8
+    #else
+        #define TO_SIMD_SFX(X) X##_s8
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGDOUBLE == 16
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f16
+        #if NPY_BITSOF_LONGDOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGDOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u16
+    #else
+        #define TO_SIMD_SFX(X) X##_s16
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGDOUBLE == 32
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f32
+        #if NPY_BITSOF_LONGDOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGDOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u32
+    #else
+        #define TO_SIMD_SFX(X) X##_s32
+    #endif
+
+#line 259
+#elif NPY_SIMD && NPY_BITSOF_LONGDOUBLE == 64
+    #if 1
+        #define TO_SIMD_SFX(X) X##_f64
+        #if NPY_BITSOF_LONGDOUBLE == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_LONGDOUBLE == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif 0
+        #define TO_SIMD_SFX(X) X##_u64
+    #else
+        #define TO_SIMD_SFX(X) X##_s64
+    #endif
+
+#endif
+
+#line 280
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(LONGDOUBLE_negative)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(npy_longdouble, npy_longdouble)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_negative)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if 1
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_negative)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_negative)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // 1
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    #line 344
+    #if UNROLL > 0
+        const npy_longdouble in_0 = *((const npy_longdouble *)(ip + 0 * istep));
+        *((npy_longdouble *)(op + 0 * ostep)) = scalar_negative(in_0);
+    #endif
+    
+#line 344
+    #if UNROLL > 1
+        const npy_longdouble in_1 = *((const npy_longdouble *)(ip + 1 * istep));
+        *((npy_longdouble *)(op + 1 * ostep)) = scalar_negative(in_1);
+    #endif
+    
+#line 344
+    #if UNROLL > 2
+        const npy_longdouble in_2 = *((const npy_longdouble *)(ip + 2 * istep));
+        *((npy_longdouble *)(op + 2 * ostep)) = scalar_negative(in_2);
+    #endif
+    
+#line 344
+    #if UNROLL > 3
+        const npy_longdouble in_3 = *((const npy_longdouble *)(ip + 3 * istep));
+        *((npy_longdouble *)(op + 3 * ostep)) = scalar_negative(in_3);
+    #endif
+    
+#line 344
+    #if UNROLL > 4
+        const npy_longdouble in_4 = *((const npy_longdouble *)(ip + 4 * istep));
+        *((npy_longdouble *)(op + 4 * ostep)) = scalar_negative(in_4);
+    #endif
+    
+#line 344
+    #if UNROLL > 5
+        const npy_longdouble in_5 = *((const npy_longdouble *)(ip + 5 * istep));
+        *((npy_longdouble *)(op + 5 * ostep)) = scalar_negative(in_5);
+    #endif
+    
+#line 344
+    #if UNROLL > 6
+        const npy_longdouble in_6 = *((const npy_longdouble *)(ip + 6 * istep));
+        *((npy_longdouble *)(op + 6 * ostep)) = scalar_negative(in_6);
+    #endif
+    
+#line 344
+    #if UNROLL > 7
+        const npy_longdouble in_7 = *((const npy_longdouble *)(ip + 7 * istep));
+        *((npy_longdouble *)(op + 7 * ostep)) = scalar_negative(in_7);
+    #endif
+    
+#line 344
+    #if UNROLL > 8
+        const npy_longdouble in_8 = *((const npy_longdouble *)(ip + 8 * istep));
+        *((npy_longdouble *)(op + 8 * ostep)) = scalar_negative(in_8);
+    #endif
+    
+#line 344
+    #if UNROLL > 9
+        const npy_longdouble in_9 = *((const npy_longdouble *)(ip + 9 * istep));
+        *((npy_longdouble *)(op + 9 * ostep)) = scalar_negative(in_9);
+    #endif
+    
+#line 344
+    #if UNROLL > 10
+        const npy_longdouble in_10 = *((const npy_longdouble *)(ip + 10 * istep));
+        *((npy_longdouble *)(op + 10 * ostep)) = scalar_negative(in_10);
+    #endif
+    
+#line 344
+    #if UNROLL > 11
+        const npy_longdouble in_11 = *((const npy_longdouble *)(ip + 11 * istep));
+        *((npy_longdouble *)(op + 11 * ostep)) = scalar_negative(in_11);
+    #endif
+    
+#line 344
+    #if UNROLL > 12
+        const npy_longdouble in_12 = *((const npy_longdouble *)(ip + 12 * istep));
+        *((npy_longdouble *)(op + 12 * ostep)) = scalar_negative(in_12);
+    #endif
+    
+#line 344
+    #if UNROLL > 13
+        const npy_longdouble in_13 = *((const npy_longdouble *)(ip + 13 * istep));
+        *((npy_longdouble *)(op + 13 * ostep)) = scalar_negative(in_13);
+    #endif
+    
+#line 344
+    #if UNROLL > 14
+        const npy_longdouble in_14 = *((const npy_longdouble *)(ip + 14 * istep));
+        *((npy_longdouble *)(op + 14 * ostep)) = scalar_negative(in_14);
+    #endif
+    
+#line 344
+    #if UNROLL > 15
+        const npy_longdouble in_15 = *((const npy_longdouble *)(ip + 15 * istep));
+        *((npy_longdouble *)(op + 15 * ostep)) = scalar_negative(in_15);
+    #endif
+    
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((npy_longdouble *)op) = scalar_negative(*(const npy_longdouble *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if 1
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+
+
+#undef NEGATIVE_CONTIG_ONLY
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.c.src
new file mode 100644
index 00000000000..1e2a81d20b2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.c.src
@@ -0,0 +1,364 @@
+/*@targets
+ ** $maxopt baseline
+ ** neon asimd
+ ** sse2 avx2 avx512_skx
+ ** vsx2
+ ** vx vxe
+ **/
+
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "numpy/npy_math.h"
+#include "simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/*******************************************************************************
+ ** Scalar ops
+ ******************************************************************************/
+#define scalar_negative(X) (-X)
+
+/*******************************************************************************
+ ** extra SIMD intrinsics
+ ******************************************************************************/
+
+#if NPY_SIMD
+
+/**begin repeat
+ * #sfx  = s8, u8, s16, u16, s32, u32, s64, u64#
+ * #ssfx =  8,  8,  16,  16,  32,  32,  64,  64#
+ */
+static NPY_INLINE npyv_@sfx@
+npyv_negative_@sfx@(npyv_@sfx@ v)
+{
+#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || @ssfx@ < 64)
+    return npyv_reinterpret_@sfx@_s@ssfx@(vnegq_s@ssfx@(npyv_reinterpret_s@ssfx@_@sfx@(v)));
+#else
+    // (x ^ -1) + 1
+    const npyv_@sfx@ m1 = npyv_setall_@sfx@((npyv_lanetype_@sfx@)-1);
+    return npyv_sub_@sfx@(npyv_xor_@sfx@(v, m1), m1);
+#endif
+}
+/**end repeat**/
+
+/**begin repeat
+ * #sfx  = f32, f64#
+ * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #fd = f, #
+ */
+#if @VCHK@
+static NPY_INLINE npyv_@sfx@
+npyv_negative_@sfx@(npyv_@sfx@ v)
+{
+#if defined(NPY_HAVE_NEON)
+    return vnegq_@sfx@(v);
+#else
+    // (v ^ signmask)
+    const npyv_@sfx@ signmask = npyv_setall_@sfx@(-0.@fd@);
+    return npyv_xor_@sfx@(v, signmask);
+#endif
+}
+#endif // @VCHK@
+/**end repeat**/
+
+#endif // NPY_SIMD
+
+/********************************************************************************
+ ** Defining the SIMD kernels
+ ********************************************************************************/
+/**begin repeat
+ * #sfx = s8, u8, s16, u16, s32, u32, s64, u64, f32, f64#
+ * #simd_chk = NPY_SIMD*8, NPY_SIMD_F32, NPY_SIMD_F64#
+ * #is_fp = 0*8, 1*2#
+ * #supports_ncontig = 0*4,1*6#
+ */
+/**begin repeat1
+ * #kind   = negative#
+ * #intrin = negative#
+ * #unroll = 4#
+ */
+#if @simd_chk@
+#if @unroll@ < 1
+#error "Unroll must be at least 1"
+#elif NPY_SIMD != 128 && @unroll@ > 2
+// Avoid memory bandwidth bottleneck for larger SIMD
+#define UNROLL 2
+#else
+#define UNROLL @unroll@
+#endif
+// contiguous inputs and output.
+static NPY_INLINE void
+simd_unary_cc_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip,
+                             npyv_lanetype_@sfx@ *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
+    /**begin repeat2
+     * #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
+     */
+    #if UNROLL > @U@
+        npyv_@sfx@ v_@U@ = npyv_load_@sfx@(ip + @U@ * vstep);
+        npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
+        npyv_store_@sfx@(op + @U@ * vstep, r_@U@);
+    #endif
+    /**end repeat2**/
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
+        npyv_@sfx@ v = npyv_load_@sfx@(ip);
+        npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
+        npyv_store_@sfx@(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, ++op) {
+        *op = scalar_@intrin@(*ip);
+    }
+}
+
+#if @supports_ncontig@
+// contiguous input, non-contiguous output
+static NPY_INLINE void
+simd_unary_cn_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip,
+                             npyv_lanetype_@sfx@ *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
+    /**begin repeat2
+     * #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
+     */
+    #if UNROLL > @U@
+        npyv_@sfx@ v_@U@ = npyv_load_@sfx@(ip + @U@ * vstep);
+        npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
+        npyv_storen_@sfx@(op + @U@ * vstep * ostride, ostride, r_@U@);
+    #endif
+    /**end repeat2**/
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
+        npyv_@sfx@ v = npyv_load_@sfx@(ip);
+        npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
+        npyv_storen_@sfx@(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ++ip, op += ostride) {
+        *op = scalar_@intrin@(*ip);
+    }
+}
+// non-contiguous input, contiguous output
+static NPY_INLINE void
+simd_unary_nc_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip, npy_intp istride,
+                             npyv_lanetype_@sfx@ *op,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
+    /**begin repeat2
+     * #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
+     */
+    #if UNROLL > @U@
+        npyv_@sfx@ v_@U@ = npyv_loadn_@sfx@(ip + @U@ * vstep * istride, istride);
+        npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
+        npyv_store_@sfx@(op + @U@ * vstep, r_@U@);
+    #endif
+    /**end repeat2**/
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
+        npyv_@sfx@ v = npyv_loadn_@sfx@(ip, istride);
+        npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
+        npyv_store_@sfx@(op, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, ++op) {
+        *op = scalar_@intrin@(*ip);
+    }
+}
+// non-contiguous input and output
+// limit unroll to 2x
+#if UNROLL > 2
+#undef UNROLL
+#define UNROLL 2
+#endif
+static NPY_INLINE void
+simd_unary_nn_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip, npy_intp istride,
+                             npyv_lanetype_@sfx@ *op, npy_intp ostride,
+                             npy_intp len)
+{
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * UNROLL;
+
+    // unrolled vector loop
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+    /**begin repeat2
+     * #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
+     */
+    #if UNROLL > @U@
+        npyv_@sfx@ v_@U@ = npyv_loadn_@sfx@(ip + @U@ * vstep * istride, istride);
+        npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
+        npyv_storen_@sfx@(op + @U@ * vstep * ostride, ostride, r_@U@);
+    #endif
+    /**end repeat2**/
+    }
+    // single vector loop
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+        npyv_@sfx@ v = npyv_loadn_@sfx@(ip, istride);
+        npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
+        npyv_storen_@sfx@(op, ostride, r);
+    }
+    // scalar finish up any remaining iterations
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = scalar_@intrin@(*ip);
+    }
+}
+#endif // @supports_ncontig@
+#undef UNROLL
+#endif // @simd_chk@
+/*end repeat1**/
+/**end repeat**/
+
+/********************************************************************************
+ ** Defining ufunc inner functions
+ ********************************************************************************/
+/**begin repeat
+ * #TYPE = UBYTE, USHORT, UINT, ULONG, ULONGLONG,
+ *         BYTE,  SHORT,  INT,  LONG,  LONGLONG,
+ *         FLOAT, DOUBLE, LONGDOUBLE#
+ *
+ * #BTYPE = BYTE, SHORT, INT,  LONG, LONGLONG,
+ *          BYTE, SHORT, INT, LONG, LONGLONG,
+ *          FLOAT, DOUBLE, LONGDOUBLE#
+ * #type = npy_ubyte, npy_ushort, npy_uint, npy_ulong, npy_ulonglong,
+ *         npy_byte, npy_short, npy_int, npy_long, npy_longlong,
+ *         npy_float, npy_double, npy_longdouble#
+ *
+ * #is_fp = 0*10, 1*3#
+ * #is_unsigned = 1*5, 0*5, 0*3#
+ * #supports_ncontig = 0*2, 1*3, 0*2, 1*3, 1*3#
+ */
+#undef TO_SIMD_SFX
+#if 0
+/**begin repeat1
+ * #len = 8, 16, 32, 64#
+ */
+#elif NPY_SIMD && NPY_BITSOF_@BTYPE@ == @len@
+    #if @is_fp@
+        #define TO_SIMD_SFX(X) X##_f@len@
+        #if NPY_BITSOF_@BTYPE@ == 32 && !NPY_SIMD_F32
+            #undef TO_SIMD_SFX
+        #endif
+        #if NPY_BITSOF_@BTYPE@ == 64 && !NPY_SIMD_F64
+            #undef TO_SIMD_SFX
+        #endif
+    #elif @is_unsigned@
+        #define TO_SIMD_SFX(X) X##_u@len@
+    #else
+        #define TO_SIMD_SFX(X) X##_s@len@
+    #endif
+/**end repeat1**/
+#endif
+
+/**begin repeat1
+ * #kind = negative#
+ * #intrin = negative#
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+    char *ip = args[0], *op = args[1];
+    npy_intp istep = steps[0], ostep = steps[1],
+             len = dimensions[0];
+#ifdef TO_SIMD_SFX
+    #undef STYPE
+    #define STYPE TO_SIMD_SFX(npyv_lanetype)
+    if (!is_mem_overlap(ip, istep, op, ostep, len)) {
+        if (IS_UNARY_CONT(@type@, @type@)) {
+            // no overlap and operands are contiguous
+            TO_SIMD_SFX(simd_unary_cc_@intrin@)(
+                (STYPE*)ip, (STYPE*)op, len
+            );
+            goto clear;
+        }
+    #if @supports_ncontig@
+        const npy_intp istride = istep / sizeof(STYPE);
+        const npy_intp ostride = ostep / sizeof(STYPE);
+        if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
+            TO_SIMD_SFX(npyv_storable_stride)(ostride))
+        {
+            if (istride == 1 && ostride != 1) {
+                // contiguous input, non-contiguous output
+                TO_SIMD_SFX(simd_unary_cn_@intrin@)(
+                    (STYPE*)ip, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+            else if (istride != 1 && ostride == 1) {
+                // non-contiguous input, contiguous output
+                TO_SIMD_SFX(simd_unary_nc_@intrin@)(
+                    (STYPE*)ip, istride, (STYPE*)op, len
+                );
+                goto clear;
+            }
+        // SSE2 does better with unrolled scalar for heavy non-contiguous
+        #if !defined(NPY_HAVE_SSE2)
+            else if (istride != 1 && ostride != 1) {
+                // non-contiguous input and output
+                TO_SIMD_SFX(simd_unary_nn_@intrin@)(
+                    (STYPE*)ip, istride, (STYPE*)op, ostride, len
+                );
+                goto clear;
+            }
+        #endif
+        }
+    #endif // @supports_ncontig@
+    }
+#endif // TO_SIMD_SFX
+#ifndef NPY_DISABLE_OPTIMIZATION
+    /*
+     * scalar unrolls
+     * 8x unroll performed best on
+     *  - Apple M1 Native / arm64
+     *  - Apple M1 Rosetta / SSE42
+     *  - iMacPro / AVX512
+     */
+    #define UNROLL 8
+    for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
+    /**begin repeat2
+     * #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
+     */
+    #if UNROLL > @U@
+        const @type@ in_@U@ = *((const @type@ *)(ip + @U@ * istep));
+        *((@type@ *)(op + @U@ * ostep)) = scalar_@intrin@(in_@U@);
+    #endif
+    /**end repeat2**/
+    }
+#endif // NPY_DISABLE_OPTIMIZATION
+    for (; len > 0; --len, ip += istep, op += ostep) {
+        *((@type@ *)op) = scalar_@intrin@(*(const @type@ *)ip);
+    }
+#ifdef TO_SIMD_SFX
+clear:
+    npyv_cleanup();
+#endif
+#if @is_fp@
+    npy_clear_floatstatus_barrier((char*)dimensions);
+#endif
+}
+/**end repeat**/
+
+#undef NEGATIVE_CONTIG_ONLY
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.h
new file mode 100644
index 00000000000..b9dbdcb1891
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary.dispatch.h
@@ -0,0 +1,20 @@
+// cache_hash:2841248447
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator (distutils/ccompiler_opt.py)
+ */
+#ifndef NPY__CPU_DISPATCH_EXPAND_
+    #define NPY__CPU_DISPATCH_EXPAND_(X) X
+#endif
+#undef NPY__CPU_DISPATCH_BASELINE_CALL
+#undef NPY__CPU_DISPATCH_CALL
+#define NPY__CPU_DISPATCH_BASELINE_CALL(CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB(__VA_ARGS__))
+#if defined(__x86_64__) || defined(_M_X64)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX512_SKX)), AVX512_SKX, __VA_ARGS__)) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX)&&CHK(F16C)&&CHK(AVX2)), AVX2, __VA_ARGS__))
+#elif defined(__aarch64__)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.avx512f.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.avx512f.c
index 60df70d7f91..070dd62ecd7 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_arithm_fp.dispatch.avx512f.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.avx512f.c
@@ -16,4 +16,4 @@
 #define NPY__CPU_TARGET_FMA3
 #define NPY__CPU_TARGET_AVX2
 #define NPY__CPU_TARGET_AVX512F
-#include "numpy/core/src/umath/loops_arithm_fp.dispatch.c"
+#include "numpy/core/src/umath/loops_unary_complex.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.c
new file mode 100644
index 00000000000..ef1df78049d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.c
@@ -0,0 +1,231 @@
+#line 1 "numpy/core/src/umath/loops_unary_complex.dispatch.c.src"
+
+/*
+ *****************************************************************************
+ **       This file was autogenerated from a template  DO NOT EDIT!!!!      **
+ **       Changes should be made to the original source (.src) file         **
+ *****************************************************************************
+ */
+
+#line 1
+/*@targets
+ ** $maxopt baseline
+ ** sse2 (avx2 fma3) avx512f
+ ** neon asimd
+ ** vsx2 vsx3
+ ** vx vxe
+ **/
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+#line 30
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_f32
+simd_cabsolute_f32(npyv_f32 re, npyv_f32 im)
+{
+    const npyv_f32 inf = npyv_setall_f32(NPY_INFINITYF);
+    const npyv_f32 nan = npyv_setall_f32(NPY_NANF);
+
+    re = npyv_abs_f32(re);
+    im = npyv_abs_f32(im);
+    /*
+     * If real or imag = INF, then convert it to inf + j*inf
+     * Handles: inf + j*nan, nan + j*inf
+     */
+    npyv_b32 re_infmask = npyv_cmpeq_f32(re, inf);
+    npyv_b32 im_infmask = npyv_cmpeq_f32(im, inf);
+    im = npyv_select_f32(re_infmask, inf, im);
+    re = npyv_select_f32(im_infmask, inf, re);
+    /*
+     * If real or imag = NAN, then convert it to nan + j*nan
+     * Handles: x + j*nan, nan + j*x
+     */
+    npyv_b32 re_nnanmask = npyv_notnan_f32(re);
+    npyv_b32 im_nnanmask = npyv_notnan_f32(im);
+    im = npyv_select_f32(re_nnanmask, im, nan);
+    re = npyv_select_f32(im_nnanmask, re, nan);
+
+    npyv_f32 larger  = npyv_max_f32(re, im);
+    npyv_f32 smaller = npyv_min_f32(im, re);
+    /*
+     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
+     */
+    npyv_b32 zeromask = npyv_cmpeq_f32(larger, npyv_zero_f32());
+    npyv_b32 infmask = npyv_cmpeq_f32(smaller, inf);
+    npyv_b32 div_mask = npyv_not_b32(npyv_or_b32(zeromask, infmask));
+
+    npyv_f32 ratio = npyv_ifdivz_f32(div_mask, smaller, larger);
+    npyv_f32 hypot = npyv_sqrt_f32(
+        npyv_muladd_f32(ratio, ratio, npyv_setall_f32(1.0f)
+    ));
+    return npyv_mul_f32(hypot, larger);
+}
+#endif // VECTOR
+
+#line 30
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_f64
+simd_cabsolute_f64(npyv_f64 re, npyv_f64 im)
+{
+    const npyv_f64 inf = npyv_setall_f64(NPY_INFINITY);
+    const npyv_f64 nan = npyv_setall_f64(NPY_NAN);
+
+    re = npyv_abs_f64(re);
+    im = npyv_abs_f64(im);
+    /*
+     * If real or imag = INF, then convert it to inf + j*inf
+     * Handles: inf + j*nan, nan + j*inf
+     */
+    npyv_b64 re_infmask = npyv_cmpeq_f64(re, inf);
+    npyv_b64 im_infmask = npyv_cmpeq_f64(im, inf);
+    im = npyv_select_f64(re_infmask, inf, im);
+    re = npyv_select_f64(im_infmask, inf, re);
+    /*
+     * If real or imag = NAN, then convert it to nan + j*nan
+     * Handles: x + j*nan, nan + j*x
+     */
+    npyv_b64 re_nnanmask = npyv_notnan_f64(re);
+    npyv_b64 im_nnanmask = npyv_notnan_f64(im);
+    im = npyv_select_f64(re_nnanmask, im, nan);
+    re = npyv_select_f64(im_nnanmask, re, nan);
+
+    npyv_f64 larger  = npyv_max_f64(re, im);
+    npyv_f64 smaller = npyv_min_f64(im, re);
+    /*
+     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
+     */
+    npyv_b64 zeromask = npyv_cmpeq_f64(larger, npyv_zero_f64());
+    npyv_b64 infmask = npyv_cmpeq_f64(smaller, inf);
+    npyv_b64 div_mask = npyv_not_b64(npyv_or_b64(zeromask, infmask));
+
+    npyv_f64 ratio = npyv_ifdivz_f64(div_mask, smaller, larger);
+    npyv_f64 hypot = npyv_sqrt_f64(
+        npyv_muladd_f64(ratio, ratio, npyv_setall_f64(1.0)
+    ));
+    return npyv_mul_f64(hypot, larger);
+}
+#endif // VECTOR
+
+
+/********************************************************************************
+ ** Defining ufunc inner functions
+ ********************************************************************************/
+#line 86
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CFLOAT_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F32
+    npy_intp len = dimensions[0];
+    npy_intp ssrc = steps[0] / sizeof(npy_float);
+    npy_intp sdst = steps[1] / sizeof(npy_float);
+
+    if (!is_mem_overlap(args[0], steps[0], args[1], steps[1], len) &&
+        npyv_loadable_stride_f32(ssrc) && npyv_storable_stride_f32(sdst)
+        && steps[0] % sizeof(npy_float) == 0
+        && steps[1] % sizeof(npy_float) == 0
+    ) {
+        const npy_float *src = (npy_float*)args[0];
+              npy_float *dst = (npy_float*)args[1];
+
+        const int vstep = npyv_nlanes_f32;
+        const int wstep = vstep * 2;
+        const int hstep = vstep / 2;
+
+        if (ssrc == 2 && sdst == 1) {
+            for (; len >= vstep; len -= vstep, src += wstep, dst += vstep) {
+                npyv_f32x2 ab = npyv_load_f32x2(src);
+                npyv_f32 r = simd_cabsolute_f32(ab.val[0], ab.val[1]);
+                npyv_store_f32(dst, r);
+            }
+        }
+        else {
+            for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+                npyv_f32 re_im0 = npyv_loadn2_f32(src, ssrc);
+                npyv_f32 re_im1 = npyv_loadn2_f32(src + ssrc*hstep, ssrc);
+                npyv_f32x2 ab = npyv_unzip_f32(re_im0, re_im1);
+                npyv_f32 r = simd_cabsolute_f32(ab.val[0], ab.val[1]);
+                npyv_storen_f32(dst, sdst, r);
+            }
+        }
+        for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+            npyv_f32 rl = npyv_loadn_tillz_f32(src, ssrc, len);
+            npyv_f32 im = npyv_loadn_tillz_f32(src + 1, ssrc, len);
+            npyv_f32 r = simd_cabsolute_f32(rl, im);
+            npyv_storen_till_f32(dst, sdst, len, r);
+        }
+        npyv_cleanup();
+        npy_clear_floatstatus_barrier((char*)&len);
+        return;
+    }
+#endif
+    UNARY_LOOP {
+        const npy_float re = ((npy_float *)ip1)[0];
+        const npy_float im = ((npy_float *)ip1)[1];
+        *((npy_float *)op1) = npy_hypotf(re, im);
+    }
+}
+
+#line 86
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(CDOUBLE_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F64
+    npy_intp len = dimensions[0];
+    npy_intp ssrc = steps[0] / sizeof(npy_double);
+    npy_intp sdst = steps[1] / sizeof(npy_double);
+
+    if (!is_mem_overlap(args[0], steps[0], args[1], steps[1], len) &&
+        npyv_loadable_stride_f64(ssrc) && npyv_storable_stride_f64(sdst)
+        && steps[0] % sizeof(npy_double) == 0
+        && steps[1] % sizeof(npy_double) == 0
+    ) {
+        const npy_double *src = (npy_double*)args[0];
+              npy_double *dst = (npy_double*)args[1];
+
+        const int vstep = npyv_nlanes_f64;
+        const int wstep = vstep * 2;
+        const int hstep = vstep / 2;
+
+        if (ssrc == 2 && sdst == 1) {
+            for (; len >= vstep; len -= vstep, src += wstep, dst += vstep) {
+                npyv_f64x2 ab = npyv_load_f64x2(src);
+                npyv_f64 r = simd_cabsolute_f64(ab.val[0], ab.val[1]);
+                npyv_store_f64(dst, r);
+            }
+        }
+        else {
+            for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+                npyv_f64 re_im0 = npyv_loadn2_f64(src, ssrc);
+                npyv_f64 re_im1 = npyv_loadn2_f64(src + ssrc*hstep, ssrc);
+                npyv_f64x2 ab = npyv_unzip_f64(re_im0, re_im1);
+                npyv_f64 r = simd_cabsolute_f64(ab.val[0], ab.val[1]);
+                npyv_storen_f64(dst, sdst, r);
+            }
+        }
+        for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+            npyv_f64 rl = npyv_loadn_tillz_f64(src, ssrc, len);
+            npyv_f64 im = npyv_loadn_tillz_f64(src + 1, ssrc, len);
+            npyv_f64 r = simd_cabsolute_f64(rl, im);
+            npyv_storen_till_f64(dst, sdst, len, r);
+        }
+        npyv_cleanup();
+        npy_clear_floatstatus_barrier((char*)&len);
+        return;
+    }
+#endif
+    UNARY_LOOP {
+        const npy_double re = ((npy_double *)ip1)[0];
+        const npy_double im = ((npy_double *)ip1)[1];
+        *((npy_double *)op1) = npy_hypot(re, im);
+    }
+}
+
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.c.src
new file mode 100644
index 00000000000..052ad464c7a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.c.src
@@ -0,0 +1,139 @@
+/*@targets
+ ** $maxopt baseline
+ ** sse2 (avx2 fma3) avx512f
+ ** neon asimd
+ ** vsx2 vsx3
+ ** vx vxe
+ **/
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+
+#include "simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/**begin repeat
+ * #type = npy_float, npy_double#
+ * #sfx = f32, f64#
+ * #bsfx = b32, b64#
+ * #usfx = b32, u64#
+ * #VECTOR = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #is_double = 0, 1#
+ * #c = f, #
+ * #INF = NPY_INFINITYF, NPY_INFINITY#
+ * #NAN = NPY_NANF, NPY_NAN#
+ */
+#if @VECTOR@
+NPY_FINLINE npyv_@sfx@
+simd_cabsolute_@sfx@(npyv_@sfx@ re, npyv_@sfx@ im)
+{
+    const npyv_@sfx@ inf = npyv_setall_@sfx@(@INF@);
+    const npyv_@sfx@ nan = npyv_setall_@sfx@(@NAN@);
+
+    re = npyv_abs_@sfx@(re);
+    im = npyv_abs_@sfx@(im);
+    /*
+     * If real or imag = INF, then convert it to inf + j*inf
+     * Handles: inf + j*nan, nan + j*inf
+     */
+    npyv_@bsfx@ re_infmask = npyv_cmpeq_@sfx@(re, inf);
+    npyv_@bsfx@ im_infmask = npyv_cmpeq_@sfx@(im, inf);
+    im = npyv_select_@sfx@(re_infmask, inf, im);
+    re = npyv_select_@sfx@(im_infmask, inf, re);
+    /*
+     * If real or imag = NAN, then convert it to nan + j*nan
+     * Handles: x + j*nan, nan + j*x
+     */
+    npyv_@bsfx@ re_nnanmask = npyv_notnan_@sfx@(re);
+    npyv_@bsfx@ im_nnanmask = npyv_notnan_@sfx@(im);
+    im = npyv_select_@sfx@(re_nnanmask, im, nan);
+    re = npyv_select_@sfx@(im_nnanmask, re, nan);
+
+    npyv_@sfx@ larger  = npyv_max_@sfx@(re, im);
+    npyv_@sfx@ smaller = npyv_min_@sfx@(im, re);
+    /*
+     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
+     */
+    npyv_@bsfx@ zeromask = npyv_cmpeq_@sfx@(larger, npyv_zero_@sfx@());
+    npyv_@bsfx@ infmask = npyv_cmpeq_@sfx@(smaller, inf);
+    npyv_@bsfx@ div_mask = npyv_not_@bsfx@(npyv_or_@bsfx@(zeromask, infmask));
+
+    npyv_@sfx@ ratio = npyv_ifdivz_@sfx@(div_mask, smaller, larger);
+    npyv_@sfx@ hypot = npyv_sqrt_@sfx@(
+        npyv_muladd_@sfx@(ratio, ratio, npyv_setall_@sfx@(1.0@c@)
+    ));
+    return npyv_mul_@sfx@(hypot, larger);
+}
+#endif // VECTOR
+/**end repeat**/
+
+/********************************************************************************
+ ** Defining ufunc inner functions
+ ********************************************************************************/
+/**begin repeat
+ * complex types
+ * #TYPE = CFLOAT, CDOUBLE#
+ * #ftype = npy_float, npy_double#
+ * #VECTOR = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #sfx = f32, f64#
+ * #c = f, #
+ * #C = F, #
+ */
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_absolute)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if @VECTOR@
+    npy_intp len = dimensions[0];
+    npy_intp ssrc = steps[0] / sizeof(@ftype@);
+    npy_intp sdst = steps[1] / sizeof(@ftype@);
+
+    if (!is_mem_overlap(args[0], steps[0], args[1], steps[1], len) &&
+        npyv_loadable_stride_@sfx@(ssrc) && npyv_storable_stride_@sfx@(sdst)
+        && steps[0] % sizeof(@ftype@) == 0
+        && steps[1] % sizeof(@ftype@) == 0
+    ) {
+        const @ftype@ *src = (@ftype@*)args[0];
+              @ftype@ *dst = (@ftype@*)args[1];
+
+        const int vstep = npyv_nlanes_@sfx@;
+        const int wstep = vstep * 2;
+        const int hstep = vstep / 2;
+
+        if (ssrc == 2 && sdst == 1) {
+            for (; len >= vstep; len -= vstep, src += wstep, dst += vstep) {
+                npyv_@sfx@x2 ab = npyv_load_@sfx@x2(src);
+                npyv_@sfx@ r = simd_cabsolute_@sfx@(ab.val[0], ab.val[1]);
+                npyv_store_@sfx@(dst, r);
+            }
+        }
+        else {
+            for (; len >= vstep; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+                npyv_@sfx@ re_im0 = npyv_loadn2_@sfx@(src, ssrc);
+                npyv_@sfx@ re_im1 = npyv_loadn2_@sfx@(src + ssrc*hstep, ssrc);
+                npyv_@sfx@x2 ab = npyv_unzip_@sfx@(re_im0, re_im1);
+                npyv_@sfx@ r = simd_cabsolute_@sfx@(ab.val[0], ab.val[1]);
+                npyv_storen_@sfx@(dst, sdst, r);
+            }
+        }
+        for (; len > 0; len -= vstep, src += ssrc*vstep, dst += sdst*vstep) {
+            npyv_@sfx@ rl = npyv_loadn_tillz_@sfx@(src, ssrc, len);
+            npyv_@sfx@ im = npyv_loadn_tillz_@sfx@(src + 1, ssrc, len);
+            npyv_@sfx@ r = simd_cabsolute_@sfx@(rl, im);
+            npyv_storen_till_@sfx@(dst, sdst, len, r);
+        }
+        npyv_cleanup();
+        npy_clear_floatstatus_barrier((char*)&len);
+        return;
+    }
+#endif
+    UNARY_LOOP {
+        const @ftype@ re = ((@ftype@ *)ip1)[0];
+        const @ftype@ im = ((@ftype@ *)ip1)[1];
+        *((@ftype@ *)op1) = npy_hypot@c@(re, im);
+    }
+}
+/**end repeat**/
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.fma3.avx2.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.fma3.avx2.c
new file mode 100644
index 00000000000..f5873849c67
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.fma3.avx2.c
@@ -0,0 +1,18 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT FMA3__AVX2
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#define NPY__CPU_TARGET_POPCNT
+#define NPY__CPU_TARGET_SSE42
+#define NPY__CPU_TARGET_AVX
+#define NPY__CPU_TARGET_F16C
+#define NPY__CPU_TARGET_FMA3
+#define NPY__CPU_TARGET_AVX2
+#include "numpy/core/src/umath/loops_unary_complex.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.h
new file mode 100644
index 00000000000..e17d2feca34
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_complex.dispatch.h
@@ -0,0 +1,20 @@
+// cache_hash:2415642914
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator (distutils/ccompiler_opt.py)
+ */
+#ifndef NPY__CPU_DISPATCH_EXPAND_
+    #define NPY__CPU_DISPATCH_EXPAND_(X) X
+#endif
+#undef NPY__CPU_DISPATCH_BASELINE_CALL
+#undef NPY__CPU_DISPATCH_CALL
+#define NPY__CPU_DISPATCH_BASELINE_CALL(CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB(__VA_ARGS__))
+#if defined(__x86_64__) || defined(_M_X64)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX512F)), AVX512F, __VA_ARGS__)) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(AVX)&&CHK(F16C)&&CHK(FMA3)&&CHK(AVX2)), FMA3__AVX2, __VA_ARGS__))
+#elif defined(__aarch64__)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c
index bb56c411cea..f686e09ade7 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c
@@ -9197,7 +9197,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_rint_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9247,7 +9247,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_floor_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9297,7 +9297,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_ceil_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9347,7 +9347,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_trunc_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9397,7 +9397,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_sqrt_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9447,7 +9447,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_absolute_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9497,7 +9497,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_square_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9547,7 +9547,7 @@ no_unroll:
 #endif // NPY_SIMD_F32
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F32
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_FLOAT_reciprocal_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f32 src0 = *(npyv_lanetype_f32*)src;
@@ -9599,7 +9599,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_rint_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
@@ -9649,7 +9649,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_floor_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
@@ -9699,7 +9699,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_ceil_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
@@ -9749,7 +9749,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_trunc_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
@@ -9799,7 +9799,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_sqrt_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
@@ -9849,7 +9849,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_absolute_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
@@ -9899,7 +9899,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_square_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
@@ -9949,7 +9949,7 @@ no_unroll:
 #endif // NPY_SIMD_F64
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if NPY_SIMD_F64
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_DOUBLE_reciprocal_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_f64 src0 = *(npyv_lanetype_f64*)src;
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c.src
index 0ac39a9b18f..c4e7b8929f1 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp.dispatch.c.src
@@ -301,7 +301,7 @@ no_unroll:
 #endif // @VCHK@
     for (; len > 0; --len, src += src_step, dst += dst_step) {
     #if @VCHK@
-        // to guarantee the same precsion and fp/domain errors for both scalars and vectors
+        // to guarantee the same precision and fp/domain errors for both scalars and vectors
         simd_@TYPE@_@kind@_CONTIG_CONTIG(src, 0, dst, 0, 1);
     #else
         const npyv_lanetype_@sfx@ src0 = *(npyv_lanetype_@sfx@*)src;
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.c
new file mode 100644
index 00000000000..6ada7e1f39c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.c
@@ -0,0 +1,3742 @@
+#line 1 "numpy/core/src/umath/loops_unary_fp_le.dispatch.c.src"
+
+/*
+ *****************************************************************************
+ **       This file was autogenerated from a template  DO NOT EDIT!!!!      **
+ **       Changes should be made to the original source (.src) file         **
+ *****************************************************************************
+ */
+
+#line 1
+/*@targets
+ ** $maxopt baseline
+ ** sse2 sse41
+ ** vsx2
+ ** neon asimd
+ **/
+
+/**
+ * Force use SSE only on x86, even if AVX2 or AVX512F are enabled
+ * through the baseline, since scatter(AVX512F) and gather very costly
+ * to handle non-contiguous memory access comparing with SSE for
+ * such small operations that this file covers.
+ */
+#define NPY_SIMD_FORCE_128
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#include <float.h>
+#include "numpy/npy_math.h"
+#include "contrib/python/numpy/py3/numpy/core/src/common/simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/**
+ * This code should really be merged into loops_unary_fp.dispatch.c.src
+ * However there is an issue with enabling the code here for VX and VXE
+ * as the shifts don't behave as expected.
+ * See the code below that references NPY__CPU_TARGET_VX and
+ * NPY_BIG_ENDIAN. Suspect that this is a big endian vector issue.
+ *
+ * Splitting the files out allows us to keep loops_unary_fp.dispatch.c.src
+ * building for VX and VXE so we don't regress performance while adding this
+ * code for other platforms.
+ */
+// TODO(@seiko2plus): add support for big-endian
+#if NPY_SIMD_BIGENDIAN
+    #undef NPY_SIMD
+    #undef NPY_SIMD_F32
+    #undef NPY_SIMD_F64
+    #define NPY_SIMD 0
+    #define NPY_SIMD_F32 0
+    #define NPY_SIMD_F64 0
+#endif
+
+/*******************************************************************************
+ ** extra SIMD intrinsics
+ ******************************************************************************/
+
+#if NPY_SIMD
+
+/**
+ * We define intrinsics for isnan, isinf, isfinite, and signbit below.  There's
+ * a few flavors of each.  We'll use f32 as an example although f64 versions
+ * are also defined.
+ * 
+ * npyv_u32 npyv_KIND_f32(npyv_f32 v)
+ *   These are mainly used for the single vector loops.  As such, result should
+ *   be bool true / false, ready to write back.
+ * 
+ * npyv_b32 _npyv_KIND_f32(npyv_f32 v)
+ *   These are used by the geneal intrinsics above as well as the multi-vector
+ *   packing intrinsics.  The multi-vector packing intrinsics are the ones
+ *   utilized in the unrolled vector loops.  Results should be vector masks
+ *   of 0x00/0xff.
+ * 
+ * npyv_u8 npyv_pack_KIND_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+ *   These are the multi-vector packing intrinsics utilized by unrolled vector
+ *   loops.  They perform the operation on all input vectors and pack the
+ *   results to a single npyv_u8.  Assuming NPY_SIMD == 128, that means we
+ *   can pack results from 4x npyv_f32 or 8x npyv_64 in a single npyv_u8.
+ *   Result should be bool true / false, ready to write back.
+ */
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_u32
+npyv_isnan_f32(npyv_f32 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u32(npyv_setall_u32(1==1));
+    npyv_u8 notnan = npyv_reinterpret_u8_u32(npyv_cvt_u32_b32(npyv_notnan_f32(v)));
+    return npyv_reinterpret_u32_u8(npyv_andc_u8(truemask, notnan));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isnan_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b32 b0 = npyv_notnan_f32(v0);
+    npyv_b32 b1 = npyv_notnan_f32(v1);
+    npyv_b32 b2 = npyv_notnan_f32(v2);
+    npyv_b32 b3 = npyv_notnan_f32(v3);
+    npyv_b8 notnan = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notnan));
+}
+#endif
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_u64
+npyv_isnan_f64(npyv_f64 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u64(npyv_setall_u64(1==1));
+    npyv_u8 notnan = npyv_reinterpret_u8_u64(npyv_cvt_u64_b64(npyv_notnan_f64(v)));
+    return npyv_reinterpret_u64_u8(npyv_andc_u8(truemask, notnan));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isnan_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                    npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b64 b0 = npyv_notnan_f64(v0);
+    npyv_b64 b1 = npyv_notnan_f64(v1);
+    npyv_b64 b2 = npyv_notnan_f64(v2);
+    npyv_b64 b3 = npyv_notnan_f64(v3);
+    npyv_b64 b4 = npyv_notnan_f64(v4);
+    npyv_b64 b5 = npyv_notnan_f64(v5);
+    npyv_b64 b6 = npyv_notnan_f64(v6);
+    npyv_b64 b7 = npyv_notnan_f64(v7);
+    npyv_b8 notnan = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notnan));
+}
+#endif
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_b32
+_npyv_isinf_f32(npyv_f32 v)
+{
+#if defined(NPY_HAVE_NEON)
+    // abs(v) > FLT_MAX
+    const npyv_f32 fltmax = npyv_setall_f32(FLT_MAX);
+    return vcagtq_f32(v, fltmax);
+#else
+    // cast out the sign and check if all exponent bits are set.
+    const npyv_u32 exp_mask = npyv_setall_u32(0xff000000);
+    npyv_u32 bits = npyv_shli_u32(npyv_reinterpret_u32_f32(v), 1);
+    return npyv_cmpeq_u32(bits, exp_mask);
+#endif
+}
+NPY_FINLINE npyv_u32
+npyv_isinf_f32(npyv_f32 v)
+{
+    const npyv_u32 truemask = npyv_setall_u32(1==1);
+    return npyv_and_u32(truemask, npyv_cvt_u32_b32(_npyv_isinf_f32(v)));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isinf_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b32 b0 = _npyv_isinf_f32(v0);
+    npyv_b32 b1 = _npyv_isinf_f32(v1);
+    npyv_b32 b2 = _npyv_isinf_f32(v2);
+    npyv_b32 b3 = _npyv_isinf_f32(v3);
+    npyv_b8 isinf = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_and_u8(truemask, npyv_cvt_u8_b8(isinf));
+}
+#endif // NPY_SIMD_F32
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_b64
+_npyv_isinf_f64(npyv_f64 v)
+{
+#if defined(NPY_HAVE_NEON)
+    // abs(v) > DBL_MAX
+    const npyv_f64 fltmax = npyv_setall_f64(DBL_MAX);
+    return vcagtq_f64(v, fltmax);
+#else
+    // cast out the sign and check if all exponent bits are set.
+    const npyv_u64 exp_mask = npyv_setall_u64(0xffe0000000000000);
+    npyv_u64 bits = npyv_shli_u64(npyv_reinterpret_u64_f64(v), 1);
+    return npyv_cmpeq_u64(bits, exp_mask);
+#endif
+}
+NPY_FINLINE npyv_u64
+npyv_isinf_f64(npyv_f64 v)
+{
+    const npyv_u64 truemask = npyv_setall_u64(1==1);
+    return npyv_and_u64(truemask, npyv_cvt_u64_b64(_npyv_isinf_f64(v)));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isinf_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                    npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b64 b0 = _npyv_isinf_f64(v0);
+    npyv_b64 b1 = _npyv_isinf_f64(v1);
+    npyv_b64 b2 = _npyv_isinf_f64(v2);
+    npyv_b64 b3 = _npyv_isinf_f64(v3);
+    npyv_b64 b4 = _npyv_isinf_f64(v4);
+    npyv_b64 b5 = _npyv_isinf_f64(v5);
+    npyv_b64 b6 = _npyv_isinf_f64(v6);
+    npyv_b64 b7 = _npyv_isinf_f64(v7);
+    npyv_b8 isinf = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_and_u8(truemask, npyv_cvt_u8_b8(isinf));
+}
+#endif // NPY_SIMD_F64
+
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_b32
+npyv_notfinite_f32(npyv_f32 v)
+{
+    // cast out the sign and check if all exponent bits are set
+    // no matter the mentissa is.
+    const npyv_u32 exp_mask = npyv_setall_u32(0x7f800000);
+    npyv_u32 bits = npyv_reinterpret_u32_f32(v);
+    bits = npyv_and_u32(bits, exp_mask);
+    return npyv_cmpeq_u32(bits, exp_mask);
+}
+NPY_FINLINE npyv_u32
+npyv_isfinite_f32(npyv_f32 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u32(npyv_setall_u32(1==1));
+    npyv_u8 notfinite = npyv_reinterpret_u8_u32(npyv_cvt_u32_b32(npyv_notfinite_f32(v)));
+    return npyv_reinterpret_u32_u8(npyv_andc_u8(truemask, notfinite));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isfinite_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // F32 exponent is 8-bits, which means we can pack multiple into
+    // a single vector.  We shift out sign bit so that we're left
+    // with only exponent in high byte.  If not all bits are set,
+    // then we've got a finite number.
+    uint8x16x4_t tbl;
+    tbl.val[0] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v0), 1));
+    tbl.val[1] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v1), 1));
+    tbl.val[2] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v2), 1));
+    tbl.val[3] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v3), 1));
+
+    const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
+    npyv_u8 r = vqtbl4q_u8(tbl, permute);
+
+    const npyv_u8 expmask = npyv_setall_u8(0xff);
+    r = npyv_cmpneq_u8(r, expmask);
+    r = vshrq_n_u8(r, 7);
+    return r;
+#else
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b32 b0 = npyv_notfinite_f32(v0);
+    npyv_b32 b1 = npyv_notfinite_f32(v1);
+    npyv_b32 b2 = npyv_notfinite_f32(v2);
+    npyv_b32 b3 = npyv_notfinite_f32(v3);
+    npyv_b8 notfinite = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notfinite));
+#endif
+}
+#endif // NPY_SIMD_F32
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_b64
+npyv_notfinite_f64(npyv_f64 v)
+{
+    // cast out the sign and check if all exponent bits are set
+    // no matter the mantissa is.
+    const npyv_u64 exp_mask = npyv_setall_u64(0x7ff0000000000000);
+    npyv_u64 bits = npyv_reinterpret_u64_f64(v);
+    bits = npyv_and_u64(bits, exp_mask);
+    return npyv_cmpeq_u64(bits, exp_mask);
+}
+NPY_FINLINE npyv_u64
+npyv_isfinite_f64(npyv_f64 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u64(npyv_setall_u64(1==1));
+    npyv_u8 notfinite = npyv_reinterpret_u8_u64(npyv_cvt_u64_b64(npyv_notfinite_f64(v)));
+    return npyv_reinterpret_u64_u8(npyv_andc_u8(truemask, notfinite));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isfinite_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                       npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // F64 exponent is 11-bits, which means we can pack multiple into
+    // a single vector.  We'll need to use u16 to fit all exponent
+    // bits.  If not all bits are set, then we've got a finite number.
+    uint8x16x4_t t0123, t4567;
+    t0123.val[0] = npyv_reinterpret_u8_f64(v0);
+    t0123.val[1] = npyv_reinterpret_u8_f64(v1);
+    t0123.val[2] = npyv_reinterpret_u8_f64(v2);
+    t0123.val[3] = npyv_reinterpret_u8_f64(v3);
+    t4567.val[0] = npyv_reinterpret_u8_f64(v4);
+    t4567.val[1] = npyv_reinterpret_u8_f64(v5);
+    t4567.val[2] = npyv_reinterpret_u8_f64(v6);
+    t4567.val[3] = npyv_reinterpret_u8_f64(v7);
+
+    const npyv_u8 permute = {6,7,14,15,  22,23,30,31,  38,39,46,47,  54,55,62,63};
+    npyv_u16 r0 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t0123, permute));
+    npyv_u16 r1 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t4567, permute));
+
+    const npyv_u16 expmask = npyv_setall_u16(0x7ff0);
+    r0 = npyv_and_u16(r0, expmask);
+    r0 = npyv_cmpneq_u16(r0, expmask);
+    r0 = npyv_shri_u16(r0, 15);
+    r1 = npyv_and_u16(r1, expmask);
+    r1 = npyv_cmpneq_u16(r1, expmask);
+    r1 = npyv_shri_u16(r1, 15);
+
+    npyv_u8 r = npyv_pack_b8_b16(r0, r1);
+    return r;
+#else
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b64 b0 = npyv_notfinite_f64(v0);
+    npyv_b64 b1 = npyv_notfinite_f64(v1);
+    npyv_b64 b2 = npyv_notfinite_f64(v2);
+    npyv_b64 b3 = npyv_notfinite_f64(v3);
+    npyv_b64 b4 = npyv_notfinite_f64(v4);
+    npyv_b64 b5 = npyv_notfinite_f64(v5);
+    npyv_b64 b6 = npyv_notfinite_f64(v6);
+    npyv_b64 b7 = npyv_notfinite_f64(v7);
+    npyv_b8 notfinite = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notfinite));
+#endif
+}
+#endif // NPY_SIMD_F64
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_u32
+npyv_signbit_f32(npyv_f32 v)
+{
+    return npyv_shri_u32(npyv_reinterpret_u32_f32(v), (sizeof(npyv_lanetype_f32)*8)-1);
+}
+NPY_FINLINE npyv_u8
+npyv_pack_signbit_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // We only need high byte for signbit, which means we can pack
+    // multiple inputs into a single vector.
+    uint8x16x4_t tbl;
+    tbl.val[0] = npyv_reinterpret_u8_f32(v0);
+    tbl.val[1] = npyv_reinterpret_u8_f32(v1);
+    tbl.val[2] = npyv_reinterpret_u8_f32(v2);
+    tbl.val[3] = npyv_reinterpret_u8_f32(v3);
+
+    const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
+    npyv_u8 r = vqtbl4q_u8(tbl, permute);
+            r = vshrq_n_u8(r, 7);
+    return r;
+#else
+    npyv_b32 b0 = npyv_cvt_b32_u32(npyv_signbit_f32(v0));
+    npyv_b32 b1 = npyv_cvt_b32_u32(npyv_signbit_f32(v1));
+    npyv_b32 b2 = npyv_cvt_b32_u32(npyv_signbit_f32(v2));
+    npyv_b32 b3 = npyv_cvt_b32_u32(npyv_signbit_f32(v3));
+    npyv_b8 signbit = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_cvt_u8_b8(signbit);
+#endif
+}
+#endif // NPY_SIMD_F32
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_u64
+npyv_signbit_f64(npyv_f64 v)
+{
+    return npyv_shri_u64(npyv_reinterpret_u64_f64(v), (sizeof(npyv_lanetype_f64)*8)-1);
+}
+NPY_FINLINE npyv_u8
+npyv_pack_signbit_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                      npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // We only need high byte for signbit, which means we can pack
+    // multiple inputs into a single vector.
+
+    // vuzp2 faster than vtbl for f64
+    npyv_u32 v01 = vuzp2q_u32(npyv_reinterpret_u32_f64(v0), npyv_reinterpret_u32_f64(v1));
+    npyv_u32 v23 = vuzp2q_u32(npyv_reinterpret_u32_f64(v2), npyv_reinterpret_u32_f64(v3));
+    npyv_u32 v45 = vuzp2q_u32(npyv_reinterpret_u32_f64(v4), npyv_reinterpret_u32_f64(v5));
+    npyv_u32 v67 = vuzp2q_u32(npyv_reinterpret_u32_f64(v6), npyv_reinterpret_u32_f64(v7));
+
+    npyv_u16 v0123 = vuzp2q_u16(npyv_reinterpret_u16_u32(v01), npyv_reinterpret_u16_u32(v23));
+    npyv_u16 v4567 = vuzp2q_u16(npyv_reinterpret_u16_u32(v45), npyv_reinterpret_u16_u32(v67));
+
+    npyv_u8 r = vuzp2q_u8(npyv_reinterpret_u8_u16(v0123), npyv_reinterpret_u8_u16(v4567));
+            r = vshrq_n_u8(r, 7);
+    return r;
+#else
+    npyv_b64 b0 = npyv_cvt_b64_u64(npyv_signbit_f64(v0));
+    npyv_b64 b1 = npyv_cvt_b64_u64(npyv_signbit_f64(v1));
+    npyv_b64 b2 = npyv_cvt_b64_u64(npyv_signbit_f64(v2));
+    npyv_b64 b3 = npyv_cvt_b64_u64(npyv_signbit_f64(v3));
+    npyv_b64 b4 = npyv_cvt_b64_u64(npyv_signbit_f64(v4));
+    npyv_b64 b5 = npyv_cvt_b64_u64(npyv_signbit_f64(v5));
+    npyv_b64 b6 = npyv_cvt_b64_u64(npyv_signbit_f64(v6));
+    npyv_b64 b7 = npyv_cvt_b64_u64(npyv_signbit_f64(v7));
+    npyv_b8 signbit = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_cvt_u8_b8(signbit);
+#endif
+}
+#endif // NPY_SIMD_F64
+
+#endif // NPY_SIMD
+
+/********************************************************************************
+ ** Defining the SIMD kernels
+ ********************************************************************************/
+/** Notes:
+ * - avoid the use of libmath to unify fp/domain errors
+ *   for both scalars and vectors among all compilers/architectures.
+ * - use intrinsic npyv_load_till_* instead of npyv_load_tillz_
+ *   to fill the remind lanes with 1.0 to avoid divide by zero fp
+ *   exception in reciprocal.
+ */
+#define CONTIG  0
+#define NCONTIG 1
+
+#line 406
+#if NPY_SIMD_F32
+#line 410
+#line 414
+static void simd_unary_isnan_FLOAT_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isnan_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isnan_FLOAT_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isnan_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isnan_FLOAT_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isnan_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isnan_FLOAT_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isnan_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+#line 410
+#line 414
+static void simd_unary_isinf_FLOAT_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isinf_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isinf_FLOAT_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isinf_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isinf_FLOAT_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isinf_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isinf_FLOAT_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isinf_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+#line 410
+#line 414
+static void simd_unary_isfinite_FLOAT_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isfinite_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isfinite_FLOAT_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isfinite_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isfinite_FLOAT_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isfinite_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isfinite_FLOAT_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_isfinite_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+#line 410
+#line 414
+static void simd_unary_signbit_FLOAT_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_signbit_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_signbit_FLOAT_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_signbit_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_signbit_FLOAT_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_signbit_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_signbit_FLOAT_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f32 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f32)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f32;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f32 v0 = npyv_load_f32(ip + vstep * 0);
+            npyv_f32 v1 = npyv_load_f32(ip + vstep * 1);
+            npyv_f32 v2 = npyv_load_f32(ip + vstep * 2);
+            npyv_f32 v3 = npyv_load_f32(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_load_f32(ip + vstep * 4);
+            npyv_f32 v5 = npyv_load_f32(ip + vstep * 5);
+            npyv_f32 v6 = npyv_load_f32(ip + vstep * 6);
+            npyv_f32 v7 = npyv_load_f32(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f32 v0 = npyv_loadn_f32(ip + istride * vstep * 0, istride);
+            npyv_f32 v1 = npyv_loadn_f32(ip + istride * vstep * 1, istride);
+            npyv_f32 v2 = npyv_loadn_f32(ip + istride * vstep * 2, istride);
+            npyv_f32 v3 = npyv_loadn_f32(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f32 v4 = npyv_loadn_f32(ip + istride * vstep * 4, istride);
+            npyv_f32 v5 = npyv_loadn_f32(ip + istride * vstep * 5, istride);
+            npyv_f32 v6 = npyv_loadn_f32(ip + istride * vstep * 6, istride);
+            npyv_f32 v7 = npyv_loadn_f32(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f32(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f32)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f32)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f32 v = npyv_load_f32(ip);
+    #else
+        npyv_f32 v = npyv_loadn_f32(ip, istride);
+    #endif
+
+        npyv_u32 r = npyv_signbit_f32(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u32(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f32)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f32)];
+        #if npyv_nlanes_f32 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f32)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f32)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+
+#endif // NPY_SIMD_F32
+
+#line 406
+#if NPY_SIMD_F64
+#line 410
+#line 414
+static void simd_unary_isnan_DOUBLE_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isnan_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isnan_DOUBLE_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isnan_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isnan_DOUBLE_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isnan_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isnan_DOUBLE_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isnan_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isnan_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isnan(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+#line 410
+#line 414
+static void simd_unary_isinf_DOUBLE_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isinf_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isinf_DOUBLE_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isinf_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isinf_DOUBLE_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isinf_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isinf_DOUBLE_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isinf_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isinf_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isinf(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+#line 410
+#line 414
+static void simd_unary_isfinite_DOUBLE_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isfinite_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isfinite_DOUBLE_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isfinite_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isfinite_DOUBLE_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isfinite_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_isfinite_DOUBLE_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_isfinite_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_isfinite_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_isfinite(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+#line 410
+#line 414
+static void simd_unary_signbit_DOUBLE_CONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_signbit_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_signbit_DOUBLE_NCONTIG_CONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if CONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // CONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // CONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_signbit_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_signbit_DOUBLE_CONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if CONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if CONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_signbit_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+#line 414
+static void simd_unary_signbit_DOUBLE_NCONTIG_NCONTIG
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_f64 *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_f64)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_f64;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if NCONTIG == CONTIG
+            // contiguous input
+            npyv_f64 v0 = npyv_load_f64(ip + vstep * 0);
+            npyv_f64 v1 = npyv_load_f64(ip + vstep * 1);
+            npyv_f64 v2 = npyv_load_f64(ip + vstep * 2);
+            npyv_f64 v3 = npyv_load_f64(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_load_f64(ip + vstep * 4);
+            npyv_f64 v5 = npyv_load_f64(ip + vstep * 5);
+            npyv_f64 v6 = npyv_load_f64(ip + vstep * 6);
+            npyv_f64 v7 = npyv_load_f64(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_f64 v0 = npyv_loadn_f64(ip + istride * vstep * 0, istride);
+            npyv_f64 v1 = npyv_loadn_f64(ip + istride * vstep * 1, istride);
+            npyv_f64 v2 = npyv_loadn_f64(ip + istride * vstep * 2, istride);
+            npyv_f64 v3 = npyv_loadn_f64(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_f64 v4 = npyv_loadn_f64(ip + istride * vstep * 4, istride);
+            npyv_f64 v5 = npyv_loadn_f64(ip + istride * vstep * 5, istride);
+            npyv_f64 v6 = npyv_loadn_f64(ip + istride * vstep * 6, istride);
+            npyv_f64 v7 = npyv_loadn_f64(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_signbit_f64(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if NCONTIG == CONTIG
+            npyv_store_u8(op, r);
+        #else // NCONTIG == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_f64)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_f64)];
+            }
+        #endif // NCONTIG == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if NCONTIG == CONTIG
+        npyv_f64 v = npyv_load_f64(ip);
+    #else
+        npyv_f64 v = npyv_loadn_f64(ip, istride);
+    #endif
+
+        npyv_u64 r = npyv_signbit_f64(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u64(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_f64)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_f64)];
+        #if npyv_nlanes_f64 == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_f64)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_f64)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_signbit(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+
+
+
+#endif // NPY_SIMD_F64
+
+
+/********************************************************************************
+ ** Defining ufunc inner functions
+ ********************************************************************************/
+#line 518
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F32
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f32);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f32(istride) &&
+        npyv_storable_stride_f32(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_isnan_FLOAT_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_isnan_FLOAT_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_isnan_FLOAT_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_isnan_FLOAT_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F32
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f32 in = *(npyv_lanetype_f32 *)ip1;
+        *((npy_bool *)op1) = (npy_isnan(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F32
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f32);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f32(istride) &&
+        npyv_storable_stride_f32(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_isinf_FLOAT_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_isinf_FLOAT_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_isinf_FLOAT_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_isinf_FLOAT_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F32
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f32 in = *(npyv_lanetype_f32 *)ip1;
+        *((npy_bool *)op1) = (npy_isinf(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F32
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f32);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f32(istride) &&
+        npyv_storable_stride_f32(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_isfinite_FLOAT_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_isfinite_FLOAT_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_isfinite_FLOAT_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_isfinite_FLOAT_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F32
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f32 in = *(npyv_lanetype_f32 *)ip1;
+        *((npy_bool *)op1) = (npy_isfinite(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(FLOAT_signbit)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F32
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f32);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f32(istride) &&
+        npyv_storable_stride_f32(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_signbit_FLOAT_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_signbit_FLOAT_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_signbit_FLOAT_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_signbit_FLOAT_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F32
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f32 in = *(npyv_lanetype_f32 *)ip1;
+        *((npy_bool *)op1) = (npy_signbit(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+
+#line 518
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_isnan)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F64
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f64);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f64(istride) &&
+        npyv_storable_stride_f64(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_isnan_DOUBLE_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_isnan_DOUBLE_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_isnan_DOUBLE_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_isnan_DOUBLE_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F64
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f64 in = *(npyv_lanetype_f64 *)ip1;
+        *((npy_bool *)op1) = (npy_isnan(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_isinf)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F64
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f64);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f64(istride) &&
+        npyv_storable_stride_f64(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_isinf_DOUBLE_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_isinf_DOUBLE_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_isinf_DOUBLE_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_isinf_DOUBLE_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F64
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f64 in = *(npyv_lanetype_f64 *)ip1;
+        *((npy_bool *)op1) = (npy_isinf(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_isfinite)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F64
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f64);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f64(istride) &&
+        npyv_storable_stride_f64(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_isfinite_DOUBLE_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_isfinite_DOUBLE_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_isfinite_DOUBLE_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_isfinite_DOUBLE_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F64
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f64 in = *(npyv_lanetype_f64 *)ip1;
+        *((npy_bool *)op1) = (npy_isfinite(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+#line 522
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_signbit)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if NPY_SIMD_F64
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_f64);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_f64(istride) &&
+        npyv_storable_stride_f64(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_signbit_DOUBLE_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_signbit_DOUBLE_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_signbit_DOUBLE_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_signbit_DOUBLE_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // NPY_SIMD_F64
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_f64 in = *(npyv_lanetype_f64 *)ip1;
+        *((npy_bool *)op1) = (npy_signbit(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+
+
+
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.c.src
new file mode 100644
index 00000000000..ba133dc1e60
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.c.src
@@ -0,0 +1,565 @@
+/*@targets
+ ** $maxopt baseline
+ ** sse2 sse41
+ ** vsx2
+ ** neon asimd
+ **/
+
+/**
+ * Force use SSE only on x86, even if AVX2 or AVX512F are enabled
+ * through the baseline, since scatter(AVX512F) and gather very costly
+ * to handle non-contiguous memory access comparing with SSE for
+ * such small operations that this file covers.
+ */
+#define NPY_SIMD_FORCE_128
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#include <float.h>
+#include "numpy/npy_math.h"
+#include "simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+/**
+ * This code should really be merged into loops_unary_fp.dispatch.c.src
+ * However there is an issue with enabling the code here for VX and VXE
+ * as the shifts don't behave as expected.
+ * See the code below that references NPY__CPU_TARGET_VX and
+ * NPY_BIG_ENDIAN. Suspect that this is a big endian vector issue.
+ *
+ * Splitting the files out allows us to keep loops_unary_fp.dispatch.c.src
+ * building for VX and VXE so we don't regress performance while adding this
+ * code for other platforms.
+ */
+// TODO(@seiko2plus): add support for big-endian
+#if NPY_SIMD_BIGENDIAN
+    #undef NPY_SIMD
+    #undef NPY_SIMD_F32
+    #undef NPY_SIMD_F64
+    #define NPY_SIMD 0
+    #define NPY_SIMD_F32 0
+    #define NPY_SIMD_F64 0
+#endif
+
+/*******************************************************************************
+ ** extra SIMD intrinsics
+ ******************************************************************************/
+
+#if NPY_SIMD
+
+/**
+ * We define intrinsics for isnan, isinf, isfinite, and signbit below.  There's
+ * a few flavors of each.  We'll use f32 as an example although f64 versions
+ * are also defined.
+ * 
+ * npyv_u32 npyv_KIND_f32(npyv_f32 v)
+ *   These are mainly used for the single vector loops.  As such, result should
+ *   be bool true / false, ready to write back.
+ * 
+ * npyv_b32 _npyv_KIND_f32(npyv_f32 v)
+ *   These are used by the geneal intrinsics above as well as the multi-vector
+ *   packing intrinsics.  The multi-vector packing intrinsics are the ones
+ *   utilized in the unrolled vector loops.  Results should be vector masks
+ *   of 0x00/0xff.
+ * 
+ * npyv_u8 npyv_pack_KIND_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+ *   These are the multi-vector packing intrinsics utilized by unrolled vector
+ *   loops.  They perform the operation on all input vectors and pack the
+ *   results to a single npyv_u8.  Assuming NPY_SIMD == 128, that means we
+ *   can pack results from 4x npyv_f32 or 8x npyv_64 in a single npyv_u8.
+ *   Result should be bool true / false, ready to write back.
+ */
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_u32
+npyv_isnan_f32(npyv_f32 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u32(npyv_setall_u32(1==1));
+    npyv_u8 notnan = npyv_reinterpret_u8_u32(npyv_cvt_u32_b32(npyv_notnan_f32(v)));
+    return npyv_reinterpret_u32_u8(npyv_andc_u8(truemask, notnan));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isnan_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b32 b0 = npyv_notnan_f32(v0);
+    npyv_b32 b1 = npyv_notnan_f32(v1);
+    npyv_b32 b2 = npyv_notnan_f32(v2);
+    npyv_b32 b3 = npyv_notnan_f32(v3);
+    npyv_b8 notnan = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notnan));
+}
+#endif
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_u64
+npyv_isnan_f64(npyv_f64 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u64(npyv_setall_u64(1==1));
+    npyv_u8 notnan = npyv_reinterpret_u8_u64(npyv_cvt_u64_b64(npyv_notnan_f64(v)));
+    return npyv_reinterpret_u64_u8(npyv_andc_u8(truemask, notnan));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isnan_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                    npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b64 b0 = npyv_notnan_f64(v0);
+    npyv_b64 b1 = npyv_notnan_f64(v1);
+    npyv_b64 b2 = npyv_notnan_f64(v2);
+    npyv_b64 b3 = npyv_notnan_f64(v3);
+    npyv_b64 b4 = npyv_notnan_f64(v4);
+    npyv_b64 b5 = npyv_notnan_f64(v5);
+    npyv_b64 b6 = npyv_notnan_f64(v6);
+    npyv_b64 b7 = npyv_notnan_f64(v7);
+    npyv_b8 notnan = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notnan));
+}
+#endif
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_b32
+_npyv_isinf_f32(npyv_f32 v)
+{
+#if defined(NPY_HAVE_NEON)
+    // abs(v) > FLT_MAX
+    const npyv_f32 fltmax = npyv_setall_f32(FLT_MAX);
+    return vcagtq_f32(v, fltmax);
+#else
+    // cast out the sign and check if all exponent bits are set.
+    const npyv_u32 exp_mask = npyv_setall_u32(0xff000000);
+    npyv_u32 bits = npyv_shli_u32(npyv_reinterpret_u32_f32(v), 1);
+    return npyv_cmpeq_u32(bits, exp_mask);
+#endif
+}
+NPY_FINLINE npyv_u32
+npyv_isinf_f32(npyv_f32 v)
+{
+    const npyv_u32 truemask = npyv_setall_u32(1==1);
+    return npyv_and_u32(truemask, npyv_cvt_u32_b32(_npyv_isinf_f32(v)));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isinf_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b32 b0 = _npyv_isinf_f32(v0);
+    npyv_b32 b1 = _npyv_isinf_f32(v1);
+    npyv_b32 b2 = _npyv_isinf_f32(v2);
+    npyv_b32 b3 = _npyv_isinf_f32(v3);
+    npyv_b8 isinf = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_and_u8(truemask, npyv_cvt_u8_b8(isinf));
+}
+#endif // NPY_SIMD_F32
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_b64
+_npyv_isinf_f64(npyv_f64 v)
+{
+#if defined(NPY_HAVE_NEON)
+    // abs(v) > DBL_MAX
+    const npyv_f64 fltmax = npyv_setall_f64(DBL_MAX);
+    return vcagtq_f64(v, fltmax);
+#else
+    // cast out the sign and check if all exponent bits are set.
+    const npyv_u64 exp_mask = npyv_setall_u64(0xffe0000000000000);
+    npyv_u64 bits = npyv_shli_u64(npyv_reinterpret_u64_f64(v), 1);
+    return npyv_cmpeq_u64(bits, exp_mask);
+#endif
+}
+NPY_FINLINE npyv_u64
+npyv_isinf_f64(npyv_f64 v)
+{
+    const npyv_u64 truemask = npyv_setall_u64(1==1);
+    return npyv_and_u64(truemask, npyv_cvt_u64_b64(_npyv_isinf_f64(v)));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isinf_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                    npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b64 b0 = _npyv_isinf_f64(v0);
+    npyv_b64 b1 = _npyv_isinf_f64(v1);
+    npyv_b64 b2 = _npyv_isinf_f64(v2);
+    npyv_b64 b3 = _npyv_isinf_f64(v3);
+    npyv_b64 b4 = _npyv_isinf_f64(v4);
+    npyv_b64 b5 = _npyv_isinf_f64(v5);
+    npyv_b64 b6 = _npyv_isinf_f64(v6);
+    npyv_b64 b7 = _npyv_isinf_f64(v7);
+    npyv_b8 isinf = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_and_u8(truemask, npyv_cvt_u8_b8(isinf));
+}
+#endif // NPY_SIMD_F64
+
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_b32
+npyv_notfinite_f32(npyv_f32 v)
+{
+    // cast out the sign and check if all exponent bits are set
+    // no matter the mentissa is.
+    const npyv_u32 exp_mask = npyv_setall_u32(0x7f800000);
+    npyv_u32 bits = npyv_reinterpret_u32_f32(v);
+    bits = npyv_and_u32(bits, exp_mask);
+    return npyv_cmpeq_u32(bits, exp_mask);
+}
+NPY_FINLINE npyv_u32
+npyv_isfinite_f32(npyv_f32 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u32(npyv_setall_u32(1==1));
+    npyv_u8 notfinite = npyv_reinterpret_u8_u32(npyv_cvt_u32_b32(npyv_notfinite_f32(v)));
+    return npyv_reinterpret_u32_u8(npyv_andc_u8(truemask, notfinite));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isfinite_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // F32 exponent is 8-bits, which means we can pack multiple into
+    // a single vector.  We shift out sign bit so that we're left
+    // with only exponent in high byte.  If not all bits are set,
+    // then we've got a finite number.
+    uint8x16x4_t tbl;
+    tbl.val[0] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v0), 1));
+    tbl.val[1] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v1), 1));
+    tbl.val[2] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v2), 1));
+    tbl.val[3] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v3), 1));
+
+    const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
+    npyv_u8 r = vqtbl4q_u8(tbl, permute);
+
+    const npyv_u8 expmask = npyv_setall_u8(0xff);
+    r = npyv_cmpneq_u8(r, expmask);
+    r = vshrq_n_u8(r, 7);
+    return r;
+#else
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b32 b0 = npyv_notfinite_f32(v0);
+    npyv_b32 b1 = npyv_notfinite_f32(v1);
+    npyv_b32 b2 = npyv_notfinite_f32(v2);
+    npyv_b32 b3 = npyv_notfinite_f32(v3);
+    npyv_b8 notfinite = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notfinite));
+#endif
+}
+#endif // NPY_SIMD_F32
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_b64
+npyv_notfinite_f64(npyv_f64 v)
+{
+    // cast out the sign and check if all exponent bits are set
+    // no matter the mantissa is.
+    const npyv_u64 exp_mask = npyv_setall_u64(0x7ff0000000000000);
+    npyv_u64 bits = npyv_reinterpret_u64_f64(v);
+    bits = npyv_and_u64(bits, exp_mask);
+    return npyv_cmpeq_u64(bits, exp_mask);
+}
+NPY_FINLINE npyv_u64
+npyv_isfinite_f64(npyv_f64 v)
+{
+    const npyv_u8 truemask = npyv_reinterpret_u8_u64(npyv_setall_u64(1==1));
+    npyv_u8 notfinite = npyv_reinterpret_u8_u64(npyv_cvt_u64_b64(npyv_notfinite_f64(v)));
+    return npyv_reinterpret_u64_u8(npyv_andc_u8(truemask, notfinite));
+}
+NPY_FINLINE npyv_u8
+npyv_pack_isfinite_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                       npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // F64 exponent is 11-bits, which means we can pack multiple into
+    // a single vector.  We'll need to use u16 to fit all exponent
+    // bits.  If not all bits are set, then we've got a finite number.
+    uint8x16x4_t t0123, t4567;
+    t0123.val[0] = npyv_reinterpret_u8_f64(v0);
+    t0123.val[1] = npyv_reinterpret_u8_f64(v1);
+    t0123.val[2] = npyv_reinterpret_u8_f64(v2);
+    t0123.val[3] = npyv_reinterpret_u8_f64(v3);
+    t4567.val[0] = npyv_reinterpret_u8_f64(v4);
+    t4567.val[1] = npyv_reinterpret_u8_f64(v5);
+    t4567.val[2] = npyv_reinterpret_u8_f64(v6);
+    t4567.val[3] = npyv_reinterpret_u8_f64(v7);
+
+    const npyv_u8 permute = {6,7,14,15,  22,23,30,31,  38,39,46,47,  54,55,62,63};
+    npyv_u16 r0 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t0123, permute));
+    npyv_u16 r1 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t4567, permute));
+
+    const npyv_u16 expmask = npyv_setall_u16(0x7ff0);
+    r0 = npyv_and_u16(r0, expmask);
+    r0 = npyv_cmpneq_u16(r0, expmask);
+    r0 = npyv_shri_u16(r0, 15);
+    r1 = npyv_and_u16(r1, expmask);
+    r1 = npyv_cmpneq_u16(r1, expmask);
+    r1 = npyv_shri_u16(r1, 15);
+
+    npyv_u8 r = npyv_pack_b8_b16(r0, r1);
+    return r;
+#else
+    const npyv_u8 truemask = npyv_setall_u8(1==1);
+    npyv_b64 b0 = npyv_notfinite_f64(v0);
+    npyv_b64 b1 = npyv_notfinite_f64(v1);
+    npyv_b64 b2 = npyv_notfinite_f64(v2);
+    npyv_b64 b3 = npyv_notfinite_f64(v3);
+    npyv_b64 b4 = npyv_notfinite_f64(v4);
+    npyv_b64 b5 = npyv_notfinite_f64(v5);
+    npyv_b64 b6 = npyv_notfinite_f64(v6);
+    npyv_b64 b7 = npyv_notfinite_f64(v7);
+    npyv_b8 notfinite = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_andc_u8(truemask, npyv_cvt_u8_b8(notfinite));
+#endif
+}
+#endif // NPY_SIMD_F64
+
+#if NPY_SIMD_F32
+NPY_FINLINE npyv_u32
+npyv_signbit_f32(npyv_f32 v)
+{
+    return npyv_shri_u32(npyv_reinterpret_u32_f32(v), (sizeof(npyv_lanetype_f32)*8)-1);
+}
+NPY_FINLINE npyv_u8
+npyv_pack_signbit_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // We only need high byte for signbit, which means we can pack
+    // multiple inputs into a single vector.
+    uint8x16x4_t tbl;
+    tbl.val[0] = npyv_reinterpret_u8_f32(v0);
+    tbl.val[1] = npyv_reinterpret_u8_f32(v1);
+    tbl.val[2] = npyv_reinterpret_u8_f32(v2);
+    tbl.val[3] = npyv_reinterpret_u8_f32(v3);
+
+    const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
+    npyv_u8 r = vqtbl4q_u8(tbl, permute);
+            r = vshrq_n_u8(r, 7);
+    return r;
+#else
+    npyv_b32 b0 = npyv_cvt_b32_u32(npyv_signbit_f32(v0));
+    npyv_b32 b1 = npyv_cvt_b32_u32(npyv_signbit_f32(v1));
+    npyv_b32 b2 = npyv_cvt_b32_u32(npyv_signbit_f32(v2));
+    npyv_b32 b3 = npyv_cvt_b32_u32(npyv_signbit_f32(v3));
+    npyv_b8 signbit = npyv_pack_b8_b32(b0, b1, b2, b3);
+    return npyv_cvt_u8_b8(signbit);
+#endif
+}
+#endif // NPY_SIMD_F32
+#if NPY_SIMD_F64
+NPY_FINLINE npyv_u64
+npyv_signbit_f64(npyv_f64 v)
+{
+    return npyv_shri_u64(npyv_reinterpret_u64_f64(v), (sizeof(npyv_lanetype_f64)*8)-1);
+}
+NPY_FINLINE npyv_u8
+npyv_pack_signbit_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                      npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+{
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    // We only need high byte for signbit, which means we can pack
+    // multiple inputs into a single vector.
+
+    // vuzp2 faster than vtbl for f64
+    npyv_u32 v01 = vuzp2q_u32(npyv_reinterpret_u32_f64(v0), npyv_reinterpret_u32_f64(v1));
+    npyv_u32 v23 = vuzp2q_u32(npyv_reinterpret_u32_f64(v2), npyv_reinterpret_u32_f64(v3));
+    npyv_u32 v45 = vuzp2q_u32(npyv_reinterpret_u32_f64(v4), npyv_reinterpret_u32_f64(v5));
+    npyv_u32 v67 = vuzp2q_u32(npyv_reinterpret_u32_f64(v6), npyv_reinterpret_u32_f64(v7));
+
+    npyv_u16 v0123 = vuzp2q_u16(npyv_reinterpret_u16_u32(v01), npyv_reinterpret_u16_u32(v23));
+    npyv_u16 v4567 = vuzp2q_u16(npyv_reinterpret_u16_u32(v45), npyv_reinterpret_u16_u32(v67));
+
+    npyv_u8 r = vuzp2q_u8(npyv_reinterpret_u8_u16(v0123), npyv_reinterpret_u8_u16(v4567));
+            r = vshrq_n_u8(r, 7);
+    return r;
+#else
+    npyv_b64 b0 = npyv_cvt_b64_u64(npyv_signbit_f64(v0));
+    npyv_b64 b1 = npyv_cvt_b64_u64(npyv_signbit_f64(v1));
+    npyv_b64 b2 = npyv_cvt_b64_u64(npyv_signbit_f64(v2));
+    npyv_b64 b3 = npyv_cvt_b64_u64(npyv_signbit_f64(v3));
+    npyv_b64 b4 = npyv_cvt_b64_u64(npyv_signbit_f64(v4));
+    npyv_b64 b5 = npyv_cvt_b64_u64(npyv_signbit_f64(v5));
+    npyv_b64 b6 = npyv_cvt_b64_u64(npyv_signbit_f64(v6));
+    npyv_b64 b7 = npyv_cvt_b64_u64(npyv_signbit_f64(v7));
+    npyv_b8 signbit = npyv_pack_b8_b64(b0, b1, b2, b3, b4, b5, b6, b7);
+    return npyv_cvt_u8_b8(signbit);
+#endif
+}
+#endif // NPY_SIMD_F64
+
+#endif // NPY_SIMD
+
+/********************************************************************************
+ ** Defining the SIMD kernels
+ ********************************************************************************/
+/** Notes:
+ * - avoid the use of libmath to unify fp/domain errors
+ *   for both scalars and vectors among all compilers/architectures.
+ * - use intrinsic npyv_load_till_* instead of npyv_load_tillz_
+ *   to fill the remind lanes with 1.0 to avoid divide by zero fp
+ *   exception in reciprocal.
+ */
+#define CONTIG  0
+#define NCONTIG 1
+
+/**begin repeat
+ * #TYPE = FLOAT, DOUBLE#
+ * #sfx  = f32, f64#
+ * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #ssfx = 32, 64#
+ */
+#if @VCHK@
+/**begin repeat1
+ * #kind = isnan, isinf, isfinite, signbit#
+ */
+/**begin repeat2
+ * #STYPE  = CONTIG, NCONTIG, CONTIG,  NCONTIG#
+ * #DTYPE  = CONTIG, CONTIG,  NCONTIG, NCONTIG#
+ */
+static void simd_unary_@kind@_@TYPE@_@STYPE@_@DTYPE@
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_@sfx@ *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_@sfx@)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        // Load vectors
+        #if @STYPE@ == CONTIG
+            // contiguous input
+            npyv_@sfx@ v0 = npyv_load_@sfx@(ip + vstep * 0);
+            npyv_@sfx@ v1 = npyv_load_@sfx@(ip + vstep * 1);
+            npyv_@sfx@ v2 = npyv_load_@sfx@(ip + vstep * 2);
+            npyv_@sfx@ v3 = npyv_load_@sfx@(ip + vstep * 3);
+            #if PACK_FACTOR == 8
+            npyv_@sfx@ v4 = npyv_load_@sfx@(ip + vstep * 4);
+            npyv_@sfx@ v5 = npyv_load_@sfx@(ip + vstep * 5);
+            npyv_@sfx@ v6 = npyv_load_@sfx@(ip + vstep * 6);
+            npyv_@sfx@ v7 = npyv_load_@sfx@(ip + vstep * 7);
+            #endif
+        #else
+            // non-contiguous input
+            npyv_@sfx@ v0 = npyv_loadn_@sfx@(ip + istride * vstep * 0, istride);
+            npyv_@sfx@ v1 = npyv_loadn_@sfx@(ip + istride * vstep * 1, istride);
+            npyv_@sfx@ v2 = npyv_loadn_@sfx@(ip + istride * vstep * 2, istride);
+            npyv_@sfx@ v3 = npyv_loadn_@sfx@(ip + istride * vstep * 3, istride);
+            #if PACK_FACTOR == 8
+            npyv_@sfx@ v4 = npyv_loadn_@sfx@(ip + istride * vstep * 4, istride);
+            npyv_@sfx@ v5 = npyv_loadn_@sfx@(ip + istride * vstep * 5, istride);
+            npyv_@sfx@ v6 = npyv_loadn_@sfx@(ip + istride * vstep * 6, istride);
+            npyv_@sfx@ v7 = npyv_loadn_@sfx@(ip + istride * vstep * 7, istride);
+            #endif
+        #endif
+
+        #if PACK_FACTOR == 4
+        npyv_u8 r = npyv_pack_@kind@_@sfx@(v0, v1, v2, v3);
+        #elif PACK_FACTOR == 8
+        npyv_u8 r = npyv_pack_@kind@_@sfx@(v0, v1, v2, v3, v4, v5, v6, v7);
+        #endif
+
+        #if @DTYPE@ == CONTIG
+            npyv_store_u8(op, r);
+        #else // @DTYPE@ == CONTIG
+            // Results are packed, so we can just loop over them
+            npy_uint8 lane[npyv_nlanes_u8];
+            npyv_store_u8(lane, r);
+            for (int ln=0; (ln * sizeof(npyv_lanetype_@sfx@)) < npyv_nlanes_u8; ++ln){
+                op[ln * ostride] = lane[ln * sizeof(npyv_lanetype_@sfx@)];
+            }
+        #endif // @DTYPE@ == CONTIG
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if @STYPE@ == CONTIG
+        npyv_@sfx@ v = npyv_load_@sfx@(ip);
+    #else
+        npyv_@sfx@ v = npyv_loadn_@sfx@(ip, istride);
+    #endif
+
+        npyv_u@ssfx@ r = npyv_@kind@_@sfx@(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u@ssfx@(r));
+
+        op[0 * ostride] = lane[0 * sizeof(npyv_lanetype_@sfx@)];
+        op[1 * ostride] = lane[1 * sizeof(npyv_lanetype_@sfx@)];
+        #if npyv_nlanes_@sfx@ == 4
+        op[2 * ostride] = lane[2 * sizeof(npyv_lanetype_@sfx@)];
+        op[3 * ostride] = lane[3 * sizeof(npyv_lanetype_@sfx@)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_@kind@(*ip) != 0);
+    }
+
+    npyv_cleanup();
+}
+/**end repeat2**/
+/**end repeat1**/
+
+#endif // @VCHK@
+/**end repeat**/
+
+/********************************************************************************
+ ** Defining ufunc inner functions
+ ********************************************************************************/
+/**begin repeat
+ * #TYPE = FLOAT, DOUBLE#
+ * #sfx  = f32, f64#
+ * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
+ */
+
+/**begin repeat1
+ * #kind = isnan, isinf, isfinite, signbit#
+ **/
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if @VCHK@
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_@sfx@);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_@sfx@(istride) &&
+        npyv_storable_stride_@sfx@(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_@kind@_@TYPE@_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_@kind@_@TYPE@_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_@kind@_@TYPE@_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_@kind@_@TYPE@_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // @VCHK@
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_@sfx@ in = *(npyv_lanetype_@sfx@ *)ip1;
+        *((npy_bool *)op1) = (npy_@kind@(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+/**end repeat1**/
+/**end repeat**/
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.h
new file mode 100644
index 00000000000..13c1aa7634b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.h
@@ -0,0 +1,19 @@
+// cache_hash:74912954
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator (distutils/ccompiler_opt.py)
+ */
+#ifndef NPY__CPU_DISPATCH_EXPAND_
+    #define NPY__CPU_DISPATCH_EXPAND_(X) X
+#endif
+#undef NPY__CPU_DISPATCH_BASELINE_CALL
+#undef NPY__CPU_DISPATCH_CALL
+#define NPY__CPU_DISPATCH_BASELINE_CALL(CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB(__VA_ARGS__))
+#if defined(__x86_64__) || defined(_M_X64)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+	NPY__CPU_DISPATCH_EXPAND_(CB((CHK(SSE)&&CHK(SSE2)&&CHK(SSE3)&&CHK(SSSE3)&&CHK(SSE41)), SSE41, __VA_ARGS__))
+#elif defined(__aarch64__)
+#define NPY__CPU_DISPATCH_CALL(CHK, CB, ...) \
+
+#endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.sse41.c b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.sse41.c
new file mode 100644
index 00000000000..4cbe55e1386
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_unary_fp_le.dispatch.sse41.c
@@ -0,0 +1,12 @@
+/**
+ * AUTOGENERATED DON'T EDIT
+ * Please make changes to the code generator              (distutils/ccompiler_opt.py)
+ */
+#define NPY__CPU_TARGET_MODE
+#define NPY__CPU_TARGET_CURRENT SSE41
+#define NPY__CPU_TARGET_SSE
+#define NPY__CPU_TARGET_SSE2
+#define NPY__CPU_TARGET_SSE3
+#define NPY__CPU_TARGET_SSSE3
+#define NPY__CPU_TARGET_SSE41
+#include "numpy/core/src/umath/loops_unary_fp_le.dispatch.c"
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h b/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h
index 03fdf65c720..acda6a76704 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h
@@ -76,7 +76,7 @@ is_mem_overlap(const void *src, npy_intp src_step, const void *dst, npy_intp dst
  * The recursion depth is O(lg n) as well.
  * when updating also update similar complex floats summation
  */
-static NPY_INLINE npy_float
+static inline npy_float
 FLOAT_pairwise_sum(char *a, npy_intp n, npy_intp stride)
 {
     if (n < 8) {
@@ -151,7 +151,7 @@ FLOAT_pairwise_sum(char *a, npy_intp n, npy_intp stride)
  * The recursion depth is O(lg n) as well.
  * when updating also update similar complex floats summation
  */
-static NPY_INLINE npy_double
+static inline npy_double
 DOUBLE_pairwise_sum(char *a, npy_intp n, npy_intp stride)
 {
     if (n < 8) {
@@ -226,7 +226,7 @@ DOUBLE_pairwise_sum(char *a, npy_intp n, npy_intp stride)
  * The recursion depth is O(lg n) as well.
  * when updating also update similar complex floats summation
  */
-static NPY_INLINE npy_longdouble
+static inline npy_longdouble
 LONGDOUBLE_pairwise_sum(char *a, npy_intp n, npy_intp stride)
 {
     if (n < 8) {
@@ -301,7 +301,7 @@ LONGDOUBLE_pairwise_sum(char *a, npy_intp n, npy_intp stride)
  * The recursion depth is O(lg n) as well.
  * when updating also update similar complex floats summation
  */
-static NPY_INLINE npy_float
+static inline npy_float
 HALF_pairwise_sum(char *a, npy_intp n, npy_intp stride)
 {
     if (n < 8) {
@@ -372,7 +372,7 @@ HALF_pairwise_sum(char *a, npy_intp n, npy_intp stride)
 
 #line 154
 /* similar to pairwise sum of real floats */
-static NPY_INLINE void
+static inline void
 CFLOAT_pairwise_sum(npy_float *rr, npy_float * ri, char * a, npy_intp n,
                     npy_intp stride)
 {
@@ -446,7 +446,7 @@ CFLOAT_pairwise_sum(npy_float *rr, npy_float * ri, char * a, npy_intp n,
 
 #line 154
 /* similar to pairwise sum of real floats */
-static NPY_INLINE void
+static inline void
 CDOUBLE_pairwise_sum(npy_double *rr, npy_double * ri, char * a, npy_intp n,
                     npy_intp stride)
 {
@@ -520,7 +520,7 @@ CDOUBLE_pairwise_sum(npy_double *rr, npy_double * ri, char * a, npy_intp n,
 
 #line 154
 /* similar to pairwise sum of real floats */
-static NPY_INLINE void
+static inline void
 CLONGDOUBLE_pairwise_sum(npy_longdouble *rr, npy_longdouble * ri, char * a, npy_intp n,
                     npy_intp stride)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h.src b/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h.src
index df92bc315c5..5640a1f0b64 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/loops_utils.h.src
@@ -74,7 +74,7 @@ is_mem_overlap(const void *src, npy_intp src_step, const void *dst, npy_intp dst
  * The recursion depth is O(lg n) as well.
  * when updating also update similar complex floats summation
  */
-static NPY_INLINE @type@
+static inline @type@
 @TYPE@_pairwise_sum(char *a, npy_intp n, npy_intp stride)
 {
     if (n < 8) {
@@ -152,7 +152,7 @@ static NPY_INLINE @type@
  * #SIMD = 1, 1, 0#
  */
 /* similar to pairwise sum of real floats */
-static NPY_INLINE void
+static inline void
 @TYPE@_pairwise_sum(@ftype@ *rr, @ftype@ * ri, char * a, npy_intp n,
                     npy_intp stride)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c b/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c
index 54a60f254f2..71b0ed95589 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c
@@ -55,7 +55,7 @@
  * 3. The slower (first) axis stride, in unit steps, must be larger than
  *    the faster axis dimension
  */
-static NPY_INLINE npy_bool
+static inline npy_bool
 is_blasable2d(npy_intp byte_stride1, npy_intp byte_stride2,
               npy_intp d1, npy_intp d2,  npy_intp itemsize)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c.src
index 4dd0c475968..127bb5e2746 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/matmul.c.src
@@ -45,7 +45,7 @@
  * 3. The slower (first) axis stride, in unit steps, must be larger than
  *    the faster axis dimension
  */
-static NPY_INLINE npy_bool
+static inline npy_bool
 is_blasable2d(npy_intp byte_stride1, npy_intp byte_stride2,
               npy_intp d1, npy_intp d2,  npy_intp itemsize)
 {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/override.c b/contrib/python/numpy/py3/numpy/core/src/umath/override.c
index d247c263986..1ee5907971d 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/override.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/override.c
@@ -23,18 +23,19 @@
  * Returns -1 on failure.
  */
 static int
-get_array_ufunc_overrides(PyObject *in_args, PyObject *out_args,
+get_array_ufunc_overrides(PyObject *in_args, PyObject *out_args, PyObject *wheremask_obj,
                           PyObject **with_override, PyObject **methods)
 {
     int i;
     int num_override_args = 0;
-    int narg, nout;
+    int narg, nout, nwhere;
 
     narg = (int)PyTuple_GET_SIZE(in_args);
     /* It is valid for out_args to be NULL: */
     nout = (out_args != NULL) ? (int)PyTuple_GET_SIZE(out_args) : 0;
+    nwhere = (wheremask_obj != NULL) ? 1: 0;
 
-    for (i = 0; i < narg + nout; ++i) {
+    for (i = 0; i < narg + nout + nwhere; ++i) {
         PyObject *obj;
         int j;
         int new_class = 1;
@@ -42,9 +43,12 @@ get_array_ufunc_overrides(PyObject *in_args, PyObject *out_args,
         if (i < narg) {
             obj = PyTuple_GET_ITEM(in_args, i);
         }
-        else {
+        else if (i < narg + nout){
             obj = PyTuple_GET_ITEM(out_args, i - narg);
         }
+        else {
+            obj = wheremask_obj;
+        }
         /*
          * Have we seen this class before?  If so, ignore.
          */
@@ -208,7 +212,7 @@ copy_positional_args_to_kwargs(const char **keywords,
  */
 NPY_NO_EXPORT int
 PyUFunc_CheckOverride(PyUFuncObject *ufunc, char *method,
-        PyObject *in_args, PyObject *out_args,
+        PyObject *in_args, PyObject *out_args, PyObject *wheremask_obj,
         PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames,
         PyObject **result)
 {
@@ -227,7 +231,7 @@ PyUFunc_CheckOverride(PyUFuncObject *ufunc, char *method,
      * Check inputs for overrides
      */
     num_override_args = get_array_ufunc_overrides(
-           in_args, out_args, with_override, array_ufunc_methods);
+           in_args, out_args, wheremask_obj, with_override, array_ufunc_methods);
     if (num_override_args == -1) {
         goto fail;
     }
@@ -311,10 +315,9 @@ PyUFunc_CheckOverride(PyUFuncObject *ufunc, char *method,
 
     /* Call __array_ufunc__ functions in correct order */
     while (1) {
-        PyObject *override_obj;
-        PyObject *override_array_ufunc;
+        PyObject *override_obj = NULL;
+        PyObject *override_array_ufunc = NULL;
 
-        override_obj = NULL;
         *result = NULL;
 
         /* Choose an overriding argument */
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/override.h b/contrib/python/numpy/py3/numpy/core/src/umath/override.h
index 4e9a323ca62..20621bb1940 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/override.h
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/override.h
@@ -6,7 +6,7 @@
 
 NPY_NO_EXPORT int
 PyUFunc_CheckOverride(PyUFuncObject *ufunc, char *method,
-        PyObject *in_args, PyObject *out_args,
+        PyObject *in_args, PyObject *out_args, PyObject *wheremask_obj,
         PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames,
         PyObject **result);
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/reduction.c b/contrib/python/numpy/py3/numpy/core/src/umath/reduction.c
index 817f99a04fa..6b913b3b8a1 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/reduction.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/reduction.c
@@ -17,6 +17,9 @@
 #include "numpy/arrayobject.h"
 
 #include "npy_pycompat.h"
+#include "array_assign.h"
+#include "array_coercion.h"
+#include "array_method.h"
 #include "ctors.h"
 
 #include "numpy/ufuncobject.h"
@@ -148,24 +151,15 @@ PyArray_CopyInitialReduceValues(
  * context     : The ArrayMethod context (with ufunc, method, and descriptors).
  * operand     : The array to be reduced.
  * out         : NULL, or the array into which to place the result.
- * wheremask   : NOT YET SUPPORTED, but this parameter is placed here
- *               so that support can be added in the future without breaking
- *               API compatibility. Pass in NULL.
+ * wheremask   : Reduction mask of valid values used for `where=`.
  * axis_flags  : Flags indicating the reduction axes of 'operand'.
- * reorderable : If True, the reduction being done is reorderable, which
- *               means specifying multiple axes of reduction at once is ok,
- *               and the reduction code may calculate the reduction in an
- *               arbitrary order. The calculation may be reordered because
- *               of cache behavior or multithreading requirements.
  * keepdims    : If true, leaves the reduction dimensions in the result
  *               with size one.
  * subok       : If true, the result uses the subclass of operand, otherwise
  *               it is always a base class ndarray.
- * identity    : If Py_None, PyArray_CopyInitialReduceValues is used, otherwise
- *               this value is used to initialize the result to
- *               the reduction's unit.
+ * initial     : Initial value, if NULL the default is fetched from the
+ *               ArrayMethod (typically as the default from the ufunc).
  * loop        : `reduce_loop` from `ufunc_object.c`.  TODO: Refactor
- * data        : Data which is passed to the inner loop.
  * buffersize  : Buffer size for the iterator. For the default, pass in 0.
  * funcname    : The name of the reduction function, for error messages.
  * errormask   : forwarded from _get_bufsize_errmask
@@ -182,9 +176,9 @@ PyArray_CopyInitialReduceValues(
 NPY_NO_EXPORT PyArrayObject *
 PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
         PyArrayObject *operand, PyArrayObject *out, PyArrayObject *wheremask,
-        npy_bool *axis_flags, int reorderable, int keepdims,
-        PyObject *identity, PyArray_ReduceLoopFunc *loop,
-        void *data, npy_intp buffersize, const char *funcname, int errormask)
+        npy_bool *axis_flags, int keepdims,
+        PyObject *initial, PyArray_ReduceLoopFunc *loop,
+        npy_intp buffersize, const char *funcname, int errormask)
 {
     assert(loop != NULL);
     PyArrayObject *result = NULL;
@@ -198,38 +192,35 @@ PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
     /* Loop auxdata (must be freed on error) */
     NpyAuxData *auxdata = NULL;
 
-    /* More than one axis means multiple orders are possible */
-    if (!reorderable && count_axes(PyArray_NDIM(operand), axis_flags) > 1) {
-        PyErr_Format(PyExc_ValueError,
-                     "reduction operation '%s' is not reorderable, "
-                     "so at most one axis may be specified",
-                     funcname);
-        return NULL;
-    }
-    /* Can only use where with an initial ( from identity or argument) */
-    if (wheremask != NULL && identity == Py_None) {
-        PyErr_Format(PyExc_ValueError,
-                     "reduction operation '%s' does not have an identity, "
-                     "so to use a where mask one has to specify 'initial'",
-                     funcname);
-        return NULL;
-    }
-
-
     /* Set up the iterator */
     op[0] = out;
     op[1] = operand;
     op_dtypes[0] = context->descriptors[0];
     op_dtypes[1] = context->descriptors[1];
 
+    /* Buffer to use when we need an initial value */
+    char *initial_buf = NULL;
+
+    /* More than one axis means multiple orders are possible */
+    if (!(context->method->flags & NPY_METH_IS_REORDERABLE)
+            && count_axes(PyArray_NDIM(operand), axis_flags) > 1) {
+        PyErr_Format(PyExc_ValueError,
+                "reduction operation '%s' is not reorderable, "
+                "so at most one axis may be specified",
+                funcname);
+        goto fail;
+    }
+
     it_flags = NPY_ITER_BUFFERED |
             NPY_ITER_EXTERNAL_LOOP |
             NPY_ITER_GROWINNER |
-            NPY_ITER_DONT_NEGATE_STRIDES |
             NPY_ITER_ZEROSIZE_OK |
             NPY_ITER_REFS_OK |
             NPY_ITER_DELAY_BUFALLOC |
             NPY_ITER_COPY_IF_OVERLAP;
+    if (!(context->method->flags & NPY_METH_IS_REORDERABLE)) {
+        it_flags |= NPY_ITER_DONT_NEGATE_STRIDES;
+    }
     op_flags[0] = NPY_ITER_READWRITE |
                   NPY_ITER_ALIGNED |
                   NPY_ITER_ALLOCATE |
@@ -297,8 +288,52 @@ PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
         goto fail;
     }
 
+    npy_bool empty_iteration = NpyIter_GetIterSize(iter) == 0;
     result = NpyIter_GetOperandArray(iter)[0];
 
+    /*
+     * Get the initial value (if it exists).  If the iteration is empty
+     * then we assume the reduction is also empty.  The reason is that when
+     * the outer iteration is empty we just won't use the initial value
+     * in any case.  (`np.sum(np.zeros((0, 3)), axis=0)` is a length 3
+     * reduction but has an empty result.)
+     */
+    if ((initial == NULL && context->method->get_reduction_initial == NULL)
+            || initial == Py_None) {
+        /* There is no initial value, or initial value was explicitly unset */
+    }
+    else {
+        /* Not all functions will need initialization, but init always: */
+        initial_buf = PyMem_Calloc(1, op_dtypes[0]->elsize);
+        if (initial_buf == NULL) {
+            PyErr_NoMemory();
+            goto fail;
+        }
+        if (initial != NULL) {
+            /* must use user provided initial value */
+            if (PyArray_Pack(op_dtypes[0], initial_buf, initial) < 0) {
+                goto fail;
+            }
+        }
+        else {
+            /*
+             * Fetch initial from ArrayMethod, we pretend the reduction is
+             * empty when the iteration is.  This may be wrong, but when it is,
+             * we will not need the identity as the result is also empty.
+             */
+            int has_initial = context->method->get_reduction_initial(
+                    context, empty_iteration, initial_buf);
+            if (has_initial < 0) {
+                goto fail;
+            }
+            if (!has_initial) {
+                /* We have no initial value available, free buffer to indicate */
+                PyMem_FREE(initial_buf);
+                initial_buf = NULL;
+            }
+        }
+    }
+
     PyArrayMethod_StridedLoop *strided_loop;
     NPY_ARRAYMETHOD_FLAGS flags = 0;
 
@@ -313,12 +348,27 @@ PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
      * Initialize the result to the reduction unit if possible,
      * otherwise copy the initial values and get a view to the rest.
      */
-    if (identity != Py_None) {
-        if (PyArray_FillWithScalar(result, identity) < 0) {
+    if (initial_buf != NULL) {
+        /* Loop provided an identity or default value, assign to result. */
+        int ret = raw_array_assign_scalar(
+                PyArray_NDIM(result), PyArray_DIMS(result),
+                PyArray_DESCR(result),
+                PyArray_BYTES(result), PyArray_STRIDES(result),
+                op_dtypes[0], initial_buf);
+        if (ret < 0) {
             goto fail;
         }
     }
     else {
+        /* Can only use where with an initial (from identity or argument) */
+        if (wheremask != NULL) {
+            PyErr_Format(PyExc_ValueError,
+                    "reduction operation '%s' does not have an identity, "
+                    "so to use a where mask one has to specify 'initial'",
+                    funcname);
+            goto fail;
+        }
+
         /*
          * For 1-D skip_first_count could be optimized to 0, but no-identity
          * reductions are not super common.
@@ -354,7 +404,7 @@ PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
         }
     }
 
-    if (NpyIter_GetIterSize(iter) != 0) {
+    if (!empty_iteration) {
         NpyIter_IterNextFunc *iternext;
         char **dataptr;
         npy_intp *strideptr;
@@ -387,6 +437,10 @@ PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
     }
     Py_INCREF(result);
 
+    if (initial_buf != NULL && PyDataType_REFCHK(PyArray_DESCR(result))) {
+        PyArray_Item_XDECREF(initial_buf, PyArray_DESCR(result));
+    }
+    PyMem_FREE(initial_buf);
     NPY_AUXDATA_FREE(auxdata);
     if (!NpyIter_Deallocate(iter)) {
         Py_DECREF(result);
@@ -395,6 +449,10 @@ PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
     return result;
 
 fail:
+    if (initial_buf != NULL && PyDataType_REFCHK(op_dtypes[0])) {
+        PyArray_Item_XDECREF(initial_buf, op_dtypes[0]);
+    }
+    PyMem_FREE(initial_buf);
     NPY_AUXDATA_FREE(auxdata);
     if (iter != NULL) {
         NpyIter_Deallocate(iter);
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/reduction.h b/contrib/python/numpy/py3/numpy/core/src/umath/reduction.h
index 2170e27a7f9..d2cbe4849ea 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/reduction.h
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/reduction.h
@@ -64,8 +64,8 @@ typedef int (PyArray_ReduceLoopFunc)(PyArrayMethod_Context *context,
 NPY_NO_EXPORT PyArrayObject *
 PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
         PyArrayObject *operand, PyArrayObject *out, PyArrayObject *wheremask,
-        npy_bool *axis_flags, int reorderable, int keepdims,
-        PyObject *identity, PyArray_ReduceLoopFunc *loop,
-        void *data, npy_intp buffersize, const char *funcname, int errormask);
+        npy_bool *axis_flags, int keepdims,
+        PyObject *initial, PyArray_ReduceLoopFunc *loop,
+        npy_intp buffersize, const char *funcname, int errormask);
 
 #endif
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c b/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c
index cdc2882f7e6..5eaf4c22b28 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c
@@ -64,7 +64,7 @@
  */
 
 #line 60
-static NPY_INLINE int
+static inline int
 byte_ctype_add(npy_byte a, npy_byte b, npy_byte *out) {
     *out = a + b;
     if ((*out^a) >= 0 || (*out^b) >= 0) {
@@ -73,7 +73,7 @@ byte_ctype_add(npy_byte a, npy_byte b, npy_byte *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 byte_ctype_subtract(npy_byte a, npy_byte b, npy_byte *out) {
     *out = a - b;
     if ((*out^a) >= 0 || (*out^~b) >= 0) {
@@ -83,7 +83,7 @@ byte_ctype_subtract(npy_byte a, npy_byte b, npy_byte *out) {
 }
 
 #line 60
-static NPY_INLINE int
+static inline int
 short_ctype_add(npy_short a, npy_short b, npy_short *out) {
     *out = a + b;
     if ((*out^a) >= 0 || (*out^b) >= 0) {
@@ -92,7 +92,7 @@ short_ctype_add(npy_short a, npy_short b, npy_short *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 short_ctype_subtract(npy_short a, npy_short b, npy_short *out) {
     *out = a - b;
     if ((*out^a) >= 0 || (*out^~b) >= 0) {
@@ -102,7 +102,7 @@ short_ctype_subtract(npy_short a, npy_short b, npy_short *out) {
 }
 
 #line 60
-static NPY_INLINE int
+static inline int
 int_ctype_add(npy_int a, npy_int b, npy_int *out) {
     *out = a + b;
     if ((*out^a) >= 0 || (*out^b) >= 0) {
@@ -111,7 +111,7 @@ int_ctype_add(npy_int a, npy_int b, npy_int *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 int_ctype_subtract(npy_int a, npy_int b, npy_int *out) {
     *out = a - b;
     if ((*out^a) >= 0 || (*out^~b) >= 0) {
@@ -121,7 +121,7 @@ int_ctype_subtract(npy_int a, npy_int b, npy_int *out) {
 }
 
 #line 60
-static NPY_INLINE int
+static inline int
 long_ctype_add(npy_long a, npy_long b, npy_long *out) {
     *out = a + b;
     if ((*out^a) >= 0 || (*out^b) >= 0) {
@@ -130,7 +130,7 @@ long_ctype_add(npy_long a, npy_long b, npy_long *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 long_ctype_subtract(npy_long a, npy_long b, npy_long *out) {
     *out = a - b;
     if ((*out^a) >= 0 || (*out^~b) >= 0) {
@@ -140,7 +140,7 @@ long_ctype_subtract(npy_long a, npy_long b, npy_long *out) {
 }
 
 #line 60
-static NPY_INLINE int
+static inline int
 longlong_ctype_add(npy_longlong a, npy_longlong b, npy_longlong *out) {
     *out = a + b;
     if ((*out^a) >= 0 || (*out^b) >= 0) {
@@ -149,7 +149,7 @@ longlong_ctype_add(npy_longlong a, npy_longlong b, npy_longlong *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_subtract(npy_longlong a, npy_longlong b, npy_longlong *out) {
     *out = a - b;
     if ((*out^a) >= 0 || (*out^~b) >= 0) {
@@ -160,7 +160,7 @@ longlong_ctype_subtract(npy_longlong a, npy_longlong b, npy_longlong *out) {
 
 
 #line 83
-static NPY_INLINE int
+static inline int
 ubyte_ctype_add(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     *out = a + b;
     if (*out >= a && *out >= b) {
@@ -169,7 +169,7 @@ ubyte_ctype_add(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_subtract(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     *out = a - b;
     if (a >= b) {
@@ -179,7 +179,7 @@ ubyte_ctype_subtract(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
 }
 
 #line 83
-static NPY_INLINE int
+static inline int
 ushort_ctype_add(npy_ushort a, npy_ushort b, npy_ushort *out) {
     *out = a + b;
     if (*out >= a && *out >= b) {
@@ -188,7 +188,7 @@ ushort_ctype_add(npy_ushort a, npy_ushort b, npy_ushort *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_subtract(npy_ushort a, npy_ushort b, npy_ushort *out) {
     *out = a - b;
     if (a >= b) {
@@ -198,7 +198,7 @@ ushort_ctype_subtract(npy_ushort a, npy_ushort b, npy_ushort *out) {
 }
 
 #line 83
-static NPY_INLINE int
+static inline int
 uint_ctype_add(npy_uint a, npy_uint b, npy_uint *out) {
     *out = a + b;
     if (*out >= a && *out >= b) {
@@ -207,7 +207,7 @@ uint_ctype_add(npy_uint a, npy_uint b, npy_uint *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 uint_ctype_subtract(npy_uint a, npy_uint b, npy_uint *out) {
     *out = a - b;
     if (a >= b) {
@@ -217,7 +217,7 @@ uint_ctype_subtract(npy_uint a, npy_uint b, npy_uint *out) {
 }
 
 #line 83
-static NPY_INLINE int
+static inline int
 ulong_ctype_add(npy_ulong a, npy_ulong b, npy_ulong *out) {
     *out = a + b;
     if (*out >= a && *out >= b) {
@@ -226,7 +226,7 @@ ulong_ctype_add(npy_ulong a, npy_ulong b, npy_ulong *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_subtract(npy_ulong a, npy_ulong b, npy_ulong *out) {
     *out = a - b;
     if (a >= b) {
@@ -236,7 +236,7 @@ ulong_ctype_subtract(npy_ulong a, npy_ulong b, npy_ulong *out) {
 }
 
 #line 83
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_add(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     *out = a + b;
     if (*out >= a && *out >= b) {
@@ -245,7 +245,7 @@ ulonglong_ctype_add(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_subtract(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     *out = a - b;
     if (a >= b) {
@@ -261,7 +261,7 @@ ulonglong_ctype_subtract(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
 
 #line 120
 #if NPY_SIZEOF_INT > NPY_SIZEOF_BYTE
-static NPY_INLINE int
+static inline int
 byte_ctype_multiply(npy_byte a, npy_byte b, npy_byte *out) {
     npy_int temp;
     temp = ((npy_int) a) * ((npy_int) b);
@@ -279,7 +279,7 @@ byte_ctype_multiply(npy_byte a, npy_byte b, npy_byte *out) {
 
 #line 120
 #if NPY_SIZEOF_INT > NPY_SIZEOF_BYTE
-static NPY_INLINE int
+static inline int
 ubyte_ctype_multiply(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     npy_uint temp;
     temp = ((npy_uint) a) * ((npy_uint) b);
@@ -297,7 +297,7 @@ ubyte_ctype_multiply(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
 
 #line 120
 #if NPY_SIZEOF_INT > NPY_SIZEOF_SHORT
-static NPY_INLINE int
+static inline int
 short_ctype_multiply(npy_short a, npy_short b, npy_short *out) {
     npy_int temp;
     temp = ((npy_int) a) * ((npy_int) b);
@@ -315,7 +315,7 @@ short_ctype_multiply(npy_short a, npy_short b, npy_short *out) {
 
 #line 120
 #if NPY_SIZEOF_INT > NPY_SIZEOF_SHORT
-static NPY_INLINE int
+static inline int
 ushort_ctype_multiply(npy_ushort a, npy_ushort b, npy_ushort *out) {
     npy_uint temp;
     temp = ((npy_uint) a) * ((npy_uint) b);
@@ -333,7 +333,7 @@ ushort_ctype_multiply(npy_ushort a, npy_ushort b, npy_ushort *out) {
 
 #line 120
 #if NPY_SIZEOF_LONGLONG > NPY_SIZEOF_INT
-static NPY_INLINE int
+static inline int
 int_ctype_multiply(npy_int a, npy_int b, npy_int *out) {
     npy_longlong temp;
     temp = ((npy_longlong) a) * ((npy_longlong) b);
@@ -351,7 +351,7 @@ int_ctype_multiply(npy_int a, npy_int b, npy_int *out) {
 
 #line 120
 #if NPY_SIZEOF_LONGLONG > NPY_SIZEOF_INT
-static NPY_INLINE int
+static inline int
 uint_ctype_multiply(npy_uint a, npy_uint b, npy_uint *out) {
     npy_ulonglong temp;
     temp = ((npy_ulonglong) a) * ((npy_ulonglong) b);
@@ -369,7 +369,7 @@ uint_ctype_multiply(npy_uint a, npy_uint b, npy_uint *out) {
 
 #line 120
 #if NPY_SIZEOF_LONGLONG > NPY_SIZEOF_LONG
-static NPY_INLINE int
+static inline int
 long_ctype_multiply(npy_long a, npy_long b, npy_long *out) {
     npy_longlong temp;
     temp = ((npy_longlong) a) * ((npy_longlong) b);
@@ -387,7 +387,7 @@ long_ctype_multiply(npy_long a, npy_long b, npy_long *out) {
 
 #line 120
 #if NPY_SIZEOF_LONGLONG > NPY_SIZEOF_LONG
-static NPY_INLINE int
+static inline int
 ulong_ctype_multiply(npy_ulong a, npy_ulong b, npy_ulong *out) {
     npy_ulonglong temp;
     temp = ((npy_ulonglong) a) * ((npy_ulonglong) b);
@@ -406,7 +406,7 @@ ulong_ctype_multiply(npy_ulong a, npy_ulong b, npy_ulong *out) {
 
 #line 146
 #if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_INT
-static NPY_INLINE int
+static inline int
 int_ctype_multiply(npy_int a, npy_int b, npy_int *out) {
     if (npy_mul_with_overflow_int(out, a, b)) {
         return NPY_FPE_OVERFLOW;
@@ -417,7 +417,7 @@ int_ctype_multiply(npy_int a, npy_int b, npy_int *out) {
 
 #line 146
 #if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_INT
-static NPY_INLINE int
+static inline int
 uint_ctype_multiply(npy_uint a, npy_uint b, npy_uint *out) {
     if (npy_mul_with_overflow_uint(out, a, b)) {
         return NPY_FPE_OVERFLOW;
@@ -428,7 +428,7 @@ uint_ctype_multiply(npy_uint a, npy_uint b, npy_uint *out) {
 
 #line 146
 #if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONG
-static NPY_INLINE int
+static inline int
 long_ctype_multiply(npy_long a, npy_long b, npy_long *out) {
     if (npy_mul_with_overflow_long(out, a, b)) {
         return NPY_FPE_OVERFLOW;
@@ -439,7 +439,7 @@ long_ctype_multiply(npy_long a, npy_long b, npy_long *out) {
 
 #line 146
 #if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONG
-static NPY_INLINE int
+static inline int
 ulong_ctype_multiply(npy_ulong a, npy_ulong b, npy_ulong *out) {
     if (npy_mul_with_overflow_ulong(out, a, b)) {
         return NPY_FPE_OVERFLOW;
@@ -450,7 +450,7 @@ ulong_ctype_multiply(npy_ulong a, npy_ulong b, npy_ulong *out) {
 
 #line 146
 #if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONGLONG
-static NPY_INLINE int
+static inline int
 longlong_ctype_multiply(npy_longlong a, npy_longlong b, npy_longlong *out) {
     if (npy_mul_with_overflow_longlong(out, a, b)) {
         return NPY_FPE_OVERFLOW;
@@ -461,7 +461,7 @@ longlong_ctype_multiply(npy_longlong a, npy_longlong b, npy_longlong *out) {
 
 #line 146
 #if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_LONGLONG
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_multiply(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     if (npy_mul_with_overflow_ulonglong(out, a, b)) {
         return NPY_FPE_OVERFLOW;
@@ -479,7 +479,7 @@ ulonglong_ctype_multiply(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 byte_ctype_divide(npy_byte a, npy_byte b, npy_byte *out) {
     if (b == 0) {
         *out = 0;
@@ -508,7 +508,7 @@ byte_ctype_divide(npy_byte a, npy_byte b, npy_byte *out) {
 
 #define byte_ctype_floor_divide byte_ctype_divide
 
-static NPY_INLINE int
+static inline int
 byte_ctype_remainder(npy_byte a, npy_byte b, npy_byte *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -541,7 +541,7 @@ byte_ctype_remainder(npy_byte a, npy_byte b, npy_byte *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_divide(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     if (b == 0) {
         *out = 0;
@@ -570,7 +570,7 @@ ubyte_ctype_divide(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
 
 #define ubyte_ctype_floor_divide ubyte_ctype_divide
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_remainder(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -603,7 +603,7 @@ ubyte_ctype_remainder(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 short_ctype_divide(npy_short a, npy_short b, npy_short *out) {
     if (b == 0) {
         *out = 0;
@@ -632,7 +632,7 @@ short_ctype_divide(npy_short a, npy_short b, npy_short *out) {
 
 #define short_ctype_floor_divide short_ctype_divide
 
-static NPY_INLINE int
+static inline int
 short_ctype_remainder(npy_short a, npy_short b, npy_short *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -665,7 +665,7 @@ short_ctype_remainder(npy_short a, npy_short b, npy_short *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_divide(npy_ushort a, npy_ushort b, npy_ushort *out) {
     if (b == 0) {
         *out = 0;
@@ -694,7 +694,7 @@ ushort_ctype_divide(npy_ushort a, npy_ushort b, npy_ushort *out) {
 
 #define ushort_ctype_floor_divide ushort_ctype_divide
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_remainder(npy_ushort a, npy_ushort b, npy_ushort *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -727,7 +727,7 @@ ushort_ctype_remainder(npy_ushort a, npy_ushort b, npy_ushort *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 int_ctype_divide(npy_int a, npy_int b, npy_int *out) {
     if (b == 0) {
         *out = 0;
@@ -756,7 +756,7 @@ int_ctype_divide(npy_int a, npy_int b, npy_int *out) {
 
 #define int_ctype_floor_divide int_ctype_divide
 
-static NPY_INLINE int
+static inline int
 int_ctype_remainder(npy_int a, npy_int b, npy_int *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -789,7 +789,7 @@ int_ctype_remainder(npy_int a, npy_int b, npy_int *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 uint_ctype_divide(npy_uint a, npy_uint b, npy_uint *out) {
     if (b == 0) {
         *out = 0;
@@ -818,7 +818,7 @@ uint_ctype_divide(npy_uint a, npy_uint b, npy_uint *out) {
 
 #define uint_ctype_floor_divide uint_ctype_divide
 
-static NPY_INLINE int
+static inline int
 uint_ctype_remainder(npy_uint a, npy_uint b, npy_uint *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -851,7 +851,7 @@ uint_ctype_remainder(npy_uint a, npy_uint b, npy_uint *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 long_ctype_divide(npy_long a, npy_long b, npy_long *out) {
     if (b == 0) {
         *out = 0;
@@ -880,7 +880,7 @@ long_ctype_divide(npy_long a, npy_long b, npy_long *out) {
 
 #define long_ctype_floor_divide long_ctype_divide
 
-static NPY_INLINE int
+static inline int
 long_ctype_remainder(npy_long a, npy_long b, npy_long *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -913,7 +913,7 @@ long_ctype_remainder(npy_long a, npy_long b, npy_long *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_divide(npy_ulong a, npy_ulong b, npy_ulong *out) {
     if (b == 0) {
         *out = 0;
@@ -942,7 +942,7 @@ ulong_ctype_divide(npy_ulong a, npy_ulong b, npy_ulong *out) {
 
 #define ulong_ctype_floor_divide ulong_ctype_divide
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_remainder(npy_ulong a, npy_ulong b, npy_ulong *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -975,7 +975,7 @@ ulong_ctype_remainder(npy_ulong a, npy_ulong b, npy_ulong *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_divide(npy_longlong a, npy_longlong b, npy_longlong *out) {
     if (b == 0) {
         *out = 0;
@@ -1004,7 +1004,7 @@ longlong_ctype_divide(npy_longlong a, npy_longlong b, npy_longlong *out) {
 
 #define longlong_ctype_floor_divide longlong_ctype_divide
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_remainder(npy_longlong a, npy_longlong b, npy_longlong *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -1037,7 +1037,7 @@ longlong_ctype_remainder(npy_longlong a, npy_longlong b, npy_longlong *out) {
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_divide(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     if (b == 0) {
         *out = 0;
@@ -1066,7 +1066,7 @@ ulonglong_ctype_divide(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
 
 #define ulonglong_ctype_floor_divide ulonglong_ctype_divide
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_remainder(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -1094,7 +1094,7 @@ ulonglong_ctype_remainder(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 byte_ctype_true_divide(npy_byte a, npy_byte b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1104,7 +1104,7 @@ byte_ctype_true_divide(npy_byte a, npy_byte b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_true_divide(npy_ubyte a, npy_ubyte b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1114,7 +1114,7 @@ ubyte_ctype_true_divide(npy_ubyte a, npy_ubyte b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 short_ctype_true_divide(npy_short a, npy_short b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1124,7 +1124,7 @@ short_ctype_true_divide(npy_short a, npy_short b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_true_divide(npy_ushort a, npy_ushort b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1134,7 +1134,7 @@ ushort_ctype_true_divide(npy_ushort a, npy_ushort b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 int_ctype_true_divide(npy_int a, npy_int b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1144,7 +1144,7 @@ int_ctype_true_divide(npy_int a, npy_int b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 uint_ctype_true_divide(npy_uint a, npy_uint b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1154,7 +1154,7 @@ uint_ctype_true_divide(npy_uint a, npy_uint b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 long_ctype_true_divide(npy_long a, npy_long b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1164,7 +1164,7 @@ long_ctype_true_divide(npy_long a, npy_long b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_true_divide(npy_ulong a, npy_ulong b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1174,7 +1174,7 @@ ulong_ctype_true_divide(npy_ulong a, npy_ulong b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_true_divide(npy_longlong a, npy_longlong b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1184,7 +1184,7 @@ longlong_ctype_true_divide(npy_longlong a, npy_longlong b, npy_double *out)
 
 #line 234
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_true_divide(npy_ulonglong a, npy_ulonglong b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -1195,7 +1195,7 @@ ulonglong_ctype_true_divide(npy_ulonglong a, npy_ulonglong b, npy_double *out)
 
 /* b will always be positive in this call */
 #line 254
-static NPY_INLINE int
+static inline int
 byte_ctype_power(npy_byte a, npy_byte b, npy_byte *out) {
     npy_byte tmp;
 
@@ -1222,7 +1222,7 @@ byte_ctype_power(npy_byte a, npy_byte b, npy_byte *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 ubyte_ctype_power(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
     npy_ubyte tmp;
 
@@ -1249,7 +1249,7 @@ ubyte_ctype_power(npy_ubyte a, npy_ubyte b, npy_ubyte *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 short_ctype_power(npy_short a, npy_short b, npy_short *out) {
     npy_short tmp;
 
@@ -1276,7 +1276,7 @@ short_ctype_power(npy_short a, npy_short b, npy_short *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 ushort_ctype_power(npy_ushort a, npy_ushort b, npy_ushort *out) {
     npy_ushort tmp;
 
@@ -1303,7 +1303,7 @@ ushort_ctype_power(npy_ushort a, npy_ushort b, npy_ushort *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 int_ctype_power(npy_int a, npy_int b, npy_int *out) {
     npy_int tmp;
 
@@ -1330,7 +1330,7 @@ int_ctype_power(npy_int a, npy_int b, npy_int *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 uint_ctype_power(npy_uint a, npy_uint b, npy_uint *out) {
     npy_uint tmp;
 
@@ -1357,7 +1357,7 @@ uint_ctype_power(npy_uint a, npy_uint b, npy_uint *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 long_ctype_power(npy_long a, npy_long b, npy_long *out) {
     npy_long tmp;
 
@@ -1384,7 +1384,7 @@ long_ctype_power(npy_long a, npy_long b, npy_long *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 ulong_ctype_power(npy_ulong a, npy_ulong b, npy_ulong *out) {
     npy_ulong tmp;
 
@@ -1411,7 +1411,7 @@ ulong_ctype_power(npy_ulong a, npy_ulong b, npy_ulong *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 longlong_ctype_power(npy_longlong a, npy_longlong b, npy_longlong *out) {
     npy_longlong tmp;
 
@@ -1438,7 +1438,7 @@ longlong_ctype_power(npy_longlong a, npy_longlong b, npy_longlong *out) {
 }
 
 #line 254
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_power(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
     npy_ulonglong tmp;
 
@@ -1470,7 +1470,7 @@ ulonglong_ctype_power(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out) {
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 byte_ctype_and(npy_byte arg1, npy_byte arg2, npy_byte *out)
 {
     *out = arg1 & arg2;
@@ -1480,7 +1480,7 @@ byte_ctype_and(npy_byte arg1, npy_byte arg2, npy_byte *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 byte_ctype_xor(npy_byte arg1, npy_byte arg2, npy_byte *out)
 {
     *out = arg1 ^ arg2;
@@ -1490,7 +1490,7 @@ byte_ctype_xor(npy_byte arg1, npy_byte arg2, npy_byte *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 byte_ctype_or(npy_byte arg1, npy_byte arg2, npy_byte *out)
 {
     *out = arg1 | arg2;
@@ -1499,14 +1499,14 @@ byte_ctype_or(npy_byte arg1, npy_byte arg2, npy_byte *out)
 
 
 
-static NPY_INLINE int
+static inline int
 byte_ctype_lshift(npy_byte arg1, npy_byte arg2, npy_byte *out)
 {
     *out = npy_lshifthh(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 byte_ctype_rshift(npy_byte arg1, npy_byte arg2, npy_byte *out)
 {
     *out = npy_rshifthh(arg1, arg2);
@@ -1518,7 +1518,7 @@ byte_ctype_rshift(npy_byte arg1, npy_byte arg2, npy_byte *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_and(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 {
     *out = arg1 & arg2;
@@ -1528,7 +1528,7 @@ ubyte_ctype_and(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_xor(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 {
     *out = arg1 ^ arg2;
@@ -1538,7 +1538,7 @@ ubyte_ctype_xor(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_or(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 {
     *out = arg1 | arg2;
@@ -1547,14 +1547,14 @@ ubyte_ctype_or(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 
 
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_lshift(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 {
     *out = npy_lshiftuhh(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_rshift(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 {
     *out = npy_rshiftuhh(arg1, arg2);
@@ -1566,7 +1566,7 @@ ubyte_ctype_rshift(npy_ubyte arg1, npy_ubyte arg2, npy_ubyte *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 short_ctype_and(npy_short arg1, npy_short arg2, npy_short *out)
 {
     *out = arg1 & arg2;
@@ -1576,7 +1576,7 @@ short_ctype_and(npy_short arg1, npy_short arg2, npy_short *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 short_ctype_xor(npy_short arg1, npy_short arg2, npy_short *out)
 {
     *out = arg1 ^ arg2;
@@ -1586,7 +1586,7 @@ short_ctype_xor(npy_short arg1, npy_short arg2, npy_short *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 short_ctype_or(npy_short arg1, npy_short arg2, npy_short *out)
 {
     *out = arg1 | arg2;
@@ -1595,14 +1595,14 @@ short_ctype_or(npy_short arg1, npy_short arg2, npy_short *out)
 
 
 
-static NPY_INLINE int
+static inline int
 short_ctype_lshift(npy_short arg1, npy_short arg2, npy_short *out)
 {
     *out = npy_lshifth(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 short_ctype_rshift(npy_short arg1, npy_short arg2, npy_short *out)
 {
     *out = npy_rshifth(arg1, arg2);
@@ -1614,7 +1614,7 @@ short_ctype_rshift(npy_short arg1, npy_short arg2, npy_short *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_and(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 {
     *out = arg1 & arg2;
@@ -1624,7 +1624,7 @@ ushort_ctype_and(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_xor(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 {
     *out = arg1 ^ arg2;
@@ -1634,7 +1634,7 @@ ushort_ctype_xor(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_or(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 {
     *out = arg1 | arg2;
@@ -1643,14 +1643,14 @@ ushort_ctype_or(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 
 
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_lshift(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 {
     *out = npy_lshiftuh(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_rshift(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 {
     *out = npy_rshiftuh(arg1, arg2);
@@ -1662,7 +1662,7 @@ ushort_ctype_rshift(npy_ushort arg1, npy_ushort arg2, npy_ushort *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 int_ctype_and(npy_int arg1, npy_int arg2, npy_int *out)
 {
     *out = arg1 & arg2;
@@ -1672,7 +1672,7 @@ int_ctype_and(npy_int arg1, npy_int arg2, npy_int *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 int_ctype_xor(npy_int arg1, npy_int arg2, npy_int *out)
 {
     *out = arg1 ^ arg2;
@@ -1682,7 +1682,7 @@ int_ctype_xor(npy_int arg1, npy_int arg2, npy_int *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 int_ctype_or(npy_int arg1, npy_int arg2, npy_int *out)
 {
     *out = arg1 | arg2;
@@ -1691,14 +1691,14 @@ int_ctype_or(npy_int arg1, npy_int arg2, npy_int *out)
 
 
 
-static NPY_INLINE int
+static inline int
 int_ctype_lshift(npy_int arg1, npy_int arg2, npy_int *out)
 {
     *out = npy_lshift(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 int_ctype_rshift(npy_int arg1, npy_int arg2, npy_int *out)
 {
     *out = npy_rshift(arg1, arg2);
@@ -1710,7 +1710,7 @@ int_ctype_rshift(npy_int arg1, npy_int arg2, npy_int *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 uint_ctype_and(npy_uint arg1, npy_uint arg2, npy_uint *out)
 {
     *out = arg1 & arg2;
@@ -1720,7 +1720,7 @@ uint_ctype_and(npy_uint arg1, npy_uint arg2, npy_uint *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 uint_ctype_xor(npy_uint arg1, npy_uint arg2, npy_uint *out)
 {
     *out = arg1 ^ arg2;
@@ -1730,7 +1730,7 @@ uint_ctype_xor(npy_uint arg1, npy_uint arg2, npy_uint *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 uint_ctype_or(npy_uint arg1, npy_uint arg2, npy_uint *out)
 {
     *out = arg1 | arg2;
@@ -1739,14 +1739,14 @@ uint_ctype_or(npy_uint arg1, npy_uint arg2, npy_uint *out)
 
 
 
-static NPY_INLINE int
+static inline int
 uint_ctype_lshift(npy_uint arg1, npy_uint arg2, npy_uint *out)
 {
     *out = npy_lshiftu(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 uint_ctype_rshift(npy_uint arg1, npy_uint arg2, npy_uint *out)
 {
     *out = npy_rshiftu(arg1, arg2);
@@ -1758,7 +1758,7 @@ uint_ctype_rshift(npy_uint arg1, npy_uint arg2, npy_uint *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 long_ctype_and(npy_long arg1, npy_long arg2, npy_long *out)
 {
     *out = arg1 & arg2;
@@ -1768,7 +1768,7 @@ long_ctype_and(npy_long arg1, npy_long arg2, npy_long *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 long_ctype_xor(npy_long arg1, npy_long arg2, npy_long *out)
 {
     *out = arg1 ^ arg2;
@@ -1778,7 +1778,7 @@ long_ctype_xor(npy_long arg1, npy_long arg2, npy_long *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 long_ctype_or(npy_long arg1, npy_long arg2, npy_long *out)
 {
     *out = arg1 | arg2;
@@ -1787,14 +1787,14 @@ long_ctype_or(npy_long arg1, npy_long arg2, npy_long *out)
 
 
 
-static NPY_INLINE int
+static inline int
 long_ctype_lshift(npy_long arg1, npy_long arg2, npy_long *out)
 {
     *out = npy_lshiftl(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 long_ctype_rshift(npy_long arg1, npy_long arg2, npy_long *out)
 {
     *out = npy_rshiftl(arg1, arg2);
@@ -1806,7 +1806,7 @@ long_ctype_rshift(npy_long arg1, npy_long arg2, npy_long *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_and(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 {
     *out = arg1 & arg2;
@@ -1816,7 +1816,7 @@ ulong_ctype_and(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_xor(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 {
     *out = arg1 ^ arg2;
@@ -1826,7 +1826,7 @@ ulong_ctype_xor(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_or(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 {
     *out = arg1 | arg2;
@@ -1835,14 +1835,14 @@ ulong_ctype_or(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 
 
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_lshift(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 {
     *out = npy_lshiftul(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_rshift(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 {
     *out = npy_rshiftul(arg1, arg2);
@@ -1854,7 +1854,7 @@ ulong_ctype_rshift(npy_ulong arg1, npy_ulong arg2, npy_ulong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_and(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 {
     *out = arg1 & arg2;
@@ -1864,7 +1864,7 @@ longlong_ctype_and(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_xor(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 {
     *out = arg1 ^ arg2;
@@ -1874,7 +1874,7 @@ longlong_ctype_xor(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_or(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 {
     *out = arg1 | arg2;
@@ -1883,14 +1883,14 @@ longlong_ctype_or(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 
 
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_lshift(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 {
     *out = npy_lshiftll(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_rshift(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 {
     *out = npy_rshiftll(arg1, arg2);
@@ -1902,7 +1902,7 @@ longlong_ctype_rshift(npy_longlong arg1, npy_longlong arg2, npy_longlong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_and(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 {
     *out = arg1 & arg2;
@@ -1912,7 +1912,7 @@ ulonglong_ctype_and(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_xor(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 {
     *out = arg1 ^ arg2;
@@ -1922,7 +1922,7 @@ ulonglong_ctype_xor(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 
 #line 294
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_or(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 {
     *out = arg1 | arg2;
@@ -1931,14 +1931,14 @@ ulonglong_ctype_or(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 
 
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_lshift(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 {
     *out = npy_lshiftull(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_rshift(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *out)
 {
     *out = npy_rshiftull(arg1, arg2);
@@ -1951,7 +1951,7 @@ ulonglong_ctype_rshift(npy_ulonglong arg1, npy_ulonglong arg2, npy_ulonglong *ou
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 float_ctype_add(npy_float a, npy_float b, npy_float *out)
 {
     *out = a + b;
@@ -1961,7 +1961,7 @@ float_ctype_add(npy_float a, npy_float b, npy_float *out)
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 float_ctype_subtract(npy_float a, npy_float b, npy_float *out)
 {
     *out = a - b;
@@ -1971,7 +1971,7 @@ float_ctype_subtract(npy_float a, npy_float b, npy_float *out)
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 float_ctype_multiply(npy_float a, npy_float b, npy_float *out)
 {
     *out = a * b;
@@ -1981,7 +1981,7 @@ float_ctype_multiply(npy_float a, npy_float b, npy_float *out)
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 float_ctype_divide(npy_float a, npy_float b, npy_float *out)
 {
     *out = a / b;
@@ -1993,21 +1993,21 @@ float_ctype_divide(npy_float a, npy_float b, npy_float *out)
 #define float_ctype_true_divide float_ctype_divide
 
 
-static NPY_INLINE int
+static inline int
 float_ctype_floor_divide(npy_float a, npy_float b, npy_float *out) {
     *out = npy_floor_dividef(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 float_ctype_remainder(npy_float a, npy_float b, npy_float *out) {
     *out = npy_remainderf(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 float_ctype_divmod(npy_float a, npy_float b, npy_float *out1, npy_float *out2) {
     *out1 = npy_divmodf(a, b, out2);
     return 0;
@@ -2019,7 +2019,7 @@ float_ctype_divmod(npy_float a, npy_float b, npy_float *out1, npy_float *out2) {
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 double_ctype_add(npy_double a, npy_double b, npy_double *out)
 {
     *out = a + b;
@@ -2029,7 +2029,7 @@ double_ctype_add(npy_double a, npy_double b, npy_double *out)
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 double_ctype_subtract(npy_double a, npy_double b, npy_double *out)
 {
     *out = a - b;
@@ -2039,7 +2039,7 @@ double_ctype_subtract(npy_double a, npy_double b, npy_double *out)
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 double_ctype_multiply(npy_double a, npy_double b, npy_double *out)
 {
     *out = a * b;
@@ -2049,7 +2049,7 @@ double_ctype_multiply(npy_double a, npy_double b, npy_double *out)
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 double_ctype_divide(npy_double a, npy_double b, npy_double *out)
 {
     *out = a / b;
@@ -2061,21 +2061,21 @@ double_ctype_divide(npy_double a, npy_double b, npy_double *out)
 #define double_ctype_true_divide double_ctype_divide
 
 
-static NPY_INLINE int
+static inline int
 double_ctype_floor_divide(npy_double a, npy_double b, npy_double *out) {
     *out = npy_floor_divide(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 double_ctype_remainder(npy_double a, npy_double b, npy_double *out) {
     *out = npy_remainder(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 double_ctype_divmod(npy_double a, npy_double b, npy_double *out1, npy_double *out2) {
     *out1 = npy_divmod(a, b, out2);
     return 0;
@@ -2087,7 +2087,7 @@ double_ctype_divmod(npy_double a, npy_double b, npy_double *out1, npy_double *ou
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_add(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 {
     *out = a + b;
@@ -2097,7 +2097,7 @@ longdouble_ctype_add(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_subtract(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 {
     *out = a - b;
@@ -2107,7 +2107,7 @@ longdouble_ctype_subtract(npy_longdouble a, npy_longdouble b, npy_longdouble *ou
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_multiply(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 {
     *out = a * b;
@@ -2117,7 +2117,7 @@ longdouble_ctype_multiply(npy_longdouble a, npy_longdouble b, npy_longdouble *ou
 
 #line 330
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_divide(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 {
     *out = a / b;
@@ -2129,21 +2129,21 @@ longdouble_ctype_divide(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 #define longdouble_ctype_true_divide longdouble_ctype_divide
 
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_floor_divide(npy_longdouble a, npy_longdouble b, npy_longdouble *out) {
     *out = npy_floor_dividel(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_remainder(npy_longdouble a, npy_longdouble b, npy_longdouble *out) {
     *out = npy_remainderl(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_divmod(npy_longdouble a, npy_longdouble b, npy_longdouble *out1, npy_longdouble *out2) {
     *out1 = npy_divmodl(a, b, out2);
     return 0;
@@ -2154,7 +2154,7 @@ longdouble_ctype_divmod(npy_longdouble a, npy_longdouble b, npy_longdouble *out1
 
 #line 370
 
-static NPY_INLINE int
+static inline int
 half_ctype_add(npy_half a, npy_half b, npy_half *out)
 {
     float res = npy_half_to_float(a) + npy_half_to_float(b);
@@ -2165,7 +2165,7 @@ half_ctype_add(npy_half a, npy_half b, npy_half *out)
 
 #line 370
 
-static NPY_INLINE int
+static inline int
 half_ctype_subtract(npy_half a, npy_half b, npy_half *out)
 {
     float res = npy_half_to_float(a) - npy_half_to_float(b);
@@ -2176,7 +2176,7 @@ half_ctype_subtract(npy_half a, npy_half b, npy_half *out)
 
 #line 370
 
-static NPY_INLINE int
+static inline int
 half_ctype_multiply(npy_half a, npy_half b, npy_half *out)
 {
     float res = npy_half_to_float(a) * npy_half_to_float(b);
@@ -2187,7 +2187,7 @@ half_ctype_multiply(npy_half a, npy_half b, npy_half *out)
 
 #line 370
 
-static NPY_INLINE int
+static inline int
 half_ctype_divide(npy_half a, npy_half b, npy_half *out)
 {
     float res = npy_half_to_float(a) / npy_half_to_float(b);
@@ -2199,7 +2199,7 @@ half_ctype_divide(npy_half a, npy_half b, npy_half *out)
 #define half_ctype_true_divide half_ctype_divide
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_floor_divide(npy_half a, npy_half b, npy_half *out)
 {
     npy_half mod;
@@ -2215,7 +2215,7 @@ half_ctype_floor_divide(npy_half a, npy_half b, npy_half *out)
 }
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_remainder(npy_half a, npy_half b, npy_half *out)
 {
     npy_half_divmod(a, b, out);
@@ -2223,7 +2223,7 @@ half_ctype_remainder(npy_half a, npy_half b, npy_half *out)
 }
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_divmod(npy_half a, npy_half b, npy_half *out1, npy_half *out2)
 {
     *out1 = npy_half_divmod(a, b, out2);
@@ -2231,7 +2231,7 @@ half_ctype_divmod(npy_half a, npy_half b, npy_half *out1, npy_half *out2)
 }
 
 #line 422
-static NPY_INLINE int
+static inline int
 cfloat_ctype_add(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 {
     out->real = a.real + b.real;
@@ -2239,7 +2239,7 @@ cfloat_ctype_add(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 cfloat_ctype_subtract(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 {
     out->real = a.real - b.real;
@@ -2252,7 +2252,7 @@ cfloat_ctype_subtract(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
  * TODO: Mark as  to work around FPEs not being issues on clang 12.
  *       This should be removed when possible.
  */
-static NPY_INLINE int
+static inline int
 cfloat_ctype_multiply( npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 {
     out->real = a.real * b.real - a.imag * b.imag;
@@ -2261,11 +2261,11 @@ cfloat_ctype_multiply( npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 }
 
 /* Use the ufunc loop directly to avoid duplicating the complicated logic */
-static NPY_INLINE int
+static inline int
 cfloat_ctype_divide(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 {
     char *args[3] = {(char *)&a, (char *)&b, (char *)out};
-    npy_intp steps[3];
+    npy_intp steps[3] = {0, 0, 0};
     npy_intp size = 1;
     CFLOAT_divide(args, &size, steps, NULL);
     return 0;
@@ -2275,7 +2275,7 @@ cfloat_ctype_divide(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 
 
 #line 422
-static NPY_INLINE int
+static inline int
 cdouble_ctype_add(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 {
     out->real = a.real + b.real;
@@ -2283,7 +2283,7 @@ cdouble_ctype_add(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 cdouble_ctype_subtract(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 {
     out->real = a.real - b.real;
@@ -2296,7 +2296,7 @@ cdouble_ctype_subtract(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
  * TODO: Mark as  to work around FPEs not being issues on clang 12.
  *       This should be removed when possible.
  */
-static NPY_INLINE int
+static inline int
 cdouble_ctype_multiply( npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 {
     out->real = a.real * b.real - a.imag * b.imag;
@@ -2305,11 +2305,11 @@ cdouble_ctype_multiply( npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 }
 
 /* Use the ufunc loop directly to avoid duplicating the complicated logic */
-static NPY_INLINE int
+static inline int
 cdouble_ctype_divide(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 {
     char *args[3] = {(char *)&a, (char *)&b, (char *)out};
-    npy_intp steps[3];
+    npy_intp steps[3] = {0, 0, 0};
     npy_intp size = 1;
     CDOUBLE_divide(args, &size, steps, NULL);
     return 0;
@@ -2319,7 +2319,7 @@ cdouble_ctype_divide(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 
 
 #line 422
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_add(npy_clongdouble a, npy_clongdouble b, npy_clongdouble *out)
 {
     out->real = a.real + b.real;
@@ -2327,7 +2327,7 @@ clongdouble_ctype_add(npy_clongdouble a, npy_clongdouble b, npy_clongdouble *out
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_subtract(npy_clongdouble a, npy_clongdouble b, npy_clongdouble *out)
 {
     out->real = a.real - b.real;
@@ -2340,7 +2340,7 @@ clongdouble_ctype_subtract(npy_clongdouble a, npy_clongdouble b, npy_clongdouble
  * TODO: Mark as  to work around FPEs not being issues on clang 12.
  *       This should be removed when possible.
  */
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_multiply( npy_clongdouble a, npy_clongdouble b, npy_clongdouble *out)
 {
     out->real = a.real * b.real - a.imag * b.imag;
@@ -2349,11 +2349,11 @@ clongdouble_ctype_multiply( npy_clongdouble a, npy_clongdouble b, npy_clongdoubl
 }
 
 /* Use the ufunc loop directly to avoid duplicating the complicated logic */
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_divide(npy_clongdouble a, npy_clongdouble b, npy_clongdouble *out)
 {
     char *args[3] = {(char *)&a, (char *)&b, (char *)out};
-    npy_intp steps[3];
+    npy_intp steps[3] = {0, 0, 0};
     npy_intp size = 1;
     CLONGDOUBLE_divide(args, &size, steps, NULL);
     return 0;
@@ -2367,7 +2367,7 @@ clongdouble_ctype_divide(npy_clongdouble a, npy_clongdouble b, npy_clongdouble *
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 byte_ctype_divmod(npy_byte a, npy_byte b, npy_byte *out, npy_byte *out2)
 {
     int res = byte_ctype_floor_divide(a, b, out);
@@ -2378,7 +2378,7 @@ byte_ctype_divmod(npy_byte a, npy_byte b, npy_byte *out, npy_byte *out2)
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_divmod(npy_ubyte a, npy_ubyte b, npy_ubyte *out, npy_ubyte *out2)
 {
     int res = ubyte_ctype_floor_divide(a, b, out);
@@ -2389,7 +2389,7 @@ ubyte_ctype_divmod(npy_ubyte a, npy_ubyte b, npy_ubyte *out, npy_ubyte *out2)
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 short_ctype_divmod(npy_short a, npy_short b, npy_short *out, npy_short *out2)
 {
     int res = short_ctype_floor_divide(a, b, out);
@@ -2400,7 +2400,7 @@ short_ctype_divmod(npy_short a, npy_short b, npy_short *out, npy_short *out2)
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_divmod(npy_ushort a, npy_ushort b, npy_ushort *out, npy_ushort *out2)
 {
     int res = ushort_ctype_floor_divide(a, b, out);
@@ -2411,7 +2411,7 @@ ushort_ctype_divmod(npy_ushort a, npy_ushort b, npy_ushort *out, npy_ushort *out
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 int_ctype_divmod(npy_int a, npy_int b, npy_int *out, npy_int *out2)
 {
     int res = int_ctype_floor_divide(a, b, out);
@@ -2422,7 +2422,7 @@ int_ctype_divmod(npy_int a, npy_int b, npy_int *out, npy_int *out2)
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 uint_ctype_divmod(npy_uint a, npy_uint b, npy_uint *out, npy_uint *out2)
 {
     int res = uint_ctype_floor_divide(a, b, out);
@@ -2433,7 +2433,7 @@ uint_ctype_divmod(npy_uint a, npy_uint b, npy_uint *out, npy_uint *out2)
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 long_ctype_divmod(npy_long a, npy_long b, npy_long *out, npy_long *out2)
 {
     int res = long_ctype_floor_divide(a, b, out);
@@ -2444,7 +2444,7 @@ long_ctype_divmod(npy_long a, npy_long b, npy_long *out, npy_long *out2)
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_divmod(npy_ulong a, npy_ulong b, npy_ulong *out, npy_ulong *out2)
 {
     int res = ulong_ctype_floor_divide(a, b, out);
@@ -2455,7 +2455,7 @@ ulong_ctype_divmod(npy_ulong a, npy_ulong b, npy_ulong *out, npy_ulong *out2)
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_divmod(npy_longlong a, npy_longlong b, npy_longlong *out, npy_longlong *out2)
 {
     int res = longlong_ctype_floor_divide(a, b, out);
@@ -2466,7 +2466,7 @@ longlong_ctype_divmod(npy_longlong a, npy_longlong b, npy_longlong *out, npy_lon
 
 #line 472
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_divmod(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out, npy_ulonglong *out2)
 {
     int res = ulonglong_ctype_floor_divide(a, b, out);
@@ -2479,7 +2479,7 @@ ulonglong_ctype_divmod(npy_ulonglong a, npy_ulonglong b, npy_ulonglong *out, npy
 
 #line 489
 
-static NPY_INLINE int
+static inline int
 float_ctype_power(npy_float a, npy_float b, npy_float *out)
 {
     *out = npy_powf(a, b);
@@ -2489,7 +2489,7 @@ float_ctype_power(npy_float a, npy_float b, npy_float *out)
 
 #line 489
 
-static NPY_INLINE int
+static inline int
 double_ctype_power(npy_double a, npy_double b, npy_double *out)
 {
     *out = npy_pow(a, b);
@@ -2499,7 +2499,7 @@ double_ctype_power(npy_double a, npy_double b, npy_double *out)
 
 #line 489
 
-static NPY_INLINE int
+static inline int
 longdouble_ctype_power(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 {
     *out = npy_powl(a, b);
@@ -2507,7 +2507,7 @@ longdouble_ctype_power(npy_longdouble a, npy_longdouble b, npy_longdouble *out)
 }
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_power(npy_half a, npy_half b, npy_half *out)
 {
     const npy_float af = npy_half_to_float(a);
@@ -2518,7 +2518,7 @@ half_ctype_power(npy_half a, npy_half b, npy_half *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 byte_ctype_negative(npy_byte a, npy_byte *out)
 {
 #if 0
@@ -2541,7 +2541,7 @@ byte_ctype_negative(npy_byte a, npy_byte *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 ubyte_ctype_negative(npy_ubyte a, npy_ubyte *out)
 {
 #if 1
@@ -2564,7 +2564,7 @@ ubyte_ctype_negative(npy_ubyte a, npy_ubyte *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 short_ctype_negative(npy_short a, npy_short *out)
 {
 #if 0
@@ -2587,7 +2587,7 @@ short_ctype_negative(npy_short a, npy_short *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 ushort_ctype_negative(npy_ushort a, npy_ushort *out)
 {
 #if 1
@@ -2610,7 +2610,7 @@ ushort_ctype_negative(npy_ushort a, npy_ushort *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 int_ctype_negative(npy_int a, npy_int *out)
 {
 #if 0
@@ -2633,7 +2633,7 @@ int_ctype_negative(npy_int a, npy_int *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 uint_ctype_negative(npy_uint a, npy_uint *out)
 {
 #if 1
@@ -2656,7 +2656,7 @@ uint_ctype_negative(npy_uint a, npy_uint *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 long_ctype_negative(npy_long a, npy_long *out)
 {
 #if 0
@@ -2679,7 +2679,7 @@ long_ctype_negative(npy_long a, npy_long *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 ulong_ctype_negative(npy_ulong a, npy_ulong *out)
 {
 #if 1
@@ -2702,7 +2702,7 @@ ulong_ctype_negative(npy_ulong a, npy_ulong *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 longlong_ctype_negative(npy_longlong a, npy_longlong *out)
 {
 #if 0
@@ -2725,7 +2725,7 @@ longlong_ctype_negative(npy_longlong a, npy_longlong *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_negative(npy_ulonglong a, npy_ulonglong *out)
 {
 #if 1
@@ -2748,7 +2748,7 @@ ulonglong_ctype_negative(npy_ulonglong a, npy_ulonglong *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 float_ctype_negative(npy_float a, npy_float *out)
 {
 #if 0
@@ -2771,7 +2771,7 @@ float_ctype_negative(npy_float a, npy_float *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 double_ctype_negative(npy_double a, npy_double *out)
 {
 #if 0
@@ -2794,7 +2794,7 @@ double_ctype_negative(npy_double a, npy_double *out)
 }
 
 #line 521
-static NPY_INLINE int
+static inline int
 longdouble_ctype_negative(npy_longdouble a, npy_longdouble *out)
 {
 #if 0
@@ -2817,7 +2817,7 @@ longdouble_ctype_negative(npy_longdouble a, npy_longdouble *out)
 }
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_negative(npy_half a, npy_half *out)
 {
     *out = a^0x8000u;
@@ -2826,7 +2826,7 @@ half_ctype_negative(npy_half a, npy_half *out)
 
 
 #line 556
-static NPY_INLINE int
+static inline int
 cfloat_ctype_negative(npy_cfloat a, npy_cfloat *out)
 {
     out->real = -a.real;
@@ -2835,7 +2835,7 @@ cfloat_ctype_negative(npy_cfloat a, npy_cfloat *out)
 }
 
 #line 556
-static NPY_INLINE int
+static inline int
 cdouble_ctype_negative(npy_cdouble a, npy_cdouble *out)
 {
     out->real = -a.real;
@@ -2844,7 +2844,7 @@ cdouble_ctype_negative(npy_cdouble a, npy_cdouble *out)
 }
 
 #line 556
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_negative(npy_clongdouble a, npy_clongdouble *out)
 {
     out->real = -a.real;
@@ -2854,7 +2854,7 @@ clongdouble_ctype_negative(npy_clongdouble a, npy_clongdouble *out)
 
 
 #line 573
-static NPY_INLINE int
+static inline int
 byte_ctype_positive(npy_byte a, npy_byte *out)
 {
     *out = a;
@@ -2862,7 +2862,7 @@ byte_ctype_positive(npy_byte a, npy_byte *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 ubyte_ctype_positive(npy_ubyte a, npy_ubyte *out)
 {
     *out = a;
@@ -2870,7 +2870,7 @@ ubyte_ctype_positive(npy_ubyte a, npy_ubyte *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 short_ctype_positive(npy_short a, npy_short *out)
 {
     *out = a;
@@ -2878,7 +2878,7 @@ short_ctype_positive(npy_short a, npy_short *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 ushort_ctype_positive(npy_ushort a, npy_ushort *out)
 {
     *out = a;
@@ -2886,7 +2886,7 @@ ushort_ctype_positive(npy_ushort a, npy_ushort *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 int_ctype_positive(npy_int a, npy_int *out)
 {
     *out = a;
@@ -2894,7 +2894,7 @@ int_ctype_positive(npy_int a, npy_int *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 uint_ctype_positive(npy_uint a, npy_uint *out)
 {
     *out = a;
@@ -2902,7 +2902,7 @@ uint_ctype_positive(npy_uint a, npy_uint *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 long_ctype_positive(npy_long a, npy_long *out)
 {
     *out = a;
@@ -2910,7 +2910,7 @@ long_ctype_positive(npy_long a, npy_long *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 ulong_ctype_positive(npy_ulong a, npy_ulong *out)
 {
     *out = a;
@@ -2918,7 +2918,7 @@ ulong_ctype_positive(npy_ulong a, npy_ulong *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 longlong_ctype_positive(npy_longlong a, npy_longlong *out)
 {
     *out = a;
@@ -2926,7 +2926,7 @@ longlong_ctype_positive(npy_longlong a, npy_longlong *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_positive(npy_ulonglong a, npy_ulonglong *out)
 {
     *out = a;
@@ -2934,7 +2934,7 @@ ulonglong_ctype_positive(npy_ulonglong a, npy_ulonglong *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 half_ctype_positive(npy_half a, npy_half *out)
 {
     *out = a;
@@ -2942,7 +2942,7 @@ half_ctype_positive(npy_half a, npy_half *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 float_ctype_positive(npy_float a, npy_float *out)
 {
     *out = a;
@@ -2950,7 +2950,7 @@ float_ctype_positive(npy_float a, npy_float *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 double_ctype_positive(npy_double a, npy_double *out)
 {
     *out = a;
@@ -2958,7 +2958,7 @@ double_ctype_positive(npy_double a, npy_double *out)
 }
 
 #line 573
-static NPY_INLINE int
+static inline int
 longdouble_ctype_positive(npy_longdouble a, npy_longdouble *out)
 {
     *out = a;
@@ -2967,7 +2967,7 @@ longdouble_ctype_positive(npy_longdouble a, npy_longdouble *out)
 
 
 #line 586
-static NPY_INLINE int
+static inline int
 cfloat_ctype_positive(npy_cfloat a, npy_cfloat *out)
 {
     out->real = a.real;
@@ -2975,7 +2975,7 @@ cfloat_ctype_positive(npy_cfloat a, npy_cfloat *out)
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 cfloat_ctype_power(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 {
     *out = npy_cpowf(a, b);
@@ -2983,7 +2983,7 @@ cfloat_ctype_power(npy_cfloat a, npy_cfloat b, npy_cfloat *out)
 }
 
 #line 586
-static NPY_INLINE int
+static inline int
 cdouble_ctype_positive(npy_cdouble a, npy_cdouble *out)
 {
     out->real = a.real;
@@ -2991,7 +2991,7 @@ cdouble_ctype_positive(npy_cdouble a, npy_cdouble *out)
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 cdouble_ctype_power(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 {
     *out = npy_cpow(a, b);
@@ -2999,7 +2999,7 @@ cdouble_ctype_power(npy_cdouble a, npy_cdouble b, npy_cdouble *out)
 }
 
 #line 586
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_positive(npy_clongdouble a, npy_clongdouble *out)
 {
     out->real = a.real;
@@ -3007,7 +3007,7 @@ clongdouble_ctype_positive(npy_clongdouble a, npy_clongdouble *out)
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_power(npy_clongdouble a, npy_clongdouble b, npy_clongdouble *out)
 {
     *out = npy_cpowl(a, b);
@@ -3044,7 +3044,7 @@ clongdouble_ctype_power(npy_clongdouble a, npy_clongdouble b, npy_clongdouble *o
 
 
 #line 617
-static NPY_INLINE int
+static inline int
 byte_ctype_absolute(npy_byte a, npy_byte *out)
 {
     if (a == NPY_MIN_BYTE) {
@@ -3056,7 +3056,7 @@ byte_ctype_absolute(npy_byte a, npy_byte *out)
 }
 
 #line 617
-static NPY_INLINE int
+static inline int
 short_ctype_absolute(npy_short a, npy_short *out)
 {
     if (a == NPY_MIN_SHORT) {
@@ -3068,7 +3068,7 @@ short_ctype_absolute(npy_short a, npy_short *out)
 }
 
 #line 617
-static NPY_INLINE int
+static inline int
 int_ctype_absolute(npy_int a, npy_int *out)
 {
     if (a == NPY_MIN_INT) {
@@ -3080,7 +3080,7 @@ int_ctype_absolute(npy_int a, npy_int *out)
 }
 
 #line 617
-static NPY_INLINE int
+static inline int
 long_ctype_absolute(npy_long a, npy_long *out)
 {
     if (a == NPY_MIN_LONG) {
@@ -3092,7 +3092,7 @@ long_ctype_absolute(npy_long a, npy_long *out)
 }
 
 #line 617
-static NPY_INLINE int
+static inline int
 longlong_ctype_absolute(npy_longlong a, npy_longlong *out)
 {
     if (a == NPY_MIN_LONGLONG) {
@@ -3105,7 +3105,7 @@ longlong_ctype_absolute(npy_longlong a, npy_longlong *out)
 
 
 #line 634
-static NPY_INLINE int
+static inline int
 float_ctype_absolute(npy_float a, npy_float *out)
 {
     *out = npy_fabsf(a);
@@ -3113,7 +3113,7 @@ float_ctype_absolute(npy_float a, npy_float *out)
 }
 
 #line 634
-static NPY_INLINE int
+static inline int
 double_ctype_absolute(npy_double a, npy_double *out)
 {
     *out = npy_fabs(a);
@@ -3121,7 +3121,7 @@ double_ctype_absolute(npy_double a, npy_double *out)
 }
 
 #line 634
-static NPY_INLINE int
+static inline int
 longdouble_ctype_absolute(npy_longdouble a, npy_longdouble *out)
 {
     *out = npy_fabsl(a);
@@ -3129,7 +3129,7 @@ longdouble_ctype_absolute(npy_longdouble a, npy_longdouble *out)
 }
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_absolute(npy_half a, npy_half *out)
 {
     *out = a&0x7fffu;
@@ -3137,7 +3137,7 @@ half_ctype_absolute(npy_half a, npy_half *out)
 }
 
 #line 655
-static NPY_INLINE int
+static inline int
 cfloat_ctype_absolute(npy_cfloat a, npy_float *out)
 {
     *out = npy_cabsf(a);
@@ -3145,7 +3145,7 @@ cfloat_ctype_absolute(npy_cfloat a, npy_float *out)
 }
 
 #line 655
-static NPY_INLINE int
+static inline int
 cdouble_ctype_absolute(npy_cdouble a, npy_double *out)
 {
     *out = npy_cabs(a);
@@ -3153,7 +3153,7 @@ cdouble_ctype_absolute(npy_cdouble a, npy_double *out)
 }
 
 #line 655
-static NPY_INLINE int
+static inline int
 clongdouble_ctype_absolute(npy_clongdouble a, npy_longdouble *out)
 {
     *out = npy_cabsl(a);
@@ -3163,7 +3163,7 @@ clongdouble_ctype_absolute(npy_clongdouble a, npy_longdouble *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 byte_ctype_invert(npy_byte a, npy_byte *out)
 {
     *out = ~a;
@@ -3173,7 +3173,7 @@ byte_ctype_invert(npy_byte a, npy_byte *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 ubyte_ctype_invert(npy_ubyte a, npy_ubyte *out)
 {
     *out = ~a;
@@ -3183,7 +3183,7 @@ ubyte_ctype_invert(npy_ubyte a, npy_ubyte *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 short_ctype_invert(npy_short a, npy_short *out)
 {
     *out = ~a;
@@ -3193,7 +3193,7 @@ short_ctype_invert(npy_short a, npy_short *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 ushort_ctype_invert(npy_ushort a, npy_ushort *out)
 {
     *out = ~a;
@@ -3203,7 +3203,7 @@ ushort_ctype_invert(npy_ushort a, npy_ushort *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 int_ctype_invert(npy_int a, npy_int *out)
 {
     *out = ~a;
@@ -3213,7 +3213,7 @@ int_ctype_invert(npy_int a, npy_int *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 uint_ctype_invert(npy_uint a, npy_uint *out)
 {
     *out = ~a;
@@ -3223,7 +3223,7 @@ uint_ctype_invert(npy_uint a, npy_uint *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 long_ctype_invert(npy_long a, npy_long *out)
 {
     *out = ~a;
@@ -3233,7 +3233,7 @@ long_ctype_invert(npy_long a, npy_long *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 ulong_ctype_invert(npy_ulong a, npy_ulong *out)
 {
     *out = ~a;
@@ -3243,7 +3243,7 @@ ulong_ctype_invert(npy_ulong a, npy_ulong *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 longlong_ctype_invert(npy_longlong a, npy_longlong *out)
 {
     *out = ~a;
@@ -3253,7 +3253,7 @@ longlong_ctype_invert(npy_longlong a, npy_longlong *out)
 
 #line 667
 
-static NPY_INLINE int
+static inline int
 ulonglong_ctype_invert(npy_ulonglong a, npy_ulonglong *out)
 {
     *out = ~a;
@@ -3394,7 +3394,7 @@ typedef enum {
      */
     CONVERT_PYSCALAR,
     /*
-     * Other object is an unkown scalar or array-like, we (typically) use
+     * Other object is an unknown scalar or array-like, we (typically) use
      * the generic path, which normally ends up in the ufunc machinery.
      */
     OTHER_IS_UNKNOWN_OBJECT,
@@ -3500,7 +3500,7 @@ typedef enum {
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_byte(PyObject *value, npy_byte *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -3832,7 +3832,7 @@ convert_to_byte(PyObject *value, npy_byte *result, npy_bool *may_need_deferring)
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_ubyte(PyObject *value, npy_ubyte *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -4164,7 +4164,7 @@ convert_to_ubyte(PyObject *value, npy_ubyte *result, npy_bool *may_need_deferrin
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_short(PyObject *value, npy_short *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -4496,7 +4496,7 @@ convert_to_short(PyObject *value, npy_short *result, npy_bool *may_need_deferrin
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_ushort(PyObject *value, npy_ushort *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -4828,7 +4828,7 @@ convert_to_ushort(PyObject *value, npy_ushort *result, npy_bool *may_need_deferr
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_int(PyObject *value, npy_int *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -5160,7 +5160,7 @@ convert_to_int(PyObject *value, npy_int *result, npy_bool *may_need_deferring)
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_uint(PyObject *value, npy_uint *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -5492,7 +5492,7 @@ convert_to_uint(PyObject *value, npy_uint *result, npy_bool *may_need_deferring)
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_long(PyObject *value, npy_long *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -5824,7 +5824,7 @@ convert_to_long(PyObject *value, npy_long *result, npy_bool *may_need_deferring)
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_ulong(PyObject *value, npy_ulong *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -6156,7 +6156,7 @@ convert_to_ulong(PyObject *value, npy_ulong *result, npy_bool *may_need_deferrin
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_longlong(PyObject *value, npy_longlong *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -6488,7 +6488,7 @@ convert_to_longlong(PyObject *value, npy_longlong *result, npy_bool *may_need_de
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_ulonglong(PyObject *value, npy_ulonglong *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -6820,7 +6820,7 @@ convert_to_ulonglong(PyObject *value, npy_ulonglong *result, npy_bool *may_need_
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_half(PyObject *value, npy_half *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -7152,7 +7152,7 @@ convert_to_half(PyObject *value, npy_half *result, npy_bool *may_need_deferring)
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_float(PyObject *value, npy_float *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -7484,7 +7484,7 @@ convert_to_float(PyObject *value, npy_float *result, npy_bool *may_need_deferrin
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_double(PyObject *value, npy_double *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -7816,7 +7816,7 @@ convert_to_double(PyObject *value, npy_double *result, npy_bool *may_need_deferr
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_longdouble(PyObject *value, npy_longdouble *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -8148,7 +8148,7 @@ convert_to_longdouble(PyObject *value, npy_longdouble *result, npy_bool *may_nee
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_cfloat(PyObject *value, npy_cfloat *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -8480,7 +8480,7 @@ convert_to_cfloat(PyObject *value, npy_cfloat *result, npy_bool *may_need_deferr
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_cdouble(PyObject *value, npy_cdouble *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -8812,7 +8812,7 @@ convert_to_cdouble(PyObject *value, npy_cdouble *result, npy_bool *may_need_defe
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_clongdouble(PyObject *value, npy_clongdouble *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -38853,7 +38853,7 @@ clongdouble_power(PyObject *a, PyObject *b, PyObject *modulo)
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38868,7 +38868,7 @@ cfloat_floor_divide(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38883,7 +38883,7 @@ cdouble_floor_divide(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38898,7 +38898,7 @@ clongdouble_floor_divide(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38913,7 +38913,7 @@ cfloat_divmod(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38928,7 +38928,7 @@ cdouble_divmod(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38943,7 +38943,7 @@ clongdouble_divmod(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38958,7 +38958,7 @@ cfloat_remainder(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -38973,7 +38973,7 @@ cdouble_remainder(PyObject *NPY_UNUSED(a), PyObject *NPY_UNUSED(b))
 
 #line 1544
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -41250,7 +41250,7 @@ static int
 emit_complexwarning(void)
 {
     static PyObject *cls = NULL;
-    npy_cache_import("numpy.core", "ComplexWarning", &cls);
+    npy_cache_import("numpy.exceptions", "ComplexWarning", &cls);
     if (cls == NULL) {
         return -1;
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c.src b/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c.src
index 6121c2fcddf..a159fdc1201 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c.src
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/scalarmath.c.src
@@ -57,7 +57,7 @@
  *  #name = byte, short, int, long, longlong#
  *  #type = npy_byte, npy_short, npy_int, npy_long, npy_longlong#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_add(@type@ a, @type@ b, @type@ *out) {
     *out = a + b;
     if ((*out^a) >= 0 || (*out^b) >= 0) {
@@ -66,7 +66,7 @@ static NPY_INLINE int
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_subtract(@type@ a, @type@ b, @type@ *out) {
     *out = a - b;
     if ((*out^a) >= 0 || (*out^~b) >= 0) {
@@ -80,7 +80,7 @@ static NPY_INLINE int
  *  #name = ubyte, ushort, uint, ulong, ulonglong#
  *  #type = npy_ubyte, npy_ushort, npy_uint, npy_ulong, npy_ulonglong#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_add(@type@ a, @type@ b, @type@ *out) {
     *out = a + b;
     if (*out >= a && *out >= b) {
@@ -89,7 +89,7 @@ static NPY_INLINE int
     return NPY_FPE_OVERFLOW;
 }
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_subtract(@type@ a, @type@ b, @type@ *out) {
     *out = a - b;
     if (a >= b) {
@@ -118,7 +118,7 @@ static NPY_INLINE int
  * #neg = (1,0)*4#
  */
 #if NPY_SIZEOF_@SIZE@ > NPY_SIZEOF_@SIZENAME@
-static NPY_INLINE int
+static inline int
 @name@_ctype_multiply(@type@ a, @type@ b, @type@ *out) {
     @big@ temp;
     temp = ((@big@) a) * ((@big@) b);
@@ -144,7 +144,7 @@ static NPY_INLINE int
  * #SIZE = INT*2, LONG*2, LONGLONG*2#
  */
 #if NPY_SIZEOF_LONGLONG == NPY_SIZEOF_@SIZE@
-static NPY_INLINE int
+static inline int
 @name@_ctype_multiply(@type@ a, @type@ b, @type@ *out) {
     if (npy_mul_with_overflow_@name@(out, a, b)) {
         return NPY_FPE_OVERFLOW;
@@ -171,7 +171,7 @@ static NPY_INLINE int
     #define DIVIDEBYZERO_CHECK (b == 0)
 #endif
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_divide(@type@ a, @type@ b, @type@ *out) {
     if (b == 0) {
         *out = 0;
@@ -200,7 +200,7 @@ static NPY_INLINE int
 
 #define @name@_ctype_floor_divide @name@_ctype_divide
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_remainder(@type@ a, @type@ b, @type@ *out) {
     if (DIVIDEBYZERO_CHECK) {
         *out = 0;
@@ -232,7 +232,7 @@ static NPY_INLINE int
  *         ulong, longlong, ulonglong#
  */
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_true_divide(npy_@name@ a, npy_@name@ b, npy_double *out)
 {
     *out = (npy_double)a / (npy_double)b;
@@ -251,7 +251,7 @@ static NPY_INLINE int
  * #upc = BYTE, UBYTE, SHORT, USHORT, INT, UINT,
  *        LONG, ULONG, LONGLONG, ULONGLONG#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_power(@type@ a, @type@ b, @type@ *out) {
     @type@ tmp;
 
@@ -292,7 +292,7 @@ static NPY_INLINE int
  * #op = &, ^, |#
  */
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_@oper@(@type@ arg1, @type@ arg2, @type@ *out)
 {
     *out = arg1 @op@ arg2;
@@ -301,14 +301,14 @@ static NPY_INLINE int
 
 /**end repeat1**/
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_lshift(@type@ arg1, @type@ arg2, @type@ *out)
 {
     *out = npy_lshift@suffix@(arg1, arg2);
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_rshift(@type@ arg1, @type@ arg2, @type@ *out)
 {
     *out = npy_rshift@suffix@(arg1, arg2);
@@ -328,7 +328,7 @@ static NPY_INLINE int
  * #oper = add, subtract, multiply, divide#
  */
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_@oper@(@type@ a, @type@ b, @type@ *out)
 {
     *out = a @OP@ b;
@@ -340,21 +340,21 @@ static NPY_INLINE int
 #define @name@_ctype_true_divide @name@_ctype_divide
 
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_floor_divide(@type@ a, @type@ b, @type@ *out) {
     *out = npy_floor_divide@c@(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_remainder(@type@ a, @type@ b, @type@ *out) {
     *out = npy_remainder@c@(a, b);
     return 0;
 }
 
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_divmod(@type@ a, @type@ b, @type@ *out1, @type@ *out2) {
     *out1 = npy_divmod@c@(a, b, out2);
     return 0;
@@ -368,7 +368,7 @@ static NPY_INLINE int
  * #oper = add, subtract, multiply, divide#
  */
 
-static NPY_INLINE int
+static inline int
 half_ctype_@oper@(npy_half a, npy_half b, npy_half *out)
 {
     float res = npy_half_to_float(a) @OP@ npy_half_to_float(b);
@@ -380,7 +380,7 @@ half_ctype_@oper@(npy_half a, npy_half b, npy_half *out)
 #define half_ctype_true_divide half_ctype_divide
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_floor_divide(npy_half a, npy_half b, npy_half *out)
 {
     npy_half mod;
@@ -396,7 +396,7 @@ half_ctype_floor_divide(npy_half a, npy_half b, npy_half *out)
 }
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_remainder(npy_half a, npy_half b, npy_half *out)
 {
     npy_half_divmod(a, b, out);
@@ -404,7 +404,7 @@ half_ctype_remainder(npy_half a, npy_half b, npy_half *out)
 }
 
 
-static NPY_INLINE int
+static inline int
 half_ctype_divmod(npy_half a, npy_half b, npy_half *out1, npy_half *out2)
 {
     *out1 = npy_half_divmod(a, b, out2);
@@ -419,7 +419,7 @@ half_ctype_divmod(npy_half a, npy_half b, npy_half *out1, npy_half *out2)
  * #rtype = npy_float, npy_double, npy_longdouble#
  * #c = f,,l#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_add(@type@ a, @type@ b, @type@ *out)
 {
     out->real = a.real + b.real;
@@ -427,7 +427,7 @@ static NPY_INLINE int
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_subtract(@type@ a, @type@ b, @type@ *out)
 {
     out->real = a.real - b.real;
@@ -440,7 +440,7 @@ static NPY_INLINE int
  * TODO: Mark as  to work around FPEs not being issues on clang 12.
  *       This should be removed when possible.
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_multiply( @type@ a, @type@ b, @type@ *out)
 {
     out->real = a.real * b.real - a.imag * b.imag;
@@ -449,11 +449,11 @@ static NPY_INLINE int
 }
 
 /* Use the ufunc loop directly to avoid duplicating the complicated logic */
-static NPY_INLINE int
+static inline int
 @name@_ctype_divide(@type@ a, @type@ b, @type@ *out)
 {
     char *args[3] = {(char *)&a, (char *)&b, (char *)out};
-    npy_intp steps[3];
+    npy_intp steps[3] = {0, 0, 0};
     npy_intp size = 1;
     @TYPE@_divide(args, &size, steps, NULL);
     return 0;
@@ -470,7 +470,7 @@ static NPY_INLINE int
  *         longlong, ulonglong#
  */
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_divmod(npy_@name@ a, npy_@name@ b, npy_@name@ *out, npy_@name@ *out2)
 {
     int res = @name@_ctype_floor_divide(a, b, out);
@@ -487,7 +487,7 @@ static NPY_INLINE int
  * #c = f,,l#
  */
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_power(@type@ a, @type@ b, @type@ *out)
 {
     *out = npy_pow@c@(a, b);
@@ -495,7 +495,7 @@ static NPY_INLINE int
 }
 
 /**end repeat**/
-static NPY_INLINE int
+static inline int
 half_ctype_power(npy_half a, npy_half b, npy_half *out)
 {
     const npy_float af = npy_half_to_float(a);
@@ -518,7 +518,7 @@ half_ctype_power(npy_half a, npy_half b, npy_half *out)
  * #uns = (0,1)*5,0*3#
  * #int = 1*10,0*3#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_negative(@type@ a, @type@ *out)
 {
 #if @uns@
@@ -541,7 +541,7 @@ static NPY_INLINE int
 }
 /**end repeat**/
 
-static NPY_INLINE int
+static inline int
 half_ctype_negative(npy_half a, npy_half *out)
 {
     *out = a^0x8000u;
@@ -553,7 +553,7 @@ half_ctype_negative(npy_half a, npy_half *out)
  * #name = cfloat, cdouble, clongdouble#
  * #type = npy_cfloat, npy_cdouble, npy_clongdouble#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_negative(@type@ a, @type@ *out)
 {
     out->real = -a.real;
@@ -570,7 +570,7 @@ static NPY_INLINE int
  *         npy_long, npy_ulong, npy_longlong, npy_ulonglong,
  *         npy_half, npy_float, npy_double, npy_longdouble#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_positive(@type@ a, @type@ *out)
 {
     *out = a;
@@ -583,7 +583,7 @@ static NPY_INLINE int
  * #type = npy_cfloat, npy_cdouble, npy_clongdouble#
  * #c = f,,l#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_positive(@type@ a, @type@ *out)
 {
     out->real = a.real;
@@ -591,7 +591,7 @@ static NPY_INLINE int
     return 0;
 }
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_power(@type@ a, @type@ b, @type@ *out)
 {
     *out = npy_cpow@c@(a, b);
@@ -614,7 +614,7 @@ static NPY_INLINE int
  * #type = npy_byte, npy_short, npy_int, npy_long, npy_longlong#
  * #NAME = BYTE, SHORT, INT, LONG, LONGLONG#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_absolute(@type@ a, @type@ *out)
 {
     if (a == NPY_MIN_@NAME@) {
@@ -631,7 +631,7 @@ static NPY_INLINE int
  * #type = npy_float, npy_double, npy_longdouble#
  * #c = f,,l#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_absolute(@type@ a, @type@ *out)
 {
     *out = npy_fabs@c@(a);
@@ -639,7 +639,7 @@ static NPY_INLINE int
 }
 /**end repeat**/
 
-static NPY_INLINE int
+static inline int
 half_ctype_absolute(npy_half a, npy_half *out)
 {
     *out = a&0x7fffu;
@@ -652,7 +652,7 @@ half_ctype_absolute(npy_half a, npy_half *out)
  * #rtype = npy_float, npy_double, npy_longdouble#
  * #c = f,,l#
  */
-static NPY_INLINE int
+static inline int
 @name@_ctype_absolute(@type@ a, @rtype@ *out)
 {
     *out = npy_cabs@c@(a);
@@ -665,7 +665,7 @@ static NPY_INLINE int
  *         ulong, longlong, ulonglong#
  */
 
-static NPY_INLINE int
+static inline int
 @name@_ctype_invert(npy_@name@ a, npy_@name@ *out)
 {
     *out = ~a;
@@ -806,7 +806,7 @@ typedef enum {
      */
     CONVERT_PYSCALAR,
     /*
-     * Other object is an unkown scalar or array-like, we (typically) use
+     * Other object is an unknown scalar or array-like, we (typically) use
      * the generic path, which normally ends up in the ufunc machinery.
      */
     OTHER_IS_UNKNOWN_OBJECT,
@@ -929,7 +929,7 @@ typedef enum {
  * @result The result value indicating what we did with `value` or what type
  *         of object it is (see `conversion_result`).
  */
-static NPY_INLINE conversion_result
+static inline conversion_result
 convert_to_@name@(PyObject *value, @type@ *result, npy_bool *may_need_deferring)
 {
     PyArray_Descr *descr;
@@ -1542,7 +1542,7 @@ static PyObject *
  *
  */
 
-/* 
+/*
  * Complex numbers do not support remainder so we manually make sure that the
  * operation is not defined.  This is/was especially important for longdoubles
  * due to their tendency to recurse for some operations, see gh-18548.
@@ -1704,7 +1704,7 @@ static int
 emit_complexwarning(void)
 {
     static PyObject *cls = NULL;
-    npy_cache_import("numpy.core", "ComplexWarning", &cls);
+    npy_cache_import("numpy.exceptions", "ComplexWarning", &cls);
     if (cls == NULL) {
         return -1;
     }
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/simd.inc b/contrib/python/numpy/py3/numpy/core/src/umath/simd.inc
deleted file mode 100644
index d9be87dd6f7..00000000000
--- a/contrib/python/numpy/py3/numpy/core/src/umath/simd.inc
+++ /dev/null
@@ -1,3192 +0,0 @@
-#line 1 "numpy/core/src/umath/simd.inc.src"
-
-/*
- *****************************************************************************
- **       This file was autogenerated from a template  DO NOT EDIT!!!!      **
- **       Changes should be made to the original source (.src) file         **
- *****************************************************************************
- */
-
-#line 1
-
-
-/*
- * This file is for the definitions of simd vectorized operations.
- *
- * Currently contains sse2 functions that are built on amd64, x32 or
- * non-generic builds (CFLAGS=-march=...)
- * In future it may contain other instruction sets like AVX or NEON detected
- * at runtime in which case it needs to be included indirectly via a file
- * compiled with special options (or use gcc target attributes) so the binary
- * stays portable.
- */
-
-
-#ifndef __NPY_SIMD_INC
-#define __NPY_SIMD_INC
-
-#include "lowlevel_strided_loops.h"
-#include "numpy/npy_common.h"
-#include "numpy/npy_math.h"
-#include "npy_simd_data.h"
-#ifdef NPY_HAVE_SSE2_INTRINSICS
-#include <emmintrin.h>
-#if !defined(_MSC_VER) || _MSC_VER >= 1600
-#include <immintrin.h>
-#else
-#undef __AVX2__
-#undef __AVX512F__
-#endif
-#endif
-#include "loops_utils.h" // nomemoverlap
-#include <assert.h>
-#include <stdlib.h>
-#include <float.h>
-#include <string.h> /* for memcpy */
-
-#define VECTOR_SIZE_BYTES 16
-
-/*
- * Dispatcher functions
- * decide whether the operation can be vectorized and run it
- * if it was run returns true and false if nothing was done
- */
-
-/*
- *****************************************************************************
- **                           CMPLX DISPATCHERS
- *****************************************************************************
- */
-
-#line 56
-
-#line 62
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_square_CFLOAT(npy_float*, npy_float*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_unary_avx512f_square_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-    if ((IS_OUTPUT_BLOCKABLE_UNARY(8, (npy_uint)(8/1), 64)) && (labs(steps[0]) < 2*2*8)) {
-        AVX512F_square_CFLOAT((npy_float*)args[1], (npy_float*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-#line 62
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_absolute_CFLOAT(npy_float*, npy_float*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_unary_avx512f_absolute_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-    if ((IS_OUTPUT_BLOCKABLE_UNARY(8, (npy_uint)(8/2), 64)) && (labs(steps[0]) < 2*8*8)) {
-        AVX512F_absolute_CFLOAT((npy_float*)args[1], (npy_float*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-#line 62
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_conjugate_CFLOAT(npy_float*, npy_float*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_unary_avx512f_conjugate_CFLOAT(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-    if ((IS_OUTPUT_BLOCKABLE_UNARY(8, (npy_uint)(8/1), 64)) && (labs(steps[0]) < 2*8*8)) {
-        AVX512F_conjugate_CFLOAT((npy_float*)args[1], (npy_float*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-
-#line 56
-
-#line 62
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_square_CDOUBLE(npy_double*, npy_double*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_unary_avx512f_square_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-    if ((IS_OUTPUT_BLOCKABLE_UNARY(16, (npy_uint)(16/1), 64)) && (labs(steps[0]) < 2*2*16)) {
-        AVX512F_square_CDOUBLE((npy_double*)args[1], (npy_double*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-#line 62
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_absolute_CDOUBLE(npy_double*, npy_double*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_unary_avx512f_absolute_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-    if ((IS_OUTPUT_BLOCKABLE_UNARY(16, (npy_uint)(16/2), 64)) && (labs(steps[0]) < 2*8*16)) {
-        AVX512F_absolute_CDOUBLE((npy_double*)args[1], (npy_double*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-#line 62
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_conjugate_CDOUBLE(npy_double*, npy_double*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_unary_avx512f_conjugate_CDOUBLE(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-    if ((IS_OUTPUT_BLOCKABLE_UNARY(16, (npy_uint)(16/1), 64)) && (labs(steps[0]) < 2*8*16)) {
-        AVX512F_conjugate_CDOUBLE((npy_double*)args[1], (npy_double*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-
-
-
-/*
- *****************************************************************************
- **                           FLOAT DISPATCHERS
- *****************************************************************************
- */
-
-#line 96
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isnan_FLOAT(npy_bool*, npy_float*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isnan_avx512_skx_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_float), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isnan_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isfinite_FLOAT(npy_bool*, npy_float*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isfinite_avx512_skx_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_float), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isfinite_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isinf_FLOAT(npy_bool*, npy_float*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isinf_avx512_skx_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_float), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isinf_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_signbit_FLOAT(npy_bool*, npy_float*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_signbit_avx512_skx_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_float), sizeof(npy_bool), 64)) {
-        AVX512_SKX_signbit_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-
-#line 96
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isnan_DOUBLE(npy_bool*, npy_double*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isnan_avx512_skx_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_double), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isnan_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isfinite_DOUBLE(npy_bool*, npy_double*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isfinite_avx512_skx_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_double), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isfinite_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isinf_DOUBLE(npy_bool*, npy_double*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isinf_avx512_skx_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_double), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isinf_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_signbit_DOUBLE(npy_bool*, npy_double*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_signbit_avx512_skx_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 1
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_double), sizeof(npy_bool), 64)) {
-        AVX512_SKX_signbit_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-
-#line 96
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isnan_LONGDOUBLE(npy_bool*, npy_longdouble*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isnan_avx512_skx_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_longdouble), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isnan_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isfinite_LONGDOUBLE(npy_bool*, npy_longdouble*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isfinite_avx512_skx_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_longdouble), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isfinite_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isinf_LONGDOUBLE(npy_bool*, npy_longdouble*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_isinf_avx512_skx_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_longdouble), sizeof(npy_bool), 64)) {
-        AVX512_SKX_isinf_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 100
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_signbit_LONGDOUBLE(npy_bool*, npy_longdouble*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_signbit_avx512_skx_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && 0
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_longdouble), sizeof(npy_bool), 64)) {
-        AVX512_SKX_signbit_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-
-
-
-#line 132
-
-#line 138
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-/* prototypes */
-static void
-sse2_negative_FLOAT(npy_float *, npy_float *, const npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_unary_simd_negative_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (IS_BLOCKABLE_UNARY(sizeof(npy_float), VECTOR_SIZE_BYTES)) {
-        sse2_negative_FLOAT((npy_float*)args[1], (npy_float*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isnan_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isnan_simd_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_float) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_float))) {
-        sse2_isnan_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isfinite_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isfinite_simd_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_float) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_float))) {
-        sse2_isfinite_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isinf_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isinf_simd_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_float) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_float))) {
-        sse2_isinf_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_signbit_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_signbit_simd_FLOAT(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_float) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_float))) {
-        sse2_signbit_FLOAT((npy_bool*)args[1], (npy_float*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-
-#line 132
-
-#line 138
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-/* prototypes */
-static void
-sse2_negative_DOUBLE(npy_double *, npy_double *, const npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_unary_simd_negative_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (IS_BLOCKABLE_UNARY(sizeof(npy_double), VECTOR_SIZE_BYTES)) {
-        sse2_negative_DOUBLE((npy_double*)args[1], (npy_double*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isnan_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isnan_simd_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_double) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_double))) {
-        sse2_isnan_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isfinite_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isfinite_simd_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_double) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_double))) {
-        sse2_isfinite_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isinf_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isinf_simd_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_double) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_double))) {
-        sse2_isinf_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_signbit_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_signbit_simd_DOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 1 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_double) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_double))) {
-        sse2_signbit_DOUBLE((npy_bool*)args[1], (npy_double*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-
-#line 132
-
-#line 138
-
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-
-/* prototypes */
-static void
-sse2_negative_LONGDOUBLE(npy_longdouble *, npy_longdouble *, const npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_unary_simd_negative_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (IS_BLOCKABLE_UNARY(sizeof(npy_longdouble), VECTOR_SIZE_BYTES)) {
-        sse2_negative_LONGDOUBLE((npy_longdouble*)args[1], (npy_longdouble*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 164
-
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isnan_LONGDOUBLE(npy_bool * op, npy_longdouble * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isnan_simd_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_longdouble) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_longdouble))) {
-        sse2_isnan_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isfinite_LONGDOUBLE(npy_bool * op, npy_longdouble * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isfinite_simd_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_longdouble) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_longdouble))) {
-        sse2_isfinite_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_isinf_LONGDOUBLE(npy_bool * op, npy_longdouble * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_isinf_simd_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_longdouble) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_longdouble))) {
-        sse2_isinf_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 164
-
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_signbit_LONGDOUBLE(npy_bool * op, npy_longdouble * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_signbit_simd_LONGDOUBLE(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if 0 && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(npy_longdouble) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(npy_longdouble))) {
-        sse2_signbit_LONGDOUBLE((npy_bool*)args[1], (npy_longdouble*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-
-
-/*
- *****************************************************************************
- **                           BOOL DISPATCHERS
- *****************************************************************************
- */
-
-#line 198
-
-#if defined NPY_HAVE_SSE2_INTRINSICS
-static void
-sse2_binary_logical_or_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2,
-                        npy_intp n);
-
-static void
-sse2_reduce_logical_or_BOOL(npy_bool * op, npy_bool * ip, npy_intp n);
-#endif
-
-static NPY_INLINE int
-run_binary_simd_logical_or_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_BINARY(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_binary_logical_or_BOOL((npy_bool*)args[2], (npy_bool*)args[0],
-                               (npy_bool*)args[1], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-static NPY_INLINE int
-run_reduce_simd_logical_or_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_REDUCE(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_reduce_logical_or_BOOL((npy_bool*)args[0], (npy_bool*)args[1],
-                                dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 198
-
-#if defined NPY_HAVE_SSE2_INTRINSICS
-static void
-sse2_binary_logical_and_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2,
-                        npy_intp n);
-
-static void
-sse2_reduce_logical_and_BOOL(npy_bool * op, npy_bool * ip, npy_intp n);
-#endif
-
-static NPY_INLINE int
-run_binary_simd_logical_and_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_BINARY(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_binary_logical_and_BOOL((npy_bool*)args[2], (npy_bool*)args[0],
-                               (npy_bool*)args[1], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-static NPY_INLINE int
-run_reduce_simd_logical_and_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_REDUCE(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_reduce_logical_and_BOOL((npy_bool*)args[0], (npy_bool*)args[1],
-                                dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-#line 242
-
-#if defined NPY_HAVE_SSE2_INTRINSICS
-static void
-sse2_absolute_BOOL(npy_bool *, npy_bool *, const npy_intp n);
-#endif
-
-static NPY_INLINE int
-run_unary_simd_absolute_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_UNARY(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_absolute_BOOL((npy_bool*)args[1], (npy_bool*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-#line 242
-
-#if defined NPY_HAVE_SSE2_INTRINSICS
-static void
-sse2_logical_not_BOOL(npy_bool *, npy_bool *, const npy_intp n);
-#endif
-
-static NPY_INLINE int
-run_unary_simd_logical_not_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_UNARY(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_logical_not_BOOL((npy_bool*)args[1], (npy_bool*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-
-#ifdef NPY_HAVE_SSE2_INTRINSICS
-
-/*
- * Vectorized operations
- */
-/*
- *****************************************************************************
- **                           FLOAT LOOPS
- *****************************************************************************
- */
-
-#line 278
-
-NPY_FINLINE npy_float sse2_horizontal_min___m128(__m128 v)
-{
-    npy_float r;
-    __m128 tmp = _mm_movehl_ps(v, v);                   /* c     d     ... */
-    __m128 m = _mm_min_ps(v, tmp);                    /* m(ac) m(bd) ... */
-    tmp = _mm_shuffle_ps(m, m, _MM_SHUFFLE(1, 1, 1, 1));/* m(bd) m(bd) ... */
-    _mm_store_ss(&r, _mm_min_ps(tmp, m));             /* m(acbd) ... */
-    return r;
-}
-
-NPY_FINLINE npy_double sse2_horizontal_min___m128d(__m128d v)
-{
-    npy_double r;
-    __m128d tmp = _mm_unpackhi_pd(v, v);    /* b     b */
-    _mm_store_sd(&r, _mm_min_pd(tmp, v)); /* m(ab) m(bb) */
-    return r;
-}
-
-#line 278
-
-NPY_FINLINE npy_float sse2_horizontal_max___m128(__m128 v)
-{
-    npy_float r;
-    __m128 tmp = _mm_movehl_ps(v, v);                   /* c     d     ... */
-    __m128 m = _mm_max_ps(v, tmp);                    /* m(ac) m(bd) ... */
-    tmp = _mm_shuffle_ps(m, m, _MM_SHUFFLE(1, 1, 1, 1));/* m(bd) m(bd) ... */
-    _mm_store_ss(&r, _mm_max_ps(tmp, m));             /* m(acbd) ... */
-    return r;
-}
-
-NPY_FINLINE npy_double sse2_horizontal_max___m128d(__m128d v)
-{
-    npy_double r;
-    __m128d tmp = _mm_unpackhi_pd(v, v);    /* b     b */
-    _mm_store_sd(&r, _mm_max_pd(tmp, v)); /* m(ab) m(bb) */
-    return r;
-}
-
-
-#line 315
-/*
- * compress 4 vectors to 4/8 bytes in op with filled with 0 or 1
- * the last vector is passed as a pointer as MSVC 2010 is unable to ignore the
- * calling convention leading to C2719 on 32 bit, see #4795
- */
-NPY_FINLINE void
-sse2_compress4_to_byte_FLOAT(__m128 r1, __m128 r2, __m128 r3, __m128 * r4,
-                              npy_bool * op)
-{
-    const __m128i mask = _mm_set1_epi8(0x1);
-    __m128i ir1 = _mm_packs_epi32(_mm_castps_si128(r1), _mm_castps_si128(r2));
-    __m128i ir2 = _mm_packs_epi32(_mm_castps_si128(r3), _mm_castps_si128(*r4));
-    __m128i rr = _mm_packs_epi16(ir1, ir2);
-#if 0
-    rr = _mm_packs_epi16(rr, rr);
-    rr = _mm_and_si128(rr, mask);
-    _mm_storel_epi64((__m128i*)op, rr);
-#else
-    rr = _mm_and_si128(rr, mask);
-    _mm_storeu_si128((__m128i*)op, rr);
-#endif
-}
-
-static void
-sse2_signbit_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_float, VECTOR_SIZE_BYTES) {
-        op[i] = npy_signbit(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_float, VECTOR_SIZE_BYTES) {
-        __m128 a = _mm_load_ps(&ip1[i]);
-        int r = _mm_movemask_ps(a);
-        if (sizeof(npy_float) == 8) {
-            op[i] = r & 1;
-            op[i + 1] = (r >> 1);
-        }
-        else {
-            op[i] = r & 1;
-            op[i + 1] = (r >> 1) & 1;
-            op[i + 2] = (r >> 2) & 1;
-            op[i + 3] = (r >> 3);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_signbit(ip1[i]) != 0;
-    }
-}
-
-#line 367
-
-static void
-sse2_isnan_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n)
-{
-#if 0 != 0 /* isinf/isfinite */
-    /* signbit mask 0x7FFFFFFF after andnot */
-    const __m128 mask = _mm_set1_ps(-0.f);
-    const __m128 ones = _mm_cmpeq_ps(_mm_setzero_ps(),
-                                             _mm_setzero_ps());
-#if 0
-    const __m128 fltmax = _mm_set1_ps(DBL_MAX);
-#else
-    const __m128 fltmax = _mm_set1_ps(FLT_MAX);
-#endif
-#endif
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_float, VECTOR_SIZE_BYTES) {
-        op[i] = npy_isnan(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_float, 4 * VECTOR_SIZE_BYTES) {
-        __m128 a = _mm_load_ps(&ip1[i + 0 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 b = _mm_load_ps(&ip1[i + 1 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 c = _mm_load_ps(&ip1[i + 2 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 d = _mm_load_ps(&ip1[i + 3 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 r1, r2, r3, r4;
-#if 0 != 0 /* isinf/isfinite */
-        /* fabs via masking of sign bit */
-        r1 = _mm_andnot_ps(mask, a);
-        r2 = _mm_andnot_ps(mask, b);
-        r3 = _mm_andnot_ps(mask, c);
-        r4 = _mm_andnot_ps(mask, d);
-#if 0 == 1 /* isfinite */
-        /* negative compare against max float, nan is always true */
-        r1 = _mm_cmpnle_ps(r1, fltmax);
-        r2 = _mm_cmpnle_ps(r2, fltmax);
-        r3 = _mm_cmpnle_ps(r3, fltmax);
-        r4 = _mm_cmpnle_ps(r4, fltmax);
-#else /* isinf */
-        r1 = _mm_cmpnlt_ps(fltmax, r1);
-        r2 = _mm_cmpnlt_ps(fltmax, r2);
-        r3 = _mm_cmpnlt_ps(fltmax, r3);
-        r4 = _mm_cmpnlt_ps(fltmax, r4);
-#endif
-        /* flip results to what we want (andnot as there is no sse not) */
-        r1 = _mm_andnot_ps(r1, ones);
-        r2 = _mm_andnot_ps(r2, ones);
-        r3 = _mm_andnot_ps(r3, ones);
-        r4 = _mm_andnot_ps(r4, ones);
-#endif
-#if 0 == 0 /* isnan */
-        r1 = _mm_cmpneq_ps(a, a);
-        r2 = _mm_cmpneq_ps(b, b);
-        r3 = _mm_cmpneq_ps(c, c);
-        r4 = _mm_cmpneq_ps(d, d);
-#endif
-        sse2_compress4_to_byte_FLOAT(r1, r2, r3, &r4, &op[i]);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_isnan(ip1[i]) != 0;
-    }
-}
-
-
-#line 367
-
-static void
-sse2_isfinite_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n)
-{
-#if 1 != 0 /* isinf/isfinite */
-    /* signbit mask 0x7FFFFFFF after andnot */
-    const __m128 mask = _mm_set1_ps(-0.f);
-    const __m128 ones = _mm_cmpeq_ps(_mm_setzero_ps(),
-                                             _mm_setzero_ps());
-#if 0
-    const __m128 fltmax = _mm_set1_ps(DBL_MAX);
-#else
-    const __m128 fltmax = _mm_set1_ps(FLT_MAX);
-#endif
-#endif
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_float, VECTOR_SIZE_BYTES) {
-        op[i] = npy_isfinite(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_float, 4 * VECTOR_SIZE_BYTES) {
-        __m128 a = _mm_load_ps(&ip1[i + 0 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 b = _mm_load_ps(&ip1[i + 1 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 c = _mm_load_ps(&ip1[i + 2 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 d = _mm_load_ps(&ip1[i + 3 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 r1, r2, r3, r4;
-#if 1 != 0 /* isinf/isfinite */
-        /* fabs via masking of sign bit */
-        r1 = _mm_andnot_ps(mask, a);
-        r2 = _mm_andnot_ps(mask, b);
-        r3 = _mm_andnot_ps(mask, c);
-        r4 = _mm_andnot_ps(mask, d);
-#if 1 == 1 /* isfinite */
-        /* negative compare against max float, nan is always true */
-        r1 = _mm_cmpnle_ps(r1, fltmax);
-        r2 = _mm_cmpnle_ps(r2, fltmax);
-        r3 = _mm_cmpnle_ps(r3, fltmax);
-        r4 = _mm_cmpnle_ps(r4, fltmax);
-#else /* isinf */
-        r1 = _mm_cmpnlt_ps(fltmax, r1);
-        r2 = _mm_cmpnlt_ps(fltmax, r2);
-        r3 = _mm_cmpnlt_ps(fltmax, r3);
-        r4 = _mm_cmpnlt_ps(fltmax, r4);
-#endif
-        /* flip results to what we want (andnot as there is no sse not) */
-        r1 = _mm_andnot_ps(r1, ones);
-        r2 = _mm_andnot_ps(r2, ones);
-        r3 = _mm_andnot_ps(r3, ones);
-        r4 = _mm_andnot_ps(r4, ones);
-#endif
-#if 1 == 0 /* isnan */
-        r1 = _mm_cmpneq_ps(a, a);
-        r2 = _mm_cmpneq_ps(b, b);
-        r3 = _mm_cmpneq_ps(c, c);
-        r4 = _mm_cmpneq_ps(d, d);
-#endif
-        sse2_compress4_to_byte_FLOAT(r1, r2, r3, &r4, &op[i]);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_isfinite(ip1[i]) != 0;
-    }
-}
-
-
-#line 367
-
-static void
-sse2_isinf_FLOAT(npy_bool * op, npy_float * ip1, npy_intp n)
-{
-#if 2 != 0 /* isinf/isfinite */
-    /* signbit mask 0x7FFFFFFF after andnot */
-    const __m128 mask = _mm_set1_ps(-0.f);
-    const __m128 ones = _mm_cmpeq_ps(_mm_setzero_ps(),
-                                             _mm_setzero_ps());
-#if 0
-    const __m128 fltmax = _mm_set1_ps(DBL_MAX);
-#else
-    const __m128 fltmax = _mm_set1_ps(FLT_MAX);
-#endif
-#endif
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_float, VECTOR_SIZE_BYTES) {
-        op[i] = npy_isinf(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_float, 4 * VECTOR_SIZE_BYTES) {
-        __m128 a = _mm_load_ps(&ip1[i + 0 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 b = _mm_load_ps(&ip1[i + 1 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 c = _mm_load_ps(&ip1[i + 2 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 d = _mm_load_ps(&ip1[i + 3 * VECTOR_SIZE_BYTES / sizeof(npy_float)]);
-        __m128 r1, r2, r3, r4;
-#if 2 != 0 /* isinf/isfinite */
-        /* fabs via masking of sign bit */
-        r1 = _mm_andnot_ps(mask, a);
-        r2 = _mm_andnot_ps(mask, b);
-        r3 = _mm_andnot_ps(mask, c);
-        r4 = _mm_andnot_ps(mask, d);
-#if 2 == 1 /* isfinite */
-        /* negative compare against max float, nan is always true */
-        r1 = _mm_cmpnle_ps(r1, fltmax);
-        r2 = _mm_cmpnle_ps(r2, fltmax);
-        r3 = _mm_cmpnle_ps(r3, fltmax);
-        r4 = _mm_cmpnle_ps(r4, fltmax);
-#else /* isinf */
-        r1 = _mm_cmpnlt_ps(fltmax, r1);
-        r2 = _mm_cmpnlt_ps(fltmax, r2);
-        r3 = _mm_cmpnlt_ps(fltmax, r3);
-        r4 = _mm_cmpnlt_ps(fltmax, r4);
-#endif
-        /* flip results to what we want (andnot as there is no sse not) */
-        r1 = _mm_andnot_ps(r1, ones);
-        r2 = _mm_andnot_ps(r2, ones);
-        r3 = _mm_andnot_ps(r3, ones);
-        r4 = _mm_andnot_ps(r4, ones);
-#endif
-#if 2 == 0 /* isnan */
-        r1 = _mm_cmpneq_ps(a, a);
-        r2 = _mm_cmpneq_ps(b, b);
-        r3 = _mm_cmpneq_ps(c, c);
-        r4 = _mm_cmpneq_ps(d, d);
-#endif
-        sse2_compress4_to_byte_FLOAT(r1, r2, r3, &r4, &op[i]);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_isinf(ip1[i]) != 0;
-    }
-}
-
-
-
-static void
-sse2_negative_FLOAT(npy_float * op, npy_float * ip, const npy_intp n)
-{
-    /*
-     * get 0x7FFFFFFF mask (everything but signbit set)
-     * float & ~mask will remove the sign, float ^ mask flips the sign
-     * this is equivalent to how the compiler implements fabs on amd64
-     */
-    const __m128 mask = _mm_set1_ps(-0.f);
-
-    /* align output to VECTOR_SIZE_BYTES bytes */
-    LOOP_BLOCK_ALIGN_VAR(op, npy_float, VECTOR_SIZE_BYTES) {
-        op[i] = -ip[i];
-    }
-    assert((npy_uintp)n < (VECTOR_SIZE_BYTES / sizeof(npy_float)) ||
-           npy_is_aligned(&op[i], VECTOR_SIZE_BYTES));
-    if (npy_is_aligned(&ip[i], VECTOR_SIZE_BYTES)) {
-        LOOP_BLOCKED(npy_float, VECTOR_SIZE_BYTES) {
-            __m128 a = _mm_load_ps(&ip[i]);
-            _mm_store_ps(&op[i], _mm_xor_ps(mask, a));
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_float, VECTOR_SIZE_BYTES) {
-            __m128 a = _mm_loadu_ps(&ip[i]);
-            _mm_store_ps(&op[i], _mm_xor_ps(mask, a));
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = -ip[i];
-    }
-}
-/**end repeat1**/
-
-
-#line 315
-/*
- * compress 4 vectors to 4/8 bytes in op with filled with 0 or 1
- * the last vector is passed as a pointer as MSVC 2010 is unable to ignore the
- * calling convention leading to C2719 on 32 bit, see #4795
- */
-NPY_FINLINE void
-sse2_compress4_to_byte_DOUBLE(__m128d r1, __m128d r2, __m128d r3, __m128d * r4,
-                              npy_bool * op)
-{
-    const __m128i mask = _mm_set1_epi8(0x1);
-    __m128i ir1 = _mm_packs_epi32(_mm_castpd_si128(r1), _mm_castpd_si128(r2));
-    __m128i ir2 = _mm_packs_epi32(_mm_castpd_si128(r3), _mm_castpd_si128(*r4));
-    __m128i rr = _mm_packs_epi16(ir1, ir2);
-#if 1
-    rr = _mm_packs_epi16(rr, rr);
-    rr = _mm_and_si128(rr, mask);
-    _mm_storel_epi64((__m128i*)op, rr);
-#else
-    rr = _mm_and_si128(rr, mask);
-    _mm_storeu_si128((__m128i*)op, rr);
-#endif
-}
-
-static void
-sse2_signbit_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_double, VECTOR_SIZE_BYTES) {
-        op[i] = npy_signbit(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_double, VECTOR_SIZE_BYTES) {
-        __m128d a = _mm_load_pd(&ip1[i]);
-        int r = _mm_movemask_pd(a);
-        if (sizeof(npy_double) == 8) {
-            op[i] = r & 1;
-            op[i + 1] = (r >> 1);
-        }
-        else {
-            op[i] = r & 1;
-            op[i + 1] = (r >> 1) & 1;
-            op[i + 2] = (r >> 2) & 1;
-            op[i + 3] = (r >> 3);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_signbit(ip1[i]) != 0;
-    }
-}
-
-#line 367
-
-static void
-sse2_isnan_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n)
-{
-#if 0 != 0 /* isinf/isfinite */
-    /* signbit mask 0x7FFFFFFF after andnot */
-    const __m128d mask = _mm_set1_pd(-0.);
-    const __m128d ones = _mm_cmpeq_pd(_mm_setzero_pd(),
-                                             _mm_setzero_pd());
-#if 1
-    const __m128d fltmax = _mm_set1_pd(DBL_MAX);
-#else
-    const __m128d fltmax = _mm_set1_pd(FLT_MAX);
-#endif
-#endif
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_double, VECTOR_SIZE_BYTES) {
-        op[i] = npy_isnan(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_double, 4 * VECTOR_SIZE_BYTES) {
-        __m128d a = _mm_load_pd(&ip1[i + 0 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d b = _mm_load_pd(&ip1[i + 1 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d c = _mm_load_pd(&ip1[i + 2 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d d = _mm_load_pd(&ip1[i + 3 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d r1, r2, r3, r4;
-#if 0 != 0 /* isinf/isfinite */
-        /* fabs via masking of sign bit */
-        r1 = _mm_andnot_pd(mask, a);
-        r2 = _mm_andnot_pd(mask, b);
-        r3 = _mm_andnot_pd(mask, c);
-        r4 = _mm_andnot_pd(mask, d);
-#if 0 == 1 /* isfinite */
-        /* negative compare against max float, nan is always true */
-        r1 = _mm_cmpnle_pd(r1, fltmax);
-        r2 = _mm_cmpnle_pd(r2, fltmax);
-        r3 = _mm_cmpnle_pd(r3, fltmax);
-        r4 = _mm_cmpnle_pd(r4, fltmax);
-#else /* isinf */
-        r1 = _mm_cmpnlt_pd(fltmax, r1);
-        r2 = _mm_cmpnlt_pd(fltmax, r2);
-        r3 = _mm_cmpnlt_pd(fltmax, r3);
-        r4 = _mm_cmpnlt_pd(fltmax, r4);
-#endif
-        /* flip results to what we want (andnot as there is no sse not) */
-        r1 = _mm_andnot_pd(r1, ones);
-        r2 = _mm_andnot_pd(r2, ones);
-        r3 = _mm_andnot_pd(r3, ones);
-        r4 = _mm_andnot_pd(r4, ones);
-#endif
-#if 0 == 0 /* isnan */
-        r1 = _mm_cmpneq_pd(a, a);
-        r2 = _mm_cmpneq_pd(b, b);
-        r3 = _mm_cmpneq_pd(c, c);
-        r4 = _mm_cmpneq_pd(d, d);
-#endif
-        sse2_compress4_to_byte_DOUBLE(r1, r2, r3, &r4, &op[i]);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_isnan(ip1[i]) != 0;
-    }
-}
-
-
-#line 367
-
-static void
-sse2_isfinite_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n)
-{
-#if 1 != 0 /* isinf/isfinite */
-    /* signbit mask 0x7FFFFFFF after andnot */
-    const __m128d mask = _mm_set1_pd(-0.);
-    const __m128d ones = _mm_cmpeq_pd(_mm_setzero_pd(),
-                                             _mm_setzero_pd());
-#if 1
-    const __m128d fltmax = _mm_set1_pd(DBL_MAX);
-#else
-    const __m128d fltmax = _mm_set1_pd(FLT_MAX);
-#endif
-#endif
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_double, VECTOR_SIZE_BYTES) {
-        op[i] = npy_isfinite(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_double, 4 * VECTOR_SIZE_BYTES) {
-        __m128d a = _mm_load_pd(&ip1[i + 0 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d b = _mm_load_pd(&ip1[i + 1 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d c = _mm_load_pd(&ip1[i + 2 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d d = _mm_load_pd(&ip1[i + 3 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d r1, r2, r3, r4;
-#if 1 != 0 /* isinf/isfinite */
-        /* fabs via masking of sign bit */
-        r1 = _mm_andnot_pd(mask, a);
-        r2 = _mm_andnot_pd(mask, b);
-        r3 = _mm_andnot_pd(mask, c);
-        r4 = _mm_andnot_pd(mask, d);
-#if 1 == 1 /* isfinite */
-        /* negative compare against max float, nan is always true */
-        r1 = _mm_cmpnle_pd(r1, fltmax);
-        r2 = _mm_cmpnle_pd(r2, fltmax);
-        r3 = _mm_cmpnle_pd(r3, fltmax);
-        r4 = _mm_cmpnle_pd(r4, fltmax);
-#else /* isinf */
-        r1 = _mm_cmpnlt_pd(fltmax, r1);
-        r2 = _mm_cmpnlt_pd(fltmax, r2);
-        r3 = _mm_cmpnlt_pd(fltmax, r3);
-        r4 = _mm_cmpnlt_pd(fltmax, r4);
-#endif
-        /* flip results to what we want (andnot as there is no sse not) */
-        r1 = _mm_andnot_pd(r1, ones);
-        r2 = _mm_andnot_pd(r2, ones);
-        r3 = _mm_andnot_pd(r3, ones);
-        r4 = _mm_andnot_pd(r4, ones);
-#endif
-#if 1 == 0 /* isnan */
-        r1 = _mm_cmpneq_pd(a, a);
-        r2 = _mm_cmpneq_pd(b, b);
-        r3 = _mm_cmpneq_pd(c, c);
-        r4 = _mm_cmpneq_pd(d, d);
-#endif
-        sse2_compress4_to_byte_DOUBLE(r1, r2, r3, &r4, &op[i]);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_isfinite(ip1[i]) != 0;
-    }
-}
-
-
-#line 367
-
-static void
-sse2_isinf_DOUBLE(npy_bool * op, npy_double * ip1, npy_intp n)
-{
-#if 2 != 0 /* isinf/isfinite */
-    /* signbit mask 0x7FFFFFFF after andnot */
-    const __m128d mask = _mm_set1_pd(-0.);
-    const __m128d ones = _mm_cmpeq_pd(_mm_setzero_pd(),
-                                             _mm_setzero_pd());
-#if 1
-    const __m128d fltmax = _mm_set1_pd(DBL_MAX);
-#else
-    const __m128d fltmax = _mm_set1_pd(FLT_MAX);
-#endif
-#endif
-    LOOP_BLOCK_ALIGN_VAR(ip1, npy_double, VECTOR_SIZE_BYTES) {
-        op[i] = npy_isinf(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(npy_double, 4 * VECTOR_SIZE_BYTES) {
-        __m128d a = _mm_load_pd(&ip1[i + 0 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d b = _mm_load_pd(&ip1[i + 1 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d c = _mm_load_pd(&ip1[i + 2 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d d = _mm_load_pd(&ip1[i + 3 * VECTOR_SIZE_BYTES / sizeof(npy_double)]);
-        __m128d r1, r2, r3, r4;
-#if 2 != 0 /* isinf/isfinite */
-        /* fabs via masking of sign bit */
-        r1 = _mm_andnot_pd(mask, a);
-        r2 = _mm_andnot_pd(mask, b);
-        r3 = _mm_andnot_pd(mask, c);
-        r4 = _mm_andnot_pd(mask, d);
-#if 2 == 1 /* isfinite */
-        /* negative compare against max float, nan is always true */
-        r1 = _mm_cmpnle_pd(r1, fltmax);
-        r2 = _mm_cmpnle_pd(r2, fltmax);
-        r3 = _mm_cmpnle_pd(r3, fltmax);
-        r4 = _mm_cmpnle_pd(r4, fltmax);
-#else /* isinf */
-        r1 = _mm_cmpnlt_pd(fltmax, r1);
-        r2 = _mm_cmpnlt_pd(fltmax, r2);
-        r3 = _mm_cmpnlt_pd(fltmax, r3);
-        r4 = _mm_cmpnlt_pd(fltmax, r4);
-#endif
-        /* flip results to what we want (andnot as there is no sse not) */
-        r1 = _mm_andnot_pd(r1, ones);
-        r2 = _mm_andnot_pd(r2, ones);
-        r3 = _mm_andnot_pd(r3, ones);
-        r4 = _mm_andnot_pd(r4, ones);
-#endif
-#if 2 == 0 /* isnan */
-        r1 = _mm_cmpneq_pd(a, a);
-        r2 = _mm_cmpneq_pd(b, b);
-        r3 = _mm_cmpneq_pd(c, c);
-        r4 = _mm_cmpneq_pd(d, d);
-#endif
-        sse2_compress4_to_byte_DOUBLE(r1, r2, r3, &r4, &op[i]);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_isinf(ip1[i]) != 0;
-    }
-}
-
-
-
-static void
-sse2_negative_DOUBLE(npy_double * op, npy_double * ip, const npy_intp n)
-{
-    /*
-     * get 0x7FFFFFFF mask (everything but signbit set)
-     * float & ~mask will remove the sign, float ^ mask flips the sign
-     * this is equivalent to how the compiler implements fabs on amd64
-     */
-    const __m128d mask = _mm_set1_pd(-0.);
-
-    /* align output to VECTOR_SIZE_BYTES bytes */
-    LOOP_BLOCK_ALIGN_VAR(op, npy_double, VECTOR_SIZE_BYTES) {
-        op[i] = -ip[i];
-    }
-    assert((npy_uintp)n < (VECTOR_SIZE_BYTES / sizeof(npy_double)) ||
-           npy_is_aligned(&op[i], VECTOR_SIZE_BYTES));
-    if (npy_is_aligned(&ip[i], VECTOR_SIZE_BYTES)) {
-        LOOP_BLOCKED(npy_double, VECTOR_SIZE_BYTES) {
-            __m128d a = _mm_load_pd(&ip[i]);
-            _mm_store_pd(&op[i], _mm_xor_pd(mask, a));
-        }
-    }
-    else {
-        LOOP_BLOCKED(npy_double, VECTOR_SIZE_BYTES) {
-            __m128d a = _mm_loadu_pd(&ip[i]);
-            _mm_store_pd(&op[i], _mm_xor_pd(mask, a));
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = -ip[i];
-    }
-}
-/**end repeat1**/
-
-
-
-/* bunch of helper functions used in ISA_exp/log_FLOAT*/
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_get_full_load_mask_ps(void)
-{
-    return _mm256_set1_ps(-1.0);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i
-fma_get_full_load_mask_pd(void)
-{
-    return _mm256_castpd_si256(_mm256_set1_pd(-1.0));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_get_partial_load_mask_ps(const npy_int num_elem, const npy_int num_lanes)
-{
-    float maskint[16] = {-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,
-                            1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0};
-    float* addr = maskint + num_lanes - num_elem;
-    return _mm256_loadu_ps(addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i
-fma_get_partial_load_mask_pd(const npy_int num_elem, const npy_int num_lanes)
-{
-    npy_int maskint[16] = {-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1};
-    npy_int* addr = maskint + 2*num_lanes - 2*num_elem;
-    return _mm256_loadu_si256((__m256i*) addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_masked_gather_ps(__m256 src,
-                     npy_float* addr,
-                     __m256i vindex,
-                     __m256 mask)
-{
-    return _mm256_mask_i32gather_ps(src, addr, vindex, mask, 4);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_masked_gather_pd(__m256d src,
-                     npy_double* addr,
-                     __m128i vindex,
-                     __m256d mask)
-{
-    return _mm256_mask_i32gather_pd(src, addr, vindex, mask, 8);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_masked_load_ps(__m256 mask, npy_float* addr)
-{
-    return _mm256_maskload_ps(addr, _mm256_cvtps_epi32(mask));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_masked_load_pd(__m256i mask, npy_double* addr)
-{
-    return _mm256_maskload_pd(addr, mask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_set_masked_lanes_ps(__m256 x, __m256 val, __m256 mask)
-{
-    return _mm256_blendv_ps(x, val, mask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_set_masked_lanes_pd(__m256d x, __m256d val, __m256d mask)
-{
-    return _mm256_blendv_pd(x, val, mask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_blend(__m256 x, __m256 y, __m256 ymask)
-{
-    return _mm256_blendv_ps(x, y, ymask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_invert_mask_ps(__m256 ymask)
-{
-    return _mm256_andnot_ps(ymask, _mm256_set1_ps(-1.0));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i
-fma_invert_mask_pd(__m256i ymask)
-{
-    return _mm256_andnot_si256(ymask, _mm256_set1_epi32(0xFFFFFFFF));
-}
-
-#line 562
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_abs_ps(__m256 x)
-{
-    return _mm256_andnot_ps(_mm256_set1_ps(-0.0), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_reciprocal_ps(__m256 x)
-{
-    return _mm256_div_ps(_mm256_set1_ps(1.0f), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_rint_ps(__m256 x)
-{
-    return _mm256_round_ps(x, _MM_FROUND_TO_NEAREST_INT);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_floor_ps(__m256 x)
-{
-    return _mm256_round_ps(x, _MM_FROUND_TO_NEG_INF);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_trunc_ps(__m256 x)
-{
-    return _mm256_round_ps(x, _MM_FROUND_TO_ZERO);
-}
-
-#line 562
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_abs_pd(__m256d x)
-{
-    return _mm256_andnot_pd(_mm256_set1_pd(-0.0), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_reciprocal_pd(__m256d x)
-{
-    return _mm256_div_pd(_mm256_set1_pd(1.0f), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_rint_pd(__m256d x)
-{
-    return _mm256_round_pd(x, _MM_FROUND_TO_NEAREST_INT);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_floor_pd(__m256d x)
-{
-    return _mm256_round_pd(x, _MM_FROUND_TO_NEG_INF);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_trunc_pd(__m256d x)
-{
-    return _mm256_round_pd(x, _MM_FROUND_TO_ZERO);
-}
-
-#endif
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_get_full_load_mask_ps(void)
-{
-    return 0xFFFF;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_get_full_load_mask_pd(void)
-{
-    return 0xFF;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_get_partial_load_mask_ps(const npy_int num_elem, const npy_int total_elem)
-{
-    return (0x0001 << num_elem) - 0x0001;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem)
-{
-    return (0x01 << num_elem) - 0x01;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_masked_gather_ps(__m512 src,
-                        npy_float* addr,
-                        __m512i vindex,
-                        __mmask16 kmask)
-{
-    return _mm512_mask_i32gather_ps(src, kmask, vindex, addr, 4);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_masked_gather_pd(__m512d src,
-                        npy_double* addr,
-                        __m256i vindex,
-                        __mmask8 kmask)
-{
-    return _mm512_mask_i32gather_pd(src, kmask, vindex, addr, 8);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_masked_load_ps(__mmask16 mask, npy_float* addr)
-{
-    return _mm512_maskz_loadu_ps(mask, (__m512 *)addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_masked_load_pd(__mmask8 mask, npy_double* addr)
-{
-    return _mm512_maskz_loadu_pd(mask, (__m512d *)addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_set_masked_lanes_ps(__m512 x, __m512 val, __mmask16 mask)
-{
-    return _mm512_mask_blend_ps(mask, x, val);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_set_masked_lanes_pd(__m512d x, __m512d val, __mmask8 mask)
-{
-    return _mm512_mask_blend_pd(mask, x, val);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_blend(__m512 x, __m512 y, __mmask16 ymask)
-{
-    return _mm512_mask_mov_ps(x, ymask, y);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_invert_mask_ps(__mmask16 ymask)
-{
-    return _mm512_knot(ymask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_invert_mask_pd(__mmask8 ymask)
-{
-    return _mm512_knot(ymask);
-}
-
-#line 693
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_abs_ps(__m512 x)
-{
-    return (__m512) _mm512_and_epi32((__m512i) x,
-				    _mm512_set1_epi32 (0x7fffffff));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_reciprocal_ps(__m512 x)
-{
-    return _mm512_div_ps(_mm512_set1_ps(1.0f), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_rint_ps(__m512 x)
-{
-    return _mm512_roundscale_ps(x, 0x08);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_floor_ps(__m512 x)
-{
-    return _mm512_roundscale_ps(x, 0x09);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_trunc_ps(__m512 x)
-{
-    return _mm512_roundscale_ps(x, 0x0B);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_hadd_ps(const __m512 x)
-{
-    return _mm512_add_ps(x, _mm512_permute_ps(x, 0xb1));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_hsub_ps(const __m512 x)
-{
-    return _mm512_sub_ps(x, _mm512_permute_ps(x, 0xb1));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_cabsolute_ps(const __m512 x1,
-                        const __m512 x2,
-                        const __m512i re_indices,
-                        const __m512i im_indices)
-{
-    __m512 inf = _mm512_set1_ps(NPY_INFINITYF);
-    __m512 nan = _mm512_set1_ps(NPY_NANF);
-    __m512 x1_abs = avx512_abs_ps(x1);
-    __m512 x2_abs = avx512_abs_ps(x2);
-    __m512 re = _mm512_permutex2var_ps(x1_abs, re_indices, x2_abs);
-    __m512 im = _mm512_permutex2var_ps(x1_abs, im_indices , x2_abs);
-    /*
-     * If real or imag = INF, then convert it to inf + j*inf
-     * Handles: inf + j*nan, nan + j*inf
-     */
-    __mmask16 re_infmask = _mm512_cmp_ps_mask(re, inf, _CMP_EQ_OQ);
-    __mmask16 im_infmask = _mm512_cmp_ps_mask(im, inf, _CMP_EQ_OQ);
-    im = _mm512_mask_mov_ps(im, re_infmask, inf);
-    re = _mm512_mask_mov_ps(re, im_infmask, inf);
-
-    /*
-     * If real or imag = NAN, then convert it to nan + j*nan
-     * Handles: x + j*nan, nan + j*x
-     */
-    __mmask16 re_nanmask = _mm512_cmp_ps_mask(re, re, _CMP_NEQ_UQ);
-    __mmask16 im_nanmask = _mm512_cmp_ps_mask(im, im, _CMP_NEQ_UQ);
-    im = _mm512_mask_mov_ps(im, re_nanmask, nan);
-    re = _mm512_mask_mov_ps(re, im_nanmask, nan);
-
-    __m512 larger  = _mm512_max_ps(re, im);
-    __m512 smaller = _mm512_min_ps(im, re);
-
-    /*
-     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
-     */
-    __mmask16 zeromask = _mm512_cmp_ps_mask(larger, _mm512_setzero_ps(), _CMP_EQ_OQ);
-    __mmask16 infmask = _mm512_cmp_ps_mask(smaller, inf, _CMP_EQ_OQ);
-    __mmask16 div_mask = _mm512_knot(_mm512_kor(zeromask, infmask));
-    __m512 ratio = _mm512_maskz_div_ps(div_mask, smaller, larger);
-    __m512 hypot = _mm512_sqrt_ps(_mm512_fmadd_ps(
-                                        ratio, ratio, _mm512_set1_ps(1.0f)));
-    return _mm512_mul_ps(hypot, larger);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_conjugate_ps(const __m512 x)
-{
-    /*
-     * __mm512_mask_xor_ps/pd requires AVX512DQ. We cast it to __m512i and
-     * use the xor_epi32/64 uinstruction instead. Cast is a zero latency instruction
-     */
-    __m512i cast_x = _mm512_castps_si512(x);
-    __m512i res = _mm512_mask_xor_epi32(cast_x, 0xAAAA,
-                                        cast_x, _mm512_set1_epi32(0x80000000));
-    return _mm512_castsi512_ps(res);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_cmul_ps(__m512 x1, __m512 x2)
-{
-    // x1 = r1, i1
-    // x2 = r2, i2
-    __m512 x3  = _mm512_permute_ps(x2, 0xb1);   // i2, r2
-    __m512 x12 = _mm512_mul_ps(x1, x2);            // r1*r2, i1*i2
-    __m512 x13 = _mm512_mul_ps(x1, x3);            // r1*i2, r2*i1
-    __m512 outreal = avx512_hsub_ps(x12);          // r1*r2 - i1*i2, r1*r2 - i1*i2
-    __m512 outimg  = avx512_hadd_ps(x13);          // r1*i2 + i1*r2, r1*i2 + i1*r2
-    return _mm512_mask_blend_ps(0xAAAA, outreal, outimg);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_csquare_ps(__m512 x)
-{
-    return avx512_cmul_ps(x, x);
-}
-
-
-#line 693
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_abs_pd(__m512d x)
-{
-    return (__m512d) _mm512_and_epi64((__m512i) x,
-				    _mm512_set1_epi64 (0x7fffffffffffffffLL));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_reciprocal_pd(__m512d x)
-{
-    return _mm512_div_pd(_mm512_set1_pd(1.0f), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_rint_pd(__m512d x)
-{
-    return _mm512_roundscale_pd(x, 0x08);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_floor_pd(__m512d x)
-{
-    return _mm512_roundscale_pd(x, 0x09);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_trunc_pd(__m512d x)
-{
-    return _mm512_roundscale_pd(x, 0x0B);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_hadd_pd(const __m512d x)
-{
-    return _mm512_add_pd(x, _mm512_permute_pd(x, 0x55));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_hsub_pd(const __m512d x)
-{
-    return _mm512_sub_pd(x, _mm512_permute_pd(x, 0x55));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_cabsolute_pd(const __m512d x1,
-                        const __m512d x2,
-                        const __m512i re_indices,
-                        const __m512i im_indices)
-{
-    __m512d inf = _mm512_set1_pd(NPY_INFINITY);
-    __m512d nan = _mm512_set1_pd(NPY_NAN);
-    __m512d x1_abs = avx512_abs_pd(x1);
-    __m512d x2_abs = avx512_abs_pd(x2);
-    __m512d re = _mm512_permutex2var_pd(x1_abs, re_indices, x2_abs);
-    __m512d im = _mm512_permutex2var_pd(x1_abs, im_indices , x2_abs);
-    /*
-     * If real or imag = INF, then convert it to inf + j*inf
-     * Handles: inf + j*nan, nan + j*inf
-     */
-    __mmask8 re_infmask = _mm512_cmp_pd_mask(re, inf, _CMP_EQ_OQ);
-    __mmask8 im_infmask = _mm512_cmp_pd_mask(im, inf, _CMP_EQ_OQ);
-    im = _mm512_mask_mov_pd(im, re_infmask, inf);
-    re = _mm512_mask_mov_pd(re, im_infmask, inf);
-
-    /*
-     * If real or imag = NAN, then convert it to nan + j*nan
-     * Handles: x + j*nan, nan + j*x
-     */
-    __mmask8 re_nanmask = _mm512_cmp_pd_mask(re, re, _CMP_NEQ_UQ);
-    __mmask8 im_nanmask = _mm512_cmp_pd_mask(im, im, _CMP_NEQ_UQ);
-    im = _mm512_mask_mov_pd(im, re_nanmask, nan);
-    re = _mm512_mask_mov_pd(re, im_nanmask, nan);
-
-    __m512d larger  = _mm512_max_pd(re, im);
-    __m512d smaller = _mm512_min_pd(im, re);
-
-    /*
-     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
-     */
-    __mmask8 zeromask = _mm512_cmp_pd_mask(larger, _mm512_setzero_pd(), _CMP_EQ_OQ);
-    __mmask8 infmask = _mm512_cmp_pd_mask(smaller, inf, _CMP_EQ_OQ);
-    __mmask8 div_mask = _mm512_knot(_mm512_kor(zeromask, infmask));
-    __m512d ratio = _mm512_maskz_div_pd(div_mask, smaller, larger);
-    __m512d hypot = _mm512_sqrt_pd(_mm512_fmadd_pd(
-                                        ratio, ratio, _mm512_set1_pd(1.0f)));
-    return _mm512_mul_pd(hypot, larger);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_conjugate_pd(const __m512d x)
-{
-    /*
-     * __mm512_mask_xor_ps/pd requires AVX512DQ. We cast it to __m512i and
-     * use the xor_epi32/64 uinstruction instead. Cast is a zero latency instruction
-     */
-    __m512i cast_x = _mm512_castpd_si512(x);
-    __m512i res = _mm512_mask_xor_epi64(cast_x, 0xAA,
-                                        cast_x, _mm512_set1_epi64(0x8000000000000000));
-    return _mm512_castsi512_pd(res);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_cmul_pd(__m512d x1, __m512d x2)
-{
-    // x1 = r1, i1
-    // x2 = r2, i2
-    __m512d x3  = _mm512_permute_pd(x2, 0x55);   // i2, r2
-    __m512d x12 = _mm512_mul_pd(x1, x2);            // r1*r2, i1*i2
-    __m512d x13 = _mm512_mul_pd(x1, x3);            // r1*i2, r2*i1
-    __m512d outreal = avx512_hsub_pd(x12);          // r1*r2 - i1*i2, r1*r2 - i1*i2
-    __m512d outimg  = avx512_hadd_pd(x13);          // r1*i2 + i1*r2, r1*i2 + i1*r2
-    return _mm512_mask_blend_pd(0xAA, outreal, outimg);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_csquare_pd(__m512d x)
-{
-    return avx512_cmul_pd(x, x);
-}
-
-
-#endif
-
-#line 827
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_sqrt_ps(__m256 x)
-{
-    return _mm256_sqrt_ps(x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_sqrt_pd(__m256d x)
-{
-    return _mm256_sqrt_pd(x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_square_ps(__m256 x)
-{
-    return _mm256_mul_ps(x,x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_square_pd(__m256d x)
-{
-    return _mm256_mul_pd(x,x);
-}
-
-#endif
-
-#line 827
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_sqrt_ps(__m512 x)
-{
-    return _mm512_sqrt_ps(x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_sqrt_pd(__m512d x)
-{
-    return _mm512_sqrt_pd(x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_square_ps(__m512 x)
-{
-    return _mm512_mul_ps(x,x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_square_pd(__m512d x)
-{
-    return _mm512_mul_pd(x,x);
-}
-
-#endif
-
-
-#line 869
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isnan_FLOAT(npy_bool* op, npy_float* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_float);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-#if 0
-    __m512 signbit = _mm512_set1_ps(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[16];
-    for (npy_int32 ii = 0; ii < 16; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m512i vindex_ip = _mm512_loadu_si512((__m512i*)&index_ip[0]);
-    __m512 zeros_f = _mm512_setzero_ps();
-    __m512i ones = _mm512_set1_epi32(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_ps(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_ps(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 0
-        x1 = _mm512_and_ps(x1,signbit);
-#endif
-
-        __mmask16 fpclassmask = _mm512_fpclass_ps_mask(x1, 0x81);
-#if 0
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi32_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 16*stride_ip;
-        op += 16;
-        num_remaining_elements -= 16;
-    }
-}
-#endif
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isfinite_FLOAT(npy_bool* op, npy_float* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_float);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-#if 0
-    __m512 signbit = _mm512_set1_ps(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[16];
-    for (npy_int32 ii = 0; ii < 16; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m512i vindex_ip = _mm512_loadu_si512((__m512i*)&index_ip[0]);
-    __m512 zeros_f = _mm512_setzero_ps();
-    __m512i ones = _mm512_set1_epi32(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_ps(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_ps(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 0
-        x1 = _mm512_and_ps(x1,signbit);
-#endif
-
-        __mmask16 fpclassmask = _mm512_fpclass_ps_mask(x1, 0x99);
-#if 1
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi32_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 16*stride_ip;
-        op += 16;
-        num_remaining_elements -= 16;
-    }
-}
-#endif
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isinf_FLOAT(npy_bool* op, npy_float* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_float);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-#if 0
-    __m512 signbit = _mm512_set1_ps(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[16];
-    for (npy_int32 ii = 0; ii < 16; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m512i vindex_ip = _mm512_loadu_si512((__m512i*)&index_ip[0]);
-    __m512 zeros_f = _mm512_setzero_ps();
-    __m512i ones = _mm512_set1_epi32(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_ps(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_ps(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 0
-        x1 = _mm512_and_ps(x1,signbit);
-#endif
-
-        __mmask16 fpclassmask = _mm512_fpclass_ps_mask(x1, 0x18);
-#if 0
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi32_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 16*stride_ip;
-        op += 16;
-        num_remaining_elements -= 16;
-    }
-}
-#endif
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_signbit_FLOAT(npy_bool* op, npy_float* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_float);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-#if 1
-    __m512 signbit = _mm512_set1_ps(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[16];
-    for (npy_int32 ii = 0; ii < 16; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m512i vindex_ip = _mm512_loadu_si512((__m512i*)&index_ip[0]);
-    __m512 zeros_f = _mm512_setzero_ps();
-    __m512i ones = _mm512_set1_epi32(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_ps(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_ps(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 1
-        x1 = _mm512_and_ps(x1,signbit);
-#endif
-
-        __mmask16 fpclassmask = _mm512_fpclass_ps_mask(x1, 0x04);
-#if 0
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi32_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 16*stride_ip;
-        op += 16;
-        num_remaining_elements -= 16;
-    }
-}
-#endif
-
-
-#line 869
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isnan_DOUBLE(npy_bool* op, npy_double* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_double);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-#if 0
-    __m512d signbit = _mm512_set1_pd(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[8];
-    for (npy_int32 ii = 0; ii < 8; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m256i vindex_ip = _mm256_loadu_si256((__m256i*)&index_ip[0]);
-    __m512d zeros_f = _mm512_setzero_pd();
-    __m512i ones = _mm512_set1_epi64(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_pd(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_pd(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 0
-        x1 = _mm512_and_pd(x1,signbit);
-#endif
-
-        __mmask8 fpclassmask = _mm512_fpclass_pd_mask(x1, 0x81);
-#if 0
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi64_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 8*stride_ip;
-        op += 8;
-        num_remaining_elements -= 8;
-    }
-}
-#endif
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isfinite_DOUBLE(npy_bool* op, npy_double* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_double);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-#if 0
-    __m512d signbit = _mm512_set1_pd(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[8];
-    for (npy_int32 ii = 0; ii < 8; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m256i vindex_ip = _mm256_loadu_si256((__m256i*)&index_ip[0]);
-    __m512d zeros_f = _mm512_setzero_pd();
-    __m512i ones = _mm512_set1_epi64(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_pd(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_pd(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 0
-        x1 = _mm512_and_pd(x1,signbit);
-#endif
-
-        __mmask8 fpclassmask = _mm512_fpclass_pd_mask(x1, 0x99);
-#if 1
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi64_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 8*stride_ip;
-        op += 8;
-        num_remaining_elements -= 8;
-    }
-}
-#endif
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_isinf_DOUBLE(npy_bool* op, npy_double* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_double);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-#if 0
-    __m512d signbit = _mm512_set1_pd(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[8];
-    for (npy_int32 ii = 0; ii < 8; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m256i vindex_ip = _mm256_loadu_si256((__m256i*)&index_ip[0]);
-    __m512d zeros_f = _mm512_setzero_pd();
-    __m512i ones = _mm512_set1_epi64(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_pd(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_pd(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 0
-        x1 = _mm512_and_pd(x1,signbit);
-#endif
-
-        __mmask8 fpclassmask = _mm512_fpclass_pd_mask(x1, 0x18);
-#if 0
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi64_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 8*stride_ip;
-        op += 8;
-        num_remaining_elements -= 8;
-    }
-}
-#endif
-
-#line 876
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_signbit_DOUBLE(npy_bool* op, npy_double* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(npy_double);
-    npy_intp num_remaining_elements = array_size;
-
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-#if 1
-    __m512d signbit = _mm512_set1_pd(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[8];
-    for (npy_int32 ii = 0; ii < 8; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    __m256i vindex_ip = _mm256_loadu_si256((__m256i*)&index_ip[0]);
-    __m512d zeros_f = _mm512_setzero_pd();
-    __m512i ones = _mm512_set1_epi64(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_pd(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_pd(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if 1
-        x1 = _mm512_and_pd(x1,signbit);
-#endif
-
-        __mmask8 fpclassmask = _mm512_fpclass_pd_mask(x1, 0x04);
-#if 0
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvtsepi64_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += 8*stride_ip;
-        op += 8;
-        num_remaining_elements -= 8;
-    }
-}
-#endif
-
-
-
-#line 950
-
-#line 955
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_square_CFLOAT(npy_float * op,
-                      npy_float * ip,
-                      const npy_intp array_size,
-                      const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(npy_float)/2;
-
-     /*
-      * Note: while generally indices are npy_intp, we ensure that our maximum index
-      * will fit in an int32 as a precondition for this function via max_stride
-      */
-    npy_int32 index_ip1[16];
-    for (npy_int32 ii = 0; ii < 16; ii=ii+2) {
-        index_ip1[ii] = ii*stride_ip1;
-        index_ip1[ii+1] = ii*stride_ip1 + 1;
-    }
-    __m512i vindex = _mm512_loadu_si512((__m512i*)index_ip1);
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-    __m512 zeros = _mm512_setzero_ps();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_ps(load_mask, ip);
-        }
-        else {
-            x1  = avx512_masked_gather_ps(zeros, ip, vindex, load_mask);
-        }
-
-        __m512 out = avx512_csquare_ps(x1);
-
-        _mm512_mask_storeu_ps(op, load_mask, out);
-        op += 16;
-        ip += 16*stride_ip1;
-        num_remaining_elements -= 16;
-    }
-}
-#endif
-
-#line 955
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_conjugate_CFLOAT(npy_float * op,
-                      npy_float * ip,
-                      const npy_intp array_size,
-                      const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(npy_float)/2;
-
-     /*
-      * Note: while generally indices are npy_intp, we ensure that our maximum index
-      * will fit in an int32 as a precondition for this function via max_stride
-      */
-    npy_int32 index_ip1[16];
-    for (npy_int32 ii = 0; ii < 16; ii=ii+2) {
-        index_ip1[ii] = ii*stride_ip1;
-        index_ip1[ii+1] = ii*stride_ip1 + 1;
-    }
-    __m512i vindex = _mm512_loadu_si512((__m512i*)index_ip1);
-    __mmask16 load_mask = avx512_get_full_load_mask_ps();
-    __m512 zeros = _mm512_setzero_ps();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-        }
-        __m512 x1;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_ps(load_mask, ip);
-        }
-        else {
-            x1  = avx512_masked_gather_ps(zeros, ip, vindex, load_mask);
-        }
-
-        __m512 out = avx512_conjugate_ps(x1);
-
-        _mm512_mask_storeu_ps(op, load_mask, out);
-        op += 16;
-        ip += 16*stride_ip1;
-        num_remaining_elements -= 16;
-    }
-}
-#endif
-
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_absolute_CFLOAT(npy_float * op,
-                        npy_float * ip,
-                        const npy_intp array_size,
-                        const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(npy_float)/2;
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum index
-     * will fit in an int32 as a precondition for this function via max_stride
-     */
-    npy_int32 index_ip[32];
-    for (npy_int32 ii = 0; ii < 2*16; ii=ii+2) {
-        index_ip[ii] = ii*stride_ip1;
-        index_ip[ii+1] = ii*stride_ip1 + 1;
-    }
-    __m512i vindex1 = _mm512_loadu_si512((__m512i*)index_ip);
-    __m512i vindex2 = _mm512_loadu_si512((__m512i*)(index_ip+16));
-
-    __mmask16 load_mask1 = avx512_get_full_load_mask_ps();
-    __mmask16 load_mask2 = avx512_get_full_load_mask_ps();
-    __mmask16 store_mask = avx512_get_full_load_mask_ps();
-    __m512 zeros = _mm512_setzero_ps();
-
-#if 1
-    __m512i re_index = _mm512_set_epi32(30,28,26,24,22,20,18,16,14,12,10,8,6,4,2,0);
-    __m512i im_index  = _mm512_set_epi32(31,29,27,25,23,21,19,17,15,13,11,9,7,5,3,1);
-#else
-    __m512i re_index = _mm512_set_epi64(14,12,10,8,6,4,2,0);
-    __m512i im_index  = _mm512_set_epi64(15,13,11,9,7,5,3,1);
-#endif
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 16) {
-            load_mask1 = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements, 16);
-            load_mask2 = 0x0000;
-            store_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements/2, 16);
-        } else if (num_remaining_elements < 2*16) {
-            load_mask1 = avx512_get_full_load_mask_ps();
-            load_mask2 = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements - 16, 16);
-            store_mask = avx512_get_partial_load_mask_ps(
-                                    num_remaining_elements/2, 16);
-        }
-        __m512 x1, x2;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_ps(load_mask1, ip);
-            x2 = avx512_masked_load_ps(load_mask2, ip+16);
-        }
-        else {
-            x1  = avx512_masked_gather_ps(zeros, ip, vindex1, load_mask1);
-            x2  = avx512_masked_gather_ps(zeros, ip, vindex2, load_mask2);
-        }
-
-        __m512 out = avx512_cabsolute_ps(x1, x2, re_index, im_index);
-
-        _mm512_mask_storeu_ps(op, store_mask, out);
-        op += 16;
-        ip += 2*16*stride_ip1;
-        num_remaining_elements -= 2*16;
-    }
-    npy_clear_floatstatus_barrier((char*)&num_remaining_elements);
-}
-
-#endif
-
-#line 950
-
-#line 955
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_square_CDOUBLE(npy_double * op,
-                      npy_double * ip,
-                      const npy_intp array_size,
-                      const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(npy_double)/2;
-
-     /*
-      * Note: while generally indices are npy_intp, we ensure that our maximum index
-      * will fit in an int32 as a precondition for this function via max_stride
-      */
-    npy_int32 index_ip1[16];
-    for (npy_int32 ii = 0; ii < 8; ii=ii+2) {
-        index_ip1[ii] = ii*stride_ip1;
-        index_ip1[ii+1] = ii*stride_ip1 + 1;
-    }
-    __m256i vindex = _mm256_loadu_si256((__m256i*)index_ip1);
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-    __m512d zeros = _mm512_setzero_pd();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_pd(load_mask, ip);
-        }
-        else {
-            x1  = avx512_masked_gather_pd(zeros, ip, vindex, load_mask);
-        }
-
-        __m512d out = avx512_csquare_pd(x1);
-
-        _mm512_mask_storeu_pd(op, load_mask, out);
-        op += 8;
-        ip += 8*stride_ip1;
-        num_remaining_elements -= 8;
-    }
-}
-#endif
-
-#line 955
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_conjugate_CDOUBLE(npy_double * op,
-                      npy_double * ip,
-                      const npy_intp array_size,
-                      const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(npy_double)/2;
-
-     /*
-      * Note: while generally indices are npy_intp, we ensure that our maximum index
-      * will fit in an int32 as a precondition for this function via max_stride
-      */
-    npy_int32 index_ip1[16];
-    for (npy_int32 ii = 0; ii < 8; ii=ii+2) {
-        index_ip1[ii] = ii*stride_ip1;
-        index_ip1[ii+1] = ii*stride_ip1 + 1;
-    }
-    __m256i vindex = _mm256_loadu_si256((__m256i*)index_ip1);
-    __mmask8 load_mask = avx512_get_full_load_mask_pd();
-    __m512d zeros = _mm512_setzero_pd();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-        }
-        __m512d x1;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_pd(load_mask, ip);
-        }
-        else {
-            x1  = avx512_masked_gather_pd(zeros, ip, vindex, load_mask);
-        }
-
-        __m512d out = avx512_conjugate_pd(x1);
-
-        _mm512_mask_storeu_pd(op, load_mask, out);
-        op += 8;
-        ip += 8*stride_ip1;
-        num_remaining_elements -= 8;
-    }
-}
-#endif
-
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_absolute_CDOUBLE(npy_double * op,
-                        npy_double * ip,
-                        const npy_intp array_size,
-                        const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(npy_double)/2;
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum index
-     * will fit in an int32 as a precondition for this function via max_stride
-     */
-    npy_int32 index_ip[32];
-    for (npy_int32 ii = 0; ii < 2*8; ii=ii+2) {
-        index_ip[ii] = ii*stride_ip1;
-        index_ip[ii+1] = ii*stride_ip1 + 1;
-    }
-    __m256i vindex1 = _mm256_loadu_si256((__m256i*)index_ip);
-    __m256i vindex2 = _mm256_loadu_si256((__m256i*)(index_ip+8));
-
-    __mmask8 load_mask1 = avx512_get_full_load_mask_pd();
-    __mmask8 load_mask2 = avx512_get_full_load_mask_pd();
-    __mmask8 store_mask = avx512_get_full_load_mask_pd();
-    __m512d zeros = _mm512_setzero_pd();
-
-#if 0
-    __m512i re_index = _mm512_set_epi32(30,28,26,24,22,20,18,16,14,12,10,8,6,4,2,0);
-    __m512i im_index  = _mm512_set_epi32(31,29,27,25,23,21,19,17,15,13,11,9,7,5,3,1);
-#else
-    __m512i re_index = _mm512_set_epi64(14,12,10,8,6,4,2,0);
-    __m512i im_index  = _mm512_set_epi64(15,13,11,9,7,5,3,1);
-#endif
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < 8) {
-            load_mask1 = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements, 8);
-            load_mask2 = 0x0000;
-            store_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements/2, 8);
-        } else if (num_remaining_elements < 2*8) {
-            load_mask1 = avx512_get_full_load_mask_pd();
-            load_mask2 = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements - 8, 8);
-            store_mask = avx512_get_partial_load_mask_pd(
-                                    num_remaining_elements/2, 8);
-        }
-        __m512d x1, x2;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_pd(load_mask1, ip);
-            x2 = avx512_masked_load_pd(load_mask2, ip+8);
-        }
-        else {
-            x1  = avx512_masked_gather_pd(zeros, ip, vindex1, load_mask1);
-            x2  = avx512_masked_gather_pd(zeros, ip, vindex2, load_mask2);
-        }
-
-        __m512d out = avx512_cabsolute_pd(x1, x2, re_index, im_index);
-
-        _mm512_mask_storeu_pd(op, store_mask, out);
-        op += 8;
-        ip += 2*8*stride_ip1;
-        num_remaining_elements -= 2*8;
-    }
-    npy_clear_floatstatus_barrier((char*)&num_remaining_elements);
-}
-
-#endif
-
-
-/*
- *****************************************************************************
- **                           BOOL LOOPS
- *****************************************************************************
- */
-
-#line 1094
-
-/*
- * convert any bit set to boolean true so vectorized and normal operations are
- * consistent, should not be required if bool is used correctly everywhere but
- * you never know
- */
-#if !0
-NPY_FINLINE __m128i byte_to_true(__m128i v)
-{
-    const __m128i zero = _mm_setzero_si128();
-    const __m128i truemask = _mm_set1_epi8(1 == 1);
-    /* get 0xFF for zeros */
-    __m128i tmp = _mm_cmpeq_epi8(v, zero);
-    /* filled with 0xFF/0x00, negate and mask to boolean true */
-    return _mm_andnot_si128(tmp, truemask);
-}
-#endif
-
-static void
-sse2_binary_logical_or_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_bool, VECTOR_SIZE_BYTES)
-        op[i] = ip1[i] || ip2[i];
-    LOOP_BLOCKED(npy_bool, VECTOR_SIZE_BYTES) {
-        __m128i a = _mm_loadu_si128((__m128i*)&ip1[i]);
-        __m128i b = _mm_loadu_si128((__m128i*)&ip2[i]);
-#if 0
-        const __m128i zero = _mm_setzero_si128();
-        /* get 0xFF for non zeros*/
-        __m128i tmp = _mm_cmpeq_epi8(a, zero);
-        /* andnot -> 0x00 for zeros xFF for non zeros, & with ip2 */
-        tmp = _mm_andnot_si128(tmp, b);
-#else
-        __m128i tmp = _mm_or_si128(a, b);
-#endif
-
-        _mm_store_si128((__m128i*)&op[i], byte_to_true(tmp));
-    }
-    LOOP_BLOCKED_END {
-        op[i] = (ip1[i] || ip2[i]);
-    }
-}
-
-
-static void
-sse2_reduce_logical_or_BOOL(npy_bool * op, npy_bool * ip, const npy_intp n)
-{
-    const __m128i zero = _mm_setzero_si128();
-    LOOP_BLOCK_ALIGN_VAR(ip, npy_bool, VECTOR_SIZE_BYTES) {
-        *op = *op || ip[i];
-        if (*op != 0) {
-            return;
-        }
-    }
-    /* unrolled once to replace a slow movmsk with a fast pmaxb */
-    LOOP_BLOCKED(npy_bool, 2 * VECTOR_SIZE_BYTES) {
-        __m128i v = _mm_load_si128((__m128i*)&ip[i]);
-        __m128i v2 = _mm_load_si128((__m128i*)&ip[i + VECTOR_SIZE_BYTES]);
-        v = _mm_cmpeq_epi8(v, zero);
-        v2 = _mm_cmpeq_epi8(v2, zero);
-#if 0
-        if ((_mm_movemask_epi8(_mm_max_epu8(v, v2)) != 0)) {
-            *op = 0;
-#else
-        if ((_mm_movemask_epi8(_mm_min_epu8(v, v2)) != 0xFFFF)) {
-            *op = 1;
-#endif
-            return;
-        }
-    }
-    LOOP_BLOCKED_END {
-        *op = *op || ip[i];
-        if (*op != 0) {
-            return;
-        }
-    }
-}
-
-
-#line 1094
-
-/*
- * convert any bit set to boolean true so vectorized and normal operations are
- * consistent, should not be required if bool is used correctly everywhere but
- * you never know
- */
-#if !1
-NPY_FINLINE __m128i byte_to_true(__m128i v)
-{
-    const __m128i zero = _mm_setzero_si128();
-    const __m128i truemask = _mm_set1_epi8(1 == 1);
-    /* get 0xFF for zeros */
-    __m128i tmp = _mm_cmpeq_epi8(v, zero);
-    /* filled with 0xFF/0x00, negate and mask to boolean true */
-    return _mm_andnot_si128(tmp, truemask);
-}
-#endif
-
-static void
-sse2_binary_logical_and_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_bool, VECTOR_SIZE_BYTES)
-        op[i] = ip1[i] && ip2[i];
-    LOOP_BLOCKED(npy_bool, VECTOR_SIZE_BYTES) {
-        __m128i a = _mm_loadu_si128((__m128i*)&ip1[i]);
-        __m128i b = _mm_loadu_si128((__m128i*)&ip2[i]);
-#if 1
-        const __m128i zero = _mm_setzero_si128();
-        /* get 0xFF for non zeros*/
-        __m128i tmp = _mm_cmpeq_epi8(a, zero);
-        /* andnot -> 0x00 for zeros xFF for non zeros, & with ip2 */
-        tmp = _mm_andnot_si128(tmp, b);
-#else
-        __m128i tmp = _mm_or_si128(a, b);
-#endif
-
-        _mm_store_si128((__m128i*)&op[i], byte_to_true(tmp));
-    }
-    LOOP_BLOCKED_END {
-        op[i] = (ip1[i] && ip2[i]);
-    }
-}
-
-
-static void
-sse2_reduce_logical_and_BOOL(npy_bool * op, npy_bool * ip, const npy_intp n)
-{
-    const __m128i zero = _mm_setzero_si128();
-    LOOP_BLOCK_ALIGN_VAR(ip, npy_bool, VECTOR_SIZE_BYTES) {
-        *op = *op && ip[i];
-        if (*op == 0) {
-            return;
-        }
-    }
-    /* unrolled once to replace a slow movmsk with a fast pmaxb */
-    LOOP_BLOCKED(npy_bool, 2 * VECTOR_SIZE_BYTES) {
-        __m128i v = _mm_load_si128((__m128i*)&ip[i]);
-        __m128i v2 = _mm_load_si128((__m128i*)&ip[i + VECTOR_SIZE_BYTES]);
-        v = _mm_cmpeq_epi8(v, zero);
-        v2 = _mm_cmpeq_epi8(v2, zero);
-#if 1
-        if ((_mm_movemask_epi8(_mm_max_epu8(v, v2)) != 0)) {
-            *op = 0;
-#else
-        if ((_mm_movemask_epi8(_mm_min_epu8(v, v2)) != 0xFFFF)) {
-            *op = 1;
-#endif
-            return;
-        }
-    }
-    LOOP_BLOCKED_END {
-        *op = *op && ip[i];
-        if (*op == 0) {
-            return;
-        }
-    }
-}
-
-
-
-#line 1185
-
-static void
-sse2_absolute_BOOL(npy_bool * op, npy_bool * ip, const npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_bool, VECTOR_SIZE_BYTES)
-        op[i] = (ip[i] != 0);
-    LOOP_BLOCKED(npy_bool, VECTOR_SIZE_BYTES) {
-        __m128i a = _mm_loadu_si128((__m128i*)&ip[i]);
-#if 0
-        const __m128i zero = _mm_setzero_si128();
-        const __m128i truemask = _mm_set1_epi8(1 == 1);
-        /* equivalent to byte_to_true but can skip the negation */
-        a = _mm_cmpeq_epi8(a, zero);
-        a = _mm_and_si128(a, truemask);
-#else
-        /* abs is kind of pointless but maybe its used for byte_to_true */
-        a = byte_to_true(a);
-#endif
-        _mm_store_si128((__m128i*)&op[i], a);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = (ip[i] != 0);
-    }
-}
-
-
-#line 1185
-
-static void
-sse2_logical_not_BOOL(npy_bool * op, npy_bool * ip, const npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, npy_bool, VECTOR_SIZE_BYTES)
-        op[i] = (ip[i] == 0);
-    LOOP_BLOCKED(npy_bool, VECTOR_SIZE_BYTES) {
-        __m128i a = _mm_loadu_si128((__m128i*)&ip[i]);
-#if 1
-        const __m128i zero = _mm_setzero_si128();
-        const __m128i truemask = _mm_set1_epi8(1 == 1);
-        /* equivalent to byte_to_true but can skip the negation */
-        a = _mm_cmpeq_epi8(a, zero);
-        a = _mm_and_si128(a, truemask);
-#else
-        /* abs is kind of pointless but maybe its used for byte_to_true */
-        a = byte_to_true(a);
-#endif
-        _mm_store_si128((__m128i*)&op[i], a);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = (ip[i] == 0);
-    }
-}
-
-
-
-#undef VECTOR_SIZE_BYTES
-#endif  /* NPY_HAVE_SSE2_INTRINSICS */
-#endif
-
-
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/simd.inc.src b/contrib/python/numpy/py3/numpy/core/src/umath/simd.inc.src
deleted file mode 100644
index d6c9a7e6538..00000000000
--- a/contrib/python/numpy/py3/numpy/core/src/umath/simd.inc.src
+++ /dev/null
@@ -1,1215 +0,0 @@
-
-
-/*
- * This file is for the definitions of simd vectorized operations.
- *
- * Currently contains sse2 functions that are built on amd64, x32 or
- * non-generic builds (CFLAGS=-march=...)
- * In future it may contain other instruction sets like AVX or NEON detected
- * at runtime in which case it needs to be included indirectly via a file
- * compiled with special options (or use gcc target attributes) so the binary
- * stays portable.
- */
-
-
-#ifndef __NPY_SIMD_INC
-#define __NPY_SIMD_INC
-
-#include "lowlevel_strided_loops.h"
-#include "numpy/npy_common.h"
-#include "numpy/npy_math.h"
-#include "npy_simd_data.h"
-#ifdef NPY_HAVE_SSE2_INTRINSICS
-#include <emmintrin.h>
-#if !defined(_MSC_VER) || _MSC_VER >= 1600
-#include <immintrin.h>
-#else
-#undef __AVX2__
-#undef __AVX512F__
-#endif
-#endif
-#include "loops_utils.h" // nomemoverlap
-#include <assert.h>
-#include <stdlib.h>
-#include <float.h>
-#include <string.h> /* for memcpy */
-
-#define VECTOR_SIZE_BYTES 16
-
-/*
- * Dispatcher functions
- * decide whether the operation can be vectorized and run it
- * if it was run returns true and false if nothing was done
- */
-
-/*
- *****************************************************************************
- **                           CMPLX DISPATCHERS
- *****************************************************************************
- */
-
-/**begin repeat
- * #TYPE = CFLOAT, CDOUBLE#
- * #type= npy_float, npy_double#
- * #esize = 8, 16#
- */
-
-/**begin repeat1
- *  #func = square, absolute, conjugate#
- *  #outsize = 1, 2, 1#
- *  #max_stride = 2, 8, 8#
- */
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_@func@_@TYPE@(@type@*, @type@*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_unary_avx512f_@func@_@TYPE@(char **args, const npy_intp *dimensions, const npy_intp *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-    if ((IS_OUTPUT_BLOCKABLE_UNARY(@esize@, (npy_uint)(@esize@/@outsize@), 64)) && (labs(steps[0]) < 2*@max_stride@*@esize@)) {
-        AVX512F_@func@_@TYPE@((@type@*)args[1], (@type@*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else
-        return 0;
-#endif
-    return 0;
-}
-
-/**end repeat1**/
-/**end repeat**/
-
-/*
- *****************************************************************************
- **                           FLOAT DISPATCHERS
- *****************************************************************************
- */
-
-/**begin repeat
- * #type = npy_float, npy_double, npy_longdouble#
- * #TYPE = FLOAT, DOUBLE, LONGDOUBLE#
- * #EXISTS = 1, 1, 0#
- */
-
-/**begin repeat1
- * #func = isnan, isfinite, isinf, signbit#
- */
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && @EXISTS@
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_@func@_@TYPE@(npy_bool*, @type@*, const npy_intp n, const npy_intp stride);
-#endif
-
-static NPY_INLINE int
-run_@func@_avx512_skx_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS && @EXISTS@
-    if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(@type@), sizeof(npy_bool), 64)) {
-        AVX512_SKX_@func@_@TYPE@((npy_bool*)args[1], (@type@*)args[0], dimensions[0], steps[0]);
-        return 1;
-    }
-    else {
-        return 0;
-    }
-#endif
-    return 0;
-}
-
-
-/**end repeat1**/
-/**end repeat**/
-
-/**begin repeat
- * Float types
- *  #type = npy_float, npy_double, npy_longdouble#
- *  #TYPE = FLOAT, DOUBLE, LONGDOUBLE#
- *  #vector = 1, 1, 0#
- *  #VECTOR = NPY_SIMD, NPY_SIMD_F64, 0 #
- */
-
-/**begin repeat1
- * #func = negative#
- * #check = IS_BLOCKABLE_UNARY#
- * #name = unary#
- */
-
-#if @vector@ && defined NPY_HAVE_SSE2_INTRINSICS
-
-/* prototypes */
-static void
-sse2_@func@_@TYPE@(@type@ *, @type@ *, const npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_@name@_simd_@func@_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if @vector@ && defined NPY_HAVE_SSE2_INTRINSICS
-    if (@check@(sizeof(@type@), VECTOR_SIZE_BYTES)) {
-        sse2_@func@_@TYPE@((@type@*)args[1], (@type@*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-/**end repeat1**/
-
-/**begin repeat1
- * #kind = isnan, isfinite, isinf, signbit#
- */
-
-#if @vector@ && defined NPY_HAVE_SSE2_INTRINSICS
-
-static void
-sse2_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, npy_intp n);
-
-#endif
-
-static NPY_INLINE int
-run_@kind@_simd_@TYPE@(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if @vector@ && defined NPY_HAVE_SSE2_INTRINSICS
-    if (steps[0] == sizeof(@type@) && steps[1] == 1 &&
-        npy_is_aligned(args[0], sizeof(@type@))) {
-        sse2_@kind@_@TYPE@((npy_bool*)args[1], (@type@*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-/**end repeat1**/
-
-/**end repeat**/
-
-/*
- *****************************************************************************
- **                           BOOL DISPATCHERS
- *****************************************************************************
- */
-
-/**begin repeat
- * # kind = logical_or, logical_and#
- */
-
-#if defined NPY_HAVE_SSE2_INTRINSICS
-static void
-sse2_binary_@kind@_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2,
-                        npy_intp n);
-
-static void
-sse2_reduce_@kind@_BOOL(npy_bool * op, npy_bool * ip, npy_intp n);
-#endif
-
-static NPY_INLINE int
-run_binary_simd_@kind@_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_BINARY(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_binary_@kind@_BOOL((npy_bool*)args[2], (npy_bool*)args[0],
-                               (npy_bool*)args[1], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-
-static NPY_INLINE int
-run_reduce_simd_@kind@_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_REDUCE(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_reduce_@kind@_BOOL((npy_bool*)args[0], (npy_bool*)args[1],
-                                dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-/**end repeat**/
-
-/**begin repeat
- * # kind = absolute, logical_not#
- */
-
-#if defined NPY_HAVE_SSE2_INTRINSICS
-static void
-sse2_@kind@_BOOL(npy_bool *, npy_bool *, const npy_intp n);
-#endif
-
-static NPY_INLINE int
-run_unary_simd_@kind@_BOOL(char **args, npy_intp const *dimensions, npy_intp const *steps)
-{
-#if defined NPY_HAVE_SSE2_INTRINSICS
-    if (sizeof(npy_bool) == 1 &&
-            IS_BLOCKABLE_UNARY(sizeof(npy_bool), VECTOR_SIZE_BYTES)) {
-        sse2_@kind@_BOOL((npy_bool*)args[1], (npy_bool*)args[0], dimensions[0]);
-        return 1;
-    }
-#endif
-    return 0;
-}
-
-/**end repeat**/
-
-#ifdef NPY_HAVE_SSE2_INTRINSICS
-
-/*
- * Vectorized operations
- */
-/*
- *****************************************************************************
- **                           FLOAT LOOPS
- *****************************************************************************
- */
-
-/**begin repeat
-* horizontal reductions on a vector
-* # VOP = min, max#
-*/
-
-NPY_FINLINE npy_float sse2_horizontal_@VOP@___m128(__m128 v)
-{
-    npy_float r;
-    __m128 tmp = _mm_movehl_ps(v, v);                   /* c     d     ... */
-    __m128 m = _mm_@VOP@_ps(v, tmp);                    /* m(ac) m(bd) ... */
-    tmp = _mm_shuffle_ps(m, m, _MM_SHUFFLE(1, 1, 1, 1));/* m(bd) m(bd) ... */
-    _mm_store_ss(&r, _mm_@VOP@_ps(tmp, m));             /* m(acbd) ... */
-    return r;
-}
-
-NPY_FINLINE npy_double sse2_horizontal_@VOP@___m128d(__m128d v)
-{
-    npy_double r;
-    __m128d tmp = _mm_unpackhi_pd(v, v);    /* b     b */
-    _mm_store_sd(&r, _mm_@VOP@_pd(tmp, v)); /* m(ab) m(bb) */
-    return r;
-}
-/**end repeat**/
-
-/**begin repeat
- *  #type = npy_float, npy_double#
- *  #TYPE = FLOAT, DOUBLE#
- *  #scalarf = npy_sqrtf, npy_sqrt#
- *  #c = f, #
- *  #vtype = __m128, __m128d#
- *  #vtype256 = __m256, __m256d#
- *  #vtype512 = __m512, __m512d#
- *  #vpre = _mm, _mm#
- *  #vpre256 = _mm256, _mm256#
- *  #vpre512 = _mm512, _mm512#
- *  #vsuf = ps, pd#
- *  #vsufs = ss, sd#
- *  #nan = NPY_NANF, NPY_NAN#
- *  #double = 0, 1#
- *  #cast = _mm_castps_si128, _mm_castpd_si128#
- */
-/*
- * compress 4 vectors to 4/8 bytes in op with filled with 0 or 1
- * the last vector is passed as a pointer as MSVC 2010 is unable to ignore the
- * calling convention leading to C2719 on 32 bit, see #4795
- */
-NPY_FINLINE void
-sse2_compress4_to_byte_@TYPE@(@vtype@ r1, @vtype@ r2, @vtype@ r3, @vtype@ * r4,
-                              npy_bool * op)
-{
-    const __m128i mask = @vpre@_set1_epi8(0x1);
-    __m128i ir1 = @vpre@_packs_epi32(@cast@(r1), @cast@(r2));
-    __m128i ir2 = @vpre@_packs_epi32(@cast@(r3), @cast@(*r4));
-    __m128i rr = @vpre@_packs_epi16(ir1, ir2);
-#if @double@
-    rr = @vpre@_packs_epi16(rr, rr);
-    rr = @vpre@_and_si128(rr, mask);
-    @vpre@_storel_epi64((__m128i*)op, rr);
-#else
-    rr = @vpre@_and_si128(rr, mask);
-    @vpre@_storeu_si128((__m128i*)op, rr);
-#endif
-}
-
-static void
-sse2_signbit_@TYPE@(npy_bool * op, @type@ * ip1, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(ip1, @type@, VECTOR_SIZE_BYTES) {
-        op[i] = npy_signbit(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(@type@, VECTOR_SIZE_BYTES) {
-        @vtype@ a = @vpre@_load_@vsuf@(&ip1[i]);
-        int r = @vpre@_movemask_@vsuf@(a);
-        if (sizeof(@type@) == 8) {
-            op[i] = r & 1;
-            op[i + 1] = (r >> 1);
-        }
-        else {
-            op[i] = r & 1;
-            op[i + 1] = (r >> 1) & 1;
-            op[i + 2] = (r >> 2) & 1;
-            op[i + 3] = (r >> 3);
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_signbit(ip1[i]) != 0;
-    }
-}
-
-/**begin repeat1
- * #kind = isnan, isfinite, isinf#
- * #var = 0, 1, 2#
- */
-
-static void
-sse2_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, npy_intp n)
-{
-#if @var@ != 0 /* isinf/isfinite */
-    /* signbit mask 0x7FFFFFFF after andnot */
-    const @vtype@ mask = @vpre@_set1_@vsuf@(-0.@c@);
-    const @vtype@ ones = @vpre@_cmpeq_@vsuf@(@vpre@_setzero_@vsuf@(),
-                                             @vpre@_setzero_@vsuf@());
-#if @double@
-    const @vtype@ fltmax = @vpre@_set1_@vsuf@(DBL_MAX);
-#else
-    const @vtype@ fltmax = @vpre@_set1_@vsuf@(FLT_MAX);
-#endif
-#endif
-    LOOP_BLOCK_ALIGN_VAR(ip1, @type@, VECTOR_SIZE_BYTES) {
-        op[i] = npy_@kind@(ip1[i]) != 0;
-    }
-    LOOP_BLOCKED(@type@, 4 * VECTOR_SIZE_BYTES) {
-        @vtype@ a = @vpre@_load_@vsuf@(&ip1[i + 0 * VECTOR_SIZE_BYTES / sizeof(@type@)]);
-        @vtype@ b = @vpre@_load_@vsuf@(&ip1[i + 1 * VECTOR_SIZE_BYTES / sizeof(@type@)]);
-        @vtype@ c = @vpre@_load_@vsuf@(&ip1[i + 2 * VECTOR_SIZE_BYTES / sizeof(@type@)]);
-        @vtype@ d = @vpre@_load_@vsuf@(&ip1[i + 3 * VECTOR_SIZE_BYTES / sizeof(@type@)]);
-        @vtype@ r1, r2, r3, r4;
-#if @var@ != 0 /* isinf/isfinite */
-        /* fabs via masking of sign bit */
-        r1 = @vpre@_andnot_@vsuf@(mask, a);
-        r2 = @vpre@_andnot_@vsuf@(mask, b);
-        r3 = @vpre@_andnot_@vsuf@(mask, c);
-        r4 = @vpre@_andnot_@vsuf@(mask, d);
-#if @var@ == 1 /* isfinite */
-        /* negative compare against max float, nan is always true */
-        r1 = @vpre@_cmpnle_@vsuf@(r1, fltmax);
-        r2 = @vpre@_cmpnle_@vsuf@(r2, fltmax);
-        r3 = @vpre@_cmpnle_@vsuf@(r3, fltmax);
-        r4 = @vpre@_cmpnle_@vsuf@(r4, fltmax);
-#else /* isinf */
-        r1 = @vpre@_cmpnlt_@vsuf@(fltmax, r1);
-        r2 = @vpre@_cmpnlt_@vsuf@(fltmax, r2);
-        r3 = @vpre@_cmpnlt_@vsuf@(fltmax, r3);
-        r4 = @vpre@_cmpnlt_@vsuf@(fltmax, r4);
-#endif
-        /* flip results to what we want (andnot as there is no sse not) */
-        r1 = @vpre@_andnot_@vsuf@(r1, ones);
-        r2 = @vpre@_andnot_@vsuf@(r2, ones);
-        r3 = @vpre@_andnot_@vsuf@(r3, ones);
-        r4 = @vpre@_andnot_@vsuf@(r4, ones);
-#endif
-#if @var@ == 0 /* isnan */
-        r1 = @vpre@_cmpneq_@vsuf@(a, a);
-        r2 = @vpre@_cmpneq_@vsuf@(b, b);
-        r3 = @vpre@_cmpneq_@vsuf@(c, c);
-        r4 = @vpre@_cmpneq_@vsuf@(d, d);
-#endif
-        sse2_compress4_to_byte_@TYPE@(r1, r2, r3, &r4, &op[i]);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = npy_@kind@(ip1[i]) != 0;
-    }
-}
-
-/**end repeat1**/
-
-static void
-sse2_negative_@TYPE@(@type@ * op, @type@ * ip, const npy_intp n)
-{
-    /*
-     * get 0x7FFFFFFF mask (everything but signbit set)
-     * float & ~mask will remove the sign, float ^ mask flips the sign
-     * this is equivalent to how the compiler implements fabs on amd64
-     */
-    const @vtype@ mask = @vpre@_set1_@vsuf@(-0.@c@);
-
-    /* align output to VECTOR_SIZE_BYTES bytes */
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, VECTOR_SIZE_BYTES) {
-        op[i] = -ip[i];
-    }
-    assert((npy_uintp)n < (VECTOR_SIZE_BYTES / sizeof(@type@)) ||
-           npy_is_aligned(&op[i], VECTOR_SIZE_BYTES));
-    if (npy_is_aligned(&ip[i], VECTOR_SIZE_BYTES)) {
-        LOOP_BLOCKED(@type@, VECTOR_SIZE_BYTES) {
-            @vtype@ a = @vpre@_load_@vsuf@(&ip[i]);
-            @vpre@_store_@vsuf@(&op[i], @vpre@_xor_@vsuf@(mask, a));
-        }
-    }
-    else {
-        LOOP_BLOCKED(@type@, VECTOR_SIZE_BYTES) {
-            @vtype@ a = @vpre@_loadu_@vsuf@(&ip[i]);
-            @vpre@_store_@vsuf@(&op[i], @vpre@_xor_@vsuf@(mask, a));
-        }
-    }
-    LOOP_BLOCKED_END {
-        op[i] = -ip[i];
-    }
-}
-/**end repeat1**/
-
-/**end repeat**/
-
-/* bunch of helper functions used in ISA_exp/log_FLOAT*/
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_get_full_load_mask_ps(void)
-{
-    return _mm256_set1_ps(-1.0);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i
-fma_get_full_load_mask_pd(void)
-{
-    return _mm256_castpd_si256(_mm256_set1_pd(-1.0));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_get_partial_load_mask_ps(const npy_int num_elem, const npy_int num_lanes)
-{
-    float maskint[16] = {-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,
-                            1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0};
-    float* addr = maskint + num_lanes - num_elem;
-    return _mm256_loadu_ps(addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i
-fma_get_partial_load_mask_pd(const npy_int num_elem, const npy_int num_lanes)
-{
-    npy_int maskint[16] = {-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1};
-    npy_int* addr = maskint + 2*num_lanes - 2*num_elem;
-    return _mm256_loadu_si256((__m256i*) addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_masked_gather_ps(__m256 src,
-                     npy_float* addr,
-                     __m256i vindex,
-                     __m256 mask)
-{
-    return _mm256_mask_i32gather_ps(src, addr, vindex, mask, 4);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_masked_gather_pd(__m256d src,
-                     npy_double* addr,
-                     __m128i vindex,
-                     __m256d mask)
-{
-    return _mm256_mask_i32gather_pd(src, addr, vindex, mask, 8);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_masked_load_ps(__m256 mask, npy_float* addr)
-{
-    return _mm256_maskload_ps(addr, _mm256_cvtps_epi32(mask));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_masked_load_pd(__m256i mask, npy_double* addr)
-{
-    return _mm256_maskload_pd(addr, mask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_set_masked_lanes_ps(__m256 x, __m256 val, __m256 mask)
-{
-    return _mm256_blendv_ps(x, val, mask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256d
-fma_set_masked_lanes_pd(__m256d x, __m256d val, __m256d mask)
-{
-    return _mm256_blendv_pd(x, val, mask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_blend(__m256 x, __m256 y, __m256 ymask)
-{
-    return _mm256_blendv_ps(x, y, ymask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256
-fma_invert_mask_ps(__m256 ymask)
-{
-    return _mm256_andnot_ps(ymask, _mm256_set1_ps(-1.0));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA __m256i
-fma_invert_mask_pd(__m256i ymask)
-{
-    return _mm256_andnot_si256(ymask, _mm256_set1_epi32(0xFFFFFFFF));
-}
-
-/**begin repeat
- *  #vsub = ps, pd#
- *  #vtype = __m256, __m256d#
- */
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@
-fma_abs_@vsub@(@vtype@ x)
-{
-    return _mm256_andnot_@vsub@(_mm256_set1_@vsub@(-0.0), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@
-fma_reciprocal_@vsub@(@vtype@ x)
-{
-    return _mm256_div_@vsub@(_mm256_set1_@vsub@(1.0f), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@
-fma_rint_@vsub@(@vtype@ x)
-{
-    return _mm256_round_@vsub@(x, _MM_FROUND_TO_NEAREST_INT);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@
-fma_floor_@vsub@(@vtype@ x)
-{
-    return _mm256_round_@vsub@(x, _MM_FROUND_TO_NEG_INF);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_FMA @vtype@
-fma_trunc_@vsub@(@vtype@ x)
-{
-    return _mm256_round_@vsub@(x, _MM_FROUND_TO_ZERO);
-}
-/**end repeat**/
-#endif
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_get_full_load_mask_ps(void)
-{
-    return 0xFFFF;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_get_full_load_mask_pd(void)
-{
-    return 0xFF;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_get_partial_load_mask_ps(const npy_int num_elem, const npy_int total_elem)
-{
-    return (0x0001 << num_elem) - 0x0001;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_get_partial_load_mask_pd(const npy_int num_elem, const npy_int total_elem)
-{
-    return (0x01 << num_elem) - 0x01;
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_masked_gather_ps(__m512 src,
-                        npy_float* addr,
-                        __m512i vindex,
-                        __mmask16 kmask)
-{
-    return _mm512_mask_i32gather_ps(src, kmask, vindex, addr, 4);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_masked_gather_pd(__m512d src,
-                        npy_double* addr,
-                        __m256i vindex,
-                        __mmask8 kmask)
-{
-    return _mm512_mask_i32gather_pd(src, kmask, vindex, addr, 8);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_masked_load_ps(__mmask16 mask, npy_float* addr)
-{
-    return _mm512_maskz_loadu_ps(mask, (__m512 *)addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_masked_load_pd(__mmask8 mask, npy_double* addr)
-{
-    return _mm512_maskz_loadu_pd(mask, (__m512d *)addr);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_set_masked_lanes_ps(__m512 x, __m512 val, __mmask16 mask)
-{
-    return _mm512_mask_blend_ps(mask, x, val);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512d
-avx512_set_masked_lanes_pd(__m512d x, __m512d val, __mmask8 mask)
-{
-    return _mm512_mask_blend_pd(mask, x, val);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __m512
-avx512_blend(__m512 x, __m512 y, __mmask16 ymask)
-{
-    return _mm512_mask_mov_ps(x, ymask, y);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask16
-avx512_invert_mask_ps(__mmask16 ymask)
-{
-    return _mm512_knot(ymask);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F __mmask8
-avx512_invert_mask_pd(__mmask8 ymask)
-{
-    return _mm512_knot(ymask);
-}
-
-/**begin repeat
- *  #vsub  = ps, pd#
- *  #type= npy_float, npy_double#
- *  #epi_vsub  = epi32, epi64#
- *  #vtype = __m512, __m512d#
- *  #mask = __mmask16, __mmask8#
- *  #and_const = 0x7fffffff, 0x7fffffffffffffffLL#
- *  #neg_mask = 0x80000000, 0x8000000000000000#
- *  #perm_ = 0xb1, 0x55#
- *  #cmpx_img_mask = 0xAAAA, 0xAA#
- *  #cmpx_re_mask = 0x5555, 0x55#
- *  #INF = NPY_INFINITYF, NPY_INFINITY#
- *  #NAN = NPY_NANF, NPY_NAN#
- */
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_abs_@vsub@(@vtype@ x)
-{
-    return (@vtype@) _mm512_and_@epi_vsub@((__m512i) x,
-				    _mm512_set1_@epi_vsub@ (@and_const@));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_reciprocal_@vsub@(@vtype@ x)
-{
-    return _mm512_div_@vsub@(_mm512_set1_@vsub@(1.0f), x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_rint_@vsub@(@vtype@ x)
-{
-    return _mm512_roundscale_@vsub@(x, 0x08);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_floor_@vsub@(@vtype@ x)
-{
-    return _mm512_roundscale_@vsub@(x, 0x09);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_trunc_@vsub@(@vtype@ x)
-{
-    return _mm512_roundscale_@vsub@(x, 0x0B);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_hadd_@vsub@(const @vtype@ x)
-{
-    return _mm512_add_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_hsub_@vsub@(const @vtype@ x)
-{
-    return _mm512_sub_@vsub@(x, _mm512_permute_@vsub@(x, @perm_@));
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_cabsolute_@vsub@(const @vtype@ x1,
-                        const @vtype@ x2,
-                        const __m512i re_indices,
-                        const __m512i im_indices)
-{
-    @vtype@ inf = _mm512_set1_@vsub@(@INF@);
-    @vtype@ nan = _mm512_set1_@vsub@(@NAN@);
-    @vtype@ x1_abs = avx512_abs_@vsub@(x1);
-    @vtype@ x2_abs = avx512_abs_@vsub@(x2);
-    @vtype@ re = _mm512_permutex2var_@vsub@(x1_abs, re_indices, x2_abs);
-    @vtype@ im = _mm512_permutex2var_@vsub@(x1_abs, im_indices , x2_abs);
-    /*
-     * If real or imag = INF, then convert it to inf + j*inf
-     * Handles: inf + j*nan, nan + j*inf
-     */
-    @mask@ re_infmask = _mm512_cmp_@vsub@_mask(re, inf, _CMP_EQ_OQ);
-    @mask@ im_infmask = _mm512_cmp_@vsub@_mask(im, inf, _CMP_EQ_OQ);
-    im = _mm512_mask_mov_@vsub@(im, re_infmask, inf);
-    re = _mm512_mask_mov_@vsub@(re, im_infmask, inf);
-
-    /*
-     * If real or imag = NAN, then convert it to nan + j*nan
-     * Handles: x + j*nan, nan + j*x
-     */
-    @mask@ re_nanmask = _mm512_cmp_@vsub@_mask(re, re, _CMP_NEQ_UQ);
-    @mask@ im_nanmask = _mm512_cmp_@vsub@_mask(im, im, _CMP_NEQ_UQ);
-    im = _mm512_mask_mov_@vsub@(im, re_nanmask, nan);
-    re = _mm512_mask_mov_@vsub@(re, im_nanmask, nan);
-
-    @vtype@ larger  = _mm512_max_@vsub@(re, im);
-    @vtype@ smaller = _mm512_min_@vsub@(im, re);
-
-    /*
-     * Calculate div_mask to prevent 0./0. and inf/inf operations in div
-     */
-    @mask@ zeromask = _mm512_cmp_@vsub@_mask(larger, _mm512_setzero_@vsub@(), _CMP_EQ_OQ);
-    @mask@ infmask = _mm512_cmp_@vsub@_mask(smaller, inf, _CMP_EQ_OQ);
-    @mask@ div_mask = _mm512_knot(_mm512_kor(zeromask, infmask));
-    @vtype@ ratio = _mm512_maskz_div_@vsub@(div_mask, smaller, larger);
-    @vtype@ hypot = _mm512_sqrt_@vsub@(_mm512_fmadd_@vsub@(
-                                        ratio, ratio, _mm512_set1_@vsub@(1.0f)));
-    return _mm512_mul_@vsub@(hypot, larger);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_conjugate_@vsub@(const @vtype@ x)
-{
-    /*
-     * __mm512_mask_xor_ps/pd requires AVX512DQ. We cast it to __m512i and
-     * use the xor_epi32/64 uinstruction instead. Cast is a zero latency instruction
-     */
-    __m512i cast_x = _mm512_cast@vsub@_si512(x);
-    __m512i res = _mm512_mask_xor_@epi_vsub@(cast_x, @cmpx_img_mask@,
-                                        cast_x, _mm512_set1_@epi_vsub@(@neg_mask@));
-    return _mm512_castsi512_@vsub@(res);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_cmul_@vsub@(@vtype@ x1, @vtype@ x2)
-{
-    // x1 = r1, i1
-    // x2 = r2, i2
-    @vtype@ x3  = _mm512_permute_@vsub@(x2, @perm_@);   // i2, r2
-    @vtype@ x12 = _mm512_mul_@vsub@(x1, x2);            // r1*r2, i1*i2
-    @vtype@ x13 = _mm512_mul_@vsub@(x1, x3);            // r1*i2, r2*i1
-    @vtype@ outreal = avx512_hsub_@vsub@(x12);          // r1*r2 - i1*i2, r1*r2 - i1*i2
-    @vtype@ outimg  = avx512_hadd_@vsub@(x13);          // r1*i2 + i1*r2, r1*i2 + i1*r2
-    return _mm512_mask_blend_@vsub@(@cmpx_img_mask@, outreal, outimg);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_AVX512F @vtype@
-avx512_csquare_@vsub@(@vtype@ x)
-{
-    return avx512_cmul_@vsub@(x, x);
-}
-
-/**end repeat**/
-#endif
-
-/**begin repeat
- * #ISA = FMA, AVX512F#
- * #isa = fma, avx512#
- * #vtype = __m256, __m512#
- * #vsize = 256, 512#
- * #or = or_ps, kor#
- * #vsub = , _mask#
- * #mask = __m256, __mmask16#
- * #fmadd = _mm256_fmadd_ps, _mm512_fmadd_ps#
- * #CHK = HAVE_ATTRIBUTE_TARGET_AVX2_WITH_INTRINSICS, HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS#
- **/
-
-#if defined @CHK@
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@
-@isa@_sqrt_ps(@vtype@ x)
-{
-    return _mm@vsize@_sqrt_ps(x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@d
-@isa@_sqrt_pd(@vtype@d x)
-{
-    return _mm@vsize@_sqrt_pd(x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@
-@isa@_square_ps(@vtype@ x)
-{
-    return _mm@vsize@_mul_ps(x,x);
-}
-
-NPY_FINLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@d
-@isa@_square_pd(@vtype@d x)
-{
-    return _mm@vsize@_mul_pd(x,x);
-}
-
-#endif
-/**end repeat**/
-
-/**begin repeat
- * #type = npy_float, npy_double#
- * #TYPE = FLOAT, DOUBLE#
- * #num_lanes = 16, 8#
- * #vsuffix = ps, pd#
- * #mask = __mmask16, __mmask8#
- * #vtype = __m512, __m512d#
- * #scale = 4, 8#
- * #vindextype = __m512i, __m256i#
- * #vindexload = _mm512_loadu_si512, _mm256_loadu_si256#
- * #episize = epi32, epi64#
- */
-
-/**begin repeat1
- * #func = isnan, isfinite, isinf, signbit#
- * #IMM8 = 0x81, 0x99, 0x18, 0x04#
- * #is_finite = 0, 1, 0, 0#
- * #is_signbit = 0, 0, 0, 1#
- */
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512_SKX_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_INLINE NPY_GCC_TARGET_AVX512_SKX void
-AVX512_SKX_@func@_@TYPE@(npy_bool* op, @type@* ip, const npy_intp array_size, const npy_intp steps)
-{
-    const npy_intp stride_ip = steps/(npy_intp)sizeof(@type@);
-    npy_intp num_remaining_elements = array_size;
-
-    @mask@ load_mask = avx512_get_full_load_mask_@vsuffix@();
-#if @is_signbit@
-    @vtype@ signbit = _mm512_set1_@vsuffix@(-0.0);
-#endif
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum
-     * index will fit in an int32 as a precondition for this function via
-     * IS_OUTPUT_BLOCKABLE_UNARY
-     */
-
-    npy_int32 index_ip[@num_lanes@];
-    for (npy_int32 ii = 0; ii < @num_lanes@; ii++) {
-        index_ip[ii] = ii*stride_ip;
-    }
-    @vindextype@ vindex_ip = @vindexload@((@vindextype@*)&index_ip[0]);
-    @vtype@ zeros_f = _mm512_setzero_@vsuffix@();
-    __m512i ones = _mm512_set1_@episize@(1);
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < @num_lanes@) {
-            load_mask = avx512_get_partial_load_mask_@vsuffix@(
-                                    num_remaining_elements, @num_lanes@);
-        }
-        @vtype@ x1;
-        if (stride_ip == 1) {
-            x1 = avx512_masked_load_@vsuffix@(load_mask, ip);
-        }
-        else {
-            x1 = avx512_masked_gather_@vsuffix@(zeros_f, ip, vindex_ip, load_mask);
-        }
-#if @is_signbit@
-        x1 = _mm512_and_@vsuffix@(x1,signbit);
-#endif
-
-        @mask@ fpclassmask = _mm512_fpclass_@vsuffix@_mask(x1, @IMM8@);
-#if @is_finite@
-        fpclassmask = _mm512_knot(fpclassmask);
-#endif
-
-        __m128i out =_mm512_maskz_cvts@episize@_epi8(fpclassmask, ones);
-        _mm_mask_storeu_epi8(op, load_mask, out);
-
-        ip += @num_lanes@*stride_ip;
-        op += @num_lanes@;
-        num_remaining_elements -= @num_lanes@;
-    }
-}
-#endif
-/**end repeat1**/
-/**end repeat**/
-
-/**begin repeat
- * #TYPE = CFLOAT, CDOUBLE#
- * #type = npy_float, npy_double#
- * #num_lanes = 16, 8#
- * #vsuffix = ps, pd#
- * #epi_vsub  = epi32, epi64#
- * #mask = __mmask16, __mmask8#
- * #vtype = __m512, __m512d#
- * #scale = 4, 8#
- * #vindextype = __m512i, __m256i#
- * #vindexload = _mm512_loadu_si512, _mm256_loadu_si256#
- * #storemask = 0xFF, 0xF#
- * #IS_FLOAT = 1, 0#
- */
-
-/**begin repeat1
- *  #func = square, conjugate#
- *  #vectorf = avx512_csquare, avx512_conjugate#
- */
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_@func@_@TYPE@(@type@ * op,
-                      @type@ * ip,
-                      const npy_intp array_size,
-                      const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(@type@)/2;
-
-     /*
-      * Note: while generally indices are npy_intp, we ensure that our maximum index
-      * will fit in an int32 as a precondition for this function via max_stride
-      */
-    npy_int32 index_ip1[16];
-    for (npy_int32 ii = 0; ii < @num_lanes@; ii=ii+2) {
-        index_ip1[ii] = ii*stride_ip1;
-        index_ip1[ii+1] = ii*stride_ip1 + 1;
-    }
-    @vindextype@ vindex = @vindexload@((@vindextype@*)index_ip1);
-    @mask@ load_mask = avx512_get_full_load_mask_@vsuffix@();
-    @vtype@ zeros = _mm512_setzero_@vsuffix@();
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < @num_lanes@) {
-            load_mask = avx512_get_partial_load_mask_@vsuffix@(
-                                    num_remaining_elements, @num_lanes@);
-        }
-        @vtype@ x1;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_@vsuffix@(load_mask, ip);
-        }
-        else {
-            x1  = avx512_masked_gather_@vsuffix@(zeros, ip, vindex, load_mask);
-        }
-
-        @vtype@ out = @vectorf@_@vsuffix@(x1);
-
-        _mm512_mask_storeu_@vsuffix@(op, load_mask, out);
-        op += @num_lanes@;
-        ip += @num_lanes@*stride_ip1;
-        num_remaining_elements -= @num_lanes@;
-    }
-}
-#endif
-/**end repeat1**/
-
-#if defined HAVE_ATTRIBUTE_TARGET_AVX512F_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
-static NPY_GCC_OPT_3 NPY_INLINE NPY_GCC_TARGET_AVX512F void
-AVX512F_absolute_@TYPE@(@type@ * op,
-                        @type@ * ip,
-                        const npy_intp array_size,
-                        const npy_intp steps)
-{
-    npy_intp num_remaining_elements = 2*array_size;
-    const npy_intp stride_ip1 = steps/(npy_intp)sizeof(@type@)/2;
-
-    /*
-     * Note: while generally indices are npy_intp, we ensure that our maximum index
-     * will fit in an int32 as a precondition for this function via max_stride
-     */
-    npy_int32 index_ip[32];
-    for (npy_int32 ii = 0; ii < 2*@num_lanes@; ii=ii+2) {
-        index_ip[ii] = ii*stride_ip1;
-        index_ip[ii+1] = ii*stride_ip1 + 1;
-    }
-    @vindextype@ vindex1 = @vindexload@((@vindextype@*)index_ip);
-    @vindextype@ vindex2 = @vindexload@((@vindextype@*)(index_ip+@num_lanes@));
-
-    @mask@ load_mask1 = avx512_get_full_load_mask_@vsuffix@();
-    @mask@ load_mask2 = avx512_get_full_load_mask_@vsuffix@();
-    @mask@ store_mask = avx512_get_full_load_mask_@vsuffix@();
-    @vtype@ zeros = _mm512_setzero_@vsuffix@();
-
-#if @IS_FLOAT@
-    __m512i re_index = _mm512_set_epi32(30,28,26,24,22,20,18,16,14,12,10,8,6,4,2,0);
-    __m512i im_index  = _mm512_set_epi32(31,29,27,25,23,21,19,17,15,13,11,9,7,5,3,1);
-#else
-    __m512i re_index = _mm512_set_epi64(14,12,10,8,6,4,2,0);
-    __m512i im_index  = _mm512_set_epi64(15,13,11,9,7,5,3,1);
-#endif
-
-    while (num_remaining_elements > 0) {
-        if (num_remaining_elements < @num_lanes@) {
-            load_mask1 = avx512_get_partial_load_mask_@vsuffix@(
-                                    num_remaining_elements, @num_lanes@);
-            load_mask2 = 0x0000;
-            store_mask = avx512_get_partial_load_mask_@vsuffix@(
-                                    num_remaining_elements/2, @num_lanes@);
-        } else if (num_remaining_elements < 2*@num_lanes@) {
-            load_mask1 = avx512_get_full_load_mask_@vsuffix@();
-            load_mask2 = avx512_get_partial_load_mask_@vsuffix@(
-                                    num_remaining_elements - @num_lanes@, @num_lanes@);
-            store_mask = avx512_get_partial_load_mask_@vsuffix@(
-                                    num_remaining_elements/2, @num_lanes@);
-        }
-        @vtype@ x1, x2;
-        if (stride_ip1 == 1) {
-            x1 = avx512_masked_load_@vsuffix@(load_mask1, ip);
-            x2 = avx512_masked_load_@vsuffix@(load_mask2, ip+@num_lanes@);
-        }
-        else {
-            x1  = avx512_masked_gather_@vsuffix@(zeros, ip, vindex1, load_mask1);
-            x2  = avx512_masked_gather_@vsuffix@(zeros, ip, vindex2, load_mask2);
-        }
-
-        @vtype@ out = avx512_cabsolute_@vsuffix@(x1, x2, re_index, im_index);
-
-        _mm512_mask_storeu_@vsuffix@(op, store_mask, out);
-        op += @num_lanes@;
-        ip += 2*@num_lanes@*stride_ip1;
-        num_remaining_elements -= 2*@num_lanes@;
-    }
-    npy_clear_floatstatus_barrier((char*)&num_remaining_elements);
-}
-
-#endif
-/**end repeat**/
-
-/*
- *****************************************************************************
- **                           BOOL LOOPS
- *****************************************************************************
- */
-
-/**begin repeat
- * # kind = logical_or, logical_and#
- * # and = 0, 1#
- * # op = ||, &&#
- * # sc = !=, ==#
- * # vpre = _mm*2#
- * # vsuf = si128*2#
- * # vtype = __m128i*2#
- * # type = npy_bool*2#
- * # vload = _mm_load_si128*2#
- * # vloadu = _mm_loadu_si128*2#
- * # vstore = _mm_store_si128*2#
- */
-
-/*
- * convert any bit set to boolean true so vectorized and normal operations are
- * consistent, should not be required if bool is used correctly everywhere but
- * you never know
- */
-#if !@and@
-NPY_FINLINE @vtype@ byte_to_true(@vtype@ v)
-{
-    const @vtype@ zero = @vpre@_setzero_@vsuf@();
-    const @vtype@ truemask = @vpre@_set1_epi8(1 == 1);
-    /* get 0xFF for zeros */
-    @vtype@ tmp = @vpre@_cmpeq_epi8(v, zero);
-    /* filled with 0xFF/0x00, negate and mask to boolean true */
-    return @vpre@_andnot_@vsuf@(tmp, truemask);
-}
-#endif
-
-static void
-sse2_binary_@kind@_BOOL(npy_bool * op, npy_bool * ip1, npy_bool * ip2, npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, VECTOR_SIZE_BYTES)
-        op[i] = ip1[i] @op@ ip2[i];
-    LOOP_BLOCKED(@type@, VECTOR_SIZE_BYTES) {
-        @vtype@ a = @vloadu@((@vtype@*)&ip1[i]);
-        @vtype@ b = @vloadu@((@vtype@*)&ip2[i]);
-#if @and@
-        const @vtype@ zero = @vpre@_setzero_@vsuf@();
-        /* get 0xFF for non zeros*/
-        @vtype@ tmp = @vpre@_cmpeq_epi8(a, zero);
-        /* andnot -> 0x00 for zeros xFF for non zeros, & with ip2 */
-        tmp = @vpre@_andnot_@vsuf@(tmp, b);
-#else
-        @vtype@ tmp = @vpre@_or_@vsuf@(a, b);
-#endif
-
-        @vstore@((@vtype@*)&op[i], byte_to_true(tmp));
-    }
-    LOOP_BLOCKED_END {
-        op[i] = (ip1[i] @op@ ip2[i]);
-    }
-}
-
-
-static void
-sse2_reduce_@kind@_BOOL(npy_bool * op, npy_bool * ip, const npy_intp n)
-{
-    const @vtype@ zero = @vpre@_setzero_@vsuf@();
-    LOOP_BLOCK_ALIGN_VAR(ip, npy_bool, VECTOR_SIZE_BYTES) {
-        *op = *op @op@ ip[i];
-        if (*op @sc@ 0) {
-            return;
-        }
-    }
-    /* unrolled once to replace a slow movmsk with a fast pmaxb */
-    LOOP_BLOCKED(npy_bool, 2 * VECTOR_SIZE_BYTES) {
-        @vtype@ v = @vload@((@vtype@*)&ip[i]);
-        @vtype@ v2 = @vload@((@vtype@*)&ip[i + VECTOR_SIZE_BYTES]);
-        v = @vpre@_cmpeq_epi8(v, zero);
-        v2 = @vpre@_cmpeq_epi8(v2, zero);
-#if @and@
-        if ((@vpre@_movemask_epi8(@vpre@_max_epu8(v, v2)) != 0)) {
-            *op = 0;
-#else
-        if ((@vpre@_movemask_epi8(@vpre@_min_epu8(v, v2)) != 0xFFFF)) {
-            *op = 1;
-#endif
-            return;
-        }
-    }
-    LOOP_BLOCKED_END {
-        *op = *op @op@ ip[i];
-        if (*op @sc@ 0) {
-            return;
-        }
-    }
-}
-
-/**end repeat**/
-
-/**begin repeat
- * # kind = absolute, logical_not#
- * # op = !=, ==#
- * # not = 0, 1#
- * # vpre = _mm*2#
- * # vsuf = si128*2#
- * # vtype = __m128i*2#
- * # type = npy_bool*2#
- * # vloadu = _mm_loadu_si128*2#
- * # vstore = _mm_store_si128*2#
- */
-
-static void
-sse2_@kind@_BOOL(@type@ * op, @type@ * ip, const npy_intp n)
-{
-    LOOP_BLOCK_ALIGN_VAR(op, @type@, VECTOR_SIZE_BYTES)
-        op[i] = (ip[i] @op@ 0);
-    LOOP_BLOCKED(@type@, VECTOR_SIZE_BYTES) {
-        @vtype@ a = @vloadu@((@vtype@*)&ip[i]);
-#if @not@
-        const @vtype@ zero = @vpre@_setzero_@vsuf@();
-        const @vtype@ truemask = @vpre@_set1_epi8(1 == 1);
-        /* equivalent to byte_to_true but can skip the negation */
-        a = @vpre@_cmpeq_epi8(a, zero);
-        a = @vpre@_and_@vsuf@(a, truemask);
-#else
-        /* abs is kind of pointless but maybe its used for byte_to_true */
-        a = byte_to_true(a);
-#endif
-        @vstore@((@vtype@*)&op[i], a);
-    }
-    LOOP_BLOCKED_END {
-        op[i] = (ip[i] @op@ 0);
-    }
-}
-
-/**end repeat**/
-
-#undef VECTOR_SIZE_BYTES
-#endif  /* NPY_HAVE_SSE2_INTRINSICS */
-#endif
-
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/string_ufuncs.cpp b/contrib/python/numpy/py3/numpy/core/src/umath/string_ufuncs.cpp
index 5a35c318b5d..5d82be6db14 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/string_ufuncs.cpp
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/string_ufuncs.cpp
@@ -16,7 +16,7 @@
 
 
 template <typename character>
-static NPY_INLINE int
+static inline int
 character_cmp(character a, character b)
 {
     if (a == b) {
@@ -37,7 +37,7 @@ character_cmp(character a, character b)
  * is always padded with zeros).
  */
 template <bool rstrip, typename character>
-static NPY_INLINE int
+static inline int
 string_cmp(int len1, const character *str1, int len2, const character *str2)
 {
     if (rstrip) {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_d_ha.s
new file mode 100644
index 00000000000..8f11c0e25d0
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_d_ha.s
@@ -0,0 +1,2669 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_acos8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_acos8_ha
+
+__svml_acos8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   __svml_dacos_ha_data_internal(%rip), %zmm8
+        vmovups   64+__svml_dacos_ha_data_internal(%rip), %zmm9
+
+/*
+ * 2*sqrt(Y) = Sh - Sl
+ * Sh ~ 2*sqrt_high(Y)
+ * Sl ~ -2*sqrt_low(Y)
+ */
+        vmovups   128+__svml_dacos_ha_data_internal(%rip), %zmm12
+        vmovups   384+__svml_dacos_ha_data_internal(%rip), %zmm1
+        vmovups   448+__svml_dacos_ha_data_internal(%rip), %zmm15
+        vmovups   512+__svml_dacos_ha_data_internal(%rip), %zmm2
+        vmovups   256+__svml_dacos_ha_data_internal(%rip), %zmm11
+        vmovaps   %zmm0, %zmm7
+
+/* x = -|arg| */
+        vorpd     %zmm7, %zmm8, %zmm6
+        vandpd    %zmm7, %zmm8, %zmm5
+        vmovups   576+__svml_dacos_ha_data_internal(%rip), %zmm0
+
+/* Y = 0.5 + 0.5*(-x) */
+        vfmadd231pd {rn-sae}, %zmm6, %zmm9, %zmm9
+
+/* x^2 */
+        vmulpd    {rn-sae}, %zmm6, %zmm6, %zmm10
+        vrsqrt14pd %zmm9, %zmm14
+        vcmppd    $17, {sae}, %zmm12, %zmm9, %k2
+        vcmppd    $17, {sae}, %zmm11, %zmm6, %k0
+        vmovups   960+__svml_dacos_ha_data_internal(%rip), %zmm11
+        vmovups   1088+__svml_dacos_ha_data_internal(%rip), %zmm12
+        vminpd    {sae}, %zmm9, %zmm10, %zmm4
+        vmovups   832+__svml_dacos_ha_data_internal(%rip), %zmm10
+        vxorpd    %zmm14, %zmm14, %zmm14{%k2}
+        vaddpd    {rn-sae}, %zmm9, %zmm9, %zmm8
+        vcmppd    $21, {sae}, %zmm9, %zmm4, %k1
+
+/* X<X^2 iff X<0 */
+        vcmppd    $21, {sae}, %zmm7, %zmm4, %k3
+        vmulpd    {rn-sae}, %zmm14, %zmm14, %zmm13
+        vmulpd    {rn-sae}, %zmm14, %zmm8, %zmm3
+
+/* polynomial */
+        vmovups   704+__svml_dacos_ha_data_internal(%rip), %zmm9
+        vfmsub231pd {rn-sae}, %zmm8, %zmm13, %zmm1
+        vmovups   640+__svml_dacos_ha_data_internal(%rip), %zmm13
+        vfmsub213pd {rn-sae}, %zmm3, %zmm14, %zmm8
+        vfmadd231pd {rn-sae}, %zmm1, %zmm15, %zmm2
+        vmovups   1216+__svml_dacos_ha_data_internal(%rip), %zmm15
+        vmulpd    {rn-sae}, %zmm1, %zmm3, %zmm14
+        vfmadd213pd {rn-sae}, %zmm0, %zmm1, %zmm2
+        vmovups   896+__svml_dacos_ha_data_internal(%rip), %zmm0
+        kmovw     %k1, %eax
+        kmovw     %k3, %ecx
+        kmovw     %k0, %edx
+        vfmadd213pd {rn-sae}, %zmm13, %zmm1, %zmm2
+        vmovups   768+__svml_dacos_ha_data_internal(%rip), %zmm1
+        vfmadd231pd {rn-sae}, %zmm4, %zmm10, %zmm0
+        vmovups   1280+__svml_dacos_ha_data_internal(%rip), %zmm10
+        vfmsub213pd {rn-sae}, %zmm8, %zmm14, %zmm2
+        vfmadd231pd {rn-sae}, %zmm4, %zmm9, %zmm1
+        vmovups   1024+__svml_dacos_ha_data_internal(%rip), %zmm8
+        vfmadd231pd {rn-sae}, %zmm4, %zmm15, %zmm10
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm9
+        vfmadd231pd {rn-sae}, %zmm4, %zmm11, %zmm8
+        vmovups   1152+__svml_dacos_ha_data_internal(%rip), %zmm11
+        vfmadd213pd {rn-sae}, %zmm0, %zmm9, %zmm1
+        vmulpd    {rn-sae}, %zmm9, %zmm9, %zmm0
+        vfmadd231pd {rn-sae}, %zmm4, %zmm12, %zmm11
+        vmovups   1408+__svml_dacos_ha_data_internal(%rip), %zmm12
+        vfmadd213pd {rn-sae}, %zmm11, %zmm9, %zmm8
+        vmovups   1344+__svml_dacos_ha_data_internal(%rip), %zmm11
+        vfmadd213pd {rn-sae}, %zmm8, %zmm0, %zmm1
+        vmovups   1728+__svml_dacos_ha_data_internal(%rip), %zmm8
+        vfmadd213pd {rn-sae}, %zmm10, %zmm9, %zmm1
+        vblendmpd %zmm2, %zmm8, %zmm2{%k1}
+        vfmadd213pd {rn-sae}, %zmm11, %zmm4, %zmm1
+        vfmadd213pd {rn-sae}, %zmm12, %zmm4, %zmm1
+        andl      %eax, %ecx
+        kmovw     %ecx, %k2
+        vmulpd    {rn-sae}, %zmm4, %zmm1, %zmm0
+        vmovups   1600+__svml_dacos_ha_data_internal(%rip), %zmm4
+        vmovups   1664+__svml_dacos_ha_data_internal(%rip), %zmm1
+        vxorpd    %zmm4, %zmm4, %zmm4{%k1}
+        vxorpd    %zmm1, %zmm1, %zmm1{%k1}
+        vorpd     1472+__svml_dacos_ha_data_internal(%rip), %zmm4, %zmm4{%k2}
+        vorpd     1536+__svml_dacos_ha_data_internal(%rip), %zmm1, %zmm1{%k2}
+        vxorpd    %zmm5, %zmm4, %zmm9
+        vblendmpd %zmm3, %zmm6, %zmm4{%k1}
+        vsubpd    {rn-sae}, %zmm2, %zmm9, %zmm6
+
+/* 2*sqrt(Y) */
+        vsubpd    {rn-sae}, %zmm2, %zmm4, %zmm3
+        vfmadd213pd {rn-sae}, %zmm6, %zmm3, %zmm0
+        vaddpd    {rn-sae}, %zmm4, %zmm0, %zmm0
+        vxorpd    %zmm5, %zmm0, %zmm5
+        vaddpd    {rn-sae}, %zmm1, %zmm5, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm7, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dacos_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_acos8_ha,@function
+	.size	__svml_acos8_ha,.-__svml_acos8_ha
+..LN__svml_acos8_ha.0:
+
+.L_2__routine_start___svml_dacos_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dacos_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    6(%rdi), %edx
+        andl      $32752, %edx
+        movb      7(%rdi), %cl
+        movsd     (%rdi), %xmm1
+        cmpl      $32752, %edx
+        je        .LBL_2_20
+
+
+        movsd     %xmm1, -32(%rsp)
+        andb      $127, %cl
+        movb      %cl, -25(%rsp)
+        movsd     -32(%rsp), %xmm12
+        movsd     4168+_vmldACosHATab(%rip), %xmm0
+        comisd    %xmm12, %xmm0
+        jbe       .LBL_2_14
+
+
+        movsd     4176+_vmldACosHATab(%rip), %xmm1
+        comisd    %xmm12, %xmm1
+        jbe       .LBL_2_10
+
+
+        comisd    4128+_vmldACosHATab(%rip), %xmm12
+        jbe       .LBL_2_9
+
+
+        movsd     4104+_vmldACosHATab(%rip), %xmm6
+        movaps    %xmm12, %xmm8
+        mulsd     %xmm12, %xmm6
+        movaps    %xmm12, %xmm7
+        movsd     %xmm6, -40(%rsp)
+        movsd     -40(%rsp), %xmm13
+        movsd     4104+_vmldACosHATab(%rip), %xmm5
+        subsd     -32(%rsp), %xmm13
+        movsd     %xmm13, -48(%rsp)
+        movsd     -40(%rsp), %xmm15
+        movsd     -48(%rsp), %xmm14
+        subsd     %xmm14, %xmm15
+        movaps    %xmm12, %xmm14
+        movsd     %xmm15, -40(%rsp)
+        movsd     -40(%rsp), %xmm6
+        subsd     %xmm6, %xmm8
+        movsd     %xmm8, -48(%rsp)
+        movsd     -40(%rsp), %xmm9
+        movaps    %xmm9, %xmm4
+        addsd     %xmm9, %xmm7
+        mulsd     %xmm9, %xmm4
+        movsd     -48(%rsp), %xmm10
+        movaps    %xmm4, %xmm11
+        mulsd     %xmm10, %xmm7
+        mulsd     %xmm4, %xmm5
+        addsd     %xmm7, %xmm11
+        movsd     4312+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm5, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4104+_vmldACosHATab(%rip), %xmm5
+        subsd     %xmm4, %xmm1
+        addsd     4304+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm1, -48(%rsp)
+        movsd     -40(%rsp), %xmm0
+        movsd     -48(%rsp), %xmm2
+        addsd     4296+_vmldACosHATab(%rip), %xmm8
+        subsd     %xmm2, %xmm0
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm3
+        addsd     4288+_vmldACosHATab(%rip), %xmm8
+        subsd     %xmm3, %xmm4
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm4, -48(%rsp)
+        movsd     -40(%rsp), %xmm6
+        mulsd     %xmm6, %xmm9
+        addsd     4280+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm6, %xmm10
+        mulsd     %xmm11, %xmm8
+        mulsd     %xmm9, %xmm5
+        addsd     4272+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm11, %xmm8
+        movaps    %xmm9, %xmm0
+        movsd     -48(%rsp), %xmm13
+        movaps    %xmm6, %xmm4
+        movsd     %xmm5, -40(%rsp)
+        addsd     %xmm13, %xmm7
+        addsd     4264+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm7, %xmm14
+        mulsd     %xmm11, %xmm8
+        addsd     %xmm14, %xmm10
+        addsd     4256+_vmldACosHATab(%rip), %xmm8
+        movsd     -40(%rsp), %xmm1
+        mulsd     %xmm11, %xmm8
+        subsd     %xmm9, %xmm1
+        addsd     %xmm10, %xmm9
+        addsd     4248+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm9, %xmm7
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm1, -48(%rsp)
+        movsd     -40(%rsp), %xmm2
+        movsd     -48(%rsp), %xmm15
+        subsd     %xmm15, %xmm2
+        addsd     4240+_vmldACosHATab(%rip), %xmm8
+        movsd     %xmm2, -40(%rsp)
+        movsd     -40(%rsp), %xmm5
+        mulsd     %xmm11, %xmm8
+        subsd     %xmm5, %xmm0
+        movsd     %xmm0, -48(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movsd     -48(%rsp), %xmm5
+        movaps    %xmm5, %xmm13
+        addsd     4232+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm3, %xmm4
+        addsd     %xmm10, %xmm13
+        mulsd     %xmm11, %xmm8
+        mulsd     %xmm13, %xmm6
+        addsd     4224+_vmldACosHATab(%rip), %xmm8
+        addsd     %xmm7, %xmm6
+        mulsd     %xmm11, %xmm8
+        movsd     4104+_vmldACosHATab(%rip), %xmm7
+        movaps    %xmm4, %xmm13
+        mulsd     %xmm4, %xmm7
+        addsd     4216+_vmldACosHATab(%rip), %xmm8
+        movsd     %xmm7, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4200+_vmldACosHATab(%rip), %xmm11
+        subsd     %xmm4, %xmm1
+        mulsd     %xmm9, %xmm11
+        addsd     %xmm6, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        movaps    %xmm12, %xmm9
+        movsd     -40(%rsp), %xmm0
+        movsd     -48(%rsp), %xmm2
+        movsd     4192+_vmldACosHATab(%rip), %xmm1
+        subsd     %xmm2, %xmm0
+        mulsd     %xmm1, %xmm3
+        mulsd     %xmm1, %xmm10
+        mulsd     %xmm4, %xmm8
+        addsd     %xmm3, %xmm9
+        mulsd     %xmm1, %xmm5
+        addsd     %xmm10, %xmm11
+        movsd     %xmm0, -40(%rsp)
+        addsd     %xmm11, %xmm8
+        movsd     -40(%rsp), %xmm7
+        addsd     %xmm5, %xmm8
+        subsd     %xmm7, %xmm13
+        movsd     %xmm13, -48(%rsp)
+        movsd     -40(%rsp), %xmm0
+        movsd     -48(%rsp), %xmm2
+        movsd     %xmm9, -40(%rsp)
+        addsd     %xmm2, %xmm6
+        movsd     -40(%rsp), %xmm10
+        movsd     %xmm3, -56(%rsp)
+        subsd     %xmm10, %xmm12
+        movsd     4208+_vmldACosHATab(%rip), %xmm4
+        addsd     %xmm12, %xmm3
+        mulsd     %xmm4, %xmm0
+        mulsd     %xmm4, %xmm6
+        movsd     %xmm3, -48(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movaps    %xmm3, %xmm12
+        movsd     -48(%rsp), %xmm7
+        addsd     %xmm0, %xmm12
+        addsd     %xmm7, %xmm8
+        movsd     %xmm12, -40(%rsp)
+        movsd     -40(%rsp), %xmm12
+        subsd     %xmm12, %xmm3
+        addsd     %xmm3, %xmm0
+        movsd     %xmm0, -48(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movsd     -48(%rsp), %xmm0
+        movsd     (%rdi), %xmm1
+        addsd     %xmm8, %xmm0
+        comisd    4184+_vmldACosHATab(%rip), %xmm1
+        addsd     %xmm0, %xmm6
+        jbe       .LBL_2_7
+
+
+        movsd     4136+_vmldACosHATab(%rip), %xmm2
+        movaps    %xmm2, %xmm0
+        subsd     %xmm3, %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4144+_vmldACosHATab(%rip), %xmm0
+        subsd     %xmm1, %xmm2
+        subsd     %xmm6, %xmm0
+        subsd     %xmm3, %xmm2
+        movsd     %xmm2, -48(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     -48(%rsp), %xmm3
+        addsd     %xmm3, %xmm0
+        jmp       .LBL_2_8
+
+.LBL_2_7:
+
+        movsd     4136+_vmldACosHATab(%rip), %xmm2
+        movaps    %xmm3, %xmm0
+        addsd     %xmm2, %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        subsd     %xmm1, %xmm2
+        addsd     %xmm2, %xmm3
+        movsd     %xmm3, -48(%rsp)
+        movsd     4144+_vmldACosHATab(%rip), %xmm3
+        movsd     -40(%rsp), %xmm1
+        addsd     %xmm3, %xmm6
+        movsd     -48(%rsp), %xmm0
+        addsd     %xmm6, %xmm0
+
+.LBL_2_8:
+
+        addsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        movsd     4144+_vmldACosHATab(%rip), %xmm1
+        addsd     %xmm0, %xmm12
+        movsd     %xmm12, -40(%rsp)
+        movsd     -40(%rsp), %xmm0
+        mulsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -56(%rsp)
+        movb      -49(%rsp), %dl
+        movb      7(%rdi), %dil
+        andb      $127, %dl
+        andb      $-128, %dil
+        orb       %dil, %dl
+        movb      %dl, -49(%rsp)
+        subsd     -56(%rsp), %xmm1
+        addsd     4136+_vmldACosHATab(%rip), %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        movaps    %xmm0, %xmm8
+        movzwl    4174+_vmldACosHATab(%rip), %r9d
+        pxor      %xmm13, %xmm13
+        andl      $-32753, %r9d
+        subsd     %xmm12, %xmm8
+        subsd     %xmm0, %xmm13
+        mulsd     %xmm8, %xmm1
+        movsd     %xmm1, -56(%rsp)
+        movzwl    -50(%rsp), %ecx
+        andl      $32752, %ecx
+        shrl      $4, %ecx
+        addl      $-1023, %ecx
+        movl      %ecx, %r8d
+        movl      %ecx, %edx
+        negl      %r8d
+        addl      $1023, %r8d
+        andl      $2047, %r8d
+        shll      $4, %r8d
+        movsd     %xmm0, -32(%rsp)
+        orl       %r8d, %r9d
+        movw      %r9w, -26(%rsp)
+        andl      $1, %edx
+        movsd     -32(%rsp), %xmm4
+        lea       _vmldACosHATab(%rip), %r8
+        mulsd     %xmm4, %xmm1
+        movl      %edx, %r10d
+        movaps    %xmm1, %xmm15
+        movsd     4112+_vmldACosHATab(%rip), %xmm6
+        addsd     %xmm1, %xmm15
+        jne       ..L54
+        movaps    %xmm1, %xmm15
+..L54:
+        mulsd     %xmm15, %xmm6
+        movaps    %xmm15, %xmm7
+        movaps    %xmm6, %xmm9
+        subl      %edx, %ecx
+        movsd     4120+_vmldACosHATab(%rip), %xmm11
+        subsd     %xmm15, %xmm9
+        addsd     %xmm1, %xmm11
+        movsd     %xmm9, -48(%rsp)
+        movsd     -48(%rsp), %xmm10
+        movsd     %xmm11, -24(%rsp)
+        subsd     %xmm10, %xmm6
+        movl      -24(%rsp), %r11d
+        movaps    %xmm6, %xmm14
+        shll      $8, %r10d
+        andl      $511, %r11d
+        addl      %r10d, %r11d
+        subsd     %xmm6, %xmm7
+        movsd     (%r8,%r11,8), %xmm5
+        addsd     %xmm7, %xmm14
+        mulsd     %xmm5, %xmm6
+        movaps    %xmm5, %xmm12
+        mulsd     %xmm5, %xmm12
+        mulsd     %xmm12, %xmm14
+        movsd     4512+_vmldACosHATab(%rip), %xmm4
+        addsd     %xmm13, %xmm14
+        mulsd     %xmm14, %xmm4
+        shrl      $1, %ecx
+        addsd     4504+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        addl      $1023, %ecx
+        andl      $2047, %ecx
+        addsd     4496+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        movzwl    4174+_vmldACosHATab(%rip), %r9d
+        shll      $4, %ecx
+        andl      $-32753, %r9d
+        movsd     %xmm0, -16(%rsp)
+        orl       %ecx, %r9d
+        movw      %r9w, -10(%rsp)
+        movsd     -16(%rsp), %xmm9
+        mulsd     %xmm9, %xmm6
+        addsd     4488+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        movsd     4104+_vmldACosHATab(%rip), %xmm3
+        mulsd     %xmm6, %xmm3
+        addsd     4480+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        movsd     %xmm3, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4352+_vmldACosHATab(%rip), %xmm11
+        subsd     %xmm6, %xmm1
+        addsd     4472+_vmldACosHATab(%rip), %xmm4
+        addsd     4360+_vmldACosHATab(%rip), %xmm11
+        mulsd     %xmm14, %xmm4
+        mulsd     %xmm8, %xmm11
+        addsd     4464+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        mulsd     %xmm15, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        addsd     %xmm4, %xmm7
+        mulsd     %xmm5, %xmm7
+        movsd     4456+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm9, %xmm7
+        addsd     4448+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movsd     -40(%rsp), %xmm2
+        movsd     -48(%rsp), %xmm0
+        movsd     4104+_vmldACosHATab(%rip), %xmm4
+        subsd     %xmm0, %xmm2
+        mulsd     %xmm8, %xmm4
+        addsd     4440+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movaps    %xmm8, %xmm0
+        movsd     %xmm2, -40(%rsp)
+        movsd     -40(%rsp), %xmm14
+        movsd     4104+_vmldACosHATab(%rip), %xmm2
+        subsd     %xmm14, %xmm6
+        addsd     4432+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movsd     %xmm6, -48(%rsp)
+        movsd     -40(%rsp), %xmm6
+        movsd     -48(%rsp), %xmm10
+        movsd     %xmm4, -40(%rsp)
+        addsd     %xmm10, %xmm7
+        addsd     4424+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movsd     -40(%rsp), %xmm3
+        movsd     4336+_vmldACosHATab(%rip), %xmm12
+        subsd     %xmm8, %xmm3
+        addsd     4416+_vmldACosHATab(%rip), %xmm5
+        addsd     4344+_vmldACosHATab(%rip), %xmm12
+        mulsd     %xmm8, %xmm5
+        addsd     %xmm11, %xmm12
+        addsd     4408+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm12
+        mulsd     %xmm8, %xmm5
+        movsd     %xmm3, -48(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     -48(%rsp), %xmm15
+        movsd     4320+_vmldACosHATab(%rip), %xmm13
+        subsd     %xmm15, %xmm1
+        addsd     4400+_vmldACosHATab(%rip), %xmm5
+        addsd     4328+_vmldACosHATab(%rip), %xmm13
+        mulsd     %xmm8, %xmm5
+        addsd     %xmm12, %xmm13
+        addsd     4392+_vmldACosHATab(%rip), %xmm5
+        movsd     %xmm1, -40(%rsp)
+        mulsd     %xmm8, %xmm5
+        movsd     -40(%rsp), %xmm4
+        subsd     %xmm4, %xmm0
+        addsd     4384+_vmldACosHATab(%rip), %xmm5
+        movsd     %xmm0, -48(%rsp)
+        movsd     -40(%rsp), %xmm4
+        movaps    %xmm4, %xmm14
+        mulsd     %xmm4, %xmm14
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm14, %xmm2
+        addsd     4376+_vmldACosHATab(%rip), %xmm5
+        movsd     -48(%rsp), %xmm3
+        movsd     %xmm2, -40(%rsp)
+        movsd     -40(%rsp), %xmm9
+        mulsd     %xmm8, %xmm5
+        subsd     %xmm14, %xmm9
+        movsd     %xmm9, -48(%rsp)
+        movsd     -40(%rsp), %xmm11
+        movsd     -48(%rsp), %xmm10
+        movsd     4336+_vmldACosHATab(%rip), %xmm0
+        subsd     %xmm10, %xmm11
+        mulsd     %xmm4, %xmm0
+        addsd     4368+_vmldACosHATab(%rip), %xmm5
+        addsd     %xmm8, %xmm4
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm3, %xmm4
+        mulsd     %xmm8, %xmm5
+        movsd     %xmm11, -40(%rsp)
+        movaps    %xmm0, %xmm1
+        movsd     -40(%rsp), %xmm12
+        mulsd     %xmm8, %xmm5
+        subsd     %xmm12, %xmm14
+        movsd     %xmm14, -48(%rsp)
+        addsd     %xmm5, %xmm13
+        movsd     -40(%rsp), %xmm9
+        mulsd     4352+_vmldACosHATab(%rip), %xmm9
+        mulsd     %xmm13, %xmm7
+        addsd     %xmm9, %xmm1
+        movsd     -48(%rsp), %xmm2
+        movsd     %xmm1, -40(%rsp)
+        addsd     %xmm2, %xmm4
+        movsd     -40(%rsp), %xmm13
+        movsd     %xmm9, -24(%rsp)
+        subsd     %xmm13, %xmm0
+        mulsd     4352+_vmldACosHATab(%rip), %xmm4
+        addsd     %xmm0, %xmm9
+        movsd     %xmm9, -48(%rsp)
+        movsd     -40(%rsp), %xmm12
+        movsd     4320+_vmldACosHATab(%rip), %xmm10
+        movsd     -48(%rsp), %xmm1
+        addsd     %xmm12, %xmm10
+        movsd     %xmm10, -40(%rsp)
+        movsd     -40(%rsp), %xmm15
+        movsd     4320+_vmldACosHATab(%rip), %xmm11
+        movsd     4104+_vmldACosHATab(%rip), %xmm9
+        subsd     %xmm15, %xmm11
+        movsd     4336+_vmldACosHATab(%rip), %xmm2
+        addsd     %xmm11, %xmm12
+        mulsd     %xmm3, %xmm2
+        movsd     %xmm12, -48(%rsp)
+        movsd     -40(%rsp), %xmm15
+        mulsd     %xmm15, %xmm9
+        movsd     -48(%rsp), %xmm0
+        movsd     %xmm9, -40(%rsp)
+        movsd     -40(%rsp), %xmm10
+        movsd     4360+_vmldACosHATab(%rip), %xmm3
+        subsd     %xmm15, %xmm10
+        mulsd     %xmm8, %xmm3
+        movsd     %xmm10, -48(%rsp)
+        movsd     -40(%rsp), %xmm11
+        movsd     -48(%rsp), %xmm13
+        subsd     %xmm13, %xmm11
+        addsd     4344+_vmldACosHATab(%rip), %xmm3
+        movsd     %xmm11, -40(%rsp)
+        movsd     -40(%rsp), %xmm14
+        mulsd     %xmm8, %xmm3
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -48(%rsp)
+        movsd     -40(%rsp), %xmm10
+        movsd     -48(%rsp), %xmm9
+        addsd     %xmm9, %xmm4
+        addsd     4328+_vmldACosHATab(%rip), %xmm3
+        addsd     %xmm2, %xmm4
+        addsd     %xmm5, %xmm3
+        addsd     %xmm1, %xmm4
+        addsd     %xmm0, %xmm4
+        addsd     %xmm3, %xmm4
+        mulsd     %xmm6, %xmm4
+        mulsd     %xmm10, %xmm6
+        addsd     %xmm7, %xmm4
+        movsd     (%rdi), %xmm7
+        comisd    4184+_vmldACosHATab(%rip), %xmm7
+        ja        .LBL_2_13
+
+
+        movsd     4152+_vmldACosHATab(%rip), %xmm2
+        movaps    %xmm2, %xmm0
+        movsd     4160+_vmldACosHATab(%rip), %xmm5
+        subsd     %xmm6, %xmm0
+        subsd     %xmm4, %xmm5
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     %xmm6, -56(%rsp)
+        subsd     %xmm1, %xmm2
+        subsd     %xmm6, %xmm2
+        movsd     %xmm2, -48(%rsp)
+        movsd     -40(%rsp), %xmm6
+        movsd     -48(%rsp), %xmm3
+        movaps    %xmm3, %xmm4
+        addsd     %xmm5, %xmm4
+
+.LBL_2_13:
+
+        addsd     %xmm4, %xmm6
+        movsd     %xmm6, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        ucomisd   %xmm0, %xmm1
+        jp        .LBL_2_15
+        je        .LBL_2_19
+
+.LBL_2_15:
+
+        xorps     .L_2il0floatpacket.201(%rip), %xmm0
+        ucomisd   %xmm0, %xmm1
+        jp        .LBL_2_16
+        je        .LBL_2_18
+
+.LBL_2_16:
+
+        movl      $1, %eax
+        pxor      %xmm1, %xmm1
+        pxor      %xmm0, %xmm0
+        divsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+
+.LBL_2_17:
+
+        ret
+
+.LBL_2_18:
+
+        movsd     4152+_vmldACosHATab(%rip), %xmm0
+        addsd     4160+_vmldACosHATab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_19:
+
+        movq      4184+_vmldACosHATab(%rip), %rdx
+        movq      %rdx, (%rsi)
+        ret
+
+.LBL_2_20:
+
+        divsd     %xmm1, %xmm1
+        movsd     %xmm1, (%rsi)
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_17
+
+
+        cmpl      $0, (%rdi)
+        sete      %al
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dacos_ha_cout_rare_internal,@function
+	.size	__svml_dacos_ha_cout_rare_internal,.-__svml_dacos_ha_cout_rare_internal
+..LN__svml_dacos_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dacos_ha_data_internal:
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	856972295
+	.long	1017226790
+	.long	856972295
+	.long	1017226790
+	.long	856972295
+	.long	1017226790
+	.long	856972295
+	.long	1017226790
+	.long	856972295
+	.long	1017226790
+	.long	856972295
+	.long	1017226790
+	.long	856972295
+	.long	1017226790
+	.long	856972295
+	.long	1017226790
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	3936260738
+	.long	1066197319
+	.long	3936260738
+	.long	1066197319
+	.long	3936260738
+	.long	1066197319
+	.long	3936260738
+	.long	1066197319
+	.long	3936260738
+	.long	1066197319
+	.long	3936260738
+	.long	1066197319
+	.long	3936260738
+	.long	1066197319
+	.long	3936260738
+	.long	1066197319
+	.long	354394453
+	.long	1067472564
+	.long	354394453
+	.long	1067472564
+	.long	354394453
+	.long	1067472564
+	.long	354394453
+	.long	1067472564
+	.long	354394453
+	.long	1067472564
+	.long	354394453
+	.long	1067472564
+	.long	354394453
+	.long	1067472564
+	.long	354394453
+	.long	1067472564
+	.long	709676628
+	.long	1067895021
+	.long	709676628
+	.long	1067895021
+	.long	709676628
+	.long	1067895021
+	.long	709676628
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+	.long	1071896576
+	.long	0
+	.long	1071894528
+	.long	0
+	.long	1071892480
+	.long	0
+	.long	1071890432
+	.long	0
+	.long	1071888384
+	.long	0
+	.long	1071886336
+	.long	0
+	.long	1071884288
+	.long	0
+	.long	1071883264
+	.long	0
+	.long	1071881216
+	.long	0
+	.long	1071879168
+	.long	0
+	.long	1071877120
+	.long	0
+	.long	1071875072
+	.long	0
+	.long	1071873024
+	.long	0
+	.long	1071872000
+	.long	0
+	.long	1071869952
+	.long	0
+	.long	1071867904
+	.long	0
+	.long	1071865856
+	.long	0
+	.long	1071864832
+	.long	0
+	.long	1071862784
+	.long	0
+	.long	1071860736
+	.long	0
+	.long	1071858688
+	.long	0
+	.long	1071856640
+	.long	0
+	.long	1071855616
+	.long	0
+	.long	1071853568
+	.long	0
+	.long	1071851520
+	.long	0
+	.long	1071850496
+	.long	0
+	.long	1071848448
+	.long	0
+	.long	1071846400
+	.long	0
+	.long	1071844352
+	.long	0
+	.long	1071843328
+	.long	0
+	.long	1071841280
+	.long	0
+	.long	1071839232
+	.long	0
+	.long	1071838208
+	.long	0
+	.long	1071836160
+	.long	0
+	.long	1071834112
+	.long	0
+	.long	1071833088
+	.long	0
+	.long	1071831040
+	.long	0
+	.long	1071830016
+	.long	0
+	.long	1071827968
+	.long	0
+	.long	1071825920
+	.long	0
+	.long	1071824896
+	.long	0
+	.long	1071822848
+	.long	0
+	.long	1071821824
+	.long	0
+	.long	1071819776
+	.long	0
+	.long	1071817728
+	.long	0
+	.long	1071816704
+	.long	0
+	.long	1071814656
+	.long	0
+	.long	1071813632
+	.long	0
+	.long	1071811584
+	.long	0
+	.long	1071810560
+	.long	0
+	.long	1071808512
+	.long	0
+	.long	1071806464
+	.long	0
+	.long	1071805440
+	.long	0
+	.long	1071803392
+	.long	0
+	.long	1071802368
+	.long	0
+	.long	1071800320
+	.long	0
+	.long	1071799296
+	.long	0
+	.long	1071797248
+	.long	0
+	.long	1071796224
+	.long	0
+	.long	1071794176
+	.long	0
+	.long	1071793152
+	.long	0
+	.long	1071791104
+	.long	0
+	.long	1071790080
+	.long	0
+	.long	1071788032
+	.long	0
+	.long	1071787008
+	.long	0
+	.long	1071784960
+	.long	0
+	.long	1071783936
+	.long	0
+	.long	1071782912
+	.long	0
+	.long	1071780864
+	.long	0
+	.long	1071779840
+	.long	0
+	.long	1071777792
+	.long	0
+	.long	1071776768
+	.long	0
+	.long	1071774720
+	.long	0
+	.long	1071773696
+	.long	0
+	.long	1071772672
+	.long	0
+	.long	1071770624
+	.long	0
+	.long	1071769600
+	.long	0
+	.long	1071767552
+	.long	0
+	.long	1071766528
+	.long	0
+	.long	1071765504
+	.long	0
+	.long	1071763456
+	.long	0
+	.long	1071762432
+	.long	0
+	.long	1071760384
+	.long	0
+	.long	1071759360
+	.long	0
+	.long	1071758336
+	.long	0
+	.long	1071756288
+	.long	0
+	.long	1071755264
+	.long	0
+	.long	1071754240
+	.long	0
+	.long	1071752192
+	.long	0
+	.long	1071751168
+	.long	0
+	.long	1071750144
+	.long	0
+	.long	1071748096
+	.long	0
+	.long	1071747072
+	.long	0
+	.long	1071746048
+	.long	0
+	.long	1071744000
+	.long	0
+	.long	1071742976
+	.long	0
+	.long	1071741952
+	.long	0
+	.long	1071739904
+	.long	0
+	.long	1071738880
+	.long	0
+	.long	1071737856
+	.long	0
+	.long	1071736832
+	.long	0
+	.long	1071734784
+	.long	0
+	.long	1071733760
+	.long	0
+	.long	1071732736
+	.long	0
+	.long	1071730688
+	.long	0
+	.long	1071729664
+	.long	0
+	.long	1071728640
+	.long	0
+	.long	1071727616
+	.long	0
+	.long	1071725568
+	.long	0
+	.long	1071724544
+	.long	0
+	.long	1071723520
+	.long	0
+	.long	1071722496
+	.long	0
+	.long	1071720448
+	.long	0
+	.long	1071719424
+	.long	0
+	.long	1071718400
+	.long	0
+	.long	1071717376
+	.long	0
+	.long	1071715328
+	.long	0
+	.long	1071714304
+	.long	0
+	.long	1071713280
+	.long	0
+	.long	1071712256
+	.long	0
+	.long	1071711232
+	.long	0
+	.long	1071709184
+	.long	0
+	.long	1071708160
+	.long	0
+	.long	1071707136
+	.long	0
+	.long	1071706112
+	.long	0
+	.long	1071705088
+	.long	0
+	.long	1071704064
+	.long	0
+	.long	1071702016
+	.long	0
+	.long	1071700992
+	.long	0
+	.long	1071699968
+	.long	0
+	.long	1071698944
+	.long	0
+	.long	1071697920
+	.long	0
+	.long	1071696896
+	.long	0
+	.long	1071694848
+	.long	0
+	.long	1071693824
+	.long	0
+	.long	1071692800
+	.long	0
+	.long	1071691776
+	.long	0
+	.long	1071690752
+	.long	0
+	.long	1071689728
+	.long	0
+	.long	1071688704
+	.long	0
+	.long	1071686656
+	.long	0
+	.long	1071685632
+	.long	0
+	.long	1071684608
+	.long	0
+	.long	1071683584
+	.long	0
+	.long	1071682560
+	.long	0
+	.long	1071681536
+	.long	0
+	.long	1071680512
+	.long	0
+	.long	1071679488
+	.long	0
+	.long	1071677440
+	.long	0
+	.long	1071676416
+	.long	0
+	.long	1071675392
+	.long	0
+	.long	1071674368
+	.long	0
+	.long	1071673344
+	.long	0
+	.long	1071672320
+	.long	0
+	.long	1071671296
+	.long	0
+	.long	1071670272
+	.long	0
+	.long	1071669248
+	.long	0
+	.long	1071668224
+	.long	0
+	.long	1071667200
+	.long	0
+	.long	1071666176
+	.long	0
+	.long	1071665152
+	.long	0
+	.long	1071663104
+	.long	0
+	.long	1071662080
+	.long	0
+	.long	1071661056
+	.long	0
+	.long	1071660032
+	.long	0
+	.long	1071659008
+	.long	0
+	.long	1071657984
+	.long	0
+	.long	1071656960
+	.long	0
+	.long	1071655936
+	.long	0
+	.long	1071654912
+	.long	0
+	.long	1071653888
+	.long	0
+	.long	1071652864
+	.long	0
+	.long	1071651840
+	.long	0
+	.long	1071650816
+	.long	0
+	.long	1071649792
+	.long	0
+	.long	1071648768
+	.long	0
+	.long	1071647744
+	.long	0
+	.long	1071646720
+	.long	0
+	.long	1071645696
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1101004800
+	.long	1073741824
+	.long	1095761920
+	.long	256
+	.long	1118830592
+	.long	0
+	.long	1017118720
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	1413754136
+	.long	1074340347
+	.long	856972295
+	.long	1017226790
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	0
+	.long	1476395008
+	.long	1069897045
+	.long	1768958041
+	.long	3189069141
+	.long	805306368
+	.long	1068708659
+	.long	3580333578
+	.long	1040816593
+	.long	3067382784
+	.long	1067899757
+	.long	3397590151
+	.long	1067392113
+	.long	2939529726
+	.long	1066854585
+	.long	1423429166
+	.long	1066517752
+	.long	1775218934
+	.long	1066178574
+	.long	1185392460
+	.long	1065859647
+	.long	289998670
+	.long	1065577550
+	.long	3179807072
+	.long	1065648121
+	.long	3781007284
+	.long	1061576176
+	.long	2482106687
+	.long	1067019199
+	.long	763519713
+	.long	3214591591
+	.long	3695107454
+	.long	1067530646
+	.long	0
+	.long	1073741824
+	.long	1124791109
+	.long	1006764147
+	.long	1476395008
+	.long	1069897045
+	.long	1953913876
+	.long	3189069141
+	.long	805306368
+	.long	1067660083
+	.long	165110192
+	.long	1039768033
+	.long	3067304082
+	.long	1065802605
+	.long	3404727379
+	.long	1064246385
+	.long	2737480376
+	.long	1062660281
+	.long	933797922
+	.long	1061274873
+	.long	1475716730
+	.long	1059887095
+	.long	1511619763
+	.long	1058519827
+	.long	556024211
+	.long	1057187555
+	.long	3482101045
+	.long	1056217350
+	.long	1174622859
+	.long	1050762633
+	.long	899668651
+	.long	1055506366
+	.long	1081094694
+	.long	3202035365
+	.long	2559814773
+	.long	1053906576
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	3218341888
+	.long	0
+	.long	1070694400
+	.long	0
+	.long	3218046976
+	.long	0
+	.long	1070391296
+	.long	0
+	.long	3217739776
+	.type	_vmldACosHATab,@object
+	.size	_vmldACosHATab,4520
+	.space 88, 0x00 	
+	.align 16
+.L_2il0floatpacket.201:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.201,@object
+	.size	.L_2il0floatpacket.201,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_d_la.s
index 993c91f507d..758149d1aef 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_h_la.s
new file mode 100644
index 00000000000..9448d928886
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_h_la.s
@@ -0,0 +1,146 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *      SelMask = (|x| >= 0.5) ? 1 : 0;
+ *      R = SelMask ? sqrt(0.5 - 0.5*|x|) : |x|
+ *      acos(|x|) = SelMask ? 2*Poly(R) : (Pi/2 - Poly(R))
+ *      acos(x) = sign(x) ? (Pi - acos(|x|)) : acos(|x|)
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_acoss32
+
+__svml_acoss32:
+
+        .cfi_startproc
+
+        vmovdqu16 __svml_hacos_data_internal(%rip), %zmm30
+        vmovdqu16 64+__svml_hacos_data_internal(%rip), %zmm1
+        vmovdqu16 128+__svml_hacos_data_internal(%rip), %zmm31
+        vmovdqu16 192+__svml_hacos_data_internal(%rip), %zmm5
+        vmovdqu16 256+__svml_hacos_data_internal(%rip), %zmm29
+        vmovdqu16 320+__svml_hacos_data_internal(%rip), %zmm7
+        vmovdqu16 384+__svml_hacos_data_internal(%rip), %zmm9
+        vmovdqu16 448+__svml_hacos_data_internal(%rip), %zmm11
+
+        kxnord  %k7, %k7, %k7
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_acos_h:
+        cmpq    $31, %rdx
+        ja .loaddata
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_acos_h
+.loaddata:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_acos_h:
+
+/* restore over-written constants */
+        vmovdqu16 %zmm31, %zmm3
+
+/* x^2 */
+        vmulph    {rn-sae}, %zmm0, %zmm0, %zmm2
+
+/* y = 0.5 -0.5*|x| */
+        vmovaps   %zmm1, %zmm10
+
+/*
+ * No callout
+ * xa = |x|
+ */
+        vpandd  %zmm30, %zmm0, %zmm12
+        vfnmadd231ph {rn-sae}, %zmm12, %zmm1, %zmm10
+
+/* SelMask=1 for |x|>=0.5 */
+        vcmpph    $21, {sae}, %zmm1, %zmm12, %k2
+
+/* set y = y*rsqrt(y) ~ sqrt(y) */
+        vrsqrtph  %zmm10, %zmm6
+
+/* SqrtMask=0 if y==+/-0 */
+        vcmpph    $4, {sae}, %zmm5, %zmm10, %k1
+
+/* set x2=y for |x|>=0.5 */
+        vminph    {sae}, %zmm10, %zmm2, %zmm8
+        vmulph    {rn-sae}, %zmm6, %zmm10, %zmm10{%k1}
+
+/* sign(x) */
+        vpxord    %zmm0, %zmm12, %zmm13
+
+/* polynomial */
+        vmovdqu16 %zmm29, %zmm0
+        vmulph    {rn-sae}, %zmm11, %zmm10, %zmm12{%k2}
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm7, %zmm8, %zmm0
+        vfmadd213ph {rn-sae}, %zmm9, %zmm8, %zmm0
+        vpxord    %zmm3, %zmm13, %zmm4
+        vsubph    {rn-sae}, %zmm4, %zmm3, %zmm3{%k2}
+        vpxord    %zmm13, %zmm12, %zmm14
+
+/* result */
+        vfnmadd213ph {rn-sae}, %zmm3, %zmm14, %zmm0
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_acos_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_acoss32,@function
+        .size	__svml_acoss32,.-__svml_acoss32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hacos_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x3800
+	.endr
+	.rept	32
+        .word	0x3e48
+	.endr
+	.rept	32
+        .word	0x0000
+	.endr
+	.rept	32
+        .word	0x2e26
+	.endr
+	.rept	32
+        .word	0x3144
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0xc000
+	.endr
+        .type	__svml_hacos_data_internal,@object
+        .size	__svml_hacos_data_internal,512
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_s_ha.s
new file mode 100644
index 00000000000..a9a8b22c94a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_s_ha.s
@@ -0,0 +1,2324 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_acosf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_acosf16_ha
+
+__svml_acosf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   __svml_sacos_ha_data_internal(%rip), %zmm7
+        vmovups   64+__svml_sacos_ha_data_internal(%rip), %zmm8
+
+/*
+ * 2*sqrt(Y) = Sh - Sl
+ * Sh ~ 2*sqrt_high(Y)
+ * Sl ~ -2*sqrt_low(Y)
+ */
+        vmovups   192+__svml_sacos_ha_data_internal(%rip), %zmm12
+        vmovups   448+__svml_sacos_ha_data_internal(%rip), %zmm1
+        vmovups   256+__svml_sacos_ha_data_internal(%rip), %zmm10
+        vmovaps   %zmm0, %zmm6
+
+/* x = -|arg| */
+        vorps     %zmm6, %zmm7, %zmm5
+        vandps    %zmm6, %zmm7, %zmm4
+
+/* Y = 0.5 + 0.5*(-x) */
+        vfmadd231ps {rn-sae}, %zmm5, %zmm8, %zmm8
+
+/* x^2 */
+        vmulps    {rn-sae}, %zmm5, %zmm5, %zmm9
+        vrsqrt14ps %zmm8, %zmm11
+        vcmpps    $17, {sae}, %zmm12, %zmm8, %k2
+        vcmpps    $22, {sae}, %zmm5, %zmm10, %k0
+        vminps    {sae}, %zmm8, %zmm9, %zmm3
+        vaddps    {rn-sae}, %zmm8, %zmm8, %zmm13
+        vandps    384+__svml_sacos_ha_data_internal(%rip), %zmm11, %zmm0
+        vcmpps    $21, {sae}, %zmm8, %zmm3, %k1
+
+/* X<X^2 iff X<0 */
+        vcmpps    $21, {sae}, %zmm6, %zmm3, %k3
+        vmovups   512+__svml_sacos_ha_data_internal(%rip), %zmm8
+        vandps    320+__svml_sacos_ha_data_internal(%rip), %zmm13, %zmm14
+        vxorps    %zmm0, %zmm0, %zmm0{%k2}
+        kmovw     %k1, %eax
+        kmovw     %k3, %ecx
+        kmovw     %k0, %edx
+        vsubps    {rn-sae}, %zmm14, %zmm13, %zmm15
+        vmulps    {rn-sae}, %zmm0, %zmm14, %zmm2
+        vmulps    {rn-sae}, %zmm0, %zmm15, %zmm12
+        vmulps    {rn-sae}, %zmm3, %zmm3, %zmm15
+
+/* polynomial */
+        vmovups   640+__svml_sacos_ha_data_internal(%rip), %zmm13
+        vfmsub231ps {rn-sae}, %zmm2, %zmm0, %zmm1
+        vmovups   768+__svml_sacos_ha_data_internal(%rip), %zmm14
+        vmulps    {rn-sae}, %zmm0, %zmm12, %zmm7
+        vmovups   832+__svml_sacos_ha_data_internal(%rip), %zmm0
+        vaddps    {rn-sae}, %zmm12, %zmm2, %zmm9
+        vaddps    {rn-sae}, %zmm7, %zmm1, %zmm10
+        vmovups   704+__svml_sacos_ha_data_internal(%rip), %zmm7
+        vfmadd231ps {rn-sae}, %zmm3, %zmm14, %zmm0
+        vmovups   576+__svml_sacos_ha_data_internal(%rip), %zmm1
+        vmulps    {rn-sae}, %zmm10, %zmm9, %zmm11
+        vfmadd231ps {rn-sae}, %zmm3, %zmm13, %zmm7
+        vmovups   896+__svml_sacos_ha_data_internal(%rip), %zmm13
+        vfmadd231ps {rn-sae}, %zmm10, %zmm8, %zmm1
+        vmovups   1216+__svml_sacos_ha_data_internal(%rip), %zmm8
+        vfmadd213ps {rn-sae}, %zmm0, %zmm15, %zmm7
+        vfmsub213ps {rn-sae}, %zmm12, %zmm11, %zmm1
+        vfmadd213ps {rn-sae}, %zmm13, %zmm3, %zmm7
+        vblendmps %zmm1, %zmm8, %zmm1{%k1}
+        vmulps    {rn-sae}, %zmm3, %zmm7, %zmm0
+        vmovups   1024+__svml_sacos_ha_data_internal(%rip), %zmm3
+        vmovups   960+__svml_sacos_ha_data_internal(%rip), %zmm7
+        vxorps    %zmm3, %zmm3, %zmm3{%k1}
+        vxorps    %zmm7, %zmm7, %zmm7{%k1}
+        andl      %eax, %ecx
+        kmovw     %ecx, %k2
+        vorps     1152+__svml_sacos_ha_data_internal(%rip), %zmm3, %zmm3{%k2}
+        vorps     1088+__svml_sacos_ha_data_internal(%rip), %zmm7, %zmm7{%k2}
+        vxorps    %zmm4, %zmm3, %zmm9
+        vblendmps %zmm2, %zmm5, %zmm3{%k1}
+        vsubps    {rn-sae}, %zmm1, %zmm9, %zmm5
+
+/* 2*sqrt(Y) */
+        vsubps    {rn-sae}, %zmm1, %zmm3, %zmm2
+        vfmadd213ps {rn-sae}, %zmm5, %zmm2, %zmm0
+        vaddps    {rn-sae}, %zmm3, %zmm0, %zmm0
+        vxorps    %zmm4, %zmm0, %zmm4
+        vaddps    {rn-sae}, %zmm7, %zmm4, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_sacos_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_acosf16_ha,@function
+	.size	__svml_acosf16_ha,.-__svml_acosf16_ha
+..LN__svml_acosf16_ha.0:
+
+.L_2__routine_start___svml_sacos_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_sacos_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    2(%rdi), %edx
+        andl      $32640, %edx
+        movss     (%rdi), %xmm1
+        cmpl      $32640, %edx
+        je        .LBL_2_20
+
+
+        cvtss2sd  %xmm1, %xmm1
+        movsd     %xmm1, -32(%rsp)
+        andb      $127, -25(%rsp)
+        movsd     -32(%rsp), %xmm12
+        movsd     4168+_vmldACosHATab(%rip), %xmm0
+        comisd    %xmm12, %xmm0
+        jbe       .LBL_2_14
+
+
+        movsd     4176+_vmldACosHATab(%rip), %xmm1
+        comisd    %xmm12, %xmm1
+        jbe       .LBL_2_10
+
+
+        comisd    4128+_vmldACosHATab(%rip), %xmm12
+        jbe       .LBL_2_9
+
+
+        movsd     4104+_vmldACosHATab(%rip), %xmm6
+        movaps    %xmm12, %xmm8
+        mulsd     %xmm12, %xmm6
+        movaps    %xmm12, %xmm7
+        movsd     %xmm6, -40(%rsp)
+        movsd     -40(%rsp), %xmm13
+        movsd     4104+_vmldACosHATab(%rip), %xmm5
+        subsd     -32(%rsp), %xmm13
+        movsd     %xmm13, -48(%rsp)
+        movsd     -40(%rsp), %xmm15
+        movsd     -48(%rsp), %xmm14
+        subsd     %xmm14, %xmm15
+        movaps    %xmm12, %xmm14
+        movsd     %xmm15, -40(%rsp)
+        movsd     -40(%rsp), %xmm6
+        subsd     %xmm6, %xmm8
+        movsd     %xmm8, -48(%rsp)
+        movsd     -40(%rsp), %xmm9
+        movaps    %xmm9, %xmm4
+        addsd     %xmm9, %xmm7
+        mulsd     %xmm9, %xmm4
+        movsd     -48(%rsp), %xmm10
+        movaps    %xmm4, %xmm11
+        mulsd     %xmm10, %xmm7
+        mulsd     %xmm4, %xmm5
+        addsd     %xmm7, %xmm11
+        movsd     4312+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm5, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4104+_vmldACosHATab(%rip), %xmm5
+        subsd     %xmm4, %xmm1
+        addsd     4304+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm1, -48(%rsp)
+        movsd     -40(%rsp), %xmm0
+        movsd     -48(%rsp), %xmm2
+        addsd     4296+_vmldACosHATab(%rip), %xmm8
+        subsd     %xmm2, %xmm0
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm3
+        addsd     4288+_vmldACosHATab(%rip), %xmm8
+        subsd     %xmm3, %xmm4
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm4, -48(%rsp)
+        movsd     -40(%rsp), %xmm6
+        mulsd     %xmm6, %xmm9
+        addsd     4280+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm6, %xmm10
+        mulsd     %xmm11, %xmm8
+        mulsd     %xmm9, %xmm5
+        addsd     4272+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm11, %xmm8
+        movaps    %xmm9, %xmm0
+        movsd     -48(%rsp), %xmm13
+        movaps    %xmm6, %xmm4
+        movsd     %xmm5, -40(%rsp)
+        addsd     %xmm13, %xmm7
+        addsd     4264+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm7, %xmm14
+        mulsd     %xmm11, %xmm8
+        addsd     %xmm14, %xmm10
+        addsd     4256+_vmldACosHATab(%rip), %xmm8
+        movsd     -40(%rsp), %xmm1
+        mulsd     %xmm11, %xmm8
+        subsd     %xmm9, %xmm1
+        addsd     %xmm10, %xmm9
+        addsd     4248+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm9, %xmm7
+        mulsd     %xmm11, %xmm8
+        movsd     %xmm1, -48(%rsp)
+        movsd     -40(%rsp), %xmm2
+        movsd     -48(%rsp), %xmm15
+        subsd     %xmm15, %xmm2
+        addsd     4240+_vmldACosHATab(%rip), %xmm8
+        movsd     %xmm2, -40(%rsp)
+        movsd     -40(%rsp), %xmm5
+        mulsd     %xmm11, %xmm8
+        subsd     %xmm5, %xmm0
+        movsd     %xmm0, -48(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movsd     -48(%rsp), %xmm5
+        movaps    %xmm5, %xmm13
+        addsd     4232+_vmldACosHATab(%rip), %xmm8
+        mulsd     %xmm3, %xmm4
+        addsd     %xmm10, %xmm13
+        mulsd     %xmm11, %xmm8
+        mulsd     %xmm13, %xmm6
+        addsd     4224+_vmldACosHATab(%rip), %xmm8
+        addsd     %xmm7, %xmm6
+        mulsd     %xmm11, %xmm8
+        movsd     4104+_vmldACosHATab(%rip), %xmm7
+        movaps    %xmm4, %xmm13
+        mulsd     %xmm4, %xmm7
+        addsd     4216+_vmldACosHATab(%rip), %xmm8
+        movsd     %xmm7, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4200+_vmldACosHATab(%rip), %xmm11
+        subsd     %xmm4, %xmm1
+        mulsd     %xmm9, %xmm11
+        addsd     %xmm6, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        movaps    %xmm12, %xmm9
+        movsd     -40(%rsp), %xmm0
+        movsd     -48(%rsp), %xmm2
+        movsd     4192+_vmldACosHATab(%rip), %xmm1
+        subsd     %xmm2, %xmm0
+        mulsd     %xmm1, %xmm3
+        mulsd     %xmm1, %xmm10
+        mulsd     %xmm4, %xmm8
+        addsd     %xmm3, %xmm9
+        mulsd     %xmm1, %xmm5
+        addsd     %xmm10, %xmm11
+        movsd     %xmm0, -40(%rsp)
+        pxor      %xmm1, %xmm1
+        movsd     -40(%rsp), %xmm7
+        addsd     %xmm11, %xmm8
+        subsd     %xmm7, %xmm13
+        addsd     %xmm5, %xmm8
+        movsd     %xmm13, -48(%rsp)
+        movsd     -40(%rsp), %xmm0
+        movsd     -48(%rsp), %xmm2
+        movsd     %xmm9, -40(%rsp)
+        addsd     %xmm2, %xmm6
+        movsd     -40(%rsp), %xmm10
+        movsd     %xmm3, -56(%rsp)
+        subsd     %xmm10, %xmm12
+        movsd     4208+_vmldACosHATab(%rip), %xmm4
+        addsd     %xmm12, %xmm3
+        mulsd     %xmm4, %xmm0
+        mulsd     %xmm4, %xmm6
+        movsd     %xmm3, -48(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movaps    %xmm3, %xmm12
+        movsd     -48(%rsp), %xmm7
+        addsd     %xmm0, %xmm12
+        addsd     %xmm7, %xmm8
+        movsd     %xmm12, -40(%rsp)
+        movsd     -40(%rsp), %xmm12
+        subsd     %xmm12, %xmm3
+        addsd     %xmm3, %xmm0
+        movsd     %xmm0, -48(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movsd     -48(%rsp), %xmm0
+        cvtss2sd  (%rdi), %xmm1
+        addsd     %xmm8, %xmm0
+        comisd    4184+_vmldACosHATab(%rip), %xmm1
+        addsd     %xmm0, %xmm6
+        jbe       .LBL_2_7
+
+
+        movsd     4136+_vmldACosHATab(%rip), %xmm2
+        movaps    %xmm2, %xmm0
+        subsd     %xmm3, %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4144+_vmldACosHATab(%rip), %xmm0
+        subsd     %xmm1, %xmm2
+        subsd     %xmm6, %xmm0
+        subsd     %xmm3, %xmm2
+        movsd     %xmm2, -48(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     -48(%rsp), %xmm3
+        addsd     %xmm3, %xmm0
+        jmp       .LBL_2_8
+
+.LBL_2_7:
+
+        movsd     4136+_vmldACosHATab(%rip), %xmm2
+        movaps    %xmm3, %xmm0
+        addsd     %xmm2, %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        subsd     %xmm1, %xmm2
+        addsd     %xmm2, %xmm3
+        movsd     %xmm3, -48(%rsp)
+        movsd     4144+_vmldACosHATab(%rip), %xmm3
+        movsd     -40(%rsp), %xmm1
+        addsd     %xmm3, %xmm6
+        movsd     -48(%rsp), %xmm0
+        addsd     %xmm6, %xmm0
+
+.LBL_2_8:
+
+        addsd     %xmm0, %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        movsd     4144+_vmldACosHATab(%rip), %xmm1
+        addsd     %xmm0, %xmm12
+        movsd     %xmm12, -40(%rsp)
+        movsd     -40(%rsp), %xmm0
+        mulsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -56(%rsp)
+        movb      -49(%rsp), %dl
+        movb      3(%rdi), %dil
+        andb      $127, %dl
+        andb      $-128, %dil
+        orb       %dil, %dl
+        movb      %dl, -49(%rsp)
+        subsd     -56(%rsp), %xmm1
+        addsd     4136+_vmldACosHATab(%rip), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        movaps    %xmm0, %xmm8
+        movzwl    4174+_vmldACosHATab(%rip), %r9d
+        pxor      %xmm13, %xmm13
+        andl      $-32753, %r9d
+        subsd     %xmm12, %xmm8
+        subsd     %xmm0, %xmm13
+        mulsd     %xmm8, %xmm1
+        movsd     %xmm1, -56(%rsp)
+        movzwl    -50(%rsp), %ecx
+        andl      $32752, %ecx
+        shrl      $4, %ecx
+        addl      $-1023, %ecx
+        movl      %ecx, %r8d
+        movl      %ecx, %edx
+        negl      %r8d
+        addl      $1023, %r8d
+        andl      $2047, %r8d
+        shll      $4, %r8d
+        movsd     %xmm0, -32(%rsp)
+        orl       %r8d, %r9d
+        movw      %r9w, -26(%rsp)
+        andl      $1, %edx
+        movsd     -32(%rsp), %xmm4
+        lea       _vmldACosHATab(%rip), %r8
+        mulsd     %xmm4, %xmm1
+        movl      %edx, %r10d
+        movaps    %xmm1, %xmm15
+        movsd     4112+_vmldACosHATab(%rip), %xmm6
+        addsd     %xmm1, %xmm15
+        jne       ..L54
+        movaps    %xmm1, %xmm15
+..L54:
+        mulsd     %xmm15, %xmm6
+        movaps    %xmm15, %xmm7
+        movaps    %xmm6, %xmm9
+        subl      %edx, %ecx
+        movsd     4120+_vmldACosHATab(%rip), %xmm11
+        subsd     %xmm15, %xmm9
+        addsd     %xmm1, %xmm11
+        movsd     %xmm9, -48(%rsp)
+        movsd     -48(%rsp), %xmm10
+        movsd     %xmm11, -24(%rsp)
+        subsd     %xmm10, %xmm6
+        movl      -24(%rsp), %r11d
+        movaps    %xmm6, %xmm14
+        shll      $8, %r10d
+        andl      $511, %r11d
+        addl      %r10d, %r11d
+        subsd     %xmm6, %xmm7
+        movsd     (%r8,%r11,8), %xmm5
+        addsd     %xmm7, %xmm14
+        mulsd     %xmm5, %xmm6
+        movaps    %xmm5, %xmm12
+        mulsd     %xmm5, %xmm12
+        mulsd     %xmm12, %xmm14
+        movsd     4512+_vmldACosHATab(%rip), %xmm4
+        addsd     %xmm13, %xmm14
+        mulsd     %xmm14, %xmm4
+        shrl      $1, %ecx
+        addsd     4504+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        addl      $1023, %ecx
+        andl      $2047, %ecx
+        addsd     4496+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        movzwl    4174+_vmldACosHATab(%rip), %r9d
+        shll      $4, %ecx
+        andl      $-32753, %r9d
+        movsd     %xmm0, -16(%rsp)
+        orl       %ecx, %r9d
+        movw      %r9w, -10(%rsp)
+        movsd     -16(%rsp), %xmm9
+        mulsd     %xmm9, %xmm6
+        addsd     4488+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        movsd     4104+_vmldACosHATab(%rip), %xmm3
+        mulsd     %xmm6, %xmm3
+        addsd     4480+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        movsd     %xmm3, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     4352+_vmldACosHATab(%rip), %xmm11
+        subsd     %xmm6, %xmm1
+        addsd     4472+_vmldACosHATab(%rip), %xmm4
+        addsd     4360+_vmldACosHATab(%rip), %xmm11
+        mulsd     %xmm14, %xmm4
+        mulsd     %xmm8, %xmm11
+        addsd     4464+_vmldACosHATab(%rip), %xmm4
+        mulsd     %xmm14, %xmm4
+        mulsd     %xmm15, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        addsd     %xmm4, %xmm7
+        mulsd     %xmm5, %xmm7
+        movsd     4456+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm9, %xmm7
+        addsd     4448+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movsd     -40(%rsp), %xmm2
+        movsd     -48(%rsp), %xmm0
+        movsd     4104+_vmldACosHATab(%rip), %xmm4
+        subsd     %xmm0, %xmm2
+        mulsd     %xmm8, %xmm4
+        addsd     4440+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movaps    %xmm8, %xmm0
+        movsd     %xmm2, -40(%rsp)
+        movsd     -40(%rsp), %xmm14
+        movsd     4104+_vmldACosHATab(%rip), %xmm2
+        subsd     %xmm14, %xmm6
+        addsd     4432+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movsd     %xmm6, -48(%rsp)
+        movsd     -40(%rsp), %xmm6
+        movsd     -48(%rsp), %xmm10
+        movsd     %xmm4, -40(%rsp)
+        addsd     %xmm10, %xmm7
+        addsd     4424+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm5
+        movsd     -40(%rsp), %xmm3
+        movsd     4336+_vmldACosHATab(%rip), %xmm12
+        subsd     %xmm8, %xmm3
+        addsd     4416+_vmldACosHATab(%rip), %xmm5
+        addsd     4344+_vmldACosHATab(%rip), %xmm12
+        mulsd     %xmm8, %xmm5
+        addsd     %xmm11, %xmm12
+        addsd     4408+_vmldACosHATab(%rip), %xmm5
+        mulsd     %xmm8, %xmm12
+        mulsd     %xmm8, %xmm5
+        movsd     %xmm3, -48(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     -48(%rsp), %xmm15
+        movsd     4320+_vmldACosHATab(%rip), %xmm13
+        subsd     %xmm15, %xmm1
+        addsd     4400+_vmldACosHATab(%rip), %xmm5
+        addsd     4328+_vmldACosHATab(%rip), %xmm13
+        mulsd     %xmm8, %xmm5
+        addsd     %xmm12, %xmm13
+        addsd     4392+_vmldACosHATab(%rip), %xmm5
+        movsd     %xmm1, -40(%rsp)
+        mulsd     %xmm8, %xmm5
+        movsd     -40(%rsp), %xmm4
+        subsd     %xmm4, %xmm0
+        addsd     4384+_vmldACosHATab(%rip), %xmm5
+        movsd     %xmm0, -48(%rsp)
+        movsd     -40(%rsp), %xmm4
+        movaps    %xmm4, %xmm14
+        mulsd     %xmm4, %xmm14
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm14, %xmm2
+        addsd     4376+_vmldACosHATab(%rip), %xmm5
+        movsd     -48(%rsp), %xmm3
+        movsd     %xmm2, -40(%rsp)
+        movsd     -40(%rsp), %xmm9
+        mulsd     %xmm8, %xmm5
+        subsd     %xmm14, %xmm9
+        movsd     %xmm9, -48(%rsp)
+        movsd     -40(%rsp), %xmm11
+        movsd     -48(%rsp), %xmm10
+        movsd     4336+_vmldACosHATab(%rip), %xmm0
+        subsd     %xmm10, %xmm11
+        mulsd     %xmm4, %xmm0
+        addsd     4368+_vmldACosHATab(%rip), %xmm5
+        addsd     %xmm8, %xmm4
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm3, %xmm4
+        mulsd     %xmm8, %xmm5
+        movsd     %xmm11, -40(%rsp)
+        movaps    %xmm0, %xmm1
+        movsd     -40(%rsp), %xmm12
+        mulsd     %xmm8, %xmm5
+        subsd     %xmm12, %xmm14
+        movsd     %xmm14, -48(%rsp)
+        addsd     %xmm5, %xmm13
+        movsd     -40(%rsp), %xmm9
+        mulsd     4352+_vmldACosHATab(%rip), %xmm9
+        mulsd     %xmm13, %xmm7
+        addsd     %xmm9, %xmm1
+        movsd     -48(%rsp), %xmm2
+        movsd     %xmm1, -40(%rsp)
+        addsd     %xmm2, %xmm4
+        movsd     -40(%rsp), %xmm13
+        movsd     %xmm9, -24(%rsp)
+        subsd     %xmm13, %xmm0
+        mulsd     4352+_vmldACosHATab(%rip), %xmm4
+        addsd     %xmm0, %xmm9
+        movsd     %xmm9, -48(%rsp)
+        movsd     -40(%rsp), %xmm12
+        movsd     4320+_vmldACosHATab(%rip), %xmm10
+        movsd     -48(%rsp), %xmm1
+        addsd     %xmm12, %xmm10
+        movsd     %xmm10, -40(%rsp)
+        movsd     -40(%rsp), %xmm15
+        movsd     4320+_vmldACosHATab(%rip), %xmm11
+        movsd     4104+_vmldACosHATab(%rip), %xmm9
+        subsd     %xmm15, %xmm11
+        movsd     4336+_vmldACosHATab(%rip), %xmm2
+        addsd     %xmm11, %xmm12
+        mulsd     %xmm3, %xmm2
+        movsd     %xmm12, -48(%rsp)
+        movsd     -40(%rsp), %xmm15
+        mulsd     %xmm15, %xmm9
+        movsd     -48(%rsp), %xmm0
+        movsd     %xmm9, -40(%rsp)
+        movsd     -40(%rsp), %xmm10
+        movsd     4360+_vmldACosHATab(%rip), %xmm3
+        subsd     %xmm15, %xmm10
+        mulsd     %xmm8, %xmm3
+        movsd     %xmm10, -48(%rsp)
+        movsd     -40(%rsp), %xmm11
+        movsd     -48(%rsp), %xmm13
+        subsd     %xmm13, %xmm11
+        addsd     4344+_vmldACosHATab(%rip), %xmm3
+        movsd     %xmm11, -40(%rsp)
+        movsd     -40(%rsp), %xmm14
+        mulsd     %xmm8, %xmm3
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -48(%rsp)
+        movsd     -40(%rsp), %xmm10
+        movsd     -48(%rsp), %xmm9
+        addsd     %xmm9, %xmm4
+        addsd     4328+_vmldACosHATab(%rip), %xmm3
+        addsd     %xmm2, %xmm4
+        addsd     %xmm5, %xmm3
+        addsd     %xmm1, %xmm4
+        addsd     %xmm0, %xmm4
+        addsd     %xmm3, %xmm4
+        mulsd     %xmm6, %xmm4
+        mulsd     %xmm10, %xmm6
+        addsd     %xmm7, %xmm4
+        pxor      %xmm7, %xmm7
+        cvtss2sd  (%rdi), %xmm7
+        comisd    4184+_vmldACosHATab(%rip), %xmm7
+        ja        .LBL_2_13
+
+
+        movsd     4152+_vmldACosHATab(%rip), %xmm2
+        movaps    %xmm2, %xmm0
+        movsd     4160+_vmldACosHATab(%rip), %xmm5
+        subsd     %xmm6, %xmm0
+        subsd     %xmm4, %xmm5
+        movsd     %xmm0, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        movsd     %xmm6, -56(%rsp)
+        subsd     %xmm1, %xmm2
+        subsd     %xmm6, %xmm2
+        movsd     %xmm2, -48(%rsp)
+        movsd     -40(%rsp), %xmm6
+        movsd     -48(%rsp), %xmm3
+        movaps    %xmm3, %xmm4
+        addsd     %xmm5, %xmm4
+
+.LBL_2_13:
+
+        addsd     %xmm4, %xmm6
+        cvtsd2ss  %xmm6, %xmm6
+        movss     %xmm6, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        ucomisd   %xmm0, %xmm1
+        jp        .LBL_2_15
+        je        .LBL_2_19
+
+.LBL_2_15:
+
+        xorps     .L_2il0floatpacket.204(%rip), %xmm0
+        ucomisd   %xmm0, %xmm1
+        jp        .LBL_2_16
+        je        .LBL_2_18
+
+.LBL_2_16:
+
+        movl      $1, %eax
+        pxor      %xmm1, %xmm1
+        pxor      %xmm0, %xmm0
+        divss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+
+
+        ret
+
+.LBL_2_18:
+
+        movsd     4152+_vmldACosHATab(%rip), %xmm0
+        addsd     4160+_vmldACosHATab(%rip), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_19:
+
+        movsd     4184+_vmldACosHATab(%rip), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_20:
+
+        divss     %xmm1, %xmm1
+        testl     $8388607, (%rdi)
+        movss     %xmm1, (%rsi)
+        sete      %al
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sacos_ha_cout_rare_internal,@function
+	.size	__svml_sacos_ha_cout_rare_internal,.-__svml_sacos_ha_cout_rare_internal
+..LN__svml_sacos_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_sacos_ha_data_internal:
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	3015425326
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
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+	.long	1071774720
+	.long	0
+	.long	1071773696
+	.long	0
+	.long	1071772672
+	.long	0
+	.long	1071770624
+	.long	0
+	.long	1071769600
+	.long	0
+	.long	1071767552
+	.long	0
+	.long	1071766528
+	.long	0
+	.long	1071765504
+	.long	0
+	.long	1071763456
+	.long	0
+	.long	1071762432
+	.long	0
+	.long	1071760384
+	.long	0
+	.long	1071759360
+	.long	0
+	.long	1071758336
+	.long	0
+	.long	1071756288
+	.long	0
+	.long	1071755264
+	.long	0
+	.long	1071754240
+	.long	0
+	.long	1071752192
+	.long	0
+	.long	1071751168
+	.long	0
+	.long	1071750144
+	.long	0
+	.long	1071748096
+	.long	0
+	.long	1071747072
+	.long	0
+	.long	1071746048
+	.long	0
+	.long	1071744000
+	.long	0
+	.long	1071742976
+	.long	0
+	.long	1071741952
+	.long	0
+	.long	1071739904
+	.long	0
+	.long	1071738880
+	.long	0
+	.long	1071737856
+	.long	0
+	.long	1071736832
+	.long	0
+	.long	1071734784
+	.long	0
+	.long	1071733760
+	.long	0
+	.long	1071732736
+	.long	0
+	.long	1071730688
+	.long	0
+	.long	1071729664
+	.long	0
+	.long	1071728640
+	.long	0
+	.long	1071727616
+	.long	0
+	.long	1071725568
+	.long	0
+	.long	1071724544
+	.long	0
+	.long	1071723520
+	.long	0
+	.long	1071722496
+	.long	0
+	.long	1071720448
+	.long	0
+	.long	1071719424
+	.long	0
+	.long	1071718400
+	.long	0
+	.long	1071717376
+	.long	0
+	.long	1071715328
+	.long	0
+	.long	1071714304
+	.long	0
+	.long	1071713280
+	.long	0
+	.long	1071712256
+	.long	0
+	.long	1071711232
+	.long	0
+	.long	1071709184
+	.long	0
+	.long	1071708160
+	.long	0
+	.long	1071707136
+	.long	0
+	.long	1071706112
+	.long	0
+	.long	1071705088
+	.long	0
+	.long	1071704064
+	.long	0
+	.long	1071702016
+	.long	0
+	.long	1071700992
+	.long	0
+	.long	1071699968
+	.long	0
+	.long	1071698944
+	.long	0
+	.long	1071697920
+	.long	0
+	.long	1071696896
+	.long	0
+	.long	1071694848
+	.long	0
+	.long	1071693824
+	.long	0
+	.long	1071692800
+	.long	0
+	.long	1071691776
+	.long	0
+	.long	1071690752
+	.long	0
+	.long	1071689728
+	.long	0
+	.long	1071688704
+	.long	0
+	.long	1071686656
+	.long	0
+	.long	1071685632
+	.long	0
+	.long	1071684608
+	.long	0
+	.long	1071683584
+	.long	0
+	.long	1071682560
+	.long	0
+	.long	1071681536
+	.long	0
+	.long	1071680512
+	.long	0
+	.long	1071679488
+	.long	0
+	.long	1071677440
+	.long	0
+	.long	1071676416
+	.long	0
+	.long	1071675392
+	.long	0
+	.long	1071674368
+	.long	0
+	.long	1071673344
+	.long	0
+	.long	1071672320
+	.long	0
+	.long	1071671296
+	.long	0
+	.long	1071670272
+	.long	0
+	.long	1071669248
+	.long	0
+	.long	1071668224
+	.long	0
+	.long	1071667200
+	.long	0
+	.long	1071666176
+	.long	0
+	.long	1071665152
+	.long	0
+	.long	1071663104
+	.long	0
+	.long	1071662080
+	.long	0
+	.long	1071661056
+	.long	0
+	.long	1071660032
+	.long	0
+	.long	1071659008
+	.long	0
+	.long	1071657984
+	.long	0
+	.long	1071656960
+	.long	0
+	.long	1071655936
+	.long	0
+	.long	1071654912
+	.long	0
+	.long	1071653888
+	.long	0
+	.long	1071652864
+	.long	0
+	.long	1071651840
+	.long	0
+	.long	1071650816
+	.long	0
+	.long	1071649792
+	.long	0
+	.long	1071648768
+	.long	0
+	.long	1071647744
+	.long	0
+	.long	1071646720
+	.long	0
+	.long	1071645696
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1101004800
+	.long	1073741824
+	.long	1095761920
+	.long	256
+	.long	1118830592
+	.long	0
+	.long	1017118720
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	1413754136
+	.long	1074340347
+	.long	856972295
+	.long	1017226790
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	0
+	.long	1476395008
+	.long	1069897045
+	.long	1768958041
+	.long	3189069141
+	.long	805306368
+	.long	1068708659
+	.long	3580333578
+	.long	1040816593
+	.long	3067382784
+	.long	1067899757
+	.long	3397590151
+	.long	1067392113
+	.long	2939529726
+	.long	1066854585
+	.long	1423429166
+	.long	1066517752
+	.long	1775218934
+	.long	1066178574
+	.long	1185392460
+	.long	1065859647
+	.long	289998670
+	.long	1065577550
+	.long	3179807072
+	.long	1065648121
+	.long	3781007284
+	.long	1061576176
+	.long	2482106687
+	.long	1067019199
+	.long	763519713
+	.long	3214591591
+	.long	3695107454
+	.long	1067530646
+	.long	0
+	.long	1073741824
+	.long	1124791109
+	.long	1006764147
+	.long	1476395008
+	.long	1069897045
+	.long	1953913876
+	.long	3189069141
+	.long	805306368
+	.long	1067660083
+	.long	165110192
+	.long	1039768033
+	.long	3067304082
+	.long	1065802605
+	.long	3404727379
+	.long	1064246385
+	.long	2737480376
+	.long	1062660281
+	.long	933797922
+	.long	1061274873
+	.long	1475716730
+	.long	1059887095
+	.long	1511619763
+	.long	1058519827
+	.long	556024211
+	.long	1057187555
+	.long	3482101045
+	.long	1056217350
+	.long	1174622859
+	.long	1050762633
+	.long	899668651
+	.long	1055506366
+	.long	1081094694
+	.long	3202035365
+	.long	2559814773
+	.long	1053906576
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	3218341888
+	.long	0
+	.long	1070694400
+	.long	0
+	.long	3218046976
+	.long	0
+	.long	1070391296
+	.long	0
+	.long	3217739776
+	.type	_vmldACosHATab,@object
+	.size	_vmldACosHATab,4520
+	.space 88, 0x00 	
+	.align 16
+.L_2il0floatpacket.204:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.204,@object
+	.size	.L_2il0floatpacket.204,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_s_la.s
index 0212411cb89..c3b9d0c56ed 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acos_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_d_ha.s
new file mode 100644
index 00000000000..686074a912b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_d_ha.s
@@ -0,0 +1,973 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *   Compute log(x+sqrt(x*x-1)) using RSQRT14/RSQRT28 for starting the
+ *  *   square root approximation, and small table lookups for log (mapping to
+ *  *   AVX3 permute instructions).
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_acosh8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_acosh8_ha
+
+__svml_acosh8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+
+/* x^2 */
+        vmulpd    {rn-sae}, %zmm0, %zmm0, %zmm1
+        vmovups   256+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm7
+
+/* polynomial computation for small inputs */
+        vmovups   576+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   640+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   704+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm9
+        vcmppd    $18, {sae}, %zmm7, %zmm0, %k1
+
+/* x^2 - 1 */
+        vsubpd    {rn-sae}, %zmm7, %zmm1, %zmm5
+        kmovw     %k1, %eax
+        vsubpd    {rn-sae}, %zmm5, %zmm1, %zmm2
+
+/* (x^2)_low */
+        vmovaps   %zmm0, %zmm12
+        vfmsub213pd {rn-sae}, %zmm1, %zmm0, %zmm12
+
+/* -One_l */
+        vsubpd    {rn-sae}, %zmm7, %zmm2, %zmm3
+
+/* not a very small input ? */
+        vmovups   384+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm2
+
+/* Yl0 */
+        vaddpd    {rn-sae}, %zmm3, %zmm12, %zmm6
+
+/* very large inputs ? */
+        vmovups   448+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm3
+
+/* Yh */
+        vaddpd    {rn-sae}, %zmm6, %zmm5, %zmm1
+        vcmppd    $21, {sae}, %zmm3, %zmm0, %k2
+        vrsqrt14pd %zmm1, %zmm15
+        vcmppd    $21, {sae}, %zmm2, %zmm1, %k3
+        vmovups   896+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm3
+        vfmadd231pd {rn-sae}, %zmm1, %zmm8, %zmm11
+
+/* Sh ~sqrt(-1+x^2) */
+        vmulpd    {rn-sae}, %zmm15, %zmm1, %zmm13
+        vfmadd213pd {rn-sae}, %zmm9, %zmm1, %zmm11
+
+/* Ylh */
+        vsubpd    {rn-sae}, %zmm5, %zmm1, %zmm4
+
+/* Sh+x */
+        vaddpd    {rn-sae}, %zmm0, %zmm13, %zmm8
+
+/* out of range inputs? */
+        vmovups   512+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm5
+        vmulpd    {rn-sae}, %zmm1, %zmm11, %zmm12
+        vcmppd    $22, {sae}, %zmm5, %zmm0, %k0
+        vmovups   960+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm5
+
+/* Yl */
+        vsubpd    {rn-sae}, %zmm4, %zmm6, %zmm14
+        vmovups   832+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm4
+        kmovw     %k0, %edx
+
+/* (Yh*R0)_low */
+        vmovaps   %zmm1, %zmm10
+        orl       %eax, %edx
+        vfmsub213pd {rn-sae}, %zmm13, %zmm15, %zmm10
+
+/* rel. error term: Eh=1-Sh*R0 */
+        vmovaps   %zmm7, %zmm6
+        vfnmadd231pd {rn-sae}, %zmm13, %zmm15, %zmm6
+
+/* Sl = (Yh*R0)_low+(R0*Yl) */
+        vfmadd213pd {rn-sae}, %zmm10, %zmm15, %zmm14
+
+/* rel. error term: Eh=(1-Sh*R0)-Sl*R0 */
+        vfnmadd231pd {rn-sae}, %zmm14, %zmm15, %zmm6
+
+/*
+ * sqrt(1+x^2) ~ Sh + Sl + Sh*Eh*poly_s
+ * poly_s = c1+c2*Eh+c3*Eh^2
+ */
+        vmovups   768+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm15
+
+/* Sh*Eh */
+        vmulpd    {rn-sae}, %zmm6, %zmm13, %zmm10
+        vfmadd231pd {rn-sae}, %zmm6, %zmm15, %zmm4
+        vmovups   1600+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm15
+        vfmadd213pd {rn-sae}, %zmm3, %zmm6, %zmm4
+
+/* Shh */
+        vsubpd    {rn-sae}, %zmm0, %zmm8, %zmm3
+        vfmadd213pd {rn-sae}, %zmm5, %zmm6, %zmm4
+
+/* Shl */
+        vsubpd    {rn-sae}, %zmm3, %zmm13, %zmm6
+
+/* Sl + Sh*Eh*poly_s */
+        vfmadd213pd {rn-sae}, %zmm14, %zmm4, %zmm10
+
+/* polynomial computation for small inputs */
+        vaddpd    {rn-sae}, %zmm10, %zmm13, %zmm14
+
+/* Xin0+Sl+Sh*Eh*poly_s ~ x+sqrt(1+x^2) */
+        vaddpd    {rn-sae}, %zmm10, %zmm8, %zmm11
+        vfmadd213pd {rn-sae}, %zmm10, %zmm12, %zmm14
+
+/* Sl_high */
+        vsubpd    {rn-sae}, %zmm8, %zmm11, %zmm3
+        vaddpd    {rn-sae}, %zmm13, %zmm14, %zmm12
+
+/* Sl_l */
+        vsubpd    {rn-sae}, %zmm3, %zmm10, %zmm5
+
+/* fixup for very large inputs */
+        vmovups   1280+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   128+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   1472+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm3
+        vmovups   1344+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm10
+        vmulpd    {rn-sae}, %zmm13, %zmm0, %zmm11{%k2}
+        vmovups   1536+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm13
+
+/* Xin_low */
+        vaddpd    {rn-sae}, %zmm6, %zmm5, %zmm9
+        vrcp14pd  %zmm11, %zmm4
+
+/* fixup for very large inputs */
+        vxorpd    %zmm9, %zmm9, %zmm9{%k2}
+
+/* round reciprocal to 1+4b mantissas */
+        vpaddq    1152+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm4, %zmm8
+
+/* Table lookups */
+        vmovups   __svml_dacosh_ha_data_internal_avx512(%rip), %zmm4
+        vandpd    1216+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm8, %zmm8
+
+/* reduced argument for log(): (Rcp*Xin-1)+Rcp*Xin_low */
+        vfmsub231pd {rn-sae}, %zmm8, %zmm11, %zmm7
+
+/* exponents */
+        vgetexppd {sae}, %zmm8, %zmm5
+
+/* Prepare table index */
+        vpsrlq    $48, %zmm8, %zmm11
+        vsubpd    {rn-sae}, %zmm10, %zmm5, %zmm5{%k2}
+        vpermt2pd 192+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm11, %zmm14
+        vpermt2pd 64+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm11, %zmm4
+        vmovups   1728+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm10
+
+/* -K*L2H + Th */
+        vmovups   1984+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm11
+        vmovaps   %zmm8, %zmm6
+        vfmadd213pd {rn-sae}, %zmm7, %zmm9, %zmm6
+
+/* Tl+Rcp*Xin_low */
+        vfmadd213pd {rn-sae}, %zmm14, %zmm9, %zmm8
+
+/* polynomials */
+        vmovups   1408+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm9
+        vfnmadd231pd {rn-sae}, %zmm5, %zmm11, %zmm4
+        vmovups   1856+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm14
+        vfmadd231pd {rn-sae}, %zmm6, %zmm9, %zmm3
+        vmovups   1664+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm9
+        vfmadd213pd {rn-sae}, %zmm13, %zmm6, %zmm3
+
+/* -K*L2L + Tl + Rcp*Xin_low */
+        vmovups   2048+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm13
+        vfmadd213pd {rn-sae}, %zmm15, %zmm6, %zmm3
+        vfnmadd213pd {rn-sae}, %zmm8, %zmm13, %zmm5
+        vmovups   1792+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   1920+__svml_dacosh_ha_data_internal_avx512(%rip), %zmm15
+        vfmadd213pd {rn-sae}, %zmm9, %zmm6, %zmm3
+
+/* (K*L2H+Th) + Rh0_h */
+        vaddpd    {rn-sae}, %zmm4, %zmm7, %zmm9
+        vfmadd213pd {rn-sae}, %zmm10, %zmm6, %zmm3
+
+/* -R_high */
+        vsubpd    {rn-sae}, %zmm9, %zmm4, %zmm4
+        vfmadd213pd {rn-sae}, %zmm8, %zmm6, %zmm3
+
+/* R^2 */
+        vmulpd    {rn-sae}, %zmm6, %zmm6, %zmm8
+        vfmadd213pd {rn-sae}, %zmm14, %zmm6, %zmm3
+
+/* Rl */
+        vaddpd    {rn-sae}, %zmm4, %zmm7, %zmm7
+        vfmadd213pd {rn-sae}, %zmm15, %zmm6, %zmm3
+
+/* (K*L2L + Tl) + R^2*Poly */
+        vfmadd213pd {rn-sae}, %zmm5, %zmm8, %zmm3
+        vaddpd    {rn-sae}, %zmm7, %zmm3, %zmm3
+        vaddpd    {rn-sae}, %zmm3, %zmm9, %zmm12{%k3}
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        vmovaps   %zmm12, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm12, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm12
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dacosh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_acosh8_ha,@function
+	.size	__svml_acosh8_ha,.-__svml_acosh8_ha
+..LN__svml_acosh8_ha.0:
+
+.L_2__routine_start___svml_dacosh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dacosh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movzwl    6(%rdi), %eax
+        andl      $32752, %eax
+        movsd     (%rdi), %xmm0
+        cmpl      $32752, %eax
+        je        .LBL_2_9
+
+.LBL_2_2:
+
+        movsd     %xmm0, -8(%rsp)
+
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_8
+
+
+        cmpl      $1072693248, -4(%rsp)
+        je        .LBL_2_7
+
+
+        cmpl      $2146435072, -4(%rsp)
+        jne       .LBL_2_8
+
+
+        movsd     %xmm0, (%rsi)
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_7:
+
+        movq      8+__dacosh_ha_CoutTab(%rip), %rax
+        movq      %rax, (%rsi)
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_8:
+
+        movsd     16+__dacosh_ha_CoutTab(%rip), %xmm0
+        movl      $1, %eax
+        mulsd     8+__dacosh_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_12
+
+
+        cmpl      $0, (%rdi)
+        je        .LBL_2_2
+
+.LBL_2_12:
+
+        mulsd     %xmm0, %xmm0
+        xorl      %eax, %eax
+        movsd     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dacosh_ha_cout_rare_internal,@function
+	.size	__svml_dacosh_ha_cout_rare_internal,.-__svml_dacosh_ha_cout_rare_internal
+..LN__svml_dacosh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dacosh_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	3222405120
+	.long	3215919664
+	.long	1848311808
+	.long	3216910087
+	.long	1890025472
+	.long	3217424176
+	.long	3348791296
+	.long	3217854455
+	.long	2880159744
+	.long	3218171740
+	.long	3256631296
+	.long	3218366859
+	.long	4139499520
+	.long	3218553303
+	.long	3971973120
+	.long	3218731811
+	.long	3348791296
+	.long	3218903031
+	.long	1605304320
+	.long	3219067535
+	.long	3827638272
+	.long	3219177074
+	.long	1584414720
+	.long	3219253343
+	.long	860823552
+	.long	3219326935
+	.long	3896934400
+	.long	3219398031
+	.long	643547136
+	.long	3219466797
+	.long	0
+	.long	0
+	.long	3496399314
+	.long	1028893491
+	.long	720371772
+	.long	1026176044
+	.long	1944193543
+	.long	3175338952
+	.long	634920691
+	.long	3175752108
+	.long	1664625295
+	.long	1029304828
+	.long	192624563
+	.long	3177103997
+	.long	3796653051
+	.long	3176138396
+	.long	3062724207
+	.long	3176680434
+	.long	634920691
+	.long	3176800684
+	.long	1913570380
+	.long	3174806221
+	.long	825194088
+	.long	3176465773
+	.long	2335489660
+	.long	3172599741
+	.long	2497625109
+	.long	1029604288
+	.long	914782743
+	.long	1029350199
+	.long	3743595607
+	.long	3175525305
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1061158912
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.type	__svml_dacosh_ha_data_internal_avx512,@object
+	.size	__svml_dacosh_ha_data_internal_avx512,2176
+	.align 8
+__dacosh_ha_CoutTab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	4293918720
+	.type	__dacosh_ha_CoutTab,@object
+	.size	__dacosh_ha_CoutTab,32
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_d_la.s
index 348ad7e16e9..af0bf20061f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_h_la.s
new file mode 100644
index 00000000000..809a065a01a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_h_la.s
@@ -0,0 +1,188 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Compute acosh(x) as log(x + sqrt(x*x - 1))
+ *
+ *   Special cases:
+ *
+ *   acosh(NaN)  = quiet NaN, and raise invalid exception
+ *   acosh(-INF) = NaN
+ *   acosh(+INF) = +INF
+ *   acosh(x)    = NaN if x < 1
+ *   acosh(1)    = +0
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_acoshs32
+
+__svml_acoshs32:
+
+        .cfi_startproc
+        vmovdqu16 __svml_hacosh_data_internal(%rip), %zmm13
+        vmovdqu16 64+__svml_hacosh_data_internal(%rip), %zmm1
+        vmovdqu16 128+__svml_hacosh_data_internal(%rip), %zmm31
+        vmovdqu16 192+__svml_hacosh_data_internal(%rip), %zmm30
+        vmovdqu16 256+__svml_hacosh_data_internal(%rip), %zmm29
+        vmovdqu16 320+__svml_hacosh_data_internal(%rip), %zmm28
+        vmovdqu16 384+__svml_hacosh_data_internal(%rip), %zmm10
+        vmovdqu16 448+__svml_hacosh_data_internal(%rip), %zmm12
+        vmovdqu16 512+__svml_hacosh_data_internal(%rip), %zmm27
+        vmovdqu16 576+__svml_hacosh_data_internal(%rip), %zmm24
+        vmovdqu16 640+__svml_hacosh_data_internal(%rip), %zmm26
+        vmovdqu16 704+__svml_hacosh_data_internal(%rip), %zmm25
+        vmovdqu16 768+__svml_hacosh_data_internal(%rip), %zmm23
+        kxnord  %k7, %k7, %k7
+/*
+ * No callout
+ * x in [1,2): acosh(x) ~ poly(x-1)*sqrt(x-1)
+ * hY = x-1
+ */
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_acosh_h:
+        cmpq    $31, %rdx
+        ja .loaddata_acosh
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $15360, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_acosh_h
+.loaddata_acosh:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_acosh_h:
+
+/* ReLoad constants */
+	vmovdqu16 %zmm31, %zmm8
+	vmovdqu16 %zmm29, %zmm6
+
+        vmovaps   %zmm0, %zmm3
+        vsubph    {rn-sae}, %zmm13, %zmm3, %zmm5
+
+/*
+ * log(x)
+ * GetMant(x), normalized to [.75,1.5) for x>=0, NaN for x<0
+ */
+        vgetmantph $11, {sae}, %zmm3, %zmm14
+        vgetexpph {sae}, %zmm3, %zmm15
+
+/*
+ * x>=2: acosh(x) = log(x) + log(1+sqrt(1-(1/x)^2))
+ * Z ~ 1/x in (0, 0.5]
+        vmovdqu16 256+__svml_hacosh_data_internal(%rip), %zmm29
+ */
+        vrcpph    %zmm3, %zmm11
+
+/* hRS ~ 1/sqrt(x-1) */
+        vrsqrtph  %zmm5, %zmm7
+        vcmpph    $21, {sae}, %zmm1, %zmm3, %k1
+        vmovdqu16 %zmm28, %zmm0
+        vfmadd213ph {rn-sae}, %zmm30, %zmm5, %zmm8
+
+/* hS ~ sqrt(x-1) */
+        vrcpph    %zmm7, %zmm9
+
+/* log(1+R)/R */
+        vmovdqu16 %zmm27, %zmm7
+        vfmadd213ph {rn-sae}, %zmm6, %zmm5, %zmm8
+
+/* mantissa - 1 */
+        vsubph    {rn-sae}, %zmm13, %zmm14, %zmm6
+        vfmadd213ph {rn-sae}, %zmm10, %zmm11, %zmm0
+
+/* exponent correction */
+        vgetexpph {sae}, %zmm14, %zmm14
+
+/* poly(x-1)*sqrt(x-1) */
+        vmulph    {rn-sae}, %zmm9, %zmm8, %zmm2
+        vfmadd213ph {rn-sae}, %zmm12, %zmm11, %zmm0
+        vsubph    {rn-sae}, %zmm14, %zmm15, %zmm8
+        vmovdqu16 %zmm24, %zmm15
+        vfmadd213ph {rn-sae}, %zmm15, %zmm6, %zmm7
+        vfmadd213ph {rn-sae}, %zmm26, %zmm6, %zmm7
+        vfmadd213ph {rn-sae}, %zmm25, %zmm6, %zmm7
+
+/* log(1+R) + log(1+sqrt(1-z*z)) */
+        vfmadd213ph {rn-sae}, %zmm0, %zmm6, %zmm7
+
+/* result for x>=2 */
+        vmovdqu16 %zmm23, %zmm0
+        vfmadd213ph {rn-sae}, %zmm7, %zmm0, %zmm8
+
+/* result = SelMask?  hPl : hPa */
+        vpblendmw %zmm8, %zmm2, %zmm0{%k1}
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_acosh_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_acoshs32,@function
+        .size	__svml_acoshs32,.-__svml_acoshs32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hacosh_data_internal:
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x4000
+	.endr
+	.rept	32
+        .word	0x24a4
+	.endr
+	.rept	32
+        .word	0xaf5e
+	.endr
+	.rept	32
+        .word	0x3da8
+	.endr
+	.rept	32
+        .word	0xb4d2
+	.endr
+	.rept	32
+        .word	0x231d
+	.endr
+	.rept	32
+        .word	0x398b
+	.endr
+	.rept	32
+        .word	0xb22b
+	.endr
+	.rept	32
+        .word	0x358b
+	.endr
+	.rept	32
+        .word	0xb807
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x398c
+	.endr
+        .type	__svml_hacosh_data_internal,@object
+        .size	__svml_hacosh_data_internal,832
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_s_ha.s
new file mode 100644
index 00000000000..8a431f0065a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_s_ha.s
@@ -0,0 +1,733 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *   Compute log(x+sqrt(x*x-1)) using RSQRT14/RSQRT28 for starting the
+ *  *   square root approximation, and small table lookups for log (mapping to
+ *  *   AVX3 permute instructions).
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_acoshf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_acoshf16_ha
+
+__svml_acoshf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm13
+
+/* x^2 */
+        vmulps    {rn-sae}, %zmm13, %zmm13, %zmm14
+        vmovups   256+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm4
+
+/* polynomial computation for small inputs */
+        vmovups   576+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm1
+
+/* very large inputs ? */
+        vmovups   448+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm6
+
+/* out of range inputs? */
+        vmovups   512+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm8
+        vcmpps    $18, {sae}, %zmm4, %zmm13, %k1
+        vcmpps    $21, {sae}, %zmm6, %zmm13, %k2
+        vcmpps    $22, {sae}, %zmm8, %zmm13, %k0
+
+/* x^2 - 1 */
+        vsubps    {rn-sae}, %zmm4, %zmm14, %zmm0
+        kmovw     %k0, %edx
+        kmovw     %k1, %eax
+        vsubps    {rn-sae}, %zmm0, %zmm14, %zmm15
+
+/* (x^2)_low */
+        vmovaps   %zmm13, %zmm12
+        orl       %eax, %edx
+        vfmsub213ps {rn-sae}, %zmm14, %zmm13, %zmm12
+
+/*
+ * sqrt(1+x^2) ~ Sh + Sl + Sh*Eh*poly_s
+ * poly_s = c1+c2*Eh
+ */
+        vmovups   640+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm14
+
+/* -One_l */
+        vsubps    {rn-sae}, %zmm4, %zmm15, %zmm11
+
+/* Yl0 */
+        vaddps    {rn-sae}, %zmm11, %zmm12, %zmm3
+
+/* not a very small input ? */
+        vmovups   384+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm11
+
+/* Yh */
+        vaddps    {rn-sae}, %zmm3, %zmm0, %zmm12
+        vrsqrt14ps %zmm12, %zmm7
+        vcmpps    $21, {sae}, %zmm11, %zmm12, %k3
+        vmulps    {rn-sae}, %zmm1, %zmm12, %zmm9
+
+/* Sh ~sqrt(-1+x^2) */
+        vmulps    {rn-sae}, %zmm7, %zmm12, %zmm10
+
+/* Ylh */
+        vsubps    {rn-sae}, %zmm0, %zmm12, %zmm2
+
+/* (Yh*R0)_low */
+        vmovaps   %zmm12, %zmm5
+        vfmsub213ps {rn-sae}, %zmm10, %zmm7, %zmm5
+
+/* Yl */
+        vsubps    {rn-sae}, %zmm2, %zmm3, %zmm0
+        vmovups   704+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm2
+
+/* Sl = (Yh*R0)_low+(R0*Yl) */
+        vfmadd213ps {rn-sae}, %zmm5, %zmm7, %zmm0
+
+/* rel. error term: Eh=1-Sh*R0 */
+        vmovaps   %zmm4, %zmm3
+        vfnmadd231ps {rn-sae}, %zmm10, %zmm7, %zmm3
+
+/* rel. error term: Eh=(1-Sh*R0)-Sl*R0 */
+        vfnmadd231ps {rn-sae}, %zmm0, %zmm7, %zmm3
+
+/* Sh*Eh */
+        vmulps    {rn-sae}, %zmm3, %zmm10, %zmm7
+        vfmadd231ps {rn-sae}, %zmm3, %zmm14, %zmm2
+
+/* polynomial */
+        vmovups   1024+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm14
+
+/* Sh+x */
+        vaddps    {rn-sae}, %zmm13, %zmm10, %zmm3
+
+/* Sl + Sh*Eh*poly_s */
+        vfmadd213ps {rn-sae}, %zmm0, %zmm2, %zmm7
+
+/* Shh */
+        vsubps    {rn-sae}, %zmm13, %zmm3, %zmm15
+
+/* polynomial computation for small inputs */
+        vaddps    {rn-sae}, %zmm7, %zmm10, %zmm0
+
+/* Xin0+Sl+Sh*Eh*poly_s ~ x+sqrt(1+x^2) */
+        vaddps    {rn-sae}, %zmm7, %zmm3, %zmm8
+
+/* Shl */
+        vsubps    {rn-sae}, %zmm15, %zmm10, %zmm2
+        vfmadd213ps {rn-sae}, %zmm7, %zmm9, %zmm0
+
+/* fixup for very large inputs */
+        vmovups   896+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   1152+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm15
+        vaddps    {rn-sae}, %zmm10, %zmm0, %zmm0
+
+/* Sl_high */
+        vsubps    {rn-sae}, %zmm3, %zmm8, %zmm10
+        vmulps    {rn-sae}, %zmm9, %zmm13, %zmm8{%k2}
+        vmovups   128+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm9
+
+/* Sl_l */
+        vsubps    {rn-sae}, %zmm10, %zmm7, %zmm3
+        vrcp14ps  %zmm8, %zmm1
+        vmovups   960+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm7
+
+/* -K*L2L + Tl + Rcp*Xin_low */
+        vmovups   1280+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm10
+
+/* round reciprocal to 1+4b mantissas */
+        vpaddd    768+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm1, %zmm5
+
+/* Xin_low */
+        vaddps    {rn-sae}, %zmm2, %zmm3, %zmm6
+
+/* Table lookups */
+        vmovups   __svml_sacosh_ha_data_internal_avx512(%rip), %zmm1
+        vandps    832+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm5, %zmm5
+
+/* fixup for very large inputs */
+        vxorps    %zmm6, %zmm6, %zmm6{%k2}
+
+/* reduced argument for log(): (Rcp*Xin-1)+Rcp*Xin_low */
+        vfmsub231ps {rn-sae}, %zmm5, %zmm8, %zmm4
+
+/* exponents */
+        vgetexpps {sae}, %zmm5, %zmm3
+
+/* Prepare table index */
+        vpsrld    $18, %zmm5, %zmm8
+        vsubps    {rn-sae}, %zmm7, %zmm3, %zmm3{%k2}
+        vpermt2ps 192+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm8, %zmm9
+        vpermt2ps 64+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm8, %zmm1
+        vmovaps   %zmm5, %zmm2
+        vfmadd213ps {rn-sae}, %zmm4, %zmm6, %zmm2
+
+/* Tl+Rcp*Xin_low */
+        vfmadd213ps {rn-sae}, %zmm9, %zmm6, %zmm5
+
+/* -K*L2H + Th */
+        vmovups   1216+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm6
+        vfnmadd231ps {rn-sae}, %zmm3, %zmm6, %zmm1
+        vfnmadd213ps {rn-sae}, %zmm5, %zmm10, %zmm3
+        vmovups   1088+__svml_sacosh_ha_data_internal_avx512(%rip), %zmm5
+
+/* (K*L2H+Th) + Rh0_h */
+        vaddps    {rn-sae}, %zmm1, %zmm4, %zmm6
+        vfmadd231ps {rn-sae}, %zmm2, %zmm14, %zmm5
+
+/* R^2 */
+        vmulps    {rn-sae}, %zmm2, %zmm2, %zmm14
+        vfmadd213ps {rn-sae}, %zmm15, %zmm2, %zmm5
+
+/* -R_high */
+        vsubps    {rn-sae}, %zmm6, %zmm1, %zmm1
+
+/* (K*L2L + Tl) + R^2*Poly */
+        vfmadd213ps {rn-sae}, %zmm3, %zmm14, %zmm5
+
+/* Rl */
+        vaddps    {rn-sae}, %zmm1, %zmm4, %zmm4
+        vaddps    {rn-sae}, %zmm4, %zmm5, %zmm1
+        vaddps    {rn-sae}, %zmm1, %zmm6, %zmm0{%k3}
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm13, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_sacosh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_acoshf16_ha,@function
+	.size	__svml_acoshf16_ha,.-__svml_acoshf16_ha
+..LN__svml_acoshf16_ha.0:
+
+.L_2__routine_start___svml_sacosh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_sacosh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movl      (%rdi), %eax
+        movl      %eax, -8(%rsp)
+        andl      $2139095040, %eax
+        cmpl      $2139095040, %eax
+        je        .LBL_2_8
+
+.LBL_2_2:
+
+        movl      -8(%rsp), %eax
+        cmpl      $1065353216, %eax
+        je        .LBL_2_7
+
+
+        cmpl      $2139095040, %eax
+        jne       .LBL_2_5
+
+
+        movl      8+__sacosh_ha__iml_sacosh_cout_tab(%rip), %eax
+        movl      %eax, (%rsi)
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_5:
+
+        movss     8+__sacosh_ha__iml_sacosh_cout_tab(%rip), %xmm0
+        movl      $1, %eax
+        mulss     4+__sacosh_ha__iml_sacosh_cout_tab(%rip), %xmm0
+        movss     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_7:
+
+        movl      4+__sacosh_ha__iml_sacosh_cout_tab(%rip), %eax
+        movl      %eax, (%rsi)
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_8:
+
+        testl     $8388607, -8(%rsp)
+        je        .LBL_2_2
+
+
+        movss     -8(%rsp), %xmm0
+        xorl      %eax, %eax
+        mulss     -8(%rsp), %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sacosh_ha_cout_rare_internal,@function
+	.size	__svml_sacosh_ha_cout_rare_internal,.-__svml_sacosh_ha_cout_rare_internal
+..LN__svml_sacosh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_sacosh_ha_data_internal_avx512:
+	.long	0
+	.long	3170631680
+	.long	3178790912
+	.long	3182919680
+	.long	3186704384
+	.long	3189022720
+	.long	3190816768
+	.long	3192561664
+	.long	3194257408
+	.long	3195912192
+	.long	3196796928
+	.long	3197583360
+	.long	3198357504
+	.long	3199111168
+	.long	3199848448
+	.long	3200569344
+	.long	3201277952
+	.long	3201966080
+	.long	3202646016
+	.long	3203309568
+	.long	3203960832
+	.long	3204524032
+	.long	3204837376
+	.long	3205146624
+	.long	3205447680
+	.long	3205744640
+	.long	3206037504
+	.long	3206324224
+	.long	3206606848
+	.long	3206883328
+	.long	3207155712
+	.long	3207424000
+	.long	2147483648
+	.long	3072770974
+	.long	943319038
+	.long	3075640037
+	.long	930648533
+	.long	3089726480
+	.long	936349528
+	.long	944943494
+	.long	897812054
+	.long	3087808175
+	.long	941839444
+	.long	3093478113
+	.long	937982919
+	.long	931430736
+	.long	924853521
+	.long	3075349253
+	.long	945558336
+	.long	3094838221
+	.long	906200662
+	.long	3084126596
+	.long	3088015279
+	.long	3089451852
+	.long	3093678154
+	.long	938521645
+	.long	3091119329
+	.long	3090949395
+	.long	933442244
+	.long	930702671
+	.long	945827699
+	.long	913590776
+	.long	3082066287
+	.long	3087046763
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	964689920
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.type	__svml_sacosh_ha_data_internal_avx512,@object
+	.size	__svml_sacosh_ha_data_internal_avx512,1344
+	.align 4
+__sacosh_ha__iml_sacosh_cout_tab:
+	.long	1065353216
+	.long	0
+	.long	2139095040
+	.type	__sacosh_ha__iml_sacosh_cout_tab,@object
+	.size	__sacosh_ha__iml_sacosh_cout_tab,12
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_s_la.s
index 68f7a495f87..c12518c3aa9 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_acosh_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_d_ha.s
new file mode 100644
index 00000000000..6f85f65b4b0
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_d_ha.s
@@ -0,0 +1,2583 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_asin8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_asin8_ha
+
+__svml_asin8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   64+__svml_dasin_ha_data_internal(%rip), %zmm8
+
+/*
+ * 2*sqrt(Y) = Sh - Sl
+ * Sh ~ 2*sqrt_high(Y)
+ * Sl ~ -2*sqrt_low(Y)
+ */
+        vmovups   128+__svml_dasin_ha_data_internal(%rip), %zmm10
+        vmovups   448+__svml_dasin_ha_data_internal(%rip), %zmm14
+        vmovups   512+__svml_dasin_ha_data_internal(%rip), %zmm1
+        vmovups   576+__svml_dasin_ha_data_internal(%rip), %zmm15
+        vmovups   256+__svml_dasin_ha_data_internal(%rip), %zmm9
+        vmovaps   %zmm0, %zmm6
+
+/* x = |arg| */
+        vandpd    __svml_dasin_ha_data_internal(%rip), %zmm6, %zmm4
+        vmovups   384+__svml_dasin_ha_data_internal(%rip), %zmm0
+
+/* x^2 */
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm7
+        vcmppd    $21, {sae}, %zmm8, %zmm4, %k2
+        vcmppd    $17, {sae}, %zmm4, %zmm9, %k0
+        vmovups   832+__svml_dasin_ha_data_internal(%rip), %zmm9
+        vxorpd    %zmm6, %zmm4, %zmm5
+        kmovw     %k0, %edx
+
+/* Y = 0.5 - 0.5*x */
+        vmovaps   %zmm8, %zmm11
+        vfnmadd231pd {rn-sae}, %zmm4, %zmm8, %zmm11
+
+/* polynomial */
+        vmovups   704+__svml_dasin_ha_data_internal(%rip), %zmm8
+        vrsqrt14pd %zmm11, %zmm13
+        vcmppd    $17, {sae}, %zmm10, %zmm11, %k1
+        vminpd    {sae}, %zmm11, %zmm7, %zmm3
+        vmovups   960+__svml_dasin_ha_data_internal(%rip), %zmm10
+        vxorpd    %zmm13, %zmm13, %zmm13{%k1}
+        vaddpd    {rn-sae}, %zmm11, %zmm11, %zmm7
+        vmovups   1088+__svml_dasin_ha_data_internal(%rip), %zmm11
+        vmulpd    {rn-sae}, %zmm13, %zmm13, %zmm12
+        vmulpd    {rn-sae}, %zmm13, %zmm7, %zmm2
+        vfmsub231pd {rn-sae}, %zmm7, %zmm12, %zmm0
+        vfmsub213pd {rn-sae}, %zmm2, %zmm13, %zmm7
+        vmovups   640+__svml_dasin_ha_data_internal(%rip), %zmm12
+        vfmadd231pd {rn-sae}, %zmm0, %zmm14, %zmm1
+        vmovups   896+__svml_dasin_ha_data_internal(%rip), %zmm14
+        vmulpd    {rn-sae}, %zmm0, %zmm2, %zmm13
+        vfmadd213pd {rn-sae}, %zmm15, %zmm0, %zmm1
+        vfmadd231pd {rn-sae}, %zmm3, %zmm9, %zmm14
+        vmovups   1216+__svml_dasin_ha_data_internal(%rip), %zmm15
+        vmovups   1280+__svml_dasin_ha_data_internal(%rip), %zmm9
+        vfmadd213pd {rn-sae}, %zmm12, %zmm0, %zmm1
+        vmovups   768+__svml_dasin_ha_data_internal(%rip), %zmm0
+        vmovups   1408+__svml_dasin_ha_data_internal(%rip), %zmm12
+        vfmadd231pd {rn-sae}, %zmm3, %zmm15, %zmm9
+        vfmsub213pd {rn-sae}, %zmm7, %zmm13, %zmm1
+        vmovups   1024+__svml_dasin_ha_data_internal(%rip), %zmm7
+        vfmadd231pd {rn-sae}, %zmm3, %zmm8, %zmm0
+        vmulpd    {rn-sae}, %zmm3, %zmm3, %zmm8
+        vfmadd231pd {rn-sae}, %zmm3, %zmm10, %zmm7
+        vmovups   1152+__svml_dasin_ha_data_internal(%rip), %zmm10
+
+/* -2*sqrt(Y), or X */
+        vsubpd    {rn-sae}, %zmm2, %zmm1, %zmm4{%k2}
+        vfmadd213pd {rn-sae}, %zmm14, %zmm8, %zmm0
+        vfmadd231pd {rn-sae}, %zmm3, %zmm11, %zmm10
+        vmovups   1344+__svml_dasin_ha_data_internal(%rip), %zmm11
+        vfmadd213pd {rn-sae}, %zmm10, %zmm8, %zmm7
+        vmulpd    {rn-sae}, %zmm8, %zmm8, %zmm10
+        vfmadd213pd {rn-sae}, %zmm7, %zmm10, %zmm0
+        vmovups   1600+__svml_dasin_ha_data_internal(%rip), %zmm7
+        vfmadd213pd {rn-sae}, %zmm9, %zmm8, %zmm0
+        vfmadd213pd {rn-sae}, %zmm11, %zmm3, %zmm0
+        vfmadd213pd {rn-sae}, %zmm12, %zmm3, %zmm0
+        vmulpd    {rn-sae}, %zmm3, %zmm0, %zmm9
+        vmovups   1472+__svml_dasin_ha_data_internal(%rip), %zmm3
+        vmovups   1536+__svml_dasin_ha_data_internal(%rip), %zmm0
+
+/* Pi2H - 2*sqrt_high(Y) */
+        vsubpd    {rn-sae}, %zmm2, %zmm3, %zmm8
+        vaddpd    {rn-sae}, %zmm1, %zmm0, %zmm1
+        vsubpd    {rn-sae}, %zmm8, %zmm3, %zmm3
+        vsubpd    {rn-sae}, %zmm3, %zmm2, %zmm2
+
+/* Low-Shl (Pi2L-2*sqrt_low(Y)), or 0 if selmask_bit==0 */
+        vsubpd    {rn-sae}, %zmm2, %zmm1, %zmm7{%k2}
+        vfmadd213pd {rn-sae}, %zmm7, %zmm4, %zmm9
+
+/* |arg| or Pi2H - 2*sqrt_high(Y) */
+        vblendmpd %zmm8, %zmm4, %zmm4{%k2}
+        vaddpd    {rn-sae}, %zmm9, %zmm4, %zmm0
+        vxorpd    %zmm5, %zmm0, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dasin_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_asin8_ha,@function
+	.size	__svml_asin8_ha,.-__svml_asin8_ha
+..LN__svml_asin8_ha.0:
+
+.L_2__routine_start___svml_dasin_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dasin_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    6(%rdi), %edx
+        andl      $32752, %edx
+        cmpl      $32752, %edx
+        je        .LBL_2_12
+
+
+        movq      (%rdi), %rdx
+        movq      %rdx, -32(%rsp)
+        shrq      $56, %rdx
+        movsd     4160+_vmldASinHATab(%rip), %xmm0
+        andl      $127, %edx
+        movb      %dl, -25(%rsp)
+        movsd     -32(%rsp), %xmm12
+        comisd    %xmm12, %xmm0
+        jb        .LBL_2_10
+
+
+        movsd     4168+_vmldASinHATab(%rip), %xmm1
+        comisd    %xmm12, %xmm1
+        jbe       .LBL_2_9
+
+
+        comisd    4136+_vmldASinHATab(%rip), %xmm12
+        jbe       .LBL_2_6
+
+
+        movsd     4104+_vmldASinHATab(%rip), %xmm13
+        movaps    %xmm12, %xmm5
+        mulsd     %xmm12, %xmm13
+        movaps    %xmm12, %xmm7
+        movsd     %xmm13, -48(%rsp)
+        movsd     -48(%rsp), %xmm14
+        movsd     4104+_vmldASinHATab(%rip), %xmm1
+        subsd     -32(%rsp), %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movsd     -48(%rsp), %xmm8
+        movsd     -56(%rsp), %xmm15
+        subsd     %xmm15, %xmm8
+        movsd     %xmm8, -48(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     4296+_vmldASinHATab(%rip), %xmm8
+        subsd     %xmm6, %xmm5
+        movsd     %xmm5, -56(%rsp)
+        movaps    %xmm12, %xmm5
+        movsd     -48(%rsp), %xmm9
+        movaps    %xmm9, %xmm13
+        addsd     %xmm9, %xmm7
+        mulsd     %xmm9, %xmm13
+        movsd     -56(%rsp), %xmm11
+        movaps    %xmm13, %xmm10
+        mulsd     %xmm11, %xmm7
+        mulsd     %xmm13, %xmm1
+        addsd     %xmm7, %xmm10
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm2
+        movsd     4104+_vmldASinHATab(%rip), %xmm1
+        subsd     %xmm13, %xmm2
+        addsd     4288+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm2, -56(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movsd     -56(%rsp), %xmm0
+        addsd     4280+_vmldASinHATab(%rip), %xmm8
+        subsd     %xmm0, %xmm3
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm3, -48(%rsp)
+        movsd     -48(%rsp), %xmm4
+        addsd     4272+_vmldASinHATab(%rip), %xmm8
+        subsd     %xmm4, %xmm13
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm13, -56(%rsp)
+        movsd     -48(%rsp), %xmm6
+        mulsd     %xmm6, %xmm9
+        addsd     4264+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm6, %xmm11
+        mulsd     %xmm10, %xmm8
+        mulsd     %xmm9, %xmm1
+        addsd     4256+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm10, %xmm8
+        movaps    %xmm9, %xmm3
+        movsd     -56(%rsp), %xmm14
+        movaps    %xmm6, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        addsd     %xmm14, %xmm7
+        addsd     4248+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm7, %xmm5
+        mulsd     %xmm10, %xmm8
+        addsd     %xmm5, %xmm11
+        addsd     4240+_vmldASinHATab(%rip), %xmm8
+        movsd     -48(%rsp), %xmm2
+        mulsd     %xmm10, %xmm8
+        subsd     %xmm9, %xmm2
+        addsd     %xmm11, %xmm9
+        addsd     4232+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm9, %xmm7
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm2, -56(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm15
+        subsd     %xmm15, %xmm0
+        addsd     4224+_vmldASinHATab(%rip), %xmm8
+        movsd     %xmm0, -48(%rsp)
+        movsd     -48(%rsp), %xmm5
+        mulsd     %xmm10, %xmm8
+        subsd     %xmm5, %xmm3
+        movsd     %xmm3, -56(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movsd     -56(%rsp), %xmm5
+        movaps    %xmm5, %xmm13
+        addsd     4216+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm3, %xmm4
+        addsd     %xmm11, %xmm13
+        mulsd     %xmm10, %xmm8
+        mulsd     %xmm13, %xmm6
+        addsd     4208+_vmldASinHATab(%rip), %xmm8
+        addsd     %xmm7, %xmm6
+        mulsd     %xmm10, %xmm8
+        movsd     4104+_vmldASinHATab(%rip), %xmm7
+        movaps    %xmm4, %xmm13
+        mulsd     %xmm4, %xmm7
+        addsd     4200+_vmldASinHATab(%rip), %xmm8
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm1
+        movsd     4184+_vmldASinHATab(%rip), %xmm10
+        subsd     %xmm4, %xmm1
+        mulsd     %xmm9, %xmm10
+        addsd     %xmm6, %xmm4
+        movsd     %xmm1, -56(%rsp)
+        movaps    %xmm12, %xmm9
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm2
+        movsd     4176+_vmldASinHATab(%rip), %xmm1
+        subsd     %xmm2, %xmm0
+        mulsd     %xmm1, %xmm3
+        mulsd     %xmm1, %xmm11
+        mulsd     %xmm4, %xmm8
+        addsd     %xmm3, %xmm9
+        mulsd     %xmm1, %xmm5
+        addsd     %xmm11, %xmm10
+        movsd     %xmm0, -48(%rsp)
+        addsd     %xmm10, %xmm8
+        movsd     -48(%rsp), %xmm7
+        addsd     %xmm5, %xmm8
+        subsd     %xmm7, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm2
+        movsd     %xmm9, -48(%rsp)
+        addsd     %xmm2, %xmm6
+        movsd     -48(%rsp), %xmm11
+        movsd     4192+_vmldASinHATab(%rip), %xmm4
+        subsd     %xmm11, %xmm12
+        mulsd     %xmm4, %xmm0
+        mulsd     %xmm4, %xmm6
+        addsd     %xmm12, %xmm3
+        movsd     %xmm3, -56(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movaps    %xmm3, %xmm12
+        movsd     -56(%rsp), %xmm7
+        addsd     %xmm0, %xmm12
+        addsd     %xmm7, %xmm8
+        movsd     %xmm12, -48(%rsp)
+        movsd     -48(%rsp), %xmm12
+        subsd     %xmm12, %xmm3
+        addsd     %xmm3, %xmm0
+        movsd     %xmm0, -56(%rsp)
+        movsd     -48(%rsp), %xmm9
+        movsd     -56(%rsp), %xmm0
+        movb      7(%rdi), %dil
+        addsd     %xmm0, %xmm8
+        andb      $-128, %dil
+        addsd     %xmm8, %xmm9
+        addsd     %xmm9, %xmm6
+        movsd     %xmm6, -40(%rsp)
+        movb      -33(%rsp), %dl
+        andb      $127, %dl
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movq      -40(%rsp), %rcx
+        movq      %rcx, (%rsi)
+        ret
+
+.LBL_2_6:
+
+        comisd    4128+_vmldASinHATab(%rip), %xmm12
+        jb        .LBL_2_8
+
+
+        addsd     %xmm0, %xmm12
+        movsd     %xmm12, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        mulsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movb      -33(%rsp), %dl
+        movb      7(%rdi), %dil
+        andb      $127, %dl
+        andb      $-128, %dil
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movq      -40(%rsp), %rcx
+        movq      %rcx, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        mulsd     %xmm12, %xmm12
+        movsd     %xmm12, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movb      7(%rdi), %dil
+        andb      $-128, %dil
+        addsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movb      -33(%rsp), %dl
+        andb      $127, %dl
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movq      -40(%rsp), %rcx
+        movq      %rcx, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        movaps    %xmm0, %xmm9
+        movzwl    4166+_vmldASinHATab(%rip), %r9d
+        pxor      %xmm15, %xmm15
+        andl      $-32753, %r9d
+        subsd     %xmm12, %xmm9
+        subsd     %xmm0, %xmm15
+        mulsd     %xmm9, %xmm1
+        movsd     %xmm1, -32(%rsp)
+        movzwl    -26(%rsp), %ecx
+        andl      $32752, %ecx
+        shrl      $4, %ecx
+        addl      $-1023, %ecx
+        movl      %ecx, %r8d
+        movl      %ecx, %edx
+        negl      %r8d
+        addl      $1023, %r8d
+        andl      $2047, %r8d
+        shll      $4, %r8d
+        movsd     %xmm0, -24(%rsp)
+        orl       %r8d, %r9d
+        movw      %r9w, -18(%rsp)
+        andl      $1, %edx
+        movsd     -24(%rsp), %xmm10
+        movl      %edx, %r10d
+        mulsd     %xmm10, %xmm1
+        lea       _vmldASinHATab(%rip), %r8
+        movaps    %xmm1, %xmm6
+        movsd     4112+_vmldASinHATab(%rip), %xmm7
+        addsd     %xmm1, %xmm6
+        jne       ..L54
+        movaps    %xmm1, %xmm6
+..L54:
+        mulsd     %xmm6, %xmm7
+        movaps    %xmm6, %xmm8
+        movaps    %xmm7, %xmm11
+        subl      %edx, %ecx
+        movsd     4120+_vmldASinHATab(%rip), %xmm13
+        subsd     %xmm6, %xmm11
+        addsd     %xmm1, %xmm13
+        movsd     %xmm11, -56(%rsp)
+        movsd     -56(%rsp), %xmm12
+        movsd     %xmm13, -16(%rsp)
+        subsd     %xmm12, %xmm7
+        movl      -16(%rsp), %r11d
+        movaps    %xmm7, %xmm4
+        shll      $8, %r10d
+        andl      $511, %r11d
+        addl      %r10d, %r11d
+        subsd     %xmm7, %xmm8
+        movsd     (%r8,%r11,8), %xmm3
+        addsd     %xmm8, %xmm4
+        mulsd     %xmm3, %xmm7
+        movaps    %xmm3, %xmm14
+        mulsd     %xmm3, %xmm14
+        mulsd     %xmm14, %xmm4
+        movsd     4496+_vmldASinHATab(%rip), %xmm5
+        addsd     %xmm15, %xmm4
+        mulsd     %xmm4, %xmm5
+        shrl      $1, %ecx
+        addsd     4488+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addl      $1023, %ecx
+        andl      $2047, %ecx
+        addsd     4480+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        movzwl    4166+_vmldASinHATab(%rip), %r9d
+        shll      $4, %ecx
+        andl      $-32753, %r9d
+        movsd     %xmm0, -8(%rsp)
+        orl       %ecx, %r9d
+        movw      %r9w, -2(%rsp)
+        movsd     -8(%rsp), %xmm12
+        mulsd     %xmm12, %xmm7
+        addsd     4472+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        movsd     4104+_vmldASinHATab(%rip), %xmm1
+        mulsd     %xmm7, %xmm1
+        addsd     4464+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     4336+_vmldASinHATab(%rip), %xmm13
+        subsd     %xmm7, %xmm0
+        addsd     4456+_vmldASinHATab(%rip), %xmm5
+        addsd     4344+_vmldASinHATab(%rip), %xmm13
+        mulsd     %xmm4, %xmm5
+        mulsd     %xmm9, %xmm13
+        addsd     4448+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        mulsd     %xmm6, %xmm5
+        movsd     %xmm0, -56(%rsp)
+        addsd     %xmm5, %xmm8
+        movsd     4440+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        mulsd     %xmm3, %xmm8
+        addsd     4432+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm12, %xmm8
+        mulsd     %xmm9, %xmm5
+        movsd     -48(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm2
+        movsd     4320+_vmldASinHATab(%rip), %xmm14
+        subsd     %xmm2, %xmm10
+        addsd     4424+_vmldASinHATab(%rip), %xmm5
+        addsd     4328+_vmldASinHATab(%rip), %xmm14
+        mulsd     %xmm9, %xmm5
+        addsd     %xmm13, %xmm14
+        addsd     4416+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm14
+        mulsd     %xmm9, %xmm5
+        movsd     %xmm10, -48(%rsp)
+        movaps    %xmm9, %xmm2
+        movsd     -48(%rsp), %xmm11
+        movsd     4104+_vmldASinHATab(%rip), %xmm3
+        subsd     %xmm11, %xmm7
+        mulsd     %xmm9, %xmm3
+        addsd     4408+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        movsd     4304+_vmldASinHATab(%rip), %xmm4
+        movsd     %xmm7, -56(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm7
+        movsd     %xmm3, -48(%rsp)
+        addsd     %xmm7, %xmm8
+        addsd     4400+_vmldASinHATab(%rip), %xmm5
+        addsd     4312+_vmldASinHATab(%rip), %xmm4
+        mulsd     %xmm9, %xmm5
+        addsd     %xmm14, %xmm4
+        addsd     4392+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        movsd     -48(%rsp), %xmm1
+        movsd     4104+_vmldASinHATab(%rip), %xmm7
+        subsd     %xmm9, %xmm1
+        addsd     4384+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        movsd     %xmm1, -56(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm15
+        addsd     4376+_vmldASinHATab(%rip), %xmm5
+        subsd     %xmm15, %xmm0
+        mulsd     %xmm9, %xmm5
+        movsd     %xmm0, -48(%rsp)
+        movsd     4320+_vmldASinHATab(%rip), %xmm0
+        addsd     4368+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        addsd     4360+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        addsd     4352+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        mulsd     %xmm9, %xmm5
+        mulsd     %xmm9, %xmm5
+        addsd     %xmm5, %xmm4
+        mulsd     %xmm4, %xmm8
+        movsd     -48(%rsp), %xmm4
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -48(%rsp), %xmm4
+        movaps    %xmm4, %xmm13
+        mulsd     %xmm4, %xmm13
+        mulsd     %xmm4, %xmm0
+        addsd     %xmm9, %xmm4
+        mulsd     %xmm13, %xmm7
+        movsd     -56(%rsp), %xmm3
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm10
+        mulsd     %xmm3, %xmm4
+        subsd     %xmm13, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -48(%rsp), %xmm11
+        movsd     -56(%rsp), %xmm1
+        movsd     4304+_vmldASinHATab(%rip), %xmm10
+        subsd     %xmm1, %xmm11
+        movsd     %xmm11, -48(%rsp)
+        movaps    %xmm0, %xmm1
+        movsd     -48(%rsp), %xmm12
+        movsd     4304+_vmldASinHATab(%rip), %xmm11
+        subsd     %xmm12, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -48(%rsp), %xmm7
+        mulsd     4336+_vmldASinHATab(%rip), %xmm7
+        movsd     -56(%rsp), %xmm2
+        addsd     %xmm7, %xmm1
+        addsd     %xmm2, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm14
+        movsd     %xmm7, -16(%rsp)
+        subsd     %xmm14, %xmm0
+        mulsd     4336+_vmldASinHATab(%rip), %xmm4
+        addsd     %xmm0, %xmm7
+        movsd     %xmm7, -56(%rsp)
+        movsd     -48(%rsp), %xmm12
+        movsd     -56(%rsp), %xmm1
+        addsd     %xmm12, %xmm10
+        movsd     %xmm10, -48(%rsp)
+        movsd     -48(%rsp), %xmm15
+        movsd     4104+_vmldASinHATab(%rip), %xmm7
+        subsd     %xmm15, %xmm11
+        movsd     4320+_vmldASinHATab(%rip), %xmm2
+        addsd     %xmm11, %xmm12
+        mulsd     %xmm3, %xmm2
+        movsd     %xmm12, -56(%rsp)
+        movsd     -48(%rsp), %xmm15
+        mulsd     %xmm15, %xmm7
+        movsd     -56(%rsp), %xmm0
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm10
+        movsd     4344+_vmldASinHATab(%rip), %xmm3
+        subsd     %xmm15, %xmm10
+        mulsd     %xmm9, %xmm3
+        movsd     %xmm10, -56(%rsp)
+        movsd     -48(%rsp), %xmm11
+        movsd     -56(%rsp), %xmm13
+        subsd     %xmm13, %xmm11
+        addsd     4328+_vmldASinHATab(%rip), %xmm3
+        movsd     %xmm11, -48(%rsp)
+        movsd     -48(%rsp), %xmm14
+        mulsd     %xmm9, %xmm3
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -56(%rsp)
+        movsd     -48(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm7
+        addsd     %xmm7, %xmm4
+        addsd     4312+_vmldASinHATab(%rip), %xmm3
+        addsd     %xmm2, %xmm4
+        addsd     %xmm5, %xmm3
+        addsd     %xmm1, %xmm4
+        addsd     %xmm0, %xmm4
+        movsd     4144+_vmldASinHATab(%rip), %xmm0
+        addsd     %xmm3, %xmm4
+        mulsd     %xmm6, %xmm4
+        mulsd     %xmm10, %xmm6
+        addsd     %xmm8, %xmm4
+        movaps    %xmm0, %xmm8
+        subsd     %xmm6, %xmm8
+        movsd     %xmm8, -48(%rsp)
+        movsd     -48(%rsp), %xmm9
+        subsd     %xmm9, %xmm0
+        subsd     %xmm6, %xmm0
+        movsd     %xmm0, -56(%rsp)
+        movsd     -48(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm1
+        movb      7(%rdi), %dil
+        andb      $-128, %dil
+        addsd     4152+_vmldASinHATab(%rip), %xmm1
+        subsd     %xmm4, %xmm1
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, -40(%rsp)
+        movb      -33(%rsp), %dl
+        andb      $127, %dl
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movq      -40(%rsp), %rcx
+        movq      %rcx, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        movl      $1, %eax
+        pxor      %xmm1, %xmm1
+        pxor      %xmm0, %xmm0
+        divsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+
+.LBL_2_11:
+
+        ret
+
+.LBL_2_12:
+
+        movsd     (%rdi), %xmm0
+        divsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_11
+
+
+        cmpl      $0, (%rdi)
+        sete      %al
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dasin_ha_cout_rare_internal,@function
+	.size	__svml_dasin_ha_cout_rare_internal,.-__svml_dasin_ha_cout_rare_internal
+..LN__svml_dasin_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dasin_ha_data_internal:
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	805306368
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	4294967040
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	4227858432
+	.long	4294967295
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1073741824
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	2570790083
+	.long	3213983744
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	1869665325
+	.long	1067712512
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294966935
+	.long	3216506879
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	4294967197
+	.long	1070596095
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	3339630857
+	.long	1067480352
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	4158370029
+	.long	3213949719
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	3216784302
+	.long	1066680132
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	2513723093
+	.long	1064982579
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	721210070
+	.long	1065941212
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	3414736215
+	.long	1066167739
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	4114132270
+	.long	1066518236
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3957258973
+	.long	1066854556
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3778730174
+	.long	1067392114
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	3014936056
+	.long	1067899757
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	859300062
+	.long	1068708659
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1431655068
+	.long	1069897045
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	0
+	.long	4294705152
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	4131758366
+	.long	1067674714
+	.long	3936260738
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+	.long	1071943680
+	.long	0
+	.long	1071941632
+	.long	0
+	.long	1071939584
+	.long	0
+	.long	1071937536
+	.long	0
+	.long	1071935488
+	.long	0
+	.long	1071933440
+	.long	0
+	.long	1071930368
+	.long	0
+	.long	1071928320
+	.long	0
+	.long	1071926272
+	.long	0
+	.long	1071924224
+	.long	0
+	.long	1071922176
+	.long	0
+	.long	1071920128
+	.long	0
+	.long	1071918080
+	.long	0
+	.long	1071916032
+	.long	0
+	.long	1071913984
+	.long	0
+	.long	1071911936
+	.long	0
+	.long	1071909888
+	.long	0
+	.long	1071907840
+	.long	0
+	.long	1071905792
+	.long	0
+	.long	1071903744
+	.long	0
+	.long	1071901696
+	.long	0
+	.long	1071900672
+	.long	0
+	.long	1071898624
+	.long	0
+	.long	1071896576
+	.long	0
+	.long	1071894528
+	.long	0
+	.long	1071892480
+	.long	0
+	.long	1071890432
+	.long	0
+	.long	1071888384
+	.long	0
+	.long	1071886336
+	.long	0
+	.long	1071884288
+	.long	0
+	.long	1071883264
+	.long	0
+	.long	1071881216
+	.long	0
+	.long	1071879168
+	.long	0
+	.long	1071877120
+	.long	0
+	.long	1071875072
+	.long	0
+	.long	1071873024
+	.long	0
+	.long	1071872000
+	.long	0
+	.long	1071869952
+	.long	0
+	.long	1071867904
+	.long	0
+	.long	1071865856
+	.long	0
+	.long	1071864832
+	.long	0
+	.long	1071862784
+	.long	0
+	.long	1071860736
+	.long	0
+	.long	1071858688
+	.long	0
+	.long	1071856640
+	.long	0
+	.long	1071855616
+	.long	0
+	.long	1071853568
+	.long	0
+	.long	1071851520
+	.long	0
+	.long	1071850496
+	.long	0
+	.long	1071848448
+	.long	0
+	.long	1071846400
+	.long	0
+	.long	1071844352
+	.long	0
+	.long	1071843328
+	.long	0
+	.long	1071841280
+	.long	0
+	.long	1071839232
+	.long	0
+	.long	1071838208
+	.long	0
+	.long	1071836160
+	.long	0
+	.long	1071834112
+	.long	0
+	.long	1071833088
+	.long	0
+	.long	1071831040
+	.long	0
+	.long	1071830016
+	.long	0
+	.long	1071827968
+	.long	0
+	.long	1071825920
+	.long	0
+	.long	1071824896
+	.long	0
+	.long	1071822848
+	.long	0
+	.long	1071821824
+	.long	0
+	.long	1071819776
+	.long	0
+	.long	1071817728
+	.long	0
+	.long	1071816704
+	.long	0
+	.long	1071814656
+	.long	0
+	.long	1071813632
+	.long	0
+	.long	1071811584
+	.long	0
+	.long	1071810560
+	.long	0
+	.long	1071808512
+	.long	0
+	.long	1071806464
+	.long	0
+	.long	1071805440
+	.long	0
+	.long	1071803392
+	.long	0
+	.long	1071802368
+	.long	0
+	.long	1071800320
+	.long	0
+	.long	1071799296
+	.long	0
+	.long	1071797248
+	.long	0
+	.long	1071796224
+	.long	0
+	.long	1071794176
+	.long	0
+	.long	1071793152
+	.long	0
+	.long	1071791104
+	.long	0
+	.long	1071790080
+	.long	0
+	.long	1071788032
+	.long	0
+	.long	1071787008
+	.long	0
+	.long	1071784960
+	.long	0
+	.long	1071783936
+	.long	0
+	.long	1071782912
+	.long	0
+	.long	1071780864
+	.long	0
+	.long	1071779840
+	.long	0
+	.long	1071777792
+	.long	0
+	.long	1071776768
+	.long	0
+	.long	1071774720
+	.long	0
+	.long	1071773696
+	.long	0
+	.long	1071772672
+	.long	0
+	.long	1071770624
+	.long	0
+	.long	1071769600
+	.long	0
+	.long	1071767552
+	.long	0
+	.long	1071766528
+	.long	0
+	.long	1071765504
+	.long	0
+	.long	1071763456
+	.long	0
+	.long	1071762432
+	.long	0
+	.long	1071760384
+	.long	0
+	.long	1071759360
+	.long	0
+	.long	1071758336
+	.long	0
+	.long	1071756288
+	.long	0
+	.long	1071755264
+	.long	0
+	.long	1071754240
+	.long	0
+	.long	1071752192
+	.long	0
+	.long	1071751168
+	.long	0
+	.long	1071750144
+	.long	0
+	.long	1071748096
+	.long	0
+	.long	1071747072
+	.long	0
+	.long	1071746048
+	.long	0
+	.long	1071744000
+	.long	0
+	.long	1071742976
+	.long	0
+	.long	1071741952
+	.long	0
+	.long	1071739904
+	.long	0
+	.long	1071738880
+	.long	0
+	.long	1071737856
+	.long	0
+	.long	1071736832
+	.long	0
+	.long	1071734784
+	.long	0
+	.long	1071733760
+	.long	0
+	.long	1071732736
+	.long	0
+	.long	1071730688
+	.long	0
+	.long	1071729664
+	.long	0
+	.long	1071728640
+	.long	0
+	.long	1071727616
+	.long	0
+	.long	1071725568
+	.long	0
+	.long	1071724544
+	.long	0
+	.long	1071723520
+	.long	0
+	.long	1071722496
+	.long	0
+	.long	1071720448
+	.long	0
+	.long	1071719424
+	.long	0
+	.long	1071718400
+	.long	0
+	.long	1071717376
+	.long	0
+	.long	1071715328
+	.long	0
+	.long	1071714304
+	.long	0
+	.long	1071713280
+	.long	0
+	.long	1071712256
+	.long	0
+	.long	1071711232
+	.long	0
+	.long	1071709184
+	.long	0
+	.long	1071708160
+	.long	0
+	.long	1071707136
+	.long	0
+	.long	1071706112
+	.long	0
+	.long	1071705088
+	.long	0
+	.long	1071704064
+	.long	0
+	.long	1071702016
+	.long	0
+	.long	1071700992
+	.long	0
+	.long	1071699968
+	.long	0
+	.long	1071698944
+	.long	0
+	.long	1071697920
+	.long	0
+	.long	1071696896
+	.long	0
+	.long	1071694848
+	.long	0
+	.long	1071693824
+	.long	0
+	.long	1071692800
+	.long	0
+	.long	1071691776
+	.long	0
+	.long	1071690752
+	.long	0
+	.long	1071689728
+	.long	0
+	.long	1071688704
+	.long	0
+	.long	1071686656
+	.long	0
+	.long	1071685632
+	.long	0
+	.long	1071684608
+	.long	0
+	.long	1071683584
+	.long	0
+	.long	1071682560
+	.long	0
+	.long	1071681536
+	.long	0
+	.long	1071680512
+	.long	0
+	.long	1071679488
+	.long	0
+	.long	1071677440
+	.long	0
+	.long	1071676416
+	.long	0
+	.long	1071675392
+	.long	0
+	.long	1071674368
+	.long	0
+	.long	1071673344
+	.long	0
+	.long	1071672320
+	.long	0
+	.long	1071671296
+	.long	0
+	.long	1071670272
+	.long	0
+	.long	1071669248
+	.long	0
+	.long	1071668224
+	.long	0
+	.long	1071667200
+	.long	0
+	.long	1071666176
+	.long	0
+	.long	1071665152
+	.long	0
+	.long	1071663104
+	.long	0
+	.long	1071662080
+	.long	0
+	.long	1071661056
+	.long	0
+	.long	1071660032
+	.long	0
+	.long	1071659008
+	.long	0
+	.long	1071657984
+	.long	0
+	.long	1071656960
+	.long	0
+	.long	1071655936
+	.long	0
+	.long	1071654912
+	.long	0
+	.long	1071653888
+	.long	0
+	.long	1071652864
+	.long	0
+	.long	1071651840
+	.long	0
+	.long	1071650816
+	.long	0
+	.long	1071649792
+	.long	0
+	.long	1071648768
+	.long	0
+	.long	1071647744
+	.long	0
+	.long	1071646720
+	.long	0
+	.long	1071645696
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1101004800
+	.long	1073741824
+	.long	1095761920
+	.long	256
+	.long	1118830592
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1017118720
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1071644672
+	.long	1476395008
+	.long	1069897045
+	.long	1768958041
+	.long	3189069141
+	.long	805306368
+	.long	1068708659
+	.long	3580333578
+	.long	1040816593
+	.long	3067382784
+	.long	1067899757
+	.long	3397590151
+	.long	1067392113
+	.long	2939529726
+	.long	1066854585
+	.long	1423429166
+	.long	1066517752
+	.long	1775218934
+	.long	1066178574
+	.long	1185392460
+	.long	1065859647
+	.long	289998670
+	.long	1065577550
+	.long	3179807072
+	.long	1065648121
+	.long	3781007284
+	.long	1061576176
+	.long	2482106687
+	.long	1067019199
+	.long	763519713
+	.long	3214591591
+	.long	3695107454
+	.long	1067530646
+	.long	0
+	.long	1073741824
+	.long	1124791109
+	.long	1006764147
+	.long	1476395008
+	.long	1069897045
+	.long	1953913876
+	.long	3189069141
+	.long	805306368
+	.long	1067660083
+	.long	165110192
+	.long	1039768033
+	.long	3067304082
+	.long	1065802605
+	.long	3404727379
+	.long	1064246385
+	.long	2737480376
+	.long	1062660281
+	.long	933797922
+	.long	1061274873
+	.long	1475716730
+	.long	1059887095
+	.long	1511619763
+	.long	1058519827
+	.long	556024211
+	.long	1057187555
+	.long	3482101045
+	.long	1056217350
+	.long	1174622859
+	.long	1050762633
+	.long	899668651
+	.long	1055506366
+	.long	1081094694
+	.long	3202035365
+	.long	2559814773
+	.long	1053906576
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	3218341888
+	.long	0
+	.long	1070694400
+	.long	0
+	.long	3218046976
+	.long	0
+	.long	1070391296
+	.long	0
+	.long	3217739776
+	.type	_vmldASinHATab,@object
+	.size	_vmldASinHATab,4504
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_d_la.s
index 09b6ce3859c..a84c68a623a 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_h_la.s
new file mode 100644
index 00000000000..1609b4f204a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_h_la.s
@@ -0,0 +1,136 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *      SelMask = (|x| >= 0.5) ? 1 : 0;
+ *      R = SelMask ? sqrt(0.5 - 0.5*|x|) : |x|
+ *      asin(x) = (SelMask ? (Pi/2 - 2*Poly(R)) : Poly(R))*(-1)^sign(x)
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_asins32
+
+__svml_asins32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hasin_data_internal(%rip), %zmm30
+        vmovdqu16 64+__svml_hasin_data_internal(%rip), %zmm1
+        vmovdqu16 128+__svml_hasin_data_internal(%rip), %zmm29
+        vmovdqu16 192+__svml_hasin_data_internal(%rip), %zmm3
+        vmovdqu16 256+__svml_hasin_data_internal(%rip), %zmm31
+        vmovdqu16 320+__svml_hasin_data_internal(%rip), %zmm5
+        vmovdqu16 384+__svml_hasin_data_internal(%rip), %zmm7
+        vmovdqu16 448+__svml_hasin_data_internal(%rip), %zmm9
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_asin_h:
+        cmpq    $31, %rdx
+        ja .loaddata_asinh
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_asin_h
+.loaddata_asinh:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_asin_h:
+
+/* x^2 */
+        vmulph    {rn-sae}, %zmm0, %zmm0, %zmm2
+
+/* y = 0.5 -0.5*|x| */
+        vmovaps   %zmm1, %zmm8
+
+/*
+ * No callout
+ * xa = |x|
+ */
+        vpandd %zmm30, %zmm0, %zmm10
+        vfnmadd231ph {rn-sae}, %zmm10, %zmm1, %zmm8
+
+/* SelMask=1 for |x|>=0.5 */
+        vcmpph    $21, {sae}, %zmm1, %zmm10, %k2
+
+/* set y = y*rsqrt(y) ~ sqrt(y) */
+        vrsqrtph  %zmm8, %zmm4
+
+/* SqrtMask=0 if y==+/-0 */
+        vcmpph    $4, {sae}, %zmm3, %zmm8, %k1
+
+/* set x2=y for |x|>=0.5 */
+        vminph    {sae}, %zmm8, %zmm2, %zmm6
+        vpblendmw %zmm29, %zmm3, %zmm11{%k2}
+        vmulph    {rn-sae}, %zmm4, %zmm8, %zmm8{%k1}
+
+        vmovdqu16 %zmm31, %zmm12
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm5, %zmm6, %zmm12
+        vfmadd213ph {rn-sae}, %zmm7, %zmm6, %zmm12
+
+/* sign(x) */
+        vpxord    %zmm0, %zmm10, %zmm13
+        vmulph    {rn-sae}, %zmm9, %zmm8, %zmm10{%k2}
+
+/* result */
+        vfmadd213ph {rn-sae}, %zmm11, %zmm10, %zmm12
+        vpxord    %zmm13, %zmm12, %zmm0
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_asin_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_asins32,@function
+        .size	__svml_asins32,.-__svml_asins32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hasin_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x3800
+	.endr
+	.rept	32
+        .word	0x3e48
+	.endr
+	.rept	32
+        .word	0x0000
+	.endr
+	.rept	32
+        .word	0x2e26
+	.endr
+	.rept	32
+        .word	0x3144
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0xc000
+	.endr
+        .type	__svml_hasin_data_internal,@object
+        .size	__svml_hasin_data_internal,512
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_s_ha.s
new file mode 100644
index 00000000000..a2a3894a79c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_s_ha.s
@@ -0,0 +1,2236 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_asinf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_asinf16_ha
+
+__svml_asinf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   __svml_sasin_ha_data_internal(%rip), %zmm6
+        vmovups   64+__svml_sasin_ha_data_internal(%rip), %zmm8
+
+/*
+ * 2*sqrt(Y) = Sh - Sl
+ * Sh ~ 2*sqrt_high(Y)
+ * Sl ~ -2*sqrt_low(Y)
+ */
+        vmovups   192+__svml_sasin_ha_data_internal(%rip), %zmm11
+        vmovups   256+__svml_sasin_ha_data_internal(%rip), %zmm9
+        vmovaps   %zmm0, %zmm5
+
+/* x = |arg| */
+        vandps    %zmm5, %zmm6, %zmm4
+        vandnps   %zmm5, %zmm6, %zmm3
+        vmovups   448+__svml_sasin_ha_data_internal(%rip), %zmm6
+
+/* x^2 */
+        vmulps    {rn-sae}, %zmm4, %zmm4, %zmm7
+        vcmpps    $17, {sae}, %zmm4, %zmm9, %k0
+        vcmpps    $21, {sae}, %zmm8, %zmm4, %k2
+        kmovw     %k0, %edx
+
+/* Y = 0.5 - 0.5*x */
+        vmovaps   %zmm8, %zmm12
+        vfnmadd231ps {rn-sae}, %zmm4, %zmm8, %zmm12
+        vmovups   512+__svml_sasin_ha_data_internal(%rip), %zmm8
+        vrsqrt14ps %zmm12, %zmm10
+        vcmpps    $17, {sae}, %zmm11, %zmm12, %k1
+        vminps    {sae}, %zmm12, %zmm7, %zmm2
+        vaddps    {rn-sae}, %zmm12, %zmm12, %zmm13
+        vandps    384+__svml_sasin_ha_data_internal(%rip), %zmm10, %zmm0
+        vandps    320+__svml_sasin_ha_data_internal(%rip), %zmm13, %zmm14
+        vxorps    %zmm0, %zmm0, %zmm0{%k1}
+        vsubps    {rn-sae}, %zmm14, %zmm13, %zmm15
+        vmulps    {rn-sae}, %zmm0, %zmm14, %zmm1
+        vmulps    {rn-sae}, %zmm0, %zmm15, %zmm12
+        vmulps    {rn-sae}, %zmm2, %zmm2, %zmm15
+
+/* polynomial */
+        vmovups   640+__svml_sasin_ha_data_internal(%rip), %zmm13
+        vfmsub231ps {rn-sae}, %zmm1, %zmm0, %zmm6
+        vmovups   768+__svml_sasin_ha_data_internal(%rip), %zmm14
+        vmulps    {rn-sae}, %zmm0, %zmm12, %zmm7
+        vmovups   576+__svml_sasin_ha_data_internal(%rip), %zmm0
+        vaddps    {rn-sae}, %zmm12, %zmm1, %zmm9
+        vaddps    {rn-sae}, %zmm7, %zmm6, %zmm10
+        vmovups   704+__svml_sasin_ha_data_internal(%rip), %zmm6
+        vmovups   1088+__svml_sasin_ha_data_internal(%rip), %zmm7
+        vfmadd231ps {rn-sae}, %zmm10, %zmm8, %zmm0
+        vmulps    {rn-sae}, %zmm10, %zmm9, %zmm11
+        vfmadd231ps {rn-sae}, %zmm2, %zmm13, %zmm6
+        vmovups   832+__svml_sasin_ha_data_internal(%rip), %zmm13
+        vfmsub213ps {rn-sae}, %zmm12, %zmm11, %zmm0
+        vfmadd231ps {rn-sae}, %zmm2, %zmm14, %zmm13
+        vmovups   896+__svml_sasin_ha_data_internal(%rip), %zmm14
+
+/* -2*sqrt(Y), or X */
+        vsubps    {rn-sae}, %zmm1, %zmm0, %zmm4{%k2}
+        vfmadd213ps {rn-sae}, %zmm13, %zmm15, %zmm6
+        vfmadd213ps {rn-sae}, %zmm14, %zmm2, %zmm6
+        vmulps    {rn-sae}, %zmm2, %zmm6, %zmm9
+        vmovups   960+__svml_sasin_ha_data_internal(%rip), %zmm2
+        vmovups   1024+__svml_sasin_ha_data_internal(%rip), %zmm6
+
+/* Pi2H - 2*sqrt_high(Y) */
+        vsubps    {rn-sae}, %zmm1, %zmm2, %zmm8
+        vaddps    {rn-sae}, %zmm0, %zmm6, %zmm0
+        vsubps    {rn-sae}, %zmm8, %zmm2, %zmm2
+        vsubps    {rn-sae}, %zmm2, %zmm1, %zmm1
+
+/* Low-Shl (Pi2L-2*sqrt_low(Y)), or 0 if selmask_bit==0 */
+        vsubps    {rn-sae}, %zmm1, %zmm0, %zmm7{%k2}
+        vfmadd213ps {rn-sae}, %zmm7, %zmm4, %zmm9
+
+/* |arg| or Pi2H - 2*sqrt_high(Y) */
+        vblendmps %zmm8, %zmm4, %zmm4{%k2}
+        vaddps    {rn-sae}, %zmm9, %zmm4, %zmm0
+        vxorps    %zmm3, %zmm0, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm5, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_sasin_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_asinf16_ha,@function
+	.size	__svml_asinf16_ha,.-__svml_asinf16_ha
+..LN__svml_asinf16_ha.0:
+
+.L_2__routine_start___svml_sasin_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_sasin_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    2(%rdi), %edx
+        andl      $32640, %edx
+        cmpl      $32640, %edx
+        je        .LBL_2_12
+
+
+        pxor      %xmm0, %xmm0
+        cvtss2sd  (%rdi), %xmm0
+        movsd     %xmm0, -32(%rsp)
+        andb      $127, -25(%rsp)
+        movsd     -32(%rsp), %xmm12
+        movsd     4160+_vmldASinHATab(%rip), %xmm0
+        comisd    %xmm12, %xmm0
+        jb        .LBL_2_10
+
+
+        movsd     4168+_vmldASinHATab(%rip), %xmm1
+        comisd    %xmm12, %xmm1
+        jbe       .LBL_2_9
+
+
+        comisd    4136+_vmldASinHATab(%rip), %xmm12
+        jbe       .LBL_2_6
+
+
+        movsd     4104+_vmldASinHATab(%rip), %xmm13
+        movaps    %xmm12, %xmm5
+        mulsd     %xmm12, %xmm13
+        movaps    %xmm12, %xmm7
+        movsd     %xmm13, -48(%rsp)
+        movsd     -48(%rsp), %xmm14
+        movsd     4104+_vmldASinHATab(%rip), %xmm1
+        subsd     -32(%rsp), %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movsd     -48(%rsp), %xmm8
+        movsd     -56(%rsp), %xmm15
+        subsd     %xmm15, %xmm8
+        movsd     %xmm8, -48(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     4296+_vmldASinHATab(%rip), %xmm8
+        subsd     %xmm6, %xmm5
+        movsd     %xmm5, -56(%rsp)
+        movaps    %xmm12, %xmm5
+        movsd     -48(%rsp), %xmm9
+        movaps    %xmm9, %xmm13
+        addsd     %xmm9, %xmm7
+        mulsd     %xmm9, %xmm13
+        movsd     -56(%rsp), %xmm11
+        movaps    %xmm13, %xmm10
+        mulsd     %xmm11, %xmm7
+        mulsd     %xmm13, %xmm1
+        addsd     %xmm7, %xmm10
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm2
+        movsd     4104+_vmldASinHATab(%rip), %xmm1
+        subsd     %xmm13, %xmm2
+        addsd     4288+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm2, -56(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movsd     -56(%rsp), %xmm0
+        addsd     4280+_vmldASinHATab(%rip), %xmm8
+        subsd     %xmm0, %xmm3
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm3, -48(%rsp)
+        movsd     -48(%rsp), %xmm4
+        addsd     4272+_vmldASinHATab(%rip), %xmm8
+        subsd     %xmm4, %xmm13
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm13, -56(%rsp)
+        movsd     -48(%rsp), %xmm6
+        mulsd     %xmm6, %xmm9
+        addsd     4264+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm6, %xmm11
+        mulsd     %xmm10, %xmm8
+        mulsd     %xmm9, %xmm1
+        addsd     4256+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm10, %xmm8
+        movaps    %xmm9, %xmm3
+        movsd     -56(%rsp), %xmm14
+        movaps    %xmm6, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        addsd     %xmm14, %xmm7
+        addsd     4248+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm7, %xmm5
+        mulsd     %xmm10, %xmm8
+        addsd     %xmm5, %xmm11
+        addsd     4240+_vmldASinHATab(%rip), %xmm8
+        movsd     -48(%rsp), %xmm2
+        mulsd     %xmm10, %xmm8
+        subsd     %xmm9, %xmm2
+        addsd     %xmm11, %xmm9
+        addsd     4232+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm9, %xmm7
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm2, -56(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm15
+        subsd     %xmm15, %xmm0
+        addsd     4224+_vmldASinHATab(%rip), %xmm8
+        movsd     %xmm0, -48(%rsp)
+        movsd     -48(%rsp), %xmm5
+        mulsd     %xmm10, %xmm8
+        subsd     %xmm5, %xmm3
+        movsd     %xmm3, -56(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movsd     -56(%rsp), %xmm5
+        movaps    %xmm5, %xmm13
+        addsd     4216+_vmldASinHATab(%rip), %xmm8
+        mulsd     %xmm3, %xmm4
+        addsd     %xmm11, %xmm13
+        mulsd     %xmm10, %xmm8
+        mulsd     %xmm13, %xmm6
+        addsd     4208+_vmldASinHATab(%rip), %xmm8
+        addsd     %xmm7, %xmm6
+        mulsd     %xmm10, %xmm8
+        movsd     4104+_vmldASinHATab(%rip), %xmm7
+        movaps    %xmm4, %xmm13
+        mulsd     %xmm4, %xmm7
+        addsd     4200+_vmldASinHATab(%rip), %xmm8
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm1
+        movsd     4184+_vmldASinHATab(%rip), %xmm10
+        subsd     %xmm4, %xmm1
+        mulsd     %xmm9, %xmm10
+        addsd     %xmm6, %xmm4
+        movsd     %xmm1, -56(%rsp)
+        movaps    %xmm12, %xmm9
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm2
+        movsd     4176+_vmldASinHATab(%rip), %xmm1
+        subsd     %xmm2, %xmm0
+        mulsd     %xmm1, %xmm3
+        mulsd     %xmm1, %xmm11
+        mulsd     %xmm4, %xmm8
+        addsd     %xmm3, %xmm9
+        mulsd     %xmm1, %xmm5
+        addsd     %xmm11, %xmm10
+        movsd     %xmm0, -48(%rsp)
+        addsd     %xmm10, %xmm8
+        movsd     -48(%rsp), %xmm7
+        addsd     %xmm5, %xmm8
+        subsd     %xmm7, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm2
+        movsd     %xmm9, -48(%rsp)
+        addsd     %xmm2, %xmm6
+        movsd     -48(%rsp), %xmm11
+        movsd     4192+_vmldASinHATab(%rip), %xmm4
+        subsd     %xmm11, %xmm12
+        mulsd     %xmm4, %xmm0
+        mulsd     %xmm4, %xmm6
+        addsd     %xmm12, %xmm3
+        movsd     %xmm3, -56(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movaps    %xmm3, %xmm12
+        movsd     -56(%rsp), %xmm7
+        addsd     %xmm0, %xmm12
+        addsd     %xmm7, %xmm8
+        movsd     %xmm12, -48(%rsp)
+        movsd     -48(%rsp), %xmm12
+        subsd     %xmm12, %xmm3
+        addsd     %xmm3, %xmm0
+        movsd     %xmm0, -56(%rsp)
+        movsd     -48(%rsp), %xmm9
+        movsd     -56(%rsp), %xmm0
+        movb      3(%rdi), %dil
+        addsd     %xmm0, %xmm8
+        andb      $-128, %dil
+        addsd     %xmm8, %xmm9
+        addsd     %xmm9, %xmm6
+        movsd     %xmm6, -40(%rsp)
+        movb      -33(%rsp), %dl
+        andb      $127, %dl
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movsd     -40(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_6:
+
+        comisd    4128+_vmldASinHATab(%rip), %xmm12
+        jb        .LBL_2_8
+
+
+        addsd     %xmm0, %xmm12
+        movsd     %xmm12, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        mulsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movb      -33(%rsp), %dl
+        movb      3(%rdi), %dil
+        andb      $127, %dl
+        andb      $-128, %dil
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movsd     -40(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        mulsd     %xmm12, %xmm12
+        movsd     %xmm12, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movb      3(%rdi), %dil
+        andb      $-128, %dil
+        addsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -40(%rsp)
+        movb      -33(%rsp), %dl
+        andb      $127, %dl
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movsd     -40(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        movaps    %xmm0, %xmm9
+        movzwl    4166+_vmldASinHATab(%rip), %r9d
+        pxor      %xmm15, %xmm15
+        andl      $-32753, %r9d
+        subsd     %xmm12, %xmm9
+        subsd     %xmm0, %xmm15
+        mulsd     %xmm9, %xmm1
+        movsd     %xmm1, -32(%rsp)
+        movzwl    -26(%rsp), %ecx
+        andl      $32752, %ecx
+        shrl      $4, %ecx
+        addl      $-1023, %ecx
+        movl      %ecx, %r8d
+        movl      %ecx, %edx
+        negl      %r8d
+        addl      $1023, %r8d
+        andl      $2047, %r8d
+        shll      $4, %r8d
+        movsd     %xmm0, -24(%rsp)
+        orl       %r8d, %r9d
+        movw      %r9w, -18(%rsp)
+        andl      $1, %edx
+        movsd     -24(%rsp), %xmm10
+        movl      %edx, %r10d
+        mulsd     %xmm10, %xmm1
+        lea       _vmldASinHATab(%rip), %r8
+        movaps    %xmm1, %xmm6
+        movsd     4112+_vmldASinHATab(%rip), %xmm7
+        addsd     %xmm1, %xmm6
+        jne       ..L54
+        movaps    %xmm1, %xmm6
+..L54:
+        mulsd     %xmm6, %xmm7
+        movaps    %xmm6, %xmm8
+        movaps    %xmm7, %xmm11
+        subl      %edx, %ecx
+        movsd     4120+_vmldASinHATab(%rip), %xmm13
+        subsd     %xmm6, %xmm11
+        addsd     %xmm1, %xmm13
+        movsd     %xmm11, -56(%rsp)
+        movsd     -56(%rsp), %xmm12
+        movsd     %xmm13, -16(%rsp)
+        subsd     %xmm12, %xmm7
+        movl      -16(%rsp), %r11d
+        movaps    %xmm7, %xmm4
+        shll      $8, %r10d
+        andl      $511, %r11d
+        addl      %r10d, %r11d
+        subsd     %xmm7, %xmm8
+        movsd     (%r8,%r11,8), %xmm3
+        addsd     %xmm8, %xmm4
+        mulsd     %xmm3, %xmm7
+        movaps    %xmm3, %xmm14
+        mulsd     %xmm3, %xmm14
+        mulsd     %xmm14, %xmm4
+        movsd     4496+_vmldASinHATab(%rip), %xmm5
+        addsd     %xmm15, %xmm4
+        mulsd     %xmm4, %xmm5
+        shrl      $1, %ecx
+        addsd     4488+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addl      $1023, %ecx
+        andl      $2047, %ecx
+        addsd     4480+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        movzwl    4166+_vmldASinHATab(%rip), %r9d
+        shll      $4, %ecx
+        andl      $-32753, %r9d
+        movsd     %xmm0, -8(%rsp)
+        orl       %ecx, %r9d
+        movw      %r9w, -2(%rsp)
+        movsd     -8(%rsp), %xmm12
+        mulsd     %xmm12, %xmm7
+        addsd     4472+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        movsd     4104+_vmldASinHATab(%rip), %xmm1
+        mulsd     %xmm7, %xmm1
+        addsd     4464+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     4336+_vmldASinHATab(%rip), %xmm13
+        subsd     %xmm7, %xmm0
+        addsd     4456+_vmldASinHATab(%rip), %xmm5
+        addsd     4344+_vmldASinHATab(%rip), %xmm13
+        mulsd     %xmm4, %xmm5
+        mulsd     %xmm9, %xmm13
+        addsd     4448+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        mulsd     %xmm6, %xmm5
+        movsd     %xmm0, -56(%rsp)
+        addsd     %xmm5, %xmm8
+        movsd     4440+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        mulsd     %xmm3, %xmm8
+        addsd     4432+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm12, %xmm8
+        mulsd     %xmm9, %xmm5
+        movsd     -48(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm2
+        movsd     4320+_vmldASinHATab(%rip), %xmm14
+        subsd     %xmm2, %xmm10
+        addsd     4424+_vmldASinHATab(%rip), %xmm5
+        addsd     4328+_vmldASinHATab(%rip), %xmm14
+        mulsd     %xmm9, %xmm5
+        addsd     %xmm13, %xmm14
+        addsd     4416+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm14
+        mulsd     %xmm9, %xmm5
+        movsd     %xmm10, -48(%rsp)
+        movaps    %xmm9, %xmm2
+        movsd     -48(%rsp), %xmm11
+        movsd     4104+_vmldASinHATab(%rip), %xmm3
+        subsd     %xmm11, %xmm7
+        mulsd     %xmm9, %xmm3
+        addsd     4408+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        movsd     4304+_vmldASinHATab(%rip), %xmm4
+        movsd     %xmm7, -56(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm7
+        movsd     %xmm3, -48(%rsp)
+        addsd     %xmm7, %xmm8
+        addsd     4400+_vmldASinHATab(%rip), %xmm5
+        addsd     4312+_vmldASinHATab(%rip), %xmm4
+        mulsd     %xmm9, %xmm5
+        addsd     %xmm14, %xmm4
+        addsd     4392+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        movsd     -48(%rsp), %xmm1
+        movsd     4104+_vmldASinHATab(%rip), %xmm7
+        subsd     %xmm9, %xmm1
+        addsd     4384+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        movsd     %xmm1, -56(%rsp)
+        movsd     -48(%rsp), %xmm0
+        movsd     -56(%rsp), %xmm15
+        addsd     4376+_vmldASinHATab(%rip), %xmm5
+        subsd     %xmm15, %xmm0
+        mulsd     %xmm9, %xmm5
+        movsd     %xmm0, -48(%rsp)
+        movsd     4320+_vmldASinHATab(%rip), %xmm0
+        addsd     4368+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        addsd     4360+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        addsd     4352+_vmldASinHATab(%rip), %xmm5
+        mulsd     %xmm9, %xmm5
+        mulsd     %xmm9, %xmm5
+        mulsd     %xmm9, %xmm5
+        addsd     %xmm5, %xmm4
+        mulsd     %xmm4, %xmm8
+        movsd     -48(%rsp), %xmm4
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -48(%rsp), %xmm4
+        movaps    %xmm4, %xmm13
+        mulsd     %xmm4, %xmm13
+        mulsd     %xmm4, %xmm0
+        addsd     %xmm9, %xmm4
+        mulsd     %xmm13, %xmm7
+        movsd     -56(%rsp), %xmm3
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm10
+        mulsd     %xmm3, %xmm4
+        subsd     %xmm13, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -48(%rsp), %xmm11
+        movsd     -56(%rsp), %xmm1
+        movsd     4304+_vmldASinHATab(%rip), %xmm10
+        subsd     %xmm1, %xmm11
+        movsd     %xmm11, -48(%rsp)
+        movaps    %xmm0, %xmm1
+        movsd     -48(%rsp), %xmm12
+        movsd     4304+_vmldASinHATab(%rip), %xmm11
+        subsd     %xmm12, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -48(%rsp), %xmm7
+        mulsd     4336+_vmldASinHATab(%rip), %xmm7
+        movsd     -56(%rsp), %xmm2
+        addsd     %xmm7, %xmm1
+        addsd     %xmm2, %xmm4
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm14
+        movsd     %xmm7, -16(%rsp)
+        subsd     %xmm14, %xmm0
+        mulsd     4336+_vmldASinHATab(%rip), %xmm4
+        addsd     %xmm0, %xmm7
+        movsd     %xmm7, -56(%rsp)
+        movsd     -48(%rsp), %xmm12
+        movsd     -56(%rsp), %xmm1
+        addsd     %xmm12, %xmm10
+        movsd     %xmm10, -48(%rsp)
+        movsd     -48(%rsp), %xmm15
+        movsd     4104+_vmldASinHATab(%rip), %xmm7
+        subsd     %xmm15, %xmm11
+        movsd     4320+_vmldASinHATab(%rip), %xmm2
+        addsd     %xmm11, %xmm12
+        mulsd     %xmm3, %xmm2
+        movsd     %xmm12, -56(%rsp)
+        movsd     -48(%rsp), %xmm15
+        mulsd     %xmm15, %xmm7
+        movsd     -56(%rsp), %xmm0
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm10
+        movsd     4344+_vmldASinHATab(%rip), %xmm3
+        subsd     %xmm15, %xmm10
+        mulsd     %xmm9, %xmm3
+        movsd     %xmm10, -56(%rsp)
+        movsd     -48(%rsp), %xmm11
+        movsd     -56(%rsp), %xmm13
+        subsd     %xmm13, %xmm11
+        addsd     4328+_vmldASinHATab(%rip), %xmm3
+        movsd     %xmm11, -48(%rsp)
+        movsd     -48(%rsp), %xmm14
+        mulsd     %xmm9, %xmm3
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -56(%rsp)
+        movsd     -48(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm7
+        addsd     %xmm7, %xmm4
+        addsd     4312+_vmldASinHATab(%rip), %xmm3
+        addsd     %xmm2, %xmm4
+        addsd     %xmm5, %xmm3
+        addsd     %xmm1, %xmm4
+        addsd     %xmm0, %xmm4
+        movsd     4144+_vmldASinHATab(%rip), %xmm0
+        addsd     %xmm3, %xmm4
+        mulsd     %xmm6, %xmm4
+        mulsd     %xmm10, %xmm6
+        addsd     %xmm8, %xmm4
+        movaps    %xmm0, %xmm8
+        subsd     %xmm6, %xmm8
+        movsd     %xmm8, -48(%rsp)
+        movsd     -48(%rsp), %xmm9
+        subsd     %xmm9, %xmm0
+        subsd     %xmm6, %xmm0
+        movsd     %xmm0, -56(%rsp)
+        movsd     -48(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm1
+        movb      3(%rdi), %dil
+        andb      $-128, %dil
+        addsd     4152+_vmldASinHATab(%rip), %xmm1
+        subsd     %xmm4, %xmm1
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, -40(%rsp)
+        movb      -33(%rsp), %dl
+        andb      $127, %dl
+        orb       %dil, %dl
+        movb      %dl, -33(%rsp)
+        movsd     -40(%rsp), %xmm3
+        cvtsd2ss  %xmm3, %xmm3
+        movss     %xmm3, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        movl      $1, %eax
+        pxor      %xmm1, %xmm1
+        pxor      %xmm0, %xmm0
+        divss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+
+
+        ret
+
+.LBL_2_12:
+
+        movss     (%rdi), %xmm0
+        divss     %xmm0, %xmm0
+        testl     $8388607, (%rdi)
+        movss     %xmm0, (%rsi)
+        sete      %al
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sasin_ha_cout_rare_internal,@function
+	.size	__svml_sasin_ha_cout_rare_internal,.-__svml_sasin_ha_cout_rare_internal
+..LN__svml_sasin_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_sasin_ha_data_internal:
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	4294963200
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294959104
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	4294965248
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	3183476740
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1048576001
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1026481159
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1019423339
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1027250868
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1033468946
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1042983679
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	4290772992
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1036306094
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	1042839218
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.long	939876771
+	.type	__svml_sasin_ha_data_internal,@object
+	.size	__svml_sasin_ha_data_internal,1472
+	.align 32
+_vmldASinHATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072689152
+	.long	0
+	.long	1072685056
+	.long	0
+	.long	1072680960
+	.long	0
+	.long	1072676864
+	.long	0
+	.long	1072672768
+	.long	0
+	.long	1072668672
+	.long	0
+	.long	1072665600
+	.long	0
+	.long	1072661504
+	.long	0
+	.long	1072657408
+	.long	0
+	.long	1072653312
+	.long	0
+	.long	1072649216
+	.long	0
+	.long	1072646144
+	.long	0
+	.long	1072642048
+	.long	0
+	.long	1072637952
+	.long	0
+	.long	1072634880
+	.long	0
+	.long	1072630784
+	.long	0
+	.long	1072626688
+	.long	0
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+	.long	1071719424
+	.long	0
+	.long	1071718400
+	.long	0
+	.long	1071717376
+	.long	0
+	.long	1071715328
+	.long	0
+	.long	1071714304
+	.long	0
+	.long	1071713280
+	.long	0
+	.long	1071712256
+	.long	0
+	.long	1071711232
+	.long	0
+	.long	1071709184
+	.long	0
+	.long	1071708160
+	.long	0
+	.long	1071707136
+	.long	0
+	.long	1071706112
+	.long	0
+	.long	1071705088
+	.long	0
+	.long	1071704064
+	.long	0
+	.long	1071702016
+	.long	0
+	.long	1071700992
+	.long	0
+	.long	1071699968
+	.long	0
+	.long	1071698944
+	.long	0
+	.long	1071697920
+	.long	0
+	.long	1071696896
+	.long	0
+	.long	1071694848
+	.long	0
+	.long	1071693824
+	.long	0
+	.long	1071692800
+	.long	0
+	.long	1071691776
+	.long	0
+	.long	1071690752
+	.long	0
+	.long	1071689728
+	.long	0
+	.long	1071688704
+	.long	0
+	.long	1071686656
+	.long	0
+	.long	1071685632
+	.long	0
+	.long	1071684608
+	.long	0
+	.long	1071683584
+	.long	0
+	.long	1071682560
+	.long	0
+	.long	1071681536
+	.long	0
+	.long	1071680512
+	.long	0
+	.long	1071679488
+	.long	0
+	.long	1071677440
+	.long	0
+	.long	1071676416
+	.long	0
+	.long	1071675392
+	.long	0
+	.long	1071674368
+	.long	0
+	.long	1071673344
+	.long	0
+	.long	1071672320
+	.long	0
+	.long	1071671296
+	.long	0
+	.long	1071670272
+	.long	0
+	.long	1071669248
+	.long	0
+	.long	1071668224
+	.long	0
+	.long	1071667200
+	.long	0
+	.long	1071666176
+	.long	0
+	.long	1071665152
+	.long	0
+	.long	1071663104
+	.long	0
+	.long	1071662080
+	.long	0
+	.long	1071661056
+	.long	0
+	.long	1071660032
+	.long	0
+	.long	1071659008
+	.long	0
+	.long	1071657984
+	.long	0
+	.long	1071656960
+	.long	0
+	.long	1071655936
+	.long	0
+	.long	1071654912
+	.long	0
+	.long	1071653888
+	.long	0
+	.long	1071652864
+	.long	0
+	.long	1071651840
+	.long	0
+	.long	1071650816
+	.long	0
+	.long	1071649792
+	.long	0
+	.long	1071648768
+	.long	0
+	.long	1071647744
+	.long	0
+	.long	1071646720
+	.long	0
+	.long	1071645696
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1101004800
+	.long	1073741824
+	.long	1095761920
+	.long	256
+	.long	1118830592
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1017118720
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1071644672
+	.long	1476395008
+	.long	1069897045
+	.long	1768958041
+	.long	3189069141
+	.long	805306368
+	.long	1068708659
+	.long	3580333578
+	.long	1040816593
+	.long	3067382784
+	.long	1067899757
+	.long	3397590151
+	.long	1067392113
+	.long	2939529726
+	.long	1066854585
+	.long	1423429166
+	.long	1066517752
+	.long	1775218934
+	.long	1066178574
+	.long	1185392460
+	.long	1065859647
+	.long	289998670
+	.long	1065577550
+	.long	3179807072
+	.long	1065648121
+	.long	3781007284
+	.long	1061576176
+	.long	2482106687
+	.long	1067019199
+	.long	763519713
+	.long	3214591591
+	.long	3695107454
+	.long	1067530646
+	.long	0
+	.long	1073741824
+	.long	1124791109
+	.long	1006764147
+	.long	1476395008
+	.long	1069897045
+	.long	1953913876
+	.long	3189069141
+	.long	805306368
+	.long	1067660083
+	.long	165110192
+	.long	1039768033
+	.long	3067304082
+	.long	1065802605
+	.long	3404727379
+	.long	1064246385
+	.long	2737480376
+	.long	1062660281
+	.long	933797922
+	.long	1061274873
+	.long	1475716730
+	.long	1059887095
+	.long	1511619763
+	.long	1058519827
+	.long	556024211
+	.long	1057187555
+	.long	3482101045
+	.long	1056217350
+	.long	1174622859
+	.long	1050762633
+	.long	899668651
+	.long	1055506366
+	.long	1081094694
+	.long	3202035365
+	.long	2559814773
+	.long	1053906576
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	3218341888
+	.long	0
+	.long	1070694400
+	.long	0
+	.long	3218046976
+	.long	0
+	.long	1070391296
+	.long	0
+	.long	3217739776
+	.type	_vmldASinHATab,@object
+	.size	_vmldASinHATab,4504
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_s_la.s
index 9d92dfbfe7c..07309c6b71f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asin_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_d_ha.s
new file mode 100644
index 00000000000..cbb578e732b
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_d_ha.s
@@ -0,0 +1,930 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *   Compute log(x+sqrt(x*x+1)) using RSQRT14/RSQRT28 for starting the
+ *  *   square root approximation, and small table lookups for log (mapping to
+ *  *   AVX3 permute instructions).
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_asinh8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_asinh8_ha
+
+__svml_asinh8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm3
+
+/* x^2 */
+        vmulpd    {rn-sae}, %zmm3, %zmm3, %zmm15
+        vmovups   256+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm9
+
+/* polynomial computation for small inputs */
+        vmovups   576+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   640+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   704+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm12
+
+/* not a very small input ? */
+        vmovups   384+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm0
+
+/* A=max(x^2, 1); */
+        vmaxpd    {sae}, %zmm15, %zmm9, %zmm4
+
+/* B=min(x^2, 1); */
+        vminpd    {sae}, %zmm15, %zmm9, %zmm10
+        vfmadd231pd {rn-sae}, %zmm15, %zmm13, %zmm14
+        vmovups   960+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm13
+
+/* 1+x^2 */
+        vaddpd    {rn-sae}, %zmm9, %zmm15, %zmm6
+
+/* |input| */
+        vandpd    320+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm3, %zmm1
+        vfmadd213pd {rn-sae}, %zmm12, %zmm15, %zmm14
+        vrsqrt14pd %zmm6, %zmm8
+        vcmppd    $21, {sae}, %zmm0, %zmm1, %k2
+
+/* B_high */
+        vsubpd    {rn-sae}, %zmm4, %zmm6, %zmm11
+
+/* sign bit */
+        vxorpd    %zmm3, %zmm1, %zmm2
+
+/* Sh ~sqrt(1+x^2) */
+        vmulpd    {rn-sae}, %zmm8, %zmm6, %zmm5
+        vmulpd    {rn-sae}, %zmm15, %zmm14, %zmm4
+
+/* B_low */
+        vsubpd    {rn-sae}, %zmm11, %zmm10, %zmm10
+
+/* (Yh*R0)_low */
+        vfmsub213pd {rn-sae}, %zmm5, %zmm8, %zmm6
+        vmovups   896+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm11
+
+/* polynomial computation for small inputs */
+        vfmadd213pd {rn-sae}, %zmm1, %zmm1, %zmm4
+
+/* (x^2)_low */
+        vmovaps   %zmm3, %zmm7
+        vfmsub213pd {rn-sae}, %zmm15, %zmm3, %zmm7
+
+/* Yl = (x^2)_low + B_low */
+        vaddpd    {rn-sae}, %zmm7, %zmm10, %zmm15
+
+/* very large inputs ? */
+        vmovups   448+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm7
+
+/* Sl = (Yh*R0)_low+(R0*Yl) */
+        vfmadd213pd {rn-sae}, %zmm6, %zmm8, %zmm15
+        vmovups   512+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm6
+        vcmppd    $21, {sae}, %zmm7, %zmm1, %k1
+        vcmppd    $22, {sae}, %zmm6, %zmm1, %k0
+        vmovups   832+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm7
+        vmovups   1344+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm6
+        kmovw     %k0, %edx
+
+/* rel. error term: Eh=1-Sh*R0 */
+        vmovaps   %zmm9, %zmm14
+        vfnmadd231pd {rn-sae}, %zmm5, %zmm8, %zmm14
+
+/* rel. error term: Eh=(1-Sh*R0)-Sl*R0 */
+        vfnmadd231pd {rn-sae}, %zmm15, %zmm8, %zmm14
+
+/*
+ * sqrt(1+x^2) ~ Sh + Sl + Sh*Eh*poly_s
+ * poly_s = c1+c2*Eh+c3*Eh^2
+ */
+        vmovups   768+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm8
+
+/* Sh*Eh */
+        vmulpd    {rn-sae}, %zmm14, %zmm5, %zmm12
+        vfmadd231pd {rn-sae}, %zmm14, %zmm8, %zmm7
+        vfmadd213pd {rn-sae}, %zmm11, %zmm14, %zmm7
+        vfmadd213pd {rn-sae}, %zmm13, %zmm14, %zmm7
+
+/* Sh+x */
+        vaddpd    {rn-sae}, %zmm1, %zmm5, %zmm14
+
+/* Sl + Sh*Eh*poly_s */
+        vfmadd213pd {rn-sae}, %zmm15, %zmm7, %zmm12
+
+/* fixup for very large inputs */
+        vmovups   1280+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm15
+
+/* Xh */
+        vsubpd    {rn-sae}, %zmm5, %zmm14, %zmm5
+
+/* Xin0+Sl+Sh*Eh*poly_s ~ x+sqrt(1+x^2) */
+        vaddpd    {rn-sae}, %zmm12, %zmm14, %zmm13
+
+/* Xl */
+        vsubpd    {rn-sae}, %zmm5, %zmm1, %zmm7
+
+/* Sl_high */
+        vsubpd    {rn-sae}, %zmm14, %zmm13, %zmm5
+        vmulpd    {rn-sae}, %zmm15, %zmm1, %zmm13{%k1}
+        vmovups   1536+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   1600+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm15
+
+/* Sl_l */
+        vsubpd    {rn-sae}, %zmm5, %zmm12, %zmm12
+        vrcp14pd  %zmm13, %zmm8
+
+/* Xin_low */
+        vaddpd    {rn-sae}, %zmm7, %zmm12, %zmm11
+
+/* round reciprocal to 1+4b mantissas */
+        vpaddq    1152+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm8, %zmm5
+
+/* fixup for very large inputs */
+        vxorpd    %zmm11, %zmm11, %zmm11{%k1}
+        vandpd    1216+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm5, %zmm10
+        vmovups   1472+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm5
+
+/* Prepare table index */
+        vpsrlq    $48, %zmm10, %zmm12
+
+/* reduced argument for log(): (Rcp*Xin-1)+Rcp*Xin_low */
+        vfmsub231pd {rn-sae}, %zmm10, %zmm13, %zmm9
+        vmovups   128+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm13
+
+/* exponents */
+        vgetexppd {sae}, %zmm10, %zmm7
+        vpermt2pd 192+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm12, %zmm13
+        vsubpd    {rn-sae}, %zmm6, %zmm7, %zmm7{%k1}
+
+/* Table lookups */
+        vmovups   __svml_dasinh_ha_data_internal_avx512(%rip), %zmm6
+        vmovaps   %zmm10, %zmm8
+        vfmadd213pd {rn-sae}, %zmm9, %zmm11, %zmm8
+
+/* Tl+Rcp*Xin_low */
+        vfmadd213pd {rn-sae}, %zmm13, %zmm11, %zmm10
+
+/* polynomials */
+        vmovups   1408+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm11
+
+/* -K*L2H + Th */
+        vmovups   1984+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm13
+        vpermt2pd 64+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm12, %zmm6
+        vfmadd231pd {rn-sae}, %zmm8, %zmm11, %zmm5
+        vmovups   1664+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   1728+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm12
+        vfnmadd231pd {rn-sae}, %zmm7, %zmm13, %zmm6
+        vfmadd213pd {rn-sae}, %zmm14, %zmm8, %zmm5
+
+/* -K*L2L + Tl + Rcp*Xin_low */
+        vmovups   2048+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm14
+
+/* (K*L2H+Th) + Rh0_h */
+        vaddpd    {rn-sae}, %zmm6, %zmm9, %zmm13
+        vfmadd213pd {rn-sae}, %zmm15, %zmm8, %zmm5
+        vfnmadd213pd {rn-sae}, %zmm10, %zmm14, %zmm7
+        vmovups   1792+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm10
+
+/* -R_high */
+        vsubpd    {rn-sae}, %zmm13, %zmm6, %zmm6
+        vfmadd213pd {rn-sae}, %zmm11, %zmm8, %zmm5
+        vmovups   1920+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm11
+
+/* Rl */
+        vaddpd    {rn-sae}, %zmm6, %zmm9, %zmm9
+        vfmadd213pd {rn-sae}, %zmm12, %zmm8, %zmm5
+
+/* R^2 */
+        vmulpd    {rn-sae}, %zmm8, %zmm8, %zmm12
+        vfmadd213pd {rn-sae}, %zmm10, %zmm8, %zmm5
+        vmovups   1856+__svml_dasinh_ha_data_internal_avx512(%rip), %zmm10
+        vfmadd213pd {rn-sae}, %zmm10, %zmm8, %zmm5
+        vfmadd213pd {rn-sae}, %zmm11, %zmm8, %zmm5
+
+/* (K*L2L + Tl) + R^2*Poly */
+        vfmadd213pd {rn-sae}, %zmm7, %zmm12, %zmm5
+        vaddpd    {rn-sae}, %zmm9, %zmm5, %zmm5
+        vaddpd    {rn-sae}, %zmm5, %zmm13, %zmm4{%k2}
+        vxorpd    %zmm2, %zmm4, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm3, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dasinh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_asinh8_ha,@function
+	.size	__svml_asinh8_ha,.-__svml_asinh8_ha
+..LN__svml_asinh8_ha.0:
+
+.L_2__routine_start___svml_dasinh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dasinh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movzwl    6(%rdi), %eax
+        andl      $32752, %eax
+        movq      (%rdi), %rdx
+        cmpl      $32752, %eax
+        jne       .LBL_2_5
+
+
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_4
+
+
+        cmpl      $0, (%rdi)
+        je        .LBL_2_5
+
+.LBL_2_4:
+
+        movsd     (%rdi), %xmm0
+        xorl      %eax, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_5:
+
+        movq      %rdx, (%rsi)
+        xorl      %eax, %eax
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dasinh_ha_cout_rare_internal,@function
+	.size	__svml_dasinh_ha_cout_rare_internal,.-__svml_dasinh_ha_cout_rare_internal
+..LN__svml_dasinh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dasinh_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	3222405120
+	.long	3215919664
+	.long	1848311808
+	.long	3216910087
+	.long	1890025472
+	.long	3217424176
+	.long	3348791296
+	.long	3217854455
+	.long	2880159744
+	.long	3218171740
+	.long	3256631296
+	.long	3218366859
+	.long	4139499520
+	.long	3218553303
+	.long	3971973120
+	.long	3218731811
+	.long	3348791296
+	.long	3218903031
+	.long	1605304320
+	.long	3219067535
+	.long	3827638272
+	.long	3219177074
+	.long	1584414720
+	.long	3219253343
+	.long	860823552
+	.long	3219326935
+	.long	3896934400
+	.long	3219398031
+	.long	643547136
+	.long	3219466797
+	.long	0
+	.long	0
+	.long	3496399314
+	.long	1028893491
+	.long	720371772
+	.long	1026176044
+	.long	1944193543
+	.long	3175338952
+	.long	634920691
+	.long	3175752108
+	.long	1664625295
+	.long	1029304828
+	.long	192624563
+	.long	3177103997
+	.long	3796653051
+	.long	3176138396
+	.long	3062724207
+	.long	3176680434
+	.long	634920691
+	.long	3176800684
+	.long	1913570380
+	.long	3174806221
+	.long	825194088
+	.long	3176465773
+	.long	2335489660
+	.long	3172599741
+	.long	2497625109
+	.long	1029604288
+	.long	914782743
+	.long	1029350199
+	.long	3743595607
+	.long	3175525305
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1066835968
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	0
+	.long	1608515584
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	2890702885
+	.long	3215382410
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	207003370
+	.long	1068708659
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	1431623933
+	.long	3217380693
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	26490386
+	.long	1070694400
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	20643840
+	.long	1070858240
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071120384
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	0
+	.long	1074790400
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.type	__svml_dasinh_ha_data_internal_avx512,@object
+	.size	__svml_dasinh_ha_data_internal_avx512,2176
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_d_la.s
index ff70160762e..79743d81f39 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_h_la.s
new file mode 100644
index 00000000000..a135052127c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_h_la.s
@@ -0,0 +1,184 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *    Compute log(x+sqrt(x*x+1)) using RSQRT for starting the
+ *    square root approximation, and small table lookups for log
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_asinhs32
+
+__svml_asinhs32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hasinh_data_internal(%rip), %zmm22
+        vmovdqu16 64+__svml_hasinh_data_internal(%rip), %zmm31
+        vmovdqu16 128+__svml_hasinh_data_internal(%rip), %zmm24
+        vmovdqu16 192+__svml_hasinh_data_internal(%rip), %zmm25
+        vmovdqu16 256+__svml_hasinh_data_internal(%rip), %zmm23
+        vmovdqu16 320+__svml_hasinh_data_internal(%rip), %zmm21
+        vmovdqu16 384+__svml_hasinh_data_internal(%rip), %zmm27
+        vmovdqu16 448+__svml_hasinh_data_internal(%rip), %zmm8
+        vmovdqu16 512+__svml_hasinh_data_internal(%rip), %zmm9
+        vmovdqu16 576+__svml_hasinh_data_internal(%rip), %zmm11
+        vmovdqu16 640+__svml_hasinh_data_internal(%rip), %zmm30
+        vmovdqu16 704+__svml_hasinh_data_internal(%rip), %zmm15
+        vmovdqu16 768+__svml_hasinh_data_internal(%rip), %zmm29
+        vmovdqu16 832+__svml_hasinh_data_internal(%rip), %zmm28
+        vmovdqu16 896+__svml_hasinh_data_internal(%rip), %zmm26
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_asinh_h:
+        cmpq    $31, %rdx
+        ja .loaddata_asinh
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_asinh_h
+.loaddata_asinh:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_asinh_h:
+
+        vmovdqu16 %zmm24, %zmm2
+        vmovdqu16 %zmm22, %zmm1
+        vmovdqu16 %zmm21, %zmm7
+
+/*
+ * No callout
+ * |x|
+ */
+        vpandd    %zmm31, %zmm0, %zmm4
+        vfmadd213ph {rn-sae}, %zmm25, %zmm4, %zmm2
+
+/*
+ * log(|x|)
+ * GetMant(x), normalized to [.75,1.5) for x>=0, NaN for x<0
+ */
+        vgetmantph $11, {sae}, %zmm4, %zmm12
+
+/*
+ * |x|>=1:  result = log(x) + log(1+sqrt(1+(1/x)^2))
+ * y = 1/|x|
+ */
+        vrcpph    %zmm4, %zmm10
+        vgetexpph {sae}, %zmm4, %zmm13
+        vcmpph    $21, {sae}, %zmm1, %zmm4, %k1
+
+/* exponent correction */
+        vgetexpph {sae}, %zmm12, %zmm14
+        vfmadd213ph {rn-sae}, %zmm23, %zmm4, %zmm2
+
+/* mantissa - 1 */
+        vsubph    {rn-sae}, %zmm1, %zmm12, %zmm5
+
+/* log(1+R)/R */
+        vmovdqu16 %zmm30, %zmm6
+        vfmadd213ph {rn-sae}, %zmm7, %zmm4, %zmm2
+        vsubph    {rn-sae}, %zmm14, %zmm13, %zmm7
+        vfmadd213ph {rn-sae}, %zmm15, %zmm5, %zmm6
+        vfmadd213ph {rn-sae}, %zmm4, %zmm4, %zmm2
+        vfmadd213ph {rn-sae}, %zmm29, %zmm5, %zmm6
+        vfmadd213ph {rn-sae}, %zmm28, %zmm5, %zmm6
+
+/* save sign */
+        vpxord    %zmm4, %zmm0, %zmm3
+        vmovdqu16 %zmm27, %zmm0
+        vfmadd213ph {rn-sae}, %zmm8, %zmm10, %zmm0
+        vfmadd213ph {rn-sae}, %zmm9, %zmm10, %zmm0
+        vfmadd213ph {rn-sae}, %zmm11, %zmm10, %zmm0
+
+/* log(1+R) + log(1+sqrt(1+y*y)) */
+        vfmadd213ph {rn-sae}, %zmm0, %zmm5, %zmm6
+
+/* result for x>=2 */
+        vfmadd213ph {rn-sae}, %zmm6, %zmm26, %zmm7
+
+/* result = SelMask?  hPl : hPa */
+        vpblendmw %zmm7, %zmm2, %zmm1{%k1}
+
+/* set sign */
+        vpxord    %zmm3, %zmm1, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_asinh_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_asinhs32,@function
+        .size	__svml_asinhs32,.-__svml_asinhs32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hasinh_data_internal:
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x2c52
+	.endr
+	.rept	32
+        .word	0xb206
+	.endr
+	.rept	32
+        .word	0x185d
+	.endr
+	.rept	32
+        .word	0x8057
+	.endr
+	.rept	32
+        .word	0xadb6
+	.endr
+	.rept	32
+        .word	0x347f
+	.endr
+	.rept	32
+        .word	0x9b9e
+	.endr
+	.rept	32
+        .word	0x398c
+	.endr
+	.rept	32
+        .word	0xb22b
+	.endr
+	.rept	32
+        .word	0x358b
+	.endr
+	.rept	32
+        .word	0xb807
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x398c
+	.endr
+        .type	__svml_hasinh_data_internal,@object
+        .size	__svml_hasinh_data_internal,960
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_s_ha.s
new file mode 100644
index 00000000000..2a65ca5dc9e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_s_ha.s
@@ -0,0 +1,695 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *   Compute log(x+sqrt(x*x+1)) using RSQRT14/RSQRT28 for starting the
+ *  *   square root approximation, and small table lookups for log (mapping to
+ *  *   AVX3 permute instructions).
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_asinhf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_asinhf16_ha
+
+__svml_asinhf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm3
+
+/* x^2 */
+        vmulps    {rn-sae}, %zmm3, %zmm3, %zmm9
+        vmovups   256+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm8
+
+/* polynomial computation for small inputs */
+        vmovups   576+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm10
+
+/* not a very small input ? */
+        vmovups   384+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm0
+
+/* 1+x^2 */
+        vaddps    {rn-sae}, %zmm8, %zmm9, %zmm15
+
+/* |input| */
+        vandps    320+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm3, %zmm1
+
+/* A=max(x^2, 1); */
+        vmaxps    {sae}, %zmm9, %zmm8, %zmm4
+        vrsqrt14ps %zmm15, %zmm6
+
+/* B=min(x^2, 1); */
+        vminps    {sae}, %zmm9, %zmm8, %zmm7
+        vcmpps    $21, {sae}, %zmm0, %zmm1, %k2
+
+/* B_high */
+        vsubps    {rn-sae}, %zmm4, %zmm15, %zmm5
+
+/* sign bit */
+        vxorps    %zmm3, %zmm1, %zmm2
+
+/* Sh ~sqrt(1+x^2) */
+        vmulps    {rn-sae}, %zmm6, %zmm15, %zmm14
+        vmulps    {rn-sae}, %zmm10, %zmm9, %zmm4
+
+/* B_low */
+        vsubps    {rn-sae}, %zmm5, %zmm7, %zmm12
+
+/* Sh+x */
+        vaddps    {rn-sae}, %zmm1, %zmm14, %zmm10
+
+/* (Yh*R0)_low */
+        vfmsub213ps {rn-sae}, %zmm14, %zmm6, %zmm15
+        vmovups   512+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm7
+
+/* polynomial computation for small inputs */
+        vfmadd213ps {rn-sae}, %zmm1, %zmm1, %zmm4
+        vcmpps    $22, {sae}, %zmm7, %zmm1, %k0
+
+/* (x^2)_low */
+        vmovaps   %zmm3, %zmm11
+        vfmsub213ps {rn-sae}, %zmm9, %zmm3, %zmm11
+        vmovups   704+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm9
+        kmovw     %k0, %edx
+
+/* Yl = (x^2)_low + B_low */
+        vaddps    {rn-sae}, %zmm11, %zmm12, %zmm13
+
+/* rel. error term: Eh=1-Sh*R0 */
+        vmovaps   %zmm8, %zmm5
+        vfnmadd231ps {rn-sae}, %zmm14, %zmm6, %zmm5
+
+/* Sl = (Yh*R0)_low+(R0*Yl) */
+        vfmadd213ps {rn-sae}, %zmm15, %zmm6, %zmm13
+
+/* very large inputs ? */
+        vmovups   448+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm15
+
+/* rel. error term: Eh=(1-Sh*R0)-Sl*R0 */
+        vfnmadd231ps {rn-sae}, %zmm13, %zmm6, %zmm5
+
+/* sqrt(1+x^2) ~ Sh + Sl + Sh*Eh*poly_s */
+        vmovups   640+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm6
+        vcmpps    $21, {sae}, %zmm15, %zmm1, %k1
+
+/* Sh*Eh */
+        vmulps    {rn-sae}, %zmm5, %zmm14, %zmm11
+        vfmadd231ps {rn-sae}, %zmm5, %zmm6, %zmm9
+        vmovups   128+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm15
+
+/* Sl + Sh*Eh*poly_s */
+        vfmadd213ps {rn-sae}, %zmm13, %zmm9, %zmm11
+
+/* Xh */
+        vsubps    {rn-sae}, %zmm14, %zmm10, %zmm13
+
+/* fixup for very large inputs */
+        vmovups   896+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm14
+
+/* Xin0+Sl+Sh*Eh*poly_s ~ x+sqrt(1+x^2) */
+        vaddps    {rn-sae}, %zmm11, %zmm10, %zmm12
+
+/* Xl */
+        vsubps    {rn-sae}, %zmm13, %zmm1, %zmm5
+
+/* Sl_high */
+        vsubps    {rn-sae}, %zmm10, %zmm12, %zmm6
+        vmulps    {rn-sae}, %zmm14, %zmm1, %zmm12{%k1}
+
+/* -K*L2L + Tl + Rcp*Xin_low */
+        vmovups   1280+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm14
+
+/* Sl_l */
+        vsubps    {rn-sae}, %zmm6, %zmm11, %zmm7
+        vrcp14ps  %zmm12, %zmm9
+
+/* Xin_low */
+        vaddps    {rn-sae}, %zmm5, %zmm7, %zmm10
+
+/* Table lookups */
+        vmovups   __svml_sasinh_ha_data_internal_avx512(%rip), %zmm5
+
+/* round reciprocal to 1+5b mantissas */
+        vpaddd    768+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm9, %zmm11
+
+/* fixup for very large inputs */
+        vxorps    %zmm10, %zmm10, %zmm10{%k1}
+        vandps    832+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm11, %zmm9
+
+/* Prepare table index */
+        vpsrld    $18, %zmm9, %zmm13
+
+/* reduced argument for log(): (Rcp*Xin-1)+Rcp*Xin_low */
+        vfmsub231ps {rn-sae}, %zmm9, %zmm12, %zmm8
+
+/* exponents */
+        vgetexpps {sae}, %zmm9, %zmm6
+        vmovups   960+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm12
+        vpermt2ps 192+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm13, %zmm15
+        vpermt2ps 64+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm13, %zmm5
+        vsubps    {rn-sae}, %zmm12, %zmm6, %zmm6{%k1}
+        vmovups   1088+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm12
+        vmovaps   %zmm9, %zmm7
+        vfmadd213ps {rn-sae}, %zmm8, %zmm10, %zmm7
+
+/* Tl+Rcp*Xin_low */
+        vfmadd213ps {rn-sae}, %zmm15, %zmm10, %zmm9
+
+/* -K*L2H + Th */
+        vmovups   1216+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm10
+
+/* R^2 */
+        vmulps    {rn-sae}, %zmm7, %zmm7, %zmm11
+        vfnmadd231ps {rn-sae}, %zmm6, %zmm10, %zmm5
+        vfnmadd213ps {rn-sae}, %zmm9, %zmm14, %zmm6
+
+/* polynomial */
+        vmovups   1024+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   1152+__svml_sasinh_ha_data_internal_avx512(%rip), %zmm10
+
+/* (K*L2H+Th) + Rh0_h */
+        vaddps    {rn-sae}, %zmm5, %zmm8, %zmm13
+        vfmadd231ps {rn-sae}, %zmm7, %zmm9, %zmm12
+
+/* -R_high */
+        vsubps    {rn-sae}, %zmm13, %zmm5, %zmm5
+        vfmadd213ps {rn-sae}, %zmm10, %zmm7, %zmm12
+
+/* Rl */
+        vaddps    {rn-sae}, %zmm5, %zmm8, %zmm8
+
+/* (K*L2L + Tl) + R^2*Poly */
+        vfmadd213ps {rn-sae}, %zmm6, %zmm11, %zmm12
+        vaddps    {rn-sae}, %zmm8, %zmm12, %zmm5
+        vaddps    {rn-sae}, %zmm5, %zmm13, %zmm4{%k2}
+        vxorps    %zmm2, %zmm4, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm3, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_sasinh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_asinhf16_ha,@function
+	.size	__svml_asinhf16_ha,.-__svml_asinhf16_ha
+..LN__svml_asinhf16_ha.0:
+
+.L_2__routine_start___svml_sasinh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_sasinh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movl      (%rdi), %eax
+        movl      %eax, -8(%rsp)
+        andl      $2139095040, %eax
+        cmpl      $2139095040, %eax
+        jne       .LBL_2_4
+
+
+        testl     $8388607, -8(%rsp)
+        je        .LBL_2_4
+
+
+        movss     -8(%rsp), %xmm0
+        xorl      %eax, %eax
+        mulss     -8(%rsp), %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_4:
+
+        movl      -8(%rsp), %eax
+        movl      %eax, (%rsi)
+        xorl      %eax, %eax
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sasinh_ha_cout_rare_internal,@function
+	.size	__svml_sasinh_ha_cout_rare_internal,.-__svml_sasinh_ha_cout_rare_internal
+..LN__svml_sasinh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_sasinh_ha_data_internal_avx512:
+	.long	0
+	.long	3170631680
+	.long	3178790912
+	.long	3182919680
+	.long	3186704384
+	.long	3189022720
+	.long	3190816768
+	.long	3192561664
+	.long	3194257408
+	.long	3195912192
+	.long	3196796928
+	.long	3197583360
+	.long	3198357504
+	.long	3199111168
+	.long	3199848448
+	.long	3200569344
+	.long	3201277952
+	.long	3201966080
+	.long	3202646016
+	.long	3203309568
+	.long	3203960832
+	.long	3204524032
+	.long	3204837376
+	.long	3205146624
+	.long	3205447680
+	.long	3205744640
+	.long	3206037504
+	.long	3206324224
+	.long	3206606848
+	.long	3206883328
+	.long	3207155712
+	.long	3207424000
+	.long	2147483648
+	.long	3072770974
+	.long	943319038
+	.long	3075640037
+	.long	930648533
+	.long	3089726480
+	.long	936349528
+	.long	944943494
+	.long	897812054
+	.long	3087808175
+	.long	941839444
+	.long	3093478113
+	.long	937982919
+	.long	931430736
+	.long	924853521
+	.long	3075349253
+	.long	945558336
+	.long	3094838221
+	.long	906200662
+	.long	3084126596
+	.long	3088015279
+	.long	3089451852
+	.long	3093678154
+	.long	938521645
+	.long	3091119329
+	.long	3090949395
+	.long	933442244
+	.long	930702671
+	.long	945827699
+	.long	913590776
+	.long	3082066287
+	.long	3087046763
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1015021568
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	1593835520
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	3190466014
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1040187392
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	1082130432
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.type	__svml_sasinh_ha_data_internal_avx512,@object
+	.size	__svml_sasinh_ha_data_internal_avx512,1344
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_s_la.s
index 8aacb0bfb02..29df3e2c76b 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_asinh_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_d_ha.s
new file mode 100644
index 00000000000..12211004fd2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_d_ha.s
@@ -0,0 +1,2726 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *      For    0.0    <= x <=  7.0/16.0: atan(x) = atan(0.0) + atan(s), where s=(x-0.0)/(1.0+0.0*x)
+ *      For  7.0/16.0 <= x <= 11.0/16.0: atan(x) = atan(0.5) + atan(s), where s=(x-0.5)/(1.0+0.5*x)
+ *      For 11.0/16.0 <= x <= 19.0/16.0: atan(x) = atan(1.0) + atan(s), where s=(x-1.0)/(1.0+1.0*x)
+ *      For 19.0/16.0 <= x <= 39.0/16.0: atan(x) = atan(1.5) + atan(s), where s=(x-1.5)/(1.0+1.5*x)
+ *      For 39.0/16.0 <= x <=    inf   : atan(x) = atan(inf) + atan(s), where s=-1.0/x
+ *      Where atan(s) ~= s+s^3*Poly11(s^2) on interval |s|<7.0/0.16.
+ * --
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_atan28_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_atan28_ha
+
+__svml_atan28_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $256, %rsp
+        xorl      %edx, %edx
+
+/*
+ * #define NO_VECTOR_ZERO_ATAN2_ARGS
+ * -------------------- Declarations -----------------------
+ * Variables
+ * Constants
+ * -------------- The end of declarations ----------------
+ * ---------------------- Implementation ---------------------
+ * Get r0~=1/B
+ * Cannot be replaced by VQRCP(D, dR0, dB);
+ * Argument Absolute values
+ */
+        vmovups   896+__svml_datan2_ha_data_internal(%rip), %zmm8
+
+/* Get PiOrZero = Pi (if x<0), or Zero (if x>0) */
+        vmovups   2880+__svml_datan2_ha_data_internal(%rip), %zmm10
+        vmovdqu   2688+__svml_datan2_ha_data_internal(%rip), %ymm2
+        kmovw     %k7, 56(%rsp)
+        kmovw     %k5, 48(%rsp)
+        vmovaps   %zmm1, %zmm11
+        vandpd    %zmm8, %zmm11, %zmm9
+        vandpd    %zmm8, %zmm0, %zmm8
+        vcmppd    $17, {sae}, %zmm10, %zmm11, %k7
+        vmovdqu   2752+__svml_datan2_ha_data_internal(%rip), %ymm10
+
+/*
+ * Determining table index (to which interval is subject y/x)
+ * Reduction of |y/x| to the interval [0;7/16].
+ * 0. If 39/16 < |y/x| <  Inf  then a=0*y-  1*x, b=0*x+  1*y, AtanHi+AtanLo=atan(Inf).
+ * 1. If 19/16 < |y/x| < 39/16 then a=1*y-1.5*x, b=1*x+1.5*y, AtanHi+AtanLo=atan(3/2).
+ * 2. If 11/16 < |y/x| < 19/16 then a=1*y-1.0*x, b=1*x+1.0*y, AtanHi+AtanLo=atan( 1 ).
+ * 3. If  7/16 < |y/x| < 11/16 then a=1*y-0.5*x, b=1*x+0.5*y, AtanHi+AtanLo=atan(1/2).
+ * 4. If   0   < |y/x| <  7/16 then a=1*y-0  *x, b=1*x+0  *y, AtanHi+AtanLo=atan( 0 ).
+ * Hence common formulas are:       a=c*y-  d*x, b=c*x+  d*y (c is mask in our case)
+ * (b is always positive)
+ */
+        vmovups   1152+__svml_datan2_ha_data_internal(%rip), %zmm1
+
+/* Check if y and x are on main path. */
+        vpsrlq    $32, %zmm9, %zmm5
+        vpsrlq    $32, %zmm8, %zmm13
+
+/* Argument signs */
+        vxorpd    %zmm9, %zmm11, %zmm6
+        vpmovqd   %zmm5, %ymm4
+        vpmovqd   %zmm13, %ymm3
+        vxorpd    %zmm8, %zmm0, %zmm7
+        vpsubd    %ymm2, %ymm4, %ymm15
+        vpsubd    %ymm2, %ymm3, %ymm5
+        vmovups   1792+__svml_datan2_ha_data_internal(%rip), %zmm2
+        vpcmpgtd  %ymm10, %ymm15, %ymm12
+        vpcmpeqd  %ymm10, %ymm15, %ymm14
+        vpcmpgtd  %ymm10, %ymm5, %ymm4
+        vpcmpeqd  %ymm10, %ymm5, %ymm10
+        vpor      %ymm14, %ymm12, %ymm13
+        vpor      %ymm10, %ymm4, %ymm3
+        vmovups   1344+__svml_datan2_ha_data_internal(%rip), %zmm15
+        vmovups   1280+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vmovups   1216+__svml_datan2_ha_data_internal(%rip), %zmm12
+        vmulpd    {rn-sae}, %zmm1, %zmm9, %zmm4
+        vmulpd    {rn-sae}, %zmm15, %zmm9, %zmm5
+        vmulpd    {rn-sae}, %zmm12, %zmm9, %zmm15
+        vmovups   768+__svml_datan2_ha_data_internal(%rip), %zmm1
+        vmovups   192+__svml_datan2_ha_data_internal(%rip), %zmm12
+        vcmppd    $17, {sae}, %zmm8, %zmm5, %k1
+        vcmppd    $17, {sae}, %zmm8, %zmm4, %k5
+        vcmppd    $17, {sae}, %zmm8, %zmm15, %k2
+        vmovups   512+__svml_datan2_ha_data_internal(%rip), %zmm15
+        vpor      %ymm3, %ymm13, %ymm10
+        vblendmpd 320+__svml_datan2_ha_data_internal(%rip), %zmm12, %zmm5{%k1}
+        vmulpd    {rn-sae}, %zmm14, %zmm9, %zmm3
+        vmovups   704+__svml_datan2_ha_data_internal(%rip), %zmm13
+        vmovups   256+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vcmppd    $17, {sae}, %zmm8, %zmm3, %k3
+        vblendmpd %zmm2, %zmm13, %zmm3{%k1}
+        vblendmpd 384+__svml_datan2_ha_data_internal(%rip), %zmm14, %zmm4{%k1}
+        vmovups   448+__svml_datan2_ha_data_internal(%rip), %zmm13
+        vblendmpd %zmm2, %zmm1, %zmm1{%k3}
+        vblendmpd 576+__svml_datan2_ha_data_internal(%rip), %zmm13, %zmm14{%k3}
+        vblendmpd %zmm1, %zmm3, %zmm3{%k2}
+        vblendmpd 640+__svml_datan2_ha_data_internal(%rip), %zmm15, %zmm13{%k3}
+        vmovups   1984+__svml_datan2_ha_data_internal(%rip), %zmm1
+        vblendmpd %zmm14, %zmm5, %zmm5{%k2}
+        vblendmpd %zmm13, %zmm4, %zmm4{%k2}
+        vmovaps   %zmm9, %zmm12
+        vxorpd    %zmm9, %zmm9, %zmm12{%k3}
+        vfmadd231pd {rn-sae}, %zmm8, %zmm3, %zmm12{%k5}
+
+/*
+ * Divide r:=a*(1/b), where a==AHi+ALo, b==BHi+BLo, 1/b~=InvHi+InvLo
+ * Get r0~=1/BHi
+ */
+        vrcp14pd  %zmm12, %zmm14
+
+/*
+ * Now refine r0 by several iterations (hidden in polynomial)
+ * e = 1-Bhi*r0
+ */
+        vmovaps   %zmm2, %zmm15
+        vfnmadd231pd {rn-sae}, %zmm12, %zmm14, %zmm15
+
+/* r0 ~= 1/Bhi*(1-e)(1+e) or 1/Bhi*(1-e)(1+e+e^2) */
+        vfmadd213pd {rn-sae}, %zmm14, %zmm15, %zmm14
+
+/* e' = 1-Bhi*r0 */
+        vfnmadd231pd {rn-sae}, %zmm12, %zmm14, %zmm2
+        vmovaps   %zmm8, %zmm13
+        vxorpd    %zmm8, %zmm8, %zmm13{%k3}
+
+/* r0 ~= 1/Bhi*(1-e')(1+e') = 1/Bhi(1-e'^2) */
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm14
+        vfnmadd231pd {rn-sae}, %zmm9, %zmm3, %zmm13{%k5}
+
+/*
+ * Now 1/B ~= R0 + InvLo
+ * Get r:=a*(1/b), where a==AHi+ALo, 1/b~=InvHi+InvLo
+ */
+        vmulpd    {rn-sae}, %zmm13, %zmm14, %zmm3
+        vmulpd    {rn-sae}, %zmm3, %zmm3, %zmm15
+        vfnmadd213pd {rn-sae}, %zmm13, %zmm3, %zmm12
+        vmulpd    {rn-sae}, %zmm15, %zmm15, %zmm2
+        vmulpd    {rn-sae}, %zmm14, %zmm12, %zmm13
+
+/* Atan polynomial approximation */
+        vmovups   1856+__svml_datan2_ha_data_internal(%rip), %zmm12
+        vmovups   1920+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vaddpd    {rn-sae}, %zmm4, %zmm13, %zmm13{%k5}
+        vfmadd231pd {rn-sae}, %zmm2, %zmm12, %zmm1
+        vmovups   2048+__svml_datan2_ha_data_internal(%rip), %zmm12
+        vfmadd231pd {rn-sae}, %zmm2, %zmm14, %zmm12
+        vmovups   2112+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm1
+        vmovups   2176+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm12
+        vmovups   2240+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm1
+        vmovups   2304+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm12
+        vmovups   2368+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm1
+        vmovups   2432+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm12
+        vmovups   2496+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm1
+        vmovups   2560+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm12
+        vfmadd213pd {rn-sae}, %zmm12, %zmm15, %zmm1
+        vmulpd    {rn-sae}, %zmm15, %zmm1, %zmm15
+
+/*
+ * Res = ( (RLo + AtanBaseLo + PiOrZeroLo*sx + Poly(R^2)*R^3 + RHi + AtanBaseHi) * sx + PiOrZeroHi) * sy
+ * Get PiOrZero = Pi (if x<0), or Zero (if x>0)
+ */
+        vxorpd    1024+__svml_datan2_ha_data_internal(%rip), %zmm6, %zmm1
+        vaddpd    {rn-sae}, %zmm1, %zmm13, %zmm13{%k7}
+        vfmadd213pd {rn-sae}, %zmm13, %zmm3, %zmm15
+        vaddpd    {rn-sae}, %zmm15, %zmm3, %zmm3
+        vaddpd    {rn-sae}, %zmm5, %zmm3, %zmm3{%k5}
+        vxorpd    %zmm6, %zmm3, %zmm1
+        vmovups   960+__svml_datan2_ha_data_internal(%rip), %zmm6
+        vaddpd    {rn-sae}, %zmm6, %zmm1, %zmm1{%k7}
+        vmovmskps %ymm10, %eax
+        vorpd     %zmm7, %zmm1, %zmm7
+
+/* =========== Special branch for fast (vector) processing of zero arguments ================ */
+        testl     %eax, %eax
+        jne       .LBL_1_12
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_2:
+
+
+/*
+ * =========== Special branch for fast (vector) processing of zero arguments ================
+ * -------------- The end of implementation ----------------
+ */
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        kmovw     48(%rsp), %k5
+	.cfi_restore 123
+        kmovw     56(%rsp), %k7
+	.cfi_restore 125
+        vmovaps   %zmm7, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_4:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm11, 128(%rsp)
+        vmovups   %zmm7, 192(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 8(%rsp)
+        kmovw     %k6, (%rsp)
+        movq      %rsi, 24(%rsp)
+        movq      %rdi, 16(%rsp)
+        movq      %r12, 40(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 32(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     8(%rsp), %k4
+	.cfi_restore 122
+        kmovw     (%rsp), %k6
+	.cfi_restore 124
+        vmovups   192(%rsp), %zmm7
+        movq      24(%rsp), %rsi
+	.cfi_restore 4
+        movq      16(%rsp), %rdi
+	.cfi_restore 5
+        movq      40(%rsp), %r12
+	.cfi_restore 12
+        movq      32(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+        lea       192(%rsp,%r12,8), %rdx
+
+        call      __svml_datan2_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 124
+
+.LBL_1_12:
+
+
+/* Check if both X & Y are not NaNs:  iXYnotNAN */
+        vcmppd    $3, {sae}, %zmm11, %zmm11, %k2
+        vcmppd    $3, {sae}, %zmm0, %zmm0, %k3
+
+/*
+ * 1) If y<x then PIO2=0
+ * 2) If y>x then PIO2=Pi/2
+ */
+        vcmppd    $17, {sae}, %zmm9, %zmm8, %k1
+        vmovups   832+__svml_datan2_ha_data_internal(%rip), %zmm14
+        vmovups   3136+__svml_datan2_ha_data_internal(%rip), %zmm4
+        vmovups   3072+__svml_datan2_ha_data_internal(%rip), %zmm12
+        vblendmpd %zmm9, %zmm8, %zmm3{%k1}
+        vpbroadcastq .L_2il0floatpacket.40(%rip), %zmm5
+        vandpd    %zmm14, %zmm11, %zmm13
+        vandpd    %zmm14, %zmm0, %zmm6
+        vxorpd    %zmm4, %zmm4, %zmm4{%k1}
+        vmovaps   %zmm5, %zmm15
+        vmovaps   %zmm5, %zmm2
+
+/* Check if at least on of Y or Y is zero: iAXAYZERO */
+        vmovaps   %zmm5, %zmm14
+        vpandnq   %zmm11, %zmm11, %zmm15{%k2}
+        vpandnq   %zmm0, %zmm0, %zmm2{%k3}
+        vandpd    %zmm2, %zmm15, %zmm1
+        vmovups   2880+__svml_datan2_ha_data_internal(%rip), %zmm2
+        vpsrlq    $32, %zmm1, %zmm1
+        vcmppd    $4, {sae}, %zmm2, %zmm9, %k5
+        vcmppd    $4, {sae}, %zmm2, %zmm8, %k7
+
+/*
+ * -------- Path for zero arguments (at least one of both) --------------
+ * Check if both args are zeros (den. is zero)
+ */
+        vcmppd    $4, {sae}, %zmm2, %zmm3, %k1
+
+/* Res = sign(Y)*(X<0)?(PIO2+PI):PIO2 */
+        vpcmpgtq  %zmm11, %zmm2, %k2
+        vpmovqd   %zmm1, %ymm1
+        vpandnq   %zmm9, %zmm9, %zmm14{%k5}
+        vmovaps   %zmm5, %zmm9
+        vpandnq   %zmm8, %zmm8, %zmm9{%k7}
+        vorpd     %zmm9, %zmm14, %zmm8
+        vpsrlq    $32, %zmm8, %zmm9
+        vpmovqd   %zmm9, %ymm14
+
+/* Check if at least on of Y or Y is zero and not NaN: iAXAYZEROnotNAN */
+        vpand     %ymm1, %ymm14, %ymm8
+
+/* Exclude from previous callout mask zero (and not NaN) arguments */
+        vpandn    %ymm10, %ymm8, %ymm10
+
+/* Merge results from main and spec path */
+        vpmovzxdq %ymm8, %zmm8
+
+/* Go to callout */
+        vmovmskps %ymm10, %edx
+        vpandnq   %zmm3, %zmm3, %zmm5{%k1}
+
+/* Set sPIO2 to zero if den. is zero */
+        vpandnq   %zmm4, %zmm5, %zmm1
+        vpandq    %zmm5, %zmm2, %zmm3
+        vporq     %zmm3, %zmm1, %zmm4
+        vorpd     %zmm13, %zmm4, %zmm9
+        vpsllq    $32, %zmm8, %zmm2
+        vaddpd    {rn-sae}, %zmm12, %zmm9, %zmm9{%k2}
+        vorpd     %zmm6, %zmm9, %zmm6
+        vpord     %zmm2, %zmm8, %zmm10
+        vpandnq   %zmm7, %zmm10, %zmm7
+        vpandq    %zmm10, %zmm6, %zmm1
+        vporq     %zmm1, %zmm7, %zmm7
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_atan28_ha,@function
+	.size	__svml_atan28_ha,.-__svml_atan28_ha
+..LN__svml_atan28_ha.0:
+
+.L_2__routine_start___svml_datan2_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_datan2_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L58:
+
+        movq      %rdx, %rcx
+        movsd     1888+__datan2_ha_CoutTab(%rip), %xmm1
+        movsd     (%rdi), %xmm2
+        movsd     (%rsi), %xmm0
+        mulsd     %xmm1, %xmm2
+        mulsd     %xmm0, %xmm1
+        movsd     %xmm2, -48(%rsp)
+        movsd     %xmm1, -40(%rsp)
+        movzwl    -42(%rsp), %r9d
+        andl      $32752, %r9d
+        movb      -33(%rsp), %al
+        movzwl    -34(%rsp), %r8d
+        andb      $-128, %al
+        andl      $32752, %r8d
+        shrl      $4, %r9d
+        movb      -41(%rsp), %dl
+        shrb      $7, %dl
+        shrb      $7, %al
+        shrl      $4, %r8d
+        cmpl      $2047, %r9d
+        je        .LBL_2_49
+
+
+        cmpl      $2047, %r8d
+        je        .LBL_2_38
+
+
+        testl     %r9d, %r9d
+        jne       .LBL_2_6
+
+
+        testl     $1048575, -44(%rsp)
+        jne       .LBL_2_6
+
+
+        cmpl      $0, -48(%rsp)
+        je        .LBL_2_31
+
+.LBL_2_6:
+
+        testl     %r8d, %r8d
+        jne       .LBL_2_9
+
+
+        testl     $1048575, -36(%rsp)
+        jne       .LBL_2_9
+
+
+        cmpl      $0, -40(%rsp)
+        je        .LBL_2_29
+
+.LBL_2_9:
+
+        negl      %r8d
+        movsd     %xmm2, -48(%rsp)
+        addl      %r9d, %r8d
+        movsd     %xmm1, -40(%rsp)
+        movb      -41(%rsp), %dil
+        movb      -33(%rsp), %sil
+        andb      $127, %dil
+        andb      $127, %sil
+        cmpl      $-54, %r8d
+        jle       .LBL_2_24
+
+
+        cmpl      $54, %r8d
+        jge       .LBL_2_21
+
+
+        movb      %sil, -33(%rsp)
+        movb      %dil, -41(%rsp)
+        testb     %al, %al
+        jne       .LBL_2_13
+
+
+        movsd     1976+__datan2_ha_CoutTab(%rip), %xmm1
+        movaps    %xmm1, %xmm0
+        jmp       .LBL_2_14
+
+.LBL_2_13:
+
+        movsd     1936+__datan2_ha_CoutTab(%rip), %xmm1
+        movsd     1944+__datan2_ha_CoutTab(%rip), %xmm0
+
+.LBL_2_14:
+
+        movsd     -48(%rsp), %xmm4
+        movsd     -40(%rsp), %xmm2
+        movaps    %xmm4, %xmm5
+        divsd     %xmm2, %xmm5
+        movzwl    -42(%rsp), %esi
+        movsd     %xmm5, -16(%rsp)
+        testl     %r9d, %r9d
+        jle       .LBL_2_37
+
+
+        cmpl      $2046, %r9d
+        jge       .LBL_2_17
+
+
+        andl      $-32753, %esi
+        addl      $-1023, %r9d
+        movsd     %xmm4, -48(%rsp)
+        addl      $16368, %esi
+        movw      %si, -42(%rsp)
+        jmp       .LBL_2_18
+
+.LBL_2_17:
+
+        movsd     1992+__datan2_ha_CoutTab(%rip), %xmm3
+        movl      $1022, %r9d
+        mulsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+
+.LBL_2_18:
+
+        negl      %r9d
+        addl      $1023, %r9d
+        andl      $2047, %r9d
+        movzwl    1894+__datan2_ha_CoutTab(%rip), %esi
+        movsd     1888+__datan2_ha_CoutTab(%rip), %xmm3
+        andl      $-32753, %esi
+        shll      $4, %r9d
+        movsd     %xmm3, -40(%rsp)
+        orl       %r9d, %esi
+        movw      %si, -34(%rsp)
+        movsd     -40(%rsp), %xmm4
+        mulsd     %xmm4, %xmm2
+        comisd    1880+__datan2_ha_CoutTab(%rip), %xmm5
+        jb        .LBL_2_20
+
+
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm12
+        movaps    %xmm2, %xmm3
+        mulsd     %xmm2, %xmm12
+        movsd     %xmm12, -72(%rsp)
+        movsd     -72(%rsp), %xmm13
+        movsd     %xmm5, -24(%rsp)
+        subsd     %xmm2, %xmm13
+        movsd     %xmm13, -64(%rsp)
+        movsd     -72(%rsp), %xmm15
+        movsd     -64(%rsp), %xmm14
+        movl      -20(%rsp), %r8d
+        movl      %r8d, %r9d
+        andl      $-524288, %r8d
+        andl      $-1048576, %r9d
+        addl      $262144, %r8d
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -72(%rsp)
+        andl      $1048575, %r8d
+        movsd     -72(%rsp), %xmm4
+        orl       %r8d, %r9d
+        movl      $0, -24(%rsp)
+        subsd     %xmm4, %xmm3
+        movl      %r9d, -20(%rsp)
+        movsd     %xmm3, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -24(%rsp), %xmm11
+        movsd     -64(%rsp), %xmm9
+        mulsd     %xmm11, %xmm5
+        mulsd     %xmm11, %xmm9
+        movsd     1968+__datan2_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm8, %xmm9
+        movaps    %xmm5, %xmm7
+        movzwl    -10(%rsp), %edi
+        addsd     %xmm9, %xmm7
+        movsd     %xmm7, -72(%rsp)
+        andl      $32752, %edi
+        movsd     -72(%rsp), %xmm6
+        shrl      $4, %edi
+        subsd     %xmm6, %xmm5
+        movl      -12(%rsp), %esi
+        addsd     %xmm5, %xmm9
+        movsd     %xmm9, -64(%rsp)
+        andl      $1048575, %esi
+        movsd     -48(%rsp), %xmm9
+        movsd     -72(%rsp), %xmm3
+        movaps    %xmm9, %xmm12
+        movsd     -64(%rsp), %xmm10
+        movaps    %xmm9, %xmm14
+        movaps    %xmm9, %xmm6
+        addsd     %xmm3, %xmm12
+        movsd     %xmm12, -72(%rsp)
+        movsd     -72(%rsp), %xmm13
+        shll      $20, %edi
+        subsd     %xmm13, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        orl       %esi, %edi
+        movsd     -72(%rsp), %xmm4
+        addl      $-1069547520, %edi
+        movsd     -64(%rsp), %xmm15
+        movl      $113, %esi
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm13
+        addsd     %xmm15, %xmm4
+        movsd     %xmm4, -56(%rsp)
+        movsd     -64(%rsp), %xmm8
+        sarl      $19, %edi
+        addsd     %xmm3, %xmm8
+        movsd     %xmm8, -64(%rsp)
+        cmpl      $113, %edi
+        movsd     -56(%rsp), %xmm7
+        cmovl     %edi, %esi
+        subsd     %xmm7, %xmm6
+        movsd     %xmm6, -56(%rsp)
+        addl      %esi, %esi
+        movsd     -64(%rsp), %xmm12
+        lea       __datan2_ha_CoutTab(%rip), %rdi
+        movsd     -56(%rsp), %xmm5
+        movslq    %esi, %rsi
+        addsd     %xmm5, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -72(%rsp), %xmm7
+        mulsd     %xmm7, %xmm13
+        movsd     -56(%rsp), %xmm8
+        movsd     %xmm13, -72(%rsp)
+        addsd     %xmm10, %xmm8
+        movsd     -72(%rsp), %xmm4
+        movaps    %xmm9, %xmm10
+        mulsd     2000+__datan2_ha_CoutTab(%rip), %xmm10
+        subsd     %xmm7, %xmm4
+        movsd     %xmm4, -64(%rsp)
+        movsd     -72(%rsp), %xmm3
+        movsd     -64(%rsp), %xmm14
+        subsd     %xmm14, %xmm3
+        movsd     %xmm3, -72(%rsp)
+        movsd     -72(%rsp), %xmm15
+        subsd     %xmm15, %xmm7
+        movsd     %xmm7, -64(%rsp)
+        movsd     -72(%rsp), %xmm7
+        movsd     -64(%rsp), %xmm4
+        movsd     %xmm10, -72(%rsp)
+        movaps    %xmm2, %xmm10
+        addsd     %xmm4, %xmm8
+        movsd     -72(%rsp), %xmm4
+        subsd     -48(%rsp), %xmm4
+        movsd     %xmm4, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm3
+        subsd     %xmm3, %xmm6
+        movaps    %xmm2, %xmm3
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        subsd     %xmm5, %xmm9
+        movsd     %xmm9, -64(%rsp)
+        movsd     -72(%rsp), %xmm12
+        movsd     -64(%rsp), %xmm9
+        mulsd     %xmm11, %xmm12
+        mulsd     %xmm11, %xmm9
+        movaps    %xmm12, %xmm11
+        addsd     %xmm9, %xmm11
+        movsd     %xmm11, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        subsd     %xmm4, %xmm12
+        addsd     %xmm9, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -72(%rsp), %xmm15
+        movsd     -64(%rsp), %xmm6
+        addsd     %xmm15, %xmm3
+        movsd     %xmm3, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm5, %xmm10
+        movsd     %xmm10, -64(%rsp)
+        movsd     -72(%rsp), %xmm13
+        movsd     -64(%rsp), %xmm11
+        addsd     %xmm11, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -64(%rsp), %xmm14
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm13
+        addsd     %xmm14, %xmm15
+        movsd     %xmm15, -64(%rsp)
+        movsd     -56(%rsp), %xmm4
+        movsd     1888+__datan2_ha_CoutTab(%rip), %xmm14
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -64(%rsp), %xmm4
+        movsd     -56(%rsp), %xmm2
+        addsd     %xmm2, %xmm4
+        movsd     %xmm4, -56(%rsp)
+        movsd     -72(%rsp), %xmm12
+        mulsd     %xmm12, %xmm3
+        movsd     -56(%rsp), %xmm5
+        movsd     %xmm3, -72(%rsp)
+        addsd     %xmm6, %xmm5
+        movsd     -72(%rsp), %xmm9
+        subsd     %xmm12, %xmm9
+        movsd     %xmm9, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm2
+        subsd     %xmm2, %xmm10
+        movsd     %xmm10, -72(%rsp)
+        movsd     -72(%rsp), %xmm11
+        subsd     %xmm11, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -72(%rsp), %xmm9
+        divsd     %xmm9, %xmm14
+        mulsd     %xmm14, %xmm13
+        movsd     -64(%rsp), %xmm10
+        movsd     %xmm13, -64(%rsp)
+        addsd     %xmm10, %xmm5
+        movsd     -64(%rsp), %xmm15
+        movsd     1888+__datan2_ha_CoutTab(%rip), %xmm12
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -56(%rsp)
+        movsd     -64(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm4
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm13
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -56(%rsp), %xmm3
+        mulsd     %xmm3, %xmm9
+        movsd     -56(%rsp), %xmm11
+        subsd     %xmm9, %xmm12
+        mulsd     %xmm11, %xmm5
+        movsd     %xmm5, -64(%rsp)
+        movsd     -64(%rsp), %xmm5
+        subsd     %xmm5, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -64(%rsp), %xmm2
+        movq      -56(%rsp), %r10
+        movsd     -64(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm4
+        movq      %r10, -40(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movaps    %xmm3, %xmm5
+        addsd     1888+__datan2_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm7, %xmm5
+        mulsd     %xmm6, %xmm2
+        mulsd     %xmm4, %xmm2
+        mulsd     %xmm2, %xmm7
+        mulsd     %xmm8, %xmm2
+        mulsd     %xmm3, %xmm8
+        addsd     %xmm2, %xmm7
+        movsd     1872+__datan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm8, %xmm7
+        movsd     %xmm7, -72(%rsp)
+        movaps    %xmm5, %xmm7
+        movsd     -72(%rsp), %xmm4
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm6
+        addsd     %xmm4, %xmm7
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm8
+        subsd     %xmm8, %xmm5
+        addsd     %xmm4, %xmm5
+        movsd     %xmm5, -64(%rsp)
+        movsd     -72(%rsp), %xmm11
+        movaps    %xmm11, %xmm2
+        mulsd     %xmm11, %xmm2
+        mulsd     %xmm11, %xmm6
+        mulsd     %xmm2, %xmm3
+        movsd     -64(%rsp), %xmm4
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm7
+        addsd     1864+__datan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm11, %xmm7
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm7, -64(%rsp)
+        movsd     -72(%rsp), %xmm9
+        movsd     -64(%rsp), %xmm8
+        addsd     1856+__datan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm8, %xmm9
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm9, -72(%rsp)
+        movsd     -72(%rsp), %xmm10
+        addsd     1848+__datan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm10, %xmm11
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm11, -64(%rsp)
+        addsd     1840+__datan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        addsd     1832+__datan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        addsd     1824+__datan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        mulsd     %xmm3, %xmm13
+        movsd     -72(%rsp), %xmm2
+        movsd     -64(%rsp), %xmm12
+        movsd     %xmm13, -72(%rsp)
+        addsd     %xmm12, %xmm4
+        movsd     -72(%rsp), %xmm14
+        subsd     %xmm3, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm15
+        subsd     %xmm15, %xmm5
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm6
+        subsd     %xmm6, %xmm3
+        movsd     %xmm3, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm5
+        movaps    %xmm6, %xmm12
+        movaps    %xmm5, %xmm3
+        mulsd     %xmm4, %xmm6
+        mulsd     %xmm4, %xmm3
+        mulsd     %xmm2, %xmm5
+        mulsd     %xmm2, %xmm12
+        addsd     %xmm3, %xmm6
+        movaps    %xmm12, %xmm7
+        movaps    %xmm12, %xmm8
+        addsd     %xmm5, %xmm6
+        addsd     %xmm2, %xmm7
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm3
+        subsd     %xmm3, %xmm8
+        movsd     %xmm8, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -64(%rsp), %xmm11
+        addsd     %xmm11, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -56(%rsp), %xmm2
+        subsd     %xmm2, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -64(%rsp), %xmm14
+        movsd     -56(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movq      -72(%rsp), %r11
+        movsd     -56(%rsp), %xmm15
+        movq      %r11, -40(%rsp)
+        addsd     %xmm15, %xmm4
+        movsd     -40(%rsp), %xmm8
+        addsd     %xmm5, %xmm4
+        movsd     %xmm4, -32(%rsp)
+        movaps    %xmm8, %xmm4
+        movaps    %xmm8, %xmm2
+        addsd     (%rdi,%rsi,8), %xmm4
+        movsd     %xmm4, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm3
+        addsd     %xmm3, %xmm5
+        movsd     %xmm5, -56(%rsp)
+        movsd     -64(%rsp), %xmm6
+        addsd     (%rdi,%rsi,8), %xmm6
+        movsd     %xmm6, -64(%rsp)
+        movsd     -56(%rsp), %xmm7
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -56(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movq      -72(%rsp), %r8
+        movq      %r8, -40(%rsp)
+
+
+        movsd     -56(%rsp), %xmm2
+        movaps    %xmm1, %xmm3
+        shrq      $56, %r8
+        addsd     -32(%rsp), %xmm2
+        shlb      $7, %dl
+        addsd     8(%rdi,%rsi,8), %xmm2
+        movb      %al, %sil
+        andb      $127, %r8b
+        shlb      $7, %sil
+        movsd     %xmm2, -32(%rsp)
+        orb       %sil, %r8b
+        movb      %r8b, -33(%rsp)
+        movsd     -40(%rsp), %xmm9
+        movaps    %xmm9, %xmm5
+        addsd     %xmm9, %xmm3
+        movsd     %xmm3, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movb      -25(%rsp), %dil
+        movb      %dil, %r9b
+        shrb      $7, %dil
+        subsd     %xmm4, %xmm5
+        movsd     %xmm5, -64(%rsp)
+        movsd     -72(%rsp), %xmm7
+        movsd     -64(%rsp), %xmm6
+        xorb      %dil, %al
+        andb      $127, %r9b
+        shlb      $7, %al
+        addsd     %xmm6, %xmm7
+        movsd     %xmm7, -56(%rsp)
+        movsd     -64(%rsp), %xmm8
+        addsd     %xmm8, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        orb       %al, %r9b
+        movsd     -56(%rsp), %xmm1
+        movb      %r9b, -25(%rsp)
+        subsd     %xmm1, %xmm9
+        movsd     %xmm9, -56(%rsp)
+        movsd     -64(%rsp), %xmm11
+        movsd     -56(%rsp), %xmm10
+        addsd     %xmm10, %xmm11
+        movsd     %xmm11, -56(%rsp)
+        movq      -72(%rsp), %rax
+        movsd     -56(%rsp), %xmm12
+        movq      %rax, -40(%rsp)
+        addsd     %xmm12, %xmm0
+        movsd     -40(%rsp), %xmm13
+        addsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -32(%rsp)
+        addsd     %xmm0, %xmm13
+        movsd     %xmm13, -24(%rsp)
+        movb      -17(%rsp), %r10b
+        andb      $127, %r10b
+        orb       %dl, %r10b
+        movb      %r10b, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_20:
+
+        movsd     -48(%rsp), %xmm12
+        movb      %al, %r8b
+        movaps    %xmm12, %xmm7
+        mulsd     2000+__datan2_ha_CoutTab(%rip), %xmm7
+        shlb      $7, %r8b
+        shlb      $7, %dl
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm13
+        movsd     1888+__datan2_ha_CoutTab(%rip), %xmm7
+        mulsd     %xmm2, %xmm13
+        subsd     -48(%rsp), %xmm8
+        movsd     %xmm8, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm9
+        subsd     %xmm9, %xmm10
+        movsd     %xmm10, -72(%rsp)
+        movsd     -72(%rsp), %xmm11
+        subsd     %xmm11, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm5
+        movsd     %xmm13, -72(%rsp)
+        movsd     -72(%rsp), %xmm14
+        subsd     %xmm2, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movsd     -64(%rsp), %xmm15
+        subsd     %xmm15, %xmm4
+        movsd     %xmm4, -72(%rsp)
+        movsd     -72(%rsp), %xmm3
+        movsd     1888+__datan2_ha_CoutTab(%rip), %xmm4
+        subsd     %xmm3, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm12
+        divsd     %xmm12, %xmm7
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm7, %xmm2
+        movsd     -64(%rsp), %xmm14
+        movsd     %xmm2, -64(%rsp)
+        movsd     -64(%rsp), %xmm8
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -56(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm9
+        subsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -56(%rsp), %xmm11
+        mulsd     %xmm11, %xmm12
+        movsd     -56(%rsp), %xmm13
+        subsd     %xmm12, %xmm4
+        mulsd     %xmm13, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -64(%rsp), %xmm15
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm13
+        subsd     %xmm15, %xmm4
+        movsd     %xmm4, -64(%rsp)
+        movsd     -64(%rsp), %xmm7
+        movq      -56(%rsp), %rsi
+        movsd     -64(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm3
+        movq      %rsi, -40(%rsp)
+        movsd     -40(%rsp), %xmm8
+        movaps    %xmm8, %xmm9
+        addsd     1888+__datan2_ha_CoutTab(%rip), %xmm7
+        mulsd     %xmm6, %xmm9
+        mulsd     %xmm5, %xmm8
+        mulsd     %xmm2, %xmm7
+        movsd     -16(%rsp), %xmm2
+        mulsd     %xmm2, %xmm2
+        mulsd     %xmm3, %xmm7
+        movsd     1872+__datan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        mulsd     %xmm7, %xmm6
+        mulsd     %xmm5, %xmm7
+        addsd     1864+__datan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm7, %xmm6
+        mulsd     %xmm2, %xmm3
+        addsd     %xmm8, %xmm6
+        addsd     1856+__datan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        movaps    %xmm9, %xmm5
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        addsd     1848+__datan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm4, %xmm5
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     2000+__datan2_ha_CoutTab(%rip), %xmm5
+        subsd     %xmm6, %xmm9
+        addsd     1840+__datan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm4, %xmm9
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm9, -64(%rsp)
+        movsd     -72(%rsp), %xmm11
+        mulsd     %xmm11, %xmm5
+        addsd     1832+__datan2_ha_CoutTab(%rip), %xmm3
+        movsd     -64(%rsp), %xmm4
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm7
+        mulsd     %xmm2, %xmm3
+        subsd     %xmm11, %xmm7
+        movsd     %xmm7, -64(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     -64(%rsp), %xmm6
+        addsd     1824+__datan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm6, %xmm8
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm8, -72(%rsp)
+        movsd     -72(%rsp), %xmm10
+        mulsd     %xmm3, %xmm13
+        subsd     %xmm10, %xmm11
+        movsd     %xmm11, -64(%rsp)
+        movsd     -72(%rsp), %xmm2
+        movsd     -64(%rsp), %xmm12
+        movsd     %xmm13, -72(%rsp)
+        addsd     %xmm12, %xmm4
+        movsd     -72(%rsp), %xmm14
+        subsd     %xmm3, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm15
+        subsd     %xmm15, %xmm5
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm6
+        subsd     %xmm6, %xmm3
+        movsd     %xmm3, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm5
+        movaps    %xmm6, %xmm12
+        movaps    %xmm5, %xmm3
+        mulsd     %xmm4, %xmm6
+        mulsd     %xmm4, %xmm3
+        mulsd     %xmm2, %xmm5
+        mulsd     %xmm2, %xmm12
+        addsd     %xmm3, %xmm6
+        movaps    %xmm12, %xmm7
+        movaps    %xmm12, %xmm8
+        addsd     %xmm5, %xmm6
+        addsd     %xmm2, %xmm7
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm3
+        subsd     %xmm3, %xmm8
+        movsd     %xmm8, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -64(%rsp), %xmm11
+        addsd     %xmm11, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -56(%rsp), %xmm2
+        subsd     %xmm2, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -64(%rsp), %xmm14
+        movsd     -56(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movq      -72(%rsp), %rdi
+        movsd     -56(%rsp), %xmm15
+        movq      %rdi, -40(%rsp)
+        addsd     %xmm15, %xmm4
+        shrq      $56, %rdi
+        addsd     %xmm5, %xmm4
+        andb      $127, %dil
+        orb       %r8b, %dil
+        movb      %dil, -33(%rsp)
+        movsd     %xmm4, -32(%rsp)
+        movaps    %xmm1, %xmm4
+        movsd     -40(%rsp), %xmm7
+        movaps    %xmm7, %xmm2
+        addsd     %xmm7, %xmm4
+        movsd     %xmm4, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movb      -25(%rsp), %r9b
+        movb      %r9b, %r10b
+        shrb      $7, %r9b
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm3
+        xorb      %r9b, %al
+        andb      $127, %r10b
+        shlb      $7, %al
+        addsd     %xmm3, %xmm5
+        movsd     %xmm5, -56(%rsp)
+        movsd     -64(%rsp), %xmm6
+        addsd     %xmm6, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        orb       %al, %r10b
+        movsd     -56(%rsp), %xmm1
+        movb      %r10b, -25(%rsp)
+        subsd     %xmm1, %xmm7
+        movsd     %xmm7, -56(%rsp)
+        movsd     -64(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm1
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movq      -72(%rsp), %rax
+        movsd     -56(%rsp), %xmm3
+        movq      %rax, -40(%rsp)
+        addsd     %xmm3, %xmm0
+        movsd     -40(%rsp), %xmm4
+        addsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -32(%rsp)
+        addsd     %xmm0, %xmm4
+        movsd     %xmm4, -24(%rsp)
+        movb      -17(%rsp), %r11b
+        andb      $127, %r11b
+        orb       %dl, %r11b
+        movb      %r11b, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_21:
+
+        cmpl      $74, %r8d
+        jge       .LBL_2_53
+
+
+        movb      %dil, -41(%rsp)
+        divsd     -48(%rsp), %xmm1
+        movsd     1928+__datan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %dl
+        subsd     %xmm1, %xmm0
+        addsd     1920+__datan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_24:
+
+        testb     %al, %al
+        jne       .LBL_2_35
+
+
+        movb      %dil, -41(%rsp)
+        movb      %sil, -33(%rsp)
+        movsd     -48(%rsp), %xmm2
+        divsd     -40(%rsp), %xmm2
+        movsd     %xmm2, -24(%rsp)
+        movzwl    -18(%rsp), %eax
+        testl     $32752, %eax
+        je        .LBL_2_27
+
+
+        movsd     1888+__datan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %dl
+        addsd     %xmm2, %xmm0
+        movsd     %xmm0, -72(%rsp)
+        movsd     -72(%rsp), %xmm1
+        mulsd     %xmm1, %xmm2
+        movsd     %xmm2, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_27:
+
+        mulsd     %xmm2, %xmm2
+        shlb      $7, %dl
+        movsd     %xmm2, -72(%rsp)
+        movsd     -72(%rsp), %xmm0
+        addsd     -24(%rsp), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_29:
+
+        testl     %r9d, %r9d
+        jne       .LBL_2_53
+
+
+        testl     $1048575, -44(%rsp)
+        jne       .LBL_2_53
+        jmp       .LBL_2_57
+
+.LBL_2_31:
+
+        jne       .LBL_2_53
+
+.LBL_2_33:
+
+        testb     %al, %al
+        jne       .LBL_2_35
+
+.LBL_2_34:
+
+        shlb      $7, %dl
+        movq      1976+__datan2_ha_CoutTab(%rip), %rax
+        movq      %rax, -24(%rsp)
+        shrq      $56, %rax
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_35:
+
+        movsd     1936+__datan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %dl
+        addsd     1944+__datan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+
+.LBL_2_36:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_37:
+
+        movsd     1984+__datan2_ha_CoutTab(%rip), %xmm3
+        movl      $-1022, %r9d
+        mulsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        jmp       .LBL_2_18
+
+.LBL_2_38:
+
+        cmpl      $2047, %r9d
+        je        .LBL_2_49
+
+.LBL_2_39:
+
+        testl     $1048575, -36(%rsp)
+        jne       .LBL_2_41
+
+
+        cmpl      $0, -40(%rsp)
+        je        .LBL_2_42
+
+.LBL_2_41:
+
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_42:
+
+        cmpl      $2047, %r9d
+        je        .LBL_2_46
+
+
+        testb     %al, %al
+        je        .LBL_2_34
+        jmp       .LBL_2_35
+
+.LBL_2_46:
+
+        testb     %al, %al
+        jne       .LBL_2_48
+
+
+        movsd     1904+__datan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %dl
+        addsd     1912+__datan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_48:
+
+        movsd     1952+__datan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %dl
+        addsd     1960+__datan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_49:
+
+        testl     $1048575, -44(%rsp)
+        jne       .LBL_2_41
+
+
+        cmpl      $0, -48(%rsp)
+        jne       .LBL_2_41
+
+
+        cmpl      $2047, %r8d
+        je        .LBL_2_39
+
+.LBL_2_53:
+
+        movsd     1920+__datan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %dl
+        addsd     1928+__datan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %dl, %al
+        movb      %al, -17(%rsp)
+        movq      -24(%rsp), %rdx
+        movq      %rdx, (%rcx)
+        jmp       .LBL_2_36
+
+.LBL_2_57:
+
+        cmpl      $0, -48(%rsp)
+        jne       .LBL_2_53
+        jmp       .LBL_2_33
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_datan2_ha_cout_rare_internal,@function
+	.size	__svml_datan2_ha_cout_rare_internal,.-__svml_datan2_ha_cout_rare_internal
+..LN__svml_datan2_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_datan2_ha_data_internal:
+	.long	0
+	.long	1072693248
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	0
+	.long	0
+	.long	0
+	.long	1073217536
+	.long	3531732635
+	.long	1072657163
+	.long	2062601149
+	.long	1013974920
+	.long	4294967295
+	.long	4294967295
+	.long	0
+	.long	1072693248
+	.long	1413754136
+	.long	1072243195
+	.long	856972295
+	.long	1015129638
+	.long	4294967295
+	.long	4294967295
+	.long	0
+	.long	1071644672
+	.long	90291023
+	.long	1071492199
+	.long	573531618
+	.long	1014639487
+	.long	4294967295
+	.long	4294967295
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	4294967295
+	.long	4294967295
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	90291023
+	.long	1071492199
+	.long	90291023
+	.long	1071492199
+	.long	90291023
+	.long	1071492199
+	.long	90291023
+	.long	1071492199
+	.long	90291023
+	.long	1071492199
+	.long	90291023
+	.long	1071492199
+	.long	90291023
+	.long	1071492199
+	.long	90291023
+	.long	1071492199
+	.long	573531618
+	.long	1014639487
+	.long	573531618
+	.long	1014639487
+	.long	573531618
+	.long	1014639487
+	.long	573531618
+	.long	1014639487
+	.long	573531618
+	.long	1014639487
+	.long	573531618
+	.long	1014639487
+	.long	573531618
+	.long	1014639487
+	.long	573531618
+	.long	1014639487
+	.long	1413754136
+	.long	1072243195
+	.long	1413754136
+	.long	1072243195
+	.long	1413754136
+	.long	1072243195
+	.long	1413754136
+	.long	1072243195
+	.long	1413754136
+	.long	1072243195
+	.long	1413754136
+	.long	1072243195
+	.long	1413754136
+	.long	1072243195
+	.long	1413754136
+	.long	1072243195
+	.long	856972295
+	.long	1015129638
+	.long	856972295
+	.long	1015129638
+	.long	856972295
+	.long	1015129638
+	.long	856972295
+	.long	1015129638
+	.long	856972295
+	.long	1015129638
+	.long	856972295
+	.long	1015129638
+	.long	856972295
+	.long	1015129638
+	.long	856972295
+	.long	1015129638
+	.long	3531732635
+	.long	1072657163
+	.long	3531732635
+	.long	1072657163
+	.long	3531732635
+	.long	1072657163
+	.long	3531732635
+	.long	1072657163
+	.long	3531732635
+	.long	1072657163
+	.long	3531732635
+	.long	1072657163
+	.long	3531732635
+	.long	1072657163
+	.long	3531732635
+	.long	1072657163
+	.long	2062601149
+	.long	1013974920
+	.long	2062601149
+	.long	1013974920
+	.long	2062601149
+	.long	1013974920
+	.long	2062601149
+	.long	1013974920
+	.long	2062601149
+	.long	1013974920
+	.long	2062601149
+	.long	1013974920
+	.long	2062601149
+	.long	1013974920
+	.long	2062601149
+	.long	1013974920
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	856972295
+	.long	1016178214
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	1073217536
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	1413754136
+	.long	1074340347
+	.long	0
+	.long	1017226816
+	.long	0
+	.long	1017226816
+	.long	0
+	.long	1017226816
+	.long	0
+	.long	1017226816
+	.long	0
+	.long	1017226816
+	.long	0
+	.long	1017226816
+	.long	0
+	.long	1017226816
+	.long	0
+	.long	1017226816
+	.long	4160749568
+	.long	4294967295
+	.long	4160749568
+	.long	4294967295
+	.long	4160749568
+	.long	4294967295
+	.long	4160749568
+	.long	4294967295
+	.long	4160749568
+	.long	4294967295
+	.long	4160749568
+	.long	4294967295
+	.long	4160749568
+	.long	4294967295
+	.long	4160749568
+	.long	4294967295
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1072889856
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1073971200
+	.long	0
+	.long	1072037888
+	.long	0
+	.long	1072037888
+	.long	0
+	.long	1072037888
+	.long	0
+	.long	1072037888
+	.long	0
+	.long	1072037888
+	.long	0
+	.long	1072037888
+	.long	0
+	.long	1072037888
+	.long	0
+	.long	1072037888
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	4293918720
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	133169152
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	3866310424
+	.long	1066132731
+	.long	3866310424
+	.long	1066132731
+	.long	3866310424
+	.long	1066132731
+	.long	3866310424
+	.long	1066132731
+	.long	3866310424
+	.long	1066132731
+	.long	3866310424
+	.long	1066132731
+	.long	3866310424
+	.long	1066132731
+	.long	3866310424
+	.long	1066132731
+	.long	529668190
+	.long	3214953687
+	.long	529668190
+	.long	3214953687
+	.long	529668190
+	.long	3214953687
+	.long	529668190
+	.long	3214953687
+	.long	529668190
+	.long	3214953687
+	.long	529668190
+	.long	3214953687
+	.long	529668190
+	.long	3214953687
+	.long	529668190
+	.long	3214953687
+	.long	1493047753
+	.long	1067887957
+	.long	1493047753
+	.long	1067887957
+	.long	1493047753
+	.long	1067887957
+	.long	1493047753
+	.long	1067887957
+	.long	1493047753
+	.long	1067887957
+	.long	1493047753
+	.long	1067887957
+	.long	1493047753
+	.long	1067887957
+	.long	1493047753
+	.long	1067887957
+	.long	1554070819
+	.long	3215629941
+	.long	1554070819
+	.long	3215629941
+	.long	1554070819
+	.long	3215629941
+	.long	1554070819
+	.long	3215629941
+	.long	1554070819
+	.long	3215629941
+	.long	1554070819
+	.long	3215629941
+	.long	1554070819
+	.long	3215629941
+	.long	1554070819
+	.long	3215629941
+	.long	3992437651
+	.long	1068372721
+	.long	3992437651
+	.long	1068372721
+	.long	3992437651
+	.long	1068372721
+	.long	3992437651
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+	.long	3192774295
+	.long	1476395008
+	.long	1073291771
+	.long	1251477510
+	.long	3192774295
+	.long	1476395008
+	.long	1073291771
+	.long	1190120835
+	.long	3192774295
+	.long	1476395008
+	.long	1073291771
+	.long	1144103328
+	.long	3192774295
+	.long	1476395008
+	.long	1073291771
+	.long	1113424990
+	.long	3192774295
+	.long	1476395008
+	.long	1073291771
+	.long	1090416237
+	.long	3192774295
+	.long	1476395008
+	.long	1073291771
+	.long	1075077068
+	.long	3192774295
+	.long	1431655765
+	.long	3218429269
+	.long	2576978363
+	.long	1070176665
+	.long	2453154343
+	.long	3217180964
+	.long	4189149139
+	.long	1069314502
+	.long	1775019125
+	.long	3216459198
+	.long	273199057
+	.long	1068739452
+	.long	874748308
+	.long	3215993277
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	1413754136
+	.long	1072243195
+	.long	856972295
+	.long	1015129638
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
+	.long	1413754136
+	.long	1074340347
+	.long	856972295
+	.long	1017226790
+	.long	2134057426
+	.long	1073928572
+	.long	1285458442
+	.long	1016756537
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	0
+	.long	0
+	.long	2144337920
+	.long	0
+	.long	1048576
+	.long	33554432
+	.long	1101004800
+	.type	__datan2_ha_CoutTab,@object
+	.size	__datan2_ha_CoutTab,2008
+	.align 8
+.L_2il0floatpacket.40:
+	.long	0xffffffff,0xffffffff
+	.type	.L_2il0floatpacket.40,@object
+	.size	.L_2il0floatpacket.40,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_d_la.s
index 1f3c838a3eb..c2c1f54c866 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_h_la.s
new file mode 100644
index 00000000000..f0635b57c44
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_h_la.s
@@ -0,0 +1,253 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Absolute arguments: xa = |x|
+ *                       ya = |y|
+ *   Argument signs: sign(x)
+ *                   sign(x)
+ *   High = (sgn_x)? Pi: 0
+ *   Mask for difference: diff_msk = (xa<ya) ? 1 : 0;
+ *   Maximum and minimum: xa1=max(xa,ya)
+ *   ya1=min(ya,xa)
+ *
+ *   if(diff_msk) High = Pi2;
+ *
+ *   Get result sign: sgn_r = -sgn_x for diff_msk=1
+ *   Divide: xa = ya1/xa1
+ *   set branch mask if xa1 is Inf/NaN/zero
+ *
+ *   Result: High + xa*Poly
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_atan2s32
+
+__svml_atan2s32:
+
+        .cfi_startproc
+
+        pushq     %rbp
+        .cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+        .cfi_def_cfa 6, 16
+        .cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $256, %rsp
+        vmovdqu16 __svml_hatan2_data_internal(%rip), %zmm5
+        vmovdqu16 384+__svml_hatan2_data_internal(%rip), %zmm13
+        vmovdqu16 448+__svml_hatan2_data_internal(%rip), %zmm14
+        vmovdqu16 512+__svml_hatan2_data_internal(%rip), %zmm15
+        vmovaps   %zmm1, %zmm4
+        vmovdqu16 64+__svml_hatan2_data_internal(%rip), %zmm1
+
+/* xa = |x| */
+        vpandd    %zmm5, %zmm4, %zmm8
+
+/* ya = |y| */
+        vpandd    %zmm5, %zmm0, %zmm7
+
+/* diff_msk = (xa<ya) ? 1 : 0; */
+        vcmpph    $17, {sae}, %zmm7, %zmm8, %k1
+
+/* xa1=max(xa,ya) */
+        vmaxph    {sae}, %zmm7, %zmm8, %zmm12
+
+/* ya1=min(ya,xa) */
+        vminph    {sae}, %zmm8, %zmm7, %zmm11
+
+/* set branch mask if xa1 is Inf/NaN/zero */
+        vfpclassph $159, %zmm12, %k0
+        kmovd     %k0, %edx
+
+/* sign(x) */
+        vpxord    %zmm4, %zmm8, %zmm2
+
+/* sgn_r = -sgn_x for diff_msk=1 */
+        vpxord    256+__svml_hatan2_data_internal(%rip), %zmm2, %zmm10
+
+/* High = (sgn_x)? Pi: 0 */
+        vpsraw    $15, %zmm2, %zmm6
+        vmovdqu16 %zmm10, %zmm2{%k1}
+
+/* xa = ya1/xa1 */
+        vdivph    {rn-sae}, %zmm12, %zmm11, %zmm10
+
+/* polynomial */
+        vmovdqu16 320+__svml_hatan2_data_internal(%rip), %zmm12
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm13, %zmm10, %zmm12
+        vfmadd213ph {rn-sae}, %zmm14, %zmm10, %zmm12
+        vfmadd213ph {rn-sae}, %zmm15, %zmm10, %zmm12
+        vfmadd213ph {rn-sae}, %zmm1, %zmm10, %zmm12
+        vpandd    128+__svml_hatan2_data_internal(%rip), %zmm6, %zmm9
+
+/* if(diff_msk) High = Pi2; */
+        vpblendmw 192+__svml_hatan2_data_internal(%rip), %zmm9, %zmm5{%k1}
+
+/* set polynomial sign */
+        vpxord    %zmm2, %zmm12, %zmm1
+
+/* High + xa*Poly */
+        vfmadd213ph {rn-sae}, %zmm5, %zmm10, %zmm1
+
+/* sign(x) */
+        vpxord    %zmm0, %zmm7, %zmm3
+
+/* set sign */
+        vpxord    %zmm3, %zmm1, %zmm1
+        testl     %edx, %edx
+
+/* Go to special inputs processing branch */
+        jne       .LBL_SPECIAL_VALUES_BRANCH
+                                # LOE rbx r12 r13 r14 r15 edx zmm0 zmm1 zmm4
+
+/* Restore registers
+ * and exit the function
+ */
+
+.LBL_EXIT:
+        vmovaps   %zmm1, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+        .cfi_def_cfa 7, 8
+        .cfi_restore 6
+        ret
+        .cfi_def_cfa 6, 16
+        .cfi_offset 6, -16
+
+/* Branch to process
+ * special inputs
+ */
+
+.LBL_SPECIAL_VALUES_BRANCH:
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm4, 128(%rsp)
+        vmovups   %zmm1, 192(%rsp)
+                                # LOE rbx r12 r13 r14 r15 edx
+
+        xorl      %eax, %eax
+        movq      %r12, 16(%rsp)
+        /*  DW_CFA_expression: r12 (r12) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -240; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 8(%rsp)
+        /*  DW_CFA_expression: r13 (r13) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -248; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+        movq      %r14, (%rsp)
+        /*  DW_CFA_expression: r14 (r14) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -256; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0e, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+                                # LOE rbx r15 r12d r13d
+
+/* Range mask
+ * bits check
+ */
+
+.LBL_RANGEMASK_CHECK:
+        btl       %r12d, %r13d
+
+/* Call scalar math function */
+        jc        .LBL_SCALAR_MATH_CALL
+                                # LOE rbx r15 r12d r13d
+
+/* Special inputs
+ * processing loop
+ */
+
+.LBL_SPECIAL_VALUES_LOOP:
+        incl      %r12d
+        cmpl      $32, %r12d
+
+/* Check bits in range mask */
+        jl        .LBL_RANGEMASK_CHECK
+                                # LOE rbx r15 r12d r13d
+
+        movq      16(%rsp), %r12
+        .cfi_restore 12
+        movq      8(%rsp), %r13
+        .cfi_restore 13
+        movq      (%rsp), %r14
+        .cfi_restore 14
+        vmovups   192(%rsp), %zmm1
+
+/* Go to exit */
+        jmp       .LBL_EXIT
+        /*  DW_CFA_expression: r12 (r12) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -240; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+        /*  DW_CFA_expression: r13 (r13) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -248; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+        /*  DW_CFA_expression: r14 (r14) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -256; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0e, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+                                # LOE rbx r12 r13 r14 r15 zmm1
+
+/* Scalar math fucntion call
+ * to process special input
+ */
+
+.LBL_SCALAR_MATH_CALL:
+        movl      %r12d, %r14d
+        vmovsh    64(%rsp,%r14,2), %xmm1
+        vmovsh    128(%rsp,%r14,2), %xmm2
+        vcvtsh2ss %xmm1, %xmm1, %xmm0
+        vcvtsh2ss %xmm2, %xmm2, %xmm1
+        vzeroupper
+        call      atan2f@PLT
+                                # LOE rbx r14 r15 r12d r13d xmm0
+
+        vmovaps   %xmm0, %xmm1
+        vxorps    %xmm0, %xmm0, %xmm0
+        vmovss    %xmm1, %xmm0, %xmm2
+        vcvtss2sh %xmm2, %xmm2, %xmm3
+        vmovsh    %xmm3, 192(%rsp,%r14,2)
+
+/* Process special inputs in loop */
+        jmp       .LBL_SPECIAL_VALUES_LOOP
+                                # LOE rbx r15 r12d r13d
+        .cfi_endproc
+
+        .type	__svml_atan2s32,@function
+        .size	__svml_atan2s32,.-__svml_atan2s32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hatan2_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x4248
+	.endr
+	.rept	32
+        .word	0x3e48
+	.endr
+	.rept	32
+        .word	0x8000
+	.endr
+	.rept	32
+        .word	0xa528
+	.endr
+	.rept	32
+        .word	0x3248
+	.endr
+	.rept	32
+        .word	0xb65d
+	.endr
+	.rept	32
+        .word	0x1f7a
+	.endr
+        .type	__svml_hatan2_data_internal,@object
+        .size	__svml_hatan2_data_internal,576
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_s_ha.s
new file mode 100644
index 00000000000..b8dd1ab060a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_s_ha.s
@@ -0,0 +1,2372 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *      For    0.0    <= x <=  7.0/16.0: atan(x) = atan(0.0) + atan(s), where s=(x-0.0)/(1.0+0.0*x)
+ *      For  7.0/16.0 <= x <= 11.0/16.0: atan(x) = atan(0.5) + atan(s), where s=(x-0.5)/(1.0+0.5*x)
+ *      For 11.0/16.0 <= x <= 19.0/16.0: atan(x) = atan(1.0) + atan(s), where s=(x-1.0)/(1.0+1.0*x)
+ *      For 19.0/16.0 <= x <= 39.0/16.0: atan(x) = atan(1.5) + atan(s), where s=(x-1.5)/(1.0+1.5*x)
+ *      For 39.0/16.0 <= x <=    inf   : atan(x) = atan(inf) + atan(s), where s=-1.0/x
+ *      Where atan(s) ~= s+s^3*Poly11(s^2) on interval |s|<7.0/0.16.
+ * --
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_atan2f16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_atan2f16_ha
+
+__svml_atan2f16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $256, %rsp
+        xorl      %edx, %edx
+
+/*
+ * #define NO_VECTOR_ZERO_ATAN2_ARGS
+ * -------------------- Declarations -----------------------
+ * Variables
+ * Constants
+ * -------------- The end of declarations ----------------
+ * -------------------- Implementation ---------------------
+ * Arguments absolute values.
+ */
+        vmovups   192+__svml_satan2_ha_data_internal(%rip), %zmm2
+
+/* Testing on working interval. */
+        vmovups   1728+__svml_satan2_ha_data_internal(%rip), %zmm12
+        vmovups   1792+__svml_satan2_ha_data_internal(%rip), %zmm14
+        vmovups   448+__svml_satan2_ha_data_internal(%rip), %zmm6
+        vmovups   576+__svml_satan2_ha_data_internal(%rip), %zmm10
+
+/*
+ * Determining to which interval
+ * is subject |y/x|.
+ * Five intervals -- five masks.
+ */
+        vmovups   384+__svml_satan2_ha_data_internal(%rip), %zmm7
+        vmovups   512+__svml_satan2_ha_data_internal(%rip), %zmm9
+        vandps    %zmm2, %zmm0, %zmm4
+        vpternlogd $255, %zmm8, %zmm8, %zmm8
+        kmovw     %k4, 56(%rsp)
+        vpsubd    %zmm12, %zmm4, %zmm15
+        vmovaps   %zmm1, %zmm5
+        vandps    %zmm2, %zmm5, %zmm3
+        vxorps    %zmm4, %zmm0, %zmm1
+        vpcmpgtd  %zmm15, %zmm14, %k3
+        vmulps    {rn-sae}, %zmm7, %zmm3, %zmm11
+        vmovups   768+__svml_satan2_ha_data_internal(%rip), %zmm7
+        vpsubd    %zmm12, %zmm3, %zmm13
+        vpternlogd $255, %zmm12, %zmm12, %zmm12
+
+/*
+ * Arguments signs.
+ * VAND(S, sSX, ATAN2_VARG2, sSIGN_MASK);
+ * VAND(S, sSY, ATAN2_VARG1, sSIGN_MASK);
+ * Arguments absolute values and signs.
+ */
+        vxorps    %zmm3, %zmm5, %zmm2
+        vpcmpgtd  %zmm13, %zmm14, %k2
+
+/* r > b1 */
+        vcmpps    $17, {sae}, %zmm4, %zmm11, %k1
+        vmulps    {rn-sae}, %zmm9, %zmm3, %zmm14
+
+/*
+ * Reduction of |y/x| to the interval [-Bound1; Bound1]
+ * 1. If   0    < |y/x| < Bound1 then a=y, b=x, AtanHi+AtanLo=atan(  0  )
+ * 2. If Bound1 < |y/x| < Bound2 then a=y-Base1*x, b=x+Base1*y, AtanHi+AtanLo=atan(Base1)
+ * 3. If Bound2 < |y/x| < Bound3 then a=y-Base2*x, b=x+Base2*y, AtanHi+AtanLo=atan(Base2)
+ * 4. If Bound3 < |y/x| < Bound4 then a=y-Base3*x, b=x+Base3*y, AtanHi+AtanLo=atan(Base3)
+ * 5. If Bound4 < |y/x| <  INF   then a=-x, b=y, AtanHi+AtanLo=atan( Inf )
+ */
+        vmovups   64+__svml_satan2_ha_data_internal(%rip), %zmm11
+
+/* r > b3 */
+        vcmpps    $17, {sae}, %zmm4, %zmm14, %k4
+        vmovups   832+__svml_satan2_ha_data_internal(%rip), %zmm14
+        vpandnd   %zmm13, %zmm13, %zmm12{%k2}
+        vpandnd   %zmm15, %zmm15, %zmm8{%k3}
+        vmulps    {rn-sae}, %zmm6, %zmm3, %zmm13
+        vmulps    {rn-sae}, %zmm10, %zmm3, %zmm15
+
+/* r > b2 */
+        vcmpps    $17, {sae}, %zmm4, %zmm13, %k2
+
+/* r > b4 */
+        vcmpps    $17, {sae}, %zmm4, %zmm15, %k3
+        vmovups   1088+__svml_satan2_ha_data_internal(%rip), %zmm15
+        vblendmps %zmm11, %zmm7, %zmm9{%k3}
+        vblendmps 1216+__svml_satan2_ha_data_internal(%rip), %zmm15, %zmm7{%k3}
+        vmovups   1344+__svml_satan2_ha_data_internal(%rip), %zmm15
+        vmovaps   %zmm3, %zmm10
+        vxorps    %zmm3, %zmm3, %zmm10{%k3}
+        vpord     %zmm8, %zmm12, %zmm12
+        vmovups   640+__svml_satan2_ha_data_internal(%rip), %zmm8
+        vptestmd  %zmm12, %zmm12, %k0
+        vblendmps 704+__svml_satan2_ha_data_internal(%rip), %zmm8, %zmm6{%k2}
+        vblendmps 960+__svml_satan2_ha_data_internal(%rip), %zmm14, %zmm8{%k2}
+
+/* PiHI + PiLO = Pi if x<0 or 0 if x>0. */
+        vmovups   __svml_satan2_ha_data_internal(%rip), %zmm14
+        vblendmps %zmm9, %zmm6, %zmm13{%k4}
+        vblendmps %zmm7, %zmm8, %zmm8{%k4}
+
+/* =========== Special branch for fast (vector) processing of zero arguments ================ */
+        kortestw  %k0, %k0
+        vfmadd231ps {rn-sae}, %zmm4, %zmm13, %zmm10{%k1}
+        vmovaps   %zmm4, %zmm9
+        vxorps    %zmm4, %zmm4, %zmm9{%k3}
+        vfnmadd231ps {rn-sae}, %zmm3, %zmm13, %zmm9{%k1}
+        vrcp14ps  %zmm10, %zmm13
+
+/* No need for extra iteration on KNC and KNL */
+        vfnmadd231ps {rn-sae}, %zmm13, %zmm10, %zmm11
+        vfmadd213ps {rn-sae}, %zmm13, %zmm11, %zmm13
+        vmulps    {rn-sae}, %zmm9, %zmm13, %zmm6
+        vmovaps   %zmm10, %zmm7
+        vfnmadd213ps {rn-sae}, %zmm9, %zmm6, %zmm7
+
+/*
+ * Add one NR iteration here to prevent loss
+ * of accuracy when sRLo is close to zero
+ */
+        vfmadd213ps {rn-sae}, %zmm6, %zmm13, %zmm7
+        vfnmadd213ps {rn-sae}, %zmm9, %zmm7, %zmm10
+        vmovups   1152+__svml_satan2_ha_data_internal(%rip), %zmm9
+        vmulps    {rn-sae}, %zmm13, %zmm10, %zmm6
+        vmovups   896+__svml_satan2_ha_data_internal(%rip), %zmm10
+        vblendmps 1280+__svml_satan2_ha_data_internal(%rip), %zmm9, %zmm11{%k3}
+        vmovups   1408+__svml_satan2_ha_data_internal(%rip), %zmm9
+        vblendmps 1024+__svml_satan2_ha_data_internal(%rip), %zmm10, %zmm10{%k2}
+        vcmpps    $17, {sae}, %zmm14, %zmm5, %k2
+
+/*
+ * Minimax polynomial calculating
+ * P1=           (A1+    A3*r4   )*r2
+ * P2=       (A0+     A2   *r4   )*r4
+ * P = (P1+P2)*r = r*r2*(A0+(A1+(A2+A3*r2)*r2)*r2)
+ */
+        vmulps    {rn-sae}, %zmm7, %zmm7, %zmm14
+        vblendmps %zmm11, %zmm10, %zmm13{%k4}
+        vmovups   1536+__svml_satan2_ha_data_internal(%rip), %zmm10
+        vmulps    {rn-sae}, %zmm14, %zmm14, %zmm11
+        vaddps    {rn-sae}, %zmm13, %zmm6, %zmm6{%k1}
+        vmovups   1472+__svml_satan2_ha_data_internal(%rip), %zmm13
+        vfmadd231ps {rn-sae}, %zmm11, %zmm15, %zmm13
+        vfmadd213ps {rn-sae}, %zmm10, %zmm9, %zmm11
+
+/*
+ * Result = ( ( AtanLo + AtanHi + Poly + R ) * sign(x) +
+ * PiOrZeroLo + PiOrZeroHi )*sign(y) =
+ * = ( ( RLo + AtanLo + PiOrZeroLo*sign(x) + Poly + RHi +
+ * AtanHi ) * sign(x) + PiOrZeroHi )*sign(y)
+ */
+        vxorps    320+__svml_satan2_ha_data_internal(%rip), %zmm2, %zmm15
+        vfmadd213ps {rn-sae}, %zmm11, %zmm14, %zmm13
+        vaddps    {rn-sae}, %zmm15, %zmm6, %zmm6{%k2}
+        vmulps    {rn-sae}, %zmm14, %zmm13, %zmm9
+        vfmadd213ps {rn-sae}, %zmm6, %zmm7, %zmm9
+        vaddps    {rn-sae}, %zmm7, %zmm9, %zmm6
+        vmovups   256+__svml_satan2_ha_data_internal(%rip), %zmm7
+        vaddps    {rn-sae}, %zmm8, %zmm6, %zmm6{%k1}
+        vorps     %zmm2, %zmm6, %zmm8
+        vaddps    {rn-sae}, %zmm7, %zmm8, %zmm8{%k2}
+        vorps     %zmm1, %zmm8, %zmm9
+        jne       .LBL_1_12
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_2:
+
+
+/*
+ * =========== Special branch for fast (vector) processing of zero arguments ================
+ * -------------- The end of implementation ----------------
+ */
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        kmovw     56(%rsp), %k4
+	.cfi_restore 122
+        vmovaps   %zmm9, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_4:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm5, 128(%rsp)
+        vmovups   %zmm9, 192(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 32(%rsp)
+        movq      %rdi, 24(%rsp)
+        movq      %r12, 48(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 40(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   192(%rsp), %zmm9
+        movq      32(%rsp), %rsi
+	.cfi_restore 4
+        movq      24(%rsp), %rdi
+	.cfi_restore 5
+        movq      48(%rsp), %r12
+	.cfi_restore 12
+        movq      40(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+        lea       192(%rsp,%r12,4), %rdx
+
+        call      __svml_satan2_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+
+/* Check if both X & Y are not NaNs:  iXYnotNAN */
+        vcmpps    $3, {sae}, %zmm5, %zmm5, %k1
+        vcmpps    $3, {sae}, %zmm0, %zmm0, %k2
+
+/* Check if at least on of Y or Y is zero: iAXAYZERO */
+        vmovups   __svml_satan2_ha_data_internal(%rip), %zmm8
+
+/*
+ * -------- Path for zero arguments (at least one of both) --------------
+ * Load and construct PIO2 and PI constants
+ * (this section is absent in HA main path)
+ * 1) If y<x then a= y, b=x, PIO2=0
+ * 2) If y>x then a=-x, b=y, PIO2=Pi/2
+ */
+        vmovups   1920+__svml_satan2_ha_data_internal(%rip), %zmm6
+        vpternlogd $255, %zmm10, %zmm10, %zmm10
+        vpternlogd $255, %zmm13, %zmm13, %zmm13
+        vpternlogd $255, %zmm11, %zmm11, %zmm11
+        vpternlogd $255, %zmm14, %zmm14, %zmm14
+        vpcmpd    $4, %zmm8, %zmm3, %k3
+        vpcmpd    $4, %zmm8, %zmm4, %k4
+        vpandnd   %zmm5, %zmm5, %zmm10{%k1}
+        vcmpps    $17, {sae}, %zmm3, %zmm4, %k1
+        vpandnd   %zmm3, %zmm3, %zmm13{%k3}
+        vblendmps %zmm3, %zmm4, %zmm3{%k1}
+
+/* Res = sign(Y)*(X<0)?(PIO2+PI):PIO2 */
+        vpcmpgtd  %zmm5, %zmm8, %k3
+        vpandnd   %zmm0, %zmm0, %zmm11{%k2}
+        vandps    %zmm11, %zmm10, %zmm7
+        vmovups   1856+__svml_satan2_ha_data_internal(%rip), %zmm10
+
+/* Check if both args are zeros (den. is zero) */
+        vcmpps    $4, {sae}, %zmm8, %zmm3, %k2
+        vxorps    %zmm10, %zmm10, %zmm10{%k1}
+        vpandnd   %zmm4, %zmm4, %zmm14{%k4}
+        vpternlogd $255, %zmm4, %zmm4, %zmm4
+        vpandnd   %zmm3, %zmm3, %zmm4{%k2}
+
+/* Set sPIO2 to zero if den. is zero */
+        vpandnd   %zmm10, %zmm4, %zmm3
+        vpandd    %zmm4, %zmm8, %zmm4
+        vpord     %zmm4, %zmm3, %zmm10
+        vorps     %zmm2, %zmm10, %zmm2
+        vaddps    {rn-sae}, %zmm6, %zmm2, %zmm2{%k3}
+        vorps     %zmm1, %zmm2, %zmm1
+        vpord     %zmm14, %zmm13, %zmm15
+
+/* Check if at least on of Y or Y is zero and not NaN: iAXAYZEROnotNAN */
+        vpandd    %zmm7, %zmm15, %zmm7
+
+/* Exclude from previous callout mask zero (and not NaN) arguments */
+        vpandnd   %zmm12, %zmm7, %zmm12
+
+/* Go to callout */
+        vptestmd  %zmm12, %zmm12, %k0
+        kmovw     %k0, %edx
+
+/* Merge results from main and spec path */
+        vpandnd   %zmm9, %zmm7, %zmm9
+        vpandd    %zmm7, %zmm1, %zmm1
+        vpord     %zmm1, %zmm9, %zmm9
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_atan2f16_ha,@function
+	.size	__svml_atan2f16_ha,.-__svml_atan2f16_ha
+..LN__svml_atan2f16_ha.0:
+
+.L_2__routine_start___svml_satan2_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_satan2_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L59:
+
+        pxor      %xmm0, %xmm0
+        movss     (%rdi), %xmm3
+        pxor      %xmm1, %xmm1
+        movss     (%rsi), %xmm2
+        movq      %rdx, %r8
+        cvtss2sd  %xmm3, %xmm0
+        cvtss2sd  %xmm2, %xmm1
+        movss     %xmm3, -32(%rsp)
+        movss     %xmm2, -28(%rsp)
+        movsd     %xmm0, -48(%rsp)
+        movsd     %xmm1, -40(%rsp)
+        movzwl    -30(%rsp), %edi
+        andl      $32640, %edi
+        movb      -25(%rsp), %dl
+        movzwl    -42(%rsp), %eax
+        andb      $-128, %dl
+        movzwl    -34(%rsp), %r9d
+        andl      $32752, %eax
+        andl      $32752, %r9d
+        shrl      $7, %edi
+        movb      -29(%rsp), %cl
+        shrb      $7, %cl
+        shrb      $7, %dl
+        shrl      $4, %eax
+        shrl      $4, %r9d
+        cmpl      $255, %edi
+        je        .LBL_2_35
+
+
+        movzwl    -26(%rsp), %esi
+        andl      $32640, %esi
+        cmpl      $32640, %esi
+        je        .LBL_2_35
+
+
+        testl     %eax, %eax
+        jne       .LBL_2_5
+
+
+        testl     $8388607, -32(%rsp)
+        je        .LBL_2_30
+
+.LBL_2_5:
+
+        testl     %r9d, %r9d
+        jne       .LBL_2_7
+
+
+        testl     $8388607, -28(%rsp)
+        je        .LBL_2_27
+
+.LBL_2_7:
+
+        negl      %r9d
+        movsd     %xmm0, -48(%rsp)
+        addl      %eax, %r9d
+        movsd     %xmm1, -40(%rsp)
+        movb      -41(%rsp), %dil
+        movb      -33(%rsp), %sil
+        andb      $127, %dil
+        andb      $127, %sil
+        cmpl      $-54, %r9d
+        jle       .LBL_2_22
+
+
+        cmpl      $54, %r9d
+        jge       .LBL_2_19
+
+
+        movb      %sil, -33(%rsp)
+        movb      %dil, -41(%rsp)
+        testb     %dl, %dl
+        jne       .LBL_2_11
+
+
+        movsd     1976+__satan2_ha_CoutTab(%rip), %xmm1
+        movaps    %xmm1, %xmm0
+        jmp       .LBL_2_12
+
+.LBL_2_11:
+
+        movsd     1936+__satan2_ha_CoutTab(%rip), %xmm1
+        movsd     1944+__satan2_ha_CoutTab(%rip), %xmm0
+
+.LBL_2_12:
+
+        movsd     -48(%rsp), %xmm4
+        movsd     -40(%rsp), %xmm2
+        movaps    %xmm4, %xmm5
+        divsd     %xmm2, %xmm5
+        movzwl    -42(%rsp), %esi
+        movsd     %xmm5, -16(%rsp)
+        testl     %eax, %eax
+        jle       .LBL_2_34
+
+
+        cmpl      $2046, %eax
+        jge       .LBL_2_15
+
+
+        andl      $-32753, %esi
+        addl      $-1023, %eax
+        movsd     %xmm4, -48(%rsp)
+        addl      $16368, %esi
+        movw      %si, -42(%rsp)
+        jmp       .LBL_2_16
+
+.LBL_2_15:
+
+        movsd     1992+__satan2_ha_CoutTab(%rip), %xmm3
+        movl      $1022, %eax
+        mulsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+
+.LBL_2_16:
+
+        negl      %eax
+        movq      1888+__satan2_ha_CoutTab(%rip), %rsi
+        addl      $1023, %eax
+        movq      %rsi, -40(%rsp)
+        andl      $2047, %eax
+        shrq      $48, %rsi
+        shll      $4, %eax
+        andl      $-32753, %esi
+        orl       %eax, %esi
+        movw      %si, -34(%rsp)
+        movsd     -40(%rsp), %xmm3
+        mulsd     %xmm3, %xmm2
+        comisd    1880+__satan2_ha_CoutTab(%rip), %xmm5
+        jb        .LBL_2_18
+
+
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm12
+        movaps    %xmm2, %xmm3
+        mulsd     %xmm2, %xmm12
+        movsd     %xmm12, -72(%rsp)
+        movsd     -72(%rsp), %xmm13
+        movsd     %xmm5, -24(%rsp)
+        subsd     %xmm2, %xmm13
+        movsd     %xmm13, -64(%rsp)
+        movsd     -72(%rsp), %xmm15
+        movsd     -64(%rsp), %xmm14
+        movl      -20(%rsp), %edi
+        movl      %edi, %r9d
+        andl      $-524288, %edi
+        andl      $-1048576, %r9d
+        addl      $262144, %edi
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -72(%rsp)
+        andl      $1048575, %edi
+        movsd     -72(%rsp), %xmm4
+        orl       %edi, %r9d
+        movl      $0, -24(%rsp)
+        subsd     %xmm4, %xmm3
+        movl      %r9d, -20(%rsp)
+        movsd     %xmm3, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -24(%rsp), %xmm11
+        movsd     -64(%rsp), %xmm9
+        mulsd     %xmm11, %xmm5
+        mulsd     %xmm11, %xmm9
+        movsd     1968+__satan2_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm8, %xmm5
+        mulsd     %xmm8, %xmm9
+        movaps    %xmm5, %xmm7
+        movzwl    -10(%rsp), %esi
+        addsd     %xmm9, %xmm7
+        movsd     %xmm7, -72(%rsp)
+        andl      $32752, %esi
+        movsd     -72(%rsp), %xmm6
+        shrl      $4, %esi
+        subsd     %xmm6, %xmm5
+        movl      -12(%rsp), %eax
+        addsd     %xmm5, %xmm9
+        movsd     %xmm9, -64(%rsp)
+        andl      $1048575, %eax
+        movsd     -48(%rsp), %xmm9
+        movsd     -72(%rsp), %xmm3
+        movaps    %xmm9, %xmm12
+        movsd     -64(%rsp), %xmm10
+        movaps    %xmm9, %xmm14
+        movaps    %xmm9, %xmm6
+        addsd     %xmm3, %xmm12
+        movsd     %xmm12, -72(%rsp)
+        movsd     -72(%rsp), %xmm13
+        shll      $20, %esi
+        subsd     %xmm13, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        orl       %eax, %esi
+        movsd     -72(%rsp), %xmm4
+        addl      $-1069547520, %esi
+        movsd     -64(%rsp), %xmm15
+        movl      $113, %eax
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm13
+        addsd     %xmm15, %xmm4
+        movsd     %xmm4, -56(%rsp)
+        movsd     -64(%rsp), %xmm8
+        sarl      $19, %esi
+        addsd     %xmm3, %xmm8
+        movsd     %xmm8, -64(%rsp)
+        cmpl      $113, %esi
+        movsd     -56(%rsp), %xmm7
+        cmovl     %esi, %eax
+        subsd     %xmm7, %xmm6
+        movsd     %xmm6, -56(%rsp)
+        addl      %eax, %eax
+        movsd     -64(%rsp), %xmm12
+        lea       __satan2_ha_CoutTab(%rip), %rsi
+        movsd     -56(%rsp), %xmm5
+        movslq    %eax, %rax
+        addsd     %xmm5, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -72(%rsp), %xmm7
+        mulsd     %xmm7, %xmm13
+        movsd     -56(%rsp), %xmm8
+        movsd     %xmm13, -72(%rsp)
+        addsd     %xmm10, %xmm8
+        movsd     -72(%rsp), %xmm4
+        movaps    %xmm9, %xmm10
+        mulsd     2000+__satan2_ha_CoutTab(%rip), %xmm10
+        subsd     %xmm7, %xmm4
+        movsd     %xmm4, -64(%rsp)
+        movsd     -72(%rsp), %xmm3
+        movsd     -64(%rsp), %xmm14
+        subsd     %xmm14, %xmm3
+        movsd     %xmm3, -72(%rsp)
+        movsd     -72(%rsp), %xmm15
+        subsd     %xmm15, %xmm7
+        movsd     %xmm7, -64(%rsp)
+        movsd     -72(%rsp), %xmm7
+        movsd     -64(%rsp), %xmm4
+        movsd     %xmm10, -72(%rsp)
+        movaps    %xmm2, %xmm10
+        addsd     %xmm4, %xmm8
+        movsd     -72(%rsp), %xmm4
+        subsd     -48(%rsp), %xmm4
+        movsd     %xmm4, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm3
+        subsd     %xmm3, %xmm6
+        movaps    %xmm2, %xmm3
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        subsd     %xmm5, %xmm9
+        movsd     %xmm9, -64(%rsp)
+        movsd     -72(%rsp), %xmm12
+        movsd     -64(%rsp), %xmm9
+        mulsd     %xmm11, %xmm12
+        mulsd     %xmm11, %xmm9
+        movaps    %xmm12, %xmm11
+        addsd     %xmm9, %xmm11
+        movsd     %xmm11, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        subsd     %xmm4, %xmm12
+        addsd     %xmm9, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -72(%rsp), %xmm15
+        movsd     -64(%rsp), %xmm6
+        addsd     %xmm15, %xmm3
+        movsd     %xmm3, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm5, %xmm10
+        movsd     %xmm10, -64(%rsp)
+        movsd     -72(%rsp), %xmm13
+        movsd     -64(%rsp), %xmm11
+        addsd     %xmm11, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -64(%rsp), %xmm14
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm13
+        addsd     %xmm14, %xmm15
+        movsd     %xmm15, -64(%rsp)
+        movsd     -56(%rsp), %xmm4
+        movsd     1888+__satan2_ha_CoutTab(%rip), %xmm14
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -64(%rsp), %xmm4
+        movsd     -56(%rsp), %xmm2
+        addsd     %xmm2, %xmm4
+        movsd     %xmm4, -56(%rsp)
+        movsd     -72(%rsp), %xmm12
+        mulsd     %xmm12, %xmm3
+        movsd     -56(%rsp), %xmm5
+        movsd     %xmm3, -72(%rsp)
+        addsd     %xmm6, %xmm5
+        movsd     -72(%rsp), %xmm9
+        subsd     %xmm12, %xmm9
+        movsd     %xmm9, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm2
+        subsd     %xmm2, %xmm10
+        movsd     %xmm10, -72(%rsp)
+        movsd     -72(%rsp), %xmm11
+        subsd     %xmm11, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -72(%rsp), %xmm9
+        divsd     %xmm9, %xmm14
+        mulsd     %xmm14, %xmm13
+        movsd     -64(%rsp), %xmm10
+        movsd     %xmm13, -64(%rsp)
+        addsd     %xmm10, %xmm5
+        movsd     -64(%rsp), %xmm15
+        movsd     1888+__satan2_ha_CoutTab(%rip), %xmm12
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, -56(%rsp)
+        movsd     -64(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm4
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm13
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -56(%rsp), %xmm3
+        mulsd     %xmm3, %xmm9
+        movsd     -56(%rsp), %xmm11
+        subsd     %xmm9, %xmm12
+        mulsd     %xmm11, %xmm5
+        movsd     %xmm5, -64(%rsp)
+        movsd     -64(%rsp), %xmm5
+        subsd     %xmm5, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -64(%rsp), %xmm2
+        movq      -56(%rsp), %r10
+        movsd     -64(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm4
+        movq      %r10, -40(%rsp)
+        movsd     -40(%rsp), %xmm3
+        movaps    %xmm3, %xmm5
+        addsd     1888+__satan2_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm7, %xmm5
+        mulsd     %xmm6, %xmm2
+        mulsd     %xmm4, %xmm2
+        mulsd     %xmm2, %xmm7
+        mulsd     %xmm8, %xmm2
+        mulsd     %xmm3, %xmm8
+        addsd     %xmm2, %xmm7
+        movsd     1872+__satan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm8, %xmm7
+        movsd     %xmm7, -72(%rsp)
+        movaps    %xmm5, %xmm7
+        movsd     -72(%rsp), %xmm4
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm6
+        addsd     %xmm4, %xmm7
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm8
+        subsd     %xmm8, %xmm5
+        addsd     %xmm4, %xmm5
+        movsd     %xmm5, -64(%rsp)
+        movsd     -72(%rsp), %xmm11
+        movaps    %xmm11, %xmm2
+        mulsd     %xmm11, %xmm2
+        mulsd     %xmm11, %xmm6
+        mulsd     %xmm2, %xmm3
+        movsd     -64(%rsp), %xmm4
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm7
+        addsd     1864+__satan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm11, %xmm7
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm7, -64(%rsp)
+        movsd     -72(%rsp), %xmm9
+        movsd     -64(%rsp), %xmm8
+        addsd     1856+__satan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm8, %xmm9
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm9, -72(%rsp)
+        movsd     -72(%rsp), %xmm10
+        addsd     1848+__satan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm10, %xmm11
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm11, -64(%rsp)
+        addsd     1840+__satan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        addsd     1832+__satan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        addsd     1824+__satan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        mulsd     %xmm3, %xmm13
+        movsd     -72(%rsp), %xmm2
+        movsd     -64(%rsp), %xmm12
+        movsd     %xmm13, -72(%rsp)
+        addsd     %xmm12, %xmm4
+        movsd     -72(%rsp), %xmm14
+        subsd     %xmm3, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm15
+        subsd     %xmm15, %xmm5
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm6
+        subsd     %xmm6, %xmm3
+        movsd     %xmm3, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm5
+        movaps    %xmm6, %xmm12
+        movaps    %xmm5, %xmm3
+        mulsd     %xmm4, %xmm6
+        mulsd     %xmm4, %xmm3
+        mulsd     %xmm2, %xmm5
+        mulsd     %xmm2, %xmm12
+        addsd     %xmm3, %xmm6
+        movaps    %xmm12, %xmm7
+        movaps    %xmm12, %xmm8
+        addsd     %xmm5, %xmm6
+        addsd     %xmm2, %xmm7
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm3
+        subsd     %xmm3, %xmm8
+        movsd     %xmm8, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -64(%rsp), %xmm11
+        addsd     %xmm11, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -56(%rsp), %xmm2
+        subsd     %xmm2, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -64(%rsp), %xmm14
+        movsd     -56(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movq      -72(%rsp), %r11
+        movsd     -56(%rsp), %xmm15
+        movq      %r11, -40(%rsp)
+        addsd     %xmm15, %xmm4
+        movsd     -40(%rsp), %xmm8
+        addsd     %xmm5, %xmm4
+        movsd     %xmm4, -32(%rsp)
+        movaps    %xmm8, %xmm4
+        movaps    %xmm8, %xmm2
+        addsd     (%rsi,%rax,8), %xmm4
+        movsd     %xmm4, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm3
+        addsd     %xmm3, %xmm5
+        movsd     %xmm5, -56(%rsp)
+        movsd     -64(%rsp), %xmm6
+        addsd     (%rsi,%rax,8), %xmm6
+        movsd     %xmm6, -64(%rsp)
+        movsd     -56(%rsp), %xmm7
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -56(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movq      -72(%rsp), %rdi
+        movq      %rdi, -40(%rsp)
+
+
+        movsd     -56(%rsp), %xmm2
+        movaps    %xmm1, %xmm3
+        shrq      $56, %rdi
+        addsd     -32(%rsp), %xmm2
+        shlb      $7, %cl
+        addsd     8(%rsi,%rax,8), %xmm2
+        movb      %dl, %al
+        andb      $127, %dil
+        shlb      $7, %al
+        movsd     %xmm2, -32(%rsp)
+        orb       %al, %dil
+        movb      %dil, -33(%rsp)
+        movsd     -40(%rsp), %xmm9
+        movaps    %xmm9, %xmm5
+        addsd     %xmm9, %xmm3
+        movsd     %xmm3, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movb      -25(%rsp), %sil
+        movb      %sil, %r9b
+        shrb      $7, %sil
+        subsd     %xmm4, %xmm5
+        movsd     %xmm5, -64(%rsp)
+        movsd     -72(%rsp), %xmm7
+        movsd     -64(%rsp), %xmm6
+        xorb      %sil, %dl
+        andb      $127, %r9b
+        shlb      $7, %dl
+        addsd     %xmm6, %xmm7
+        movsd     %xmm7, -56(%rsp)
+        movsd     -64(%rsp), %xmm8
+        addsd     %xmm8, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        orb       %dl, %r9b
+        movsd     -56(%rsp), %xmm1
+        movb      %r9b, -25(%rsp)
+        subsd     %xmm1, %xmm9
+        movsd     %xmm9, -56(%rsp)
+        movsd     -64(%rsp), %xmm11
+        movsd     -56(%rsp), %xmm10
+        addsd     %xmm10, %xmm11
+        movsd     %xmm11, -56(%rsp)
+        movq      -72(%rsp), %rdx
+        movsd     -56(%rsp), %xmm12
+        movq      %rdx, -40(%rsp)
+        addsd     %xmm12, %xmm0
+        movsd     -40(%rsp), %xmm13
+        addsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -32(%rsp)
+        addsd     %xmm0, %xmm13
+        movsd     %xmm13, -24(%rsp)
+        movb      -17(%rsp), %r10b
+        andb      $127, %r10b
+        orb       %cl, %r10b
+        movb      %r10b, -17(%rsp)
+        movsd     -24(%rsp), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_18:
+
+        movsd     -48(%rsp), %xmm12
+        movb      %dl, %dil
+        movaps    %xmm12, %xmm7
+        mulsd     2000+__satan2_ha_CoutTab(%rip), %xmm7
+        shlb      $7, %dil
+        shlb      $7, %cl
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm13
+        movsd     1888+__satan2_ha_CoutTab(%rip), %xmm7
+        mulsd     %xmm2, %xmm13
+        subsd     -48(%rsp), %xmm8
+        movsd     %xmm8, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm9
+        subsd     %xmm9, %xmm10
+        movsd     %xmm10, -72(%rsp)
+        movsd     -72(%rsp), %xmm11
+        subsd     %xmm11, %xmm12
+        movsd     %xmm12, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm5
+        movsd     %xmm13, -72(%rsp)
+        movsd     -72(%rsp), %xmm14
+        subsd     %xmm2, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movsd     -64(%rsp), %xmm15
+        subsd     %xmm15, %xmm4
+        movsd     %xmm4, -72(%rsp)
+        movsd     -72(%rsp), %xmm3
+        movsd     1888+__satan2_ha_CoutTab(%rip), %xmm4
+        subsd     %xmm3, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm12
+        divsd     %xmm12, %xmm7
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm7, %xmm2
+        movsd     -64(%rsp), %xmm14
+        movsd     %xmm2, -64(%rsp)
+        movsd     -64(%rsp), %xmm8
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -56(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm9
+        subsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -56(%rsp), %xmm11
+        mulsd     %xmm11, %xmm12
+        movsd     -56(%rsp), %xmm13
+        subsd     %xmm12, %xmm4
+        mulsd     %xmm13, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -64(%rsp), %xmm15
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm13
+        subsd     %xmm15, %xmm4
+        movsd     %xmm4, -64(%rsp)
+        movsd     -64(%rsp), %xmm7
+        movq      -56(%rsp), %rax
+        movsd     -64(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm3
+        movq      %rax, -40(%rsp)
+        movsd     -40(%rsp), %xmm8
+        movaps    %xmm8, %xmm9
+        addsd     1888+__satan2_ha_CoutTab(%rip), %xmm7
+        mulsd     %xmm6, %xmm9
+        mulsd     %xmm5, %xmm8
+        mulsd     %xmm2, %xmm7
+        movsd     -16(%rsp), %xmm2
+        mulsd     %xmm2, %xmm2
+        mulsd     %xmm3, %xmm7
+        movsd     1872+__satan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        mulsd     %xmm7, %xmm6
+        mulsd     %xmm5, %xmm7
+        addsd     1864+__satan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm7, %xmm6
+        mulsd     %xmm2, %xmm3
+        addsd     %xmm8, %xmm6
+        addsd     1856+__satan2_ha_CoutTab(%rip), %xmm3
+        mulsd     %xmm2, %xmm3
+        movaps    %xmm9, %xmm5
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        addsd     1848+__satan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm4, %xmm5
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     2000+__satan2_ha_CoutTab(%rip), %xmm5
+        subsd     %xmm6, %xmm9
+        addsd     1840+__satan2_ha_CoutTab(%rip), %xmm3
+        addsd     %xmm4, %xmm9
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm9, -64(%rsp)
+        movsd     -72(%rsp), %xmm11
+        mulsd     %xmm11, %xmm5
+        addsd     1832+__satan2_ha_CoutTab(%rip), %xmm3
+        movsd     -64(%rsp), %xmm4
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm7
+        mulsd     %xmm2, %xmm3
+        subsd     %xmm11, %xmm7
+        movsd     %xmm7, -64(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     -64(%rsp), %xmm6
+        addsd     1824+__satan2_ha_CoutTab(%rip), %xmm3
+        subsd     %xmm6, %xmm8
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm8, -72(%rsp)
+        movsd     -72(%rsp), %xmm10
+        mulsd     %xmm3, %xmm13
+        subsd     %xmm10, %xmm11
+        movsd     %xmm11, -64(%rsp)
+        movsd     -72(%rsp), %xmm2
+        movsd     -64(%rsp), %xmm12
+        movsd     %xmm13, -72(%rsp)
+        addsd     %xmm12, %xmm4
+        movsd     -72(%rsp), %xmm14
+        subsd     %xmm3, %xmm14
+        movsd     %xmm14, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm15
+        subsd     %xmm15, %xmm5
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm6
+        subsd     %xmm6, %xmm3
+        movsd     %xmm3, -64(%rsp)
+        movsd     -72(%rsp), %xmm6
+        movsd     -64(%rsp), %xmm5
+        movaps    %xmm6, %xmm12
+        movaps    %xmm5, %xmm3
+        mulsd     %xmm4, %xmm6
+        mulsd     %xmm4, %xmm3
+        mulsd     %xmm2, %xmm5
+        mulsd     %xmm2, %xmm12
+        addsd     %xmm3, %xmm6
+        movaps    %xmm12, %xmm7
+        movaps    %xmm12, %xmm8
+        addsd     %xmm5, %xmm6
+        addsd     %xmm2, %xmm7
+        movsd     %xmm6, -72(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     %xmm7, -72(%rsp)
+        movsd     -72(%rsp), %xmm3
+        subsd     %xmm3, %xmm8
+        movsd     %xmm8, -64(%rsp)
+        movsd     -72(%rsp), %xmm10
+        movsd     -64(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -56(%rsp)
+        movsd     -64(%rsp), %xmm11
+        addsd     %xmm11, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -56(%rsp), %xmm2
+        subsd     %xmm2, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -64(%rsp), %xmm14
+        movsd     -56(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movq      -72(%rsp), %rsi
+        movsd     -56(%rsp), %xmm15
+        movq      %rsi, -40(%rsp)
+        addsd     %xmm15, %xmm4
+        shrq      $56, %rsi
+        addsd     %xmm5, %xmm4
+        andb      $127, %sil
+        orb       %dil, %sil
+        movb      %sil, -33(%rsp)
+        movsd     %xmm4, -32(%rsp)
+        movaps    %xmm1, %xmm4
+        movsd     -40(%rsp), %xmm7
+        movaps    %xmm7, %xmm2
+        addsd     %xmm7, %xmm4
+        movsd     %xmm4, -72(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movb      -25(%rsp), %r9b
+        movb      %r9b, %r10b
+        shrb      $7, %r9b
+        subsd     %xmm4, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm5
+        movsd     -64(%rsp), %xmm3
+        xorb      %r9b, %dl
+        andb      $127, %r10b
+        shlb      $7, %dl
+        addsd     %xmm3, %xmm5
+        movsd     %xmm5, -56(%rsp)
+        movsd     -64(%rsp), %xmm6
+        addsd     %xmm6, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        orb       %dl, %r10b
+        movsd     -56(%rsp), %xmm1
+        movb      %r10b, -25(%rsp)
+        subsd     %xmm1, %xmm7
+        movsd     %xmm7, -56(%rsp)
+        movsd     -64(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm1
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movq      -72(%rsp), %rdx
+        movsd     -56(%rsp), %xmm3
+        movq      %rdx, -40(%rsp)
+        addsd     %xmm3, %xmm0
+        movsd     -40(%rsp), %xmm4
+        addsd     -32(%rsp), %xmm0
+        movsd     %xmm0, -32(%rsp)
+        addsd     %xmm0, %xmm4
+        movsd     %xmm4, -24(%rsp)
+        movb      -17(%rsp), %r11b
+        andb      $127, %r11b
+        orb       %cl, %r11b
+        movb      %r11b, -17(%rsp)
+        movsd     -24(%rsp), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_19:
+
+        cmpl      $74, %r9d
+        jge       .LBL_2_21
+
+
+        movb      %dil, -41(%rsp)
+        divsd     -48(%rsp), %xmm1
+        movsd     1928+__satan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %cl
+        subsd     %xmm1, %xmm0
+        addsd     1920+__satan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_21:
+
+        movsd     1920+__satan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %cl
+        addsd     1928+__satan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_22:
+
+        testb     %dl, %dl
+        jne       .LBL_2_32
+
+
+        movb      %dil, -41(%rsp)
+        pxor      %xmm0, %xmm0
+        movb      %sil, -33(%rsp)
+        movsd     -48(%rsp), %xmm2
+        divsd     -40(%rsp), %xmm2
+        cvtsd2ss  %xmm2, %xmm0
+        movss     %xmm0, -8(%rsp)
+        movzwl    -6(%rsp), %eax
+        movsd     %xmm2, -24(%rsp)
+        testl     $32640, %eax
+        je        .LBL_2_25
+
+
+        movsd     1888+__satan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %cl
+        addsd     %xmm2, %xmm0
+        movsd     %xmm0, -72(%rsp)
+        movsd     -72(%rsp), %xmm1
+        mulsd     %xmm1, %xmm2
+        movsd     %xmm2, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm2
+        cvtsd2ss  %xmm2, %xmm2
+        movss     %xmm2, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_25:
+
+        movsd     -24(%rsp), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        shlb      $7, %cl
+        movss     %xmm0, -8(%rsp)
+        movss     -8(%rsp), %xmm2
+        movss     -8(%rsp), %xmm1
+        mulss     %xmm1, %xmm2
+        movss     %xmm2, -8(%rsp)
+        movss     -8(%rsp), %xmm3
+        cvtss2sd  %xmm3, %xmm3
+        addsd     -24(%rsp), %xmm3
+        movsd     %xmm3, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm4
+        cvtsd2ss  %xmm4, %xmm4
+        movss     %xmm4, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_27:
+
+        testl     %eax, %eax
+        jne       .LBL_2_21
+
+
+        testl     $8388607, -32(%rsp)
+        jne       .LBL_2_21
+
+.LBL_2_30:
+
+        testb     %dl, %dl
+        jne       .LBL_2_32
+
+.LBL_2_31:
+
+        shlb      $7, %cl
+        movq      1976+__satan2_ha_CoutTab(%rip), %rax
+        movq      %rax, -24(%rsp)
+        shrq      $56, %rax
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_32:
+
+        movsd     1936+__satan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %cl
+        addsd     1944+__satan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%r8)
+
+.LBL_2_33:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_34:
+
+        movsd     1984+__satan2_ha_CoutTab(%rip), %xmm3
+        movl      $-1022, %eax
+        mulsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        jmp       .LBL_2_16
+
+.LBL_2_35:
+
+        cmpl      $2047, %eax
+        je        .LBL_2_48
+
+.LBL_2_36:
+
+        cmpl      $2047, %r9d
+        je        .LBL_2_46
+
+.LBL_2_37:
+
+        movzwl    -26(%rsp), %eax
+        andl      $32640, %eax
+        cmpl      $32640, %eax
+        jne       .LBL_2_21
+
+
+        cmpl      $255, %edi
+        je        .LBL_2_43
+
+
+        testb     %dl, %dl
+        je        .LBL_2_31
+        jmp       .LBL_2_32
+
+.LBL_2_43:
+
+        testb     %dl, %dl
+        jne       .LBL_2_45
+
+
+        movsd     1904+__satan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %cl
+        addsd     1912+__satan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_45:
+
+        movsd     1952+__satan2_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %cl
+        addsd     1960+__satan2_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %al
+        andb      $127, %al
+        orb       %cl, %al
+        movb      %al, -17(%rsp)
+        movsd     -24(%rsp), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_46:
+
+        testl     $8388607, -28(%rsp)
+        je        .LBL_2_37
+
+.LBL_2_47:
+
+        addss     %xmm2, %xmm3
+        movss     %xmm3, (%r8)
+        jmp       .LBL_2_33
+
+.LBL_2_48:
+
+        testl     $8388607, -32(%rsp)
+        jne       .LBL_2_47
+        jmp       .LBL_2_36
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_satan2_ha_cout_rare_internal,@function
+	.size	__svml_satan2_ha_cout_rare_internal,.-__svml_satan2_ha_cout_rare_internal
+..LN__svml_satan2_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_satan2_ha_data_internal:
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	1078530016
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	3047930835
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1052770304
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1060655596
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1071644672
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1076625408
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1073741824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	1055744824
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	833369960
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	1061752792
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	875071576
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	1066252048
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	3034196096
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	1070141400
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	883460184
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	1034818345
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	3188674126
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	1045211707
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	3198855788
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	4294967295
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	2172649472
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	4219469824
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.long	1078530011
+	.type	__svml_satan2_ha_data_internal,@object
+	.size	__svml_satan2_ha_data_internal,1984
+	.align 32
+__satan2_ha_CoutTab:
+	.long	3892314112
+	.long	1069799150
+	.long	2332892550
+	.long	1039715405
+	.long	1342177280
+	.long	1070305495
+	.long	270726690
+	.long	1041535749
+	.long	939524096
+	.long	1070817911
+	.long	2253973841
+	.long	3188654726
+	.long	3221225472
+	.long	1071277294
+	.long	3853927037
+	.long	1043226911
+	.long	2818572288
+	.long	1071767563
+	.long	2677759107
+	.long	1044314101
+	.long	3355443200
+	.long	1072103591
+	.long	1636578514
+	.long	3191094734
+	.long	1476395008
+	.long	1072475260
+	.long	1864703685
+	.long	3188646936
+	.long	805306368
+	.long	1072747407
+	.long	192551812
+	.long	3192726267
+	.long	2013265920
+	.long	1072892781
+	.long	2240369452
+	.long	1043768538
+	.long	0
+	.long	1072999953
+	.long	3665168337
+	.long	3192705970
+	.long	402653184
+	.long	1073084787
+	.long	1227953434
+	.long	3192313277
+	.long	2013265920
+	.long	1073142981
+	.long	3853283127
+	.long	1045277487
+	.long	805306368
+	.long	1073187261
+	.long	1676192264
+	.long	3192868861
+	.long	134217728
+	.long	1073217000
+	.long	4290763938
+	.long	1042034855
+	.long	671088640
+	.long	1073239386
+	.long	994303084
+	.long	3189643768
+	.long	402653184
+	.long	1073254338
+	.long	1878067156
+	.long	1042652475
+	.long	1610612736
+	.long	1073265562
+	.long	670314820
+	.long	1045138554
+	.long	3221225472
+	.long	1073273048
+	.long	691126919
+	.long	3189987794
+	.long	3489660928
+	.long	1073278664
+	.long	1618990832
+	.long	3188194509
+	.long	1207959552
+	.long	1073282409
+	.long	2198872939
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+	.long	1073285217
+	.long	2633982383
+	.long	1042307894
+	.long	939524096
+	.long	1073287090
+	.long	1059367786
+	.long	3189114230
+	.long	2281701376
+	.long	1073288494
+	.long	3158525533
+	.long	1044484961
+	.long	3221225472
+	.long	1073289430
+	.long	286581777
+	.long	1044893263
+	.long	4026531840
+	.long	1073290132
+	.long	2000245215
+	.long	3191647611
+	.long	134217728
+	.long	1073290601
+	.long	4205071590
+	.long	1045035927
+	.long	536870912
+	.long	1073290952
+	.long	2334392229
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+	.long	1073291186
+	.long	2281458177
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+	.long	3087007744
+	.long	1073291361
+	.long	691611507
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+	.long	3221225472
+	.long	1073291478
+	.long	1816229550
+	.long	1044363390
+	.long	2281701376
+	.long	1073291566
+	.long	1993843750
+	.long	3189837440
+	.long	134217728
+	.long	1073291625
+	.long	3654754496
+	.long	1044970837
+	.long	4026531840
+	.long	1073291668
+	.long	3224300229
+	.long	3191935390
+	.long	805306368
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+	.long	2988777976
+	.long	3188950659
+	.long	536870912
+	.long	1073291720
+	.long	1030371341
+	.long	1043402665
+	.long	3221225472
+	.long	1073291734
+	.long	1524463765
+	.long	1044361356
+	.long	3087007744
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+	.long	2754295320
+	.long	1044731036
+	.long	134217728
+	.long	1073291753
+	.long	3099629057
+	.long	1044970710
+	.long	2281701376
+	.long	1073291758
+	.long	962914160
+	.long	3189838838
+	.long	805306368
+	.long	1073291762
+	.long	3543908206
+	.long	3188950786
+	.long	4026531840
+	.long	1073291764
+	.long	1849909620
+	.long	3191935434
+	.long	3221225472
+	.long	1073291766
+	.long	1641333636
+	.long	1044361352
+	.long	536870912
+	.long	1073291768
+	.long	1373968792
+	.long	1043402654
+	.long	134217728
+	.long	1073291769
+	.long	2033191599
+	.long	1044970710
+	.long	3087007744
+	.long	1073291769
+	.long	4117947437
+	.long	1044731035
+	.long	805306368
+	.long	1073291770
+	.long	315378368
+	.long	3188950787
+	.long	2281701376
+	.long	1073291770
+	.long	2428571750
+	.long	3189838838
+	.long	3221225472
+	.long	1073291770
+	.long	1608007466
+	.long	1044361352
+	.long	4026531840
+	.long	1073291770
+	.long	1895711420
+	.long	3191935434
+	.long	134217728
+	.long	1073291771
+	.long	2031108713
+	.long	1044970710
+	.long	536870912
+	.long	1073291771
+	.long	1362518342
+	.long	1043402654
+	.long	805306368
+	.long	1073291771
+	.long	317461253
+	.long	3188950787
+	.long	939524096
+	.long	1073291771
+	.long	4117231784
+	.long	1044731035
+	.long	1073741824
+	.long	1073291771
+	.long	1607942376
+	.long	1044361352
+	.long	1207959552
+	.long	1073291771
+	.long	2428929577
+	.long	3189838838
+	.long	1207959552
+	.long	1073291771
+	.long	2031104645
+	.long	1044970710
+	.long	1342177280
+	.long	1073291771
+	.long	1895722602
+	.long	3191935434
+	.long	1342177280
+	.long	1073291771
+	.long	317465322
+	.long	3188950787
+	.long	1342177280
+	.long	1073291771
+	.long	1362515546
+	.long	1043402654
+	.long	1342177280
+	.long	1073291771
+	.long	1607942248
+	.long	1044361352
+	.long	1342177280
+	.long	1073291771
+	.long	4117231610
+	.long	1044731035
+	.long	1342177280
+	.long	1073291771
+	.long	2031104637
+	.long	1044970710
+	.long	1342177280
+	.long	1073291771
+	.long	1540251232
+	.long	1045150466
+	.long	1342177280
+	.long	1073291771
+	.long	2644671394
+	.long	1045270303
+	.long	1342177280
+	.long	1073291771
+	.long	2399244691
+	.long	1045360181
+	.long	1342177280
+	.long	1073291771
+	.long	803971124
+	.long	1045420100
+	.long	1476395008
+	.long	1073291771
+	.long	3613709523
+	.long	3192879152
+	.long	1476395008
+	.long	1073291771
+	.long	2263862659
+	.long	3192849193
+	.long	1476395008
+	.long	1073291771
+	.long	177735686
+	.long	3192826724
+	.long	1476395008
+	.long	1073291771
+	.long	1650295902
+	.long	3192811744
+	.long	1476395008
+	.long	1073291771
+	.long	2754716064
+	.long	3192800509
+	.long	1476395008
+	.long	1073291771
+	.long	3490996172
+	.long	3192793019
+	.long	1476395008
+	.long	1073291771
+	.long	1895722605
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+	.long	1476395008
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+	.long	2263862659
+	.long	3192783657
+	.long	1476395008
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+	.long	3613709523
+	.long	3192780848
+	.long	1476395008
+	.long	1073291771
+	.long	1650295902
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+	.long	1476395008
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+	.long	1650295902
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+	.long	1476395008
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+	.long	3613709523
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+	.long	3490996172
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+	.long	1476395008
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+	.long	1650295902
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+	.long	1466225875
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+	.long	1343512524
+	.long	3192774295
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+	.long	4189149139
+	.long	1069314502
+	.long	1775019125
+	.long	3216459198
+	.long	273199057
+	.long	1068739452
+	.long	874748308
+	.long	3215993277
+	.long	0
+	.long	1069547520
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	1413754136
+	.long	1072243195
+	.long	856972295
+	.long	1015129638
+	.long	1413754136
+	.long	1073291771
+	.long	856972295
+	.long	1016178214
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+	.long	856972295
+	.long	1017226790
+	.long	2134057426
+	.long	1073928572
+	.long	1285458442
+	.long	1016756537
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	0
+	.long	0
+	.long	2144337920
+	.long	0
+	.long	1048576
+	.long	33554432
+	.long	1101004800
+	.type	__satan2_ha_CoutTab,@object
+	.size	__satan2_ha_CoutTab,2008
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_s_la.s
index 6a1fe9cc3bb..ed77b56a4e5 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan2_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_d_ha.s
new file mode 100644
index 00000000000..dc6e5e12f47
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_d_ha.s
@@ -0,0 +1,1386 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *      For    0.0    <= x <=  7.0/16.0: atan(x) = atan(0.0) + atan(s), where s=(x-0.0)/(1.0+0.0*x)
+ *      For  7.0/16.0 <= x <= 11.0/16.0: atan(x) = atan(0.5) + atan(s), where s=(x-0.5)/(1.0+0.5*x)
+ *      For 11.0/16.0 <= x <= 19.0/16.0: atan(x) = atan(1.0) + atan(s), where s=(x-1.0)/(1.0+1.0*x)
+ *      For 19.0/16.0 <= x <= 39.0/16.0: atan(x) = atan(1.5) + atan(s), where s=(x-1.5)/(1.0+1.5*x)
+ *      For 39.0/16.0 <= x <=    inf   : atan(x) = atan(inf) + atan(s), where s=-1.0/x
+ *      Where atan(s) ~= s+s^3*Poly11(s^2) on interval |s|<7.0/0.16.
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_atan8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_atan8_ha
+
+__svml_atan8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        vmovups   128+__svml_datan_ha_data_internal_avx512(%rip), %zmm8
+
+/* saturate X range, initialize Y */
+        vmovups   320+__svml_datan_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   64+__svml_datan_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   256+__svml_datan_ha_data_internal_avx512(%rip), %zmm15
+
+/* round to 2 bits after binary point */
+        vmovups   192+__svml_datan_ha_data_internal_avx512(%rip), %zmm2
+
+        vandpd    __svml_datan_ha_data_internal_avx512(%rip), %zmm0, %zmm3
+        vcmppd    $17, {sae}, %zmm8, %zmm3, %k1
+        vminpd    {sae}, %zmm3, %zmm9, %zmm7
+        vmovups   960+__svml_datan_ha_data_internal_avx512(%rip), %zmm8
+
+/* table lookup sequence */
+        vmovups   448+__svml_datan_ha_data_internal_avx512(%rip), %zmm9
+        vreducepd $40, {sae}, %zmm3, %zmm2{%k1}
+        vaddpd    {rn-sae}, %zmm10, %zmm3, %zmm5
+
+/* initialize DiffX=-1 */
+        vxorpd    %zmm0, %zmm3, %zmm6
+        knotw     %k1, %k3
+        vsubpd    {rn-sae}, %zmm10, %zmm5, %zmm11
+        vpermt2pd 512+__svml_datan_ha_data_internal_avx512(%rip), %zmm5, %zmm9
+        vmovups   576+__svml_datan_ha_data_internal_avx512(%rip), %zmm10
+        vfmadd213pd {rn-sae}, %zmm15, %zmm11, %zmm7{%k1}
+        vcmppd    $29, {sae}, %zmm8, %zmm5, %k2
+        vmovups   1344+__svml_datan_ha_data_internal_avx512(%rip), %zmm8
+
+/* R+Rl = DiffX/Y */
+        vrcp14pd  %zmm7, %zmm0
+
+/* (x*x0)_high */
+        vsubpd    {rn-sae}, %zmm15, %zmm7, %zmm12
+        vpermt2pd 640+__svml_datan_ha_data_internal_avx512(%rip), %zmm5, %zmm10
+
+/* (x*x0)_low */
+        vfmsub213pd {rn-sae}, %zmm12, %zmm11, %zmm3
+        vmovups   704+__svml_datan_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   832+__svml_datan_ha_data_internal_avx512(%rip), %zmm12
+        vpermt2pd 768+__svml_datan_ha_data_internal_avx512(%rip), %zmm5, %zmm11
+        vpermt2pd 896+__svml_datan_ha_data_internal_avx512(%rip), %zmm5, %zmm12
+        vblendmpd %zmm10, %zmm9, %zmm5{%k2}
+
+/* eps=1-Y*Rcp */
+        vmovaps   %zmm15, %zmm13
+        vfnmadd231pd {rn-sae}, %zmm7, %zmm0, %zmm13
+
+/* eps^2 */
+        vmulpd    {rn-sae}, %zmm13, %zmm13, %zmm14
+
+/* Rcp+Rcp*eps */
+        vfmadd213pd {rn-sae}, %zmm0, %zmm13, %zmm0
+        vblendmpd %zmm12, %zmm11, %zmm13{%k2}
+
+/* Rcp+Rcp*eps */
+        vfmadd213pd {rn-sae}, %zmm0, %zmm14, %zmm0
+        vmovups   1152+__svml_datan_ha_data_internal_avx512(%rip), %zmm14
+
+/* Diff*Rcp1 */
+        vmulpd    {rn-sae}, %zmm2, %zmm0, %zmm4
+
+/* (x*x0)_low*R */
+        vmulpd    {rn-sae}, %zmm0, %zmm3, %zmm1
+
+/* eps1=1-Y*Rcp1 */
+        vfnmadd213pd {rn-sae}, %zmm15, %zmm0, %zmm7
+        vmovups   1216+__svml_datan_ha_data_internal_avx512(%rip), %zmm3
+        vmovups   1280+__svml_datan_ha_data_internal_avx512(%rip), %zmm15
+
+/* (Diff*Rcp1)_low */
+        vfmsub213pd {rn-sae}, %zmm4, %zmm0, %zmm2
+
+/* R_low = (Diff*Rcp1)_low + eps1*(DiffX*Rcp1) */
+        vfmadd213pd {rn-sae}, %zmm2, %zmm4, %zmm7
+
+/* polynomial evaluation */
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm2
+
+/* R_low = R_low - (X*X0)_low*Rcp1*(Diff*Rcp1) */
+        vfnmadd231pd {rn-sae}, %zmm4, %zmm1, %zmm7{%k1}
+
+/* set table value to Pi/2 for large X */
+        vblendmpd 1024+__svml_datan_ha_data_internal_avx512(%rip), %zmm5, %zmm1{%k3}
+        vblendmpd 1088+__svml_datan_ha_data_internal_avx512(%rip), %zmm13, %zmm5{%k3}
+        vmulpd    {rn-sae}, %zmm2, %zmm2, %zmm9
+        vmulpd    {rn-sae}, %zmm4, %zmm2, %zmm11
+        vfmadd231pd {rn-sae}, %zmm2, %zmm14, %zmm3
+        vfmadd231pd {rn-sae}, %zmm2, %zmm15, %zmm8
+        vaddpd    {rn-sae}, %zmm5, %zmm7, %zmm10
+        vaddpd    {rn-sae}, %zmm4, %zmm1, %zmm0
+        vmovups   1408+__svml_datan_ha_data_internal_avx512(%rip), %zmm7
+        vmovups   1472+__svml_datan_ha_data_internal_avx512(%rip), %zmm5
+        vfmadd213pd {rn-sae}, %zmm8, %zmm9, %zmm3
+        vsubpd    {rn-sae}, %zmm1, %zmm0, %zmm1
+        vfmadd213pd {rn-sae}, %zmm5, %zmm7, %zmm2
+        vsubpd    {rn-sae}, %zmm1, %zmm4, %zmm4
+        vfmadd213pd {rn-sae}, %zmm2, %zmm9, %zmm3
+        vaddpd    {rn-sae}, %zmm4, %zmm10, %zmm1
+        vfmadd213pd {rn-sae}, %zmm1, %zmm11, %zmm3
+        vaddpd    {rn-sae}, %zmm0, %zmm3, %zmm0
+        vxorpd    %zmm6, %zmm0, %zmm0
+
+/* no invcbrt in libm, so taking it out here */
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_atan8_ha,@function
+	.size	__svml_atan8_ha,.-__svml_atan8_ha
+..LN__svml_atan8_ha.0:
+
+.L_2__routine_start___svml_datan_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_datan_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L5:
+
+        movzwl    6(%rdi), %r8d
+        andl      $32752, %r8d
+        shrl      $4, %r8d
+        cmpl      $2047, %r8d
+        je        .LBL_2_12
+
+
+        movq      (%rdi), %rdx
+        movq      %rdx, -16(%rsp)
+        shrq      $56, %rdx
+        movb      7(%rdi), %al
+        andl      $127, %edx
+        movb      %dl, -9(%rsp)
+        movsd     -16(%rsp), %xmm0
+        shrb      $7, %al
+        comisd    1888+__datan_ha_CoutTab(%rip), %xmm0
+        movl      -12(%rsp), %ecx
+        jb        .LBL_2_6
+
+
+        movsd     1896+__datan_ha_CoutTab(%rip), %xmm1
+        comisd    %xmm0, %xmm1
+        jbe       .LBL_2_5
+
+
+        movl      4(%rdi), %edx
+        movl      %ecx, %edi
+        andl      $-524288, %ecx
+        andl      $-1048576, %edi
+        addl      $262144, %ecx
+        movaps    %xmm0, %xmm9
+        andl      $1048575, %ecx
+        movaps    %xmm0, %xmm10
+        movsd     %xmm0, -56(%rsp)
+        orl       %ecx, %edi
+        movl      $0, -56(%rsp)
+        andl      $1048575, %edx
+        movl      %edi, -52(%rsp)
+        lea       __datan_ha_CoutTab(%rip), %rcx
+        movsd     1928+__datan_ha_CoutTab(%rip), %xmm4
+        movsd     -56(%rsp), %xmm15
+        shll      $20, %r8d
+        subsd     -56(%rsp), %xmm9
+        mulsd     1928+__datan_ha_CoutTab(%rip), %xmm10
+        shlb      $7, %al
+        mulsd     %xmm9, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        orl       %edx, %r8d
+        movsd     -48(%rsp), %xmm5
+        addl      $-1069547520, %r8d
+        sarl      $18, %r8d
+        subsd     %xmm9, %xmm5
+        movsd     %xmm5, -40(%rsp)
+        andl      $-2, %r8d
+        movsd     -48(%rsp), %xmm7
+        movsd     -40(%rsp), %xmm6
+        movslq    %r8d, %r8
+        subsd     %xmm6, %xmm7
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm8
+        movsd     1904+__datan_ha_CoutTab(%rip), %xmm6
+        subsd     %xmm8, %xmm9
+        movsd     %xmm9, -40(%rsp)
+        movsd     -48(%rsp), %xmm2
+        movsd     -40(%rsp), %xmm3
+        movsd     %xmm10, -48(%rsp)
+        movsd     -48(%rsp), %xmm11
+        movsd     1904+__datan_ha_CoutTab(%rip), %xmm8
+        subsd     -16(%rsp), %xmm11
+        movsd     %xmm11, -40(%rsp)
+        movsd     -48(%rsp), %xmm13
+        movsd     -40(%rsp), %xmm12
+        subsd     %xmm12, %xmm13
+        movsd     %xmm13, -48(%rsp)
+        movsd     -48(%rsp), %xmm14
+        subsd     %xmm14, %xmm0
+        movsd     1904+__datan_ha_CoutTab(%rip), %xmm14
+        movsd     %xmm0, -40(%rsp)
+        movsd     -48(%rsp), %xmm5
+        movsd     -40(%rsp), %xmm4
+        mulsd     %xmm15, %xmm5
+        mulsd     %xmm15, %xmm4
+        movaps    %xmm5, %xmm1
+        addsd     %xmm4, %xmm1
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        subsd     %xmm0, %xmm5
+        addsd     %xmm4, %xmm5
+        movsd     1928+__datan_ha_CoutTab(%rip), %xmm4
+        movsd     %xmm5, -40(%rsp)
+        movsd     -48(%rsp), %xmm11
+        movsd     -40(%rsp), %xmm1
+        addsd     %xmm11, %xmm6
+        movsd     %xmm6, -48(%rsp)
+        movsd     -48(%rsp), %xmm7
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -40(%rsp)
+        movsd     -48(%rsp), %xmm10
+        movsd     -40(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -32(%rsp)
+        movsd     -40(%rsp), %xmm12
+        movsd     1928+__datan_ha_CoutTab(%rip), %xmm10
+        addsd     %xmm11, %xmm12
+        movsd     %xmm12, -40(%rsp)
+        movsd     -32(%rsp), %xmm13
+        movsd     1904+__datan_ha_CoutTab(%rip), %xmm11
+        subsd     %xmm13, %xmm14
+        movsd     %xmm14, -32(%rsp)
+        movsd     -40(%rsp), %xmm0
+        movsd     -32(%rsp), %xmm15
+        addsd     %xmm15, %xmm0
+        movsd     %xmm0, -32(%rsp)
+        movsd     -48(%rsp), %xmm9
+        mulsd     %xmm9, %xmm4
+        movsd     -32(%rsp), %xmm0
+        movsd     %xmm4, -48(%rsp)
+        addsd     %xmm1, %xmm0
+        movsd     -48(%rsp), %xmm5
+        subsd     %xmm9, %xmm5
+        movsd     %xmm5, -40(%rsp)
+        movsd     -48(%rsp), %xmm7
+        movsd     -40(%rsp), %xmm6
+        subsd     %xmm6, %xmm7
+        movsd     1904+__datan_ha_CoutTab(%rip), %xmm6
+        movsd     %xmm7, -48(%rsp)
+        movsd     -48(%rsp), %xmm8
+        subsd     %xmm8, %xmm9
+        movsd     %xmm9, -40(%rsp)
+        movsd     -48(%rsp), %xmm4
+        divsd     %xmm4, %xmm11
+        mulsd     %xmm11, %xmm10
+        movsd     -40(%rsp), %xmm5
+        movsd     %xmm10, -40(%rsp)
+        addsd     %xmm0, %xmm5
+        movsd     -40(%rsp), %xmm12
+        subsd     %xmm11, %xmm12
+        movsd     %xmm12, -32(%rsp)
+        movsd     -40(%rsp), %xmm10
+        movsd     -32(%rsp), %xmm13
+        subsd     %xmm13, %xmm10
+        movsd     %xmm10, -32(%rsp)
+        movsd     -32(%rsp), %xmm14
+        mulsd     %xmm14, %xmm4
+        movsd     -32(%rsp), %xmm15
+        subsd     %xmm4, %xmm6
+        mulsd     %xmm15, %xmm5
+        movsd     %xmm5, -40(%rsp)
+        movsd     -40(%rsp), %xmm1
+        subsd     %xmm1, %xmm6
+        movsd     %xmm6, -40(%rsp)
+        movsd     -40(%rsp), %xmm4
+        movsd     -32(%rsp), %xmm5
+        movsd     -40(%rsp), %xmm0
+        movaps    %xmm5, %xmm7
+        movsd     -32(%rsp), %xmm1
+        mulsd     %xmm3, %xmm5
+        addsd     1904+__datan_ha_CoutTab(%rip), %xmm4
+        mulsd     %xmm2, %xmm7
+        mulsd     %xmm0, %xmm4
+        mulsd     %xmm1, %xmm4
+        mulsd     %xmm4, %xmm3
+        mulsd     %xmm4, %xmm2
+        addsd     %xmm3, %xmm5
+        movsd     1872+__datan_ha_CoutTab(%rip), %xmm6
+        addsd     %xmm2, %xmm5
+        movsd     %xmm5, -48(%rsp)
+        movaps    %xmm7, %xmm2
+        movsd     -48(%rsp), %xmm4
+        addsd     %xmm4, %xmm2
+        movsd     %xmm2, -48(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movsd     (%rcx,%r8,8), %xmm2
+        subsd     %xmm3, %xmm7
+        addsd     %xmm4, %xmm7
+        movsd     %xmm7, -40(%rsp)
+        movsd     -48(%rsp), %xmm3
+        movaps    %xmm3, %xmm5
+        movaps    %xmm3, %xmm0
+        mulsd     %xmm3, %xmm5
+        addsd     %xmm2, %xmm0
+        mulsd     %xmm5, %xmm6
+        movsd     -40(%rsp), %xmm10
+        movsd     %xmm0, -48(%rsp)
+        movsd     -48(%rsp), %xmm1
+        addsd     1864+__datan_ha_CoutTab(%rip), %xmm6
+        subsd     %xmm1, %xmm2
+        mulsd     %xmm5, %xmm6
+        addsd     %xmm3, %xmm2
+        addsd     1856+__datan_ha_CoutTab(%rip), %xmm6
+        mulsd     %xmm5, %xmm6
+        movsd     %xmm2, -40(%rsp)
+        movsd     -48(%rsp), %xmm9
+        movsd     -40(%rsp), %xmm8
+        addsd     1848+__datan_ha_CoutTab(%rip), %xmm6
+        mulsd     %xmm5, %xmm6
+        addsd     1840+__datan_ha_CoutTab(%rip), %xmm6
+        mulsd     %xmm5, %xmm6
+        addsd     1832+__datan_ha_CoutTab(%rip), %xmm6
+        mulsd     %xmm5, %xmm6
+        addsd     1824+__datan_ha_CoutTab(%rip), %xmm6
+        mulsd     %xmm5, %xmm6
+        mulsd     %xmm3, %xmm6
+        addsd     %xmm6, %xmm10
+        addsd     8(%rcx,%r8,8), %xmm10
+        addsd     %xmm8, %xmm10
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -24(%rsp)
+        movb      -17(%rsp), %r9b
+        andb      $127, %r9b
+        orb       %al, %r9b
+        movb      %r9b, -17(%rsp)
+        movq      -24(%rsp), %rax
+        movq      %rax, (%rsi)
+        jmp       .LBL_2_11
+
+.LBL_2_5:
+
+        movsd     1912+__datan_ha_CoutTab(%rip), %xmm0
+        shlb      $7, %al
+        addsd     1920+__datan_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %dl
+        andb      $127, %dl
+        orb       %al, %dl
+        movb      %dl, -17(%rsp)
+        movq      -24(%rsp), %rax
+        movq      %rax, (%rsi)
+        jmp       .LBL_2_11
+
+.LBL_2_6:
+
+        comisd    1880+__datan_ha_CoutTab(%rip), %xmm0
+        jb        .LBL_2_8
+
+
+        movaps    %xmm0, %xmm1
+        mulsd     %xmm0, %xmm1
+        shlb      $7, %al
+        movsd     1872+__datan_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1864+__datan_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1856+__datan_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1848+__datan_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1840+__datan_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1832+__datan_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1824+__datan_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        mulsd     %xmm0, %xmm2
+        addsd     %xmm0, %xmm2
+        movsd     %xmm2, -24(%rsp)
+        movb      -17(%rsp), %dl
+        andb      $127, %dl
+        orb       %al, %dl
+        movb      %dl, -17(%rsp)
+        movq      -24(%rsp), %rax
+        movq      %rax, (%rsi)
+        jmp       .LBL_2_11
+
+.LBL_2_8:
+
+        movzwl    -10(%rsp), %edx
+        testl     $32752, %edx
+        je        .LBL_2_10
+
+
+        movsd     1904+__datan_ha_CoutTab(%rip), %xmm1
+        shlb      $7, %al
+        addsd     %xmm0, %xmm1
+        movsd     %xmm1, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        mulsd     -16(%rsp), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %dl
+        andb      $127, %dl
+        orb       %al, %dl
+        movb      %dl, -17(%rsp)
+        movq      -24(%rsp), %rax
+        movq      %rax, (%rsi)
+        jmp       .LBL_2_11
+
+.LBL_2_10:
+
+        mulsd     %xmm0, %xmm0
+        shlb      $7, %al
+        movsd     %xmm0, -48(%rsp)
+        movsd     -48(%rsp), %xmm0
+        addsd     -16(%rsp), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %dl
+        andb      $127, %dl
+        orb       %al, %dl
+        movb      %dl, -17(%rsp)
+        movq      -24(%rsp), %rax
+        movq      %rax, (%rsi)
+
+.LBL_2_11:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_12:
+
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_15
+
+
+        cmpl      $0, (%rdi)
+        jne       .LBL_2_15
+
+
+        movsd     1912+__datan_ha_CoutTab(%rip), %xmm0
+        movb      7(%rdi), %al
+        andb      $-128, %al
+        addsd     1920+__datan_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movb      -17(%rsp), %dl
+        andb      $127, %dl
+        orb       %al, %dl
+        movb      %dl, -17(%rsp)
+        movq      -24(%rsp), %rcx
+        movq      %rcx, (%rsi)
+        jmp       .LBL_2_11
+
+.LBL_2_15:
+
+        movsd     (%rdi), %xmm0
+        addsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        jmp       .LBL_2_11
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_datan_ha_cout_rare_internal,@function
+	.size	__svml_datan_ha_cout_rare_internal,.-__svml_datan_ha_cout_rare_internal
+..LN__svml_datan_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_datan_ha_data_internal_avx512:
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1125646336
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	1075806208
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	1206910976
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	4180443357
+	.long	1070553973
+	.long	90291023
+	.long	1071492199
+	.long	2737217249
+	.long	1071945615
+	.long	1413754136
+	.long	1072243195
+	.long	1468297118
+	.long	1072475260
+	.long	3531732635
+	.long	1072657163
+	.long	744202399
+	.long	1072747407
+	.long	2464923204
+	.long	1072805601
+	.long	1436891685
+	.long	1072853231
+	.long	2037009832
+	.long	1072892781
+	.long	1826698067
+	.long	1072926058
+	.long	1803191648
+	.long	1072954391
+	.long	2205372832
+	.long	1072978772
+	.long	4234512805
+	.long	1072999952
+	.long	3932628503
+	.long	1073018509
+	.long	2501811453
+	.long	1073034892
+	.long	866379431
+	.long	1073049455
+	.long	1376865888
+	.long	1073062480
+	.long	3290094269
+	.long	1073074195
+	.long	354764887
+	.long	1073084787
+	.long	3332975497
+	.long	1073094406
+	.long	1141460092
+	.long	1073103181
+	.long	745761286
+	.long	1073111216
+	.long	1673304509
+	.long	1073118600
+	.long	983388243
+	.long	1073125409
+	.long	3895509104
+	.long	1073131706
+	.long	2128523669
+	.long	1073137548
+	.long	2075485693
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diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_d_la.s
index d77f0cf09ab..201060b6b53 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_h_la.s
new file mode 100644
index 00000000000..5c8014948bb
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_h_la.s
@@ -0,0 +1,139 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Absolute argument and sign: xa = |x|, sign(x)
+ *   Selection mask: SelMask=1 for |x|>1.0
+ *   Reciprocal:  y=RCP(xa)
+ *   xa=y for |x|>1
+ *   High = Pi/2 for |x|>1, 0 otherwise
+ *   Result: High + xa*Poly
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_atans32
+
+__svml_atans32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hatan_data_internal(%rip), %zmm30
+        vmovdqu16 64+__svml_hatan_data_internal(%rip), %zmm5
+        vmovdqu16 128+__svml_hatan_data_internal(%rip), %zmm31
+        vmovdqu16 192+__svml_hatan_data_internal(%rip), %zmm1
+        vmovdqu16 256+__svml_hatan_data_internal(%rip), %zmm28
+        vmovdqu16 320+__svml_hatan_data_internal(%rip), %zmm29
+        vmovdqu16 384+__svml_hatan_data_internal(%rip), %zmm2
+        vmovdqu16 448+__svml_hatan_data_internal(%rip), %zmm3
+        vmovdqu16 512+__svml_hatan_data_internal(%rip), %zmm4
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_atan_h:
+        cmpq    $31, %rdx
+        ja .loaddata_atanh
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_atan_h
+.loaddata_atanh:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_atan_h:
+
+        vmovdqu16 %zmm29, %zmm9
+
+/*
+ * No callout
+ * xa = |x|
+ */
+        vpandd %zmm30, %zmm0, %zmm7
+
+/* SelMask=1 for |x|>1.0 */
+        vcmpph    $22, {sae}, %zmm5, %zmm7, %k1
+
+/* High = Pi/2 for |x|>1, 0 otherwise */
+        vpblendmw %zmm31, %zmm1, %zmm8{%k1}
+
+/* sign(x) */
+        vpxord    %zmm0, %zmm7, %zmm10
+
+/* xa=y for |x|>1 */
+        vrcpph    %zmm7, %zmm7{%k1}
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm2, %zmm7, %zmm9
+        vfmadd213ph {rn-sae}, %zmm3, %zmm7, %zmm9
+        vfmadd213ph {rn-sae}, %zmm4, %zmm7, %zmm9
+        vfmadd213ph {rn-sae}, %zmm5, %zmm7, %zmm9
+
+/* change sign of xa, for |x|>1 */
+        vpxord %zmm28, %zmm7, %zmm6
+        vmovdqu16 %zmm6, %zmm7{%k1}
+
+/* High + xa*Poly */
+        vfmadd213ph {rn-sae}, %zmm8, %zmm7, %zmm9
+
+/* set sign */
+        vpxord    %zmm10, %zmm9, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_atan_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_atans32,@function
+        .size	__svml_atans32,.-__svml_atans32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hatan_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x3e48
+	.endr
+	.rept	32
+        .word	0x0000
+	.endr
+	.rept	32
+        .word	0x8000
+	.endr
+	.rept	32
+        .word	0xa528
+	.endr
+	.rept	32
+        .word	0x3248
+	.endr
+	.rept	32
+        .word	0xb65d
+	.endr
+	.rept	32
+        .word	0x1f7a
+	.endr
+        .type	__svml_hatan_data_internal,@object
+        .size	__svml_hatan_data_internal,576
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_s_ha.s
new file mode 100644
index 00000000000..4fa48cdd1a7
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_s_ha.s
@@ -0,0 +1,396 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *      For    0.0    <= x <=  7.0/16.0: atan(x) = atan(0.0) + atan(s), where s=(x-0.0)/(1.0+0.0*x)
+ *      For  7.0/16.0 <= x <= 11.0/16.0: atan(x) = atan(0.5) + atan(s), where s=(x-0.5)/(1.0+0.5*x)
+ *      For 11.0/16.0 <= x <= 19.0/16.0: atan(x) = atan(1.0) + atan(s), where s=(x-1.0)/(1.0+1.0*x)
+ *      For 19.0/16.0 <= x <= 39.0/16.0: atan(x) = atan(1.5) + atan(s), where s=(x-1.5)/(1.0+1.5*x)
+ *      For 39.0/16.0 <= x <=    inf   : atan(x) = atan(inf) + atan(s), where s=-1.0/x
+ *      Where atan(s) ~= s+s^3*Poly11(s^2) on interval |s|<7.0/0.16.
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_atanf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_atanf16_ha
+
+__svml_atanf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        vmovups   128+__svml_satan_ha_data_internal_avx512(%rip), %zmm4
+
+/* saturate X range, initialize Y */
+        vmovups   320+__svml_satan_ha_data_internal_avx512(%rip), %zmm5
+        vmovups   64+__svml_satan_ha_data_internal_avx512(%rip), %zmm6
+        vmovups   256+__svml_satan_ha_data_internal_avx512(%rip), %zmm10
+
+/* round to 2 bits after binary point */
+        vmovups   192+__svml_satan_ha_data_internal_avx512(%rip), %zmm13
+
+/* table lookup sequence */
+        vmovups   448+__svml_satan_ha_data_internal_avx512(%rip), %zmm15
+
+        vandps    __svml_satan_ha_data_internal_avx512(%rip), %zmm0, %zmm11
+        vcmpps    $17, {sae}, %zmm4, %zmm11, %k1
+        vminps    {sae}, %zmm11, %zmm5, %zmm2
+        vmovups   576+__svml_satan_ha_data_internal_avx512(%rip), %zmm4
+        vaddps    {rn-sae}, %zmm6, %zmm11, %zmm3
+
+/* initialize DiffX=-1 */
+        vxorps    %zmm0, %zmm11, %zmm1
+        vreduceps $40, {sae}, %zmm11, %zmm13{%k1}
+        vsubps    {rn-sae}, %zmm6, %zmm3, %zmm7
+        vpermt2ps 512+__svml_satan_ha_data_internal_avx512(%rip), %zmm3, %zmm15
+        vpermt2ps 640+__svml_satan_ha_data_internal_avx512(%rip), %zmm3, %zmm4
+        vfmadd213ps {rn-sae}, %zmm10, %zmm7, %zmm2{%k1}
+        vmovups   832+__svml_satan_ha_data_internal_avx512(%rip), %zmm3
+        vmovups   896+__svml_satan_ha_data_internal_avx512(%rip), %zmm6
+
+/* (x*x0)_high */
+        vsubps    {rn-sae}, %zmm10, %zmm2, %zmm8
+
+/* R+Rl = DiffX/Y */
+        vrcp14ps  %zmm2, %zmm12
+
+/* (x*x0)_low */
+        vfmsub213ps {rn-sae}, %zmm8, %zmm7, %zmm11
+        knotw     %k1, %k2
+
+/* eps=1-Y*Rcp */
+        vmovaps   %zmm10, %zmm9
+        vfnmadd231ps {rn-sae}, %zmm2, %zmm12, %zmm9
+
+/* set table value to Pi/2 for large X */
+        vblendmps 704+__svml_satan_ha_data_internal_avx512(%rip), %zmm15, %zmm7{%k2}
+        vblendmps 768+__svml_satan_ha_data_internal_avx512(%rip), %zmm4, %zmm5{%k2}
+
+/* Rcp+Rcp*eps */
+        vfmadd213ps {rn-sae}, %zmm12, %zmm9, %zmm12
+
+/* Diff*Rcp1 */
+        vmulps    {rn-sae}, %zmm13, %zmm12, %zmm0
+
+/* (x*x0)_low*R */
+        vmulps    {rn-sae}, %zmm12, %zmm11, %zmm14
+
+/* eps1=1-Y*Rcp1 */
+        vfnmadd213ps {rn-sae}, %zmm10, %zmm12, %zmm2
+
+/* (Diff*Rcp1)_low */
+        vfmsub213ps {rn-sae}, %zmm0, %zmm12, %zmm13
+
+/* polynomial evaluation */
+        vmulps    {rn-sae}, %zmm0, %zmm0, %zmm12
+
+/* R_low = (Diff*Rcp1)_low + eps1*(DiffX*Rcp1) */
+        vfmadd213ps {rn-sae}, %zmm13, %zmm0, %zmm2
+        vaddps    {rn-sae}, %zmm0, %zmm7, %zmm13
+        vmulps    {rn-sae}, %zmm0, %zmm12, %zmm10
+
+/* R_low = R_low - (X*X0)_low*Rcp1*(Diff*Rcp1) */
+        vfnmadd231ps {rn-sae}, %zmm0, %zmm14, %zmm2{%k1}
+        vfmadd231ps {rn-sae}, %zmm12, %zmm3, %zmm6
+        vsubps    {rn-sae}, %zmm7, %zmm13, %zmm8
+        vaddps    {rn-sae}, %zmm5, %zmm2, %zmm9
+        vmovups   960+__svml_satan_ha_data_internal_avx512(%rip), %zmm2
+        vsubps    {rn-sae}, %zmm8, %zmm0, %zmm0
+        vfmadd213ps {rn-sae}, %zmm2, %zmm6, %zmm12
+        vaddps    {rn-sae}, %zmm0, %zmm9, %zmm11
+        vfmadd213ps {rn-sae}, %zmm11, %zmm10, %zmm12
+        vaddps    {rn-sae}, %zmm13, %zmm12, %zmm14
+        vxorps    %zmm1, %zmm14, %zmm0
+
+/* no invcbrt in libm, so taking it out here */
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_atanf16_ha,@function
+	.size	__svml_atanf16_ha,.-__svml_atanf16_ha
+..LN__svml_atanf16_ha.0:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_satan_ha_data_internal_avx512:
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1241513984
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	1089994752
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	1333788672
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1048239024
+	.long	1055744824
+	.long	1059372157
+	.long	1061752795
+	.long	1063609315
+	.long	1065064543
+	.long	1065786489
+	.long	1066252045
+	.long	1066633083
+	.long	1066949484
+	.long	1067215699
+	.long	1067442363
+	.long	1067637412
+	.long	1067806856
+	.long	1067955311
+	.long	1068086373
+	.long	1068202874
+	.long	1068307075
+	.long	1068400798
+	.long	1068485529
+	.long	1068562486
+	.long	1068632682
+	.long	1068696961
+	.long	1068756035
+	.long	1068810506
+	.long	1068860887
+	.long	1068907620
+	.long	1068951084
+	.long	1068991608
+	.long	1069029480
+	.long	1069064949
+	.long	0
+	.long	2975494116
+	.long	833369962
+	.long	835299256
+	.long	2998648110
+	.long	2995239174
+	.long	3000492182
+	.long	860207626
+	.long	3008447516
+	.long	3005590622
+	.long	3000153675
+	.long	860754741
+	.long	859285590
+	.long	844944488
+	.long	2993069463
+	.long	858157665
+	.long	3006142000
+	.long	3007693206
+	.long	3009342234
+	.long	847469400
+	.long	3006114683
+	.long	852829553
+	.long	847325583
+	.long	860305056
+	.long	846145135
+	.long	2997638646
+	.long	855837703
+	.long	2979047222
+	.long	2995344192
+	.long	854092798
+	.long	3000498637
+	.long	859965578
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	1070141403
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3007036718
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	3188697310
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	1045219554
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.long	3198855850
+	.type	__svml_satan_ha_data_internal_avx512,@object
+	.size	__svml_satan_ha_data_internal_avx512,1024
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_s_la.s
index 743ebf4917b..98682426885 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atan_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_d_ha.s
new file mode 100644
index 00000000000..f62a95631cc
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_d_ha.s
@@ -0,0 +1,696 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *   Compute 0.5*[log(1+x)-log(1-x)], using small table
+ *  *   lookups that map to AVX3 permute instructions
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_atanh8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_atanh8_ha
+
+__svml_atanh8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   256+__svml_datanh_ha_data_internal_avx512(%rip), %zmm3
+
+/* round reciprocals to 1+4b mantissas */
+        vmovups   384+__svml_datanh_ha_data_internal_avx512(%rip), %zmm4
+        vmovups   448+__svml_datanh_ha_data_internal_avx512(%rip), %zmm10
+        vandpd    320+__svml_datanh_ha_data_internal_avx512(%rip), %zmm0, %zmm14
+
+/* 1+y */
+        vaddpd    {rn-sae}, %zmm3, %zmm14, %zmm2
+
+/* 1-y */
+        vsubpd    {rn-sae}, %zmm14, %zmm3, %zmm7
+        vxorpd    %zmm14, %zmm0, %zmm1
+
+/* Yp_high */
+        vsubpd    {rn-sae}, %zmm3, %zmm2, %zmm8
+
+/* -Ym_high */
+        vsubpd    {rn-sae}, %zmm3, %zmm7, %zmm13
+
+/* RcpP ~ 1/Yp */
+        vrcp14pd  %zmm2, %zmm5
+
+/* RcpM ~ 1/Ym */
+        vrcp14pd  %zmm7, %zmm6
+
+/* input outside (-1, 1) ? */
+        vcmppd    $21, {sae}, %zmm3, %zmm14, %k0
+        vpaddq    %zmm4, %zmm5, %zmm12
+        vpaddq    %zmm4, %zmm6, %zmm11
+
+/* Yp_low */
+        vsubpd    {rn-sae}, %zmm8, %zmm14, %zmm9
+        vandpd    %zmm10, %zmm12, %zmm15
+        vandpd    %zmm10, %zmm11, %zmm5
+
+/* Ym_low */
+        vaddpd    {rn-sae}, %zmm13, %zmm14, %zmm14
+
+/* Reduced argument: Rp = (RcpP*Yp - 1)+RcpP*Yp_low */
+        vfmsub213pd {rn-sae}, %zmm3, %zmm15, %zmm2
+
+/* exponents */
+        vgetexppd {sae}, %zmm15, %zmm6
+        vmovups   128+__svml_datanh_ha_data_internal_avx512(%rip), %zmm8
+
+/* Table lookups */
+        vmovups   __svml_datanh_ha_data_internal_avx512(%rip), %zmm10
+
+/* polynomials */
+        vmovups   512+__svml_datanh_ha_data_internal_avx512(%rip), %zmm4
+        vfmadd231pd {rn-sae}, %zmm15, %zmm9, %zmm2
+        vpsrlq    $48, %zmm5, %zmm9
+
+/* Prepare table index */
+        vpsrlq    $48, %zmm15, %zmm12
+        vmovups   64+__svml_datanh_ha_data_internal_avx512(%rip), %zmm15
+        kmovw     %k0, %edx
+
+/* Reduced argument: Rm = (RcpM*Ym - 1)+RcpM*Ym_low */
+        vmovaps   %zmm3, %zmm13
+        vfmsub231pd {rn-sae}, %zmm5, %zmm7, %zmm13
+        vmovups   192+__svml_datanh_ha_data_internal_avx512(%rip), %zmm7
+        vfnmadd231pd {rn-sae}, %zmm5, %zmm14, %zmm13
+        vgetexppd {sae}, %zmm5, %zmm5
+        vmovups   704+__svml_datanh_ha_data_internal_avx512(%rip), %zmm14
+
+/* Km-Kp */
+        vsubpd    {rn-sae}, %zmm6, %zmm5, %zmm6
+        vmovups   576+__svml_datanh_ha_data_internal_avx512(%rip), %zmm5
+        vmovaps   %zmm12, %zmm11
+        vpermi2pd %zmm7, %zmm8, %zmm12
+        vpermt2pd %zmm7, %zmm9, %zmm8
+        vpermi2pd %zmm15, %zmm10, %zmm11
+        vpermt2pd %zmm15, %zmm9, %zmm10
+        vsubpd    {rn-sae}, %zmm12, %zmm8, %zmm8
+        vmovups   640+__svml_datanh_ha_data_internal_avx512(%rip), %zmm9
+
+/* K*L2H + Th */
+        vmovups   1088+__svml_datanh_ha_data_internal_avx512(%rip), %zmm12
+
+/* K*L2L + Tl */
+        vmovups   1152+__svml_datanh_ha_data_internal_avx512(%rip), %zmm15
+        vmovaps   %zmm5, %zmm7
+        vfmadd231pd {rn-sae}, %zmm2, %zmm4, %zmm7
+        vfmadd231pd {rn-sae}, %zmm13, %zmm4, %zmm5
+
+/* table values */
+        vsubpd    {rn-sae}, %zmm11, %zmm10, %zmm4
+        vfmadd213pd {rn-sae}, %zmm9, %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm9, %zmm13, %zmm5
+        vmovups   768+__svml_datanh_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   832+__svml_datanh_ha_data_internal_avx512(%rip), %zmm11
+        vfmadd231pd {rn-sae}, %zmm6, %zmm12, %zmm4
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm14, %zmm13, %zmm5
+        vfmadd213pd {rn-sae}, %zmm8, %zmm15, %zmm6
+        vmovups   896+__svml_datanh_ha_data_internal_avx512(%rip), %zmm8
+
+/* Rp^2 */
+        vmulpd    {rn-sae}, %zmm2, %zmm2, %zmm9
+        vfmadd213pd {rn-sae}, %zmm10, %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm10, %zmm13, %zmm5
+
+/* (K*L2H+Th) + Rph */
+        vaddpd    {rn-sae}, %zmm4, %zmm2, %zmm10
+        vfmadd213pd {rn-sae}, %zmm11, %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm11, %zmm13, %zmm5
+
+/* Rm^2 */
+        vmulpd    {rn-sae}, %zmm13, %zmm13, %zmm11
+        vfmadd213pd {rn-sae}, %zmm8, %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm8, %zmm13, %zmm5
+        vmovups   960+__svml_datanh_ha_data_internal_avx512(%rip), %zmm8
+
+/* -Rp_high */
+        vsubpd    {rn-sae}, %zmm10, %zmm4, %zmm4
+
+/* (K*L2H+Th) + (Rph - Rmh) */
+        vsubpd    {rn-sae}, %zmm13, %zmm10, %zmm3
+        vfmadd213pd {rn-sae}, %zmm8, %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm8, %zmm13, %zmm5
+        vmovups   1024+__svml_datanh_ha_data_internal_avx512(%rip), %zmm8
+        vfmadd213pd {rn-sae}, %zmm8, %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm8, %zmm13, %zmm5
+
+/* Rpl */
+        vaddpd    {rn-sae}, %zmm4, %zmm2, %zmm2
+
+/* -Rm_high */
+        vsubpd    {rn-sae}, %zmm10, %zmm3, %zmm4
+
+/* (K*L2L + Tl) + Rp^2*PolyP */
+        vfmadd213pd {rn-sae}, %zmm6, %zmm9, %zmm7
+
+/* Rpl - Rm^2*PolyM */
+        vfnmadd213pd {rn-sae}, %zmm2, %zmm11, %zmm5
+
+/* Rml */
+        vaddpd    {rn-sae}, %zmm4, %zmm13, %zmm2
+        vorpd     1216+__svml_datanh_ha_data_internal_avx512(%rip), %zmm1, %zmm4
+
+/* low part */
+        vaddpd    {rn-sae}, %zmm5, %zmm7, %zmm5
+        vsubpd    {rn-sae}, %zmm2, %zmm5, %zmm1
+        vaddpd    {rn-sae}, %zmm1, %zmm3, %zmm1
+        vmulpd    {rn-sae}, %zmm4, %zmm1, %zmm1
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        vmovaps   %zmm1, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm1, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm1
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_datanh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_atanh8_ha,@function
+	.size	__svml_atanh8_ha,.-__svml_atanh8_ha
+..LN__svml_atanh8_ha.0:
+
+.L_2__routine_start___svml_datanh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_datanh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movzwl    6(%rdi), %eax
+        andl      $32752, %eax
+        movsd     (%rdi), %xmm0
+        movb      7(%rdi), %dl
+        andb      $127, %dl
+        movsd     %xmm0, -8(%rsp)
+        cmpl      $32752, %eax
+        je        .LBL_2_6
+
+.LBL_2_2:
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_5
+
+
+        movb      %dl, -1(%rsp)
+        cmpl      $1072693248, -4(%rsp)
+        jne       .LBL_2_5
+
+
+        divsd     8+__datanh_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        movl      $2, %eax
+        ret
+
+.LBL_2_5:
+
+        movsd     8+__datanh_ha_CoutTab(%rip), %xmm0
+        movl      $1, %eax
+        mulsd     16+__datanh_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_6:
+
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_8
+
+
+        cmpl      $0, (%rdi)
+        je        .LBL_2_2
+
+.LBL_2_8:
+
+        mulsd     %xmm0, %xmm0
+        xorl      %eax, %eax
+        movsd     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_datanh_ha_cout_rare_internal,@function
+	.size	__svml_datanh_ha_cout_rare_internal,.-__svml_datanh_ha_cout_rare_internal
+..LN__svml_datanh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_datanh_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	3222405120
+	.long	1068436016
+	.long	1848311808
+	.long	1069426439
+	.long	1890025472
+	.long	1069940528
+	.long	3348791296
+	.long	1070370807
+	.long	2880159744
+	.long	1070688092
+	.long	3256631296
+	.long	1070883211
+	.long	4139499520
+	.long	1071069655
+	.long	3971973120
+	.long	1071248163
+	.long	3348791296
+	.long	1071419383
+	.long	1605304320
+	.long	1071583887
+	.long	3827638272
+	.long	1071693426
+	.long	1584414720
+	.long	1071769695
+	.long	860823552
+	.long	1071843287
+	.long	3896934400
+	.long	1071914383
+	.long	643547136
+	.long	1071983149
+	.long	0
+	.long	0
+	.long	3496399314
+	.long	3176377139
+	.long	720371772
+	.long	3173659692
+	.long	1944193543
+	.long	1027855304
+	.long	634920691
+	.long	1028268460
+	.long	1664625295
+	.long	3176788476
+	.long	192624563
+	.long	1029620349
+	.long	3796653051
+	.long	1028654748
+	.long	3062724207
+	.long	1029196786
+	.long	634920691
+	.long	1029317036
+	.long	1913570380
+	.long	1027322573
+	.long	825194088
+	.long	1028982125
+	.long	2335489660
+	.long	1025116093
+	.long	2497625109
+	.long	3177087936
+	.long	914782743
+	.long	3176833847
+	.long	3743595607
+	.long	1028041657
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	32768
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	0
+	.long	4294901760
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1075921768
+	.long	3216615856
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	1847891832
+	.long	1069318246
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	2315602889
+	.long	3217031163
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	4145174257
+	.long	1069697314
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	1436264246
+	.long	3217380693
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	2579396527
+	.long	1070176665
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	4294966373
+	.long	3218079743
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	1431655617
+	.long	1070945621
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	0
+	.long	3219128320
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.type	__svml_datanh_ha_data_internal_avx512,@object
+	.size	__svml_datanh_ha_data_internal_avx512,1280
+	.align 8
+__datanh_ha_CoutTab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	4293918720
+	.type	__datanh_ha_CoutTab,@object
+	.size	__datanh_ha_CoutTab,32
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_d_la.s
index a6a21bc5ab7..63379dac2c9 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_h_la.s
new file mode 100644
index 00000000000..f027c4da19e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_h_la.s
@@ -0,0 +1,191 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Compute atanh(x) as 0.5 * log((1 + x)/(1 - x))
+ *
+ *   Special cases:
+ *
+ *   atanh(0)  = 0
+ *   atanh(+1) = +INF
+ *   atanh(-1) = -INF
+ *   atanh(x)  = NaN if |x| > 1, or if x is a NaN or INF
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_atanhs32
+
+__svml_atanhs32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hatanh_data_internal(%rip), %zmm31
+        vmovdqu16 64+__svml_hatanh_data_internal(%rip), %zmm30
+        vmovdqu16 128+__svml_hatanh_data_internal(%rip), %zmm29
+        vmovdqu16 192+__svml_hatanh_data_internal(%rip), %zmm28
+        vmovdqu16 256+__svml_hatanh_data_internal(%rip), %zmm27
+        vmovdqu16 320+__svml_hatanh_data_internal(%rip), %zmm26
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_atanh_h:
+        cmpq    $31, %rdx
+        ja .loaddata_atanhh
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_atanh_h
+.loaddata_atanhh:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_atanh_h:
+
+/*
+ * reduced argument: 4*(1-|x|)
+ * scaling by 4 to get around limited FP16 range for coefficient representation
+ */
+        vmovdqu16 %zmm27, %zmm3
+
+/* prepare special case mask */
+        vpandd    %zmm28, %zmm0, %zmm2
+        vfnmadd213ph {rn-sae}, %zmm3, %zmm2, %zmm3
+
+/* get table index */
+        vpsrlw    $9, %zmm3, %zmm4
+
+/* Special result */
+        vrsqrtph  %zmm3, %zmm7
+        vcmpph    $18, {sae}, %zmm26, %zmm3, %k1
+
+/* look up poly coefficients: c1, c2, c3 */
+        vpermw    %zmm29, %zmm4, %zmm10
+        vpermw    %zmm30, %zmm4, %zmm5
+        vpermw    %zmm31, %zmm4, %zmm6
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm5, %zmm3, %zmm10
+        vfmadd213ph {rn-sae}, %zmm6, %zmm3, %zmm10
+
+/* x+x*Poly */
+        vfmadd213ph {rn-sae}, %zmm0, %zmm0, %zmm10
+        vpandnd   %zmm0, %zmm28, %zmm8
+        vpord     %zmm8, %zmm7, %zmm11
+        vpblendmw %zmm11, %zmm10, %zmm0{%k1}
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_atanh_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_atanhs32,@function
+        .size	__svml_atanhs32,.-__svml_atanhs32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hatanh_data_internal:
+        .word	0x3770
+        .word	0x35ab
+	.rept	10
+        .word	0x0000
+	.endr
+        .word	0x439e
+        .word	0x4345
+        .word	0x42ed
+        .word	0x4294
+        .word	0x423c
+        .word	0x41e3
+        .word	0x418a
+        .word	0x4132
+        .word	0x40da
+        .word	0x4081
+        .word	0x4029
+        .word	0x3fa2
+        .word	0x3ef4
+        .word	0x3e46
+        .word	0x3d9a
+        .word	0x3cef
+        .word	0x3c48
+        .word	0x3b47
+        .word	0x3a08
+        .word	0x38d4
+        .word	0xb412
+        .word	0xb1b1
+	.rept	10
+        .word	0x0000
+	.endr
+        .word	0xde80
+        .word	0xdc99
+        .word	0xda7d
+        .word	0xd896
+        .word	0xd677
+        .word	0xd491
+        .word	0xd26e
+        .word	0xd088
+        .word	0xce5d
+        .word	0xcc79
+        .word	0xca41
+        .word	0xc85f
+        .word	0xc613
+        .word	0xc437
+        .word	0xc1cc
+        .word	0xbff0
+        .word	0xbd60
+        .word	0xbb38
+        .word	0xb8c7
+        .word	0xb641
+        .word	0x288f
+        .word	0x25b7
+	.rept	10
+        .word	0x0000
+	.endr
+        .word	0x7940
+        .word	0x754a
+        .word	0x713f
+        .word	0x6d49
+        .word	0x693d
+        .word	0x6546
+        .word	0x613a
+        .word	0x5d42
+        .word	0x5934
+        .word	0x553a
+        .word	0x5129
+        .word	0x4d2b
+        .word	0x4915
+        .word	0x4510
+        .word	0x40f3
+        .word	0x3ce4
+        .word	0x38bd
+        .word	0x34a5
+        .word	0x307b
+        .word	0x2c6f
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x4400
+	.endr
+	.rept	32
+        .word	0x0000
+	.endr
+        .type	__svml_hatanh_data_internal,@object
+        .size	__svml_hatanh_data_internal,384
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_s_ha.s
new file mode 100644
index 00000000000..cea606cfb1d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_s_ha.s
@@ -0,0 +1,578 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *   Compute 0.5*[log(1+x)-log(1-x)], using small table
+ *  *   lookups that map to AVX3 permute instructions
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_atanhf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_atanhf16_ha
+
+__svml_atanhf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   256+__svml_satanh_ha_data_internal_avx512(%rip), %zmm7
+
+/* round reciprocals to 1+5b mantissas */
+        vmovups   384+__svml_satanh_ha_data_internal_avx512(%rip), %zmm2
+        vmovups   448+__svml_satanh_ha_data_internal_avx512(%rip), %zmm3
+        vmovaps   %zmm0, %zmm13
+        vandps    320+__svml_satanh_ha_data_internal_avx512(%rip), %zmm13, %zmm9
+
+/* 1+y */
+        vaddps    {rn-sae}, %zmm7, %zmm9, %zmm12
+
+/* 1-y */
+        vsubps    {rn-sae}, %zmm9, %zmm7, %zmm11
+        vxorps    %zmm9, %zmm13, %zmm14
+
+/* Yp_high */
+        vsubps    {rn-sae}, %zmm7, %zmm12, %zmm5
+
+/* -Ym_high */
+        vsubps    {rn-sae}, %zmm7, %zmm11, %zmm8
+
+/* RcpP ~ 1/Yp */
+        vrcp14ps  %zmm12, %zmm15
+
+/* RcpM ~ 1/Ym */
+        vrcp14ps  %zmm11, %zmm0
+
+/* input outside (-1, 1) ? */
+        vcmpps    $21, {sae}, %zmm7, %zmm9, %k0
+        vpaddd    %zmm2, %zmm15, %zmm1
+        vpaddd    %zmm2, %zmm0, %zmm4
+
+/* Yp_low */
+        vsubps    {rn-sae}, %zmm5, %zmm9, %zmm6
+        vandps    %zmm3, %zmm1, %zmm10
+        vandps    %zmm3, %zmm4, %zmm15
+
+/* Ym_low */
+        vaddps    {rn-sae}, %zmm8, %zmm9, %zmm8
+
+/* Reduced argument: Rp = (RcpP*Yp - 1)+RcpP*Yp_low */
+        vfmsub213ps {rn-sae}, %zmm7, %zmm10, %zmm12
+
+/* exponents */
+        vgetexpps {sae}, %zmm10, %zmm1
+
+/* Table lookups */
+        vmovups   __svml_satanh_ha_data_internal_avx512(%rip), %zmm9
+
+/* Reduced argument: Rm = (RcpM*Ym - 1)+RcpM*Ym_low */
+        vfmsub231ps {rn-sae}, %zmm15, %zmm11, %zmm7
+        vmovups   128+__svml_satanh_ha_data_internal_avx512(%rip), %zmm11
+        vfmadd231ps {rn-sae}, %zmm10, %zmm6, %zmm12
+        vmovups   192+__svml_satanh_ha_data_internal_avx512(%rip), %zmm0
+        vgetexpps {sae}, %zmm15, %zmm2
+        vfnmadd231ps {rn-sae}, %zmm15, %zmm8, %zmm7
+
+/* Prepare table index */
+        vpsrld    $18, %zmm10, %zmm6
+        vpsrld    $18, %zmm15, %zmm5
+        vmovups   64+__svml_satanh_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   640+__svml_satanh_ha_data_internal_avx512(%rip), %zmm15
+
+/* Km-Kp */
+        vsubps    {rn-sae}, %zmm1, %zmm2, %zmm4
+        vmovups   576+__svml_satanh_ha_data_internal_avx512(%rip), %zmm1
+        kmovw     %k0, %edx
+        vmovaps   %zmm6, %zmm3
+        vpermi2ps %zmm10, %zmm9, %zmm3
+        vpermt2ps %zmm10, %zmm5, %zmm9
+        vpermi2ps %zmm0, %zmm11, %zmm6
+        vpermt2ps %zmm0, %zmm5, %zmm11
+
+/* table values */
+        vsubps    {rn-sae}, %zmm3, %zmm9, %zmm3
+
+/* K*L2H + Th */
+        vmovups   704+__svml_satanh_ha_data_internal_avx512(%rip), %zmm5
+
+/* polynomials */
+        vmovups   512+__svml_satanh_ha_data_internal_avx512(%rip), %zmm10
+        vsubps    {rn-sae}, %zmm6, %zmm11, %zmm8
+
+/* K*L2L + Tl */
+        vmovups   768+__svml_satanh_ha_data_internal_avx512(%rip), %zmm6
+        vfmadd231ps {rn-sae}, %zmm4, %zmm5, %zmm3
+
+/* Rp^2 */
+        vmulps    {rn-sae}, %zmm12, %zmm12, %zmm5
+        vfmadd213ps {rn-sae}, %zmm8, %zmm6, %zmm4
+
+/* Rm^2 */
+        vmulps    {rn-sae}, %zmm7, %zmm7, %zmm8
+
+/* (K*L2H+Th) + Rph */
+        vaddps    {rn-sae}, %zmm3, %zmm12, %zmm6
+        vmovaps   %zmm1, %zmm2
+        vfmadd231ps {rn-sae}, %zmm12, %zmm10, %zmm2
+        vfmadd231ps {rn-sae}, %zmm7, %zmm10, %zmm1
+
+/* -Rp_high */
+        vsubps    {rn-sae}, %zmm6, %zmm3, %zmm3
+
+/* (K*L2H+Th) + (Rph - Rmh) */
+        vsubps    {rn-sae}, %zmm7, %zmm6, %zmm0
+        vfmadd213ps {rn-sae}, %zmm15, %zmm12, %zmm2
+        vfmadd213ps {rn-sae}, %zmm15, %zmm7, %zmm1
+
+/* Rpl */
+        vaddps    {rn-sae}, %zmm3, %zmm12, %zmm12
+
+/* -Rm_high */
+        vsubps    {rn-sae}, %zmm6, %zmm0, %zmm3
+
+/* (K*L2L + Tl) + Rp^2*PolyP */
+        vfmadd213ps {rn-sae}, %zmm4, %zmm5, %zmm2
+
+/* Rpl - Rm^2*PolyM */
+        vfnmadd213ps {rn-sae}, %zmm12, %zmm8, %zmm1
+
+/* Rml */
+        vaddps    {rn-sae}, %zmm3, %zmm7, %zmm3
+
+/* low part */
+        vaddps    {rn-sae}, %zmm1, %zmm2, %zmm7
+        vorps     832+__svml_satanh_ha_data_internal_avx512(%rip), %zmm14, %zmm1
+        vsubps    {rn-sae}, %zmm3, %zmm7, %zmm14
+        vaddps    {rn-sae}, %zmm14, %zmm0, %zmm0
+        vmulps    {rn-sae}, %zmm1, %zmm0, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm13, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_satanh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_atanhf16_ha,@function
+	.size	__svml_atanhf16_ha,.-__svml_atanhf16_ha
+..LN__svml_atanhf16_ha.0:
+
+.L_2__routine_start___svml_satanh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_satanh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movzwl    2(%rdi), %edx
+        movss     (%rdi), %xmm1
+        andl      $32640, %edx
+        movb      3(%rdi), %al
+        andb      $127, %al
+        movss     %xmm1, -8(%rsp)
+        movb      %al, -5(%rsp)
+        cmpl      $32640, %edx
+        je        .LBL_2_6
+
+
+        cmpl      $1065353216, -8(%rsp)
+        jne       .LBL_2_4
+
+
+        divss     4+__satanh_ha__imlsAtanhTab(%rip), %xmm1
+        movss     %xmm1, (%rsi)
+        movl      $2, %eax
+        ret
+
+.LBL_2_4:
+
+        movss     8+__satanh_ha__imlsAtanhTab(%rip), %xmm0
+        movl      $1, %eax
+        mulss     4+__satanh_ha__imlsAtanhTab(%rip), %xmm0
+        movss     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_6:
+
+        cmpl      $2139095040, -8(%rsp)
+        jne       .LBL_2_8
+
+
+        movss     4+__satanh_ha__imlsAtanhTab(%rip), %xmm0
+        movl      $1, %eax
+        mulss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        mulss     (%rdi), %xmm1
+        xorl      %eax, %eax
+        movss     %xmm1, (%rsi)
+
+
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_satanh_ha_cout_rare_internal,@function
+	.size	__svml_satanh_ha_cout_rare_internal,.-__svml_satanh_ha_cout_rare_internal
+..LN__svml_satanh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_satanh_ha_data_internal_avx512:
+	.long	0
+	.long	1023148032
+	.long	1031307264
+	.long	1035436032
+	.long	1039220736
+	.long	1041539072
+	.long	1043333120
+	.long	1045078016
+	.long	1046773760
+	.long	1048428544
+	.long	1049313280
+	.long	1050099712
+	.long	1050873856
+	.long	1051627520
+	.long	1052364800
+	.long	1053085696
+	.long	1053794304
+	.long	1054482432
+	.long	1055162368
+	.long	1055825920
+	.long	1056477184
+	.long	1057040384
+	.long	1057353728
+	.long	1057662976
+	.long	1057964032
+	.long	1058260992
+	.long	1058553856
+	.long	1058840576
+	.long	1059123200
+	.long	1059399680
+	.long	1059672064
+	.long	1059940352
+	.long	0
+	.long	925287326
+	.long	3090802686
+	.long	928156389
+	.long	3078132181
+	.long	942242832
+	.long	3083833176
+	.long	3092427142
+	.long	3045295702
+	.long	940324527
+	.long	3089323092
+	.long	945994465
+	.long	3085466567
+	.long	3078914384
+	.long	3072337169
+	.long	927865605
+	.long	3093041984
+	.long	947354573
+	.long	3053684310
+	.long	936642948
+	.long	940531631
+	.long	941968204
+	.long	946194506
+	.long	3086005293
+	.long	943635681
+	.long	943465747
+	.long	3080925892
+	.long	3078186319
+	.long	3093311347
+	.long	3061074424
+	.long	934582639
+	.long	939563115
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.type	__svml_satanh_ha_data_internal_avx512,@object
+	.size	__svml_satanh_ha_data_internal_avx512,896
+	.align 4
+__satanh_ha__imlsAtanhTab:
+	.long	1065353216
+	.long	0
+	.long	2139095040
+	.type	__satanh_ha__imlsAtanhTab,@object
+	.size	__satanh_ha__imlsAtanhTab,12
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_s_la.s
index 2ae97c49277..2f899834bff 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_atanh_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_d_ha.s
new file mode 100644
index 00000000000..e56144626aa
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_d_ha.s
@@ -0,0 +1,936 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *   x=2^{3*k+j} * 1.b1 b2 ... b5 b6 ... b52
+ *   Let r=(x*2^{-3k-j} - 1.b1 b2 ... b5 1)* rcp[b1 b2 ..b5],
+ *   where rcp[b1 b2 .. b5]=1/(1.b1 b2 b3 b4 b5 1) in double precision
+ *   cbrt(2^j * 1. b1 b2 .. b5 1) is approximated as T[j][b1..b5]+D[j][b1..b5]
+ *   (T stores the high 53 bits, D stores the low order bits)
+ *   Result=2^k*T+(2^k*T*r)*P+2^k*D
+ *   where P=p1+p2*r+..+p8*r^7
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_cbrt8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_cbrt8_ha
+
+__svml_cbrt8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+
+/* GetExp(x) */
+        vgetexppd {sae}, %zmm0, %zmm9
+        vmovups   384+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm10
+        vgetmantpd $0, {sae}, %zmm0, %zmm2
+
+/* exponent/3 */
+        vmovups   512+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   576+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm12
+
+/* exponent%3 (to be used as index) */
+        vmovups   640+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm13
+
+/* Table lookup */
+        vmovups   128+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm1
+
+/* Reduced argument: R = DblRcp*Mantissa - 1 */
+        vmovups   704+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm4
+
+/* DblRcp ~ 1/Mantissa */
+        vrcp14pd  %zmm2, %zmm15
+        vaddpd    {rn-sae}, %zmm10, %zmm9, %zmm14
+        vandpd    448+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm0, %zmm8
+
+/* round DblRcp to 3 fractional bits (RN mode, no Precision exception) */
+        vrndscalepd $72, {sae}, %zmm15, %zmm3
+        vfmsub231pd {rn-sae}, %zmm14, %zmm11, %zmm12
+        vmovups   256+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm11
+
+/* polynomial */
+        vmovups   768+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   896+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm10
+        vfmsub231pd {rn-sae}, %zmm3, %zmm2, %zmm4
+        vrndscalepd $9, {sae}, %zmm12, %zmm7
+
+/* Prepare table index */
+        vpsrlq    $49, %zmm3, %zmm0
+        vmovups   832+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm3
+        vfnmadd231pd {rn-sae}, %zmm7, %zmm13, %zmm14
+        vmovups   1024+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm13
+        vfmadd231pd {rn-sae}, %zmm4, %zmm9, %zmm3
+        vmovups   1088+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm9
+
+/* Table lookup: 2^(exponent%3) */
+        vpermpd   __svml_dcbrt_ha_data_internal_avx512(%rip), %zmm14, %zmm6
+        vpermt2pd 192+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm0, %zmm1
+        vpermt2pd 320+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm0, %zmm11
+        vpermpd   64+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm14, %zmm5
+
+/* (Sh*Th)_high */
+        vmulpd    {rn-sae}, %zmm1, %zmm6, %zmm2
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm0
+        vmovups   960+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm14
+        vfmadd231pd {rn-sae}, %zmm4, %zmm13, %zmm9
+        vfmadd231pd {rn-sae}, %zmm4, %zmm10, %zmm14
+        vmovups   1216+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm10
+        vfmadd213pd {rn-sae}, %zmm14, %zmm0, %zmm3
+
+/* (Sh*Th)_low */
+        vmovaps   %zmm6, %zmm12
+        vfmsub213pd {rn-sae}, %zmm2, %zmm1, %zmm12
+        vfmadd213pd {rn-sae}, %zmm9, %zmm0, %zmm3
+
+/* (Sh*Th)_low + Sh*Tl */
+        vfmadd213pd {rn-sae}, %zmm12, %zmm11, %zmm6
+
+/* (Sh*Th)_low + Sh*Tl + Sl*Th */
+        vfmadd213pd {rn-sae}, %zmm6, %zmm1, %zmm5
+        vmovups   1152+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm1
+        vmovups   1280+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm6
+        vfmadd231pd {rn-sae}, %zmm4, %zmm1, %zmm10
+        vmovups   1344+__svml_dcbrt_ha_data_internal_avx512(%rip), %zmm1
+        vfmadd213pd {rn-sae}, %zmm10, %zmm0, %zmm3
+        vfmadd231pd {rn-sae}, %zmm4, %zmm6, %zmm1
+
+/* (Sh*Th)_h*R */
+        vmulpd    {rn-sae}, %zmm4, %zmm2, %zmm4
+        vfmadd213pd {rn-sae}, %zmm1, %zmm0, %zmm3
+
+/* Low + ((Sh*Th)_h*R)*Poly */
+        vfmadd213pd {rn-sae}, %zmm5, %zmm3, %zmm4
+
+/*
+ * branch-free
+ * ShTh + Low + ((Sh*Th)_h*R)*Poly
+ */
+        vaddpd    {rn-sae}, %zmm2, %zmm4, %zmm5
+        vscalefpd {rn-sae}, %zmm7, %zmm5, %zmm7
+        vorpd     %zmm8, %zmm7, %zmm0
+
+/* no invcbrt in libm, so taking it out here */
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_cbrt8_ha,@function
+	.size	__svml_cbrt8_ha,.-__svml_cbrt8_ha
+..LN__svml_cbrt8_ha.0:
+
+.L_2__routine_start___svml_dcbrt_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dcbrt_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L5:
+
+        movq      %rsi, %r8
+        movzwl    6(%rdi), %r9d
+        andl      $32752, %r9d
+        shrl      $4, %r9d
+        movb      7(%rdi), %sil
+        movsd     (%rdi), %xmm1
+        cmpl      $2047, %r9d
+        je        .LBL_2_9
+
+
+        ucomisd   432+__dcbrt_ha__vmldCbrtTab(%rip), %xmm1
+        jp        .LBL_2_3
+        je        .LBL_2_8
+
+.LBL_2_3:
+
+        movb      %sil, %al
+        lea       440+__dcbrt_ha__vmldCbrtTab(%rip), %rdx
+        andb      $-128, %al
+        andb      $127, %sil
+        shrb      $7, %al
+        xorl      %edi, %edi
+        movsd     %xmm1, -56(%rsp)
+        movzbl    %al, %ecx
+        movb      %sil, -49(%rsp)
+        movsd     (%rdx,%rcx,8), %xmm5
+        testl     %r9d, %r9d
+        jne       .LBL_2_5
+
+
+        movsd     -56(%rsp), %xmm0
+        movl      $100, %edi
+        mulsd     360+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        movsd     %xmm0, -56(%rsp)
+        jmp       .LBL_2_6
+
+.LBL_2_5:
+
+        movsd     -56(%rsp), %xmm0
+
+.LBL_2_6:
+
+        movzwl    -50(%rsp), %esi
+        movl      $1431655766, %eax
+        andl      $32752, %esi
+        lea       __dcbrt_ha__vmldCbrtTab(%rip), %r11
+        shrl      $4, %esi
+        movsd     %xmm0, -40(%rsp)
+        movsd     368+__dcbrt_ha__vmldCbrtTab(%rip), %xmm14
+        imull     %esi
+        movl      $1431655766, %eax
+        lea       (%rdx,%rdx,2), %ecx
+        negl      %ecx
+        addl      %esi, %ecx
+        subl      %ecx, %esi
+        addl      %ecx, %ecx
+        addl      $-1023, %esi
+        imull     %esi
+        sarl      $31, %esi
+        subl      %esi, %edx
+        addl      $1023, %edx
+        subl      %edi, %edx
+        movzwl    -34(%rsp), %edi
+        andl      $2047, %edx
+        andl      $-32753, %edi
+        addl      $16368, %edi
+        movw      %di, -34(%rsp)
+        movsd     -40(%rsp), %xmm11
+        movaps    %xmm11, %xmm6
+        mulsd     376+__dcbrt_ha__vmldCbrtTab(%rip), %xmm6
+        movsd     %xmm6, -32(%rsp)
+        movsd     -32(%rsp), %xmm7
+        movl      -36(%rsp), %r10d
+        andl      $1048575, %r10d
+        subsd     -40(%rsp), %xmm7
+        movsd     %xmm7, -24(%rsp)
+        movsd     -32(%rsp), %xmm9
+        movsd     -24(%rsp), %xmm8
+        shrl      $15, %r10d
+        subsd     %xmm8, %xmm9
+        movsd     %xmm9, -32(%rsp)
+        movsd     -32(%rsp), %xmm10
+        movsd     (%r11,%r10,8), %xmm4
+        subsd     %xmm10, %xmm11
+        movaps    %xmm4, %xmm12
+        movaps    %xmm4, %xmm13
+        mulsd     %xmm4, %xmm12
+        movsd     %xmm11, -24(%rsp)
+        movsd     -32(%rsp), %xmm2
+        mulsd     %xmm12, %xmm2
+        mulsd     %xmm2, %xmm13
+        movsd     440+__dcbrt_ha__vmldCbrtTab(%rip), %xmm6
+        movsd     -24(%rsp), %xmm3
+        subsd     %xmm13, %xmm6
+        mulsd     %xmm12, %xmm3
+        mulsd     %xmm6, %xmm14
+        mulsd     %xmm3, %xmm4
+        movsd     %xmm14, -32(%rsp)
+        movsd     -32(%rsp), %xmm15
+        xorps     .L_2il0floatpacket.81(%rip), %xmm4
+        subsd     %xmm6, %xmm15
+        movsd     %xmm15, -24(%rsp)
+        movsd     -32(%rsp), %xmm1
+        movsd     -24(%rsp), %xmm0
+        movsd     256+__dcbrt_ha__vmldCbrtTab(%rip), %xmm9
+        subsd     %xmm0, %xmm1
+        movsd     %xmm1, -32(%rsp)
+        movsd     -32(%rsp), %xmm13
+        movsd     352+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        subsd     %xmm13, %xmm6
+        movsd     %xmm6, -24(%rsp)
+        movsd     -32(%rsp), %xmm1
+        movsd     -24(%rsp), %xmm7
+        movaps    %xmm1, %xmm8
+        movsd     256+__dcbrt_ha__vmldCbrtTab(%rip), %xmm11
+        addsd     %xmm7, %xmm4
+        movsd     256+__dcbrt_ha__vmldCbrtTab(%rip), %xmm7
+        addsd     %xmm4, %xmm8
+        mulsd     %xmm8, %xmm0
+        movslq    %ecx, %rcx
+        addsd     344+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        movq      440+__dcbrt_ha__vmldCbrtTab(%rip), %r9
+        movq      %r9, -48(%rsp)
+        shrq      $48, %r9
+        addsd     336+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        andl      $-32753, %r9d
+        shll      $4, %edx
+        addsd     328+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        orl       %edx, %r9d
+        movw      %r9w, -42(%rsp)
+        addsd     320+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        addsd     312+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        addsd     304+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        addsd     296+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        addsd     288+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        addsd     280+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        addsd     272+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm8, %xmm0
+        addsd     %xmm0, %xmm9
+        movsd     %xmm9, -32(%rsp)
+        movsd     -32(%rsp), %xmm10
+        movsd     368+__dcbrt_ha__vmldCbrtTab(%rip), %xmm9
+        subsd     %xmm10, %xmm11
+        movsd     %xmm11, -24(%rsp)
+        movsd     -32(%rsp), %xmm14
+        movsd     -24(%rsp), %xmm12
+        addsd     %xmm12, %xmm14
+        movsd     %xmm14, -16(%rsp)
+        movaps    %xmm2, %xmm14
+        movsd     -24(%rsp), %xmm6
+        addsd     %xmm0, %xmm6
+        movsd     %xmm6, -24(%rsp)
+        movsd     -16(%rsp), %xmm15
+        subsd     %xmm15, %xmm7
+        movsd     %xmm7, -16(%rsp)
+        movsd     -24(%rsp), %xmm8
+        movsd     -16(%rsp), %xmm0
+        addsd     %xmm0, %xmm8
+        movsd     %xmm8, -16(%rsp)
+        movaps    %xmm1, %xmm8
+        movsd     -32(%rsp), %xmm13
+        mulsd     %xmm13, %xmm9
+        movsd     -16(%rsp), %xmm0
+        movsd     %xmm9, -32(%rsp)
+        movsd     -32(%rsp), %xmm10
+        subsd     %xmm13, %xmm10
+        addsd     264+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        movsd     %xmm10, -24(%rsp)
+        movsd     -32(%rsp), %xmm11
+        movsd     -24(%rsp), %xmm6
+        subsd     %xmm6, %xmm11
+        movsd     %xmm11, -32(%rsp)
+        movsd     -32(%rsp), %xmm12
+        subsd     %xmm12, %xmm13
+        movsd     %xmm13, -24(%rsp)
+        movsd     -32(%rsp), %xmm7
+        movsd     -24(%rsp), %xmm6
+        mulsd     %xmm7, %xmm8
+        addsd     %xmm0, %xmm6
+        mulsd     %xmm4, %xmm7
+        mulsd     %xmm6, %xmm4
+        mulsd     %xmm6, %xmm1
+        addsd     %xmm4, %xmm7
+        movsd     368+__dcbrt_ha__vmldCbrtTab(%rip), %xmm4
+        addsd     %xmm1, %xmm7
+        mulsd     %xmm8, %xmm4
+        movsd     %xmm7, -32(%rsp)
+        movsd     -32(%rsp), %xmm10
+        movsd     %xmm4, -32(%rsp)
+        movsd     -32(%rsp), %xmm0
+        subsd     %xmm8, %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movsd     -32(%rsp), %xmm1
+        movsd     -24(%rsp), %xmm4
+        subsd     %xmm4, %xmm1
+        movsd     %xmm1, -32(%rsp)
+        movsd     -32(%rsp), %xmm6
+        subsd     %xmm6, %xmm8
+        movsd     %xmm8, -24(%rsp)
+        movsd     -32(%rsp), %xmm9
+        movsd     -24(%rsp), %xmm7
+        movaps    %xmm9, %xmm1
+        mulsd     %xmm3, %xmm9
+        addsd     %xmm7, %xmm10
+        mulsd     %xmm2, %xmm1
+        movaps    %xmm10, %xmm11
+        movaps    %xmm1, %xmm12
+        mulsd     %xmm3, %xmm10
+        addsd     %xmm2, %xmm12
+        mulsd     %xmm2, %xmm11
+        addsd     %xmm9, %xmm10
+        addsd     %xmm10, %xmm11
+        movsd     %xmm11, -32(%rsp)
+        movsd     -32(%rsp), %xmm0
+        movsd     %xmm12, -32(%rsp)
+        movsd     -32(%rsp), %xmm13
+        subsd     %xmm13, %xmm14
+        movsd     %xmm14, -24(%rsp)
+        movsd     -32(%rsp), %xmm9
+        movsd     -24(%rsp), %xmm15
+        addsd     %xmm15, %xmm9
+        movsd     %xmm9, -16(%rsp)
+        movsd     -24(%rsp), %xmm10
+        addsd     %xmm10, %xmm1
+        movsd     %xmm1, -24(%rsp)
+        movsd     -16(%rsp), %xmm4
+        subsd     %xmm4, %xmm2
+        movsd     368+__dcbrt_ha__vmldCbrtTab(%rip), %xmm4
+        movsd     %xmm2, -16(%rsp)
+        movsd     -24(%rsp), %xmm1
+        movsd     -16(%rsp), %xmm2
+        addsd     %xmm2, %xmm1
+        movsd     %xmm1, -16(%rsp)
+        movsd     -32(%rsp), %xmm9
+        mulsd     %xmm9, %xmm4
+        movsd     -16(%rsp), %xmm11
+        movsd     %xmm4, -32(%rsp)
+        movsd     -32(%rsp), %xmm6
+        subsd     %xmm9, %xmm6
+        movsd     %xmm6, -24(%rsp)
+        movsd     -32(%rsp), %xmm7
+        movsd     -24(%rsp), %xmm2
+        subsd     %xmm2, %xmm7
+        movsd     %xmm7, -32(%rsp)
+        movsd     -32(%rsp), %xmm8
+        subsd     %xmm8, %xmm9
+        movsd     %xmm9, -24(%rsp)
+        movsd     -32(%rsp), %xmm12
+        movsd     -24(%rsp), %xmm10
+        addsd     %xmm0, %xmm10
+        addsd     %xmm3, %xmm10
+        movsd     392(%r11,%rcx,8), %xmm3
+        movaps    %xmm3, %xmm0
+        addsd     %xmm10, %xmm11
+        mulsd     %xmm12, %xmm3
+        mulsd     %xmm11, %xmm0
+        movsd     384(%r11,%rcx,8), %xmm10
+        addsd     %xmm3, %xmm0
+        mulsd     %xmm10, %xmm11
+        mulsd     %xmm10, %xmm12
+        addsd     %xmm11, %xmm0
+        movsd     %xmm0, -32(%rsp)
+        movsd     -32(%rsp), %xmm3
+        addsd     %xmm3, %xmm12
+        mulsd     -48(%rsp), %xmm12
+        mulsd     %xmm12, %xmm5
+        movsd     %xmm5, (%r8)
+
+.LBL_2_7:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_8:
+
+        movsd     440+__dcbrt_ha__vmldCbrtTab(%rip), %xmm0
+        mulsd     %xmm0, %xmm1
+        movsd     %xmm1, (%r8)
+        jmp       .LBL_2_7
+
+.LBL_2_9:
+
+        addsd     %xmm1, %xmm1
+        movsd     %xmm1, (%r8)
+        jmp       .LBL_2_7
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dcbrt_ha_cout_rare_internal,@function
+	.size	__svml_dcbrt_ha_cout_rare_internal,.-__svml_dcbrt_ha_cout_rare_internal
+..LN__svml_dcbrt_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dcbrt_ha_data_internal_avx512:
+	.long	0
+	.long	1072693248
+	.long	4186796683
+	.long	1072965794
+	.long	2772266557
+	.long	1073309182
+	.long	0
+	.long	0
+	.long	0
+	.long	3220176896
+	.long	4186796683
+	.long	3220449442
+	.long	2772266557
+	.long	3220792830
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1418634270
+	.long	3162364962
+	.long	2576690953
+	.long	3164558313
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1418634270
+	.long	1014881314
+	.long	2576690953
+	.long	1017074665
+	.long	0
+	.long	0
+	.long	4186796683
+	.long	1072965794
+	.long	1554061055
+	.long	1072914931
+	.long	3992368458
+	.long	1072871093
+	.long	3714535808
+	.long	1072832742
+	.long	954824104
+	.long	1072798779
+	.long	3256858690
+	.long	1072768393
+	.long	3858344660
+	.long	1072740974
+	.long	1027250248
+	.long	1072716050
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1418634270
+	.long	3162364962
+	.long	629721892
+	.long	1016287007
+	.long	1776620500
+	.long	3163956186
+	.long	648592220
+	.long	1016269578
+	.long	1295766103
+	.long	3161896715
+	.long	1348094586
+	.long	3164476360
+	.long	2407028709
+	.long	1015925873
+	.long	497428409
+	.long	1014435402
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	1431655766
+	.long	1070945621
+	.long	1431655766
+	.long	1070945621
+	.long	1431655766
+	.long	1070945621
+	.long	1431655766
+	.long	1070945621
+	.long	1431655766
+	.long	1070945621
+	.long	1431655766
+	.long	1070945621
+	.long	1431655766
+	.long	1070945621
+	.long	1431655766
+	.long	1070945621
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1126170624
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1074266112
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	1792985698
+	.long	3213372987
+	.long	1792985698
+	.long	3213372987
+	.long	1792985698
+	.long	3213372987
+	.long	1792985698
+	.long	3213372987
+	.long	1792985698
+	.long	3213372987
+	.long	1792985698
+	.long	3213372987
+	.long	1792985698
+	.long	3213372987
+	.long	1792985698
+	.long	3213372987
+	.long	3135539317
+	.long	1066129956
+	.long	3135539317
+	.long	1066129956
+	.long	3135539317
+	.long	1066129956
+	.long	3135539317
+	.long	1066129956
+	.long	3135539317
+	.long	1066129956
+	.long	3135539317
+	.long	1066129956
+	.long	3135539317
+	.long	1066129956
+	.long	3135539317
+	.long	1066129956
+	.long	2087834975
+	.long	3213899448
+	.long	2087834975
+	.long	3213899448
+	.long	2087834975
+	.long	3213899448
+	.long	2087834975
+	.long	3213899448
+	.long	2087834975
+	.long	3213899448
+	.long	2087834975
+	.long	3213899448
+	.long	2087834975
+	.long	3213899448
+	.long	2087834975
+	.long	3213899448
+	.long	2476259604
+	.long	1066628333
+	.long	2476259604
+	.long	1066628333
+	.long	2476259604
+	.long	1066628333
+	.long	2476259604
+	.long	1066628333
+	.long	2476259604
+	.long	1066628333
+	.long	2476259604
+	.long	1066628333
+	.long	2476259604
+	.long	1066628333
+	.long	2476259604
+	.long	1066628333
+	.long	2012366478
+	.long	3214412045
+	.long	2012366478
+	.long	3214412045
+	.long	2012366478
+	.long	3214412045
+	.long	2012366478
+	.long	3214412045
+	.long	2012366478
+	.long	3214412045
+	.long	2012366478
+	.long	3214412045
+	.long	2012366478
+	.long	3214412045
+	.long	2012366478
+	.long	3214412045
+	.long	1104999785
+	.long	1067378449
+	.long	1104999785
+	.long	1067378449
+	.long	1104999785
+	.long	1067378449
+	.long	1104999785
+	.long	1067378449
+	.long	1104999785
+	.long	1067378449
+	.long	1104999785
+	.long	1067378449
+	.long	1104999785
+	.long	1067378449
+	.long	1104999785
+	.long	1067378449
+	.long	3534763582
+	.long	3215266280
+	.long	3534763582
+	.long	3215266280
+	.long	3534763582
+	.long	3215266280
+	.long	3534763582
+	.long	3215266280
+	.long	3534763582
+	.long	3215266280
+	.long	3534763582
+	.long	3215266280
+	.long	3534763582
+	.long	3215266280
+	.long	3534763582
+	.long	3215266280
+	.long	1007386161
+	.long	1068473053
+	.long	1007386161
+	.long	1068473053
+	.long	1007386161
+	.long	1068473053
+	.long	1007386161
+	.long	1068473053
+	.long	1007386161
+	.long	1068473053
+	.long	1007386161
+	.long	1068473053
+	.long	1007386161
+	.long	1068473053
+	.long	1007386161
+	.long	1068473053
+	.long	477218625
+	.long	3216798151
+	.long	477218625
+	.long	3216798151
+	.long	477218625
+	.long	3216798151
+	.long	477218625
+	.long	3216798151
+	.long	477218625
+	.long	3216798151
+	.long	477218625
+	.long	3216798151
+	.long	477218625
+	.long	3216798151
+	.long	477218625
+	.long	3216798151
+	.long	1431655767
+	.long	1070945621
+	.long	1431655767
+	.long	1070945621
+	.long	1431655767
+	.long	1070945621
+	.long	1431655767
+	.long	1070945621
+	.long	1431655767
+	.long	1070945621
+	.long	1431655767
+	.long	1070945621
+	.long	1431655767
+	.long	1070945621
+	.long	1431655767
+	.long	1070945621
+	.type	__svml_dcbrt_ha_data_internal_avx512,@object
+	.size	__svml_dcbrt_ha_data_internal_avx512,1408
+	.align 32
+__dcbrt_ha__vmldCbrtTab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072668672
+	.long	0
+	.long	1072644096
+	.long	0
+	.long	1072627712
+	.long	0
+	.long	1072611328
+	.long	0
+	.long	1072586752
+	.long	0
+	.long	1072570368
+	.long	0
+	.long	1072553984
+	.long	0
+	.long	1072537600
+	.long	0
+	.long	1072521216
+	.long	0
+	.long	1072504832
+	.long	0
+	.long	1072488448
+	.long	0
+	.long	1072480256
+	.long	0
+	.long	1072463872
+	.long	0
+	.long	1072447488
+	.long	0
+	.long	1072439296
+	.long	0
+	.long	1072422912
+	.long	0
+	.long	1072414720
+	.long	0
+	.long	1072398336
+	.long	0
+	.long	1072390144
+	.long	0
+	.long	1072373760
+	.long	0
+	.long	1072365568
+	.long	0
+	.long	1072357376
+	.long	0
+	.long	1072340992
+	.long	0
+	.long	1072332800
+	.long	0
+	.long	1072324608
+	.long	0
+	.long	1072308224
+	.long	0
+	.long	1072300032
+	.long	0
+	.long	1072291840
+	.long	0
+	.long	1072283648
+	.long	0
+	.long	1072275456
+	.long	0
+	.long	1072267264
+	.long	1431655765
+	.long	1071994197
+	.long	1431655765
+	.long	1015371093
+	.long	1908874354
+	.long	1071761180
+	.long	1007461464
+	.long	1071618781
+	.long	565592401
+	.long	1071446176
+	.long	241555088
+	.long	1071319599
+	.long	943963244
+	.long	1071221150
+	.long	2330668378
+	.long	1071141453
+	.long	2770428108
+	.long	1071075039
+	.long	3622256836
+	.long	1071018464
+	.long	1497196870
+	.long	1070969433
+	.long	280472551
+	.long	1070926345
+	.long	1585032765
+	.long	1070888044
+	.long	0
+	.long	1387266048
+	.long	33554432
+	.long	1101004800
+	.long	512
+	.long	1117782016
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	4160749568
+	.long	1072965794
+	.long	2921479643
+	.long	1043912488
+	.long	2684354560
+	.long	1073309182
+	.long	4060791142
+	.long	1045755320
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3220176896
+	.type	__dcbrt_ha__vmldCbrtTab,@object
+	.size	__dcbrt_ha__vmldCbrtTab,456
+	.space 8, 0x00 	
+	.align 16
+.L_2il0floatpacket.81:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.81,@object
+	.size	.L_2il0floatpacket.81,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_d_la.s
index 32dbfcc951e..c557980d39f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_h_la.s
new file mode 100644
index 00000000000..86b4a7056d0
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_h_la.s
@@ -0,0 +1,166 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *     x=2^{3*k+j} * 1.b1 b2 ... b5 b6 ... b52
+ *     Let r=(x*2^{-3k-j} - 1.b1 b2 ... b5 1)* rcp[b1 b2 ..b5],
+ *     where rcp[b1 b2 .. b5]=1/(1.b1 b2 b3 b4 b5 1) in single precision
+ *     cbrtf(2^j * 1. b1 b2 .. b5 1) is approximated as T[j][b1..b5]+D[j][b1..b5]
+ *     (T stores the high 24 bits, D stores the low order bits)
+ *     Result=2^k*T+(2^k*T*r)*P+2^k*D
+ *      where P=p1+p2*r+..
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_cbrts32
+
+__svml_cbrts32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hcbrt_data_internal(%rip), %zmm31
+        vmovdqu16 64+__svml_hcbrt_data_internal(%rip), %zmm29
+        vmovdqu16 128+__svml_hcbrt_data_internal(%rip), %zmm2
+        vmovdqu16 192+__svml_hcbrt_data_internal(%rip), %zmm28
+        vmovdqu16 256+__svml_hcbrt_data_internal(%rip), %zmm4
+        vmovdqu16 320+__svml_hcbrt_data_internal(%rip), %zmm30
+        vmovdqu16 384+__svml_hcbrt_data_internal(%rip), %zmm8
+        vmovdqu16 448+__svml_hcbrt_data_internal(%rip), %zmm9
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_cbrt_h:
+        cmpq    $31, %rdx
+        ja .loaddata_cbrth
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_cbrt_h
+.loaddata_cbrth:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_cbrt_h:
+
+        vmovdqu16 %zmm31, %zmm6
+        vmovdqu16 %zmm30, %zmm10
+        vgetexpph {sae}, %zmm0, %zmm3
+
+/* mantissa(|x|) */
+        vgetmantph $4, {sae}, %zmm0, %zmm1
+
+/* check for +/-Inf, +/-0 */
+        vfpclassph $30, %zmm0, %k1
+        vaddph    {rn-sae}, %zmm4, %zmm3, %zmm5
+        vsubph    {rn-sae}, %zmm2, %zmm1, %zmm11
+        vpermt2w  %zmm29, %zmm5, %zmm6
+        vfmadd213ph {rn-sae}, %zmm8, %zmm11, %zmm10
+        vfmadd213ph {rn-sae}, %zmm9, %zmm11, %zmm10
+        vmulph    {rn-sae}, %zmm11, %zmm10, %zmm12
+
+/*
+ * No callout
+ * sign
+ */
+        vpandd    %zmm28, %zmm0, %zmm7
+
+/* add sign */
+        vpord     %zmm7, %zmm6, %zmm13
+        vfmadd231ph {rn-sae}, %zmm13, %zmm12, %zmm13
+
+/* fixup for +/-Inf, +/-0 */
+        vmovdqu16 %zmm0, %zmm13{%k1}
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm13, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_cbrt_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_cbrts32,@function
+        .size	__svml_cbrts32,.-__svml_cbrts32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hcbrt_data_internal:
+        .word	0x3c00
+        .word	0x3d0a
+        .word	0x3e59
+        .word	0x4000
+        .word	0x410a
+        .word	0x4259
+        .word	0x4400
+        .word	0x450a
+        .word	0x4659
+        .word	0x4800
+        .word	0x490a
+        .word	0x4a59
+        .word	0x4c00
+        .word	0x4d0a
+        .word	0x4e59
+        .word	0x5000
+	.rept	24
+        .word	0x0000
+	.endr
+        .word	0x1c00
+        .word	0x1d0a
+        .word	0x1e59
+        .word	0x2000
+        .word	0x210a
+        .word	0x2259
+        .word	0x2400
+        .word	0x250a
+        .word	0x2659
+        .word	0x2800
+        .word	0x290a
+        .word	0x2a59
+        .word	0x2c00
+        .word	0x2d0a
+        .word	0x2e59
+        .word	0x3000
+        .word	0x310a
+        .word	0x3259
+        .word	0x3400
+        .word	0x350a
+        .word	0x3659
+        .word	0x3800
+        .word	0x390a
+        .word	0x3a59
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x8000
+	.endr
+	.rept	32
+        .word	0x6600
+	.endr
+	.rept	32
+        .word	0x277c
+	.endr
+	.rept	32
+        .word	0xae84
+	.endr
+	.rept	32
+        .word	0x3554
+	.endr
+        .type	__svml_hcbrt_data_internal,@object
+        .size	__svml_hcbrt_data_internal,512
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_s_ha.s
new file mode 100644
index 00000000000..50b57948fd1
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_s_ha.s
@@ -0,0 +1,1037 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *     x=2^{3*k+j} * 1.b1 b2 ... b5 b6 ... b52
+ *     Let r=(x*2^{-3k-j} - 1.b1 b2 ... b5 1)* rcp[b1 b2 ..b5],
+ *     where rcp[b1 b2 .. b5]=1/(1.b1 b2 b3 b4 b5 1) in single precision
+ *     cbrtf(2^j * 1. b1 b2 .. b5 1) is approximated as T[j][b1..b5]+D[j][b1..b5]
+ *     (T stores the high 24 bits, D stores the low order bits)
+ *     Result=2^k*T+(2^k*T*r)*P+2^k*D
+ *      where P=p1+p2*r+..
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_cbrtf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_cbrtf16_ha
+
+__svml_cbrtf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+
+/* GetExp(x) */
+        vgetexpps {sae}, %zmm0, %zmm4
+        vmovups   384+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm5
+        vgetmantps $0, {sae}, %zmm0, %zmm11
+
+/* exponent/3 */
+        vmovups   512+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm6
+        vmovups   576+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm7
+
+/* exponent%3 (to be used as index) */
+        vmovups   640+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   256+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm15
+
+/* polynomial */
+        vmovups   768+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm14
+
+/* DblRcp ~ 1/Mantissa */
+        vrcp14ps  %zmm11, %zmm10
+        vaddps    {rn-sae}, %zmm5, %zmm4, %zmm9
+        vandps    448+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm0, %zmm3
+
+/* round DblRcp to 3 fractional bits (RN mode, no Precision exception) */
+        vrndscaleps $88, {sae}, %zmm10, %zmm12
+        vfmsub231ps {rn-sae}, %zmm9, %zmm6, %zmm7
+
+/* Reduced argument: R = DblRcp*Mantissa - 1 */
+        vmovups   704+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm4
+        vmovups   832+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm5
+
+/* Prepare table index */
+        vpsrld    $19, %zmm12, %zmm13
+        vrndscaleps $9, {sae}, %zmm7, %zmm2
+        vfmsub231ps {rn-sae}, %zmm12, %zmm11, %zmm4
+
+/* Table lookup */
+        vmovups   128+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm11
+        vpermt2ps 320+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm13, %zmm15
+        vfnmadd231ps {rn-sae}, %zmm2, %zmm8, %zmm9
+        vfmadd231ps {rn-sae}, %zmm4, %zmm14, %zmm5
+        vmovups   896+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm14
+
+/* Table lookup: 2^(exponent%3) */
+        vpermps   __svml_scbrt_ha_data_internal_avx512(%rip), %zmm9, %zmm0
+        vpermt2ps 192+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm13, %zmm11
+        vpermps   64+__svml_scbrt_ha_data_internal_avx512(%rip), %zmm9, %zmm1
+        vfmadd213ps {rn-sae}, %zmm14, %zmm4, %zmm5
+
+/* (Sh*Th)_high */
+        vmulps    {rn-sae}, %zmm11, %zmm0, %zmm6
+
+/* (Sh*Th)_low */
+        vmovaps   %zmm0, %zmm13
+        vfmsub213ps {rn-sae}, %zmm6, %zmm11, %zmm13
+
+/* (Sh*Th)_low + Sh*Tl */
+        vfmadd213ps {rn-sae}, %zmm13, %zmm15, %zmm0
+
+/* (Sh*Th)_low + Sh*Tl + Sl*Th */
+        vfmadd213ps {rn-sae}, %zmm0, %zmm11, %zmm1
+
+/* (Sh*Th)_h*R */
+        vmulps    {rn-sae}, %zmm4, %zmm6, %zmm0
+
+/* Low + ((Sh*Th)_h*R)*Poly */
+        vfmadd213ps {rn-sae}, %zmm1, %zmm5, %zmm0
+
+/*
+ * branch-free
+ * ShTh + Low + ((Sh*Th)_h*R)*Poly
+ */
+        vaddps    {rn-sae}, %zmm6, %zmm0, %zmm1
+        vscalefps {rn-sae}, %zmm2, %zmm1, %zmm2
+        vorps     %zmm3, %zmm2, %zmm0
+
+/* no invcbrt in libm, so taking it out here */
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_cbrtf16_ha,@function
+	.size	__svml_cbrtf16_ha,.-__svml_cbrtf16_ha
+..LN__svml_cbrtf16_ha.0:
+
+.L_2__routine_start___svml_scbrt_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_scbrt_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L5:
+
+        movq      %rsi, %r9
+        movl      $1065353216, -24(%rsp)
+        movss     (%rdi), %xmm0
+        movss     -24(%rsp), %xmm1
+        mulss     %xmm0, %xmm1
+        movss     %xmm1, -4(%rsp)
+        movzwl    -2(%rsp), %eax
+        andl      $32640, %eax
+        shrl      $7, %eax
+        cmpl      $255, %eax
+        je        .LBL_2_9
+
+
+        pxor      %xmm0, %xmm0
+        ucomiss   %xmm0, %xmm1
+        jp        .LBL_2_3
+        je        .LBL_2_8
+
+.LBL_2_3:
+
+        testl     %eax, %eax
+        jne       .LBL_2_5
+
+
+        movl      $2122317824, -24(%rsp)
+        movl      $713031680, -20(%rsp)
+        jmp       .LBL_2_6
+
+.LBL_2_5:
+
+        movl      $1065353216, %eax
+        movl      %eax, -24(%rsp)
+        movl      %eax, -20(%rsp)
+
+.LBL_2_6:
+
+        movss     -24(%rsp), %xmm0
+        lea       __scbrt_ha_vscbrt_ha_cout_data(%rip), %rsi
+        mulss     %xmm0, %xmm1
+        movd      %xmm1, %ecx
+        movss     %xmm1, -4(%rsp)
+        movl      %ecx, %r10d
+        movl      %ecx, %edi
+        andl      $8388607, %r10d
+        movl      %ecx, %r11d
+        shrl      $23, %edi
+        andl      $8257536, %r11d
+        orl       $-1082130432, %r10d
+        orl       $-1081999360, %r11d
+        movl      %r10d, -16(%rsp)
+        movl      %ecx, %edx
+        movzbl    %dil, %r8d
+        andl      $2147483647, %ecx
+        movl      %r11d, -12(%rsp)
+        andl      $-256, %edi
+        movss     -16(%rsp), %xmm1
+        addl      $2139095040, %ecx
+        shrl      $16, %edx
+        subss     -12(%rsp), %xmm1
+        andl      $124, %edx
+        lea       (%r8,%r8,4), %r10d
+        mulss     (%rsi,%rdx), %xmm1
+        lea       (%r10,%r10), %r11d
+        movss     .L_2il0floatpacket.35(%rip), %xmm4
+        lea       (%r11,%r11), %eax
+        addl      %eax, %eax
+        lea       (%r10,%r11,8), %r10d
+        addl      %eax, %eax
+        decl      %r8d
+        mulss     %xmm1, %xmm4
+        shll      $7, %r8d
+        lea       (%r10,%rax,8), %r11d
+        lea       (%r11,%rax,8), %r10d
+        shrl      $12, %r10d
+        addss     .L_2il0floatpacket.34(%rip), %xmm4
+        mulss     %xmm1, %xmm4
+        lea       85(%r10), %eax
+        orl       %edi, %eax
+        xorl      %edi, %edi
+        cmpl      $-16777217, %ecx
+        addss     .L_2il0floatpacket.33(%rip), %xmm4
+        setg      %dil
+        shll      $7, %r10d
+        negl      %edi
+        subl      %r10d, %r8d
+        addl      %r10d, %r10d
+        subl      %r10d, %r8d
+        notl      %edi
+        addl      %r8d, %edx
+        andl      %edx, %edi
+        shll      $23, %eax
+        addl      %edi, %edi
+        movl      %eax, -8(%rsp)
+        movss     128(%rdi,%rsi), %xmm5
+        movss     -8(%rsp), %xmm2
+        mulss     %xmm1, %xmm4
+        mulss     %xmm2, %xmm5
+        addss     .L_2il0floatpacket.32(%rip), %xmm4
+        mulss     %xmm5, %xmm1
+        movss     132(%rsi,%rdi), %xmm3
+        mulss     %xmm1, %xmm4
+        mulss     %xmm2, %xmm3
+        addss     %xmm3, %xmm4
+        addss     %xmm4, %xmm5
+        mulss     -20(%rsp), %xmm5
+        movss     %xmm5, (%r9)
+
+.LBL_2_7:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_8:
+
+        movss     %xmm1, (%r9)
+        jmp       .LBL_2_7
+
+.LBL_2_9:
+
+        addss     %xmm0, %xmm0
+        movss     %xmm0, (%r9)
+        jmp       .LBL_2_7
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_scbrt_ha_cout_rare_internal,@function
+	.size	__svml_scbrt_ha_cout_rare_internal,.-__svml_scbrt_ha_cout_rare_internal
+..LN__svml_scbrt_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_scbrt_ha_data_internal_avx512:
+	.long	1065353216
+	.long	1067533592
+	.long	1070280693
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2999865775
+	.long	849849800
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1067533592
+	.long	1067322155
+	.long	1067126683
+	.long	1066945178
+	.long	1066775983
+	.long	1066617708
+	.long	1066469175
+	.long	1066329382
+	.long	1066197466
+	.long	1066072682
+	.long	1065954382
+	.long	1065841998
+	.long	1065735031
+	.long	1065633040
+	.long	1065535634
+	.long	1065442463
+	.long	1065353216
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2999865775
+	.long	849353281
+	.long	2992093760
+	.long	858369405
+	.long	861891413
+	.long	3001900484
+	.long	2988845984
+	.long	3009185201
+	.long	3001209163
+	.long	847824101
+	.long	839380496
+	.long	845124191
+	.long	851391835
+	.long	856440803
+	.long	2989578734
+	.long	852890174
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1249902592
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1077936128
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	1031603580
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	3185812323
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.long	1051372202
+	.type	__svml_scbrt_ha_data_internal_avx512,@object
+	.size	__svml_scbrt_ha_data_internal_avx512,960
+	.align 64
+__scbrt_ha_vscbrt_ha_cout_data:
+	.long	3212578753
+	.long	3212085645
+	.long	3211621124
+	.long	3211182772
+	.long	3210768440
+	.long	3210376206
+	.long	3210004347
+	.long	3209651317
+	.long	3209315720
+	.long	3208996296
+	.long	3208691905
+	.long	3208401508
+	.long	3208124163
+	.long	3207859009
+	.long	3207605259
+	.long	3207362194
+	.long	3207129151
+	.long	3206905525
+	.long	3206690755
+	.long	3206484326
+	.long	3206285761
+	.long	3206094618
+	.long	3205910490
+	.long	3205732998
+	.long	3205561788
+	.long	3205396533
+	.long	3205236929
+	.long	3205082689
+	.long	3204933547
+	.long	3204789256
+	.long	3204649583
+	.long	3204514308
+	.long	1065396681
+	.long	839340838
+	.long	1065482291
+	.long	867750258
+	.long	1065566215
+	.long	851786446
+	.long	1065648532
+	.long	853949398
+	.long	1065729317
+	.long	864938789
+	.long	1065808640
+	.long	864102364
+	.long	1065886565
+	.long	864209792
+	.long	1065963152
+	.long	865422805
+	.long	1066038457
+	.long	867593594
+	.long	1066112533
+	.long	854482593
+	.long	1066185428
+	.long	848298042
+	.long	1066257188
+	.long	860064854
+	.long	1066327857
+	.long	844792593
+	.long	1066397474
+	.long	870701309
+	.long	1066466079
+	.long	872023170
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+	.long	863561479
+	.long	1066731064
+	.long	869115319
+	.long	1066795108
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+	.long	1066920751
+	.long	871954398
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+	.long	864500406
+	.long	1067337305
+	.long	850523240
+	.long	1067394143
+	.long	808125243
+	.long	1067450368
+	.long	0
+	.long	1067505996
+	.long	861173761
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+	.long	823158129
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+	.long	869245699
+	.long	1069508563
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+	.long	1069586236
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+	.long	1069887762
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+	.long	1069960982
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+	.long	1070033387
+	.long	864252033
+	.long	1070104998
+	.long	867276215
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+	.long	868189817
+	.long	1070245925
+	.long	849541095
+	.long	1070349689
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+	.long	1070485588
+	.long	843967686
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+	.long	1070749478
+	.long	862339487
+	.long	1070877717
+	.long	850054662
+	.long	1071003634
+	.long	864048556
+	.long	1071127332
+	.long	868027089
+	.long	1071248907
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+	.long	865557362
+	.long	1071715659
+	.long	870297525
+	.long	1071827839
+	.long	863416216
+	.long	1071938350
+	.long	869675693
+	.long	1072047254
+	.long	865888071
+	.long	1072154608
+	.long	825332584
+	.long	1072260465
+	.long	843309506
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+	.long	870885636
+	.long	1072467891
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+	.long	3173551943
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+	.long	3173551943
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+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	1031591658
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
+	.long	3185806905
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+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	1051372203
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8388607
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
+	.long	8257536
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+	.long	3212836864
+	.long	3212836864
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+	.long	3212836864
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+	.long	3212836864
+	.long	3212836864
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+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
+	.long	3212967936
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+	.long	3212967936
+	.long	3212967936
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+	.long	3212967936
+	.long	124
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+	.long	124
+	.long	124
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+	.long	255
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+	.long	256
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+	.long	85
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+	.long	1
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+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	1
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	2155872256
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.long	4278190079
+	.type	__scbrt_ha_vscbrt_ha_cout_data,@object
+	.size	__scbrt_ha_vscbrt_ha_cout_data,1920
+	.align 4
+.L_2il0floatpacket.28:
+	.long	0x007fffff
+	.type	.L_2il0floatpacket.28,@object
+	.size	.L_2il0floatpacket.28,4
+	.align 4
+.L_2il0floatpacket.29:
+	.long	0x007e0000
+	.type	.L_2il0floatpacket.29,@object
+	.size	.L_2il0floatpacket.29,4
+	.align 4
+.L_2il0floatpacket.30:
+	.long	0xbf800000
+	.type	.L_2il0floatpacket.30,@object
+	.size	.L_2il0floatpacket.30,4
+	.align 4
+.L_2il0floatpacket.31:
+	.long	0xbf820000
+	.type	.L_2il0floatpacket.31,@object
+	.size	.L_2il0floatpacket.31,4
+	.align 4
+.L_2il0floatpacket.32:
+	.long	0x3eaaaaab
+	.type	.L_2il0floatpacket.32,@object
+	.size	.L_2il0floatpacket.32,4
+	.align 4
+.L_2il0floatpacket.33:
+	.long	0xbde38e39
+	.type	.L_2il0floatpacket.33,@object
+	.size	.L_2il0floatpacket.33,4
+	.align 4
+.L_2il0floatpacket.34:
+	.long	0x3d7cd6ea
+	.type	.L_2il0floatpacket.34,@object
+	.size	.L_2il0floatpacket.34,4
+	.align 4
+.L_2il0floatpacket.35:
+	.long	0xbd288f47
+	.type	.L_2il0floatpacket.35,@object
+	.size	.L_2il0floatpacket.35,4
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_s_la.s
index ba099072665..b15e2f010e0 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cbrt_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_d_ha.s
new file mode 100644
index 00000000000..7ab1561b1ee
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_d_ha.s
@@ -0,0 +1,17663 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *     (high accuracy implementation: < 1 ulp)
+ * 
+ *     Argument representation:
+ *     arg = N*Pi/2^k + r = B + (z+e)
+ *             B = N*Pi/2^k
+ *             z+e = r
+ * 
+ *     Result calculation:
+ *     cos(arg) = cos(B+z+e) = sin(B'+z+e) =
+ *     = [ (SHi+Sigma*z)+CHL*z ] + [ (CHL+Sigma)*PS + PC*SHi + e*(CHL+Sigma-SHi*z) + SLo ]
+ *             B' = B+Pi/2
+ *             Sigma = 2^round(log[2](cosB'))
+ *             CHL = cosB' - Sigma
+ *             SHi+SLo = sinB'
+ *             PS = sin(z) - z
+ *             PC = cos(z)- 1
+ *     Sigma, CHL, SHi, SLo are stored in table
+ *     PS, PC are approximated by polynomials
+ * 
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_cos8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_cos8_ha
+
+__svml_cos8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        xorl      %edx, %edx
+
+/* start arg. reduction */
+        vmovups   192+__svml_dcos_ha_data_internal(%rip), %zmm13
+        vmovups   128+__svml_dcos_ha_data_internal(%rip), %zmm6
+        vmovups   256+__svml_dcos_ha_data_internal(%rip), %zmm8
+        vmovups   320+__svml_dcos_ha_data_internal(%rip), %zmm10
+        vmovups   384+__svml_dcos_ha_data_internal(%rip), %zmm14
+        vmovups   64+__svml_dcos_ha_data_internal(%rip), %zmm5
+
+/* start polynomial evaluation */
+        vmovups   896+__svml_dcos_ha_data_internal(%rip), %zmm12
+
+/* Table lookup: Th, Tl/Th */
+        vmovups   512+__svml_dcos_ha_data_internal(%rip), %zmm2
+        vmovups   640+__svml_dcos_ha_data_internal(%rip), %zmm1
+        vmovaps   %zmm0, %zmm11
+        vfmadd213pd {rn-sae}, %zmm13, %zmm11, %zmm6
+        vmovups   768+__svml_dcos_ha_data_internal(%rip), %zmm0
+        vandpd    __svml_dcos_ha_data_internal(%rip), %zmm11, %zmm4
+
+/*
+ * mask will be used to decide whether long arg. reduction is needed
+ * dN
+ */
+        vsubpd    {rn-sae}, %zmm13, %zmm6, %zmm9
+        vpermt2pd 576+__svml_dcos_ha_data_internal(%rip), %zmm6, %zmm2
+        vpermt2pd 704+__svml_dcos_ha_data_internal(%rip), %zmm6, %zmm1
+        vpermt2pd 832+__svml_dcos_ha_data_internal(%rip), %zmm6, %zmm0
+        vfnmadd213pd {rn-sae}, %zmm11, %zmm9, %zmm8
+        vcmppd    $22, {sae}, %zmm5, %zmm4, %k1
+        vpsrlq    $4, %zmm6, %zmm6
+
+/* will branch if long arg. reduction needed */
+        kortestw  %k1, %k1
+
+/* continue argument reduction */
+        vmovaps   %zmm10, %zmm7
+        vfnmadd213pd {rn-sae}, %zmm8, %zmm9, %zmm7
+        vmovaps   %zmm14, %zmm3
+        vfnmadd213pd {rn-sae}, %zmm7, %zmm9, %zmm3
+
+/* (dN*dPI2)_h */
+        vsubpd    {rn-sae}, %zmm7, %zmm8, %zmm15
+        vmovups   1152+__svml_dcos_ha_data_internal(%rip), %zmm8
+
+/* dR^2 */
+        vmulpd    {rn-sae}, %zmm3, %zmm3, %zmm4
+
+/* -(dN*dPI2)_l */
+        vfnmadd231pd {rn-sae}, %zmm9, %zmm10, %zmm15
+
+/* -(dN*dPI3)_h */
+        vsubpd    {rn-sae}, %zmm7, %zmm3, %zmm13
+        vmovups   960+__svml_dcos_ha_data_internal(%rip), %zmm7
+
+/* (dN*dPI3)_l */
+        vfmadd213pd {rn-sae}, %zmm13, %zmm14, %zmm9
+        vmovups   1024+__svml_dcos_ha_data_internal(%rip), %zmm14
+        vmovups   1088+__svml_dcos_ha_data_internal(%rip), %zmm13
+        vfmadd231pd {rn-sae}, %zmm4, %zmm12, %zmm7
+
+/* R_low */
+        vsubpd    {rn-sae}, %zmm9, %zmm15, %zmm10
+        vfmadd231pd {rn-sae}, %zmm4, %zmm14, %zmm13
+        vfmadd213pd {rn-sae}, %zmm8, %zmm4, %zmm7
+        vmovups   1280+__svml_dcos_ha_data_internal(%rip), %zmm8
+        vmovups   1216+__svml_dcos_ha_data_internal(%rip), %zmm15
+        vmovups   1344+__svml_dcos_ha_data_internal(%rip), %zmm14
+
+/* Sl */
+        vpsllq    $32, %zmm0, %zmm9
+        vfmadd213pd {rn-sae}, %zmm8, %zmm4, %zmm7
+        vfmadd213pd {rn-sae}, %zmm15, %zmm4, %zmm13
+
+/* Ch - R*Sh */
+        vmovaps   %zmm2, %zmm5
+        vfnmadd213pd {rn-sae}, %zmm1, %zmm3, %zmm5
+        vfmadd213pd {rn-sae}, %zmm14, %zmm4, %zmm13
+
+/* (R*Sh)_high */
+        vsubpd    {rn-sae}, %zmm5, %zmm1, %zmm12
+
+/* Sh + Ch*R */
+        vmovaps   %zmm1, %zmm8
+
+/* (R*Ch_low) */
+        vfmsub231pd {rn-sae}, %zmm2, %zmm3, %zmm12
+        vfmadd213pd {rn-sae}, %zmm2, %zmm3, %zmm8
+
+/* R*Sh */
+        vmulpd    {rn-sae}, %zmm3, %zmm2, %zmm2
+
+/* Ch*dCPoly */
+        vmulpd    {rn-sae}, %zmm7, %zmm1, %zmm1
+
+/* (R*Sh_low) + R*Sl */
+        vfmadd213pd {rn-sae}, %zmm12, %zmm3, %zmm9
+
+/* Cl - R_low*(Sh+Ch*R) */
+        vfnmadd213pd {rn-sae}, %zmm0, %zmm10, %zmm8
+        vpsllq    $63, %zmm6, %zmm0
+
+/* Ch*dCPoly - R*Sh*dSPoly */
+        vfnmadd213pd {rn-sae}, %zmm1, %zmm2, %zmm13
+
+/* dLow = Cl - R_low*(Sh+Ch*R) - [(R*Sh_low) + R*Sl] */
+        vsubpd    {rn-sae}, %zmm9, %zmm8, %zmm3
+
+/* R^2*(Sh*dCPoly + R*Ch*dSPoly) + dLow */
+        vfmadd213pd {rn-sae}, %zmm3, %zmm4, %zmm13
+        vaddpd    {rn-sae}, %zmm13, %zmm5, %zmm3
+        vxorpd    %zmm0, %zmm3, %zmm0
+        jne       .LBL_1_12
+
+.LBL_1_2:
+
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_4:
+
+        vmovups   %zmm11, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dcos_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+
+/* ............Check for Inf and NaNs................. */
+        vmovups   16512+__svml_dcos_ha_data_internal_ha(%rip), %zmm7
+
+/*
+ * Get the (2^a / 2pi) mod 1 values from the table.
+ * Because VLANG doesn't have L-type gather, we need a trivial cast
+ */
+        lea       __svml_dcos_ha_reduction_data_internal(%rip), %rax
+        vmovups   %zmm0, (%rsp)
+        vandpd    %zmm11, %zmm7, %zmm8
+        vpbroadcastq .L_2il0floatpacket.26(%rip), %zmm9
+        vcmppd    $4, {sae}, %zmm7, %zmm8, %k2
+        vmovaps   %zmm9, %zmm6
+
+/*
+ * The output is _VRES_Z (high) + _VRES_E (low), and the integer part is _VRES_IND
+ * Now resume the original code.
+ * ..................... Table look-up ........................
+ */
+        lea       __svml_dcos_ha_data_internal_ha(%rip), %rcx
+        vpandq    .L_2il0floatpacket.27(%rip){1to8}, %zmm11, %zmm3
+        vpandnq   %zmm8, %zmm8, %zmm6{%k2}
+        vpsrlq    $52, %zmm3, %zmm12
+        vcmppd    $3, {sae}, %zmm6, %zmm6, %k0
+        vpsllq    $1, %zmm12, %zmm1
+
+/*
+ * Break the P_xxx and m into 32-bit chunks ready for
+ * the long multiplication via 32x32->64 multiplications
+ */
+        vpbroadcastq .L_2il0floatpacket.30(%rip), %zmm6
+        kxnorw    %k0, %k0, %k3
+        kxnorw    %k0, %k0, %k2
+        kmovw     %k0, %edx
+        vpaddq    %zmm12, %zmm1, %zmm10
+        vpsllq    $3, %zmm10, %zmm4
+        vpmovqd   %zmm4, %ymm0
+        vpxord    %zmm7, %zmm7, %zmm7
+        vgatherdpd (%rax,%ymm0), %zmm7{%k3}
+        kxnorw    %k0, %k0, %k3
+        vpxord    %zmm8, %zmm8, %zmm8
+        vpxord    %zmm2, %zmm2, %zmm2
+        vgatherdpd 8(%rax,%ymm0), %zmm8{%k2}
+        vgatherdpd 16(%rax,%ymm0), %zmm2{%k3}
+        vpsrlq    $32, %zmm7, %zmm0
+        vpsrlq    $32, %zmm8, %zmm15
+        vpsrlq    $32, %zmm2, %zmm13
+
+/*
+ * Also get the significand as an integer
+ * NB: adding in the integer bit is wrong for denorms!
+ * To make this work for denorms we should do something slightly different
+ */
+        vpandq    .L_2il0floatpacket.28(%rip){1to8}, %zmm11, %zmm5
+        vpaddq    .L_2il0floatpacket.29(%rip){1to8}, %zmm5, %zmm3
+        vpsrlq    $32, %zmm3, %zmm10
+        vpmullq   %zmm13, %zmm10, %zmm1
+        vpandq    %zmm6, %zmm7, %zmm5
+        vpandq    %zmm6, %zmm8, %zmm12
+        vpandq    %zmm6, %zmm2, %zmm7
+        vpandq    %zmm6, %zmm3, %zmm14
+
+/* Now do the big multiplication and carry propagation */
+        vpmullq   %zmm5, %zmm10, %zmm4
+        vpmullq   %zmm15, %zmm10, %zmm3
+        vpmullq   %zmm12, %zmm10, %zmm2
+        vpmullq   %zmm7, %zmm10, %zmm8
+        vpmullq   %zmm15, %zmm14, %zmm10
+        vpmullq   %zmm13, %zmm14, %zmm15
+        vpmullq   %zmm0, %zmm14, %zmm7
+        vpmullq   %zmm12, %zmm14, %zmm0
+        vpmullq   %zmm5, %zmm14, %zmm5
+        vpsrlq    $32, %zmm15, %zmm13
+        vpsrlq    $32, %zmm0, %zmm14
+        vpsrlq    $32, %zmm5, %zmm12
+        vpsrlq    $32, %zmm10, %zmm15
+        vpaddq    %zmm13, %zmm1, %zmm1
+        vpaddq    %zmm14, %zmm2, %zmm13
+        vpaddq    %zmm12, %zmm4, %zmm4
+        vpaddq    %zmm15, %zmm3, %zmm2
+        vpandq    %zmm6, %zmm0, %zmm14
+        vpandq    %zmm6, %zmm10, %zmm12
+        vpandq    %zmm6, %zmm5, %zmm10
+        vpaddq    %zmm1, %zmm14, %zmm5
+        vpaddq    %zmm2, %zmm10, %zmm2
+        vpsrlq    $32, %zmm8, %zmm1
+        vpaddq    %zmm5, %zmm1, %zmm8
+        vpsrlq    $32, %zmm8, %zmm10
+        vpandq    %zmm6, %zmm7, %zmm0
+        vpaddq    %zmm13, %zmm12, %zmm7
+        vpaddq    %zmm4, %zmm0, %zmm3
+        vpaddq    %zmm7, %zmm10, %zmm14
+
+/*
+ * Now round at the 2^-9 bit position for reduction mod pi/2^8
+ * instead of the original 2pi (but still with the same 2pi scaling).
+ * Use a shifter of 2^43 + 2^42.
+ * The N we get is our final version; it has an offset of
+ * 2^9 because of the implicit integer bit, and anyway for negative
+ * starting value it's a 2s complement thing. But we need to mask
+ * off the exponent part anyway so it's fine.
+ */
+        vpbroadcastq .L_2il0floatpacket.33(%rip), %zmm7
+        vpsrlq    $32, %zmm14, %zmm4
+        vpaddq    %zmm2, %zmm4, %zmm15
+        vpsllq    $32, %zmm14, %zmm4
+        vpsrlq    $32, %zmm15, %zmm0
+        vpaddq    %zmm3, %zmm0, %zmm13
+
+/* Assemble reduced argument from the pieces */
+        vpandq    %zmm6, %zmm8, %zmm1
+        vpandq    %zmm6, %zmm15, %zmm10
+        vpsllq    $32, %zmm13, %zmm6
+        vpaddq    %zmm1, %zmm4, %zmm13
+        vpaddq    %zmm10, %zmm6, %zmm10
+        vpsrlq    $12, %zmm10, %zmm0
+        vpandq    .L_2il0floatpacket.36(%rip){1to8}, %zmm13, %zmm3
+        vpsllq    $28, %zmm3, %zmm12
+        vpsrlq    $24, %zmm13, %zmm13
+
+/*
+ * We want to incorporate the original sign now too.
+ * Do it here for convenience in getting the right N value,
+ * though we could wait right to the end if we were prepared
+ * to modify the sign of N later too.
+ * So get the appropriate sign mask now (or sooner).
+ */
+        vpandq    .L_2il0floatpacket.31(%rip){1to8}, %zmm11, %zmm14
+
+/*
+ * Create floating-point high part, implicitly adding integer bit 1
+ * Incorporate overall sign at this stage too.
+ */
+        vpxorq    .L_2il0floatpacket.32(%rip){1to8}, %zmm14, %zmm5
+        vporq     %zmm5, %zmm0, %zmm8
+        vaddpd    {rn-sae}, %zmm8, %zmm7, %zmm2
+        vsubpd    {rn-sae}, %zmm7, %zmm2, %zmm6
+        vsubpd    {rn-sae}, %zmm6, %zmm8, %zmm15
+
+/*
+ * If the magnitude of the input is <= 2^-20, then
+ * just pass through the input, since no reduction will be needed and
+ * the main path will only work accurately if the reduced argument is
+ * about >= 2^-70 (which it is for all large pi multiples)
+ */
+        vpbroadcastq .L_2il0floatpacket.42(%rip), %zmm8
+        vandpd    .L_2il0floatpacket.41(%rip){1to8}, %zmm11, %zmm6
+        vcmppd    $26, {sae}, %zmm8, %zmm6, %k2
+        vcmppd    $22, {sae}, %zmm8, %zmm6, %k3
+
+/*
+ * Create floating-point low and medium parts, respectively
+ * lo_23, ... lo_0, 0, ..., 0
+ * hi_11, ... hi_0, lo_63, ..., lo_24
+ * then subtract off the implicitly added integer bits,
+ * 2^-104 and 2^-52, respectively.
+ * Put the original sign into all of them at this stage.
+ */
+        vpxorq    .L_2il0floatpacket.35(%rip){1to8}, %zmm14, %zmm0
+        vporq     %zmm0, %zmm12, %zmm4
+        vsubpd    {rn-sae}, %zmm0, %zmm4, %zmm3
+        vpxorq    .L_2il0floatpacket.37(%rip){1to8}, %zmm14, %zmm0
+        vpandq    .L_2il0floatpacket.38(%rip){1to8}, %zmm10, %zmm14
+        vpsllq    $40, %zmm14, %zmm10
+        vmovups   17152+__svml_dcos_ha_data_internal_ha(%rip), %zmm14
+        vporq     %zmm13, %zmm10, %zmm10
+        vporq     %zmm0, %zmm10, %zmm4
+        vsubpd    {rn-sae}, %zmm0, %zmm4, %zmm5
+        vpbroadcastq .L_2il0floatpacket.40(%rip), %zmm4
+
+/* Now add them up into 2 reasonably aligned pieces */
+        vaddpd    {rn-sae}, %zmm5, %zmm15, %zmm7
+        vsubpd    {rn-sae}, %zmm7, %zmm15, %zmm15
+        vaddpd    {rn-sae}, %zmm15, %zmm5, %zmm10
+
+/*
+ * Now multiply those numbers all by 2 pi, reasonably accurately.
+ * (RHi + RLo) * (pi_lead + pi_trail) ~=
+ * RHi * pi_lead + (RHi * pi_trail + RLo * pi_lead)
+ */
+        vpbroadcastq .L_2il0floatpacket.39(%rip), %zmm5
+        vaddpd    {rn-sae}, %zmm3, %zmm10, %zmm12
+        vmulpd    {rn-sae}, %zmm5, %zmm7, %zmm1
+        vmovaps   %zmm5, %zmm0
+        vfmsub213pd {rn-sae}, %zmm1, %zmm7, %zmm0
+        vfmadd213pd {rn-sae}, %zmm0, %zmm4, %zmm7
+        vfmadd213pd {rn-sae}, %zmm7, %zmm5, %zmm12
+        vmovaps   %zmm9, %zmm3
+
+/* Grab our final N value as an integer, appropriately masked mod 2^9 */
+        vpandq    .L_2il0floatpacket.34(%rip){1to8}, %zmm2, %zmm2
+        vpsllq    $5, %zmm2, %zmm5
+        vpmovqd   %zmm5, %ymm5
+        vpandnq   %zmm6, %zmm6, %zmm3{%k2}
+        kxnorw    %k0, %k0, %k2
+        vandpd    %zmm1, %zmm3, %zmm4
+        vandpd    %zmm12, %zmm3, %zmm0
+        vmovups   16960+__svml_dcos_ha_data_internal_ha(%rip), %zmm3
+        vpandnq   %zmm6, %zmm6, %zmm9{%k3}
+        vandpd    %zmm11, %zmm9, %zmm10
+        kxnorw    %k0, %k0, %k3
+        vorpd     %zmm4, %zmm10, %zmm4
+
+/* ................. Reconstruction: res_hi ................... */
+        vmovaps   %zmm4, %zmm2
+        vmovaps   %zmm4, %zmm10
+        vpxord    %zmm9, %zmm9, %zmm9
+        vgatherdpd (%rcx,%ymm5), %zmm9{%k2}
+
+/* VGATHER_TABLE( L2D, dSLo, TAB_HA._dT, lIndex, 0, T_ITEM_SIZE, T_ITEM_GRAN, 3 ); */
+        kxnorw    %k0, %k0, %k2
+        vpxord    %zmm7, %zmm7, %zmm7
+        vpxord    %zmm6, %zmm6, %zmm6
+        vgatherdpd 16(%rcx,%ymm5), %zmm7{%k3}
+        vgatherdpd 8(%rcx,%ymm5), %zmm6{%k2}
+        kxnorw    %k0, %k0, %k3
+        vfmadd213pd {rn-sae}, %zmm7, %zmm6, %zmm2
+        vfmadd132pd {rn-sae}, %zmm9, %zmm2, %zmm10
+
+/* ................. Reconstruction: res_lo ................... */
+        vsubpd    {rn-sae}, %zmm2, %zmm7, %zmm1
+        vsubpd    {rn-sae}, %zmm10, %zmm2, %zmm8
+        vfmadd231pd {rn-sae}, %zmm4, %zmm6, %zmm1
+        vmovups   17024+__svml_dcos_ha_data_internal_ha(%rip), %zmm2
+        vfmadd231pd {rn-sae}, %zmm9, %zmm4, %zmm8
+        vaddpd    {rn-sae}, %zmm1, %zmm8, %zmm13
+        vaddpd    {rn-sae}, %zmm6, %zmm9, %zmm1
+
+/* ............... Polynomial approximation ................... */
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm8
+        vmovups   17216+__svml_dcos_ha_data_internal_ha(%rip), %zmm6
+        vmovups   17088+__svml_dcos_ha_data_internal_ha(%rip), %zmm9
+        vfmadd231pd {rn-sae}, %zmm8, %zmm2, %zmm3
+        vfmadd231pd {rn-sae}, %zmm8, %zmm6, %zmm14
+        vmulpd    {rn-sae}, %zmm8, %zmm3, %zmm12
+        vfmadd213pd {rn-sae}, %zmm9, %zmm8, %zmm14
+        vmulpd    {rn-sae}, %zmm4, %zmm12, %zmm15
+        vmulpd    {rn-sae}, %zmm8, %zmm14, %zmm2
+
+/* ................. Reconstruction: res_lo+corr ................... */
+        vfnmadd213pd {rn-sae}, %zmm1, %zmm7, %zmm4
+        vfmadd213pd {rn-sae}, %zmm13, %zmm1, %zmm15
+        vfmadd213pd {rn-sae}, %zmm15, %zmm7, %zmm2
+        vpxord    %zmm7, %zmm7, %zmm7
+        vgatherdpd 24(%rcx,%ymm5), %zmm7{%k3}
+        vfmadd213pd {rn-sae}, %zmm7, %zmm4, %zmm0
+        vaddpd    {rn-sae}, %zmm0, %zmm2, %zmm1
+
+/* .................. Final reconstruction ................... */
+        vmovups   (%rsp), %zmm0
+        vaddpd    {rn-sae}, %zmm1, %zmm10, %zmm0{%k1}
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_cos8_ha,@function
+	.size	__svml_cos8_ha,.-__svml_cos8_ha
+..LN__svml_cos8_ha.0:
+
+.L_2__routine_start___svml_dcos_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dcos_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L63:
+
+        movzwl    6(%rdi), %eax
+        andl      $32752, %eax
+        movsd     (%rdi), %xmm1
+        movb      7(%rdi), %dl
+        andb      $127, %dl
+        movsd     %xmm1, -8(%rsp)
+        cmpl      $32752, %eax
+        jne       .LBL_2_6
+
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_5
+
+
+        movb      %dl, -1(%rsp)
+        cmpl      $2146435072, -4(%rsp)
+        jne       .LBL_2_5
+
+
+        movsd     __dcos_ha_CoutTab(%rip), %xmm0
+        movl      $1, %eax
+        mulsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_5:
+
+        mulsd     %xmm1, %xmm1
+        xorl      %eax, %eax
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_6:
+
+        xorl      %eax, %eax
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dcos_ha_cout_rare_internal,@function
+	.size	__svml_dcos_ha_cout_rare_internal,.-__svml_dcos_ha_cout_rare_internal
+..LN__svml_dcos_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dcos_ha_data_internal:
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	0
+	.long	0
+	.long	1013556747
+	.long	1070135480
+	.long	2796464483
+	.long	1071152610
+	.long	967731400
+	.long	1071761211
+	.long	1719614413
+	.long	1072079006
+	.long	688824739
+	.long	1072339814
+	.long	3476196678
+	.long	1072533611
+	.long	3489094832
+	.long	1072652951
+	.long	0
+	.long	1072693248
+	.long	3489094832
+	.long	1072652951
+	.long	3476196678
+	.long	1072533611
+	.long	688824739
+	.long	1072339814
+	.long	1719614413
+	.long	1072079006
+	.long	967731400
+	.long	1071761211
+	.long	2796464483
+	.long	1071152610
+	.long	1013556747
+	.long	1070135480
+	.long	0
+	.long	1072693248
+	.long	3489094832
+	.long	1072652951
+	.long	3476196678
+	.long	1072533611
+	.long	688824739
+	.long	1072339814
+	.long	1719614413
+	.long	1072079006
+	.long	967731400
+	.long	1071761211
+	.long	2796464483
+	.long	1071152610
+	.long	1013556747
+	.long	1070135480
+	.long	0
+	.long	0
+	.long	1013556747
+	.long	3217619128
+	.long	2796464483
+	.long	3218636258
+	.long	967731400
+	.long	3219244859
+	.long	1719614413
+	.long	3219562654
+	.long	688824739
+	.long	3219823462
+	.long	3476196678
+	.long	3220017259
+	.long	3489094832
+	.long	3220136599
+	.long	0
+	.long	0
+	.long	3160567065
+	.long	1014325783
+	.long	3160878317
+	.long	1014257638
+	.long	1015752157
+	.long	1010431536
+	.long	3163282740
+	.long	3163282740
+	.long	1010431536
+	.long	1015752157
+	.long	1014257638
+	.long	3160878317
+	.long	1014325783
+	.long	3160567065
+	.long	0
+	.long	0
+	.long	1014325783
+	.long	1013083417
+	.long	1014257638
+	.long	1013394669
+	.long	1010431536
+	.long	3163235805
+	.long	3163282740
+	.long	1015799092
+	.long	1015752157
+	.long	3157915184
+	.long	3160878317
+	.long	3161741286
+	.long	3160567065
+	.long	3161809431
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.type	__svml_dcos_ha_data_internal,@object
+	.size	__svml_dcos_ha_data_internal,1408
+	.align 64
+__svml_dcos_ha_data_internal_ha:
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	210035182
+	.long	1064007864
+	.long	0
+	.long	3213885440
+	.long	379294427
+	.long	1072693090
+	.long	3221225472
+	.long	1015405987
+	.long	561145352
+	.long	1065056936
+	.long	0
+	.long	3214934016
+	.long	1619315981
+	.long	1072692616
+	.long	1073741824
+	.long	3162584397
+	.long	2896342633
+	.long	3212231467
+	.long	0
+	.long	3214934016
+	.long	4026464093
+	.long	1072691826
+	.long	3221225472
+	.long	3163075789
+	.long	1003003824
+	.long	1066107496
+	.long	0
+	.long	3215982592
+	.long	3816385918
+	.long	1072690721
+	.long	536870912
+	.long	3161216956
+	.long	3237733378
+	.long	1062425136
+	.long	0
+	.long	3215982592
+	.long	1703834029
+	.long	1072689301
+	.long	3758096384
+	.long	1015028364
+	.long	2523975598
+	.long	3213273348
+	.long	0
+	.long	3215982592
+	.long	2902558778
+	.long	1072687565
+	.long	3221225472
+	.long	1013260780
+	.long	711626851
+	.long	3214400320
+	.long	0
+	.long	3215982592
+	.long	4240268041
+	.long	1072685514
+	.long	536870912
+	.long	1013588963
+	.long	257503056
+	.long	1067164005
+	.long	0
+	.long	3217031168
+	.long	2748392742
+	.long	1072683149
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+	.long	1738598206
+	.long	603040673
+	.long	1300196342
+	.long	4210654302
+	.long	1565126321
+	.long	3801392314
+	.long	3477196413
+	.long	1206081346
+	.long	2600392684
+	.long	4126341309
+	.long	3130252643
+	.long	3307817333
+	.long	2659425531
+	.long	2412162692
+	.long	905818072
+	.long	3957715323
+	.long	1965537991
+	.long	2320667370
+	.long	1023883767
+	.long	529358088
+	.long	1811636145
+	.long	3620463350
+	.long	3931075983
+	.long	346367444
+	.long	2047767535
+	.long	1058716176
+	.long	3623272290
+	.long	2945959404
+	.long	3567184671
+	.long	692734889
+	.long	4095535070
+	.long	2117432353
+	.long	2951577284
+	.long	1596951513
+	.long	2839402047
+	.long	1385469779
+	.long	3896102844
+	.long	4234864706
+	.long	1608187272
+	.long	3193903027
+	.long	1383836798
+	.long	2770939558
+	.long	3497238392
+	.long	4174762117
+	.long	3216374545
+	.long	2092838759
+	.long	2767673597
+	.long	1246911821
+	.long	2699509489
+	.long	4054556938
+	.long	2137781795
+	.long	4185677519
+	.long	1240379898
+	.long	2493823642
+	.long	1104051682
+	.long	3814146581
+	.long	4275563591
+	.long	4076387742
+	.long	2480759797
+	.long	692679989
+	.long	2208103365
+	.long	3333325867
+	.long	4256159887
+	.long	3857808189
+	.long	666552299
+	.long	1385359979
+	.long	121239434
+	.long	2371684438
+	.long	4217352479
+	.long	3420649082
+	.long	1333104599
+	.long	2770719959
+	.long	242478868
+	.long	448401580
+	.long	4139737663
+	.long	2546330868
+	.long	2666209199
+	.long	1246472623
+	.long	484957737
+	.long	896803160
+	.long	3984508030
+	.type	__svml_dcos_ha_reduction_data_internal,@object
+	.size	__svml_dcos_ha_reduction_data_internal,49152
+	.space 512, 0x00 	
+	.align 8
+.L_2il0floatpacket.26:
+	.long	0xffffffff,0xffffffff
+	.type	.L_2il0floatpacket.26,@object
+	.size	.L_2il0floatpacket.26,8
+	.align 8
+.L_2il0floatpacket.27:
+	.long	0x00000000,0x7ff00000
+	.type	.L_2il0floatpacket.27,@object
+	.size	.L_2il0floatpacket.27,8
+	.align 8
+.L_2il0floatpacket.28:
+	.long	0xffffffff,0x000fffff
+	.type	.L_2il0floatpacket.28,@object
+	.size	.L_2il0floatpacket.28,8
+	.align 8
+.L_2il0floatpacket.29:
+	.long	0x00000000,0x00100000
+	.type	.L_2il0floatpacket.29,@object
+	.size	.L_2il0floatpacket.29,8
+	.align 8
+.L_2il0floatpacket.30:
+	.long	0xffffffff,0x00000000
+	.type	.L_2il0floatpacket.30,@object
+	.size	.L_2il0floatpacket.30,8
+	.align 8
+.L_2il0floatpacket.31:
+	.long	0x00000000,0x80000000
+	.type	.L_2il0floatpacket.31,@object
+	.size	.L_2il0floatpacket.31,8
+	.align 8
+.L_2il0floatpacket.32:
+	.long	0x00000000,0x3ff00000
+	.type	.L_2il0floatpacket.32,@object
+	.size	.L_2il0floatpacket.32,8
+	.align 8
+.L_2il0floatpacket.33:
+	.long	0x00000000,0x42a80000
+	.type	.L_2il0floatpacket.33,@object
+	.size	.L_2il0floatpacket.33,8
+	.align 8
+.L_2il0floatpacket.34:
+	.long	0x000001ff,0x00000000
+	.type	.L_2il0floatpacket.34,@object
+	.size	.L_2il0floatpacket.34,8
+	.align 8
+.L_2il0floatpacket.35:
+	.long	0x00000000,0x39700000
+	.type	.L_2il0floatpacket.35,@object
+	.size	.L_2il0floatpacket.35,8
+	.align 8
+.L_2il0floatpacket.36:
+	.long	0x00ffffff,0x00000000
+	.type	.L_2il0floatpacket.36,@object
+	.size	.L_2il0floatpacket.36,8
+	.align 8
+.L_2il0floatpacket.37:
+	.long	0x00000000,0x3cb00000
+	.type	.L_2il0floatpacket.37,@object
+	.size	.L_2il0floatpacket.37,8
+	.align 8
+.L_2il0floatpacket.38:
+	.long	0x00000fff,0x00000000
+	.type	.L_2il0floatpacket.38,@object
+	.size	.L_2il0floatpacket.38,8
+	.align 8
+.L_2il0floatpacket.39:
+	.long	0x54442d18,0x401921fb
+	.type	.L_2il0floatpacket.39,@object
+	.size	.L_2il0floatpacket.39,8
+	.align 8
+.L_2il0floatpacket.40:
+	.long	0x33145c07,0x3cb1a626
+	.type	.L_2il0floatpacket.40,@object
+	.size	.L_2il0floatpacket.40,8
+	.align 8
+.L_2il0floatpacket.41:
+	.long	0xffffffff,0x7fffffff
+	.type	.L_2il0floatpacket.41,@object
+	.size	.L_2il0floatpacket.41,8
+	.align 8
+.L_2il0floatpacket.42:
+	.long	0x00000000,0x3eb00000
+	.type	.L_2il0floatpacket.42,@object
+	.size	.L_2il0floatpacket.42,8
+	.align 8
+__dcos_ha_CoutTab:
+	.long	0
+	.long	0
+	.long	0
+	.long	2146435072
+	.type	__dcos_ha_CoutTab,@object
+	.size	__dcos_ha_CoutTab,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_d_la.s
index d5db3a28e1b..043e3cecaba 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_h_la.s
new file mode 100644
index 00000000000..48bc59794bf
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_h_la.s
@@ -0,0 +1,283 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *  1) Range reduction to [-Pi/2; +Pi/2] interval
+ *     a) We remove sign using AND operation
+ *     b) Add Pi/2 value to argument X for Cos to Sin transformation
+ *     c) Getting octant Y by 1/Pi multiplication
+ *     d) Add "Right Shifter" value
+ *     e) Treat obtained value as integer for destination sign setting.
+ *        Shift first bit of this value to the last (sign) position
+ *     f) Subtract "Right Shifter"  value
+ *     g) Subtract 0.5 from result for octant correction
+ *     h) Subtract Y*PI from X argument, where PI divided to 4 parts:
+ *        X = X - Y*PI1 - Y*PI2 - Y*PI3 - Y*PI4;
+ *  2) Polynomial (minimax for sin within [-Pi/2; +Pi/2] interval)
+ *     a) Calculate X^2 = X * X
+ *     b) Calculate polynomial:
+ *        R = X + X * X^2 * (A3 + x^2 * (A5 +
+ *  3) Destination sign setting
+ *     a) Set shifted destination sign using XOR operation:
+ *        R = XOR( R, S );
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_coss32
+
+__svml_coss32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hcos_data_internal(%rip), %zmm30
+        vmovdqu16 64+__svml_hcos_data_internal(%rip), %zmm29
+        vmovdqu16 128+__svml_hcos_data_internal(%rip), %zmm31
+        vmovdqu16 192+__svml_hcos_data_internal(%rip), %zmm28
+        vmovdqu16 256+__svml_hcos_data_internal(%rip), %zmm27
+        vmovdqu16 320+__svml_hcos_data_internal(%rip), %zmm26
+        vmovdqu16 384+__svml_hcos_data_internal(%rip), %zmm22
+        vmovdqu16 448+__svml_hcos_data_internal(%rip), %zmm25
+        vmovdqu16 576+__svml_hcos_data_internal(%rip), %zmm24
+        vmovdqu16 640+__svml_hcos_data_internal(%rip), %zmm21
+        vmovdqu16 704+__svml_hcos_data_internal(%rip), %zmm23
+        vmovdqu16 768+__svml_hcos_data_internal(%rip), %zmm20
+        vmovdqu16 896+__svml_hcos_data_internal(%rip), %zmm19
+        vmovdqu16 832+__svml_hcos_data_internal(%rip), %zmm18
+        vmovdqu16 960+__svml_hcos_data_internal(%rip), %zmm17
+        vmovdqu16 1024+__svml_hcos_data_internal(%rip), %zmm16
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__cos_h:
+        cmpq    $31, %rdx
+        ja .loaddata__cos_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_cos_h
+.loaddata__cos_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+
+.funcbegin_cos_h:
+
+/* Check for +/- INF */
+        vfpclassph $24, %zmm0, %k6
+/* Dummy vsubph instruction to raise an invalid */
+        vsubph  %zmm0, %zmm0, %zmm8{%k6}{z}
+
+        vmovdqu16 %zmm22, %zmm8
+        vmovdqu16 %zmm21, %zmm15
+
+/*
+ * Variables:
+ * H
+ * S
+ * HM
+ * No callout
+ * Copy argument
+ */
+        vpandd    %zmm30, %zmm0, %zmm1
+        vaddph    {rn-sae}, %zmm31, %zmm1, %zmm9
+
+/* |sX| > threshold? */
+        vpcmpgtw  %zmm29, %zmm1, %k1
+        vfmadd213ph {rn-sae}, %zmm28, %zmm27, %zmm9
+
+/* fN0 = (int)(sX/pi)*(2^(-5)) */
+        vsubph    {rn-sae}, %zmm28, %zmm9, %zmm5
+
+/*
+ * sign bit, will treat hY as integer value to look at last bit
+ * shift to FP16 sign position
+ */
+        vpsllw    $15, %zmm9, %zmm11
+
+/* hN = ((int)(sX/pi)-0.5)*(2^(-5)) */
+        vsubph    {rn-sae}, %zmm26, %zmm5, %zmm10
+        vfmadd213ph {rn-sae}, %zmm1, %zmm10, %zmm8
+        vfmadd213ph {rn-sae}, %zmm8, %zmm25, %zmm10
+
+/* hR*hR */
+        vmulph    {rn-sae}, %zmm10, %zmm10, %zmm14
+        vfmadd231ph {rn-sae}, %zmm14, %zmm24, %zmm15
+        vfmadd213ph {rn-sae}, %zmm23, %zmm14, %zmm15
+
+/* short path */
+        kortestd  %k1, %k1
+
+/* set sign of R */
+        vpxord    %zmm11, %zmm10, %zmm2
+
+/* hR2*hR */
+        vmulph    {rn-sae}, %zmm2, %zmm14, %zmm0
+
+/* hR + hR3*fPoly */
+        vfmadd213ph {rn-sae}, %zmm2, %zmm15, %zmm0
+
+/* Go to exit */
+        jne       .LBL_EXIT
+                                # LOE rbx rbp r12 r13 r14 r15 zmm0 zmm1 k1
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__cos_h
+
+        ret
+
+/* Restore registers
+ * and exit the function
+ */
+
+.LBL_EXIT:
+        vmovdqu16 %zmm20, %zmm4
+        vmovdqu16   1152+__svml_hcos_data_internal(%rip), %zmm12
+        vmovdqu16   %zmm4, %zmm11
+
+/* convert to FP32 */
+        vextractf32x8 $1, %zmm1, %ymm3
+        vcvtph2psx %ymm1, %zmm8
+        vcvtph2psx %ymm3, %zmm10
+        vfmadd231ps {rz-sae}, %zmm8, %zmm19, %zmm11
+        vfmadd231ps {rz-sae}, %zmm10, %zmm19, %zmm4
+        vsubps    {rn-sae}, %zmm18, %zmm11, %zmm2
+        vsubps    {rn-sae}, %zmm18, %zmm4, %zmm1
+
+/* sign bit, will treat sY as integer value to look at last bit */
+        vpslld    $31, %zmm4, %zmm5
+        vpslld    $31, %zmm11, %zmm3
+        vfmsub231ps {rn-sae}, %zmm2, %zmm17, %zmm8
+        vfmsub231ps {rn-sae}, %zmm1, %zmm17, %zmm10
+
+/* c2*sR2 + c1 */
+        vmovdqu16   1088+__svml_hcos_data_internal(%rip), %zmm4
+        vfmadd213ps {rn-sae}, %zmm8, %zmm16, %zmm2
+        vfmadd213ps {rn-sae}, %zmm10, %zmm16, %zmm1
+
+/* sR*sR */
+        vmulps    {rn-sae}, %zmm2, %zmm2, %zmm13
+        vmulps    {rn-sae}, %zmm1, %zmm1, %zmm14
+        vmovdqu16   %zmm4, %zmm15
+        vfmadd231ps {rn-sae}, %zmm13, %zmm12, %zmm15
+        vfmadd231ps {rn-sae}, %zmm14, %zmm12, %zmm4
+
+/* sR2*sR */
+        vmulps    {rn-sae}, %zmm2, %zmm13, %zmm12
+        vmulps    {rn-sae}, %zmm1, %zmm14, %zmm13
+
+/* sR + sR3*sPoly */
+        vfmadd213ps {rn-sae}, %zmm2, %zmm15, %zmm12
+        vfmadd213ps {rn-sae}, %zmm1, %zmm4, %zmm13
+
+/* add sign bit to result (logical XOR between sPoly and i32_sgn bits) */
+        vxorps    %zmm3, %zmm12, %zmm1
+        vxorps    %zmm5, %zmm13, %zmm6
+        vcvtps2phx %zmm1, %ymm2
+        vcvtps2phx %zmm6, %ymm7
+        vinsertf32x8 $1, %ymm7, %zmm2, %zmm8
+
+/*
+ * ensure results are always exactly the same for common arguments
+ * (return fast path result for common args)
+ */
+        vpblendmw %zmm8, %zmm0, %zmm0{%k1}
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__cos_h
+        ret
+                                # LOE rbx rbp r12 r13 r14 r15 zmm0
+        .cfi_endproc
+
+        .type	__svml_coss32,@function
+        .size	__svml_coss32,.-__svml_coss32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hcos_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x4bd9
+	.endr
+	.rept	32
+        .word	0x3e48
+	.endr
+	.rept	32
+        .word	0x5200
+	.endr
+	.rept	32
+        .word	0x2118
+	.endr
+	.rept	32
+        .word	0x2400
+	.endr
+	.rept	32
+        .word	0xd648
+	.endr
+	.rept	32
+        .word	0xa7ed
+	.endr
+	.rept	32
+        .word	0x0001
+	.endr
+	.rept	32
+        .word	0x8a2d
+	.endr
+	.rept	32
+        .word	0x2042
+	.endr
+	.rept	32
+        .word	0xb155
+	.endr
+	.rept	16
+        .long	0x4b000000
+	.endr
+	.rept	16
+        .long	0x4affffff
+	.endr
+	.rept	16
+        .long	0x3ea2f983
+	.endr
+	.rept	16
+        .long	0x40490fdb
+	.endr
+	.rept	16
+        .long	0xb3bbbd2e
+	.endr
+	.rept	16
+        .long	0xbe2a026e
+	.endr
+	.rept	16
+        .long	0x3bf9f9b6
+	.endr
+	.rept	16
+        .long	0x3f000000
+	.endr
+	.rept	16
+        .long	0x3fc90fdb
+	.endr
+        .type	__svml_hcos_data_internal,@object
+        .size	__svml_hcos_data_internal,1344
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_s_ha.s
new file mode 100644
index 00000000000..5ab48a900bd
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_s_ha.s
@@ -0,0 +1,2859 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *  1) Range reduction to [-Pi/2; +Pi/2] interval
+ *     a) We remove sign using AND operation
+ *     b) Add Pi/2 value to argument X for Cos to Sin transformation
+ *     c) Getting octant Y by 1/Pi multiplication
+ *     d) Add "Right Shifter" value
+ *     e) Treat obtained value as integer for destination sign setting.
+ *        Shift first bit of this value to the last (sign) position
+ *     f) Subtract "Right Shifter"  value
+ *     g) Subtract 0.5 from result for octant correction
+ *     h) Subtract Y*PI from X argument, where PI divided to 4 parts:
+ *        X = X - Y*PI1 - Y*PI2 - Y*PI3 - Y*PI4;
+ *  2) Polynomial (minimax for sin within [-Pi/2; +Pi/2] interval)
+ *     a) Calculate X^2 = X * X
+ *     b) Calculate polynomial:
+ *        R = X + X * X^2 * (A3 + x^2 * (A5 + .....
+ *  3) Destination sign setting
+ *     a) Set shifted destination sign using XOR operation:
+ *        R = XOR( R, S );
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_cosf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_cosf16_ha
+
+__svml_cosf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        xorl      %edx, %edx
+        vmovups   5504+__svml_scos_ha_data_internal(%rip), %zmm10
+
+/* Constants */
+        vmovups   5440+__svml_scos_ha_data_internal(%rip), %zmm7
+        vmovups   5376+__svml_scos_ha_data_internal(%rip), %zmm5
+
+/*
+ * Check for large and special arguments
+ * VAND( S, sAX, _VARG1, sAbsMask );
+ */
+        vmovups   4160+__svml_scos_ha_data_internal(%rip), %zmm4
+        vmovups   5568+__svml_scos_ha_data_internal(%rip), %zmm9
+        vmovups   4992+__svml_scos_ha_data_internal(%rip), %zmm15
+        vmovaps   %zmm0, %zmm11
+        vandps    4096+__svml_scos_ha_data_internal(%rip), %zmm11, %zmm3
+
+/* b) Add Pi/2 value to argument X for Cos to Sin transformation */
+        vaddps    {rn-sae}, %zmm10, %zmm3, %zmm6
+        vpternlogd $255, %zmm10, %zmm10, %zmm10
+        vcmpps    $18, {sae}, %zmm4, %zmm3, %k1
+
+/*
+ * c) Getting octant Y by 1/Pi multiplication
+ * d) Add "Right Shifter" value
+ */
+        vfmadd213ps {rn-sae}, %zmm7, %zmm5, %zmm6
+
+/* f) Subtract "Right Shifter"  value */
+        vsubps    {rn-sae}, %zmm7, %zmm6, %zmm8
+
+/*
+ * e) Treat obtained value as integer for destination sign setting.
+ * Shift first bit of this value to the last (sign) position
+ */
+        vpslld    $31, %zmm6, %zmm2
+        vmovups   5248+__svml_scos_ha_data_internal(%rip), %zmm6
+        vmovups   5312+__svml_scos_ha_data_internal(%rip), %zmm7
+
+/* g) Subtract 0.5 from result for octant correction */
+        vsubps    {rn-sae}, %zmm9, %zmm8, %zmm12
+        vmovups   5184+__svml_scos_ha_data_internal(%rip), %zmm8
+        vmovups   5120+__svml_scos_ha_data_internal(%rip), %zmm9
+        vpandnd   %zmm3, %zmm3, %zmm10{%k1}
+
+/* Convert SP N to DP */
+        vextractf32x8 $1, %zmm12, %ymm13
+        vextractf32x8 $1, %zmm3, %ymm14
+        vcvtps2pd {sae}, %ymm12, %zmm1
+        vcvtps2pd {sae}, %ymm13, %zmm0
+        vcvtps2pd {sae}, %ymm3, %zmm4
+        vcvtps2pd {sae}, %ymm14, %zmm5
+        vptestmd  %zmm10, %zmm10, %k0
+        vfnmadd231pd {rn-sae}, %zmm1, %zmm15, %zmm4
+        vfnmadd231pd {rn-sae}, %zmm0, %zmm15, %zmm5
+        vmovups   5056+__svml_scos_ha_data_internal(%rip), %zmm15
+        kortestw  %k0, %k0
+        vfnmadd213pd {rn-sae}, %zmm5, %zmm15, %zmm0
+        vfnmadd213pd {rn-sae}, %zmm4, %zmm15, %zmm1
+
+/*
+ * 2) Polynomial (minimax for sin within [-Pi/2; +Pi/2] interval)
+ * a) Calculate X^2 = X * X
+ * b) Calculate polynomial:
+ * R = X + X * X^2 * (A3 + x^2 * (A5 + .....
+ */
+        vmulpd    {rn-sae}, %zmm0, %zmm0, %zmm14
+        vmulpd    {rn-sae}, %zmm1, %zmm1, %zmm13
+        vmovaps   %zmm6, %zmm12
+        vfmadd231pd {rn-sae}, %zmm14, %zmm7, %zmm6
+        vfmadd231pd {rn-sae}, %zmm13, %zmm7, %zmm12
+        vfmadd213pd {rn-sae}, %zmm8, %zmm14, %zmm6
+        vfmadd213pd {rn-sae}, %zmm8, %zmm13, %zmm12
+        vfmadd213pd {rn-sae}, %zmm9, %zmm14, %zmm6
+        vfmadd213pd {rn-sae}, %zmm9, %zmm13, %zmm12
+        vmulpd    {rn-sae}, %zmm14, %zmm6, %zmm5
+        vmulpd    {rn-sae}, %zmm13, %zmm12, %zmm4
+        vfmadd213pd {rn-sae}, %zmm0, %zmm0, %zmm5
+        vfmadd213pd {rn-sae}, %zmm1, %zmm1, %zmm4
+
+/* Convert back to SP */
+        vcvtpd2ps {rn-sae}, %zmm5, %ymm0
+        vcvtpd2ps {rn-sae}, %zmm4, %ymm1
+        vinsertf32x8 $1, %ymm0, %zmm1, %zmm1
+
+/*
+ * 3) Destination sign setting
+ * a) Set shifted destination sign using XOR operation:
+ * R = XOR( R, S );
+ */
+        vxorps    %zmm2, %zmm1, %zmm0
+        jne       .LBL_1_12
+
+.LBL_1_2:
+
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_4:
+
+        vmovups   %zmm11, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_scos_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+        vmovups   4224+__svml_scos_ha_data_internal(%rip), %zmm6
+
+/*
+ * Get the (2^a / 2pi) mod 1 values from the table.
+ * Because VLANG doesn't have I-type gather, we need a trivial cast
+ */
+        lea       __svml_scos_ha_reduction_data_internal(%rip), %rax
+        vmovups   %zmm0, (%rsp)
+        vandps    %zmm3, %zmm6, %zmm4
+        vpternlogd $255, %zmm7, %zmm7, %zmm7
+        vcmpps    $4, {sae}, %zmm6, %zmm4, %k1
+
+/*
+ * Break the P_xxx and m into 16-bit chunks ready for
+ * the long multiplication via 16x16->32 multiplications
+ */
+        vmovups   .L_2il0floatpacket.25(%rip), %zmm6
+
+/* ..................... Table look-up ........................ */
+        lea       __svml_scos_ha_data_internal(%rip), %rcx
+        vpandd    .L_2il0floatpacket.22(%rip), %zmm11, %zmm1
+        vpandnd   %zmm4, %zmm4, %zmm7{%k1}
+        vpsrld    $23, %zmm1, %zmm8
+        vptestmd  %zmm7, %zmm7, %k0
+        vpslld    $1, %zmm8, %zmm12
+        kxnorw    %k0, %k0, %k2
+        kxnorw    %k0, %k0, %k3
+        kxnorw    %k0, %k0, %k1
+        kmovw     %k0, %edx
+        vpaddd    %zmm8, %zmm12, %zmm2
+        vpslld    $2, %zmm2, %zmm5
+        vpxord    %zmm4, %zmm4, %zmm4
+        vpxord    %zmm7, %zmm7, %zmm7
+        vpxord    %zmm14, %zmm14, %zmm14
+        vgatherdps (%rax,%zmm5), %zmm4{%k2}
+        vgatherdps 4(%rax,%zmm5), %zmm7{%k3}
+        vgatherdps 8(%rax,%zmm5), %zmm14{%k1}
+        vpsrld    $16, %zmm4, %zmm9
+        vpsrld    $16, %zmm7, %zmm15
+        vpsrld    $16, %zmm14, %zmm13
+        kxnorw    %k0, %k0, %k1
+
+/*
+ * Also get the significand as an integer
+ * NB: adding in the integer bit is wrong for denorms!
+ * To make this work for denorms we should do something slightly different
+ */
+        vpandd    .L_2il0floatpacket.23(%rip), %zmm11, %zmm3
+        vpaddd    .L_2il0floatpacket.24(%rip), %zmm3, %zmm1
+        vpsrld    $16, %zmm1, %zmm8
+        vpandd    %zmm6, %zmm14, %zmm5
+        vpandd    %zmm6, %zmm1, %zmm14
+        vpandd    %zmm6, %zmm7, %zmm12
+        vpmulld   %zmm15, %zmm8, %zmm3
+        vpmulld   %zmm9, %zmm14, %zmm7
+        vpmulld   %zmm15, %zmm14, %zmm9
+        vpmulld   %zmm13, %zmm14, %zmm15
+        vpmulld   %zmm13, %zmm8, %zmm1
+        vpsrld    $16, %zmm15, %zmm13
+        vpsrld    $16, %zmm9, %zmm15
+        vpaddd    %zmm13, %zmm1, %zmm1
+        vpandd    %zmm6, %zmm4, %zmm0
+
+/* Now do the big multiplication and carry propagation */
+        vpmulld   %zmm0, %zmm8, %zmm4
+        vpmulld   %zmm12, %zmm8, %zmm2
+        vpmulld   %zmm5, %zmm8, %zmm8
+        vpmulld   %zmm0, %zmm14, %zmm5
+        vpmulld   %zmm12, %zmm14, %zmm0
+        vpsrld    $16, %zmm0, %zmm14
+        vpsrld    $16, %zmm5, %zmm12
+        vpsrld    $16, %zmm8, %zmm8
+        vpaddd    %zmm14, %zmm2, %zmm13
+        vpaddd    %zmm15, %zmm3, %zmm14
+        vpaddd    %zmm12, %zmm4, %zmm3
+        vpandd    %zmm6, %zmm0, %zmm0
+        vpandd    %zmm6, %zmm5, %zmm12
+        vpaddd    %zmm1, %zmm0, %zmm5
+        vpaddd    %zmm14, %zmm12, %zmm0
+        vpaddd    %zmm5, %zmm8, %zmm15
+        vpandd    %zmm6, %zmm7, %zmm2
+        vpaddd    %zmm3, %zmm2, %zmm14
+        vpsrld    $16, %zmm15, %zmm3
+        vpandd    %zmm6, %zmm9, %zmm9
+        vpaddd    %zmm13, %zmm9, %zmm9
+        vpaddd    %zmm9, %zmm3, %zmm12
+        vpsrld    $16, %zmm12, %zmm4
+        vpslld    $16, %zmm12, %zmm2
+        vpaddd    %zmm0, %zmm4, %zmm13
+
+/*
+ * Now round at the 2^-8 bit position for reduction mod pi/2^7
+ * instead of the original 2pi (but still with the same 2pi scaling).
+ * Use a shifter of 2^15 + 2^14.
+ * The N we get is our final version; it has an offset of
+ * 2^8 because of the implicit integer bit, and anyway for negative
+ * starting value it's a 2s complement thing. But we need to mask
+ * off the exponent part anyway so it's fine.
+ */
+        vmovups   .L_2il0floatpacket.28(%rip), %zmm4
+        vpsrld    $16, %zmm13, %zmm7
+        vpaddd    %zmm14, %zmm7, %zmm1
+
+/* Assemble reduced argument from the pieces */
+        vpandd    %zmm6, %zmm15, %zmm3
+        vpandd    %zmm6, %zmm13, %zmm8
+        vpslld    $16, %zmm1, %zmm6
+        vpaddd    %zmm8, %zmm6, %zmm5
+        vpaddd    %zmm3, %zmm2, %zmm8
+        vpsrld    $9, %zmm5, %zmm9
+
+/*
+ * We want to incorporate the original sign now too.
+ * Do it here for convenience in getting the right N value,
+ * though we could wait right to the end if we were prepared
+ * to modify the sign of N later too.
+ * So get the appropriate sign mask now (or sooner).
+ */
+        vpandd    .L_2il0floatpacket.26(%rip), %zmm11, %zmm2
+
+/*
+ * Create floating-point high part, implicitly adding integer bit 1
+ * Incorporate overall sign at this stage too.
+ */
+        vpxord    .L_2il0floatpacket.27(%rip), %zmm2, %zmm6
+        vpord     %zmm6, %zmm9, %zmm0
+        vaddps    {rn-sae}, %zmm0, %zmm4, %zmm14
+        vsubps    {rn-sae}, %zmm4, %zmm14, %zmm7
+        vsubps    {rn-sae}, %zmm7, %zmm0, %zmm13
+        vpternlogd $255, %zmm0, %zmm0, %zmm0
+        vpandd    .L_2il0floatpacket.31(%rip), %zmm8, %zmm15
+        vpandd    .L_2il0floatpacket.33(%rip), %zmm5, %zmm5
+
+/*
+ * Create floating-point low and medium parts, respectively
+ * lo_17, ... lo_0, 0, ..., 0
+ * hi_8, ... hi_0, lo_31, ..., lo_18
+ * then subtract off the implicitly added integer bits,
+ * 2^-46 and 2^-23, respectively.
+ * Put the original sign into all of them at this stage.
+ */
+        vpxord    .L_2il0floatpacket.30(%rip), %zmm2, %zmm3
+        vpslld    $5, %zmm15, %zmm1
+        vpsrld    $18, %zmm8, %zmm8
+        vpxord    .L_2il0floatpacket.32(%rip), %zmm2, %zmm9
+        vpslld    $14, %zmm5, %zmm2
+        vpord     %zmm3, %zmm1, %zmm12
+        vpord     %zmm8, %zmm2, %zmm5
+        vsubps    {rn-sae}, %zmm3, %zmm12, %zmm15
+        vmovups   .L_2il0floatpacket.35(%rip), %zmm2
+        vpord     %zmm9, %zmm5, %zmm3
+        vsubps    {rn-sae}, %zmm9, %zmm3, %zmm6
+
+/*
+ * Now multiply those numbers all by 2 pi, reasonably accurately.
+ * (RHi + RLo) * (pi_lead + pi_trail) ~=
+ * RHi * pi_lead + (RHi * pi_trail + RLo * pi_lead)
+ */
+        vmovups   .L_2il0floatpacket.34(%rip), %zmm5
+
+/*
+ * If the magnitude of the input is <= 2^-20, then
+ * just pass through the input, since no reduction will be needed and
+ * the main path will only work accurately if the reduced argument is
+ * about >= 2^-40 (which it is for all large pi multiples)
+ */
+        vmovups   .L_2il0floatpacket.37(%rip), %zmm9
+
+/* Now add them up into 2 reasonably aligned pieces */
+        vaddps    {rn-sae}, %zmm6, %zmm13, %zmm4
+        vsubps    {rn-sae}, %zmm4, %zmm13, %zmm13
+        vmulps    {rn-sae}, %zmm5, %zmm4, %zmm7
+        vaddps    {rn-sae}, %zmm13, %zmm6, %zmm8
+        vandps    .L_2il0floatpacket.36(%rip), %zmm11, %zmm6
+        vaddps    {rn-sae}, %zmm15, %zmm8, %zmm8
+        vpternlogd $255, %zmm15, %zmm15, %zmm15
+        vcmpps    $22, {sae}, %zmm9, %zmm6, %k3
+        vcmpps    $26, {sae}, %zmm9, %zmm6, %k2
+        vmovaps   %zmm5, %zmm3
+        vfmsub213ps {rn-sae}, %zmm7, %zmm4, %zmm3
+        vfmadd213ps {rn-sae}, %zmm3, %zmm2, %zmm4
+        vfmadd213ps {rn-sae}, %zmm4, %zmm5, %zmm8
+
+/* Grab our final N value as an integer, appropriately masked mod 2^8 */
+        vpandd    .L_2il0floatpacket.29(%rip), %zmm14, %zmm14
+        vpslld    $4, %zmm14, %zmm13
+        vpandnd   %zmm6, %zmm6, %zmm0{%k3}
+        vandps    %zmm11, %zmm0, %zmm1
+        kxnorw    %k0, %k0, %k3
+        vpxord    %zmm0, %zmm0, %zmm0
+        vgatherdps (%rcx,%zmm13), %zmm0{%k1}
+        kxnorw    %k0, %k0, %k1
+        vpandnd   %zmm6, %zmm6, %zmm15{%k2}
+        kxnorw    %k0, %k0, %k2
+        vandps    %zmm7, %zmm15, %zmm12
+        vandps    %zmm8, %zmm15, %zmm3
+        vorps     %zmm12, %zmm1, %zmm2
+
+/* ............... Polynomial approximation ................... */
+        vmovups   4352+__svml_scos_ha_data_internal(%rip), %zmm12
+
+/*
+ * The output is _VRES_R (high) + _VRES_E (low), and the integer part is _VRES_IND
+ * Set sRp2 = _VRES_R^2 and then resume the original code.
+ */
+        vmulps    {rn-sae}, %zmm2, %zmm2, %zmm4
+
+/* ................. Reconstruction: res_hi ................... */
+        vmovaps   %zmm2, %zmm14
+        vmovaps   %zmm2, %zmm8
+        vpxord    %zmm9, %zmm9, %zmm9
+        vpxord    %zmm7, %zmm7, %zmm7
+        vgatherdps 4(%rcx,%zmm13), %zmm9{%k2}
+        vgatherdps 12(%rcx,%zmm13), %zmm7{%k1}
+        vfmadd213ps {rn-sae}, %zmm9, %zmm7, %zmm14
+        vaddps    {rn-sae}, %zmm7, %zmm0, %zmm15
+        vfmadd132ps {rn-sae}, %zmm0, %zmm14, %zmm8
+        vsubps    {rn-sae}, %zmm14, %zmm9, %zmm1
+        vsubps    {rn-sae}, %zmm8, %zmm14, %zmm6
+        vfmadd231ps {rn-sae}, %zmm2, %zmm7, %zmm1
+        vmovups   4416+__svml_scos_ha_data_internal(%rip), %zmm7
+        vfmadd231ps {rn-sae}, %zmm0, %zmm2, %zmm6
+        vmovups   4480+__svml_scos_ha_data_internal(%rip), %zmm0
+        vaddps    {rn-sae}, %zmm1, %zmm6, %zmm6
+        vfmadd231ps {rn-sae}, %zmm4, %zmm0, %zmm7
+        vpxord    %zmm5, %zmm5, %zmm5
+        vgatherdps 8(%rcx,%zmm13), %zmm5{%k3}
+        vmovups   4288+__svml_scos_ha_data_internal(%rip), %zmm13
+        vfmadd231ps {rn-sae}, %zmm4, %zmm12, %zmm13
+        vmulps    {rn-sae}, %zmm4, %zmm13, %zmm14
+        vmulps    {rn-sae}, %zmm4, %zmm7, %zmm4
+        vmulps    {rn-sae}, %zmm2, %zmm14, %zmm1
+
+/* .......... Reconstruction: res_lo=corr+polS+polC+res_lo0 ...... */
+        vfnmadd213ps {rn-sae}, %zmm15, %zmm9, %zmm2
+        vfmadd213ps {rn-sae}, %zmm5, %zmm2, %zmm3
+        vfmadd213ps {rn-sae}, %zmm6, %zmm2, %zmm1
+        vfmadd213ps {rn-sae}, %zmm3, %zmm9, %zmm4
+        vaddps    {rn-sae}, %zmm1, %zmm4, %zmm2
+
+/* .................. Final reconstruction ................... */
+        vaddps    {rn-sae}, %zmm2, %zmm8, %zmm3
+
+/*
+ * ----------------------------------------------------------
+ * End of large arguments path
+ * ----------------------------------------------------------
+ * Merge results from main and large paths:
+ */
+        vpandnd   (%rsp), %zmm10, %zmm5
+        vpandd    %zmm10, %zmm3, %zmm10
+        vpord     %zmm10, %zmm5, %zmm0
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_cosf16_ha,@function
+	.size	__svml_cosf16_ha,.-__svml_cosf16_ha
+..LN__svml_cosf16_ha.0:
+
+.L_2__routine_start___svml_scos_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_scos_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L63:
+
+        movl      (%rdi), %edx
+        movzwl    2(%rdi), %eax
+        movl      %edx, -8(%rsp)
+        andl      $32640, %eax
+        shrl      $24, %edx
+        andl      $127, %edx
+        cmpl      $32640, %eax
+        jne       .LBL_2_6
+
+
+        movb      %dl, -5(%rsp)
+        cmpl      $2139095040, -8(%rsp)
+        jne       .LBL_2_4
+
+
+        movss     (%rdi), %xmm0
+        movl      $1, %eax
+        mulss     __scos_ha__vmlsCosCoutTab(%rip), %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_4:
+
+        movss     (%rdi), %xmm0
+        xorl      %eax, %eax
+        mulss     (%rdi), %xmm0
+        movss     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_6:
+
+        xorl      %eax, %eax
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_scos_ha_cout_rare_internal,@function
+	.size	__svml_scos_ha_cout_rare_internal,.-__svml_scos_ha_cout_rare_internal
+..LN__svml_scos_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+.L_2il0floatpacket.22:
+	.long	0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000
+	.type	.L_2il0floatpacket.22,@object
+	.size	.L_2il0floatpacket.22,64
+	.align 64
+.L_2il0floatpacket.23:
+	.long	0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff
+	.type	.L_2il0floatpacket.23,@object
+	.size	.L_2il0floatpacket.23,64
+	.align 64
+.L_2il0floatpacket.24:
+	.long	0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000
+	.type	.L_2il0floatpacket.24,@object
+	.size	.L_2il0floatpacket.24,64
+	.align 64
+.L_2il0floatpacket.25:
+	.long	0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff
+	.type	.L_2il0floatpacket.25,@object
+	.size	.L_2il0floatpacket.25,64
+	.align 64
+.L_2il0floatpacket.26:
+	.long	0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000
+	.type	.L_2il0floatpacket.26,@object
+	.size	.L_2il0floatpacket.26,64
+	.align 64
+.L_2il0floatpacket.27:
+	.long	0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000
+	.type	.L_2il0floatpacket.27,@object
+	.size	.L_2il0floatpacket.27,64
+	.align 64
+.L_2il0floatpacket.28:
+	.long	0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000
+	.type	.L_2il0floatpacket.28,@object
+	.size	.L_2il0floatpacket.28,64
+	.align 64
+.L_2il0floatpacket.29:
+	.long	0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff
+	.type	.L_2il0floatpacket.29,@object
+	.size	.L_2il0floatpacket.29,64
+	.align 64
+.L_2il0floatpacket.30:
+	.long	0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000
+	.type	.L_2il0floatpacket.30,@object
+	.size	.L_2il0floatpacket.30,64
+	.align 64
+.L_2il0floatpacket.31:
+	.long	0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff
+	.type	.L_2il0floatpacket.31,@object
+	.size	.L_2il0floatpacket.31,64
+	.align 64
+.L_2il0floatpacket.32:
+	.long	0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000
+	.type	.L_2il0floatpacket.32,@object
+	.size	.L_2il0floatpacket.32,64
+	.align 64
+.L_2il0floatpacket.33:
+	.long	0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff
+	.type	.L_2il0floatpacket.33,@object
+	.size	.L_2il0floatpacket.33,64
+	.align 64
+.L_2il0floatpacket.34:
+	.long	0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb
+	.type	.L_2il0floatpacket.34,@object
+	.size	.L_2il0floatpacket.34,64
+	.align 64
+.L_2il0floatpacket.35:
+	.long	0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e
+	.type	.L_2il0floatpacket.35,@object
+	.size	.L_2il0floatpacket.35,64
+	.align 64
+.L_2il0floatpacket.36:
+	.long	0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff
+	.type	.L_2il0floatpacket.36,@object
+	.size	.L_2il0floatpacket.36,64
+	.align 64
+.L_2il0floatpacket.37:
+	.long	0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000
+	.type	.L_2il0floatpacket.37,@object
+	.size	.L_2il0floatpacket.37,64
+	.align 64
+__svml_scos_ha_data_internal:
+	.long	0
+	.long	1065353216
+	.long	0
+	.long	0
+	.long	1004262721
+	.long	1065348163
+	.long	814009613
+	.long	3170893824
+	.long	1012667202
+	.long	1065333007
+	.long	836655967
+	.long	3179282432
+	.long	3157608485
+	.long	1065307757
+	.long	852498564
+	.long	3179282432
+	.long	1021119272
+	.long	1065272429
+	.long	838093129
+	.long	3187671040
+	.long	992588201
+	.long	1065227044
+	.long	3002197507
+	.long	3187671040
+	.long	3165783068
+	.long	1065171628
+	.long	2969000681
+	.long	3187671040
+	.long	3174843017
+	.long	1065106216
+	.long	2989610635
+	.long	3187671040
+	.long	1029761272
+	.long	1065030846
+	.long	855602635
+	.long	3196059648
+	.long	1023221605
+	.long	1064945565
+	.long	851856985
+	.long	3196059648
+	.long	1004930693
+	.long	1064850424
+	.long	2987244005
+	.long	3196059648
+	.long	3163089201
+	.long	1064745479
+	.long	846006572
+	.long	3196059648
+	.long	3173319052
+	.long	1064630795
+	.long	2996018466
+	.long	3196059648
+	.long	3179441043
+	.long	1064506439
+	.long	851742225
+	.long	3196059648
+	.long	3182555983
+	.long	1064372488
+	.long	840880349
+	.long	3196059648
+	.long	1041201069
+	.long	1064229022
+	.long	2995991516
+	.long	3204448256
+	.long	1039156139
+	.long	1064076126
+	.long	854796500
+	.long	3204448256
+	.long	1036128472
+	.long	1063913895
+	.long	3001754476
+	.long	3204448256
+	.long	1033133567
+	.long	1063742424
+	.long	2998678409
+	.long	3204448256
+	.long	1028547674
+	.long	1063561817
+	.long	823789818
+	.long	3204448256
+	.long	1021989220
+	.long	1063372184
+	.long	3001545765
+	.long	3204448256
+	.long	1005106760
+	.long	1063173637
+	.long	851900755
+	.long	3204448256
+	.long	3160870706
+	.long	1062966298
+	.long	841166280
+	.long	3204448256
+	.long	3171899818
+	.long	1062750291
+	.long	2994560960
+	.long	3204448256
+	.long	3177422237
+	.long	1062525745
+	.long	848357914
+	.long	3204448256
+	.long	3181068627
+	.long	1062292797
+	.long	806113028
+	.long	3204448256
+	.long	3183738367
+	.long	1062051586
+	.long	847147240
+	.long	3204448256
+	.long	3186359946
+	.long	1061802258
+	.long	848897600
+	.long	3204448256
+	.long	3188301413
+	.long	1061544963
+	.long	850481524
+	.long	3204448256
+	.long	3189561687
+	.long	1061279856
+	.long	851442039
+	.long	3204448256
+	.long	3190795559
+	.long	1061007097
+	.long	832220140
+	.long	3204448256
+	.long	3192002283
+	.long	1060726850
+	.long	2994798599
+	.long	3204448256
+	.long	1050015258
+	.long	1060439283
+	.long	844097402
+	.long	3212836864
+	.long	1049440125
+	.long	1060144571
+	.long	2997759282
+	.long	3212836864
+	.long	1048879630
+	.long	1059842890
+	.long	2998350134
+	.long	3212836864
+	.long	1048092223
+	.long	1059534422
+	.long	2986574659
+	.long	3212836864
+	.long	1047031795
+	.long	1059219353
+	.long	841032635
+	.long	3212836864
+	.long	1046002615
+	.long	1058897873
+	.long	848430348
+	.long	3212836864
+	.long	1045005303
+	.long	1058570176
+	.long	2982650867
+	.long	3212836864
+	.long	1044040460
+	.long	1058236458
+	.long	852349230
+	.long	3212836864
+	.long	1043108667
+	.long	1057896922
+	.long	2991207143
+	.long	3212836864
+	.long	1042210485
+	.long	1057551771
+	.long	2998815566
+	.long	3212836864
+	.long	1041346455
+	.long	1057201213
+	.long	2992349186
+	.long	3212836864
+	.long	1040517098
+	.long	1056726311
+	.long	2978016425
+	.long	3212836864
+	.long	1039258436
+	.long	1056004842
+	.long	2986287417
+	.long	3212836864
+	.long	1037741368
+	.long	1055273845
+	.long	2983839604
+	.long	3212836864
+	.long	1036296515
+	.long	1054533760
+	.long	840832460
+	.long	3212836864
+	.long	1034924748
+	.long	1053785034
+	.long	829045603
+	.long	3212836864
+	.long	1033626892
+	.long	1053028117
+	.long	836097324
+	.long	3212836864
+	.long	1032403730
+	.long	1052263466
+	.long	2993707942
+	.long	3212836864
+	.long	1030713214
+	.long	1051491540
+	.long	2988789250
+	.long	3212836864
+	.long	1028569994
+	.long	1050712805
+	.long	2990442912
+	.long	3212836864
+	.long	1026580309
+	.long	1049927729
+	.long	846027248
+	.long	3212836864
+	.long	1024745356
+	.long	1049136787
+	.long	824999326
+	.long	3212836864
+	.long	1022722308
+	.long	1048104908
+	.long	2971391005
+	.long	3212836864
+	.long	1019677780
+	.long	1046502419
+	.long	833086710
+	.long	3212836864
+	.long	1016948784
+	.long	1044891074
+	.long	2967836285
+	.long	3212836864
+	.long	1014052363
+	.long	1043271842
+	.long	823224313
+	.long	3212836864
+	.long	1009865986
+	.long	1041645699
+	.long	837346836
+	.long	3212836864
+	.long	1006005820
+	.long	1039839859
+	.long	2970970319
+	.long	3212836864
+	.long	1000196465
+	.long	1036565814
+	.long	2960495349
+	.long	3212836864
+	.long	993104536
+	.long	1033283845
+	.long	2975014497
+	.long	3212836864
+	.long	983425480
+	.long	1028193072
+	.long	2968461951
+	.long	3212836864
+	.long	966649823
+	.long	1019808432
+	.long	2953169304
+	.long	3212836864
+	.long	0
+	.long	0
+	.long	0
+	.long	3212836864
+	.long	966649823
+	.long	3167292080
+	.long	805685656
+	.long	3212836864
+	.long	983425480
+	.long	3175676720
+	.long	820978303
+	.long	3212836864
+	.long	993104536
+	.long	3180767493
+	.long	827530849
+	.long	3212836864
+	.long	1000196465
+	.long	3184049462
+	.long	813011701
+	.long	3212836864
+	.long	1006005820
+	.long	3187323507
+	.long	823486671
+	.long	3212836864
+	.long	1009865986
+	.long	3189129347
+	.long	2984830484
+	.long	3212836864
+	.long	1014052363
+	.long	3190755490
+	.long	2970707961
+	.long	3212836864
+	.long	1016948784
+	.long	3192374722
+	.long	820352637
+	.long	3212836864
+	.long	1019677780
+	.long	3193986067
+	.long	2980570358
+	.long	3212836864
+	.long	1022722308
+	.long	3195588556
+	.long	823907357
+	.long	3212836864
+	.long	1024745356
+	.long	3196620435
+	.long	2972482974
+	.long	3212836864
+	.long	1026580309
+	.long	3197411377
+	.long	2993510896
+	.long	3212836864
+	.long	1028569994
+	.long	3198196453
+	.long	842959264
+	.long	3212836864
+	.long	1030713214
+	.long	3198975188
+	.long	841305602
+	.long	3212836864
+	.long	1032403730
+	.long	3199747114
+	.long	846224294
+	.long	3212836864
+	.long	1033626892
+	.long	3200511765
+	.long	2983580972
+	.long	3212836864
+	.long	1034924748
+	.long	3201268682
+	.long	2976529251
+	.long	3212836864
+	.long	1036296515
+	.long	3202017408
+	.long	2988316108
+	.long	3212836864
+	.long	1037741368
+	.long	3202757493
+	.long	836355956
+	.long	3212836864
+	.long	1039258436
+	.long	3203488490
+	.long	838803769
+	.long	3212836864
+	.long	1040517098
+	.long	3204209959
+	.long	830532777
+	.long	3212836864
+	.long	1041346455
+	.long	3204684861
+	.long	844865538
+	.long	3212836864
+	.long	1042210485
+	.long	3205035419
+	.long	851331918
+	.long	3212836864
+	.long	1043108667
+	.long	3205380570
+	.long	843723495
+	.long	3212836864
+	.long	1044040460
+	.long	3205720106
+	.long	2999832878
+	.long	3212836864
+	.long	1045005303
+	.long	3206053824
+	.long	835167219
+	.long	3212836864
+	.long	1046002615
+	.long	3206381521
+	.long	2995913996
+	.long	3212836864
+	.long	1047031795
+	.long	3206703001
+	.long	2988516283
+	.long	3212836864
+	.long	1048092223
+	.long	3207018070
+	.long	839091011
+	.long	3212836864
+	.long	1048879630
+	.long	3207326538
+	.long	850866486
+	.long	3212836864
+	.long	1049440125
+	.long	3207628219
+	.long	850275634
+	.long	3212836864
+	.long	1050015258
+	.long	3207922931
+	.long	2991581050
+	.long	3212836864
+	.long	3192002283
+	.long	3208210498
+	.long	847314951
+	.long	3204448256
+	.long	3190795559
+	.long	3208490745
+	.long	2979703788
+	.long	3204448256
+	.long	3189561687
+	.long	3208763504
+	.long	2998925687
+	.long	3204448256
+	.long	3188301413
+	.long	3209028611
+	.long	2997965172
+	.long	3204448256
+	.long	3186359946
+	.long	3209285906
+	.long	2996381248
+	.long	3204448256
+	.long	3183738367
+	.long	3209535234
+	.long	2994630888
+	.long	3204448256
+	.long	3181068627
+	.long	3209776445
+	.long	2953596676
+	.long	3204448256
+	.long	3177422237
+	.long	3210009393
+	.long	2995841562
+	.long	3204448256
+	.long	3171899818
+	.long	3210233939
+	.long	847077312
+	.long	3204448256
+	.long	3160870706
+	.long	3210449946
+	.long	2988649928
+	.long	3204448256
+	.long	1005106760
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+	.long	4165906339
+	.long	3932797825
+	.long	957371559
+	.long	4036845383
+	.long	3570628355
+	.long	1914743119
+	.long	3778723471
+	.long	2846289414
+	.long	3829486239
+	.long	3262479647
+	.long	1397611533
+	.long	3364005183
+	.long	2229991998
+	.long	2795223067
+	.long	2433043071
+	.long	165016701
+	.long	1295478838
+	.long	571118846
+	.long	330033402
+	.long	2590957677
+	.long	1142237692
+	.long	660066805
+	.long	886948059
+	.long	2284475384
+	.long	1320133610
+	.long	1773896118
+	.long	273983472
+	.long	2640267220
+	.long	3547792237
+	.long	547966945
+	.long	985567145
+	.long	2800617179
+	.long	1095933890
+	.long	1971134291
+	.long	1306267062
+	.long	2191867780
+	.long	3942268582
+	.long	2612534124
+	.long	88768265
+	.long	3589569869
+	.long	930100952
+	.long	177536531
+	.long	2884172442
+	.long	1860201905
+	.long	355073063
+	.long	1473377588
+	.long	3720403810
+	.long	710146126
+	.long	2946755177
+	.long	3145840325
+	.long	1420292253
+	.long	1598543059
+	.long	1996713354
+	.long	2840584506
+	.long	3197086118
+	.long	3993426708
+	.long	1386201717
+	.long	2099204941
+	.long	3691886121
+	.long	2772403434
+	.long	4198409883
+	.long	3088804946
+	.long	1249839573
+	.long	4101852471
+	.long	1882642597
+	.long	2499679147
+	.long	3908737646
+	.long	3765285194
+	.long	704390999
+	.long	3522507997
+	.long	3235603093
+	.long	1408781999
+	.long	2750048699
+	.long	2176238891
+	.long	2817563999
+	.long	1205130103
+	.long	57510486
+	.long	1340160702
+	.long	2410260206
+	.long	115020972
+	.long	2680321405
+	.long	525553116
+	.long	230041945
+	.long	1065675514
+	.long	1051106232
+	.long	460083891
+	.long	2131351028
+	.long	2102212464
+	.long	920167782
+	.long	4262702056
+	.long	4204424928
+	.long	1840335564
+	.long	4230436817
+	.long	4113882560
+	.long	3680671129
+	.long	4165906339
+	.long	3932797825
+	.long	3066374962
+	.long	4036845383
+	.long	3570628355
+	.long	1837782628
+	.long	3778723471
+	.long	2846289414
+	.long	3675565257
+	.long	3262479647
+	.long	1397611533
+	.long	3056163219
+	.long	2229991998
+	.long	2795223067
+	.long	1817359143
+	.long	165016701
+	.long	1295478838
+	.long	3634718287
+	.long	330033402
+	.long	2590957677
+	.long	2974469278
+	.long	660066805
+	.long	886948059
+	.long	1653971260
+	.long	1320133610
+	.long	1773896118
+	.long	3307942520
+	.long	2640267220
+	.long	3547792237
+	.long	2320917745
+	.long	985567145
+	.long	2800617179
+	.long	346868194
+	.long	1971134291
+	.long	1306267062
+	.long	693736388
+	.long	3942268582
+	.long	2612534124
+	.long	1387472776
+	.long	3589569869
+	.long	930100952
+	.long	2774945552
+	.long	2884172442
+	.long	1860201905
+	.long	1254923809
+	.long	1473377588
+	.long	3720403810
+	.long	2509847619
+	.long	2946755177
+	.long	3145840325
+	.long	724727943
+	.long	1598543059
+	.long	1996713354
+	.long	1449455886
+	.long	3197086118
+	.long	3993426708
+	.long	2898911772
+	.long	2099204941
+	.long	3691886121
+	.long	1502856249
+	.long	4198409883
+	.long	3088804946
+	.long	3005712498
+	.long	4101852471
+	.long	1882642597
+	.long	1716457700
+	.long	3908737646
+	.long	3765285194
+	.long	3432915400
+	.long	3522507997
+	.long	3235603093
+	.long	2570863504
+	.long	2750048699
+	.long	2176238891
+	.long	846759712
+	.long	1205130103
+	.long	57510486
+	.long	1693519425
+	.long	2410260206
+	.long	115020972
+	.long	3387038850
+	.long	525553116
+	.long	230041945
+	.long	2479110404
+	.long	1051106232
+	.long	460083891
+	.long	663253512
+	.long	2102212464
+	.long	920167782
+	.long	1326507024
+	.long	4204424928
+	.long	1840335564
+	.long	2653014048
+	.long	4113882560
+	.long	3680671129
+	.long	1011060801
+	.long	3932797825
+	.long	3066374962
+	.long	2022121603
+	.long	3570628355
+	.long	1837782628
+	.long	4044243207
+	.long	2846289414
+	.long	3675565257
+	.long	3793519119
+	.long	1397611533
+	.long	3056163219
+	.long	3292070943
+	.long	2795223067
+	.long	1817359143
+	.long	2289174591
+	.long	1295478838
+	.long	3634718287
+	.long	283381887
+	.long	2590957677
+	.long	2974469278
+	.long	566763775
+	.type	__svml_scos_ha_reduction_data_internal,@object
+	.size	__svml_scos_ha_reduction_data_internal,3072
+	.align 4
+__scos_ha__vmlsCosCoutTab:
+	.long	0
+	.long	2139095040
+	.type	__scos_ha__vmlsCosCoutTab,@object
+	.size	__scos_ha__vmlsCosCoutTab,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_s_la.s
index 4581bf6de8c..e1db29e2528 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cos_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_d_ha.s
new file mode 100644
index 00000000000..a82bdc6922e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_d_ha.s
@@ -0,0 +1,1952 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_cosh8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_cosh8_ha
+
+__svml_cosh8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   4672+__svml_dcosh_ha_data_internal(%rip), %zmm1
+        vmovups   384+__svml_dcosh_ha_data_internal(%rip), %zmm8
+
+/*
+ * ............... Load argument ...........................
+ * dM = x*2^K/log(2) + RShifter
+ */
+        vmovups   3968+__svml_dcosh_ha_data_internal(%rip), %zmm7
+        vmovups   4032+__svml_dcosh_ha_data_internal(%rip), %zmm9
+        vmovups   4096+__svml_dcosh_ha_data_internal(%rip), %zmm6
+        vmovups   128+__svml_dcosh_ha_data_internal(%rip), %zmm4
+        vmovups   256+__svml_dcosh_ha_data_internal(%rip), %zmm5
+        vmovups   832+__svml_dcosh_ha_data_internal(%rip), %zmm11
+        vmovups   768+__svml_dcosh_ha_data_internal(%rip), %zmm13
+        vmovups   512+__svml_dcosh_ha_data_internal(%rip), %zmm14
+        vmovups   576+__svml_dcosh_ha_data_internal(%rip), %zmm12
+        vmovaps   %zmm0, %zmm15
+
+/* ............... Abs argument ............................ */
+        vandnpd   %zmm15, %zmm1, %zmm10
+        vfmadd213pd {rn-sae}, %zmm8, %zmm10, %zmm7
+
+/*
+ * ...............Check for overflow\underflow .............
+ * 
+ */
+        vpsrlq    $32, %zmm10, %zmm0
+
+/* dN = dM - RShifter */
+        vsubpd    {rn-sae}, %zmm8, %zmm7, %zmm8
+        vpmovqd   %zmm0, %ymm2
+        vpermt2pd 192+__svml_dcosh_ha_data_internal(%rip), %zmm7, %zmm4
+        vpermt2pd 320+__svml_dcosh_ha_data_internal(%rip), %zmm7, %zmm5
+
+/* dR = dX - dN*Log2_hi/2^K */
+        vfnmadd231pd {rn-sae}, %zmm9, %zmm8, %zmm10
+
+/* dR = dX - dN*Log2_hi/2^K */
+        vfnmadd231pd {rn-sae}, %zmm6, %zmm8, %zmm10
+        vpsllq    $48, %zmm7, %zmm6
+        vpcmpgtd  4736+__svml_dcosh_ha_data_internal(%rip), %ymm2, %ymm3
+        vmulpd    {rn-sae}, %zmm10, %zmm10, %zmm2
+        vmovmskps %ymm3, %edx
+
+/* .............. Index and lookup ......................... */
+        vmovups   __svml_dcosh_ha_data_internal(%rip), %zmm3
+        vpermt2pd 64+__svml_dcosh_ha_data_internal(%rip), %zmm7, %zmm3
+
+/* lM now is an EXP(2^N) */
+        vpandq    4608+__svml_dcosh_ha_data_internal(%rip), %zmm6, %zmm7
+        vpaddq    %zmm7, %zmm4, %zmm0
+        vpaddq    %zmm7, %zmm3, %zmm1
+        vpsubq    %zmm7, %zmm5, %zmm3
+
+/*
+ * poly(r) = Gmjp(1 + a2*r^2 + a4*r^4) + Gmjn*(r+ a3*r^3 +a5*r^5)       =
+ * = Gmjp_h +Gmjp_l+ Gmjp*r^2*(a2 + a4*r^2) + Gmjn*(r+ r^3*(a3 +a5*r^2)
+ */
+        vmovups   704+__svml_dcosh_ha_data_internal(%rip), %zmm4
+        vaddpd    {rn-sae}, %zmm3, %zmm1, %zmm5
+        vsubpd    {rn-sae}, %zmm3, %zmm1, %zmm9
+        vfmadd231pd {rn-sae}, %zmm2, %zmm11, %zmm4
+        vmovups   640+__svml_dcosh_ha_data_internal(%rip), %zmm11
+        vfmadd213pd {rn-sae}, %zmm12, %zmm2, %zmm4
+        vfmadd231pd {rn-sae}, %zmm2, %zmm13, %zmm11
+
+/* dM=r^2*(a3 +a5*r^2) */
+        vmulpd    {rn-sae}, %zmm2, %zmm4, %zmm13
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm11
+
+/* dM= r + r^3*(a3 +a5*r^2) */
+        vfmadd213pd {rn-sae}, %zmm10, %zmm10, %zmm13
+
+/* dOut=r^2*(a2 + a4*r^2) */
+        vmulpd    {rn-sae}, %zmm2, %zmm11, %zmm12
+
+/* dOut=Gmjp*r^2*(a2 + a4*r^2)+Gmjp_l(1) */
+        vfmadd213pd {rn-sae}, %zmm0, %zmm5, %zmm12
+
+/* dOut=Gmjp*r^2*(a2 + a4*r^2)+Gmjp_l(1) + Gmjn*(r+ r^3*(a3+a5*r^2) */
+        vfmadd213pd {rn-sae}, %zmm12, %zmm9, %zmm13
+
+/* dOut=Gmjp*r^2*(a2 + a4*r^2)+Gmjp_l(1) + Gmjn*(r+ r^3*(a3+a5*r^2) + Gmjp_l(2) */
+        vaddpd    {rn-sae}, %zmm3, %zmm13, %zmm10
+
+/* Gmjp_h +Gmjp_l+ Gmjp*r^2*(a2 + a4*r^2) + Gmjn*(r+ r^3*(a3+a5*r^2) */
+        vaddpd    {rn-sae}, %zmm1, %zmm10, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm15, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dcosh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_cosh8_ha,@function
+	.size	__svml_cosh8_ha,.-__svml_cosh8_ha
+..LN__svml_cosh8_ha.0:
+
+.L_2__routine_start___svml_dcosh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dcosh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movq      %rsi, %r8
+        movzwl    6(%rdi), %edx
+        xorl      %eax, %eax
+        andl      $32752, %edx
+        cmpl      $32752, %edx
+        je        .LBL_2_12
+
+
+        movq      (%rdi), %rdx
+        movq      %rdx, -8(%rsp)
+        shrq      $56, %rdx
+        andl      $127, %edx
+        movb      %dl, -1(%rsp)
+        movzwl    -2(%rsp), %ecx
+        andl      $32752, %ecx
+        cmpl      $15504, %ecx
+        jle       .LBL_2_10
+
+
+        movsd     -8(%rsp), %xmm0
+        movsd     1096+__dcosh_ha_CoutTab(%rip), %xmm1
+        comisd    %xmm0, %xmm1
+        jbe       .LBL_2_9
+
+
+        movq      1128+__dcosh_ha_CoutTab(%rip), %rdx
+        movq      %rdx, -8(%rsp)
+        comisd    1144+__dcosh_ha_CoutTab(%rip), %xmm0
+        jb        .LBL_2_8
+
+
+        movsd     1040+__dcosh_ha_CoutTab(%rip), %xmm1
+        lea       __dcosh_ha_CoutTab(%rip), %r9
+        mulsd     %xmm0, %xmm1
+        addsd     1048+__dcosh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -40(%rsp)
+        movsd     -40(%rsp), %xmm2
+        movsd     1088+__dcosh_ha_CoutTab(%rip), %xmm1
+        movl      -40(%rsp), %edx
+        movl      %edx, %esi
+        andl      $63, %esi
+        subsd     1048+__dcosh_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm2, -32(%rsp)
+        lea       (%rsi,%rsi), %ecx
+        movsd     -32(%rsp), %xmm3
+        lea       1(%rsi,%rsi), %edi
+        mulsd     1104+__dcosh_ha_CoutTab(%rip), %xmm3
+        movsd     -32(%rsp), %xmm4
+        subsd     %xmm3, %xmm0
+        mulsd     1112+__dcosh_ha_CoutTab(%rip), %xmm4
+        shrl      $6, %edx
+        subsd     %xmm4, %xmm0
+        mulsd     %xmm0, %xmm1
+        addl      $1022, %edx
+        andl      $2047, %edx
+        addsd     1080+__dcosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1072+__dcosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1064+__dcosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1056+__dcosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     %xmm0, %xmm1
+        movsd     (%r9,%rcx,8), %xmm0
+        mulsd     %xmm0, %xmm1
+        addsd     (%r9,%rdi,8), %xmm1
+        addsd     %xmm0, %xmm1
+        cmpl      $2046, %edx
+        ja        .LBL_2_7
+
+
+        movq      1128+__dcosh_ha_CoutTab(%rip), %rcx
+        shrq      $48, %rcx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm1, %xmm0
+        movsd     %xmm0, (%r8)
+        ret
+
+.LBL_2_7:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -2(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        mulsd     1024+__dcosh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, (%r8)
+        ret
+
+.LBL_2_8:
+
+        movsd     1040+__dcosh_ha_CoutTab(%rip), %xmm1
+        lea       __dcosh_ha_CoutTab(%rip), %rcx
+        movzwl    -2(%rsp), %esi
+        andl      $-32753, %esi
+        movsd     1080+__dcosh_ha_CoutTab(%rip), %xmm14
+        mulsd     %xmm0, %xmm1
+        addsd     1048+__dcosh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -40(%rsp)
+        movsd     -40(%rsp), %xmm2
+        movl      -40(%rsp), %r10d
+        movl      %r10d, %r9d
+        shrl      $6, %r9d
+        subsd     1048+__dcosh_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm2, -32(%rsp)
+        lea       1023(%r9), %edi
+        andl      $63, %r10d
+        addl      $1022, %r9d
+        movsd     -32(%rsp), %xmm3
+        andl      $2047, %r9d
+        negl      %edi
+        shll      $4, %r9d
+        addl      $-4, %edi
+        mulsd     1104+__dcosh_ha_CoutTab(%rip), %xmm3
+        lea       (%r10,%r10), %edx
+        movsd     (%rcx,%rdx,8), %xmm15
+        negl      %edx
+        movsd     -32(%rsp), %xmm4
+        orl       %r9d, %esi
+        andl      $2047, %edi
+        lea       1(%r10,%r10), %r11d
+        mulsd     1112+__dcosh_ha_CoutTab(%rip), %xmm4
+        subsd     %xmm3, %xmm0
+        movw      %si, -2(%rsp)
+        andl      $-32753, %esi
+        shll      $4, %edi
+        subsd     %xmm4, %xmm0
+        movsd     -8(%rsp), %xmm6
+        orl       %edi, %esi
+        movw      %si, -2(%rsp)
+        lea       128(%rdx), %esi
+        mulsd     %xmm6, %xmm15
+        movaps    %xmm0, %xmm5
+        mulsd     %xmm0, %xmm5
+        movsd     -8(%rsp), %xmm7
+        movaps    %xmm15, %xmm8
+        movsd     (%rcx,%rsi,8), %xmm11
+        addl      $129, %edx
+        mulsd     %xmm7, %xmm11
+        movaps    %xmm15, %xmm10
+        mulsd     %xmm5, %xmm14
+        addsd     %xmm11, %xmm8
+        subsd     %xmm11, %xmm15
+        addsd     1064+__dcosh_ha_CoutTab(%rip), %xmm14
+        movsd     %xmm8, -24(%rsp)
+        movsd     (%rcx,%r11,8), %xmm12
+        movsd     (%rcx,%rdx,8), %xmm13
+        movsd     -24(%rsp), %xmm9
+        mulsd     %xmm6, %xmm12
+        subsd     %xmm9, %xmm10
+        mulsd     %xmm7, %xmm13
+        mulsd     %xmm5, %xmm14
+        addsd     %xmm11, %xmm10
+        mulsd     %xmm0, %xmm14
+        movsd     1088+__dcosh_ha_CoutTab(%rip), %xmm1
+        movaps    %xmm12, %xmm11
+        mulsd     %xmm5, %xmm1
+        subsd     %xmm13, %xmm12
+        mulsd     %xmm15, %xmm14
+        mulsd     %xmm0, %xmm12
+        addsd     1072+__dcosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm15, %xmm0
+        mulsd     %xmm5, %xmm1
+        addsd     %xmm12, %xmm11
+        movsd     %xmm10, -16(%rsp)
+        addsd     %xmm13, %xmm11
+        addsd     1056+__dcosh_ha_CoutTab(%rip), %xmm1
+        addsd     %xmm14, %xmm11
+        mulsd     %xmm5, %xmm1
+        addsd     %xmm0, %xmm11
+        movsd     -24(%rsp), %xmm3
+        mulsd     %xmm3, %xmm1
+        movsd     -16(%rsp), %xmm2
+        addsd     %xmm1, %xmm11
+        addsd     %xmm2, %xmm11
+        movsd     %xmm11, -24(%rsp)
+        movsd     -24(%rsp), %xmm0
+        addsd     %xmm0, %xmm3
+        movsd     %xmm3, (%r8)
+        ret
+
+.LBL_2_9:
+
+        movsd     1120+__dcosh_ha_CoutTab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%r8)
+        ret
+
+.LBL_2_10:
+
+        movsd     1136+__dcosh_ha_CoutTab(%rip), %xmm0
+        addsd     -8(%rsp), %xmm0
+        movsd     %xmm0, (%r8)
+
+
+        ret
+
+.LBL_2_12:
+
+        movsd     (%rdi), %xmm0
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%r8)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dcosh_ha_cout_rare_internal,@function
+	.size	__svml_dcosh_ha_cout_rare_internal,.-__svml_dcosh_ha_cout_rare_internal
+..LN__svml_dcosh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dcosh_ha_data_internal:
+	.long	0
+	.long	1071644672
+	.long	1828292879
+	.long	1071691096
+	.long	1014845819
+	.long	1071739576
+	.long	1853186616
+	.long	1071790202
+	.long	171030293
+	.long	1071843070
+	.long	1276261410
+	.long	1071898278
+	.long	3577096743
+	.long	1071955930
+	.long	3712504873
+	.long	1072016135
+	.long	1719614413
+	.long	1072079006
+	.long	1944781191
+	.long	1072144660
+	.long	1110089947
+	.long	1072213221
+	.long	2191782032
+	.long	1072284817
+	.long	2572866477
+	.long	1072359583
+	.long	3716502172
+	.long	1072437659
+	.long	3707479175
+	.long	1072519192
+	.long	2728693978
+	.long	1072604335
+	.long	0
+	.long	0
+	.long	1255956747
+	.long	1015588398
+	.long	3117910646
+	.long	3161559105
+	.long	3066496371
+	.long	1015656574
+	.long	3526460132
+	.long	1014428778
+	.long	300981948
+	.long	1014684169
+	.long	2951496418
+	.long	1013793687
+	.long	88491949
+	.long	1015427660
+	.long	330458198
+	.long	3163282740
+	.long	3993278767
+	.long	3161724279
+	.long	1451641639
+	.long	1015474673
+	.long	2960257726
+	.long	1013742662
+	.long	878562433
+	.long	1015521741
+	.long	2303740125
+	.long	1014042725
+	.long	3613079303
+	.long	1014164738
+	.long	396109971
+	.long	3163462691
+	.long	0
+	.long	1071644672
+	.long	2728693978
+	.long	1071555759
+	.long	3707479175
+	.long	1071470616
+	.long	3716502172
+	.long	1071389083
+	.long	2572866477
+	.long	1071311007
+	.long	2191782032
+	.long	1071236241
+	.long	1110089947
+	.long	1071164645
+	.long	1944781191
+	.long	1071096084
+	.long	1719614413
+	.long	1071030430
+	.long	3712504873
+	.long	1070967559
+	.long	3577096743
+	.long	1070907354
+	.long	1276261410
+	.long	1070849702
+	.long	171030293
+	.long	1070794494
+	.long	1853186616
+	.long	1070741626
+	.long	1014845819
+	.long	1070691000
+	.long	1828292879
+	.long	1070642520
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	4
+	.long	1071644672
+	.long	4
+	.long	1071644672
+	.long	4
+	.long	1071644672
+	.long	4
+	.long	1071644672
+	.long	4
+	.long	1071644672
+	.long	4
+	.long	1071644672
+	.long	4
+	.long	1071644672
+	.long	4
+	.long	1071644672
+	.long	1431655747
+	.long	1069897045
+	.long	1431655747
+	.long	1069897045
+	.long	1431655747
+	.long	1069897045
+	.long	1431655747
+	.long	1069897045
+	.long	1431655747
+	.long	1069897045
+	.long	1431655747
+	.long	1069897045
+	.long	1431655747
+	.long	1069897045
+	.long	1431655747
+	.long	1069897045
+	.long	1430802231
+	.long	1067799893
+	.long	1430802231
+	.long	1067799893
+	.long	1430802231
+	.long	1067799893
+	.long	1430802231
+	.long	1067799893
+	.long	1430802231
+	.long	1067799893
+	.long	1430802231
+	.long	1067799893
+	.long	1430802231
+	.long	1067799893
+	.long	1430802231
+	.long	1067799893
+	.long	287861260
+	.long	1065423121
+	.long	287861260
+	.long	1065423121
+	.long	287861260
+	.long	1065423121
+	.long	287861260
+	.long	1065423121
+	.long	287861260
+	.long	1065423121
+	.long	287861260
+	.long	1065423121
+	.long	287861260
+	.long	1065423121
+	.long	287861260
+	.long	1065423121
+	.long	3658019094
+	.long	1062650243
+	.long	3658019094
+	.long	1062650243
+	.long	3658019094
+	.long	1062650243
+	.long	3658019094
+	.long	1062650243
+	.long	3658019094
+	.long	1062650243
+	.long	3658019094
+	.long	1062650243
+	.long	3658019094
+	.long	1062650243
+	.long	3658019094
+	.long	1062650243
+	.long	1993999322
+	.long	1059717517
+	.long	1993999322
+	.long	1059717517
+	.long	1993999322
+	.long	1059717517
+	.long	1993999322
+	.long	1059717517
+	.long	1993999322
+	.long	1059717517
+	.long	1993999322
+	.long	1059717517
+	.long	1993999322
+	.long	1059717517
+	.long	1993999322
+	.long	1059717517
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	0
+	.long	0
+	.long	1071644672
+	.long	2851812149
+	.long	1071650365
+	.long	2595802551
+	.long	1015767337
+	.long	730821105
+	.long	1071633346
+	.long	1048019041
+	.long	1071656090
+	.long	1398474845
+	.long	3160510595
+	.long	2174652632
+	.long	1071622081
+	.long	3899555717
+	.long	1071661845
+	.long	427280750
+	.long	3162546972
+	.long	2912730644
+	.long	1071610877
+	.long	3541402996
+	.long	1071667632
+	.long	2759177317
+	.long	1014854626
+	.long	1533953344
+	.long	1071599734
+	.long	702412510
+	.long	1071673451
+	.long	3803266087
+	.long	3162280415
+	.long	929806999
+	.long	1071588651
+	.long	410360776
+	.long	1071679301
+	.long	1269990655
+	.long	1011975870
+	.long	3999357479
+	.long	1071577627
+	.long	3402036099
+	.long	1071685182
+	.long	405889334
+	.long	1015105656
+	.long	764307441
+	.long	1071566664
+	.long	1828292879
+	.long	1071691096
+	.long	1255956747
+	.long	1015588398
+	.long	2728693978
+	.long	1071555759
+	.long	728909815
+	.long	1071697042
+	.long	383930225
+	.long	1015029468
+	.long	4224142467
+	.long	1071544913
+	.long	852742562
+	.long	1071703020
+	.long	667253587
+	.long	1009793559
+	.long	3884662774
+	.long	1071534126
+	.long	2952712987
+	.long	1071709030
+	.long	3293494651
+	.long	3160120301
+	.long	351641897
+	.long	1071523398
+	.long	3490863953
+	.long	1071715073
+	.long	960797498
+	.long	3162948880
+	.long	863738719
+	.long	1071512727
+	.long	3228316108
+	.long	1071721149
+	.long	3010241991
+	.long	3158422804
+	.long	4076975200
+	.long	1071502113
+	.long	2930322912
+	.long	1071727258
+	.long	2599499422
+	.long	3162714047
+	.long	64696965
+	.long	1071491558
+	.long	3366293073
+	.long	1071733400
+	.long	3119426314
+	.long	1014120554
+	.long	382305176
+	.long	1071481059
+	.long	1014845819
+	.long	1071739576
+	.long	3117910646
+	.long	3161559105
+	.long	3707479175
+	.long	1071470616
+	.long	948735466
+	.long	1071745785
+	.long	3516338028
+	.long	3162574883
+	.long	135105010
+	.long	1071460231
+	.long	3949972341
+	.long	1071752027
+	.long	2068408548
+	.long	1014913868
+	.long	1242007932
+	.long	1071449901
+	.long	2214878420
+	.long	1071758304
+	.long	892270087
+	.long	3163116422
+	.long	1432208378
+	.long	1071439627
+	.long	828946858
+	.long	1071764615
+	.long	10642492
+	.long	1015939438
+	.long	3706687593
+	.long	1071429408
+	.long	586995997
+	.long	1071770960
+	.long	41662348
+	.long	3162627992
+	.long	2483480501
+	.long	1071419245
+	.long	2288159958
+	.long	1071777339
+	.long	2169144469
+	.long	1014876021
+	.long	777507147
+	.long	1071409137
+	.long	2440944790
+	.long	1071783753
+	.long	2492769774
+	.long	1014147454
+	.long	1610600570
+	.long	1071399083
+	.long	1853186616
+	.long	1071790202
+	.long	3066496371
+	.long	1015656574
+	.long	3716502172
+	.long	1071389083
+	.long	1337108031
+	.long	1071796686
+	.long	3203724452
+	.long	1014677845
+	.long	1540824585
+	.long	1071379138
+	.long	1709341917
+	.long	1071803205
+	.long	2571168217
+	.long	1014152499
+	.long	2420883922
+	.long	1071369246
+	.long	3790955393
+	.long	1071809759
+	.long	2352942462
+	.long	3163180090
+	.long	815859274
+	.long	1071359408
+	.long	4112506593
+	.long	1071816349
+	.long	2947355221
+	.long	1014371048
+	.long	4076559943
+	.long	1071349622
+	.long	3504003472
+	.long	1071822975
+	.long	3594001060
+	.long	3157330652
+	.long	2380618042
+	.long	1071339890
+	.long	2799960843
+	.long	1071829637
+	.long	1423655381
+	.long	1015022151
+	.long	3092190715
+	.long	1071330210
+	.long	2839424854
+	.long	1071836335
+	.long	1171596163
+	.long	1013041679
+	.long	697153126
+	.long	1071320583
+	.long	171030293
+	.long	1071843070
+	.long	3526460132
+	.long	1014428778
+	.long	2572866477
+	.long	1071311007
+	.long	4232894513
+	.long	1071849840
+	.long	2383938684
+	.long	1014668519
+	.long	3218338682
+	.long	1071301483
+	.long	2992903935
+	.long	1071856648
+	.long	2218154406
+	.long	1015228193
+	.long	1434058175
+	.long	1071292011
+	.long	1603444721
+	.long	1071863493
+	.long	1548633640
+	.long	3162201326
+	.long	321958744
+	.long	1071282590
+	.long	926591435
+	.long	1071870375
+	.long	3208833762
+	.long	3162913514
+	.long	2990417245
+	.long	1071273219
+	.long	1829099622
+	.long	1071877294
+	.long	1016661181
+	.long	3163461005
+	.long	3964284211
+	.long	1071263899
+	.long	887463927
+	.long	1071884251
+	.long	3596744163
+	.long	3160794166
+	.long	2069751141
+	.long	1071254630
+	.long	3272845541
+	.long	1071891245
+	.long	928852419
+	.long	3163488248
+	.long	434316067
+	.long	1071245411
+	.long	1276261410
+	.long	1071898278
+	.long	300981948
+	.long	1014684169
+	.long	2191782032
+	.long	1071236241
+	.long	78413852
+	.long	1071905349
+	.long	4183226867
+	.long	3163017251
+	.long	1892288442
+	.long	1071227121
+	.long	569847338
+	.long	1071912458
+	.long	472945272
+	.long	3159290729
+	.long	2682146384
+	.long	1071218050
+	.long	3645941911
+	.long	1071919605
+	.long	3814685081
+	.long	3161573341
+	.long	3418903055
+	.long	1071209028
+	.long	1617004845
+	.long	1071926792
+	.long	82804944
+	.long	1010342778
+	.long	2966275557
+	.long	1071200055
+	.long	3978100823
+	.long	1071934017
+	.long	3513027190
+	.long	1015845963
+	.long	194117574
+	.long	1071191131
+	.long	3049340112
+	.long	1071941282
+	.long	3062915824
+	.long	1013170595
+	.long	2568320822
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+	.long	2288159958
+	.long	1072825915
+	.long	2169144469
+	.long	1015924597
+	.long	1853186616
+	.long	1072838778
+	.long	3066496371
+	.long	1016705150
+	.long	1709341917
+	.long	1072851781
+	.long	2571168217
+	.long	1015201075
+	.long	4112506593
+	.long	1072864925
+	.long	2947355221
+	.long	1015419624
+	.long	2799960843
+	.long	1072878213
+	.long	1423655381
+	.long	1016070727
+	.long	171030293
+	.long	1072891646
+	.long	3526460132
+	.long	1015477354
+	.long	2992903935
+	.long	1072905224
+	.long	2218154406
+	.long	1016276769
+	.long	926591435
+	.long	1072918951
+	.long	3208833762
+	.long	3163962090
+	.long	887463927
+	.long	1072932827
+	.long	3596744163
+	.long	3161842742
+	.long	1276261410
+	.long	1072946854
+	.long	300981948
+	.long	1015732745
+	.long	569847338
+	.long	1072961034
+	.long	472945272
+	.long	3160339305
+	.long	1617004845
+	.long	1072975368
+	.long	82804944
+	.long	1011391354
+	.long	3049340112
+	.long	1072989858
+	.long	3062915824
+	.long	1014219171
+	.long	3577096743
+	.long	1073004506
+	.long	2951496418
+	.long	1014842263
+	.long	1990012071
+	.long	1073019314
+	.long	3529070563
+	.long	3163861769
+	.long	1453150082
+	.long	1073034283
+	.long	498154669
+	.long	3162536638
+	.long	917841882
+	.long	1073049415
+	.long	18715565
+	.long	1016707884
+	.long	3712504873
+	.long	1073064711
+	.long	88491949
+	.long	1016476236
+	.long	363667784
+	.long	1073080175
+	.long	813753950
+	.long	1016833785
+	.long	2956612997
+	.long	1073095806
+	.long	2118169751
+	.long	3163784129
+	.long	2186617381
+	.long	1073111608
+	.long	2270764084
+	.long	3164321289
+	.long	1719614413
+	.long	1073127582
+	.long	330458198
+	.long	3164331316
+	.long	1013258799
+	.long	1073143730
+	.long	1748797611
+	.long	3161177658
+	.long	3907805044
+	.long	1073160053
+	.long	2257091225
+	.long	3162598983
+	.long	1447192521
+	.long	1073176555
+	.long	1462857171
+	.long	3163563097
+	.long	1944781191
+	.long	1073193236
+	.long	3993278767
+	.long	3162772855
+	.long	919555682
+	.long	1073210099
+	.long	3121969534
+	.long	1013996802
+	.long	2571947539
+	.long	1073227145
+	.long	3558159064
+	.long	3164425245
+	.long	2604962541
+	.long	1073244377
+	.long	2614425274
+	.long	3164587768
+	.long	1110089947
+	.long	1073261797
+	.long	1451641639
+	.long	1016523249
+	.long	2568320822
+	.long	1073279406
+	.long	2732824428
+	.long	1015401491
+	.long	2966275557
+	.long	1073297207
+	.long	2176155324
+	.long	3160891335
+	.long	2682146384
+	.long	1073315202
+	.long	2082178513
+	.long	3164411995
+	.long	2191782032
+	.long	1073333393
+	.long	2960257726
+	.long	1014791238
+	.long	2069751141
+	.long	1073351782
+	.long	1562170675
+	.long	3163773257
+	.long	2990417245
+	.long	1073370371
+	.long	3683467745
+	.long	3164417902
+	.long	1434058175
+	.long	1073389163
+	.long	251133233
+	.long	1016134345
+	.long	2572866477
+	.long	1073408159
+	.long	878562433
+	.long	1016570317
+	.long	3092190715
+	.long	1073427362
+	.long	814012168
+	.long	3160571998
+	.long	4076559943
+	.long	1073446774
+	.long	2119478331
+	.long	3161806927
+	.long	2420883922
+	.long	1073466398
+	.long	2049810052
+	.long	1015168464
+	.long	3716502172
+	.long	1073486235
+	.long	2303740125
+	.long	1015091301
+	.long	777507147
+	.long	1073506289
+	.long	4282924205
+	.long	1016236109
+	.long	3706687593
+	.long	1073526560
+	.long	3521726939
+	.long	1014301643
+	.long	1242007932
+	.long	1073547053
+	.long	1132034716
+	.long	3164388407
+	.long	3707479175
+	.long	1073567768
+	.long	3613079303
+	.long	1015213314
+	.long	64696965
+	.long	1073588710
+	.long	1768797490
+	.long	1016865536
+	.long	863738719
+	.long	1073609879
+	.long	1326992220
+	.long	3163661773
+	.long	3884662774
+	.long	1073631278
+	.long	2158611599
+	.long	1015258761
+	.long	2728693978
+	.long	1073652911
+	.long	396109971
+	.long	3164511267
+	.long	3999357479
+	.long	1073674779
+	.long	2258941616
+	.long	1016973300
+	.long	1533953344
+	.long	1073696886
+	.long	769171851
+	.long	1016714209
+	.long	2174652632
+	.long	1073719233
+	.long	4087714590
+	.long	1015498835
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	0
+	.long	1697350398
+	.long	1079448903
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1071644672
+	.long	1431652600
+	.long	1069897045
+	.long	1431670732
+	.long	1067799893
+	.long	984555731
+	.long	1065423122
+	.long	472530941
+	.long	1062650218
+	.long	2411329662
+	.long	1082536910
+	.long	4277796864
+	.long	1065758274
+	.long	3164486458
+	.long	1025308570
+	.long	4294967295
+	.long	2146435071
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	3875694624
+	.long	1077247184
+	.type	__dcosh_ha_CoutTab,@object
+	.size	__dcosh_ha_CoutTab,1152
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_d_la.s
index 5e70a7dc0bc..d8ef3121eea 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_h_la.s
new file mode 100644
index 00000000000..0c66121a71c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_h_la.s
@@ -0,0 +1,136 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Compute cosh(x) as (exp(x)+exp(-x))/2,
+ *   where exp is calculated as
+ *   exp(M*ln2 + ln2*(j/2^k) + r) = 2^M * 2^(j/2^k) * exp(r)
+ *
+ *   Special cases:
+ *
+ *   cosh(NaN) = quiet NaN, and raise invalid exception
+ *   cosh(INF) = that INF
+ *   cosh(0)   = 1
+ *   cosh(x) overflows for big x and returns MAXLOG+log(2)
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_coshs32
+
+__svml_coshs32:
+
+        .cfi_startproc
+        vmovdqu16 __svml_hcosh_data_internal(%rip), %zmm31
+        vmovdqu16 64+__svml_hcosh_data_internal(%rip), %zmm30
+        vmovdqu16 128+__svml_hcosh_data_internal(%rip), %zmm2
+        vmovdqu16 192+__svml_hcosh_data_internal(%rip), %zmm3
+        vmovdqu16 256+__svml_hcosh_data_internal(%rip), %zmm4
+        vmovdqu16 320+__svml_hcosh_data_internal(%rip), %zmm5
+        vmovdqu16 384+__svml_hcosh_data_internal(%rip), %zmm29
+        vmovdqu16 448+__svml_hcosh_data_internal(%rip), %zmm6
+        vmovdqu16 512+__svml_hcosh_data_internal(%rip), %zmm9
+        vmovdqu16 576+__svml_hcosh_data_internal(%rip), %zmm12
+        kxnord  %k7, %k7, %k7
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_cosh_h:
+        cmpq    $31, %rdx
+        ja .loaddata_coshh
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_cosh_h
+.loaddata_coshh:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_cosh_h:
+
+/* Shifter + x*log2(e) */
+        vmovdqu16 %zmm30, %zmm1
+        vmovdqu16 %zmm29, %zmm10
+
+/* poly + 0.25*mpoly ~ (exp(x)+exp(-x))*0.5 */
+        vpandd %zmm31, %zmm0, %zmm7
+        vfmadd213ph {rz-sae}, %zmm2, %zmm7, %zmm1
+        vsubph    {rn-sae}, %zmm2, %zmm1, %zmm8
+        vfnmadd231ph {rn-sae}, %zmm8, %zmm3, %zmm7
+
+/* hN - 1 */
+        vsubph    {rn-sae}, %zmm9, %zmm8, %zmm11
+        vfnmadd231ph {rn-sae}, %zmm8, %zmm4, %zmm7
+
+/* exp(R) */
+        vfmadd231ph {rn-sae}, %zmm7, %zmm5, %zmm10
+        vfmadd213ph {rn-sae}, %zmm6, %zmm7, %zmm10
+        vfmadd213ph {rn-sae}, %zmm9, %zmm7, %zmm10
+
+/* poly*R+1 */
+        vfmadd213ph {rn-sae}, %zmm9, %zmm7, %zmm10
+        vscalefph {rn-sae}, %zmm11, %zmm10, %zmm13
+        vrcpph    %zmm13, %zmm0
+        vfmadd213ph {rn-sae}, %zmm13, %zmm12, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_cosh_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_coshs32,@function
+        .size	__svml_coshs32,.-__svml_coshs32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hcosh_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x3dc5
+	.endr
+	.rept	32
+        .word	0x6600
+	.endr
+	.rept	32
+        .word	0x398c
+	.endr
+	.rept	32
+        .word	0x8af4
+	.endr
+	.rept	32
+        .word	0x2b17
+	.endr
+	.rept	32
+        .word	0x3122
+	.endr
+	.rept	32
+        .word	0x3802
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x3400
+	.endr
+        .type	__svml_hcosh_data_internal,@object
+        .size	__svml_hcosh_data_internal,640
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_s_ha.s
new file mode 100644
index 00000000000..f49a7f0edcd
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_s_ha.s
@@ -0,0 +1,2139 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_coshf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_coshf16_ha
+
+__svml_coshf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   1024+__svml_scosh_ha_data_internal(%rip), %zmm5
+        vmovups   384+__svml_scosh_ha_data_internal(%rip), %zmm7
+
+/*
+ * ............... Load argument ............................
+ * dM = x/log(2) + RShifter
+ */
+        vmovups   768+__svml_scosh_ha_data_internal(%rip), %zmm11
+        vmovups   896+__svml_scosh_ha_data_internal(%rip), %zmm8
+        vmovups   960+__svml_scosh_ha_data_internal(%rip), %zmm10
+
+/* x^2 */
+        vmovups   640+__svml_scosh_ha_data_internal(%rip), %zmm3
+
+/* ... */
+        vmovups   704+__svml_scosh_ha_data_internal(%rip), %zmm2
+
+/* ............... G1,G2 2^N,2^(-N) ........... */
+        vmovups   __svml_scosh_ha_data_internal(%rip), %zmm13
+        vmovups   256+__svml_scosh_ha_data_internal(%rip), %zmm15
+        vmovups   128+__svml_scosh_ha_data_internal(%rip), %zmm14
+
+/* ...............Check for overflow\underflow ............. */
+        vpternlogd $255, %zmm6, %zmm6, %zmm6
+        vmovaps   %zmm0, %zmm4
+
+/*
+ * -------------------- Implementation  -------------------
+ * ............... Abs argument ............................
+ */
+        vandnps   %zmm4, %zmm5, %zmm1
+        vfmadd213ps {rn-sae}, %zmm7, %zmm1, %zmm11
+        vpcmpd    $1, 512+__svml_scosh_ha_data_internal(%rip), %zmm1, %k1
+
+/* iM now is an EXP(2^N) */
+        vpslld    $18, %zmm11, %zmm12
+
+/*
+ * ................... R ...................................
+ * sN = sM - RShifter
+ */
+        vsubps    {rn-sae}, %zmm7, %zmm11, %zmm9
+        vpermt2ps 64+__svml_scosh_ha_data_internal(%rip), %zmm11, %zmm13
+        vpermt2ps 320+__svml_scosh_ha_data_internal(%rip), %zmm11, %zmm15
+        vpermt2ps 192+__svml_scosh_ha_data_internal(%rip), %zmm11, %zmm14
+        vpandnd   %zmm1, %zmm1, %zmm6{%k1}
+
+/* sR = sX - sN*Log2_hi */
+        vfnmadd231ps {rn-sae}, %zmm8, %zmm9, %zmm1
+        vptestmd  %zmm6, %zmm6, %k0
+
+/* sR = (sX - sN*Log2_hi) - sN*Log2_lo */
+        vfnmadd231ps {rn-sae}, %zmm10, %zmm9, %zmm1
+        kmovw     %k0, %edx
+        vmulps    {rn-sae}, %zmm1, %zmm1, %zmm0
+        vmulps    {rn-sae}, %zmm0, %zmm2, %zmm2
+
+/* sOut = r^2*(a2) */
+        vmulps    {rn-sae}, %zmm0, %zmm3, %zmm0
+
+/* sSinh_r = r + r*(r^2*(a3)) */
+        vfmadd213ps {rn-sae}, %zmm1, %zmm1, %zmm2
+        vpandd    1216+__svml_scosh_ha_data_internal(%rip), %zmm12, %zmm5
+        vpaddd    %zmm5, %zmm13, %zmm8
+        vpsubd    %zmm5, %zmm15, %zmm7
+        vpaddd    %zmm5, %zmm14, %zmm14
+
+/* sG2 = 2^N*Th + 2^(-N)*T_h */
+        vaddps    {rn-sae}, %zmm7, %zmm8, %zmm15
+
+/* sG1 = 2^N*Th - 2^(-N)*T_h */
+        vsubps    {rn-sae}, %zmm7, %zmm8, %zmm6
+
+/* res = sG1*(r + r*(r^2*(a3))) + sG2*(1+r^2*(a2)) */
+        vfmadd213ps {rn-sae}, %zmm14, %zmm15, %zmm0
+        vfmadd213ps {rn-sae}, %zmm0, %zmm6, %zmm2
+        vaddps    {rn-sae}, %zmm7, %zmm2, %zmm1
+        vaddps    {rn-sae}, %zmm8, %zmm1, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm4, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_scosh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_coshf16_ha,@function
+	.size	__svml_coshf16_ha,.-__svml_coshf16_ha
+..LN__svml_coshf16_ha.0:
+
+.L_2__routine_start___svml_scosh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_scosh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movq      %rsi, %r8
+        movzwl    2(%rdi), %edx
+        xorl      %eax, %eax
+        andl      $32640, %edx
+        cmpl      $32640, %edx
+        je        .LBL_2_12
+
+
+        pxor      %xmm0, %xmm0
+        cvtss2sd  (%rdi), %xmm0
+        movsd     %xmm0, -8(%rsp)
+        andb      $127, -1(%rsp)
+        movzwl    -2(%rsp), %edx
+        andl      $32752, %edx
+        cmpl      $15504, %edx
+        jle       .LBL_2_10
+
+
+        movsd     -8(%rsp), %xmm0
+        movsd     1096+__scosh_ha_CoutTab(%rip), %xmm1
+        comisd    %xmm0, %xmm1
+        jbe       .LBL_2_9
+
+
+        movq      1128+__scosh_ha_CoutTab(%rip), %rdx
+        movq      %rdx, -8(%rsp)
+        comisd    1144+__scosh_ha_CoutTab(%rip), %xmm0
+        jb        .LBL_2_8
+
+
+        movsd     1040+__scosh_ha_CoutTab(%rip), %xmm1
+        lea       __scosh_ha_CoutTab(%rip), %r9
+        mulsd     %xmm0, %xmm1
+        addsd     1048+__scosh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -40(%rsp)
+        movsd     -40(%rsp), %xmm2
+        movsd     1088+__scosh_ha_CoutTab(%rip), %xmm1
+        movl      -40(%rsp), %edx
+        movl      %edx, %esi
+        andl      $63, %esi
+        subsd     1048+__scosh_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm2, -32(%rsp)
+        lea       (%rsi,%rsi), %ecx
+        movsd     -32(%rsp), %xmm3
+        lea       1(%rsi,%rsi), %edi
+        mulsd     1104+__scosh_ha_CoutTab(%rip), %xmm3
+        movsd     -32(%rsp), %xmm4
+        subsd     %xmm3, %xmm0
+        mulsd     1112+__scosh_ha_CoutTab(%rip), %xmm4
+        shrl      $6, %edx
+        subsd     %xmm4, %xmm0
+        mulsd     %xmm0, %xmm1
+        addl      $1022, %edx
+        andl      $2047, %edx
+        addsd     1080+__scosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1072+__scosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1064+__scosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1056+__scosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     %xmm0, %xmm1
+        movsd     (%r9,%rcx,8), %xmm0
+        mulsd     %xmm0, %xmm1
+        addsd     (%r9,%rdi,8), %xmm1
+        addsd     %xmm0, %xmm1
+        cmpl      $2046, %edx
+        ja        .LBL_2_7
+
+
+        movq      1128+__scosh_ha_CoutTab(%rip), %rcx
+        shrq      $48, %rcx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm1, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r8)
+        ret
+
+.LBL_2_7:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -2(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        mulsd     1024+__scosh_ha_CoutTab(%rip), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%r8)
+        ret
+
+.LBL_2_8:
+
+        movsd     1040+__scosh_ha_CoutTab(%rip), %xmm1
+        lea       __scosh_ha_CoutTab(%rip), %rcx
+        movzwl    -2(%rsp), %esi
+        andl      $-32753, %esi
+        movsd     1080+__scosh_ha_CoutTab(%rip), %xmm14
+        mulsd     %xmm0, %xmm1
+        addsd     1048+__scosh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -40(%rsp)
+        movsd     -40(%rsp), %xmm2
+        movl      -40(%rsp), %r10d
+        movl      %r10d, %r9d
+        shrl      $6, %r9d
+        subsd     1048+__scosh_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm2, -32(%rsp)
+        lea       1023(%r9), %edi
+        movsd     -32(%rsp), %xmm3
+        addl      $1022, %r9d
+        mulsd     1104+__scosh_ha_CoutTab(%rip), %xmm3
+        andl      $63, %r10d
+        movsd     -32(%rsp), %xmm4
+        lea       (%r10,%r10), %edx
+        mulsd     1112+__scosh_ha_CoutTab(%rip), %xmm4
+        subsd     %xmm3, %xmm0
+        andl      $2047, %r9d
+        negl      %edi
+        movsd     (%rcx,%rdx,8), %xmm15
+        negl      %edx
+        shll      $4, %r9d
+        addl      $-4, %edi
+        orl       %r9d, %esi
+        andl      $2047, %edi
+        movw      %si, -2(%rsp)
+        andl      $-32753, %esi
+        shll      $4, %edi
+        lea       1(%r10,%r10), %r11d
+        movsd     -8(%rsp), %xmm6
+        orl       %edi, %esi
+        movw      %si, -2(%rsp)
+        lea       128(%rdx), %esi
+        addl      $129, %edx
+        subsd     %xmm4, %xmm0
+        mulsd     %xmm6, %xmm15
+        movaps    %xmm0, %xmm5
+        movaps    %xmm15, %xmm8
+        mulsd     %xmm0, %xmm5
+        movaps    %xmm15, %xmm10
+        movsd     (%rcx,%r11,8), %xmm2
+        mulsd     %xmm6, %xmm2
+        mulsd     %xmm5, %xmm14
+        movsd     -8(%rsp), %xmm7
+        movaps    %xmm2, %xmm12
+        movsd     (%rcx,%rdx,8), %xmm13
+        mulsd     %xmm7, %xmm13
+        addsd     1064+__scosh_ha_CoutTab(%rip), %xmm14
+        movsd     1088+__scosh_ha_CoutTab(%rip), %xmm1
+        subsd     %xmm13, %xmm12
+        mulsd     %xmm5, %xmm1
+        mulsd     %xmm5, %xmm14
+        mulsd     %xmm0, %xmm12
+        addsd     1072+__scosh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm14
+        addsd     %xmm12, %xmm2
+        mulsd     %xmm5, %xmm1
+        addsd     %xmm13, %xmm2
+        addsd     1056+__scosh_ha_CoutTab(%rip), %xmm1
+        movsd     (%rcx,%rsi,8), %xmm11
+        mulsd     %xmm7, %xmm11
+        mulsd     %xmm5, %xmm1
+        addsd     %xmm11, %xmm8
+        subsd     %xmm11, %xmm15
+        movsd     %xmm8, -24(%rsp)
+        movsd     -24(%rsp), %xmm9
+        mulsd     %xmm15, %xmm14
+        subsd     %xmm9, %xmm10
+        mulsd     %xmm15, %xmm0
+        addsd     %xmm11, %xmm10
+        addsd     %xmm14, %xmm2
+        movsd     %xmm10, -16(%rsp)
+        addsd     %xmm0, %xmm2
+        movsd     -24(%rsp), %xmm3
+        mulsd     %xmm3, %xmm1
+        movsd     -16(%rsp), %xmm6
+        addsd     %xmm1, %xmm2
+        addsd     %xmm6, %xmm2
+        movsd     %xmm2, -24(%rsp)
+        movsd     -24(%rsp), %xmm0
+        addsd     %xmm0, %xmm3
+        cvtsd2ss  %xmm3, %xmm3
+        movss     %xmm3, (%r8)
+        ret
+
+.LBL_2_9:
+
+        movsd     1120+__scosh_ha_CoutTab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r8)
+        ret
+
+.LBL_2_10:
+
+        movsd     1136+__scosh_ha_CoutTab(%rip), %xmm0
+        addsd     -8(%rsp), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r8)
+
+
+        ret
+
+.LBL_2_12:
+
+        movss     (%rdi), %xmm0
+        mulss     %xmm0, %xmm0
+        movss     %xmm0, (%r8)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_scosh_ha_cout_rare_internal,@function
+	.size	__svml_scosh_ha_cout_rare_internal,.-__svml_scosh_ha_cout_rare_internal
+..LN__svml_scosh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_scosh_ha_data_internal:
+	.long	1056964608
+	.long	1057148295
+	.long	1057336003
+	.long	1057527823
+	.long	1057723842
+	.long	1057924154
+	.long	1058128851
+	.long	1058338032
+	.long	1058551792
+	.long	1058770234
+	.long	1058993458
+	.long	1059221571
+	.long	1059454679
+	.long	1059692891
+	.long	1059936319
+	.long	1060185078
+	.long	1060439283
+	.long	1060699055
+	.long	1060964516
+	.long	1061235789
+	.long	1061513002
+	.long	1061796286
+	.long	1062085772
+	.long	1062381598
+	.long	1062683901
+	.long	1062992824
+	.long	1063308511
+	.long	1063631111
+	.long	1063960775
+	.long	1064297658
+	.long	1064641917
+	.long	1064993715
+	.long	0
+	.long	2999887785
+	.long	852465809
+	.long	3003046475
+	.long	2984291233
+	.long	3001644133
+	.long	854021668
+	.long	2997748242
+	.long	849550193
+	.long	2995541347
+	.long	851518274
+	.long	809701978
+	.long	2997656926
+	.long	2996185864
+	.long	2980965110
+	.long	3002882728
+	.long	844097402
+	.long	848217591
+	.long	2999013352
+	.long	2992006718
+	.long	831170615
+	.long	3002278818
+	.long	833158180
+	.long	3000769962
+	.long	2991891850
+	.long	2999994908
+	.long	2979965785
+	.long	2982419430
+	.long	2982221534
+	.long	2999469642
+	.long	833168438
+	.long	2987538264
+	.long	1056964608
+	.long	1056605107
+	.long	1056253309
+	.long	1055909050
+	.long	1055572167
+	.long	1055242503
+	.long	1054919903
+	.long	1054604216
+	.long	1054295293
+	.long	1053992990
+	.long	1053697164
+	.long	1053407678
+	.long	1053124394
+	.long	1052847181
+	.long	1052575908
+	.long	1052310447
+	.long	1052050675
+	.long	1051796470
+	.long	1051547711
+	.long	1051304283
+	.long	1051066071
+	.long	1050832963
+	.long	1050604850
+	.long	1050381626
+	.long	1050163184
+	.long	1049949424
+	.long	1049740243
+	.long	1049535546
+	.long	1049335234
+	.long	1049139215
+	.long	1048947395
+	.long	1048759687
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	31
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1118743630
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1056964879
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1042983629
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	849703008
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	1060204544
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	939916788
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
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+	.type	__svml_scosh_ha_data_internal,@object
+	.size	__svml_scosh_ha_data_internal,5568
+	.align 32
+__scosh_ha_CoutTab:
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+	.type	__scosh_ha_CoutTab,@object
+	.size	__scosh_ha_CoutTab,1152
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_s_la.s
index 6713ad8efd5..58b5ec2d035 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_cosh_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp10_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp10_h_la.s
new file mode 100644
index 00000000000..df38cb11ff6
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp10_h_la.s
@@ -0,0 +1,123 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   exp10(x)  = 2^x/log10(2) = 2^n * (1 + T[j]) * (1 + P(y))
+ *
+ *   x = m*log10(2)/K + y,  y in [-log10(2)/K..log10(2)/K]
+ *   m = n*K + j,           m,n,j - signed integer, j in [-K/2..K/2]
+ *
+ *   values of 2^j/K are tabulated
+ *
+ *   P(y) is a minimax polynomial approximation of exp10(x)-1
+ *   on small interval [-log10(2)/K..log10(2)/K]
+ *
+ *  Special cases:
+ *
+ *   exp10(NaN)  = NaN
+ *   exp10(+INF) = +INF
+ *   exp10(-INF) = 0
+ *   exp10(x)    = 1 for subnormals
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_exp10s32
+
+__svml_exp10s32:
+
+        .cfi_startproc
+
+/* restrict input range to [-8.0,6.0] */
+        vmovdqu16 64+__svml_hexp10_data_internal(%rip), %zmm1
+        vmovdqu16 __svml_hexp10_data_internal(%rip), %zmm3
+
+/* (2^11*1.5 + bias) + x*log2(e) */
+        vmovdqu16 128+__svml_hexp10_data_internal(%rip), %zmm4
+        vmovdqu16 192+__svml_hexp10_data_internal(%rip), %zmm6
+        vmovdqu16 256+__svml_hexp10_data_internal(%rip), %zmm7
+
+/* polynomial ~ 2^R */
+        vmovdqu16 320+__svml_hexp10_data_internal(%rip), %zmm12
+        vmovdqu16 384+__svml_hexp10_data_internal(%rip), %zmm9
+        vmovdqu16 448+__svml_hexp10_data_internal(%rip), %zmm10
+        vmovdqu16 512+__svml_hexp10_data_internal(%rip), %zmm11
+
+/* fixup for input=NaN */
+        vmovdqu16 576+__svml_hexp10_data_internal(%rip), %zmm14
+        vminph    {sae}, %zmm1, %zmm0, %zmm2
+        vcmpph    $22, {sae}, %zmm14, %zmm0, %k1
+        vmaxph    {sae}, %zmm3, %zmm2, %zmm13
+        vmovaps   %zmm4, %zmm5
+        vfmadd231ph {rn-sae}, %zmm13, %zmm6, %zmm5
+
+/* N = 2*(int)(x*log2(e)/2) */
+        vsubph    {rn-sae}, %zmm4, %zmm5, %zmm8
+
+/* 2^(N/2) */
+        vpsllw    $10, %zmm5, %zmm15
+
+/* reduced arg.: x*log2(e) - N */
+        vfmsub231ph {rn-sae}, %zmm13, %zmm6, %zmm8
+        vpblendmw %zmm0, %zmm15, %zmm0{%k1}
+        vfmadd213ph {rn-sae}, %zmm8, %zmm7, %zmm13
+
+/* start polynomial */
+        vfmadd213ph {rn-sae}, %zmm9, %zmm13, %zmm12
+        vfmadd213ph {rn-sae}, %zmm10, %zmm13, %zmm12
+        vfmadd213ph {rn-sae}, %zmm11, %zmm13, %zmm12
+        vmulph    {rn-sae}, %zmm13, %zmm12, %zmm1
+        vfmadd213ph {rn-sae}, %zmm15, %zmm15, %zmm1
+
+/* result:  (1+poly)*2^(N/2)*2^(N/2) */
+        vmulph    {rn-sae}, %zmm0, %zmm1, %zmm0
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_exp10s32,@function
+        .size	__svml_exp10s32,.-__svml_exp10s32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hexp10_data_internal:
+	.rept	32
+        .word	0xc800
+	.endr
+	.rept	32
+        .word	0x4600
+	.endr
+	.rept	32
+        .word	0x6a0f
+	.endr
+	.rept	32
+        .word	0x42a5
+	.endr
+	.rept	32
+        .word	0x8d88
+	.endr
+	.rept	32
+        .word	0x2110
+	.endr
+	.rept	32
+        .word	0x2b52
+	.endr
+	.rept	32
+        .word	0x33af
+	.endr
+	.rept	32
+        .word	0x398b
+	.endr
+	.rept	32
+        .word	0x7c00
+	.endr
+        .type	__svml_hexp10_data_internal,@object
+        .size	__svml_hexp10_data_internal,640
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_d_ha.s
new file mode 100644
index 00000000000..e6b758cf965
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_d_ha.s
@@ -0,0 +1,885 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_exp28_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_exp28_ha
+
+__svml_exp28_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   576+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm6
+
+/* c6*r   + c5 */
+        vmovups   512+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm14
+
+/*
+ * Reduced argument
+ * where VREDUCE is available
+ */
+        vreducepd $65, {sae}, %zmm0, %zmm10
+        vmovups   448+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm7
+        vmovups   640+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm3
+        vmovups   384+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   __svml_dexp2_ha_data_internal_avx512(%rip), %zmm15
+        vfmadd231pd {rn-sae}, %zmm10, %zmm6, %zmm14
+        vmovups   320+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   256+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   128+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   768+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm2
+
+/* c6*r^2 + c5*r + c4 */
+        vfmadd213pd {rn-sae}, %zmm7, %zmm10, %zmm14
+
+/*
+ * Integer form of K+0.b1b2b3b4 in lower bits - call K_plus_f0
+ * Mantisssa of normalized single precision FP: 1.b1b2...b52
+ */
+        vaddpd    {rd-sae}, %zmm3, %zmm0, %zmm4
+        vandpd    704+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm0, %zmm1
+
+/* c6*r^3 + c5*r^2 + c4*r + c3 */
+        vfmadd213pd {rn-sae}, %zmm8, %zmm10, %zmm14
+        vcmppd    $29, {sae}, %zmm2, %zmm1, %k0
+
+/* c6*r^4 + c5*r^3 + c4*r^2 + c3*r + c2 */
+        vfmadd213pd {rn-sae}, %zmm9, %zmm10, %zmm14
+        kmovw     %k0, %edx
+
+/* c6*r^5 + c5*r^4 + c4*r^3 + c3*r^2 + c2*r + c1 */
+        vfmadd213pd {rn-sae}, %zmm11, %zmm10, %zmm14
+
+/* Table value: 2^(0.b1b2b3b4) */
+        vpandq    832+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm4, %zmm5
+        vpermt2pd 64+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm5, %zmm15
+        vpermt2pd 192+__svml_dexp2_ha_data_internal_avx512(%rip), %zmm5, %zmm13
+
+/* Th*r */
+        vmulpd    {rn-sae}, %zmm10, %zmm15, %zmm12
+
+/* Tl+(T*r*(c6*r^5 + c5*r^4 + c4*r^3 + c3*r^2 + c2*r + c1)) */
+        vfmadd213pd {rn-sae}, %zmm13, %zmm12, %zmm14
+
+/* Th+Tl+(T*r*(c6*r^5 + c5*r^4 + c4*r^3 + c3*r^2 + c2*r + c1)) */
+        vaddpd    {rn-sae}, %zmm15, %zmm14, %zmm1
+
+/* Scaling placed at the end to avoid accuracy loss when T*r*scale underflows */
+        vscalefpd {rn-sae}, %zmm0, %zmm1, %zmm1
+
+/*
+ * ===================================================================
+ * =============================== LA / EP ===========================
+ * ===================================================================
+ */
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        vmovaps   %zmm1, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm1, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm1
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dexp2_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_exp28_ha,@function
+	.size	__svml_exp28_ha,.-__svml_exp28_ha
+..LN__svml_exp28_ha.0:
+
+.L_2__routine_start___svml_dexp2_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dexp2_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    6(%rdi), %edx
+        andl      $32752, %edx
+        movsd     (%rdi), %xmm5
+        movsd     %xmm5, -8(%rsp)
+        cmpl      $32752, %edx
+        je        .LBL_2_12
+
+
+        movsd     1072+__dexp2_ha__imldExp2HATab(%rip), %xmm0
+        comisd    %xmm5, %xmm0
+        jbe       .LBL_2_10
+
+
+        comisd    1088+__dexp2_ha__imldExp2HATab(%rip), %xmm5
+        jbe       .LBL_2_9
+
+
+        movsd     1024+__dexp2_ha__imldExp2HATab(%rip), %xmm0
+        movaps    %xmm5, %xmm3
+        lea       __dexp2_ha__imldExp2HATab(%rip), %r10
+        addsd     %xmm5, %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movsd     -24(%rsp), %xmm1
+        movl      -24(%rsp), %r8d
+        movl      %r8d, %ecx
+        andl      $63, %r8d
+        subsd     1024+__dexp2_ha__imldExp2HATab(%rip), %xmm1
+        movsd     %xmm1, -16(%rsp)
+        lea       1(%r8,%r8), %r9d
+        movsd     -16(%rsp), %xmm2
+        lea       (%r8,%r8), %edi
+        movsd     1064+__dexp2_ha__imldExp2HATab(%rip), %xmm1
+        subsd     %xmm2, %xmm3
+        mulsd     %xmm3, %xmm1
+        movsd     (%r10,%rdi,8), %xmm4
+        shrl      $6, %ecx
+        addsd     1056+__dexp2_ha__imldExp2HATab(%rip), %xmm1
+        comisd    1080+__dexp2_ha__imldExp2HATab(%rip), %xmm5
+        mulsd     %xmm3, %xmm1
+        movq      1112+__dexp2_ha__imldExp2HATab(%rip), %rdx
+        movq      %rdx, -8(%rsp)
+        lea       1023(%rcx), %edx
+        addsd     1048+__dexp2_ha__imldExp2HATab(%rip), %xmm1
+        mulsd     %xmm3, %xmm1
+        addsd     1040+__dexp2_ha__imldExp2HATab(%rip), %xmm1
+        mulsd     %xmm3, %xmm1
+        addsd     1032+__dexp2_ha__imldExp2HATab(%rip), %xmm1
+        mulsd     %xmm3, %xmm1
+        addsd     (%r10,%r9,8), %xmm1
+        mulsd     %xmm4, %xmm1
+        addsd     %xmm4, %xmm1
+        jb        .LBL_2_8
+
+
+        andl      $2047, %edx
+        cmpl      $2046, %edx
+        ja        .LBL_2_7
+
+
+        movq      1112+__dexp2_ha__imldExp2HATab(%rip), %rcx
+        shrq      $48, %rcx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm1, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_7:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -2(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm1, %xmm0
+        mulsd     1128+__dexp2_ha__imldExp2HATab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        addl      $1123, %ecx
+        andl      $2047, %ecx
+        movzwl    -2(%rsp), %eax
+        shll      $4, %ecx
+        andl      $-32753, %eax
+        orl       %ecx, %eax
+        movw      %ax, -2(%rsp)
+        movl      $4, %eax
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm1, %xmm0
+        mulsd     1136+__dexp2_ha__imldExp2HATab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        movsd     1096+__dexp2_ha__imldExp2HATab(%rip), %xmm0
+        movl      $4, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        movsd     1104+__dexp2_ha__imldExp2HATab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_12:
+
+        movb      -1(%rsp), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_14
+
+.LBL_2_13:
+
+        mulsd     %xmm5, %xmm5
+        movsd     %xmm5, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        testl     $1048575, -4(%rsp)
+        jne       .LBL_2_13
+
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_13
+
+
+        movq      1112+__dexp2_ha__imldExp2HATab(%rip), %rdx
+        movq      %rdx, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dexp2_ha_cout_rare_internal,@function
+	.size	__svml_dexp2_ha_cout_rare_internal,.-__svml_dexp2_ha_cout_rare_internal
+..LN__svml_dexp2_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dexp2_ha_data_internal_avx512:
+	.long	0
+	.long	1072693248
+	.long	1828292879
+	.long	1072739672
+	.long	1014845819
+	.long	1072788152
+	.long	1853186616
+	.long	1072838778
+	.long	171030293
+	.long	1072891646
+	.long	1276261410
+	.long	1072946854
+	.long	3577096743
+	.long	1073004506
+	.long	3712504873
+	.long	1073064711
+	.long	1719614413
+	.long	1073127582
+	.long	1944781191
+	.long	1073193236
+	.long	1110089947
+	.long	1073261797
+	.long	2191782032
+	.long	1073333393
+	.long	2572866477
+	.long	1073408159
+	.long	3716502172
+	.long	1073486235
+	.long	3707479175
+	.long	1073567768
+	.long	2728693978
+	.long	1073652911
+	.long	0
+	.long	0
+	.long	1255956747
+	.long	1016636974
+	.long	3117910646
+	.long	3162607681
+	.long	3066496371
+	.long	1016705150
+	.long	3526460132
+	.long	1015477354
+	.long	300981948
+	.long	1015732745
+	.long	2951496418
+	.long	1014842263
+	.long	88491949
+	.long	1016476236
+	.long	330458198
+	.long	3164331316
+	.long	3993278767
+	.long	3162772855
+	.long	1451641639
+	.long	1016523249
+	.long	2960257726
+	.long	1014791238
+	.long	878562433
+	.long	1016570317
+	.long	2303740125
+	.long	1015091301
+	.long	3613079303
+	.long	1015213314
+	.long	396109971
+	.long	3164511267
+	.long	4277811595
+	.long	1072049730
+	.long	4277811595
+	.long	1072049730
+	.long	4277811595
+	.long	1072049730
+	.long	4277811595
+	.long	1072049730
+	.long	4277811595
+	.long	1072049730
+	.long	4277811595
+	.long	1072049730
+	.long	4277811595
+	.long	1072049730
+	.long	4277811595
+	.long	1072049730
+	.long	4286862682
+	.long	1070514109
+	.long	4286862682
+	.long	1070514109
+	.long	4286862682
+	.long	1070514109
+	.long	4286862682
+	.long	1070514109
+	.long	4286862682
+	.long	1070514109
+	.long	4286862682
+	.long	1070514109
+	.long	4286862682
+	.long	1070514109
+	.long	4286862682
+	.long	1070514109
+	.long	3568142009
+	.long	1068264200
+	.long	3568142009
+	.long	1068264200
+	.long	3568142009
+	.long	1068264200
+	.long	3568142009
+	.long	1068264200
+	.long	3568142009
+	.long	1068264200
+	.long	3568142009
+	.long	1068264200
+	.long	3568142009
+	.long	1068264200
+	.long	3568142009
+	.long	1068264200
+	.long	454500946
+	.long	1065595565
+	.long	454500946
+	.long	1065595565
+	.long	454500946
+	.long	1065595565
+	.long	454500946
+	.long	1065595565
+	.long	454500946
+	.long	1065595565
+	.long	454500946
+	.long	1065595565
+	.long	454500946
+	.long	1065595565
+	.long	454500946
+	.long	1065595565
+	.long	655609113
+	.long	1062590279
+	.long	655609113
+	.long	1062590279
+	.long	655609113
+	.long	1062590279
+	.long	655609113
+	.long	1062590279
+	.long	655609113
+	.long	1062590279
+	.long	655609113
+	.long	1062590279
+	.long	655609113
+	.long	1062590279
+	.long	655609113
+	.long	1062590279
+	.long	4112922395
+	.long	1059365335
+	.long	4112922395
+	.long	1059365335
+	.long	4112922395
+	.long	1059365335
+	.long	4112922395
+	.long	1059365335
+	.long	4112922395
+	.long	1059365335
+	.long	4112922395
+	.long	1059365335
+	.long	4112922395
+	.long	1059365335
+	.long	4112922395
+	.long	1059365335
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	1083174911
+	.long	0
+	.long	1083174911
+	.long	0
+	.long	1083174911
+	.long	0
+	.long	1083174911
+	.long	0
+	.long	1083174911
+	.long	0
+	.long	1083174911
+	.long	0
+	.long	1083174911
+	.long	0
+	.long	1083174911
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.type	__svml_dexp2_ha_data_internal_avx512,@object
+	.size	__svml_dexp2_ha_data_internal_avx512,896
+	.align 32
+__dexp2_ha__imldExp2HATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	1048019041
+	.long	1072704666
+	.long	2631457885
+	.long	3161546771
+	.long	3541402996
+	.long	1072716208
+	.long	896005651
+	.long	1015861842
+	.long	410360776
+	.long	1072727877
+	.long	1642514529
+	.long	1012987726
+	.long	1828292879
+	.long	1072739672
+	.long	1568897901
+	.long	1016568486
+	.long	852742562
+	.long	1072751596
+	.long	1882168529
+	.long	1010744893
+	.long	3490863953
+	.long	1072763649
+	.long	707771662
+	.long	3163903570
+	.long	2930322912
+	.long	1072775834
+	.long	3117806614
+	.long	3163670819
+	.long	1014845819
+	.long	1072788152
+	.long	3936719688
+	.long	3162512149
+	.long	3949972341
+	.long	1072800603
+	.long	1058231231
+	.long	1015777676
+	.long	828946858
+	.long	1072813191
+	.long	1044000608
+	.long	1016786167
+	.long	2288159958
+	.long	1072825915
+	.long	1151779725
+	.long	1015705409
+	.long	1853186616
+	.long	1072838778
+	.long	3819481236
+	.long	1016499965
+	.long	1709341917
+	.long	1072851781
+	.long	2552227826
+	.long	1015039787
+	.long	4112506593
+	.long	1072864925
+	.long	1829350193
+	.long	1015216097
+	.long	2799960843
+	.long	1072878213
+	.long	1913391796
+	.long	1015756674
+	.long	171030293
+	.long	1072891646
+	.long	1303423926
+	.long	1015238005
+	.long	2992903935
+	.long	1072905224
+	.long	1574172746
+	.long	1016061241
+	.long	926591435
+	.long	1072918951
+	.long	3427487848
+	.long	3163704045
+	.long	887463927
+	.long	1072932827
+	.long	1049900754
+	.long	3161575912
+	.long	1276261410
+	.long	1072946854
+	.long	2804567149
+	.long	1015390024
+	.long	569847338
+	.long	1072961034
+	.long	1209502043
+	.long	3159926671
+	.long	1617004845
+	.long	1072975368
+	.long	1623370769
+	.long	1011049453
+	.long	3049340112
+	.long	1072989858
+	.long	3667985273
+	.long	1013894369
+	.long	3577096743
+	.long	1073004506
+	.long	3145379760
+	.long	1014403278
+	.long	1990012071
+	.long	1073019314
+	.long	7447438
+	.long	3163526196
+	.long	1453150082
+	.long	1073034283
+	.long	3171891295
+	.long	3162037958
+	.long	917841882
+	.long	1073049415
+	.long	419288974
+	.long	1016280325
+	.long	3712504873
+	.long	1073064711
+	.long	3793507337
+	.long	1016095713
+	.long	363667784
+	.long	1073080175
+	.long	728023093
+	.long	1016345318
+	.long	2956612997
+	.long	1073095806
+	.long	1005538728
+	.long	3163304901
+	.long	2186617381
+	.long	1073111608
+	.long	2018924632
+	.long	3163803357
+	.long	1719614413
+	.long	1073127582
+	.long	3210617384
+	.long	3163796463
+	.long	1013258799
+	.long	1073143730
+	.long	3094194670
+	.long	3160631279
+	.long	3907805044
+	.long	1073160053
+	.long	2119843535
+	.long	3161988964
+	.long	1447192521
+	.long	1073176555
+	.long	508946058
+	.long	3162904882
+	.long	1944781191
+	.long	1073193236
+	.long	3108873501
+	.long	3162190556
+	.long	919555682
+	.long	1073210099
+	.long	2882956373
+	.long	1013312481
+	.long	2571947539
+	.long	1073227145
+	.long	4047189812
+	.long	3163777462
+	.long	2604962541
+	.long	1073244377
+	.long	3631372142
+	.long	3163870288
+	.long	1110089947
+	.long	1073261797
+	.long	3253791412
+	.long	1015920431
+	.long	2568320822
+	.long	1073279406
+	.long	1509121860
+	.long	1014756995
+	.long	2966275557
+	.long	1073297207
+	.long	2339118633
+	.long	3160254904
+	.long	2682146384
+	.long	1073315202
+	.long	586480042
+	.long	3163702083
+	.long	2191782032
+	.long	1073333393
+	.long	730975783
+	.long	1014083580
+	.long	2069751141
+	.long	1073351782
+	.long	576856675
+	.long	3163014404
+	.long	2990417245
+	.long	1073370371
+	.long	3552361237
+	.long	3163667409
+	.long	1434058175
+	.long	1073389163
+	.long	1853053619
+	.long	1015310724
+	.long	2572866477
+	.long	1073408159
+	.long	2462790535
+	.long	1015814775
+	.long	3092190715
+	.long	1073427362
+	.long	1457303226
+	.long	3159737305
+	.long	4076559943
+	.long	1073446774
+	.long	950899508
+	.long	3160987380
+	.long	2420883922
+	.long	1073466398
+	.long	174054861
+	.long	1014300631
+	.long	3716502172
+	.long	1073486235
+	.long	816778419
+	.long	1014197934
+	.long	777507147
+	.long	1073506289
+	.long	3507050924
+	.long	1015341199
+	.long	3706687593
+	.long	1073526560
+	.long	1821514088
+	.long	1013410604
+	.long	1242007932
+	.long	1073547053
+	.long	1073740399
+	.long	3163532637
+	.long	3707479175
+	.long	1073567768
+	.long	2789017511
+	.long	1014276997
+	.long	64696965
+	.long	1073588710
+	.long	3586233004
+	.long	1015962192
+	.long	863738719
+	.long	1073609879
+	.long	129252895
+	.long	3162690849
+	.long	3884662774
+	.long	1073631278
+	.long	1614448851
+	.long	1014281732
+	.long	2728693978
+	.long	1073652911
+	.long	2413007344
+	.long	3163551506
+	.long	3999357479
+	.long	1073674779
+	.long	1101668360
+	.long	1015989180
+	.long	1533953344
+	.long	1073696886
+	.long	835814894
+	.long	1015702697
+	.long	2174652632
+	.long	1073719233
+	.long	1301400989
+	.long	1014466875
+	.long	0
+	.long	1121452032
+	.long	4277811695
+	.long	1072049730
+	.long	4286751290
+	.long	1070514109
+	.long	3607585384
+	.long	1068264200
+	.long	871937163
+	.long	1065595565
+	.long	3302507530
+	.long	1062590576
+	.long	0
+	.long	1083179008
+	.long	0
+	.long	3230658560
+	.long	0
+	.long	3230714880
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	967835648
+	.long	0
+	.long	0
+	.type	__dexp2_ha__imldExp2HATab,@object
+	.size	__dexp2_ha__imldExp2HATab,1152
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_d_la.s
index af4f281e3c7..8806509b781 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_h_la.s
new file mode 100644
index 00000000000..00e4921c391
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_h_la.s
@@ -0,0 +1,141 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *     2^X = 2^Xo  x  2^{X-Xo}
+ *     2^X = 2^K  x  2^fo  x  2^{X-Xo}
+ *     2^X = 2^K  x  2^fo  x  2^r
+ *
+ *     2^K  --> Manual scaling
+ *     2^fo --> Table lookup
+ *     r    --> 1 + poly    (r = X - Xo)
+ *
+ *     Xo = K  +  fo
+ *     Xo = K  +  0.x1x2x3x4
+ *
+ *     r = X - Xo
+ *       = Vreduce(X, imm)
+ *       = X - VRndScale(X, imm),    where Xo = VRndScale(X, imm)
+ *
+ *     Rnd(S + X) = S + Xo,    where S is selected as S = 2^19 x 1.5
+ *         S + X = S + floor(X) + 0.x1x2x3x4
+ *     Rnd(S + X) = Rnd(2^19 x 1.5 + X)
+ *     (Note: 2^exp x 1.b1b2b3 ... b23,  2^{exp-23} = 2^-4 for exp=19)
+ *
+ *     exp2(x) =  2^K  x  2^fo  x (1 + poly(r)),   where 2^r = 1 + poly(r)
+ *
+ *     Scale back:
+ *     dest = src1 x 2^floor(src2)
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_exp2s32
+
+__svml_exp2s32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hexp2_data_internal(%rip), %zmm31
+        vmovdqu16 64+__svml_hexp2_data_internal(%rip), %zmm1
+        vmovdqu16 128+__svml_hexp2_data_internal(%rip), %zmm2
+        vmovdqu16 192+__svml_hexp2_data_internal(%rip), %zmm4
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_exp2_h:
+        cmpq    $31, %rdx
+        ja .loaddata_exp2_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_exp2_h
+.loaddata_exp2_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_exp2_h:
+
+/*
+ * No callout
+ * reduce argument to [0, 1), i.e. x-floor(x)
+ */
+        vreduceph $9, {sae}, %zmm0, %zmm3
+        vmovdqu16 %zmm31, %zmm5
+
+/* start polynomial */
+        vfmadd213ph {rn-sae}, %zmm1, %zmm3, %zmm5
+        vfmadd213ph {rn-sae}, %zmm2, %zmm3, %zmm5
+        vfmadd213ph {rn-sae}, %zmm4, %zmm3, %zmm5
+
+/* poly*2^floor(x) */
+        vscalefph {rn-sae}, %zmm0, %zmm5, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_exp2_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_exp2s32,@function
+        .size	__svml_exp2s32,.-__svml_exp2s32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hexp2_data_internal:
+	.rept	32
+        .word	0x2d12
+	.endr
+	.rept	32
+        .word	0x332e
+	.endr
+	.rept	32
+        .word	0x3992
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0xce80
+	.endr
+	.rept	32
+        .word	0x4c00
+	.endr
+	.rept	32
+        .word	0x6a0f
+	.endr
+	.rept	32
+        .word	0x2110
+	.endr
+	.rept	32
+        .word	0x2b52
+	.endr
+	.rept	32
+        .word	0x33af
+	.endr
+	.rept	32
+        .word	0x398b
+	.endr
+	.rept	32
+        .word	0x7c00
+	.endr
+        .type	__svml_hexp2_data_internal,@object
+        .size	__svml_hexp2_data_internal,768
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_s_ha.s
new file mode 100644
index 00000000000..15641ac2ad7
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_s_ha.s
@@ -0,0 +1,757 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_exp2f16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_exp2f16_ha
+
+__svml_exp2f16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   384+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm3
+        vmovups   320+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm5
+        vmovups   256+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm11
+
+/*
+ * Reduced argument
+ * where VREDUCE is available
+ */
+        vreduceps $65, {sae}, %zmm0, %zmm7
+        vmovups   192+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm6
+        vmovups   128+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   512+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm2
+
+/* c4*r + c3 */
+        vfmadd231ps {rn-sae}, %zmm7, %zmm5, %zmm11
+
+/*
+ * Integer form of K+0.b1b2b3b4 in lower bits - call K_plus_f0
+ * Mantisssa of normalized single precision FP: 1.b1b2...b23
+ */
+        vaddps    {rd-sae}, %zmm3, %zmm0, %zmm4
+        vandps    448+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm0, %zmm1
+
+/* c4*r^2+c3*r+c2 */
+        vfmadd213ps {rn-sae}, %zmm6, %zmm7, %zmm11
+        vcmpps    $30, {sae}, %zmm2, %zmm1, %k0
+
+/* c4*r^3+c3*r^2+c2*r+c1 */
+        vfmadd213ps {rn-sae}, %zmm8, %zmm7, %zmm11
+
+/* Table value: 2^(0.b1b2b3b4) */
+        vpermps   __svml_sexp2_ha_data_internal_avx512(%rip), %zmm4, %zmm12
+        vpermps   64+__svml_sexp2_ha_data_internal_avx512(%rip), %zmm4, %zmm10
+        kmovw     %k0, %edx
+
+/* Th*r */
+        vmulps    {rn-sae}, %zmm7, %zmm12, %zmm9
+
+/* Tl + (Th*r*(c4*r^3+c3*r^2+c2*r+c1)) */
+        vfmadd213ps {rn-sae}, %zmm10, %zmm9, %zmm11
+
+/* Th + (Tl + (Th*r*(c4*r^3+c3*r^2+c2*r+c1))) */
+        vaddps    {rn-sae}, %zmm12, %zmm11, %zmm13
+
+/* Scaling placed at the end to avoid accuracy loss when T*r*scale underflows */
+        vscalefps {rn-sae}, %zmm0, %zmm13, %zmm1
+
+/*
+ * ===================================================================
+ * =============================== LA / EP ===========================
+ * ===================================================================
+ */
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        vmovaps   %zmm1, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm1, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm1
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_sexp2_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_exp2f16_ha,@function
+	.size	__svml_exp2f16_ha,.-__svml_exp2f16_ha
+..LN__svml_exp2f16_ha.0:
+
+.L_2__routine_start___svml_sexp2_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_sexp2_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        pxor      %xmm7, %xmm7
+        movzwl    2(%rdi), %edx
+        xorl      %eax, %eax
+        andl      $32640, %edx
+        movss     (%rdi), %xmm0
+        cvtss2sd  %xmm0, %xmm7
+        cmpl      $32640, %edx
+        je        .LBL_2_8
+
+
+        movsd     1072+__sexp2_ha__imldExp2HATab(%rip), %xmm0
+        comisd    %xmm7, %xmm0
+        jbe       .LBL_2_6
+
+
+        comisd    1088+__sexp2_ha__imldExp2HATab(%rip), %xmm7
+        jbe       .LBL_2_5
+
+
+        movsd     1024+__sexp2_ha__imldExp2HATab(%rip), %xmm1
+        movaps    %xmm7, %xmm4
+        movsd     1064+__sexp2_ha__imldExp2HATab(%rip), %xmm6
+        addsd     %xmm7, %xmm1
+        cmpltsd   1080+__sexp2_ha__imldExp2HATab(%rip), %xmm7
+        movsd     %xmm1, -16(%rsp)
+        lea       __sexp2_ha__imldExp2HATab(%rip), %rdi
+        movsd     -16(%rsp), %xmm2
+        movl      -16(%rsp), %r8d
+        movl      %r8d, %edx
+        shrl      $6, %r8d
+        subsd     1024+__sexp2_ha__imldExp2HATab(%rip), %xmm2
+        movsd     %xmm2, -8(%rsp)
+        addl      $1023, %r8d
+        movsd     -8(%rsp), %xmm3
+        andl      $2047, %r8d
+        andl      $63, %edx
+        subsd     %xmm3, %xmm4
+        mulsd     %xmm4, %xmm6
+        lea       1(%rdx,%rdx), %ecx
+        movzwl    1118+__sexp2_ha__imldExp2HATab(%rip), %r9d
+        lea       (%rdx,%rdx), %eax
+        movsd     (%rdi,%rax,8), %xmm5
+        andl      $-32753, %r9d
+        movsd     1112+__sexp2_ha__imldExp2HATab(%rip), %xmm0
+        shll      $4, %r8d
+        addsd     1056+__sexp2_ha__imldExp2HATab(%rip), %xmm6
+        movd      %xmm7, %eax
+        movsd     %xmm0, -24(%rsp)
+        mulsd     %xmm4, %xmm6
+        orl       %r8d, %r9d
+        movw      %r9w, -18(%rsp)
+        andl      $4, %eax
+        addsd     1048+__sexp2_ha__imldExp2HATab(%rip), %xmm6
+        mulsd     %xmm4, %xmm6
+        addsd     1040+__sexp2_ha__imldExp2HATab(%rip), %xmm6
+        mulsd     %xmm4, %xmm6
+        addsd     1032+__sexp2_ha__imldExp2HATab(%rip), %xmm6
+        mulsd     %xmm4, %xmm6
+        addsd     (%rdi,%rcx,8), %xmm6
+        mulsd     %xmm5, %xmm6
+        addsd     %xmm5, %xmm6
+        mulsd     -24(%rsp), %xmm6
+        cvtsd2ss  %xmm6, %xmm6
+        movss     %xmm6, (%rsi)
+        ret
+
+.LBL_2_5:
+
+        movsd     1096+__sexp2_ha__imldExp2HATab(%rip), %xmm0
+        movl      $4, %eax
+        mulsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_6:
+
+        movsd     1104+__sexp2_ha__imldExp2HATab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_8:
+
+        movb      3(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_10
+
+.LBL_2_9:
+
+        mulss     %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        testl     $8388607, (%rdi)
+        jne       .LBL_2_9
+
+
+        movsd     1112+__sexp2_ha__imldExp2HATab(%rip), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sexp2_ha_cout_rare_internal,@function
+	.size	__svml_sexp2_ha_cout_rare_internal,.-__svml_sexp2_ha_cout_rare_internal
+..LN__svml_sexp2_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_sexp2_ha_data_internal_avx512:
+	.long	1065353216
+	.long	1065724611
+	.long	1066112450
+	.long	1066517459
+	.long	1066940400
+	.long	1067382066
+	.long	1067843287
+	.long	1068324927
+	.long	1068827891
+	.long	1069353124
+	.long	1069901610
+	.long	1070474380
+	.long	1071072509
+	.long	1071697119
+	.long	1072349383
+	.long	1073030525
+	.long	0
+	.long	860854417
+	.long	2992679841
+	.long	862410276
+	.long	857938801
+	.long	859906882
+	.long	3006045534
+	.long	2989353718
+	.long	852486010
+	.long	3007401960
+	.long	839559223
+	.long	841546788
+	.long	3000280458
+	.long	2988354393
+	.long	2990610142
+	.long	841557046
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1047920129
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1029917730
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1008798169
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.long	1123811328
+	.type	__svml_sexp2_ha_data_internal_avx512,@object
+	.size	__svml_sexp2_ha_data_internal_avx512,576
+	.align 32
+__sexp2_ha__imldExp2HATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	1048019041
+	.long	1072704666
+	.long	2631457885
+	.long	3161546771
+	.long	3541402996
+	.long	1072716208
+	.long	896005651
+	.long	1015861842
+	.long	410360776
+	.long	1072727877
+	.long	1642514529
+	.long	1012987726
+	.long	1828292879
+	.long	1072739672
+	.long	1568897901
+	.long	1016568486
+	.long	852742562
+	.long	1072751596
+	.long	1882168529
+	.long	1010744893
+	.long	3490863953
+	.long	1072763649
+	.long	707771662
+	.long	3163903570
+	.long	2930322912
+	.long	1072775834
+	.long	3117806614
+	.long	3163670819
+	.long	1014845819
+	.long	1072788152
+	.long	3936719688
+	.long	3162512149
+	.long	3949972341
+	.long	1072800603
+	.long	1058231231
+	.long	1015777676
+	.long	828946858
+	.long	1072813191
+	.long	1044000608
+	.long	1016786167
+	.long	2288159958
+	.long	1072825915
+	.long	1151779725
+	.long	1015705409
+	.long	1853186616
+	.long	1072838778
+	.long	3819481236
+	.long	1016499965
+	.long	1709341917
+	.long	1072851781
+	.long	2552227826
+	.long	1015039787
+	.long	4112506593
+	.long	1072864925
+	.long	1829350193
+	.long	1015216097
+	.long	2799960843
+	.long	1072878213
+	.long	1913391796
+	.long	1015756674
+	.long	171030293
+	.long	1072891646
+	.long	1303423926
+	.long	1015238005
+	.long	2992903935
+	.long	1072905224
+	.long	1574172746
+	.long	1016061241
+	.long	926591435
+	.long	1072918951
+	.long	3427487848
+	.long	3163704045
+	.long	887463927
+	.long	1072932827
+	.long	1049900754
+	.long	3161575912
+	.long	1276261410
+	.long	1072946854
+	.long	2804567149
+	.long	1015390024
+	.long	569847338
+	.long	1072961034
+	.long	1209502043
+	.long	3159926671
+	.long	1617004845
+	.long	1072975368
+	.long	1623370769
+	.long	1011049453
+	.long	3049340112
+	.long	1072989858
+	.long	3667985273
+	.long	1013894369
+	.long	3577096743
+	.long	1073004506
+	.long	3145379760
+	.long	1014403278
+	.long	1990012071
+	.long	1073019314
+	.long	7447438
+	.long	3163526196
+	.long	1453150082
+	.long	1073034283
+	.long	3171891295
+	.long	3162037958
+	.long	917841882
+	.long	1073049415
+	.long	419288974
+	.long	1016280325
+	.long	3712504873
+	.long	1073064711
+	.long	3793507337
+	.long	1016095713
+	.long	363667784
+	.long	1073080175
+	.long	728023093
+	.long	1016345318
+	.long	2956612997
+	.long	1073095806
+	.long	1005538728
+	.long	3163304901
+	.long	2186617381
+	.long	1073111608
+	.long	2018924632
+	.long	3163803357
+	.long	1719614413
+	.long	1073127582
+	.long	3210617384
+	.long	3163796463
+	.long	1013258799
+	.long	1073143730
+	.long	3094194670
+	.long	3160631279
+	.long	3907805044
+	.long	1073160053
+	.long	2119843535
+	.long	3161988964
+	.long	1447192521
+	.long	1073176555
+	.long	508946058
+	.long	3162904882
+	.long	1944781191
+	.long	1073193236
+	.long	3108873501
+	.long	3162190556
+	.long	919555682
+	.long	1073210099
+	.long	2882956373
+	.long	1013312481
+	.long	2571947539
+	.long	1073227145
+	.long	4047189812
+	.long	3163777462
+	.long	2604962541
+	.long	1073244377
+	.long	3631372142
+	.long	3163870288
+	.long	1110089947
+	.long	1073261797
+	.long	3253791412
+	.long	1015920431
+	.long	2568320822
+	.long	1073279406
+	.long	1509121860
+	.long	1014756995
+	.long	2966275557
+	.long	1073297207
+	.long	2339118633
+	.long	3160254904
+	.long	2682146384
+	.long	1073315202
+	.long	586480042
+	.long	3163702083
+	.long	2191782032
+	.long	1073333393
+	.long	730975783
+	.long	1014083580
+	.long	2069751141
+	.long	1073351782
+	.long	576856675
+	.long	3163014404
+	.long	2990417245
+	.long	1073370371
+	.long	3552361237
+	.long	3163667409
+	.long	1434058175
+	.long	1073389163
+	.long	1853053619
+	.long	1015310724
+	.long	2572866477
+	.long	1073408159
+	.long	2462790535
+	.long	1015814775
+	.long	3092190715
+	.long	1073427362
+	.long	1457303226
+	.long	3159737305
+	.long	4076559943
+	.long	1073446774
+	.long	950899508
+	.long	3160987380
+	.long	2420883922
+	.long	1073466398
+	.long	174054861
+	.long	1014300631
+	.long	3716502172
+	.long	1073486235
+	.long	816778419
+	.long	1014197934
+	.long	777507147
+	.long	1073506289
+	.long	3507050924
+	.long	1015341199
+	.long	3706687593
+	.long	1073526560
+	.long	1821514088
+	.long	1013410604
+	.long	1242007932
+	.long	1073547053
+	.long	1073740399
+	.long	3163532637
+	.long	3707479175
+	.long	1073567768
+	.long	2789017511
+	.long	1014276997
+	.long	64696965
+	.long	1073588710
+	.long	3586233004
+	.long	1015962192
+	.long	863738719
+	.long	1073609879
+	.long	129252895
+	.long	3162690849
+	.long	3884662774
+	.long	1073631278
+	.long	1614448851
+	.long	1014281732
+	.long	2728693978
+	.long	1073652911
+	.long	2413007344
+	.long	3163551506
+	.long	3999357479
+	.long	1073674779
+	.long	1101668360
+	.long	1015989180
+	.long	1533953344
+	.long	1073696886
+	.long	835814894
+	.long	1015702697
+	.long	2174652632
+	.long	1073719233
+	.long	1301400989
+	.long	1014466875
+	.long	0
+	.long	1121452032
+	.long	4277811695
+	.long	1072049730
+	.long	4286751290
+	.long	1070514109
+	.long	3607585384
+	.long	1068264200
+	.long	871937163
+	.long	1065595565
+	.long	3302507530
+	.long	1062590576
+	.long	0
+	.long	1080033280
+	.long	0
+	.long	3227484160
+	.long	0
+	.long	3227697152
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	967835648
+	.long	0
+	.long	0
+	.type	__sexp2_ha__imldExp2HATab,@object
+	.size	__sexp2_ha__imldExp2HATab,1152
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_s_la.s
index 3affa77dc88..a57e0f07696 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp2_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_d_ha.s
new file mode 100644
index 00000000000..0a589e9ec9f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_d_ha.s
@@ -0,0 +1,1105 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *   Argument representation:
+ *   N = rint(X*2^k/ln2) = 2^k*M+j
+ *   X = N*ln2/2^k + r = M*ln2 + ln2*(j/2^k) + r
+ *   then -ln2/2^(k+1) < r < ln2/2^(k+1)
+ *   Alternatively:
+ *   N = trunc(X*2^k/ln2)
+ *   then 0 < r < ln2/2^k
+ * 
+ *   Result calculation:
+ *   exp(X) = exp(M*ln2 + ln2*(j/2^k) + r)
+ *   = 2^M * 2^(j/2^k) * exp(r)
+ *   2^M is calculated by bit manipulation
+ *   2^(j/2^k) is stored in table
+ *   exp(r) is approximated by polynomial
+ * 
+ *   The table lookup is skipped if k = 0.
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_exp8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_exp8_ha
+
+__svml_exp8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   256+__svml_dexp_ha_data_internal_avx512(%rip), %zmm4
+        vmovups   320+__svml_dexp_ha_data_internal_avx512(%rip), %zmm2
+        vmovups   384+__svml_dexp_ha_data_internal_avx512(%rip), %zmm5
+        vmovups   448+__svml_dexp_ha_data_internal_avx512(%rip), %zmm3
+        vmovups   576+__svml_dexp_ha_data_internal_avx512(%rip), %zmm6
+
+/*
+ * polynomial approximation
+ * p56 = c6*r+c5
+ */
+        vmovups   640+__svml_dexp_ha_data_internal_avx512(%rip), %zmm12
+        vmovups   704+__svml_dexp_ha_data_internal_avx512(%rip), %zmm7
+
+/* p34 = c4*r+c3 */
+        vmovups   768+__svml_dexp_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   832+__svml_dexp_ha_data_internal_avx512(%rip), %zmm8
+
+/* p12 = c2*r+c1 */
+        vmovups   896+__svml_dexp_ha_data_internal_avx512(%rip), %zmm11
+
+/* Table lookup: Tlr, Th(1 + Tlr) = 2^(j/2^k). */
+        vmovups   128+__svml_dexp_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   1024+__svml_dexp_ha_data_internal_avx512(%rip), %zmm15
+        vmovaps   %zmm0, %zmm1
+
+/* 2^(52-4)*1.5 + x * log2(e) in round-to-zero mode */
+        vfmadd213pd {rz-sae}, %zmm2, %zmm1, %zmm4
+
+/* remove sign of x by "and" operation */
+        vandpd    960+__svml_dexp_ha_data_internal_avx512(%rip), %zmm1, %zmm14
+
+/* N ~ x*log2(e), round-to-zero to 4 fractional bits */
+        vsubpd    {rn-sae}, %zmm2, %zmm4, %zmm0
+        vpermt2pd 192+__svml_dexp_ha_data_internal_avx512(%rip), %zmm4, %zmm13
+
+/* Table lookup: Th. */
+        vmovups   __svml_dexp_ha_data_internal_avx512(%rip), %zmm2
+
+/* R = x - N*ln(2)_high */
+        vfnmadd213pd {rn-sae}, %zmm1, %zmm0, %zmm5
+
+/* compare against threshold */
+        vcmppd    $29, {sae}, %zmm15, %zmm14, %k0
+
+/* R = R - N*ln(2)_low = x - N*ln(2) */
+        vfnmadd231pd {rn-sae}, %zmm0, %zmm3, %zmm5
+        vpermt2pd 64+__svml_dexp_ha_data_internal_avx512(%rip), %zmm4, %zmm2
+
+/* set mask for overflow/underflow */
+        kmovw     %k0, %edx
+
+/* ensure |R|<2 even for special cases */
+        vandpd    512+__svml_dexp_ha_data_internal_avx512(%rip), %zmm5, %zmm3
+
+/* r2 = r*r */
+        vmulpd    {rn-sae}, %zmm3, %zmm3, %zmm10
+        vfmadd231pd {rn-sae}, %zmm3, %zmm6, %zmm12
+        vfmadd231pd {rn-sae}, %zmm3, %zmm7, %zmm9
+        vfmadd231pd {rn-sae}, %zmm3, %zmm8, %zmm11
+
+/* p36 = (c6*r+c5)*r^2+c4*r+c3 */
+        vfmadd213pd {rn-sae}, %zmm9, %zmm10, %zmm12
+
+/* p16 = ((c6*r+c5)*r^2+c4*r+c3)*r^2+c2*r+c1 */
+        vfmadd213pd {rn-sae}, %zmm11, %zmm10, %zmm12
+
+/* p = (((c6*r+c5)*r^2+c4*r+c3)*r^2+c2*r+c1)*r + Tlr */
+        vfmadd213pd {rn-sae}, %zmm13, %zmm12, %zmm3
+
+/*
+ * 2^M * Th
+ * Th(1+p)
+ */
+        vfmadd213pd {rn-sae}, %zmm2, %zmm2, %zmm3
+
+/* exp(x) = 2^M*Th*(1+p)~2^M*Th(1+Tlr)*exp(r) */
+        vscalefpd {rn-sae}, %zmm0, %zmm3, %zmm0
+
+/*
+ * Check general callout condition
+ * Check VML specific mode related condition,
+ * no check in case of other libraries
+ * Above HA/LA/EP (non lrb2) sequences produce
+ * correct results even without going to callout.
+ * Callout was only needed to raise flags
+ * and set errno. If caller doesn't need that
+ * then it is safe to proceed without callout
+ */
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm1, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dexp_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_exp8_ha,@function
+	.size	__svml_exp8_ha,.-__svml_exp8_ha
+..LN__svml_exp8_ha.0:
+
+.L_2__routine_start___svml_dexp_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dexp_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    6(%rdi), %edx
+        andl      $32752, %edx
+        shrl      $4, %edx
+        movsd     (%rdi), %xmm6
+        movsd     %xmm6, -8(%rsp)
+        cmpl      $2047, %edx
+        je        .LBL_2_17
+
+
+        cmpl      $970, %edx
+        jle       .LBL_2_15
+
+
+        movsd     1080+_imldExpHATab(%rip), %xmm0
+        comisd    %xmm6, %xmm0
+        jb        .LBL_2_14
+
+
+        comisd    1096+_imldExpHATab(%rip), %xmm6
+        jb        .LBL_2_13
+
+
+        movsd     1024+_imldExpHATab(%rip), %xmm0
+        movaps    %xmm6, %xmm5
+        mulsd     %xmm6, %xmm0
+        lea       _imldExpHATab(%rip), %r10
+        movsd     %xmm0, -24(%rsp)
+        movsd     -24(%rsp), %xmm1
+        movq      1136+_imldExpHATab(%rip), %rdx
+        movq      %rdx, -8(%rsp)
+        addsd     1032+_imldExpHATab(%rip), %xmm1
+        movsd     %xmm1, -16(%rsp)
+        movsd     -16(%rsp), %xmm2
+        movl      -16(%rsp), %r8d
+        movl      %r8d, %ecx
+        andl      $63, %r8d
+        subsd     1032+_imldExpHATab(%rip), %xmm2
+        movsd     %xmm2, -24(%rsp)
+        lea       1(%r8,%r8), %r9d
+        movsd     -24(%rsp), %xmm3
+        lea       (%r8,%r8), %edi
+        mulsd     1104+_imldExpHATab(%rip), %xmm3
+        movsd     -24(%rsp), %xmm4
+        subsd     %xmm3, %xmm5
+        mulsd     1112+_imldExpHATab(%rip), %xmm4
+        movsd     1072+_imldExpHATab(%rip), %xmm2
+        subsd     %xmm4, %xmm5
+        mulsd     %xmm5, %xmm2
+        shrl      $6, %ecx
+        addsd     1064+_imldExpHATab(%rip), %xmm2
+        comisd    1088+_imldExpHATab(%rip), %xmm6
+        mulsd     %xmm5, %xmm2
+        movsd     (%r10,%rdi,8), %xmm0
+        lea       1023(%rcx), %edx
+        addsd     1056+_imldExpHATab(%rip), %xmm2
+        mulsd     %xmm5, %xmm2
+        addsd     1048+_imldExpHATab(%rip), %xmm2
+        mulsd     %xmm5, %xmm2
+        addsd     1040+_imldExpHATab(%rip), %xmm2
+        mulsd     %xmm5, %xmm2
+        mulsd     %xmm5, %xmm2
+        addsd     %xmm5, %xmm2
+        addsd     (%r10,%r9,8), %xmm2
+        mulsd     %xmm0, %xmm2
+        jb        .LBL_2_9
+
+
+        andl      $2047, %edx
+        addsd     %xmm0, %xmm2
+        cmpl      $2046, %edx
+        ja        .LBL_2_8
+
+
+        movq      1136+_imldExpHATab(%rip), %rcx
+        shrq      $48, %rcx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm0, %xmm2
+        movsd     %xmm2, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -2(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm0, %xmm2
+        mulsd     1152+_imldExpHATab(%rip), %xmm2
+        movsd     %xmm2, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        addl      $1083, %ecx
+        andl      $2047, %ecx
+        movl      %ecx, %eax
+        movzwl    -2(%rsp), %edx
+        shll      $4, %eax
+        andl      $-32753, %edx
+        orl       %eax, %edx
+        movw      %dx, -2(%rsp)
+        movsd     -8(%rsp), %xmm1
+        mulsd     %xmm1, %xmm2
+        mulsd     %xmm0, %xmm1
+        movaps    %xmm1, %xmm0
+        addsd     %xmm2, %xmm0
+        cmpl      $50, %ecx
+        ja        .LBL_2_11
+
+
+        mulsd     1160+_imldExpHATab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        jmp       .LBL_2_12
+
+.LBL_2_11:
+
+        movsd     %xmm0, -72(%rsp)
+        movsd     -72(%rsp), %xmm0
+        subsd     %xmm0, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        movsd     -64(%rsp), %xmm1
+        addsd     %xmm2, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        movsd     -72(%rsp), %xmm2
+        mulsd     1168+_imldExpHATab(%rip), %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movsd     -56(%rsp), %xmm3
+        addsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm5
+        subsd     %xmm5, %xmm6
+        movsd     %xmm6, -40(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     -40(%rsp), %xmm7
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -32(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -32(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -32(%rsp)
+        movsd     -40(%rsp), %xmm11
+        mulsd     1160+_imldExpHATab(%rip), %xmm11
+        movsd     %xmm11, -40(%rsp)
+        movsd     -32(%rsp), %xmm12
+        mulsd     1160+_imldExpHATab(%rip), %xmm12
+        movsd     %xmm12, -32(%rsp)
+        movsd     -40(%rsp), %xmm14
+        movsd     -32(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        movsd     %xmm14, (%rsi)
+
+.LBL_2_12:
+
+        movl      $4, %eax
+        ret
+
+.LBL_2_13:
+
+        movsd     1120+_imldExpHATab(%rip), %xmm0
+        movl      $4, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        movsd     1128+_imldExpHATab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_15:
+
+        movsd     1144+_imldExpHATab(%rip), %xmm0
+        addsd     %xmm6, %xmm0
+        movsd     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_17:
+
+        movb      -1(%rsp), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_19
+
+.LBL_2_18:
+
+        mulsd     %xmm6, %xmm6
+        movsd     %xmm6, (%rsi)
+        ret
+
+.LBL_2_19:
+
+        testl     $1048575, -4(%rsp)
+        jne       .LBL_2_18
+
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_18
+
+
+        movq      1136+_imldExpHATab(%rip), %rdx
+        movq      %rdx, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dexp_ha_cout_rare_internal,@function
+	.size	__svml_dexp_ha_cout_rare_internal,.-__svml_dexp_ha_cout_rare_internal
+..LN__svml_dexp_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dexp_ha_data_internal_avx512:
+	.long	0
+	.long	1072693248
+	.long	1828292879
+	.long	1072739672
+	.long	1014845819
+	.long	1072788152
+	.long	1853186616
+	.long	1072838778
+	.long	171030293
+	.long	1072891646
+	.long	1276261410
+	.long	1072946854
+	.long	3577096743
+	.long	1073004506
+	.long	3712504873
+	.long	1073064711
+	.long	1719614413
+	.long	1073127582
+	.long	1944781191
+	.long	1073193236
+	.long	1110089947
+	.long	1073261797
+	.long	2191782032
+	.long	1073333393
+	.long	2572866477
+	.long	1073408159
+	.long	3716502172
+	.long	1073486235
+	.long	3707479175
+	.long	1073567768
+	.long	2728693978
+	.long	1073652911
+	.long	0
+	.long	0
+	.long	1568897901
+	.long	1016568486
+	.long	3936719688
+	.long	3162512149
+	.long	3819481236
+	.long	1016499965
+	.long	1303423926
+	.long	1015238005
+	.long	2804567149
+	.long	1015390024
+	.long	3145379760
+	.long	1014403278
+	.long	3793507337
+	.long	1016095713
+	.long	3210617384
+	.long	3163796463
+	.long	3108873501
+	.long	3162190556
+	.long	3253791412
+	.long	1015920431
+	.long	730975783
+	.long	1014083580
+	.long	2462790535
+	.long	1015814775
+	.long	816778419
+	.long	1014197934
+	.long	2789017511
+	.long	1014276997
+	.long	2413007344
+	.long	3163551506
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	915672068
+	.long	1062682904
+	.long	915672068
+	.long	1062682904
+	.long	915672068
+	.long	1062682904
+	.long	915672068
+	.long	1062682904
+	.long	915672068
+	.long	1062682904
+	.long	915672068
+	.long	1062682904
+	.long	915672068
+	.long	1062682904
+	.long	915672068
+	.long	1062682904
+	.long	3150079424
+	.long	1065422876
+	.long	3150079424
+	.long	1065422876
+	.long	3150079424
+	.long	1065422876
+	.long	3150079424
+	.long	1065422876
+	.long	3150079424
+	.long	1065422876
+	.long	3150079424
+	.long	1065422876
+	.long	3150079424
+	.long	1065422876
+	.long	3150079424
+	.long	1065422876
+	.long	606955774
+	.long	1067799895
+	.long	606955774
+	.long	1067799895
+	.long	606955774
+	.long	1067799895
+	.long	606955774
+	.long	1067799895
+	.long	606955774
+	.long	1067799895
+	.long	606955774
+	.long	1067799895
+	.long	606955774
+	.long	1067799895
+	.long	606955774
+	.long	1067799895
+	.long	1402181426
+	.long	1069897045
+	.long	1402181426
+	.long	1069897045
+	.long	1402181426
+	.long	1069897045
+	.long	1402181426
+	.long	1069897045
+	.long	1402181426
+	.long	1069897045
+	.long	1402181426
+	.long	1069897045
+	.long	1402181426
+	.long	1069897045
+	.long	1402181426
+	.long	1069897045
+	.long	53256
+	.long	1071644672
+	.long	53256
+	.long	1071644672
+	.long	53256
+	.long	1071644672
+	.long	53256
+	.long	1071644672
+	.long	53256
+	.long	1071644672
+	.long	53256
+	.long	1071644672
+	.long	53256
+	.long	1071644672
+	.long	53256
+	.long	1071644672
+	.long	4294967152
+	.long	1072693247
+	.long	4294967152
+	.long	1072693247
+	.long	4294967152
+	.long	1072693247
+	.long	4294967152
+	.long	1072693247
+	.long	4294967152
+	.long	1072693247
+	.long	4294967152
+	.long	1072693247
+	.long	4294967152
+	.long	1072693247
+	.long	4294967152
+	.long	1072693247
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	0
+	.long	1005584384
+	.long	0
+	.long	1005584384
+	.long	0
+	.long	1005584384
+	.long	0
+	.long	1005584384
+	.long	0
+	.long	1005584384
+	.long	0
+	.long	1005584384
+	.long	0
+	.long	1005584384
+	.long	0
+	.long	1005584384
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	32752
+	.long	0
+	.long	32752
+	.long	0
+	.long	32752
+	.long	0
+	.long	32752
+	.long	0
+	.long	32752
+	.long	0
+	.long	32752
+	.long	0
+	.long	32752
+	.long	0
+	.long	32752
+	.long	0
+	.type	__svml_dexp_ha_data_internal_avx512,@object
+	.size	__svml_dexp_ha_data_internal_avx512,1280
+	.align 32
+_imldExpHATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	1048019041
+	.long	1072704666
+	.long	2631457885
+	.long	3161546771
+	.long	3541402996
+	.long	1072716208
+	.long	896005651
+	.long	1015861842
+	.long	410360776
+	.long	1072727877
+	.long	1642514529
+	.long	1012987726
+	.long	1828292879
+	.long	1072739672
+	.long	1568897901
+	.long	1016568486
+	.long	852742562
+	.long	1072751596
+	.long	1882168529
+	.long	1010744893
+	.long	3490863953
+	.long	1072763649
+	.long	707771662
+	.long	3163903570
+	.long	2930322912
+	.long	1072775834
+	.long	3117806614
+	.long	3163670819
+	.long	1014845819
+	.long	1072788152
+	.long	3936719688
+	.long	3162512149
+	.long	3949972341
+	.long	1072800603
+	.long	1058231231
+	.long	1015777676
+	.long	828946858
+	.long	1072813191
+	.long	1044000608
+	.long	1016786167
+	.long	2288159958
+	.long	1072825915
+	.long	1151779725
+	.long	1015705409
+	.long	1853186616
+	.long	1072838778
+	.long	3819481236
+	.long	1016499965
+	.long	1709341917
+	.long	1072851781
+	.long	2552227826
+	.long	1015039787
+	.long	4112506593
+	.long	1072864925
+	.long	1829350193
+	.long	1015216097
+	.long	2799960843
+	.long	1072878213
+	.long	1913391796
+	.long	1015756674
+	.long	171030293
+	.long	1072891646
+	.long	1303423926
+	.long	1015238005
+	.long	2992903935
+	.long	1072905224
+	.long	1574172746
+	.long	1016061241
+	.long	926591435
+	.long	1072918951
+	.long	3427487848
+	.long	3163704045
+	.long	887463927
+	.long	1072932827
+	.long	1049900754
+	.long	3161575912
+	.long	1276261410
+	.long	1072946854
+	.long	2804567149
+	.long	1015390024
+	.long	569847338
+	.long	1072961034
+	.long	1209502043
+	.long	3159926671
+	.long	1617004845
+	.long	1072975368
+	.long	1623370769
+	.long	1011049453
+	.long	3049340112
+	.long	1072989858
+	.long	3667985273
+	.long	1013894369
+	.long	3577096743
+	.long	1073004506
+	.long	3145379760
+	.long	1014403278
+	.long	1990012071
+	.long	1073019314
+	.long	7447438
+	.long	3163526196
+	.long	1453150082
+	.long	1073034283
+	.long	3171891295
+	.long	3162037958
+	.long	917841882
+	.long	1073049415
+	.long	419288974
+	.long	1016280325
+	.long	3712504873
+	.long	1073064711
+	.long	3793507337
+	.long	1016095713
+	.long	363667784
+	.long	1073080175
+	.long	728023093
+	.long	1016345318
+	.long	2956612997
+	.long	1073095806
+	.long	1005538728
+	.long	3163304901
+	.long	2186617381
+	.long	1073111608
+	.long	2018924632
+	.long	3163803357
+	.long	1719614413
+	.long	1073127582
+	.long	3210617384
+	.long	3163796463
+	.long	1013258799
+	.long	1073143730
+	.long	3094194670
+	.long	3160631279
+	.long	3907805044
+	.long	1073160053
+	.long	2119843535
+	.long	3161988964
+	.long	1447192521
+	.long	1073176555
+	.long	508946058
+	.long	3162904882
+	.long	1944781191
+	.long	1073193236
+	.long	3108873501
+	.long	3162190556
+	.long	919555682
+	.long	1073210099
+	.long	2882956373
+	.long	1013312481
+	.long	2571947539
+	.long	1073227145
+	.long	4047189812
+	.long	3163777462
+	.long	2604962541
+	.long	1073244377
+	.long	3631372142
+	.long	3163870288
+	.long	1110089947
+	.long	1073261797
+	.long	3253791412
+	.long	1015920431
+	.long	2568320822
+	.long	1073279406
+	.long	1509121860
+	.long	1014756995
+	.long	2966275557
+	.long	1073297207
+	.long	2339118633
+	.long	3160254904
+	.long	2682146384
+	.long	1073315202
+	.long	586480042
+	.long	3163702083
+	.long	2191782032
+	.long	1073333393
+	.long	730975783
+	.long	1014083580
+	.long	2069751141
+	.long	1073351782
+	.long	576856675
+	.long	3163014404
+	.long	2990417245
+	.long	1073370371
+	.long	3552361237
+	.long	3163667409
+	.long	1434058175
+	.long	1073389163
+	.long	1853053619
+	.long	1015310724
+	.long	2572866477
+	.long	1073408159
+	.long	2462790535
+	.long	1015814775
+	.long	3092190715
+	.long	1073427362
+	.long	1457303226
+	.long	3159737305
+	.long	4076559943
+	.long	1073446774
+	.long	950899508
+	.long	3160987380
+	.long	2420883922
+	.long	1073466398
+	.long	174054861
+	.long	1014300631
+	.long	3716502172
+	.long	1073486235
+	.long	816778419
+	.long	1014197934
+	.long	777507147
+	.long	1073506289
+	.long	3507050924
+	.long	1015341199
+	.long	3706687593
+	.long	1073526560
+	.long	1821514088
+	.long	1013410604
+	.long	1242007932
+	.long	1073547053
+	.long	1073740399
+	.long	3163532637
+	.long	3707479175
+	.long	1073567768
+	.long	2789017511
+	.long	1014276997
+	.long	64696965
+	.long	1073588710
+	.long	3586233004
+	.long	1015962192
+	.long	863738719
+	.long	1073609879
+	.long	129252895
+	.long	3162690849
+	.long	3884662774
+	.long	1073631278
+	.long	1614448851
+	.long	1014281732
+	.long	2728693978
+	.long	1073652911
+	.long	2413007344
+	.long	3163551506
+	.long	3999357479
+	.long	1073674779
+	.long	1101668360
+	.long	1015989180
+	.long	1533953344
+	.long	1073696886
+	.long	835814894
+	.long	1015702697
+	.long	2174652632
+	.long	1073719233
+	.long	1301400989
+	.long	1014466875
+	.long	1697350398
+	.long	1079448903
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1071644672
+	.long	1431652600
+	.long	1069897045
+	.long	1431670732
+	.long	1067799893
+	.long	984555731
+	.long	1065423122
+	.long	472530941
+	.long	1062650218
+	.long	4277811695
+	.long	1082535490
+	.long	3715808466
+	.long	3230016299
+	.long	3576508497
+	.long	3230091536
+	.long	4277796864
+	.long	1065758274
+	.long	3164486458
+	.long	1025308570
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1009778688
+	.long	0
+	.long	1106771968
+	.type	_imldExpHATab,@object
+	.size	_imldExpHATab,1176
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_d_la.s
index dd5f03a4459..54b2b2c5eef 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_h_la.s
new file mode 100644
index 00000000000..2cb9fc7ef26
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_h_la.s
@@ -0,0 +1,194 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Typical exp() implementation, except that:
+ *    - tables are small, allowing for fast gathers
+ *    - all arguments processed in the main path
+ *        - final VSCALEF assists branch-free design (correct overflow/underflow and special case responses)
+ *        - a VAND is used to ensure the reduced argument |R|<2, even for large inputs
+ *        - RZ mode used to avoid oveflow to +/-Inf for x*log2(e); helps with special case handling
+ *        - SAE used to avoid spurious flag settings
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_exps32
+
+__svml_exps32:
+
+        .cfi_startproc
+
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hexp_data_internal(%rip), %zmm31
+        vmovdqu16 64+__svml_hexp_data_internal(%rip), %zmm28
+        vmovdqu16 128+__svml_hexp_data_internal(%rip), %zmm27
+        vmovdqu16 192+__svml_hexp_data_internal(%rip), %zmm3
+        vmovdqu16 256+__svml_hexp_data_internal(%rip), %zmm4
+        vmovdqu16 320+__svml_hexp_data_internal(%rip), %zmm30
+        vmovdqu16 448+__svml_hexp_data_internal(%rip), %zmm29
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_exp_h:
+        cmpq    $31, %rdx
+        ja .loaddata_exp_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_exp_h
+.loaddata_exp_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_exp_h:
+
+        vmovdqu16 %zmm28, %zmm2
+        vmovdqu16 %zmm27, %zmm5
+
+/*
+ * Variables:
+ * W
+ * H
+ * HM
+ * No callout
+ * Copy argument
+ * Check sign of input (to adjust shifter)
+ */
+        vpxord    %zmm1, %zmm1, %zmm1
+        vpcmpgtw  %zmm1, %zmm0, %k1
+        vpsubw %zmm31, %zmm2, %zmm2{%k1}
+        vfmadd213ph {rz-sae}, %zmm2, %zmm0, %zmm5
+        vsubph    {rn-sae}, %zmm2, %zmm5, %zmm8
+
+/*
+ * 2^(k/32) (k in 0..31)
+ * table value: index in last 5 bits of S
+ */
+        vpermw %zmm30, %zmm5, %zmm6
+        vfmadd231ph {rn-sae}, %zmm8, %zmm3, %zmm0
+        vfmadd231ph {rn-sae}, %zmm8, %zmm4, %zmm0
+
+/* T+T*R */
+        vfmadd213ph {rn-sae}, %zmm6, %zmm0, %zmm6
+
+/* Fixup for sign of underflow result */
+        vpandd %zmm29, %zmm6, %zmm7
+
+/* (T+T*R)*2^floor(N) */
+        vscalefph {rn-sae}, %zmm8, %zmm7, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_exp_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_exps32,@function
+        .size	__svml_exps32,.-__svml_exps32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hexp_data_internal:
+	.rept	32
+        .word	0x0100
+	.endr
+	.rept	32
+        .word	0x5300
+	.endr
+	.rept	32
+        .word	0x3dc5
+	.endr
+	.rept	32
+        .word	0xb98c
+	.endr
+	.rept	32
+        .word	0x0af4
+	.endr
+        .word	0x3c00
+        .word	0x3c16
+        .word	0x3c2d
+        .word	0x3c45
+        .word	0x3c5d
+        .word	0x3c75
+        .word	0x3c8e
+        .word	0x3ca8
+        .word	0x3cc2
+        .word	0x3cdc
+        .word	0x3cf8
+        .word	0x3d14
+        .word	0x3d30
+        .word	0x3d4d
+        .word	0x3d6b
+        .word	0x3d89
+        .word	0x3da8
+        .word	0x3dc8
+        .word	0x3de8
+        .word	0x3e09
+        .word	0x3e2b
+        .word	0x3e4e
+        .word	0x3e71
+        .word	0x3e95
+        .word	0x3eba
+        .word	0x3ee0
+        .word	0x3f06
+        .word	0x3f2e
+        .word	0x3f56
+        .word	0x3f7f
+        .word	0x3fa9
+        .word	0x3fd4
+	.rept	32
+        .word	0xbfff
+	.endr
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0xcc80
+	.endr
+	.rept	32
+        .word	0x4a00
+	.endr
+	.rept	32
+        .word	0x6a0f
+	.endr
+	.rept	32
+        .word	0x3dc5
+	.endr
+	.rept	32
+        .word	0x0d1e
+	.endr
+	.rept	32
+        .word	0x2110
+	.endr
+	.rept	32
+        .word	0x2b52
+	.endr
+	.rept	32
+        .word	0x33af
+	.endr
+	.rept	32
+        .word	0x398b
+	.endr
+	.rept	32
+        .word	0x7c00
+	.endr
+        .type	__svml_hexp_data_internal,@object
+        .size	__svml_hexp_data_internal,1152
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_s_ha.s
new file mode 100644
index 00000000000..84e5e1d8a5e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_s_ha.s
@@ -0,0 +1,1012 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *   Argument representation:
+ *   M = rint(X*2^k/ln2) = 2^k*N+j
+ *   X = M*ln2/2^k + r = N*ln2 + ln2*(j/2^k) + r
+ *   then -ln2/2^(k+1) < r < ln2/2^(k+1)
+ *   Alternatively:
+ *   M = trunc(X*2^k/ln2)
+ *   then 0 < r < ln2/2^k
+ * 
+ *   Result calculation:
+ *   exp(X) = exp(N*ln2 + ln2*(j/2^k) + r)
+ *   = 2^N * 2^(j/2^k) * exp(r)
+ *   2^N is calculated by bit manipulation
+ *   2^(j/2^k) is computed from table lookup
+ *   exp(r) is approximated by polynomial
+ * 
+ *   The table lookup is skipped if k = 0.
+ *   For low accuracy approximation, exp(r) ~ 1 or 1+r.
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_expf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_expf16_ha
+
+__svml_expf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   256+__svml_sexp_ha_data_internal_avx512(%rip), %zmm3
+        vmovups   320+__svml_sexp_ha_data_internal_avx512(%rip), %zmm1
+        vmovups   384+__svml_sexp_ha_data_internal_avx512(%rip), %zmm4
+        vmovups   448+__svml_sexp_ha_data_internal_avx512(%rip), %zmm2
+
+/* Table lookup: Tlr */
+        vmovups   __svml_sexp_ha_data_internal_avx512(%rip), %zmm6
+
+/* Table lookup: Th,  Th(1 + Tlr) = 2^(j/2^k) */
+        vmovups   128+__svml_sexp_ha_data_internal_avx512(%rip), %zmm12
+
+/* 2^(23-4)*1.5 + x * log2(e) in round-to-zero mode */
+        vfmadd213ps {rz-sae}, %zmm1, %zmm0, %zmm3
+        vmovups   768+__svml_sexp_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   576+__svml_sexp_ha_data_internal_avx512(%rip), %zmm5
+        vmovups   640+__svml_sexp_ha_data_internal_avx512(%rip), %zmm8
+
+/* N ~ x*log2(e), round-to-zero to 5 fractional bits */
+        vsubps    {rn-sae}, %zmm1, %zmm3, %zmm13
+
+/* remove sign of x by "and" operation */
+        vandps    704+__svml_sexp_ha_data_internal_avx512(%rip), %zmm0, %zmm10
+        vpermt2ps 64+__svml_sexp_ha_data_internal_avx512(%rip), %zmm3, %zmm6
+        vpermt2ps 192+__svml_sexp_ha_data_internal_avx512(%rip), %zmm3, %zmm12
+
+/* R = x - N*ln(2)_high */
+        vfnmadd213ps {rn-sae}, %zmm0, %zmm13, %zmm4
+
+/* compare against threshold */
+        vcmpps    $29, {sae}, %zmm11, %zmm10, %k0
+
+/* 2^M * Th */
+        vscalefps {rn-sae}, %zmm13, %zmm12, %zmm14
+
+/* R = R - N*ln(2)_low = x - N*ln(2) */
+        vfnmadd231ps {rn-sae}, %zmm13, %zmm2, %zmm4
+
+/* set mask for overflow/underflow */
+        kmovw     %k0, %edx
+
+/* ensure |R|<2 even for special cases */
+        vandps    512+__svml_sexp_ha_data_internal_avx512(%rip), %zmm4, %zmm7
+
+/* R*R */
+        vmulps    {rn-sae}, %zmm7, %zmm7, %zmm1
+
+/* p23 = c3*R+c2, polynomial approximation */
+        vfmadd231ps {rn-sae}, %zmm7, %zmm5, %zmm8
+
+/* R+Tlr */
+        vaddps    {rn-sae}, %zmm7, %zmm6, %zmm9
+
+/* p = R+Tlr+R^2*p23 */
+        vfmadd213ps {rn-sae}, %zmm9, %zmm8, %zmm1
+
+/* exp(x) = 2^M*Th(1+p) ~ 2^M*Th(1+Tlr)(1+R+R^2*p23) */
+        vfmadd213ps {rn-sae}, %zmm14, %zmm14, %zmm1
+
+/*
+ * Check general callout condition
+ * Check VML specific mode related condition,
+ * no check in case of other libraries
+ * Above HA/LA/EP sequences produce
+ * correct results even without going to callout.
+ * Callout was only needed to raise flags
+ * and set errno. If caller doesn't need that
+ * then it is safe to proceed without callout
+ */
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        vmovaps   %zmm1, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm1, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm1
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_sexp_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_expf16_ha,@function
+	.size	__svml_expf16_ha,.-__svml_expf16_ha
+..LN__svml_expf16_ha.0:
+
+.L_2__routine_start___svml_sexp_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_sexp_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    2(%rdi), %edx
+        andl      $32640, %edx
+        shrl      $7, %edx
+        cmpl      $255, %edx
+        je        .LBL_2_17
+
+
+        pxor      %xmm7, %xmm7
+        cvtss2sd  (%rdi), %xmm7
+        cmpl      $74, %edx
+        jle       .LBL_2_15
+
+
+        movsd     1080+_vmldExpHATab(%rip), %xmm0
+        comisd    %xmm7, %xmm0
+        jb        .LBL_2_14
+
+
+        comisd    1096+_vmldExpHATab(%rip), %xmm7
+        jb        .LBL_2_13
+
+
+        movsd     1024+_vmldExpHATab(%rip), %xmm1
+        movaps    %xmm7, %xmm6
+        mulsd     %xmm7, %xmm1
+        lea       _vmldExpHATab(%rip), %r10
+        movsd     %xmm1, -16(%rsp)
+        movsd     -16(%rsp), %xmm2
+        movsd     1072+_vmldExpHATab(%rip), %xmm1
+        movsd     1136+_vmldExpHATab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        addsd     1032+_vmldExpHATab(%rip), %xmm2
+        movsd     %xmm2, -8(%rsp)
+        movsd     -8(%rsp), %xmm3
+        movl      -8(%rsp), %r8d
+        movl      %r8d, %ecx
+        andl      $63, %r8d
+        subsd     1032+_vmldExpHATab(%rip), %xmm3
+        movsd     %xmm3, -16(%rsp)
+        lea       1(%r8,%r8), %r9d
+        movsd     -16(%rsp), %xmm5
+        lea       (%r8,%r8), %edi
+        movsd     -16(%rsp), %xmm4
+        mulsd     1112+_vmldExpHATab(%rip), %xmm4
+        mulsd     1104+_vmldExpHATab(%rip), %xmm5
+        subsd     %xmm4, %xmm6
+        shrl      $6, %ecx
+        subsd     %xmm5, %xmm6
+        comisd    1088+_vmldExpHATab(%rip), %xmm7
+        mulsd     %xmm6, %xmm1
+        movsd     (%r10,%rdi,8), %xmm0
+        lea       1023(%rcx), %edx
+        addsd     1064+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     1056+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     1048+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     1040+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     %xmm6, %xmm1
+        addsd     (%r10,%r9,8), %xmm1
+        mulsd     %xmm0, %xmm1
+        jb        .LBL_2_9
+
+
+        andl      $2047, %edx
+        addsd     %xmm0, %xmm1
+        cmpl      $2046, %edx
+        ja        .LBL_2_8
+
+
+        movzwl    1142+_vmldExpHATab(%rip), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -18(%rsp)
+        movsd     -24(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -18(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -18(%rsp)
+        movsd     -24(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        mulsd     1152+_vmldExpHATab(%rip), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        addl      $1083, %ecx
+        andl      $2047, %ecx
+        movl      %ecx, %eax
+        movzwl    -18(%rsp), %edx
+        shll      $4, %eax
+        andl      $-32753, %edx
+        orl       %eax, %edx
+        movw      %dx, -18(%rsp)
+        movsd     -24(%rsp), %xmm3
+        mulsd     %xmm3, %xmm1
+        mulsd     %xmm0, %xmm3
+        movaps    %xmm1, %xmm2
+        addsd     %xmm3, %xmm2
+        cmpl      $50, %ecx
+        ja        .LBL_2_11
+
+
+        movsd     1160+_vmldExpHATab(%rip), %xmm0
+        mulsd     %xmm2, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        jmp       .LBL_2_12
+
+.LBL_2_11:
+
+        movsd     %xmm2, -72(%rsp)
+        movsd     -72(%rsp), %xmm0
+        subsd     %xmm0, %xmm3
+        movsd     %xmm3, -64(%rsp)
+        movsd     -64(%rsp), %xmm2
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm1
+        mulsd     1168+_vmldExpHATab(%rip), %xmm1
+        movsd     %xmm1, -56(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movsd     -56(%rsp), %xmm3
+        addsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm5
+        subsd     %xmm5, %xmm6
+        movsd     %xmm6, -40(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     -40(%rsp), %xmm7
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -32(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -32(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -32(%rsp)
+        movsd     -40(%rsp), %xmm11
+        mulsd     1160+_vmldExpHATab(%rip), %xmm11
+        movsd     %xmm11, -40(%rsp)
+        movsd     -32(%rsp), %xmm12
+        mulsd     1160+_vmldExpHATab(%rip), %xmm12
+        movsd     %xmm12, -32(%rsp)
+        movsd     -40(%rsp), %xmm14
+        movsd     -32(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        cvtsd2ss  %xmm14, %xmm14
+        movss     %xmm14, (%rsi)
+
+.LBL_2_12:
+
+        movl      $4, %eax
+        ret
+
+.LBL_2_13:
+
+        movsd     1120+_vmldExpHATab(%rip), %xmm0
+        movl      $4, %eax
+        mulsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        movsd     1128+_vmldExpHATab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_15:
+
+        movsd     1144+_vmldExpHATab(%rip), %xmm0
+        addsd     %xmm7, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_17:
+
+        movb      3(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_19
+
+.LBL_2_18:
+
+        movss     (%rdi), %xmm0
+        mulss     %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_19:
+
+        testl     $8388607, (%rdi)
+        jne       .LBL_2_18
+
+
+        movsd     1136+_vmldExpHATab(%rip), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sexp_ha_cout_rare_internal,@function
+	.size	__svml_sexp_ha_cout_rare_internal,.-__svml_sexp_ha_cout_rare_internal
+..LN__svml_sexp_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_sexp_ha_data_internal_avx512:
+	.long	0
+	.long	3007986186
+	.long	860277610
+	.long	3010384254
+	.long	2991457809
+	.long	3008462297
+	.long	860562562
+	.long	3004532446
+	.long	856238081
+	.long	3001480295
+	.long	857441778
+	.long	815380209
+	.long	3003456168
+	.long	3001196762
+	.long	2986372182
+	.long	3006683458
+	.long	848495278
+	.long	851809756
+	.long	3003311522
+	.long	2995654817
+	.long	833868005
+	.long	3004843819
+	.long	835836658
+	.long	3003498340
+	.long	2994528642
+	.long	3002229827
+	.long	2981408986
+	.long	2983889551
+	.long	2983366846
+	.long	3000350873
+	.long	833659207
+	.long	2987748092
+	.long	1065353216
+	.long	1065536903
+	.long	1065724611
+	.long	1065916431
+	.long	1066112450
+	.long	1066312762
+	.long	1066517459
+	.long	1066726640
+	.long	1066940400
+	.long	1067158842
+	.long	1067382066
+	.long	1067610179
+	.long	1067843287
+	.long	1068081499
+	.long	1068324927
+	.long	1068573686
+	.long	1068827891
+	.long	1069087663
+	.long	1069353124
+	.long	1069624397
+	.long	1069901610
+	.long	1070184894
+	.long	1070474380
+	.long	1070770206
+	.long	1071072509
+	.long	1071381432
+	.long	1071697119
+	.long	1072019719
+	.long	1072349383
+	.long	1072686266
+	.long	1073030525
+	.long	1073382323
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1220542464
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1042983923
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	1056964854
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	1118743631
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	796917760
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.type	__svml_sexp_ha_data_internal_avx512,@object
+	.size	__svml_sexp_ha_data_internal_avx512,960
+	.align 32
+_vmldExpHATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	1048019041
+	.long	1072704666
+	.long	2631457885
+	.long	3161546771
+	.long	3541402996
+	.long	1072716208
+	.long	896005651
+	.long	1015861842
+	.long	410360776
+	.long	1072727877
+	.long	1642514529
+	.long	1012987726
+	.long	1828292879
+	.long	1072739672
+	.long	1568897901
+	.long	1016568486
+	.long	852742562
+	.long	1072751596
+	.long	1882168529
+	.long	1010744893
+	.long	3490863953
+	.long	1072763649
+	.long	707771662
+	.long	3163903570
+	.long	2930322912
+	.long	1072775834
+	.long	3117806614
+	.long	3163670819
+	.long	1014845819
+	.long	1072788152
+	.long	3936719688
+	.long	3162512149
+	.long	3949972341
+	.long	1072800603
+	.long	1058231231
+	.long	1015777676
+	.long	828946858
+	.long	1072813191
+	.long	1044000608
+	.long	1016786167
+	.long	2288159958
+	.long	1072825915
+	.long	1151779725
+	.long	1015705409
+	.long	1853186616
+	.long	1072838778
+	.long	3819481236
+	.long	1016499965
+	.long	1709341917
+	.long	1072851781
+	.long	2552227826
+	.long	1015039787
+	.long	4112506593
+	.long	1072864925
+	.long	1829350193
+	.long	1015216097
+	.long	2799960843
+	.long	1072878213
+	.long	1913391796
+	.long	1015756674
+	.long	171030293
+	.long	1072891646
+	.long	1303423926
+	.long	1015238005
+	.long	2992903935
+	.long	1072905224
+	.long	1574172746
+	.long	1016061241
+	.long	926591435
+	.long	1072918951
+	.long	3427487848
+	.long	3163704045
+	.long	887463927
+	.long	1072932827
+	.long	1049900754
+	.long	3161575912
+	.long	1276261410
+	.long	1072946854
+	.long	2804567149
+	.long	1015390024
+	.long	569847338
+	.long	1072961034
+	.long	1209502043
+	.long	3159926671
+	.long	1617004845
+	.long	1072975368
+	.long	1623370769
+	.long	1011049453
+	.long	3049340112
+	.long	1072989858
+	.long	3667985273
+	.long	1013894369
+	.long	3577096743
+	.long	1073004506
+	.long	3145379760
+	.long	1014403278
+	.long	1990012071
+	.long	1073019314
+	.long	7447438
+	.long	3163526196
+	.long	1453150082
+	.long	1073034283
+	.long	3171891295
+	.long	3162037958
+	.long	917841882
+	.long	1073049415
+	.long	419288974
+	.long	1016280325
+	.long	3712504873
+	.long	1073064711
+	.long	3793507337
+	.long	1016095713
+	.long	363667784
+	.long	1073080175
+	.long	728023093
+	.long	1016345318
+	.long	2956612997
+	.long	1073095806
+	.long	1005538728
+	.long	3163304901
+	.long	2186617381
+	.long	1073111608
+	.long	2018924632
+	.long	3163803357
+	.long	1719614413
+	.long	1073127582
+	.long	3210617384
+	.long	3163796463
+	.long	1013258799
+	.long	1073143730
+	.long	3094194670
+	.long	3160631279
+	.long	3907805044
+	.long	1073160053
+	.long	2119843535
+	.long	3161988964
+	.long	1447192521
+	.long	1073176555
+	.long	508946058
+	.long	3162904882
+	.long	1944781191
+	.long	1073193236
+	.long	3108873501
+	.long	3162190556
+	.long	919555682
+	.long	1073210099
+	.long	2882956373
+	.long	1013312481
+	.long	2571947539
+	.long	1073227145
+	.long	4047189812
+	.long	3163777462
+	.long	2604962541
+	.long	1073244377
+	.long	3631372142
+	.long	3163870288
+	.long	1110089947
+	.long	1073261797
+	.long	3253791412
+	.long	1015920431
+	.long	2568320822
+	.long	1073279406
+	.long	1509121860
+	.long	1014756995
+	.long	2966275557
+	.long	1073297207
+	.long	2339118633
+	.long	3160254904
+	.long	2682146384
+	.long	1073315202
+	.long	586480042
+	.long	3163702083
+	.long	2191782032
+	.long	1073333393
+	.long	730975783
+	.long	1014083580
+	.long	2069751141
+	.long	1073351782
+	.long	576856675
+	.long	3163014404
+	.long	2990417245
+	.long	1073370371
+	.long	3552361237
+	.long	3163667409
+	.long	1434058175
+	.long	1073389163
+	.long	1853053619
+	.long	1015310724
+	.long	2572866477
+	.long	1073408159
+	.long	2462790535
+	.long	1015814775
+	.long	3092190715
+	.long	1073427362
+	.long	1457303226
+	.long	3159737305
+	.long	4076559943
+	.long	1073446774
+	.long	950899508
+	.long	3160987380
+	.long	2420883922
+	.long	1073466398
+	.long	174054861
+	.long	1014300631
+	.long	3716502172
+	.long	1073486235
+	.long	816778419
+	.long	1014197934
+	.long	777507147
+	.long	1073506289
+	.long	3507050924
+	.long	1015341199
+	.long	3706687593
+	.long	1073526560
+	.long	1821514088
+	.long	1013410604
+	.long	1242007932
+	.long	1073547053
+	.long	1073740399
+	.long	3163532637
+	.long	3707479175
+	.long	1073567768
+	.long	2789017511
+	.long	1014276997
+	.long	64696965
+	.long	1073588710
+	.long	3586233004
+	.long	1015962192
+	.long	863738719
+	.long	1073609879
+	.long	129252895
+	.long	3162690849
+	.long	3884662774
+	.long	1073631278
+	.long	1614448851
+	.long	1014281732
+	.long	2728693978
+	.long	1073652911
+	.long	2413007344
+	.long	3163551506
+	.long	3999357479
+	.long	1073674779
+	.long	1101668360
+	.long	1015989180
+	.long	1533953344
+	.long	1073696886
+	.long	835814894
+	.long	1015702697
+	.long	2174652632
+	.long	1073719233
+	.long	1301400989
+	.long	1014466875
+	.long	1697350398
+	.long	1079448903
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1071644672
+	.long	1431652600
+	.long	1069897045
+	.long	1431670732
+	.long	1067799893
+	.long	984555731
+	.long	1065423122
+	.long	472530941
+	.long	1062650218
+	.long	3758096384
+	.long	1079389762
+	.long	3758096384
+	.long	3226850697
+	.long	2147483648
+	.long	3227123254
+	.long	4277796864
+	.long	1065758274
+	.long	3164486458
+	.long	1025308570
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1009778688
+	.long	0
+	.long	1106771968
+	.type	_vmldExpHATab,@object
+	.size	_vmldExpHATab,1176
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_s_la.s
index 7bb40a33d66..18594649bab 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_exp_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_d_ha.s
new file mode 100644
index 00000000000..a57ec137966
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_d_ha.s
@@ -0,0 +1,1134 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  After computing exp(x) in high-low parts, an accurate computation is performed to obtain exp(x)-1
+ *  *  Typical exp() implementation, except that:
+ *  *   - tables are small (16 elements), allowing for fast gathers
+ *  *   - all arguments processed in the main path
+ *  *       - final VSCALEF assists branch-free design (correct overflow/underflow and special case responses)
+ *  *       - a VAND is used to ensure the reduced argument |R|<2, even for large inputs
+ *  *       - RZ mode used to avoid oveflow to +/-Inf for x*log2(e); helps with special case handling
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_expm18_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_expm18_ha
+
+__svml_expm18_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   256+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   320+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   512+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   576+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm11
+
+/* set Z0=max(Z0, -128.0) */
+        vmovups   640+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   384+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm7
+        vmovups   __svml_dexpm1_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   128+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm4
+        vmovaps   %zmm0, %zmm6
+
+/* 2^(52-4)*1.5 + x * log2(e) */
+        vfmadd213pd {rn-sae}, %zmm8, %zmm6, %zmm9
+
+/* ensure |R|<2 even for special cases */
+        vmovups   704+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm0
+        vcmppd    $21, {sae}, %zmm7, %zmm6, %k0
+
+/* polynomial */
+        vmovups   768+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm7
+
+/* Z0 ~ x*log2(e), rounded to 4 fractional bits */
+        vsubpd    {rn-sae}, %zmm8, %zmm9, %zmm12
+        vpermt2pd 64+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm9, %zmm14
+        vpermt2pd 192+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm9, %zmm4
+        vandpd    448+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm6, %zmm5
+
+/* R = x - Z0*log(2) */
+        vfnmadd213pd {rn-sae}, %zmm6, %zmm12, %zmm10
+        vmovups   896+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   1024+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm9
+        vmaxpd    {sae}, %zmm13, %zmm12, %zmm15
+        kmovw     %k0, %edx
+
+/* scale Th */
+        vscalefpd {rn-sae}, %zmm15, %zmm14, %zmm3
+
+/* Th - 1 */
+        vmovups   1216+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm15
+        vmovaps   %zmm11, %zmm2
+        vfnmadd213pd {rn-sae}, %zmm10, %zmm12, %zmm2
+        vcmppd    $17, {sae}, %zmm15, %zmm3, %k1
+
+/* (Z0*L2L)_high */
+        vsubpd    {rn-sae}, %zmm2, %zmm10, %zmm1
+        vandpd    %zmm0, %zmm2, %zmm2
+        vmovups   960+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm10
+
+/* Rl = -(Z0*L2L)_low */
+        vfnmadd231pd {rn-sae}, %zmm12, %zmm11, %zmm1
+        vmovups   1088+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   1152+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm12
+        vfmadd231pd {rn-sae}, %zmm2, %zmm8, %zmm10
+        vfmadd231pd {rn-sae}, %zmm2, %zmm9, %zmm11
+        vandpd    %zmm0, %zmm1, %zmm13
+        vmulpd    {rn-sae}, %zmm2, %zmm2, %zmm0
+        vmovups   832+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm1
+
+/* Rl+Tlr */
+        vaddpd    {rn-sae}, %zmm4, %zmm13, %zmm14
+        vfmadd231pd {rn-sae}, %zmm2, %zmm7, %zmm1
+        vmovups   1280+__svml_dexpm1_ha_data_internal_avx512(%rip), %zmm7
+
+/* Rl+Tlr + Tlr*R */
+        vfmadd213pd {rn-sae}, %zmm14, %zmm2, %zmm4
+        vfmadd213pd {rn-sae}, %zmm10, %zmm0, %zmm1
+        vblendmpd %zmm7, %zmm3, %zmm8{%k1}
+        vblendmpd %zmm3, %zmm7, %zmm10{%k1}
+        vfmadd213pd {rn-sae}, %zmm11, %zmm0, %zmm1
+        vaddpd    {rn-sae}, %zmm7, %zmm3, %zmm11
+        vfmadd213pd {rn-sae}, %zmm12, %zmm2, %zmm1
+        vsubpd    {rn-sae}, %zmm11, %zmm8, %zmm9
+
+/* polynomial + Tlr*(1+R) */
+        vfmadd213pd {rn-sae}, %zmm4, %zmm0, %zmm1
+        vaddpd    {rn-sae}, %zmm10, %zmm9, %zmm12
+
+/* Th-1 + (Th*R)_high */
+        vmovaps   %zmm11, %zmm0
+        vfmadd231pd {rn-sae}, %zmm3, %zmm2, %zmm0
+
+/* (Th*R)_low */
+        vsubpd    {rn-sae}, %zmm11, %zmm0, %zmm4
+        vfmsub231pd {rn-sae}, %zmm3, %zmm2, %zmm4
+
+/* Tl1 + Th*(polynomial + Tlr*(1+R)) */
+        vfmadd213pd {rn-sae}, %zmm12, %zmm1, %zmm3
+
+/* (Th*R)_low + Tl1 + Th*(polynomial + Tlr*(1+R)) */
+        vaddpd    {rn-sae}, %zmm4, %zmm3, %zmm3
+        vaddpd    {rn-sae}, %zmm3, %zmm0, %zmm0
+        vorpd     %zmm5, %zmm0, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dexpm1_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_expm18_ha,@function
+	.size	__svml_expm18_ha,.-__svml_expm18_ha
+..LN__svml_expm18_ha.0:
+
+.L_2__routine_start___svml_dexpm1_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dexpm1_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movsd     (%rdi), %xmm6
+        pxor      %xmm0, %xmm0
+        movzwl    6(%rdi), %edx
+        comisd    %xmm6, %xmm0
+        ja        .LBL_2_18
+
+
+        andl      $32752, %edx
+        shrl      $4, %edx
+        movsd     %xmm6, -8(%rsp)
+        cmpl      $2047, %edx
+        je        .LBL_2_19
+
+
+        cmpl      $970, %edx
+        jle       .LBL_2_16
+
+
+        movsd     1080+_imldExpHATab(%rip), %xmm0
+        comisd    %xmm6, %xmm0
+        jb        .LBL_2_15
+
+
+        comisd    1096+_imldExpHATab(%rip), %xmm6
+        jb        .LBL_2_14
+
+
+        movsd     1024+_imldExpHATab(%rip), %xmm0
+        movaps    %xmm6, %xmm5
+        mulsd     %xmm6, %xmm0
+        lea       _imldExpHATab(%rip), %r10
+        movsd     %xmm0, -24(%rsp)
+        movsd     -24(%rsp), %xmm1
+        movq      1136+_imldExpHATab(%rip), %rdx
+        movq      %rdx, -8(%rsp)
+        addsd     1032+_imldExpHATab(%rip), %xmm1
+        movsd     %xmm1, -16(%rsp)
+        movsd     -16(%rsp), %xmm2
+        movl      -16(%rsp), %r8d
+        movl      %r8d, %ecx
+        andl      $63, %r8d
+        subsd     1032+_imldExpHATab(%rip), %xmm2
+        movsd     %xmm2, -24(%rsp)
+        lea       1(%r8,%r8), %r9d
+        movsd     -24(%rsp), %xmm3
+        lea       (%r8,%r8), %edi
+        mulsd     1104+_imldExpHATab(%rip), %xmm3
+        movsd     -24(%rsp), %xmm4
+        subsd     %xmm3, %xmm5
+        mulsd     1112+_imldExpHATab(%rip), %xmm4
+        movsd     1072+_imldExpHATab(%rip), %xmm2
+        subsd     %xmm4, %xmm5
+        mulsd     %xmm5, %xmm2
+        shrl      $6, %ecx
+        addsd     1064+_imldExpHATab(%rip), %xmm2
+        comisd    1088+_imldExpHATab(%rip), %xmm6
+        mulsd     %xmm5, %xmm2
+        movsd     (%r10,%rdi,8), %xmm0
+        lea       1023(%rcx), %edx
+        addsd     1056+_imldExpHATab(%rip), %xmm2
+        mulsd     %xmm5, %xmm2
+        addsd     1048+_imldExpHATab(%rip), %xmm2
+        mulsd     %xmm5, %xmm2
+        addsd     1040+_imldExpHATab(%rip), %xmm2
+        mulsd     %xmm5, %xmm2
+        mulsd     %xmm5, %xmm2
+        addsd     %xmm5, %xmm2
+        addsd     (%r10,%r9,8), %xmm2
+        mulsd     %xmm0, %xmm2
+        jb        .LBL_2_10
+
+
+        andl      $2047, %edx
+        addsd     %xmm0, %xmm2
+        cmpl      $2046, %edx
+        ja        .LBL_2_9
+
+
+        movq      1136+_imldExpHATab(%rip), %rcx
+        shrq      $48, %rcx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm0, %xmm2
+        movsd     %xmm2, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -2(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -2(%rsp)
+        movsd     -8(%rsp), %xmm0
+        mulsd     %xmm0, %xmm2
+        mulsd     1152+_imldExpHATab(%rip), %xmm2
+        movsd     %xmm2, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        addl      $1083, %ecx
+        andl      $2047, %ecx
+        movl      %ecx, %eax
+        movzwl    -2(%rsp), %edx
+        shll      $4, %eax
+        andl      $-32753, %edx
+        orl       %eax, %edx
+        movw      %dx, -2(%rsp)
+        movsd     -8(%rsp), %xmm1
+        mulsd     %xmm1, %xmm2
+        mulsd     %xmm0, %xmm1
+        movaps    %xmm1, %xmm0
+        addsd     %xmm2, %xmm0
+        cmpl      $50, %ecx
+        ja        .LBL_2_12
+
+
+        mulsd     1160+_imldExpHATab(%rip), %xmm0
+        movsd     %xmm0, (%rsi)
+        jmp       .LBL_2_13
+
+.LBL_2_12:
+
+        movsd     %xmm0, -72(%rsp)
+        movsd     -72(%rsp), %xmm0
+        subsd     %xmm0, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        movsd     -64(%rsp), %xmm1
+        addsd     %xmm2, %xmm1
+        movsd     %xmm1, -64(%rsp)
+        movsd     -72(%rsp), %xmm2
+        mulsd     1168+_imldExpHATab(%rip), %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movsd     -56(%rsp), %xmm3
+        addsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm5
+        subsd     %xmm5, %xmm6
+        movsd     %xmm6, -40(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     -40(%rsp), %xmm7
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -32(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -32(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -32(%rsp)
+        movsd     -40(%rsp), %xmm11
+        mulsd     1160+_imldExpHATab(%rip), %xmm11
+        movsd     %xmm11, -40(%rsp)
+        movsd     -32(%rsp), %xmm12
+        mulsd     1160+_imldExpHATab(%rip), %xmm12
+        movsd     %xmm12, -32(%rsp)
+        movsd     -40(%rsp), %xmm14
+        movsd     -32(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        movsd     %xmm14, (%rsi)
+
+.LBL_2_13:
+
+        movl      $4, %eax
+        ret
+
+.LBL_2_14:
+
+        movsd     1120+_imldExpHATab(%rip), %xmm0
+        movl      $4, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_15:
+
+        movsd     1128+_imldExpHATab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_16:
+
+        movsd     1144+_imldExpHATab(%rip), %xmm0
+        addsd     %xmm6, %xmm0
+        movsd     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_18:
+
+        movq      $0xbff0000000000000, %rax
+        movq      %rax, (%rsi)
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_19:
+
+        movb      -1(%rsp), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_21
+
+.LBL_2_20:
+
+        mulsd     %xmm6, %xmm6
+        movsd     %xmm6, (%rsi)
+        ret
+
+.LBL_2_21:
+
+        testl     $1048575, -4(%rsp)
+        jne       .LBL_2_20
+
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_20
+
+
+        movq      1136+_imldExpHATab(%rip), %rdx
+        movq      %rdx, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dexpm1_ha_cout_rare_internal,@function
+	.size	__svml_dexpm1_ha_cout_rare_internal,.-__svml_dexpm1_ha_cout_rare_internal
+..LN__svml_dexpm1_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dexpm1_ha_data_internal_avx512:
+	.long	0
+	.long	1072693248
+	.long	1828292879
+	.long	1072739672
+	.long	1014845819
+	.long	1072788152
+	.long	1853186616
+	.long	1072838778
+	.long	171030293
+	.long	1072891646
+	.long	1276261410
+	.long	1072946854
+	.long	3577096743
+	.long	1073004506
+	.long	3712504873
+	.long	1073064711
+	.long	1719614413
+	.long	1073127582
+	.long	1944781191
+	.long	1073193236
+	.long	1110089947
+	.long	1073261797
+	.long	2191782032
+	.long	1073333393
+	.long	2572866477
+	.long	1073408159
+	.long	3716502172
+	.long	1073486235
+	.long	3707479175
+	.long	1073567768
+	.long	2728693978
+	.long	1073652911
+	.long	0
+	.long	0
+	.long	1568897901
+	.long	1016568486
+	.long	3936719688
+	.long	3162512149
+	.long	3819481236
+	.long	1016499965
+	.long	1303423926
+	.long	1015238005
+	.long	2804567149
+	.long	1015390024
+	.long	3145379760
+	.long	1014403278
+	.long	3793507337
+	.long	1016095713
+	.long	3210617384
+	.long	3163796463
+	.long	3108873501
+	.long	3162190556
+	.long	3253791412
+	.long	1015920431
+	.long	730975783
+	.long	1014083580
+	.long	2462790535
+	.long	1015814775
+	.long	816778419
+	.long	1014197934
+	.long	2789017511
+	.long	1014276997
+	.long	2413007344
+	.long	3163551506
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	16368
+	.long	1123549184
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	1287323204
+	.long	1082531232
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	4277811695
+	.long	1072049730
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	993624127
+	.long	1014676638
+	.long	0
+	.long	3227516928
+	.long	0
+	.long	3227516928
+	.long	0
+	.long	3227516928
+	.long	0
+	.long	3227516928
+	.long	0
+	.long	3227516928
+	.long	0
+	.long	3227516928
+	.long	0
+	.long	3227516928
+	.long	0
+	.long	3227516928
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4294967295
+	.long	3221225471
+	.long	4106095538
+	.long	1056571896
+	.long	4106095538
+	.long	1056571896
+	.long	4106095538
+	.long	1056571896
+	.long	4106095538
+	.long	1056571896
+	.long	4106095538
+	.long	1056571896
+	.long	4106095538
+	.long	1056571896
+	.long	4106095538
+	.long	1056571896
+	.long	4106095538
+	.long	1056571896
+	.long	4105251267
+	.long	1059717624
+	.long	4105251267
+	.long	1059717624
+	.long	4105251267
+	.long	1059717624
+	.long	4105251267
+	.long	1059717624
+	.long	4105251267
+	.long	1059717624
+	.long	4105251267
+	.long	1059717624
+	.long	4105251267
+	.long	1059717624
+	.long	4105251267
+	.long	1059717624
+	.long	372262401
+	.long	1062650220
+	.long	372262401
+	.long	1062650220
+	.long	372262401
+	.long	1062650220
+	.long	372262401
+	.long	1062650220
+	.long	372262401
+	.long	1062650220
+	.long	372262401
+	.long	1062650220
+	.long	372262401
+	.long	1062650220
+	.long	372262401
+	.long	1062650220
+	.long	279336721
+	.long	1065423121
+	.long	279336721
+	.long	1065423121
+	.long	279336721
+	.long	1065423121
+	.long	279336721
+	.long	1065423121
+	.long	279336721
+	.long	1065423121
+	.long	279336721
+	.long	1065423121
+	.long	279336721
+	.long	1065423121
+	.long	279336721
+	.long	1065423121
+	.long	1431655946
+	.long	1067799893
+	.long	1431655946
+	.long	1067799893
+	.long	1431655946
+	.long	1067799893
+	.long	1431655946
+	.long	1067799893
+	.long	1431655946
+	.long	1067799893
+	.long	1431655946
+	.long	1067799893
+	.long	1431655946
+	.long	1067799893
+	.long	1431655946
+	.long	1067799893
+	.long	1431655929
+	.long	1069897045
+	.long	1431655929
+	.long	1069897045
+	.long	1431655929
+	.long	1069897045
+	.long	1431655929
+	.long	1069897045
+	.long	1431655929
+	.long	1069897045
+	.long	1431655929
+	.long	1069897045
+	.long	1431655929
+	.long	1069897045
+	.long	1431655929
+	.long	1069897045
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	3220176896
+	.type	__svml_dexpm1_ha_data_internal_avx512,@object
+	.size	__svml_dexpm1_ha_data_internal_avx512,1344
+	.align 32
+_imldExpHATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	1048019041
+	.long	1072704666
+	.long	2631457885
+	.long	3161546771
+	.long	3541402996
+	.long	1072716208
+	.long	896005651
+	.long	1015861842
+	.long	410360776
+	.long	1072727877
+	.long	1642514529
+	.long	1012987726
+	.long	1828292879
+	.long	1072739672
+	.long	1568897901
+	.long	1016568486
+	.long	852742562
+	.long	1072751596
+	.long	1882168529
+	.long	1010744893
+	.long	3490863953
+	.long	1072763649
+	.long	707771662
+	.long	3163903570
+	.long	2930322912
+	.long	1072775834
+	.long	3117806614
+	.long	3163670819
+	.long	1014845819
+	.long	1072788152
+	.long	3936719688
+	.long	3162512149
+	.long	3949972341
+	.long	1072800603
+	.long	1058231231
+	.long	1015777676
+	.long	828946858
+	.long	1072813191
+	.long	1044000608
+	.long	1016786167
+	.long	2288159958
+	.long	1072825915
+	.long	1151779725
+	.long	1015705409
+	.long	1853186616
+	.long	1072838778
+	.long	3819481236
+	.long	1016499965
+	.long	1709341917
+	.long	1072851781
+	.long	2552227826
+	.long	1015039787
+	.long	4112506593
+	.long	1072864925
+	.long	1829350193
+	.long	1015216097
+	.long	2799960843
+	.long	1072878213
+	.long	1913391796
+	.long	1015756674
+	.long	171030293
+	.long	1072891646
+	.long	1303423926
+	.long	1015238005
+	.long	2992903935
+	.long	1072905224
+	.long	1574172746
+	.long	1016061241
+	.long	926591435
+	.long	1072918951
+	.long	3427487848
+	.long	3163704045
+	.long	887463927
+	.long	1072932827
+	.long	1049900754
+	.long	3161575912
+	.long	1276261410
+	.long	1072946854
+	.long	2804567149
+	.long	1015390024
+	.long	569847338
+	.long	1072961034
+	.long	1209502043
+	.long	3159926671
+	.long	1617004845
+	.long	1072975368
+	.long	1623370769
+	.long	1011049453
+	.long	3049340112
+	.long	1072989858
+	.long	3667985273
+	.long	1013894369
+	.long	3577096743
+	.long	1073004506
+	.long	3145379760
+	.long	1014403278
+	.long	1990012071
+	.long	1073019314
+	.long	7447438
+	.long	3163526196
+	.long	1453150082
+	.long	1073034283
+	.long	3171891295
+	.long	3162037958
+	.long	917841882
+	.long	1073049415
+	.long	419288974
+	.long	1016280325
+	.long	3712504873
+	.long	1073064711
+	.long	3793507337
+	.long	1016095713
+	.long	363667784
+	.long	1073080175
+	.long	728023093
+	.long	1016345318
+	.long	2956612997
+	.long	1073095806
+	.long	1005538728
+	.long	3163304901
+	.long	2186617381
+	.long	1073111608
+	.long	2018924632
+	.long	3163803357
+	.long	1719614413
+	.long	1073127582
+	.long	3210617384
+	.long	3163796463
+	.long	1013258799
+	.long	1073143730
+	.long	3094194670
+	.long	3160631279
+	.long	3907805044
+	.long	1073160053
+	.long	2119843535
+	.long	3161988964
+	.long	1447192521
+	.long	1073176555
+	.long	508946058
+	.long	3162904882
+	.long	1944781191
+	.long	1073193236
+	.long	3108873501
+	.long	3162190556
+	.long	919555682
+	.long	1073210099
+	.long	2882956373
+	.long	1013312481
+	.long	2571947539
+	.long	1073227145
+	.long	4047189812
+	.long	3163777462
+	.long	2604962541
+	.long	1073244377
+	.long	3631372142
+	.long	3163870288
+	.long	1110089947
+	.long	1073261797
+	.long	3253791412
+	.long	1015920431
+	.long	2568320822
+	.long	1073279406
+	.long	1509121860
+	.long	1014756995
+	.long	2966275557
+	.long	1073297207
+	.long	2339118633
+	.long	3160254904
+	.long	2682146384
+	.long	1073315202
+	.long	586480042
+	.long	3163702083
+	.long	2191782032
+	.long	1073333393
+	.long	730975783
+	.long	1014083580
+	.long	2069751141
+	.long	1073351782
+	.long	576856675
+	.long	3163014404
+	.long	2990417245
+	.long	1073370371
+	.long	3552361237
+	.long	3163667409
+	.long	1434058175
+	.long	1073389163
+	.long	1853053619
+	.long	1015310724
+	.long	2572866477
+	.long	1073408159
+	.long	2462790535
+	.long	1015814775
+	.long	3092190715
+	.long	1073427362
+	.long	1457303226
+	.long	3159737305
+	.long	4076559943
+	.long	1073446774
+	.long	950899508
+	.long	3160987380
+	.long	2420883922
+	.long	1073466398
+	.long	174054861
+	.long	1014300631
+	.long	3716502172
+	.long	1073486235
+	.long	816778419
+	.long	1014197934
+	.long	777507147
+	.long	1073506289
+	.long	3507050924
+	.long	1015341199
+	.long	3706687593
+	.long	1073526560
+	.long	1821514088
+	.long	1013410604
+	.long	1242007932
+	.long	1073547053
+	.long	1073740399
+	.long	3163532637
+	.long	3707479175
+	.long	1073567768
+	.long	2789017511
+	.long	1014276997
+	.long	64696965
+	.long	1073588710
+	.long	3586233004
+	.long	1015962192
+	.long	863738719
+	.long	1073609879
+	.long	129252895
+	.long	3162690849
+	.long	3884662774
+	.long	1073631278
+	.long	1614448851
+	.long	1014281732
+	.long	2728693978
+	.long	1073652911
+	.long	2413007344
+	.long	3163551506
+	.long	3999357479
+	.long	1073674779
+	.long	1101668360
+	.long	1015989180
+	.long	1533953344
+	.long	1073696886
+	.long	835814894
+	.long	1015702697
+	.long	2174652632
+	.long	1073719233
+	.long	1301400989
+	.long	1014466875
+	.long	1697350398
+	.long	1079448903
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1071644672
+	.long	1431652600
+	.long	1069897045
+	.long	1431670732
+	.long	1067799893
+	.long	984555731
+	.long	1065423122
+	.long	472530941
+	.long	1062650218
+	.long	4277811695
+	.long	1082535490
+	.long	3715808466
+	.long	3230016299
+	.long	3576508497
+	.long	3230091536
+	.long	4277796864
+	.long	1065758274
+	.long	3164486458
+	.long	1025308570
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1009778688
+	.long	0
+	.long	1106771968
+	.type	_imldExpHATab,@object
+	.size	_imldExpHATab,1176
+	.align 8
+.L_2il0floatpacket.79:
+	.long	0x00000000,0xbff00000
+	.type	.L_2il0floatpacket.79,@object
+	.size	.L_2il0floatpacket.79,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_d_la.s
index 0e52bb606bd..13622689ed5 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_h_la.s
new file mode 100644
index 00000000000..f459d880efe
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_h_la.s
@@ -0,0 +1,154 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   After computing exp(x), an accurate computation is performed to obtain exp(x)-1
+ *   Typical exp() implementation, except that:
+ *    - tables are small, allowing for fast gathers
+ *    - all arguments processed in the main path
+ *        - final VSCALEF assists branch-free design (correct overflow/underflow and special case responses)
+ *        - a VAND is used to ensure the reduced argument |R|<2, even for large inputs
+ *        - RZ mode used to avoid oveflow to +/-Inf for x*log2(e); helps with special case handling
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_expm1s32
+
+__svml_expm1s32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovups   __svml_hexpm1_data_internal(%rip), %zmm30
+        vmovups   64+__svml_hexpm1_data_internal(%rip), %zmm31
+        vmovups   128+__svml_hexpm1_data_internal(%rip), %zmm6
+        vmovups   192+__svml_hexpm1_data_internal(%rip), %zmm29
+        vmovups   256+__svml_hexpm1_data_internal(%rip), %zmm7
+        vmovups   320+__svml_hexpm1_data_internal(%rip), %zmm8
+        vmovups   384+__svml_hexpm1_data_internal(%rip), %zmm15
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray_expm1_h:
+        cmpq    $31, %rdx
+        ja .loaddata_expm1_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $15360, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_expm1_h
+.loaddata_expm1_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_expm1_h:
+
+/* No callout */
+        vmovups   %zmm30, %zmm2
+        vmovups   %zmm29, %zmm11
+        vmovups   %zmm31, %zmm5
+
+/* polynomial ~ expm1(R)/R */
+        vmovaps   %zmm11, %zmm9
+        vextractf32x8 $1, %zmm0, %ymm1
+        vcvtph2psx %ymm0, %zmm10
+        vcvtph2psx %ymm1, %zmm12
+
+/* Shifter + x * log2(e) */
+        vmulps    {rn-sae}, %zmm2, %zmm10, %zmm3
+        vmulps    {rn-sae}, %zmm2, %zmm12, %zmm4
+
+/* N ~ x*log2(e) */
+        vrndscaleps $8, {sae}, %zmm3, %zmm13
+        vrndscaleps $8, {sae}, %zmm4, %zmm14
+
+/* R = x - N*ln(2)_high */
+        vfmadd231ps {rn-sae}, %zmm13, %zmm5, %zmm10
+        vfmadd231ps {rn-sae}, %zmm14, %zmm5, %zmm12
+        vscalefps {rn-sae}, %zmm13, %zmm15, %zmm13
+        vscalefps {rn-sae}, %zmm14, %zmm15, %zmm14
+        vfmadd231ps {rn-sae}, %zmm10, %zmm6, %zmm9
+        vfmadd231ps {rn-sae}, %zmm12, %zmm6, %zmm11
+
+/* fixup for overflow and special cases */
+        vfpclassps $14, %zmm13, %k0
+        vfpclassps $14, %zmm14, %k2
+
+/* 2^N - 1 */
+        vsubps    {rn-sae}, %zmm15, %zmm13, %zmm2
+        vsubps    {rn-sae}, %zmm15, %zmm14, %zmm4
+        vfmadd213ps {rn-sae}, %zmm7, %zmm10, %zmm9
+        vfmadd213ps {rn-sae}, %zmm7, %zmm12, %zmm11
+        knotw     %k0, %k1
+        knotw     %k2, %k3
+        vfmadd213ps {rn-sae}, %zmm8, %zmm10, %zmm9
+        vfmadd213ps {rn-sae}, %zmm8, %zmm12, %zmm11
+
+/* exp(R) - 1 */
+        vmulps    {rn-sae}, %zmm10, %zmm9, %zmm0
+        vmulps    {rn-sae}, %zmm12, %zmm11, %zmm1
+
+/* result */
+        vfmadd231ps {rn-sae}, %zmm0, %zmm13, %zmm2{%k1}
+        vfmadd231ps {rn-sae}, %zmm1, %zmm14, %zmm4{%k3}
+        vcvtps2phx %zmm2, %ymm3
+        vcvtps2phx %zmm4, %ymm5
+        vinsertf32x8 $1, %ymm5, %zmm3, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray_expm1_h
+
+/*
+ * #else  _LA_, _EP_
+ * #endif  _LA_, _EP_
+ */
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_expm1s32,@function
+        .size	__svml_expm1s32,.-__svml_expm1s32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hexpm1_data_internal:
+	.rept	16
+        .long	0x3fb8aa3b
+	.endr
+	.rept	16
+        .long	0xbf317218
+	.endr
+	.rept	16
+        .long	0x3d2bb1be
+	.endr
+	.rept	16
+        .long	0x3e2bb1c1
+	.endr
+	.rept	16
+        .long	0x3efffeaf
+	.endr
+	.rept	16
+        .long	0x3f7fff03
+	.endr
+	.rept	16
+        .long	0x3f800000
+	.endr
+        .type	__svml_hexpm1_data_internal,@object
+        .size	__svml_hexpm1_data_internal,448
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_s_ha.s
new file mode 100644
index 00000000000..8abae959d6f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_s_ha.s
@@ -0,0 +1,1030 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  After computing exp(x) in high-low parts, an accurate computation is performed to obtain exp(x)-1
+ *  *  Typical exp() implementation, except that:
+ *  *   - tables are small (32 elements), allowing for fast gathers
+ *  *   - all arguments processed in the main path
+ *  *       - final VSCALEF assists branch-free design (correct overflow/underflow and special case responses)
+ *  *       - a VAND is used to ensure the reduced argument |R|<2, even for large inputs
+ *  *       - RZ mode used to avoid oveflow to +/-Inf for x*log2(e); helps with special case handling
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_expm1f16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_expm1f16_ha
+
+__svml_expm1f16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   128+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   192+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   384+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   448+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm11
+
+/* set Z0=max(Z0, -64.0) */
+        vmovups   512+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   256+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm7
+        vmovaps   %zmm0, %zmm6
+
+/* 2^(23-4)*1.5 + x * log2(e) */
+        vfmadd213ps {rn-sae}, %zmm8, %zmm6, %zmm9
+
+/* ensure |R|<2 even for special cases */
+        vmovups   576+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm0
+        vcmpps    $21, {sae}, %zmm7, %zmm6, %k0
+
+/* polynomial */
+        vmovups   640+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm7
+
+/* Z0 ~ x*log2(e), rounded to 4 fractional bits */
+        vsubps    {rn-sae}, %zmm8, %zmm9, %zmm12
+        vpermps   __svml_sexpm1_ha_data_internal_avx512(%rip), %zmm9, %zmm14
+        vpermps   64+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm9, %zmm4
+        vandps    320+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm6, %zmm5
+
+/* R = x - Z0*log(2) */
+        vfnmadd213ps {rn-sae}, %zmm6, %zmm12, %zmm10
+        vmovups   768+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm8
+        vmaxps    {sae}, %zmm13, %zmm12, %zmm15
+        kmovw     %k0, %edx
+
+/* scale Th */
+        vscalefps {rn-sae}, %zmm15, %zmm14, %zmm3
+        vmovaps   %zmm11, %zmm2
+        vfnmadd213ps {rn-sae}, %zmm10, %zmm12, %zmm2
+
+/* (Z0*L2L)_high */
+        vsubps    {rn-sae}, %zmm2, %zmm10, %zmm1
+        vandps    %zmm0, %zmm2, %zmm2
+
+/* Rl = -(Z0*L2L)_low */
+        vfnmadd231ps {rn-sae}, %zmm12, %zmm11, %zmm1
+
+/* Th - 1 */
+        vmovups   832+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   896+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm12
+        vandps    %zmm0, %zmm1, %zmm9
+        vmovups   704+__svml_sexpm1_ha_data_internal_avx512(%rip), %zmm1
+        vmulps    {rn-sae}, %zmm2, %zmm2, %zmm0
+        vcmpps    $17, {sae}, %zmm11, %zmm3, %k1
+        vfmadd231ps {rn-sae}, %zmm2, %zmm7, %zmm1
+
+/* Rl+Tlr */
+        vaddps    {rn-sae}, %zmm4, %zmm9, %zmm10
+        vaddps    {rn-sae}, %zmm12, %zmm3, %zmm7
+        vfmadd213ps {rn-sae}, %zmm8, %zmm2, %zmm1
+
+/* Rl+Tlr + Tlr*R */
+        vfmadd213ps {rn-sae}, %zmm10, %zmm2, %zmm4
+        vblendmps %zmm12, %zmm3, %zmm13{%k1}
+        vblendmps %zmm3, %zmm12, %zmm15{%k1}
+
+/* polynomial + Tlr*(1+R) */
+        vfmadd213ps {rn-sae}, %zmm4, %zmm0, %zmm1
+        vsubps    {rn-sae}, %zmm7, %zmm13, %zmm14
+
+/* Th-1 + (Th*R)_high */
+        vmovaps   %zmm7, %zmm0
+        vfmadd231ps {rn-sae}, %zmm3, %zmm2, %zmm0
+        vaddps    {rn-sae}, %zmm15, %zmm14, %zmm8
+
+/* (Th*R)_low */
+        vsubps    {rn-sae}, %zmm7, %zmm0, %zmm4
+        vfmsub231ps {rn-sae}, %zmm3, %zmm2, %zmm4
+
+/* Tl1 + Th*(polynomial + Tlr*(1+R)) */
+        vfmadd213ps {rn-sae}, %zmm8, %zmm1, %zmm3
+
+/* (Th*R)_low + Tl1 + Th*(polynomial + Tlr*(1+R)) */
+        vaddps    {rn-sae}, %zmm4, %zmm3, %zmm3
+        vaddps    {rn-sae}, %zmm3, %zmm0, %zmm0
+        vorps     %zmm5, %zmm0, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_sexpm1_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_expm1f16_ha,@function
+	.size	__svml_expm1f16_ha,.-__svml_expm1f16_ha
+..LN__svml_expm1f16_ha.0:
+
+.L_2__routine_start___svml_sexpm1_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_sexpm1_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movss     (%rdi), %xmm7
+        pxor      %xmm0, %xmm0
+        comiss    %xmm7, %xmm0
+        ja        .LBL_2_18
+
+
+        movzwl    2(%rdi), %edx
+        andl      $32640, %edx
+        shrl      $7, %edx
+        cmpl      $255, %edx
+        je        .LBL_2_19
+
+
+        cvtss2sd  %xmm7, %xmm7
+        cmpl      $74, %edx
+        jle       .LBL_2_16
+
+
+        movsd     1080+_vmldExpHATab(%rip), %xmm0
+        comisd    %xmm7, %xmm0
+        jb        .LBL_2_15
+
+
+        comisd    1096+_vmldExpHATab(%rip), %xmm7
+        jb        .LBL_2_14
+
+
+        movsd     1024+_vmldExpHATab(%rip), %xmm1
+        movaps    %xmm7, %xmm6
+        mulsd     %xmm7, %xmm1
+        lea       _vmldExpHATab(%rip), %r10
+        movsd     %xmm1, -16(%rsp)
+        movsd     -16(%rsp), %xmm2
+        movsd     1072+_vmldExpHATab(%rip), %xmm1
+        movsd     1136+_vmldExpHATab(%rip), %xmm0
+        movsd     %xmm0, -24(%rsp)
+        addsd     1032+_vmldExpHATab(%rip), %xmm2
+        movsd     %xmm2, -8(%rsp)
+        movsd     -8(%rsp), %xmm3
+        movl      -8(%rsp), %r8d
+        movl      %r8d, %ecx
+        andl      $63, %r8d
+        subsd     1032+_vmldExpHATab(%rip), %xmm3
+        movsd     %xmm3, -16(%rsp)
+        lea       1(%r8,%r8), %r9d
+        movsd     -16(%rsp), %xmm5
+        lea       (%r8,%r8), %edi
+        movsd     -16(%rsp), %xmm4
+        mulsd     1112+_vmldExpHATab(%rip), %xmm4
+        mulsd     1104+_vmldExpHATab(%rip), %xmm5
+        subsd     %xmm4, %xmm6
+        shrl      $6, %ecx
+        subsd     %xmm5, %xmm6
+        comisd    1088+_vmldExpHATab(%rip), %xmm7
+        mulsd     %xmm6, %xmm1
+        movsd     (%r10,%rdi,8), %xmm0
+        lea       1023(%rcx), %edx
+        addsd     1064+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     1056+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     1048+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     1040+_vmldExpHATab(%rip), %xmm1
+        mulsd     %xmm6, %xmm1
+        mulsd     %xmm6, %xmm1
+        addsd     %xmm6, %xmm1
+        addsd     (%r10,%r9,8), %xmm1
+        mulsd     %xmm0, %xmm1
+        jb        .LBL_2_10
+
+
+        andl      $2047, %edx
+        addsd     %xmm0, %xmm1
+        cmpl      $2046, %edx
+        ja        .LBL_2_9
+
+
+        movzwl    1142+_vmldExpHATab(%rip), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -18(%rsp)
+        movsd     -24(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -18(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -18(%rsp)
+        movsd     -24(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        mulsd     1152+_vmldExpHATab(%rip), %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        addl      $1083, %ecx
+        andl      $2047, %ecx
+        movl      %ecx, %eax
+        movzwl    -18(%rsp), %edx
+        shll      $4, %eax
+        andl      $-32753, %edx
+        orl       %eax, %edx
+        movw      %dx, -18(%rsp)
+        movsd     -24(%rsp), %xmm3
+        mulsd     %xmm3, %xmm1
+        mulsd     %xmm0, %xmm3
+        movaps    %xmm1, %xmm2
+        addsd     %xmm3, %xmm2
+        cmpl      $50, %ecx
+        ja        .LBL_2_12
+
+
+        movsd     1160+_vmldExpHATab(%rip), %xmm0
+        mulsd     %xmm2, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        jmp       .LBL_2_13
+
+.LBL_2_12:
+
+        movsd     %xmm2, -72(%rsp)
+        movsd     -72(%rsp), %xmm0
+        subsd     %xmm0, %xmm3
+        movsd     %xmm3, -64(%rsp)
+        movsd     -64(%rsp), %xmm2
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, -64(%rsp)
+        movsd     -72(%rsp), %xmm1
+        mulsd     1168+_vmldExpHATab(%rip), %xmm1
+        movsd     %xmm1, -56(%rsp)
+        movsd     -72(%rsp), %xmm4
+        movsd     -56(%rsp), %xmm3
+        addsd     %xmm3, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        movsd     -48(%rsp), %xmm6
+        movsd     -56(%rsp), %xmm5
+        subsd     %xmm5, %xmm6
+        movsd     %xmm6, -40(%rsp)
+        movsd     -72(%rsp), %xmm8
+        movsd     -40(%rsp), %xmm7
+        subsd     %xmm7, %xmm8
+        movsd     %xmm8, -32(%rsp)
+        movsd     -64(%rsp), %xmm10
+        movsd     -32(%rsp), %xmm9
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, -32(%rsp)
+        movsd     -40(%rsp), %xmm11
+        mulsd     1160+_vmldExpHATab(%rip), %xmm11
+        movsd     %xmm11, -40(%rsp)
+        movsd     -32(%rsp), %xmm12
+        mulsd     1160+_vmldExpHATab(%rip), %xmm12
+        movsd     %xmm12, -32(%rsp)
+        movsd     -40(%rsp), %xmm14
+        movsd     -32(%rsp), %xmm13
+        addsd     %xmm13, %xmm14
+        cvtsd2ss  %xmm14, %xmm14
+        movss     %xmm14, (%rsi)
+
+.LBL_2_13:
+
+        movl      $4, %eax
+        ret
+
+.LBL_2_14:
+
+        movsd     1120+_vmldExpHATab(%rip), %xmm0
+        movl      $4, %eax
+        mulsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_15:
+
+        movsd     1128+_vmldExpHATab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_16:
+
+        movsd     1144+_vmldExpHATab(%rip), %xmm0
+        addsd     %xmm7, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_18:
+
+        movl      $-1082130432, (%rsi)
+        ret
+
+.LBL_2_19:
+
+        movb      3(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_21
+
+.LBL_2_20:
+
+        mulss     %xmm7, %xmm7
+        movss     %xmm7, (%rsi)
+        ret
+
+.LBL_2_21:
+
+        testl     $8388607, (%rdi)
+        jne       .LBL_2_20
+
+
+        movsd     1136+_vmldExpHATab(%rip), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sexpm1_ha_cout_rare_internal,@function
+	.size	__svml_sexpm1_ha_cout_rare_internal,.-__svml_sexpm1_ha_cout_rare_internal
+..LN__svml_sexpm1_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_sexpm1_ha_data_internal_avx512:
+	.long	1065353216
+	.long	1065724611
+	.long	1066112450
+	.long	1066517459
+	.long	1066940400
+	.long	1067382066
+	.long	1067843287
+	.long	1068324927
+	.long	1068827891
+	.long	1069353124
+	.long	1069901610
+	.long	1070474380
+	.long	1071072509
+	.long	1071697119
+	.long	1072349383
+	.long	1073030525
+	.long	0
+	.long	860277610
+	.long	2991457809
+	.long	860562562
+	.long	856238081
+	.long	857441778
+	.long	3003456168
+	.long	2986372182
+	.long	848495278
+	.long	3003311522
+	.long	833868005
+	.long	835836658
+	.long	2994528642
+	.long	2981408986
+	.long	2983366846
+	.long	833659207
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1228931072
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	1118652779
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	1060205080
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	2969756424
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3263168512
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	3221225471
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1026207428
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1042984644
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1056964608
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.type	__svml_sexpm1_ha_data_internal_avx512,@object
+	.size	__svml_sexpm1_ha_data_internal_avx512,960
+	.align 32
+_vmldExpHATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	1048019041
+	.long	1072704666
+	.long	2631457885
+	.long	3161546771
+	.long	3541402996
+	.long	1072716208
+	.long	896005651
+	.long	1015861842
+	.long	410360776
+	.long	1072727877
+	.long	1642514529
+	.long	1012987726
+	.long	1828292879
+	.long	1072739672
+	.long	1568897901
+	.long	1016568486
+	.long	852742562
+	.long	1072751596
+	.long	1882168529
+	.long	1010744893
+	.long	3490863953
+	.long	1072763649
+	.long	707771662
+	.long	3163903570
+	.long	2930322912
+	.long	1072775834
+	.long	3117806614
+	.long	3163670819
+	.long	1014845819
+	.long	1072788152
+	.long	3936719688
+	.long	3162512149
+	.long	3949972341
+	.long	1072800603
+	.long	1058231231
+	.long	1015777676
+	.long	828946858
+	.long	1072813191
+	.long	1044000608
+	.long	1016786167
+	.long	2288159958
+	.long	1072825915
+	.long	1151779725
+	.long	1015705409
+	.long	1853186616
+	.long	1072838778
+	.long	3819481236
+	.long	1016499965
+	.long	1709341917
+	.long	1072851781
+	.long	2552227826
+	.long	1015039787
+	.long	4112506593
+	.long	1072864925
+	.long	1829350193
+	.long	1015216097
+	.long	2799960843
+	.long	1072878213
+	.long	1913391796
+	.long	1015756674
+	.long	171030293
+	.long	1072891646
+	.long	1303423926
+	.long	1015238005
+	.long	2992903935
+	.long	1072905224
+	.long	1574172746
+	.long	1016061241
+	.long	926591435
+	.long	1072918951
+	.long	3427487848
+	.long	3163704045
+	.long	887463927
+	.long	1072932827
+	.long	1049900754
+	.long	3161575912
+	.long	1276261410
+	.long	1072946854
+	.long	2804567149
+	.long	1015390024
+	.long	569847338
+	.long	1072961034
+	.long	1209502043
+	.long	3159926671
+	.long	1617004845
+	.long	1072975368
+	.long	1623370769
+	.long	1011049453
+	.long	3049340112
+	.long	1072989858
+	.long	3667985273
+	.long	1013894369
+	.long	3577096743
+	.long	1073004506
+	.long	3145379760
+	.long	1014403278
+	.long	1990012071
+	.long	1073019314
+	.long	7447438
+	.long	3163526196
+	.long	1453150082
+	.long	1073034283
+	.long	3171891295
+	.long	3162037958
+	.long	917841882
+	.long	1073049415
+	.long	419288974
+	.long	1016280325
+	.long	3712504873
+	.long	1073064711
+	.long	3793507337
+	.long	1016095713
+	.long	363667784
+	.long	1073080175
+	.long	728023093
+	.long	1016345318
+	.long	2956612997
+	.long	1073095806
+	.long	1005538728
+	.long	3163304901
+	.long	2186617381
+	.long	1073111608
+	.long	2018924632
+	.long	3163803357
+	.long	1719614413
+	.long	1073127582
+	.long	3210617384
+	.long	3163796463
+	.long	1013258799
+	.long	1073143730
+	.long	3094194670
+	.long	3160631279
+	.long	3907805044
+	.long	1073160053
+	.long	2119843535
+	.long	3161988964
+	.long	1447192521
+	.long	1073176555
+	.long	508946058
+	.long	3162904882
+	.long	1944781191
+	.long	1073193236
+	.long	3108873501
+	.long	3162190556
+	.long	919555682
+	.long	1073210099
+	.long	2882956373
+	.long	1013312481
+	.long	2571947539
+	.long	1073227145
+	.long	4047189812
+	.long	3163777462
+	.long	2604962541
+	.long	1073244377
+	.long	3631372142
+	.long	3163870288
+	.long	1110089947
+	.long	1073261797
+	.long	3253791412
+	.long	1015920431
+	.long	2568320822
+	.long	1073279406
+	.long	1509121860
+	.long	1014756995
+	.long	2966275557
+	.long	1073297207
+	.long	2339118633
+	.long	3160254904
+	.long	2682146384
+	.long	1073315202
+	.long	586480042
+	.long	3163702083
+	.long	2191782032
+	.long	1073333393
+	.long	730975783
+	.long	1014083580
+	.long	2069751141
+	.long	1073351782
+	.long	576856675
+	.long	3163014404
+	.long	2990417245
+	.long	1073370371
+	.long	3552361237
+	.long	3163667409
+	.long	1434058175
+	.long	1073389163
+	.long	1853053619
+	.long	1015310724
+	.long	2572866477
+	.long	1073408159
+	.long	2462790535
+	.long	1015814775
+	.long	3092190715
+	.long	1073427362
+	.long	1457303226
+	.long	3159737305
+	.long	4076559943
+	.long	1073446774
+	.long	950899508
+	.long	3160987380
+	.long	2420883922
+	.long	1073466398
+	.long	174054861
+	.long	1014300631
+	.long	3716502172
+	.long	1073486235
+	.long	816778419
+	.long	1014197934
+	.long	777507147
+	.long	1073506289
+	.long	3507050924
+	.long	1015341199
+	.long	3706687593
+	.long	1073526560
+	.long	1821514088
+	.long	1013410604
+	.long	1242007932
+	.long	1073547053
+	.long	1073740399
+	.long	3163532637
+	.long	3707479175
+	.long	1073567768
+	.long	2789017511
+	.long	1014276997
+	.long	64696965
+	.long	1073588710
+	.long	3586233004
+	.long	1015962192
+	.long	863738719
+	.long	1073609879
+	.long	129252895
+	.long	3162690849
+	.long	3884662774
+	.long	1073631278
+	.long	1614448851
+	.long	1014281732
+	.long	2728693978
+	.long	1073652911
+	.long	2413007344
+	.long	3163551506
+	.long	3999357479
+	.long	1073674779
+	.long	1101668360
+	.long	1015989180
+	.long	1533953344
+	.long	1073696886
+	.long	835814894
+	.long	1015702697
+	.long	2174652632
+	.long	1073719233
+	.long	1301400989
+	.long	1014466875
+	.long	1697350398
+	.long	1079448903
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1071644672
+	.long	1431652600
+	.long	1069897045
+	.long	1431670732
+	.long	1067799893
+	.long	984555731
+	.long	1065423122
+	.long	472530941
+	.long	1062650218
+	.long	3758096384
+	.long	1079389762
+	.long	3758096384
+	.long	3226850697
+	.long	2147483648
+	.long	3227123254
+	.long	4277796864
+	.long	1065758274
+	.long	3164486458
+	.long	1025308570
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	1009778688
+	.long	0
+	.long	1106771968
+	.type	_vmldExpHATab,@object
+	.size	_vmldExpHATab,1176
+	.align 4
+.L_2il0floatpacket.76:
+	.long	0xbf800000
+	.type	.L_2il0floatpacket.76,@object
+	.size	.L_2il0floatpacket.76,4
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_s_la.s
index 9cbefbba561..d64cf612462 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_expm1_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_ln_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_ln_h_la.s
new file mode 100644
index 00000000000..0972f2b4eae
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_ln_h_la.s
@@ -0,0 +1,211 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *    log(x) = VGETEXP(x)*log(2) + log(VGETMANT(x))
+ *    VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *    mx = VGETMANT(x) is in [1,2) for all x>=0
+ *    log(mx) = -log(RCP(mx)) + log(1 +(mx*RCP(mx)-1))
+ *    and the table lookup for log(RCP(mx))
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_logs32
+
+__svml_logs32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hlog_data_internal(%rip), %zmm31
+        vmovdqu16 64+__svml_hlog_data_internal(%rip), %zmm30
+        vmovdqu16 128+__svml_hlog_data_internal(%rip), %zmm29
+        vmovdqu16 192+__svml_hlog_data_internal(%rip), %zmm28
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__ln_h:
+        cmpq    $31, %rdx
+        ja .loaddata__ln_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $15360, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_ln_h
+.loaddata__ln_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_ln_h:
+
+/*
+ * Variables:
+ * H
+ * No callout
+ * Copy argument
+ * GetMant(x), normalized to [.75,1.5) for x>=0, NaN for x<0
+ */
+        vgetmantph $11, {sae}, %zmm0, %zmm2
+
+/* Get exponent */
+        vgetexpph {sae}, %zmm0, %zmm4
+
+/* exponent corrrection */
+        vgetexpph {sae}, %zmm2, %zmm5
+
+/* table index */
+        vpsrlw    $5, %zmm2, %zmm3
+
+/* exponent corrrection */
+        vsubph    {rn-sae}, %zmm5, %zmm4, %zmm7
+        vsubph    {rn-sae}, %zmm31, %zmm2, %zmm0
+
+/* polynomial coefficients */
+        vpermw    %zmm29, %zmm3, %zmm9
+        vpermw    %zmm28, %zmm3, %zmm6
+        vmulph    {rn-sae}, %zmm30, %zmm7, %zmm10
+
+/* hC0+hC1*R */
+        vfmadd213ph {rn-sae}, %zmm6, %zmm0, %zmm9
+
+/* result res = R*P + Expon */
+        vfmadd213ph {rn-sae}, %zmm10, %zmm9, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__ln_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_logs32,@function
+        .size	__svml_logs32,.-__svml_logs32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hlog_data_internal:
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x398c
+	.endr
+        .word	0xb7d6
+        .word	0xb787
+        .word	0xb73c
+        .word	0xb6f6
+        .word	0xb6b5
+        .word	0xb677
+        .word	0xb63e
+        .word	0xb607
+        .word	0xb5d3
+        .word	0xb5a3
+        .word	0xb575
+        .word	0xb549
+        .word	0xb520
+        .word	0xb4f8
+        .word	0xb4d3
+        .word	0xb4af
+        .word	0xb9c4
+        .word	0xb99d
+        .word	0xb978
+        .word	0xb955
+        .word	0xb933
+        .word	0xb912
+        .word	0xb8f3
+        .word	0xb8d5
+        .word	0xb8b8
+        .word	0xb89c
+        .word	0xb882
+        .word	0xb868
+        .word	0xb850
+        .word	0xb838
+        .word	0xb821
+        .word	0xb80b
+        .word	0x3c00
+        .word	0x3bff
+        .word	0x3bfc
+        .word	0x3bf9
+        .word	0x3bf5
+        .word	0x3bf0
+        .word	0x3beb
+        .word	0x3be5
+        .word	0x3bde
+        .word	0x3bd8
+        .word	0x3bd0
+        .word	0x3bc9
+        .word	0x3bc1
+        .word	0x3bb9
+        .word	0x3bb1
+        .word	0x3ba9
+        .word	0x3bc4
+        .word	0x3bcd
+        .word	0x3bd5
+        .word	0x3bdc
+        .word	0x3be2
+        .word	0x3be8
+        .word	0x3bed
+        .word	0x3bf1
+        .word	0x3bf5
+        .word	0x3bf8
+        .word	0x3bfa
+        .word	0x3bfc
+        .word	0x3bfe
+        .word	0x3bff
+	.rept	2
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x001f
+	.endr
+	.rept	32
+        .word	0x0000
+	.endr
+	.rept	16
+        .long	0x00000001
+	.endr
+	.rept	16
+        .long	0x0000007f
+	.endr
+	.rept	16
+        .long	0x3f800000
+	.endr
+	.rept	16
+        .long	0x3e51367b
+	.endr
+	.rept	16
+        .long	0xbebfd356
+	.endr
+	.rept	16
+        .long	0x3ef9e953
+	.endr
+	.rept	16
+        .long	0xbf389f48
+	.endr
+	.rept	16
+        .long	0x3fb8a7e4
+	.endr
+	.rept	16
+        .long	0x3f317218
+	.endr
+	.rept	32
+        .word	0xfc00
+	.endr
+        .type	__svml_hlog_data_internal,@object
+        .size	__svml_hlog_data_internal,1024
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_d_ha.s
new file mode 100644
index 00000000000..bfd0fd68b9a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_d_ha.s
@@ -0,0 +1,1205 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  log10(x) = VGETEXP(x)*log10(2) + log10(VGETMANT(x))
+ *  *       VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *  *   mx = VGETMANT(x) is in [1,2) for all x>=0
+ *  *   log10(mx) = -log10(RCP(mx)) + log10(1 +(mx*RCP(mx)-1))
+ *  *      RCP(mx) is rounded to 4 fractional bits,
+ *  *      and the table lookup for log(RCP(mx)) is based on a small permute instruction
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_log108_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_log108_ha
+
+__svml_log108_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm5
+
+/* GetMant(x), normalized to [1,2) for x>=0, NaN for x<0 */
+        vgetmantpd $8, {sae}, %zmm5, %zmm4
+        vmovups   256+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm9
+
+/* Prepare exponent correction: DblRcp<0.75? */
+        vmovups   320+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm8
+
+/* GetExp(x) */
+        vgetexppd {sae}, %zmm5, %zmm3
+
+/* Table lookup */
+        vmovups   __svml_dlog10_ha_data_internal_avx512(%rip), %zmm2
+        vmovups   128+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm15
+        vmovups   512+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm12
+
+/* K*log10(2)+Th */
+        vmovups   1024+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm13
+
+/* Start polynomial evaluation */
+        vmovups   384+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   448+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm1
+
+/* K*log10(2)_low+Tl */
+        vmovups   1088+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm14
+
+/* DblRcp ~ 1/Mantissa */
+        vrcp14pd  %zmm4, %zmm6
+
+/* x<=0? */
+        vfpclasspd $94, %zmm5, %k2
+
+/* round DblRcp to 4 fractional bits (RN mode, no Precision exception) */
+        vrndscalepd $88, {sae}, %zmm6, %zmm7
+        vmovups   576+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm6
+        kmovw     %k2, %edx
+
+/* Reduced argument: R = DblRcp*Mantissa - 1 */
+        vfmsub213pd {rn-sae}, %zmm9, %zmm7, %zmm4
+        vcmppd    $17, {sae}, %zmm8, %zmm7, %k1
+        vfmadd231pd {rn-sae}, %zmm4, %zmm12, %zmm6
+        vmovups   640+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm12
+        vfmadd231pd {rn-sae}, %zmm4, %zmm11, %zmm1
+
+/* R^2 */
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm0
+
+/* Prepare table index */
+        vpsrlq    $48, %zmm7, %zmm10
+        vmovups   832+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm7
+        vmulpd    {rn-sae}, %zmm0, %zmm0, %zmm8
+        vfmadd213pd {rn-sae}, %zmm6, %zmm0, %zmm1
+
+/* add 1 to Expon if DblRcp<0.75 */
+        vaddpd    {rn-sae}, %zmm9, %zmm3, %zmm3{%k1}
+        vpermt2pd 64+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm10, %zmm2
+        vpermt2pd 192+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm10, %zmm15
+
+/* K+Th+(c0h*R)_h */
+        vmovups   960+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm10
+        vfmadd231pd {rn-sae}, %zmm3, %zmm13, %zmm2
+        vmovups   768+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   704+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm9
+        vfmadd213pd {rn-sae}, %zmm15, %zmm14, %zmm3
+        vfmadd231pd {rn-sae}, %zmm4, %zmm13, %zmm7
+        vfmadd231pd {rn-sae}, %zmm4, %zmm12, %zmm9
+        vmovaps   %zmm10, %zmm11
+        vfmadd213pd {rn-sae}, %zmm2, %zmm4, %zmm11
+        vfmadd213pd {rn-sae}, %zmm7, %zmm0, %zmm9
+        vmovups   896+__svml_dlog10_ha_data_internal_avx512(%rip), %zmm0
+
+/* (c0h*R)_h */
+        vsubpd    {rn-sae}, %zmm2, %zmm11, %zmm2
+
+/* polynomial */
+        vfmadd213pd {rn-sae}, %zmm9, %zmm8, %zmm1
+        vxorpd    %zmm2, %zmm2, %zmm2{%k2}
+        vfmadd213pd {rn-sae}, %zmm0, %zmm4, %zmm1
+
+/* c0hR_low */
+        vfmsub231pd {rn-sae}, %zmm10, %zmm4, %zmm2
+        vaddpd    {rn-sae}, %zmm2, %zmm3, %zmm3
+        vfmadd213pd {rn-sae}, %zmm3, %zmm4, %zmm1
+        vaddpd    {rn-sae}, %zmm1, %zmm11, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm5, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dlog10_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_log108_ha,@function
+	.size	__svml_log108_ha,.-__svml_log108_ha
+..LN__svml_log108_ha.0:
+
+.L_2__routine_start___svml_dlog10_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dlog10_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    6(%rdi), %edx
+        andl      $32752, %edx
+        cmpl      $32752, %edx
+        je        .LBL_2_12
+
+
+        movsd     (%rdi), %xmm2
+        xorl      %ecx, %ecx
+        movsd     %xmm2, -8(%rsp)
+        movzwl    -2(%rsp), %edx
+        testl     $32752, %edx
+        jne       .LBL_2_4
+
+
+        movsd     1600+__dlog10_ha_CoutTab(%rip), %xmm0
+        movl      $-60, %ecx
+        mulsd     %xmm0, %xmm2
+        movsd     %xmm2, -8(%rsp)
+
+.LBL_2_4:
+
+        movsd     1608+__dlog10_ha_CoutTab(%rip), %xmm0
+        comisd    %xmm0, %xmm2
+        jbe       .LBL_2_8
+
+
+        movaps    %xmm2, %xmm1
+        subsd     .L_2il0floatpacket.91(%rip), %xmm1
+        movsd     %xmm1, -16(%rsp)
+        andb      $127, -9(%rsp)
+        movsd     -16(%rsp), %xmm0
+        comisd    1592+__dlog10_ha_CoutTab(%rip), %xmm0
+        jbe       .LBL_2_7
+
+
+        movsd     %xmm2, -16(%rsp)
+        pxor      %xmm7, %xmm7
+        movzwl    -10(%rsp), %edi
+        lea       __dlog10_ha_CoutTab(%rip), %r10
+        andl      $-32753, %edi
+        addl      $16368, %edi
+        movw      %di, -10(%rsp)
+        movsd     -16(%rsp), %xmm3
+        movaps    %xmm3, %xmm1
+        movaps    %xmm3, %xmm2
+        movsd     1688+__dlog10_ha_CoutTab(%rip), %xmm5
+        movzwl    -2(%rsp), %edx
+        andl      $32752, %edx
+        addsd     1576+__dlog10_ha_CoutTab(%rip), %xmm1
+        addsd     1584+__dlog10_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm1, -24(%rsp)
+        movl      -24(%rsp), %r8d
+        movsd     %xmm2, -24(%rsp)
+        andl      $127, %r8d
+        movsd     -24(%rsp), %xmm8
+        movsd     1560+__dlog10_ha_CoutTab(%rip), %xmm9
+        movsd     1568+__dlog10_ha_CoutTab(%rip), %xmm0
+        shrl      $4, %edx
+        subsd     1584+__dlog10_ha_CoutTab(%rip), %xmm8
+        lea       (%r8,%r8,2), %r9d
+        movsd     (%r10,%r9,8), %xmm6
+        lea       -1023(%rcx,%rdx), %ecx
+        cvtsi2sd  %ecx, %xmm7
+        subsd     %xmm8, %xmm3
+        mulsd     %xmm6, %xmm8
+        mulsd     %xmm7, %xmm9
+        subsd     1624+__dlog10_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm6
+        mulsd     %xmm0, %xmm7
+        addsd     8(%r10,%r9,8), %xmm9
+        addsd     16(%r10,%r9,8), %xmm7
+        addsd     %xmm8, %xmm9
+        movaps    %xmm8, %xmm4
+        addsd     %xmm6, %xmm4
+        mulsd     %xmm4, %xmm5
+        addsd     1680+__dlog10_ha_CoutTab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addsd     1672+__dlog10_ha_CoutTab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addsd     1664+__dlog10_ha_CoutTab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addsd     1656+__dlog10_ha_CoutTab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addsd     1648+__dlog10_ha_CoutTab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addsd     1640+__dlog10_ha_CoutTab(%rip), %xmm5
+        mulsd     %xmm4, %xmm5
+        addsd     1632+__dlog10_ha_CoutTab(%rip), %xmm5
+        mulsd     %xmm5, %xmm8
+        mulsd     %xmm6, %xmm5
+        addsd     %xmm5, %xmm7
+        addsd     %xmm6, %xmm7
+        addsd     %xmm7, %xmm8
+        addsd     %xmm8, %xmm9
+        movsd     %xmm9, (%rsi)
+        ret
+
+.LBL_2_7:
+
+        movsd     1624+__dlog10_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm0, %xmm1
+        movsd     1688+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1680+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1672+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1664+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1656+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1648+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1640+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1632+__dlog10_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        ucomisd   %xmm0, %xmm2
+        jp        .LBL_2_9
+        je        .LBL_2_11
+
+.LBL_2_9:
+
+        divsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        movl      $1, %eax
+
+
+        ret
+
+.LBL_2_11:
+
+        movsd     1616+__dlog10_ha_CoutTab(%rip), %xmm1
+        movl      $2, %eax
+        xorps     .L_2il0floatpacket.90(%rip), %xmm1
+        divsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_12:
+
+        movb      7(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_14
+
+.LBL_2_13:
+
+        movsd     (%rdi), %xmm0
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_13
+
+
+        cmpl      $0, (%rdi)
+        jne       .LBL_2_13
+
+
+        movsd     1608+__dlog10_ha_CoutTab(%rip), %xmm0
+        movl      $1, %eax
+        divsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dlog10_ha_cout_rare_internal,@function
+	.size	__svml_dlog10_ha_cout_rare_internal,.-__svml_dlog10_ha_cout_rare_internal
+..LN__svml_dlog10_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dlog10_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	721420288
+	.long	3214603769
+	.long	3590979584
+	.long	3215601833
+	.long	1438908416
+	.long	3216186160
+	.long	948305920
+	.long	3216559896
+	.long	2869821440
+	.long	3216915393
+	.long	516521984
+	.long	3217142759
+	.long	2145648640
+	.long	3217304702
+	.long	733741056
+	.long	1069546492
+	.long	3513843712
+	.long	1069249052
+	.long	3459645440
+	.long	1068963280
+	.long	1085800448
+	.long	1068688295
+	.long	3613786112
+	.long	1068347678
+	.long	1803419648
+	.long	1067836310
+	.long	3436707840
+	.long	1067234191
+	.long	930611200
+	.long	1066155272
+	.long	0
+	.long	0
+	.long	3686878645
+	.long	3175924212
+	.long	2120733314
+	.long	1025513021
+	.long	1829252717
+	.long	3176941056
+	.long	3391913567
+	.long	3176268582
+	.long	1445614576
+	.long	3174694461
+	.long	577138096
+	.long	1029194402
+	.long	717064894
+	.long	1025503698
+	.long	1625232067
+	.long	1029570781
+	.long	2107125367
+	.long	1029044389
+	.long	1740576090
+	.long	1029619435
+	.long	4015256301
+	.long	3177184346
+	.long	1535607671
+	.long	1029013126
+	.long	2000266743
+	.long	1028805283
+	.long	761604295
+	.long	1028127597
+	.long	803304759
+	.long	1025708071
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	675808112
+	.long	1068024536
+	.long	675808112
+	.long	1068024536
+	.long	675808112
+	.long	1068024536
+	.long	675808112
+	.long	1068024536
+	.long	675808112
+	.long	1068024536
+	.long	675808112
+	.long	1068024536
+	.long	675808112
+	.long	1068024536
+	.long	675808112
+	.long	1068024536
+	.long	2516752404
+	.long	3215710221
+	.long	2516752404
+	.long	3215710221
+	.long	2516752404
+	.long	3215710221
+	.long	2516752404
+	.long	3215710221
+	.long	2516752404
+	.long	3215710221
+	.long	2516752404
+	.long	3215710221
+	.long	2516752404
+	.long	3215710221
+	.long	2516752404
+	.long	3215710221
+	.long	4085995682
+	.long	1068483574
+	.long	4085995682
+	.long	1068483574
+	.long	4085995682
+	.long	1068483574
+	.long	4085995682
+	.long	1068483574
+	.long	4085995682
+	.long	1068483574
+	.long	4085995682
+	.long	1068483574
+	.long	4085995682
+	.long	1068483574
+	.long	4085995682
+	.long	1068483574
+	.long	879025280
+	.long	3216148390
+	.long	879025280
+	.long	3216148390
+	.long	879025280
+	.long	3216148390
+	.long	879025280
+	.long	3216148390
+	.long	879025280
+	.long	3216148390
+	.long	879025280
+	.long	3216148390
+	.long	879025280
+	.long	3216148390
+	.long	879025280
+	.long	3216148390
+	.long	2004821977
+	.long	1068907618
+	.long	2004821977
+	.long	1068907618
+	.long	2004821977
+	.long	1068907618
+	.long	2004821977
+	.long	1068907618
+	.long	2004821977
+	.long	1068907618
+	.long	2004821977
+	.long	1068907618
+	.long	2004821977
+	.long	1068907618
+	.long	2004821977
+	.long	1068907618
+	.long	356255395
+	.long	3216755579
+	.long	356255395
+	.long	3216755579
+	.long	356255395
+	.long	3216755579
+	.long	356255395
+	.long	3216755579
+	.long	356255395
+	.long	3216755579
+	.long	356255395
+	.long	3216755579
+	.long	356255395
+	.long	3216755579
+	.long	356255395
+	.long	3216755579
+	.long	1668235916
+	.long	1069713319
+	.long	1668235916
+	.long	1069713319
+	.long	1668235916
+	.long	1069713319
+	.long	1668235916
+	.long	1069713319
+	.long	1668235916
+	.long	1069713319
+	.long	1668235916
+	.long	1069713319
+	.long	1668235916
+	.long	1069713319
+	.long	1668235916
+	.long	1069713319
+	.long	354870491
+	.long	3217804155
+	.long	354870491
+	.long	3217804155
+	.long	354870491
+	.long	3217804155
+	.long	354870491
+	.long	3217804155
+	.long	354870491
+	.long	3217804155
+	.long	354870491
+	.long	3217804155
+	.long	354870491
+	.long	3217804155
+	.long	354870491
+	.long	3217804155
+	.long	2867927077
+	.long	1013556733
+	.long	2867927077
+	.long	1013556733
+	.long	2867927077
+	.long	1013556733
+	.long	2867927077
+	.long	1013556733
+	.long	2867927077
+	.long	1013556733
+	.long	2867927077
+	.long	1013556733
+	.long	2867927077
+	.long	1013556733
+	.long	2867927077
+	.long	1013556733
+	.long	354870542
+	.long	1071369083
+	.long	354870542
+	.long	1071369083
+	.long	354870542
+	.long	1071369083
+	.long	354870542
+	.long	1071369083
+	.long	354870542
+	.long	1071369083
+	.long	354870542
+	.long	1071369083
+	.long	354870542
+	.long	1071369083
+	.long	354870542
+	.long	1071369083
+	.long	1352597504
+	.long	1070810131
+	.long	1352597504
+	.long	1070810131
+	.long	1352597504
+	.long	1070810131
+	.long	1352597504
+	.long	1070810131
+	.long	1352597504
+	.long	1070810131
+	.long	1352597504
+	.long	1070810131
+	.long	1352597504
+	.long	1070810131
+	.long	1352597504
+	.long	1070810131
+	.long	3296479949
+	.long	1031700412
+	.long	3296479949
+	.long	1031700412
+	.long	3296479949
+	.long	1031700412
+	.long	3296479949
+	.long	1031700412
+	.long	3296479949
+	.long	1031700412
+	.long	3296479949
+	.long	1031700412
+	.long	3296479949
+	.long	1031700412
+	.long	3296479949
+	.long	1031700412
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.type	__svml_dlog10_ha_data_internal_avx512,@object
+	.size	__svml_dlog10_ha_data_internal_avx512,1344
+	.align 32
+__dlog10_ha_CoutTab:
+	.long	0
+	.long	1071366144
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1071337728
+	.long	184549376
+	.long	1065092008
+	.long	2099961998
+	.long	3178897324
+	.long	0
+	.long	1071309312
+	.long	931135488
+	.long	1066155272
+	.long	2365712557
+	.long	3178155773
+	.long	0
+	.long	1071280896
+	.long	603979776
+	.long	1066752445
+	.long	709057215
+	.long	1031474920
+	.long	0
+	.long	1071252480
+	.long	3437232128
+	.long	1067234191
+	.long	1515412199
+	.long	3179085970
+	.long	0
+	.long	1071238272
+	.long	1105723392
+	.long	1067464226
+	.long	153915826
+	.long	3178000698
+	.long	0
+	.long	1071209856
+	.long	3328442368
+	.long	1067711223
+	.long	3899912278
+	.long	3177135692
+	.long	0
+	.long	1071181440
+	.long	341835776
+	.long	1067962480
+	.long	2509208190
+	.long	3176531222
+	.long	0
+	.long	1071167232
+	.long	2884632576
+	.long	1068089751
+	.long	1030636902
+	.long	1025224143
+	.long	0
+	.long	1071138816
+	.long	3613917184
+	.long	1068347678
+	.long	3527163461
+	.long	3177529532
+	.long	0
+	.long	1071124608
+	.long	3549954048
+	.long	1068478374
+	.long	3498894081
+	.long	3173000425
+	.long	0
+	.long	1071096192
+	.long	1623785472
+	.long	1068621140
+	.long	2889825554
+	.long	3176375375
+	.long	0
+	.long	1071081984
+	.long	1085800448
+	.long	1068688295
+	.long	4015256301
+	.long	3177184346
+	.long	0
+	.long	1071053568
+	.long	3652976640
+	.long	1068824490
+	.long	3600693529
+	.long	3175753877
+	.long	0
+	.long	1071039360
+	.long	1592393728
+	.long	1068893555
+	.long	231073830
+	.long	3177087939
+	.long	0
+	.long	1071025152
+	.long	3459645440
+	.long	1068963280
+	.long	1740576090
+	.long	1029619435
+	.long	0
+	.long	1070996736
+	.long	3774611456
+	.long	1069104765
+	.long	3858552785
+	.long	1028603845
+	.long	0
+	.long	1070982528
+	.long	845086720
+	.long	1069176552
+	.long	3138879731
+	.long	1029120443
+	.long	0
+	.long	1070968320
+	.long	3513843712
+	.long	1069249052
+	.long	2107125367
+	.long	1029044389
+	.long	0
+	.long	1070954112
+	.long	434503680
+	.long	1069322282
+	.long	3827602229
+	.long	1028932700
+	.long	0
+	.long	1070939904
+	.long	3613851648
+	.long	1069396254
+	.long	1223751955
+	.long	3176465139
+	.long	0
+	.long	1070911488
+	.long	733741056
+	.long	1069546492
+	.long	1625232067
+	.long	1029570781
+	.long	0
+	.long	1070897280
+	.long	1511620608
+	.long	1069585154
+	.long	3044605139
+	.long	1028090775
+	.long	0
+	.long	1070883072
+	.long	1337196544
+	.long	1069623706
+	.long	2602639001
+	.long	3175938675
+	.long	0
+	.long	1070868864
+	.long	2572533760
+	.long	1069662670
+	.long	3067107955
+	.long	1022933137
+	.long	0
+	.long	1070854656
+	.long	559611904
+	.long	1069702056
+	.long	764145786
+	.long	3174041535
+	.long	0
+	.long	1070840448
+	.long	485818368
+	.long	1069741872
+	.long	2037567072
+	.long	3175580956
+	.long	0
+	.long	1070826240
+	.long	259604480
+	.long	1069782128
+	.long	4012068429
+	.long	1027865895
+	.long	0
+	.long	1070812032
+	.long	3454042112
+	.long	1069822833
+	.long	2867680007
+	.long	3174202478
+	.long	0
+	.long	1070797824
+	.long	2188754944
+	.long	1069863999
+	.long	2538655286
+	.long	3175840981
+	.long	0
+	.long	1070783616
+	.long	2965241856
+	.long	1069905635
+	.long	1338936972
+	.long	3176093950
+	.long	0
+	.long	1070769408
+	.long	966279168
+	.long	1069947753
+	.long	1774547674
+	.long	3175051484
+	.long	0
+	.long	1070755200
+	.long	1604042752
+	.long	1069990363
+	.long	2557470738
+	.long	3174667448
+	.long	0
+	.long	1070740992
+	.long	3417833472
+	.long	1070033477
+	.long	2268255117
+	.long	3175678264
+	.long	0
+	.long	1070740992
+	.long	3417833472
+	.long	1070033477
+	.long	2268255117
+	.long	3175678264
+	.long	0
+	.long	1070726784
+	.long	2451292160
+	.long	1070077108
+	.long	3757728941
+	.long	1027943275
+	.long	0
+	.long	1070712576
+	.long	929644544
+	.long	1070121268
+	.long	899045708
+	.long	1027944939
+	.long	0
+	.long	1070698368
+	.long	3057254400
+	.long	1070165969
+	.long	3880649376
+	.long	3172972504
+	.long	0
+	.long	1070684160
+	.long	2231091200
+	.long	1070211226
+	.long	521319256
+	.long	1027600177
+	.long	0
+	.long	1070684160
+	.long	2231091200
+	.long	1070211226
+	.long	521319256
+	.long	1027600177
+	.long	0
+	.long	1070669952
+	.long	2620162048
+	.long	1070257052
+	.long	1385613369
+	.long	3176104036
+	.long	0
+	.long	1070655744
+	.long	2096726016
+	.long	1070303462
+	.long	3138305819
+	.long	3173646777
+	.long	0
+	.long	1070641536
+	.long	944717824
+	.long	1070350471
+	.long	1065120110
+	.long	1027539054
+	.long	0
+	.long	1070641536
+	.long	944717824
+	.long	1070350471
+	.long	1065120110
+	.long	1027539054
+	.long	0
+	.long	1070627328
+	.long	1985789952
+	.long	1070398094
+	.long	3635943864
+	.long	3173136490
+	.long	0
+	.long	1070613120
+	.long	2123825152
+	.long	1070446348
+	.long	1125219725
+	.long	3175615738
+	.long	0
+	.long	1070598912
+	.long	1078378496
+	.long	1070495250
+	.long	603852726
+	.long	3174570526
+	.long	0
+	.long	1070598912
+	.long	1078378496
+	.long	1070495250
+	.long	603852726
+	.long	3174570526
+	.long	0
+	.long	1070573312
+	.long	1537933312
+	.long	1070544817
+	.long	998069198
+	.long	1026662908
+	.long	0
+	.long	1070544896
+	.long	733773824
+	.long	1070595068
+	.long	4061058002
+	.long	3174036009
+	.long	0
+	.long	1070544896
+	.long	733773824
+	.long	1070595068
+	.long	4061058002
+	.long	3174036009
+	.long	0
+	.long	1070516480
+	.long	3897544704
+	.long	1070621058
+	.long	951856294
+	.long	1026731877
+	.long	0
+	.long	1070516480
+	.long	3897544704
+	.long	1070621058
+	.long	951856294
+	.long	1026731877
+	.long	0
+	.long	1070488064
+	.long	493535232
+	.long	1070646897
+	.long	3852369308
+	.long	3173264746
+	.long	0
+	.long	1070459648
+	.long	463249408
+	.long	1070673107
+	.long	2853152111
+	.long	3174564937
+	.long	0
+	.long	1070459648
+	.long	463249408
+	.long	1070673107
+	.long	2853152111
+	.long	3174564937
+	.long	0
+	.long	1070431232
+	.long	3186585600
+	.long	1070699699
+	.long	1874718356
+	.long	3174139933
+	.long	0
+	.long	1070431232
+	.long	3186585600
+	.long	1070699699
+	.long	1874718356
+	.long	3174139933
+	.long	0
+	.long	1070402816
+	.long	1525858304
+	.long	1070726686
+	.long	3039843523
+	.long	1024724665
+	.long	0
+	.long	1070402816
+	.long	1525858304
+	.long	1070726686
+	.long	3039843523
+	.long	1024724665
+	.long	0
+	.long	1070374400
+	.long	3425300480
+	.long	1070754078
+	.long	1303046649
+	.long	1022401701
+	.long	0
+	.long	1070374400
+	.long	3425300480
+	.long	1070754078
+	.long	1303046649
+	.long	1022401701
+	.long	0
+	.long	1070345984
+	.long	1980465152
+	.long	1070781889
+	.long	3188656319
+	.long	1027271390
+	.long	0
+	.long	1070345984
+	.long	1980465152
+	.long	1070781889
+	.long	3188656319
+	.long	1027271390
+	.long	0
+	.long	1070317568
+	.long	1352630272
+	.long	1070810131
+	.long	3090895658
+	.long	3174564915
+	.long	1352630272
+	.long	1070810131
+	.long	3090895658
+	.long	3174564915
+	.long	64
+	.long	1120927744
+	.long	0
+	.long	1096810496
+	.long	0
+	.long	1064828928
+	.long	0
+	.long	1135607808
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1071366144
+	.long	3207479559
+	.long	1062894188
+	.long	3698831637
+	.long	3220339442
+	.long	3700832817
+	.long	1073506818
+	.long	1691624569
+	.long	3221787401
+	.long	2065628764
+	.long	1075227551
+	.long	1770847080
+	.long	3223701774
+	.long	3786517112
+	.long	1077250450
+	.long	1316351650
+	.long	3225793313
+	.type	__dlog10_ha_CoutTab,@object
+	.size	__dlog10_ha_CoutTab,1696
+	.align 16
+.L_2il0floatpacket.90:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.90,@object
+	.size	.L_2il0floatpacket.90,16
+	.align 8
+.L_2il0floatpacket.91:
+	.long	0x00000000,0x3ff00000
+	.type	.L_2il0floatpacket.91,@object
+	.size	.L_2il0floatpacket.91,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_d_la.s
index 86a0bc84cb4..4c974672886 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_h_la.s
new file mode 100644
index 00000000000..37bfaef6fc6
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_h_la.s
@@ -0,0 +1,151 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *    log10(x) = VGETEXP(x)*log10(2) + log10(VGETMANT(x))
+ *    VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *    mx = VGETMANT(x) is in [1,2) for all x>=0
+ *    log10(mx) = -log10(RCP(mx)) + log10(1 +(mx*RCP(mx)-1))
+ *    and the table lookup for log(RCP(mx))
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_log10s32
+
+__svml_log10s32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 64+__svml_hlog10_data_internal(%rip), %zmm31
+        vmovdqu16 384+__svml_hlog10_data_internal(%rip), %zmm4
+        vmovdqu16 128+__svml_hlog10_data_internal(%rip), %zmm5
+        vmovdqu16 192+__svml_hlog10_data_internal(%rip), %zmm6
+        vmovdqu16 256+__svml_hlog10_data_internal(%rip), %zmm7
+        vmovdqu16 320+__svml_hlog10_data_internal(%rip), %zmm8
+        vmovdqu16 __svml_hlog10_data_internal(%rip), %zmm11
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__log10_h:
+        cmpq    $31, %rdx
+        ja .loaddata__log10_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $15360, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_log10_h
+.loaddata__log10_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_log10_h:
+        
+        vmovdqu16 %zmm31, %zmm9
+
+/* Get exponent */
+        vgetexpph {sae}, %zmm0, %zmm1
+
+/* GetMant(x), normalized to [.75,1.5) for x>=0, NaN for x<0 */
+        vgetmantph $11, {sae}, %zmm0, %zmm3
+        vsubph    {rn-sae}, %zmm4, %zmm3, %zmm10
+
+/* exponent corrrection */
+        vgetexpph {sae}, %zmm3, %zmm2
+        vfmadd213ph {rn-sae}, %zmm5, %zmm10, %zmm9
+        vsubph    {rn-sae}, %zmm2, %zmm1, %zmm0
+        vfmadd213ph {rn-sae}, %zmm6, %zmm10, %zmm9
+        vfmadd213ph {rn-sae}, %zmm7, %zmm10, %zmm9
+        vfmadd213ph {rn-sae}, %zmm8, %zmm10, %zmm9
+        vmulph    {rn-sae}, %zmm10, %zmm9, %zmm12
+        vfmadd213ph {rn-sae}, %zmm12, %zmm11, %zmm0
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__log10_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_log10s32,@function
+        .size	__svml_log10s32,.-__svml_log10s32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hlog10_data_internal:
+	.rept	32
+        .word	0x34d1
+	.endr
+	.rept	32
+        .word	0x2bad
+	.endr
+	.rept	32
+        .word	0xaf2b
+	.endr
+	.rept	32
+        .word	0x30b4
+	.endr
+	.rept	32
+        .word	0xb2f3
+	.endr
+	.rept	32
+        .word	0x36f3
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x001f
+	.endr
+	.rept	32
+        .word	0x0000
+	.endr
+	.rept	16
+        .long	0x00000001
+	.endr
+	.rept	16
+        .long	0x0000007f
+	.endr
+	.rept	16
+        .long	0x3f800000
+	.endr
+	.rept	16
+        .long	0x3e51367b
+	.endr
+	.rept	16
+        .long	0xbebfd356
+	.endr
+	.rept	16
+        .long	0x3ef9e953
+	.endr
+	.rept	16
+        .long	0xbf389f48
+	.endr
+	.rept	16
+        .long	0x3fb8a7e4
+	.endr
+	.rept	16
+        .long	0x3e9a209b
+	.endr
+	.rept	32
+        .word	0xfc00
+	.endr
+        .type	__svml_hlog10_data_internal,@object
+        .size	__svml_hlog10_data_internal,1216
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_s_ha.s
new file mode 100644
index 00000000000..bc2e2d5a83f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_s_ha.s
@@ -0,0 +1,952 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  log10(x) = VGETEXP(x)*log10(2) + log10(VGETMANT(x))
+ *  *       VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *  *   mx = VGETMANT(x) is in [1,2) for all x>=0
+ *  *   log10(mx) = -log10(RCP(mx)) + log10(1 +(mx*RCP(mx)-1))
+ *  *      RCP(mx) is rounded to 4 fractional bits,
+ *  *      and the table lookup for log(RCP(mx)) is based on a small permute instruction
+ *  *
+ *  *   LA, EP versions use interval interpolation (16 intervals)
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_log10f16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_log10f16_ha
+
+__svml_log10f16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm2
+
+/* GetMant(x), normalized to [1,2) for x>=0, NaN for x<0 */
+        vgetmantps $10, {sae}, %zmm2, %zmm4
+
+/* Reduced argument: R = SglRcp*Mantissa - 1 */
+        vmovups   256+__svml_slog10_ha_data_internal_avx512(%rip), %zmm1
+
+/* GetExp(x) */
+        vgetexpps {sae}, %zmm2, %zmm7
+
+/* Table lookup */
+        vmovups   __svml_slog10_ha_data_internal_avx512(%rip), %zmm0
+
+/* Start polynomial evaluation */
+        vmovups   320+__svml_slog10_ha_data_internal_avx512(%rip), %zmm12
+        vmovups   640+__svml_slog10_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   448+__svml_slog10_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   512+__svml_slog10_ha_data_internal_avx512(%rip), %zmm15
+        vmovups   128+__svml_slog10_ha_data_internal_avx512(%rip), %zmm11
+
+/* K*log10(2)_low+Tl */
+        vmovups   704+__svml_slog10_ha_data_internal_avx512(%rip), %zmm10
+
+/* SglRcp ~ 1/Mantissa */
+        vrcp14ps  %zmm4, %zmm3
+
+/* x<=0? */
+        vfpclassps $94, %zmm2, %k1
+
+/* round SglRcp to 5 fractional bits (RN mode, no Precision exception) */
+        vrndscaleps $88, {sae}, %zmm3, %zmm5
+        kmovw     %k1, %edx
+        vgetexpps {sae}, %zmm5, %zmm8
+        vfmsub231ps {rn-sae}, %zmm5, %zmm4, %zmm1
+        vmovups   384+__svml_slog10_ha_data_internal_avx512(%rip), %zmm4
+
+/* Prepare table index */
+        vpsrld    $18, %zmm5, %zmm6
+
+/* K*log(2)_high+Th */
+        vsubps    {rn-sae}, %zmm8, %zmm7, %zmm3
+        vfmadd231ps {rn-sae}, %zmm1, %zmm12, %zmm4
+
+/* K+Th+(R*c1)_h */
+        vmovups   576+__svml_slog10_ha_data_internal_avx512(%rip), %zmm12
+        vfmadd231ps {rn-sae}, %zmm1, %zmm13, %zmm15
+        vmulps    {rn-sae}, %zmm1, %zmm1, %zmm14
+        vpermt2ps 64+__svml_slog10_ha_data_internal_avx512(%rip), %zmm6, %zmm0
+        vpermt2ps 192+__svml_slog10_ha_data_internal_avx512(%rip), %zmm6, %zmm11
+
+/* poly */
+        vfmadd213ps {rn-sae}, %zmm15, %zmm14, %zmm4
+        vfmadd231ps {rn-sae}, %zmm3, %zmm9, %zmm0
+        vfmadd213ps {rn-sae}, %zmm11, %zmm10, %zmm3
+        vmovaps   %zmm12, %zmm5
+        vfmadd213ps {rn-sae}, %zmm0, %zmm1, %zmm5
+        vfmadd213ps {rn-sae}, %zmm3, %zmm1, %zmm4
+
+/* (R*c1)_h */
+        vsubps    {rn-sae}, %zmm0, %zmm5, %zmm0
+        vxorps    %zmm0, %zmm0, %zmm0{%k1}
+
+/* (R*c1h)_l */
+        vfmsub213ps {rn-sae}, %zmm0, %zmm12, %zmm1
+        vaddps    {rn-sae}, %zmm1, %zmm4, %zmm1
+        vaddps    {rn-sae}, %zmm1, %zmm5, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm2, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_slog10_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_log10f16_ha,@function
+	.size	__svml_log10f16_ha,.-__svml_log10f16_ha
+..LN__svml_log10f16_ha.0:
+
+.L_2__routine_start___svml_slog10_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_slog10_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    2(%rdi), %edx
+        andl      $32640, %edx
+        cmpl      $32640, %edx
+        je        .LBL_2_12
+
+
+        movss     (%rdi), %xmm2
+        xorl      %ecx, %ecx
+        movss     %xmm2, -16(%rsp)
+        movzwl    -14(%rsp), %edx
+        testl     $32640, %edx
+        jne       .LBL_2_4
+
+
+        mulss     .L_2il0floatpacket.83(%rip), %xmm2
+        movl      $-40, %ecx
+        movss     %xmm2, -16(%rsp)
+
+.LBL_2_4:
+
+        pxor      %xmm0, %xmm0
+        comiss    %xmm0, %xmm2
+        jbe       .LBL_2_8
+
+
+        movaps    %xmm2, %xmm1
+        subss     .L_2il0floatpacket.99(%rip), %xmm1
+        movss     %xmm1, -20(%rsp)
+        andb      $127, -17(%rsp)
+        movss     -20(%rsp), %xmm0
+        comiss    .L_2il0floatpacket.84(%rip), %xmm0
+        jbe       .LBL_2_7
+
+
+        movss     %xmm2, -20(%rsp)
+        pxor      %xmm8, %xmm8
+        movzwl    -18(%rsp), %edi
+        lea       __slog10_ha_CoutTab(%rip), %r10
+        andl      $-32641, %edi
+        addl      $16256, %edi
+        movw      %di, -18(%rsp)
+        movss     -20(%rsp), %xmm3
+        movaps    %xmm3, %xmm0
+        movss     .L_2il0floatpacket.86(%rip), %xmm2
+        movaps    %xmm2, %xmm1
+        addss     .L_2il0floatpacket.85(%rip), %xmm0
+        addss     %xmm3, %xmm1
+        movss     %xmm0, -24(%rsp)
+        movl      -24(%rsp), %r8d
+        movss     %xmm1, -24(%rsp)
+        andl      $127, %r8d
+        movss     -24(%rsp), %xmm9
+        movss     .L_2il0floatpacket.95(%rip), %xmm6
+        subss     %xmm2, %xmm9
+        movzwl    -14(%rsp), %edx
+        lea       (%r8,%r8,2), %r9d
+        movss     (%r10,%r9,4), %xmm7
+        andl      $32640, %edx
+        shrl      $7, %edx
+        subss     %xmm9, %xmm3
+        mulss     %xmm7, %xmm9
+        mulss     %xmm3, %xmm7
+        subss     .L_2il0floatpacket.87(%rip), %xmm9
+        movaps    %xmm9, %xmm4
+        lea       -127(%rcx,%rdx), %ecx
+        cvtsi2ss  %ecx, %xmm8
+        addss     %xmm7, %xmm4
+        mulss     %xmm4, %xmm6
+        movss     .L_2il0floatpacket.96(%rip), %xmm10
+        mulss     %xmm8, %xmm10
+        addss     .L_2il0floatpacket.94(%rip), %xmm6
+        addss     4(%r10,%r9,4), %xmm10
+        mulss     %xmm4, %xmm6
+        addss     %xmm9, %xmm10
+        addss     .L_2il0floatpacket.93(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        movss     .L_2il0floatpacket.97(%rip), %xmm5
+        mulss     %xmm5, %xmm8
+        addss     .L_2il0floatpacket.92(%rip), %xmm6
+        addss     8(%r10,%r9,4), %xmm8
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.91(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.90(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.89(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.88(%rip), %xmm6
+        mulss     %xmm6, %xmm9
+        mulss     %xmm7, %xmm6
+        addss     %xmm6, %xmm8
+        addss     %xmm7, %xmm8
+        addss     %xmm8, %xmm9
+        addss     %xmm9, %xmm10
+        movss     %xmm10, (%rsi)
+        ret
+
+.LBL_2_7:
+
+        movss     .L_2il0floatpacket.87(%rip), %xmm0
+        mulss     %xmm0, %xmm1
+        movss     .L_2il0floatpacket.95(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.94(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.93(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.92(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.91(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.90(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.89(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.88(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     %xmm1, %xmm2
+        movss     %xmm2, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        ucomiss   %xmm0, %xmm2
+        jp        .LBL_2_9
+        je        .LBL_2_11
+
+.LBL_2_9:
+
+        divss     %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        movl      $1, %eax
+
+
+        ret
+
+.LBL_2_11:
+
+        movss     .L_2il0floatpacket.98(%rip), %xmm1
+        movl      $2, %eax
+        divss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_12:
+
+        movb      3(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_14
+
+.LBL_2_13:
+
+        movss     (%rdi), %xmm0
+        mulss     %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        testl     $8388607, (%rdi)
+        jne       .LBL_2_13
+
+
+        movl      $1, %eax
+        pxor      %xmm1, %xmm1
+        pxor      %xmm0, %xmm0
+        divss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_slog10_ha_cout_rare_internal,@function
+	.size	__svml_slog10_ha_cout_rare_internal,.-__svml_slog10_ha_cout_rare_internal
+..LN__svml_slog10_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_slog10_ha_data_internal_avx512:
+	.long	1050288128
+	.long	1049839616
+	.long	1049405440
+	.long	1048981504
+	.long	1048567808
+	.long	1047769088
+	.long	1046990848
+	.long	1046233088
+	.long	1045495808
+	.long	1044779008
+	.long	1044074496
+	.long	1043390464
+	.long	1042718720
+	.long	1042063360
+	.long	1041424384
+	.long	1040797696
+	.long	1040179200
+	.long	1038974976
+	.long	1037803520
+	.long	1036648448
+	.long	1035509760
+	.long	1034403840
+	.long	1033314304
+	.long	1032241152
+	.long	1030586368
+	.long	1028521984
+	.long	1026490368
+	.long	1024507904
+	.long	1021673472
+	.long	1017839616
+	.long	1013055488
+	.long	1004535808
+	.long	916096252
+	.long	922116432
+	.long	3080225825
+	.long	937573077
+	.long	3035959522
+	.long	907149878
+	.long	932083293
+	.long	937852153
+	.long	932873468
+	.long	3083585687
+	.long	923506544
+	.long	3080111720
+	.long	922618327
+	.long	929046259
+	.long	3073359178
+	.long	3075170456
+	.long	3027570914
+	.long	932146099
+	.long	3086063431
+	.long	3082882946
+	.long	937977627
+	.long	3064614024
+	.long	3065085585
+	.long	934503987
+	.long	923757491
+	.long	927716856
+	.long	937794272
+	.long	3074368796
+	.long	929297206
+	.long	3083361151
+	.long	3065901602
+	.long	912917177
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	3185469915
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	1041515222
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	3193854937
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	2990071104
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1054759897
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	1050288128
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	916096252
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.type	__svml_slog10_ha_data_internal_avx512,@object
+	.size	__svml_slog10_ha_data_internal_avx512,960
+	.align 32
+__slog10_ha_CoutTab:
+	.long	1121868800
+	.long	0
+	.long	0
+	.long	1121641104
+	.long	1004535808
+	.long	912917177
+	.long	1121413408
+	.long	1013055488
+	.long	3065901602
+	.long	1121185712
+	.long	1017839616
+	.long	3083361151
+	.long	1120958016
+	.long	1021673472
+	.long	929297206
+	.long	1120844168
+	.long	1023524864
+	.long	3077496589
+	.long	1120616472
+	.long	1025499136
+	.long	3070500046
+	.long	1120388776
+	.long	1027506176
+	.long	912271551
+	.long	1120274928
+	.long	1028521984
+	.long	927716856
+	.long	1120047232
+	.long	1030586368
+	.long	923757491
+	.long	1119933384
+	.long	1031634944
+	.long	3056752848
+	.long	1119705688
+	.long	1032775680
+	.long	917029265
+	.long	1119591840
+	.long	1033314304
+	.long	3065085585
+	.long	1119364144
+	.long	1034403840
+	.long	3064614024
+	.long	1119250296
+	.long	1034954752
+	.long	921091539
+	.long	1119136448
+	.long	1035513856
+	.long	3057436454
+	.long	1118908752
+	.long	1036644352
+	.long	922468856
+	.long	1118794904
+	.long	1037219840
+	.long	3049155845
+	.long	1118681056
+	.long	1037799424
+	.long	904301451
+	.long	1118567208
+	.long	1038385152
+	.long	908617625
+	.long	1118453360
+	.long	1038977024
+	.long	905362229
+	.long	1118225664
+	.long	1040179200
+	.long	3027570914
+	.long	1118111816
+	.long	1040488448
+	.long	882280038
+	.long	1117997968
+	.long	1040796672
+	.long	911375775
+	.long	1117884120
+	.long	1041108480
+	.long	904500572
+	.long	1117770272
+	.long	1041423872
+	.long	3057579304
+	.long	1117656424
+	.long	1041742336
+	.long	3053334705
+	.long	1117542576
+	.long	1042064384
+	.long	3053389931
+	.long	1117428728
+	.long	1042390016
+	.long	3051561465
+	.long	1117314880
+	.long	1042719232
+	.long	3011187895
+	.long	1117201032
+	.long	1043052544
+	.long	3059907089
+	.long	1117087184
+	.long	1043389440
+	.long	3057005374
+	.long	1116973336
+	.long	1043729920
+	.long	911932638
+	.long	1116859488
+	.long	1044075008
+	.long	892958461
+	.long	1116859488
+	.long	1044075008
+	.long	892958461
+	.long	1116745640
+	.long	1044424192
+	.long	3048660547
+	.long	1116631792
+	.long	1044777472
+	.long	3049032043
+	.long	1116517944
+	.long	1045134848
+	.long	906867152
+	.long	1116404096
+	.long	1045496832
+	.long	911484894
+	.long	1116404096
+	.long	1045496832
+	.long	911484894
+	.long	1116290248
+	.long	1045863424
+	.long	912580963
+	.long	1116176400
+	.long	1046235136
+	.long	3058440244
+	.long	1116062552
+	.long	1046610944
+	.long	895945194
+	.long	1116062552
+	.long	1046610944
+	.long	895945194
+	.long	1115948704
+	.long	1046991872
+	.long	904357324
+	.long	1115834856
+	.long	1047377920
+	.long	902293870
+	.long	1115721008
+	.long	1047769088
+	.long	907149878
+	.long	1115721008
+	.long	1047769088
+	.long	907149878
+	.long	1115529456
+	.long	1048165888
+	.long	3052029263
+	.long	1115301760
+	.long	1048567808
+	.long	3035959522
+	.long	1115301760
+	.long	1048567808
+	.long	3035959522
+	.long	1115074064
+	.long	1048775680
+	.long	892998645
+	.long	1115074064
+	.long	1048775680
+	.long	892998645
+	.long	1114846368
+	.long	1048982400
+	.long	881767775
+	.long	1114618672
+	.long	1049192064
+	.long	893839142
+	.long	1114618672
+	.long	1049192064
+	.long	893839142
+	.long	1114390976
+	.long	1049404800
+	.long	896498651
+	.long	1114390976
+	.long	1049404800
+	.long	896498651
+	.long	1114163280
+	.long	1049620736
+	.long	3033695903
+	.long	1114163280
+	.long	1049620736
+	.long	3033695903
+	.long	1113935584
+	.long	1049839872
+	.long	3029986056
+	.long	1113935584
+	.long	1049839872
+	.long	3029986056
+	.long	1113707888
+	.long	1050062336
+	.long	884671939
+	.long	1113707888
+	.long	1050062336
+	.long	884671939
+	.long	1113480192
+	.long	1050288256
+	.long	894707678
+	.long	1050279936
+	.long	964848148
+	.long	1207959616
+	.long	1174405120
+	.long	1002438656
+	.long	1400897536
+	.long	0
+	.long	1065353216
+	.long	1121868800
+	.long	3212771328
+	.long	3079888218
+	.long	870463078
+	.long	2957202361
+	.long	749987585
+	.long	2838272395
+	.long	631921661
+	.long	2720751022
+	.type	__slog10_ha_CoutTab,@object
+	.size	__slog10_ha_CoutTab,848
+	.align 4
+.L_2il0floatpacket.83:
+	.long	0x53800000
+	.type	.L_2il0floatpacket.83,@object
+	.size	.L_2il0floatpacket.83,4
+	.align 4
+.L_2il0floatpacket.84:
+	.long	0x3bc00000
+	.type	.L_2il0floatpacket.84,@object
+	.size	.L_2il0floatpacket.84,4
+	.align 4
+.L_2il0floatpacket.85:
+	.long	0x48000040
+	.type	.L_2il0floatpacket.85,@object
+	.size	.L_2il0floatpacket.85,4
+	.align 4
+.L_2il0floatpacket.86:
+	.long	0x46000000
+	.type	.L_2il0floatpacket.86,@object
+	.size	.L_2il0floatpacket.86,4
+	.align 4
+.L_2il0floatpacket.87:
+	.long	0x42de5c00
+	.type	.L_2il0floatpacket.87,@object
+	.size	.L_2il0floatpacket.87,4
+	.align 4
+.L_2il0floatpacket.88:
+	.long	0xbf7f0000
+	.type	.L_2il0floatpacket.88,@object
+	.size	.L_2il0floatpacket.88,4
+	.align 4
+.L_2il0floatpacket.89:
+	.long	0xb7935d5a
+	.type	.L_2il0floatpacket.89,@object
+	.size	.L_2il0floatpacket.89,4
+	.align 4
+.L_2il0floatpacket.90:
+	.long	0x33e23666
+	.type	.L_2il0floatpacket.90,@object
+	.size	.L_2il0floatpacket.90,4
+	.align 4
+.L_2il0floatpacket.91:
+	.long	0xb04353b9
+	.type	.L_2il0floatpacket.91,@object
+	.size	.L_2il0floatpacket.91,4
+	.align 4
+.L_2il0floatpacket.92:
+	.long	0x2cb3e701
+	.type	.L_2il0floatpacket.92,@object
+	.size	.L_2il0floatpacket.92,4
+	.align 4
+.L_2il0floatpacket.93:
+	.long	0xa92c998b
+	.type	.L_2il0floatpacket.93,@object
+	.size	.L_2il0floatpacket.93,4
+	.align 4
+.L_2il0floatpacket.94:
+	.long	0x25aa5bfd
+	.type	.L_2il0floatpacket.94,@object
+	.size	.L_2il0floatpacket.94,4
+	.align 4
+.L_2il0floatpacket.95:
+	.long	0xa22b5dae
+	.type	.L_2il0floatpacket.95,@object
+	.size	.L_2il0floatpacket.95,4
+	.align 4
+.L_2il0floatpacket.96:
+	.long	0x3e9a0000
+	.type	.L_2il0floatpacket.96,@object
+	.size	.L_2il0floatpacket.96,4
+	.align 4
+.L_2il0floatpacket.97:
+	.long	0x39826a14
+	.type	.L_2il0floatpacket.97,@object
+	.size	.L_2il0floatpacket.97,4
+	.align 4
+.L_2il0floatpacket.98:
+	.long	0xbf800000
+	.type	.L_2il0floatpacket.98,@object
+	.size	.L_2il0floatpacket.98,4
+	.align 4
+.L_2il0floatpacket.99:
+	.long	0x3f800000
+	.type	.L_2il0floatpacket.99,@object
+	.size	.L_2il0floatpacket.99,4
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_s_la.s
index 1a02ca02f25..c824675b170 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log10_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_d_ha.s
new file mode 100644
index 00000000000..28ade0ff953
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_d_ha.s
@@ -0,0 +1,1205 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  Compute 1+_VARG1 in high-low parts.  The low part will be
+ *  *  incorporated in the reduced argument (with proper scaling).
+ *  *  log(x) = VGETEXP(x)*log(2) + log(VGETMANT(x))
+ *  *       VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *  *   mx = VGETMANT(x) is in [1,2) for all x>=0
+ *  *   log(mx) = -log(RCP(mx)) + log(1 +(mx*RCP(mx)-1))
+ *  *      RCP(mx) is rounded to 4 fractional bits,
+ *  *      and the table lookup for log(RCP(mx)) is based on a small permute instruction
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_log1p8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_log1p8_ha
+
+__svml_log1p8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   256+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   320+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm3
+        vmovaps   %zmm0, %zmm10
+
+/* compute 1+x as high, low parts */
+        vmaxpd    {sae}, %zmm10, %zmm8, %zmm12
+        vminpd    {sae}, %zmm10, %zmm8, %zmm14
+        vaddpd    {rn-sae}, %zmm10, %zmm8, %zmm13
+        vandpd    %zmm3, %zmm10, %zmm11
+
+/* GetMant(x), normalized to [1,2) for x>=0, NaN for x<0 */
+        vgetmantpd $8, {sae}, %zmm13, %zmm7
+
+/* GetExp(x) */
+        vgetexppd {sae}, %zmm13, %zmm9
+        vsubpd    {rn-sae}, %zmm12, %zmm13, %zmm15
+
+/* DblRcp ~ 1/Mantissa */
+        vrcp14pd  %zmm7, %zmm2
+
+/* Prepare exponent correction: DblRcp<0.75? */
+        vmovups   384+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm13
+
+/* Xl */
+        vsubpd    {rn-sae}, %zmm15, %zmm14, %zmm0
+        vxorpd    %zmm3, %zmm9, %zmm6
+
+/* round DblRcp to 4 fractional bits (RN mode, no Precision exception) */
+        vrndscalepd $88, {sae}, %zmm2, %zmm12
+        vmovups   576+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   512+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm3
+
+/* K*log(2)_high+Th */
+        vmovups   960+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm15
+
+/* Xl*2^(-Expon) */
+        vscalefpd {rn-sae}, %zmm6, %zmm0, %zmm4
+        vcmppd    $17, {sae}, %zmm13, %zmm12, %k1
+
+/* Reduced argument: R = DblRcp*Mantissa - 1 */
+        vfmsub213pd {rn-sae}, %zmm8, %zmm12, %zmm7
+        vmovups   128+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm0
+        vmulpd    {rn-sae}, %zmm4, %zmm12, %zmm1
+
+/* Table lookup */
+        vmovups   __svml_dlog1p_ha_data_internal_avx512(%rip), %zmm4
+
+/* add 1 to Expon if DblRcp<0.75 */
+        vaddpd    {rn-sae}, %zmm8, %zmm9, %zmm9{%k1}
+        vaddpd    {rn-sae}, %zmm1, %zmm7, %zmm6
+
+/* Start polynomial evaluation */
+        vmovups   448+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm8
+
+/* R^2 */
+        vmulpd    {rn-sae}, %zmm6, %zmm6, %zmm2
+        vfmadd231pd {rn-sae}, %zmm6, %zmm8, %zmm3
+        vmovups   896+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm8
+        vsubpd    {rn-sae}, %zmm7, %zmm6, %zmm5
+
+/* Prepare table index */
+        vpsrlq    $48, %zmm12, %zmm7
+        vmovups   768+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm12
+        vsubpd    {rn-sae}, %zmm5, %zmm1, %zmm5
+        vpermt2pd 192+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm7, %zmm0
+        vpermt2pd 64+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm7, %zmm4
+        vmovups   640+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm1
+        vmovups   832+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm7
+        vfmadd231pd {rn-sae}, %zmm9, %zmm15, %zmm4
+
+/* Rl+Tl */
+        vaddpd    {rn-sae}, %zmm5, %zmm0, %zmm0
+        vfmadd231pd {rn-sae}, %zmm6, %zmm14, %zmm1
+        vmovups   704+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm5
+        vfmadd231pd {rn-sae}, %zmm6, %zmm7, %zmm8
+
+/* K*log(2)_high+Th+Rh */
+        vaddpd    {rn-sae}, %zmm6, %zmm4, %zmm14
+        vfmadd213pd {rn-sae}, %zmm1, %zmm2, %zmm3
+        vfmadd231pd {rn-sae}, %zmm6, %zmm5, %zmm12
+        vmulpd    {rn-sae}, %zmm2, %zmm2, %zmm1
+        vfmadd213pd {rn-sae}, %zmm8, %zmm2, %zmm12
+
+/*
+ * Here we should use a zeroing mask (when available): VSUBMASKZ(D, Rh, GuardMask, High, Th)
+ * Rh
+ */
+        vsubpd    {rn-sae}, %zmm4, %zmm14, %zmm4
+
+/* polynomial */
+        vfmadd213pd {rn-sae}, %zmm12, %zmm1, %zmm3
+
+/*
+ * Rl
+ * When VSUBMASKZ is available, simply use VSUB(D, R, R, Rh) here
+ * VMSUB(D, R, GuardMask, R, Th);
+ */
+        vsubpd    {rn-sae}, %zmm4, %zmm6, %zmm13
+
+/* K*log(2)_low + Tl */
+        vmovups   1024+__svml_dlog1p_ha_data_internal_avx512(%rip), %zmm6
+        vfmadd213pd {rn-sae}, %zmm13, %zmm2, %zmm3
+        vcmppd    $4, {sae}, %zmm13, %zmm13, %k0
+        vfmadd213pd {rn-sae}, %zmm0, %zmm6, %zmm9
+        kmovw     %k0, %edx
+        vaddpd    {rn-sae}, %zmm9, %zmm3, %zmm9
+        vaddpd    {rn-sae}, %zmm9, %zmm14, %zmm0
+        vorpd     %zmm11, %zmm0, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm10, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dlog1p_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_log1p8_ha,@function
+	.size	__svml_log1p8_ha,.-__svml_log1p8_ha
+..LN__svml_log1p8_ha.0:
+
+.L_2__routine_start___svml_dlog1p_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dlog1p_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movsd     .L_2il0floatpacket.82(%rip), %xmm3
+        movsd     (%rdi), %xmm2
+        addsd     %xmm3, %xmm2
+        movsd     %xmm2, -16(%rsp)
+        movzwl    -10(%rsp), %edx
+        andl      $32752, %edx
+        cmpl      $32752, %edx
+        je        .LBL_2_12
+
+
+        movsd     %xmm2, -8(%rsp)
+        xorl      %ecx, %ecx
+        movzwl    -2(%rsp), %edx
+        testl     $32752, %edx
+        jne       .LBL_2_4
+
+
+        mulsd     1600+__dlog1p_ha_CoutTab(%rip), %xmm2
+        movl      $-60, %ecx
+        movsd     %xmm2, -8(%rsp)
+
+.LBL_2_4:
+
+        movsd     1608+__dlog1p_ha_CoutTab(%rip), %xmm0
+        comisd    %xmm0, %xmm2
+        jbe       .LBL_2_8
+
+
+        movaps    %xmm2, %xmm1
+        subsd     %xmm3, %xmm1
+        movsd     %xmm1, -16(%rsp)
+        andb      $127, -9(%rsp)
+        movsd     -16(%rsp), %xmm0
+        comisd    1592+__dlog1p_ha_CoutTab(%rip), %xmm0
+        jbe       .LBL_2_7
+
+
+        movsd     %xmm2, -16(%rsp)
+        pxor      %xmm7, %xmm7
+        movzwl    -10(%rsp), %edi
+        lea       __dlog1p_ha_CoutTab(%rip), %r10
+        andl      $-32753, %edi
+        addl      $16368, %edi
+        movw      %di, -10(%rsp)
+        movsd     -16(%rsp), %xmm4
+        movaps    %xmm4, %xmm1
+        movaps    %xmm4, %xmm2
+        movsd     1672+__dlog1p_ha_CoutTab(%rip), %xmm8
+        movzwl    -2(%rsp), %edx
+        andl      $32752, %edx
+        addsd     1576+__dlog1p_ha_CoutTab(%rip), %xmm1
+        addsd     1584+__dlog1p_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm1, -24(%rsp)
+        movl      -24(%rsp), %r8d
+        movsd     %xmm2, -24(%rsp)
+        andl      $127, %r8d
+        movsd     -24(%rsp), %xmm5
+        movsd     1560+__dlog1p_ha_CoutTab(%rip), %xmm9
+        movsd     1568+__dlog1p_ha_CoutTab(%rip), %xmm0
+        shrl      $4, %edx
+        subsd     1584+__dlog1p_ha_CoutTab(%rip), %xmm5
+        lea       (%r8,%r8,2), %r9d
+        movsd     (%r10,%r9,8), %xmm6
+        lea       -1023(%rcx,%rdx), %ecx
+        cvtsi2sd  %ecx, %xmm7
+        subsd     %xmm5, %xmm4
+        mulsd     %xmm6, %xmm5
+        mulsd     %xmm7, %xmm9
+        subsd     %xmm3, %xmm5
+        mulsd     %xmm4, %xmm6
+        mulsd     %xmm0, %xmm7
+        addsd     8(%r10,%r9,8), %xmm9
+        addsd     16(%r10,%r9,8), %xmm7
+        addsd     %xmm5, %xmm9
+        addsd     %xmm6, %xmm7
+        movaps    %xmm5, %xmm3
+        addsd     %xmm6, %xmm3
+        mulsd     %xmm3, %xmm8
+        addsd     1664+__dlog1p_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1656+__dlog1p_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1648+__dlog1p_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1640+__dlog1p_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1632+__dlog1p_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1624+__dlog1p_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     %xmm7, %xmm8
+        addsd     %xmm8, %xmm9
+        movsd     %xmm9, (%rsi)
+        ret
+
+.LBL_2_7:
+
+        movsd     1672+__dlog1p_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1664+__dlog1p_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1656+__dlog1p_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1648+__dlog1p_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1640+__dlog1p_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1632+__dlog1p_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1624+__dlog1p_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     %xmm1, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        ucomisd   %xmm0, %xmm2
+        jp        .LBL_2_9
+        je        .LBL_2_11
+
+.LBL_2_9:
+
+        divsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        movl      $1, %eax
+
+
+        ret
+
+.LBL_2_11:
+
+        movsd     1616+__dlog1p_ha_CoutTab(%rip), %xmm1
+        movl      $2, %eax
+        xorps     .L_2il0floatpacket.81(%rip), %xmm1
+        divsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_12:
+
+        movb      -9(%rsp), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_14
+
+.LBL_2_13:
+
+        mulsd     %xmm2, %xmm2
+        movsd     %xmm2, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        testl     $1048575, -12(%rsp)
+        jne       .LBL_2_13
+
+
+        cmpl      $0, -16(%rsp)
+        jne       .LBL_2_13
+
+
+        movsd     1608+__dlog1p_ha_CoutTab(%rip), %xmm0
+        movl      $1, %eax
+        divsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dlog1p_ha_cout_rare_internal,@function
+	.size	__svml_dlog1p_ha_cout_rare_internal,.-__svml_dlog1p_ha_cout_rare_internal
+..LN__svml_dlog1p_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dlog1p_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	3222405120
+	.long	3215919664
+	.long	1848311808
+	.long	3216910087
+	.long	1890025472
+	.long	3217424176
+	.long	3348791296
+	.long	3217854455
+	.long	2880159744
+	.long	3218171740
+	.long	3256631296
+	.long	3218366859
+	.long	4139499520
+	.long	3218553303
+	.long	288669696
+	.long	1070754146
+	.long	1823703040
+	.long	1070569756
+	.long	1015742464
+	.long	1070240749
+	.long	1800667136
+	.long	1069924160
+	.long	2183659520
+	.long	1069619086
+	.long	1566113792
+	.long	1069101918
+	.long	3047030784
+	.long	1068533144
+	.long	2313682944
+	.long	1067467101
+	.long	0
+	.long	0
+	.long	3496399314
+	.long	1028893491
+	.long	720371772
+	.long	1026176044
+	.long	1944193543
+	.long	3175338952
+	.long	634920691
+	.long	3175752108
+	.long	1664625295
+	.long	1029304828
+	.long	192624563
+	.long	3177103997
+	.long	3796653051
+	.long	3176138396
+	.long	1005287031
+	.long	1029411448
+	.long	3433090547
+	.long	1029291198
+	.long	1779090477
+	.long	3176336968
+	.long	3242817150
+	.long	1029626109
+	.long	3430202884
+	.long	3175842849
+	.long	2270669051
+	.long	1028809259
+	.long	1375653371
+	.long	1028457284
+	.long	4246237509
+	.long	3176626033
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.type	__svml_dlog1p_ha_data_internal_avx512,@object
+	.size	__svml_dlog1p_ha_data_internal_avx512,1280
+	.align 32
+__dlog1p_ha_CoutTab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1072660480
+	.long	1486880768
+	.long	1066410070
+	.long	1813744607
+	.long	3179892593
+	.long	0
+	.long	1072629760
+	.long	377487360
+	.long	1067416219
+	.long	919019713
+	.long	3179241129
+	.long	0
+	.long	1072599040
+	.long	1513619456
+	.long	1067944025
+	.long	874573033
+	.long	3178512940
+	.long	0
+	.long	1072570368
+	.long	3221749760
+	.long	1068427825
+	.long	4181665006
+	.long	3177478212
+	.long	0
+	.long	1072541696
+	.long	4162322432
+	.long	1068708823
+	.long	627020255
+	.long	1028629941
+	.long	0
+	.long	1072513024
+	.long	183107584
+	.long	1068957907
+	.long	2376703469
+	.long	1030233118
+	.long	0
+	.long	1072486400
+	.long	1053425664
+	.long	1069192557
+	.long	696277142
+	.long	1030474863
+	.long	0
+	.long	1072459776
+	.long	3996123136
+	.long	1069430535
+	.long	2630798680
+	.long	1028792016
+	.long	0
+	.long	1072435200
+	.long	3452764160
+	.long	1069600382
+	.long	624954044
+	.long	3177101741
+	.long	0
+	.long	1072409600
+	.long	207650816
+	.long	1069717971
+	.long	3272735636
+	.long	3175176575
+	.long	0
+	.long	1072386048
+	.long	2647228416
+	.long	1069827627
+	.long	3594228712
+	.long	1029303785
+	.long	0
+	.long	1072362496
+	.long	2712010752
+	.long	1069938736
+	.long	3653242769
+	.long	3176839013
+	.long	0
+	.long	1072338944
+	.long	374439936
+	.long	1070051337
+	.long	4072775574
+	.long	3176577495
+	.long	0
+	.long	1072316416
+	.long	3707174912
+	.long	1070160474
+	.long	1486946159
+	.long	1023930920
+	.long	0
+	.long	1072294912
+	.long	1443954688
+	.long	1070265993
+	.long	293532967
+	.long	3176278277
+	.long	0
+	.long	1072273408
+	.long	127762432
+	.long	1070372856
+	.long	3404145447
+	.long	3177023955
+	.long	0
+	.long	1072252928
+	.long	2053832704
+	.long	1070475911
+	.long	1575076358
+	.long	1029048544
+	.long	0
+	.long	1072232448
+	.long	3194093568
+	.long	1070580248
+	.long	1864169120
+	.long	1026866084
+	.long	0
+	.long	1072212992
+	.long	3917201408
+	.long	1070638340
+	.long	2362145246
+	.long	3175606197
+	.long	0
+	.long	1072193536
+	.long	3417112576
+	.long	1070689116
+	.long	70087871
+	.long	3174183577
+	.long	0
+	.long	1072175104
+	.long	4226777088
+	.long	1070737793
+	.long	1620410586
+	.long	3174700065
+	.long	0
+	.long	1072156672
+	.long	3168870400
+	.long	1070787042
+	.long	311238082
+	.long	1025781772
+	.long	0
+	.long	1072139264
+	.long	2150580224
+	.long	1070834092
+	.long	1664262457
+	.long	3175299224
+	.long	0
+	.long	1072120832
+	.long	4095672320
+	.long	1070884491
+	.long	1657121015
+	.long	3174674199
+	.long	0
+	.long	1072104448
+	.long	2595577856
+	.long	1070929805
+	.long	2014006823
+	.long	3175423830
+	.long	0
+	.long	1072087040
+	.long	3747176448
+	.long	1070978493
+	.long	144991708
+	.long	3171552042
+	.long	0
+	.long	1072070656
+	.long	1050435584
+	.long	1071024840
+	.long	3386227432
+	.long	1027876916
+	.long	0
+	.long	1072055296
+	.long	255516672
+	.long	1071068760
+	.long	2637594316
+	.long	1028049573
+	.long	0
+	.long	1072038912
+	.long	1640783872
+	.long	1071116120
+	.long	893247007
+	.long	1028452162
+	.long	0
+	.long	1072023552
+	.long	2940411904
+	.long	1071161011
+	.long	813240633
+	.long	1027664048
+	.long	0
+	.long	1072009216
+	.long	882917376
+	.long	1071203348
+	.long	2376597551
+	.long	3175828767
+	.long	0
+	.long	1071993856
+	.long	213966848
+	.long	1071249188
+	.long	2977204125
+	.long	1028350609
+	.long	0
+	.long	1071979520
+	.long	2921504768
+	.long	1071292428
+	.long	523218347
+	.long	1028007004
+	.long	0
+	.long	1071965184
+	.long	3186655232
+	.long	1071336119
+	.long	2352907891
+	.long	1026967097
+	.long	0
+	.long	1071951872
+	.long	2653364224
+	.long	1071377101
+	.long	2453418583
+	.long	3174349512
+	.long	0
+	.long	1071938560
+	.long	3759783936
+	.long	1071418487
+	.long	3685870403
+	.long	3175415611
+	.long	0
+	.long	1071925248
+	.long	2468364288
+	.long	1071460286
+	.long	1578908842
+	.long	3175510517
+	.long	0
+	.long	1071911936
+	.long	81903616
+	.long	1071502506
+	.long	770710269
+	.long	1026742353
+	.long	0
+	.long	1071899648
+	.long	2799321088
+	.long	1071541858
+	.long	3822266185
+	.long	1028434427
+	.long	0
+	.long	1071886336
+	.long	2142265344
+	.long	1071584911
+	.long	175901806
+	.long	3173871540
+	.long	0
+	.long	1071874048
+	.long	2944024576
+	.long	1071625048
+	.long	2747360403
+	.long	1027672159
+	.long	0
+	.long	1071862784
+	.long	3434301440
+	.long	1071653426
+	.long	4194662196
+	.long	3173893003
+	.long	0
+	.long	1071850496
+	.long	1547755520
+	.long	1071673870
+	.long	4248764681
+	.long	3172759087
+	.long	0
+	.long	1071839232
+	.long	4246986752
+	.long	1071692786
+	.long	2840205638
+	.long	3174430911
+	.long	0
+	.long	1071826944
+	.long	3418390528
+	.long	1071713619
+	.long	3041880823
+	.long	1025440860
+	.long	0
+	.long	1071816704
+	.long	4143093760
+	.long	1071731139
+	.long	2727587401
+	.long	3173965207
+	.long	0
+	.long	1071805440
+	.long	3121326080
+	.long	1071750582
+	.long	3173887692
+	.long	3174190163
+	.long	0
+	.long	1071794176
+	.long	1852893184
+	.long	1071770207
+	.long	3951060252
+	.long	1027348295
+	.long	0
+	.long	1071783936
+	.long	3636379648
+	.long	1071788208
+	.long	1684924001
+	.long	3174777086
+	.long	0
+	.long	1071773696
+	.long	516505600
+	.long	1071806366
+	.long	429181199
+	.long	3173211033
+	.long	0
+	.long	1071763456
+	.long	4186185728
+	.long	1071824681
+	.long	2044904577
+	.long	3174967132
+	.long	0
+	.long	1071753216
+	.long	877596672
+	.long	1071843159
+	.long	1396318105
+	.long	3173959727
+	.long	0
+	.long	1071742976
+	.long	2912784384
+	.long	1071861800
+	.long	448136789
+	.long	3174814192
+	.long	0
+	.long	1071733760
+	.long	3722825728
+	.long	1071878720
+	.long	714165913
+	.long	3173439560
+	.long	0
+	.long	1071723520
+	.long	2522374144
+	.long	1071897682
+	.long	3227240353
+	.long	3173394323
+	.long	0
+	.long	1071714304
+	.long	4165410816
+	.long	1071914895
+	.long	1365684961
+	.long	3174365060
+	.long	0
+	.long	1071705088
+	.long	3477135360
+	.long	1071932251
+	.long	368482985
+	.long	3174140821
+	.long	0
+	.long	1071695872
+	.long	2079455232
+	.long	1071949752
+	.long	1320576317
+	.long	1026822714
+	.long	0
+	.long	1071687680
+	.long	851795968
+	.long	1071965432
+	.long	3702467026
+	.long	1025224125
+	.long	0
+	.long	1071678464
+	.long	647743488
+	.long	1071983213
+	.long	772992109
+	.long	3174038459
+	.long	0
+	.long	1071670272
+	.long	26537984
+	.long	1071999146
+	.long	2360214276
+	.long	3174861275
+	.long	0
+	.long	1071661056
+	.long	1547061248
+	.long	1072017216
+	.long	2886781435
+	.long	1026423395
+	.long	0
+	.long	1071652864
+	.long	2854492160
+	.long	1072033410
+	.long	215631550
+	.long	1025638968
+	.long	0
+	.long	1071644672
+	.long	4277811200
+	.long	1072049730
+	.long	2479318832
+	.long	1026487127
+	.long	4277811200
+	.long	1072049730
+	.long	2479318832
+	.long	1026487127
+	.long	64
+	.long	1120927744
+	.long	0
+	.long	1094713344
+	.long	0
+	.long	1065615360
+	.long	0
+	.long	1135607808
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3219128320
+	.long	1431655955
+	.long	1070945621
+	.long	610
+	.long	3218079744
+	.long	2545118337
+	.long	1070176665
+	.long	1378399119
+	.long	3217380693
+	.long	612435357
+	.long	1069697472
+	.long	94536557
+	.long	3217031348
+	.type	__dlog1p_ha_CoutTab,@object
+	.size	__dlog1p_ha_CoutTab,1680
+	.align 16
+.L_2il0floatpacket.81:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.81,@object
+	.size	.L_2il0floatpacket.81,16
+	.align 8
+.L_2il0floatpacket.82:
+	.long	0x00000000,0x3ff00000
+	.type	.L_2il0floatpacket.82,@object
+	.size	.L_2il0floatpacket.82,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_d_la.s
index 35bd4177601..d1cf448d4a9 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_h_la.s
new file mode 100644
index 00000000000..81892303f7c
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_h_la.s
@@ -0,0 +1,163 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   log(x) = VGETEXP(x)*log(2) + log(VGETMANT(x))
+ *   VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *   mx = VGETMANT(x) is in [1,2) for all x>=0
+ *   log(mx) = -log(RCP(mx)) + log(1 +(mx*RCP(mx)-1))
+ *   and the table lookup for log(RCP(mx))
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_log1ps32
+
+__svml_log1ps32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hlog1p_data_internal(%rip), %zmm6
+        vmovdqu16 64+__svml_hlog1p_data_internal(%rip), %zmm29
+        vmovdqu16 128+__svml_hlog1p_data_internal(%rip), %zmm28
+        vmovdqu16 192+__svml_hlog1p_data_internal(%rip), %zmm27
+        vmovdqu16 256+__svml_hlog1p_data_internal(%rip), %zmm14
+        vmovdqu16 320+__svml_hlog1p_data_internal(%rip), %zmm15
+        vmovdqu16 384+__svml_hlog1p_data_internal(%rip), %zmm31
+        vmovdqu16 448+__svml_hlog1p_data_internal(%rip), %zmm30
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__log1p_h:
+        cmpq    $31, %rdx
+        ja .loaddata__log1p_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_log1p_h
+.loaddata__log1p_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_log1p_h:
+
+/* No callout */
+        vmovdqu16 %zmm27, %zmm11
+
+/* x+1.0 */
+        vaddph    {rn-sae}, %zmm6, %zmm0, %zmm1
+
+/* A = max(x, 1.0) */
+        vmaxph    {sae}, %zmm6, %zmm0, %zmm2
+
+/* B = min(x, 1.0); */
+        vminph    {sae}, %zmm6, %zmm0, %zmm3
+
+/* input is zero or +Inf? */
+        vfpclassph $14, %zmm0, %k1
+
+/* reduce mantissa to [.75, 1.5) */
+        vgetmantph $11, {sae}, %zmm1, %zmm5
+
+/* Bh */
+        vsubph    {rn-sae}, %zmm2, %zmm1, %zmm4
+
+/* exponent */
+        vgetexpph {sae}, %zmm1, %zmm8
+
+/* exponent correction */
+        vgetexpph {sae}, %zmm5, %zmm7
+
+/* Mant_low */
+        vsubph    {rn-sae}, %zmm4, %zmm3, %zmm9
+
+/* reduced argument */
+        vsubph    {rn-sae}, %zmm6, %zmm5, %zmm12
+
+/* -exponent */
+        vsubph    {rn-sae}, %zmm8, %zmm7, %zmm1
+
+/* mant_low, whith proper scale */
+        vscalefph {rn-sae}, %zmm1, %zmm9, %zmm10
+
+/* fixup in case Mant_low=NaN */
+        vpandd %zmm29, %zmm10, %zmm13
+        vmovdqu16 %zmm28, %zmm10
+
+/* start polynomial */
+        vfmadd213ph {rn-sae}, %zmm11, %zmm12, %zmm10
+
+/* apply correction to reduced argument */
+        vaddph    {rn-sae}, %zmm13, %zmm12, %zmm11
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm14, %zmm11, %zmm10
+        vfmadd213ph {rn-sae}, %zmm15, %zmm11, %zmm10
+        vfmadd213ph {rn-sae}, %zmm31, %zmm11, %zmm10
+
+/* Poly*R+R */
+        vfmadd213ph {rn-sae}, %zmm11, %zmm11, %zmm10
+
+/* result:  -m_expon*log(2)+poly */
+        vfnmadd213ph {rn-sae}, %zmm10, %zmm30, %zmm1
+
+/* fixup for +0/-0/+Inf */
+        vpblendmw %zmm0, %zmm1, %zmm0{%k1}
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__log1p_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_log1ps32,@function
+        .size	__svml_log1ps32,.-__svml_log1ps32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hlog1p_data_internal:
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0xbfff
+	.endr
+	.rept	32
+        .word	0x3088
+	.endr
+	.rept	32
+        .word	0xb428
+	.endr
+	.rept	32
+        .word	0x356a
+	.endr
+	.rept	32
+        .word	0xb800
+	.endr
+	.rept	32
+        .word	0x833f
+	.endr
+	.rept	32
+        .word	0x398c
+	.endr
+        .type	__svml_hlog1p_data_internal,@object
+        .size	__svml_hlog1p_data_internal,512
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_s_ha.s
new file mode 100644
index 00000000000..25cf31ad15a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_s_ha.s
@@ -0,0 +1,959 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  Compute 1+_VARG1 in high-low parts.  The low part will be
+ *  *  incorporated in the reduced argument (with proper scaling).
+ *  *  log(x) = VGETEXP(x)*log(2) + log(VGETMANT(x))
+ *  *       VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *  *   mx = VGETMANT(x) is in [1,2) for all x>=0
+ *  *   log(mx) = -log(RCP(mx)) + log(1 +(mx*RCP(mx)-1))
+ *  *      RCP(mx) is rounded to 5 fractional bits,
+ *  *      and the table lookup for log(RCP(mx)) is based on a small permute instruction
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_log1pf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_log1pf16_ha
+
+__svml_log1pf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   256+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm4
+        vmovups   320+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm11
+        vmovaps   %zmm0, %zmm6
+
+/* compute 1+x as high, low parts */
+        vmaxps    {sae}, %zmm6, %zmm4, %zmm7
+        vminps    {sae}, %zmm6, %zmm4, %zmm9
+        vaddps    {rn-sae}, %zmm6, %zmm4, %zmm8
+        vandps    %zmm11, %zmm6, %zmm5
+
+/* GetMant(x), normalized to [1,2) for x>=0, NaN for x<0 */
+        vgetmantps $8, {sae}, %zmm8, %zmm3
+
+/* GetExp(x) */
+        vgetexpps {sae}, %zmm8, %zmm2
+        vsubps    {rn-sae}, %zmm7, %zmm8, %zmm10
+
+/* SglRcp ~ 1/Mantissa */
+        vrcp14ps  %zmm3, %zmm12
+
+/* Xl */
+        vsubps    {rn-sae}, %zmm10, %zmm9, %zmm13
+
+/* -expon */
+        vxorps    %zmm11, %zmm2, %zmm14
+
+/* round SglRcp to 5 fractional bits (RN mode, no Precision exception) */
+        vrndscaleps $104, {sae}, %zmm12, %zmm7
+        vmovups   128+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   640+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm9
+
+/* Start polynomial evaluation */
+        vmovups   448+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm12
+
+/* Xl*2^(-Expon) */
+        vscalefps {rn-sae}, %zmm14, %zmm13, %zmm15
+
+/* Reduced argument: R = SglRcp*Mantissa - 1 */
+        vfmsub213ps {rn-sae}, %zmm4, %zmm7, %zmm3
+        vgetexpps {sae}, %zmm7, %zmm8
+        vmovups   576+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm13
+
+/* K*log(2)_low + Tl */
+        vmovups   704+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm14
+        vmulps    {rn-sae}, %zmm15, %zmm7, %zmm0
+
+/* exponent correction */
+        vsubps    {rn-sae}, %zmm4, %zmm2, %zmm4
+
+/* Prepare table index */
+        vpsrld    $18, %zmm7, %zmm2
+        vaddps    {rn-sae}, %zmm0, %zmm3, %zmm1
+        vpermt2ps 192+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm2, %zmm10
+        vsubps    {rn-sae}, %zmm3, %zmm1, %zmm3
+        vsubps    {rn-sae}, %zmm3, %zmm0, %zmm11
+
+/* Table lookup */
+        vmovups   __svml_slog1p_ha_data_internal_avx512(%rip), %zmm0
+        vmovups   512+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm3
+
+/* Rl+Tl */
+        vaddps    {rn-sae}, %zmm11, %zmm10, %zmm15
+        vpermt2ps 64+__svml_slog1p_ha_data_internal_avx512(%rip), %zmm2, %zmm0
+
+/* K*log(2)_high+Th */
+        vsubps    {rn-sae}, %zmm8, %zmm4, %zmm2
+        vfmadd231ps {rn-sae}, %zmm1, %zmm12, %zmm3
+        vmulps    {rn-sae}, %zmm1, %zmm1, %zmm12
+        vfmadd231ps {rn-sae}, %zmm2, %zmm9, %zmm0
+        vfmadd213ps {rn-sae}, %zmm13, %zmm1, %zmm3
+        vfmadd213ps {rn-sae}, %zmm15, %zmm14, %zmm2
+
+/* K*log(2)_high+Th+Rh */
+        vaddps    {rn-sae}, %zmm1, %zmm0, %zmm4
+        vfmadd213ps {rn-sae}, %zmm2, %zmm12, %zmm3
+
+/* Rh */
+        vsubps    {rn-sae}, %zmm0, %zmm4, %zmm0
+
+/* Rl */
+        vsubps    {rn-sae}, %zmm0, %zmm1, %zmm1
+        vcmpps    $4, {sae}, %zmm1, %zmm1, %k0
+        vaddps    {rn-sae}, %zmm1, %zmm3, %zmm0
+        kmovw     %k0, %edx
+        vaddps    {rn-sae}, %zmm0, %zmm4, %zmm1
+        vorps     %zmm5, %zmm1, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_slog1p_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_log1pf16_ha,@function
+	.size	__svml_log1pf16_ha,.-__svml_log1pf16_ha
+..LN__svml_log1pf16_ha.0:
+
+.L_2__routine_start___svml_slog1p_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_slog1p_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movss     .L_2il0floatpacket.93(%rip), %xmm1
+        xorb      %r8b, %r8b
+        movss     (%rdi), %xmm5
+        addss     %xmm1, %xmm5
+        movss     %xmm5, -20(%rsp)
+        movzwl    -18(%rsp), %edx
+        andl      $32640, %edx
+        cmpl      $32640, %edx
+        je        .LBL_2_15
+
+
+        movss     %xmm5, -16(%rsp)
+        xorl      %ecx, %ecx
+        movzwl    -14(%rsp), %edx
+        testl     $32640, %edx
+        jne       .LBL_2_4
+
+
+        mulss     .L_2il0floatpacket.78(%rip), %xmm5
+        movb      $1, %r8b
+        movss     %xmm5, -16(%rsp)
+        movl      $-40, %ecx
+
+.LBL_2_4:
+
+        pxor      %xmm3, %xmm3
+        comiss    %xmm3, %xmm5
+        jbe       .LBL_2_10
+
+
+        movaps    %xmm5, %xmm2
+        subss     %xmm1, %xmm2
+        movss     %xmm2, -20(%rsp)
+        andb      $127, -17(%rsp)
+        movss     -20(%rsp), %xmm0
+        comiss    .L_2il0floatpacket.79(%rip), %xmm0
+        jbe       .LBL_2_9
+
+
+        movzwl    -14(%rsp), %edx
+        pxor      %xmm6, %xmm6
+        andl      $32640, %edx
+        shrl      $7, %edx
+        lea       -127(%rcx,%rdx), %ecx
+        cvtsi2ss  %ecx, %xmm6
+        cmpb      $1, %r8b
+        je        .LBL_2_13
+
+
+        movss     .L_2il0floatpacket.89(%rip), %xmm4
+        movss     .L_2il0floatpacket.90(%rip), %xmm0
+        mulss     %xmm6, %xmm4
+        mulss     %xmm0, %xmm6
+
+.LBL_2_8:
+
+        movss     %xmm5, -20(%rsp)
+        movaps    %xmm4, %xmm9
+        movzwl    -18(%rsp), %edx
+        lea       __slog1p_ha_CoutTab(%rip), %r8
+        andl      $-32641, %edx
+        addl      $16256, %edx
+        movw      %dx, -18(%rsp)
+        movss     -20(%rsp), %xmm8
+        movaps    %xmm8, %xmm2
+        movss     .L_2il0floatpacket.92(%rip), %xmm7
+        addss     .L_2il0floatpacket.91(%rip), %xmm2
+        movss     %xmm2, -24(%rsp)
+        movl      -24(%rsp), %ecx
+        andl      $127, %ecx
+        lea       (%rcx,%rcx,2), %edi
+        movss     4(%r8,%rdi,4), %xmm5
+        movss     (%r8,%rdi,4), %xmm0
+        addss     %xmm5, %xmm9
+        addss     8(%r8,%rdi,4), %xmm6
+        movaps    %xmm9, %xmm3
+        subss     %xmm4, %xmm3
+        movss     %xmm3, -24(%rsp)
+        movss     -24(%rsp), %xmm4
+        subss     %xmm4, %xmm5
+        movss     %xmm5, -24(%rsp)
+        movss     -24(%rsp), %xmm10
+        addss     %xmm6, %xmm10
+        movaps    %xmm7, %xmm6
+        addss     %xmm8, %xmm6
+        movss     %xmm6, -24(%rsp)
+        movss     -24(%rsp), %xmm12
+        subss     %xmm7, %xmm12
+        subss     %xmm12, %xmm8
+        mulss     %xmm0, %xmm12
+        subss     %xmm1, %xmm12
+        mulss     %xmm8, %xmm0
+        movaps    %xmm0, %xmm15
+        movaps    %xmm12, %xmm2
+        addss     %xmm10, %xmm15
+        addss     %xmm9, %xmm12
+        addss     %xmm0, %xmm2
+        movaps    %xmm15, %xmm1
+        movaps    %xmm12, %xmm13
+        subss     %xmm10, %xmm1
+        addss     %xmm15, %xmm13
+        movss     %xmm1, -24(%rsp)
+        movss     -24(%rsp), %xmm11
+        subss     %xmm11, %xmm0
+        movss     %xmm0, -24(%rsp)
+        movss     -24(%rsp), %xmm0
+        movss     %xmm13, (%rsi)
+        subss     %xmm12, %xmm13
+        movss     .L_2il0floatpacket.86(%rip), %xmm12
+        mulss     %xmm2, %xmm12
+        movss     %xmm13, -24(%rsp)
+        movss     -24(%rsp), %xmm14
+        addss     .L_2il0floatpacket.85(%rip), %xmm12
+        subss     %xmm14, %xmm15
+        mulss     %xmm2, %xmm12
+        movss     %xmm15, -24(%rsp)
+        movss     -24(%rsp), %xmm1
+        addss     .L_2il0floatpacket.84(%rip), %xmm12
+        mulss     %xmm2, %xmm12
+        addss     .L_2il0floatpacket.83(%rip), %xmm12
+        mulss     %xmm2, %xmm12
+        addss     .L_2il0floatpacket.82(%rip), %xmm12
+        mulss     %xmm2, %xmm12
+        addss     .L_2il0floatpacket.81(%rip), %xmm12
+        mulss     %xmm2, %xmm12
+        addss     .L_2il0floatpacket.80(%rip), %xmm12
+        mulss     %xmm2, %xmm12
+        mulss     %xmm2, %xmm12
+        addss     %xmm12, %xmm0
+        addss     %xmm0, %xmm1
+        movss     %xmm1, -24(%rsp)
+        movss     -24(%rsp), %xmm3
+        addss     (%rsi), %xmm3
+        movss     %xmm3, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        movss     .L_2il0floatpacket.86(%rip), %xmm0
+        mulss     %xmm2, %xmm0
+        addss     .L_2il0floatpacket.85(%rip), %xmm0
+        mulss     %xmm2, %xmm0
+        addss     .L_2il0floatpacket.84(%rip), %xmm0
+        mulss     %xmm2, %xmm0
+        addss     .L_2il0floatpacket.83(%rip), %xmm0
+        mulss     %xmm2, %xmm0
+        addss     .L_2il0floatpacket.82(%rip), %xmm0
+        mulss     %xmm2, %xmm0
+        addss     .L_2il0floatpacket.81(%rip), %xmm0
+        mulss     %xmm2, %xmm0
+        addss     .L_2il0floatpacket.80(%rip), %xmm0
+        mulss     %xmm2, %xmm0
+        mulss     %xmm2, %xmm0
+        addss     %xmm2, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_10:
+
+        ucomiss   %xmm3, %xmm5
+        jp        .LBL_2_11
+        je        .LBL_2_14
+
+.LBL_2_11:
+
+        divss     %xmm3, %xmm3
+        movss     %xmm3, (%rsi)
+        movl      $1, %eax
+
+
+        ret
+
+.LBL_2_13:
+
+        movss     .L_2il0floatpacket.88(%rip), %xmm0
+        mulss     %xmm0, %xmm6
+        movaps    %xmm6, %xmm4
+        movaps    %xmm3, %xmm6
+        jmp       .LBL_2_8
+
+.LBL_2_14:
+
+        movss     .L_2il0floatpacket.87(%rip), %xmm0
+        movl      $2, %eax
+        divss     %xmm3, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_15:
+
+        movb      -17(%rsp), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_17
+
+.LBL_2_16:
+
+        mulss     %xmm5, %xmm5
+        movss     %xmm5, (%rsi)
+        ret
+
+.LBL_2_17:
+
+        testl     $8388607, -20(%rsp)
+        jne       .LBL_2_16
+
+
+        movl      $1, %eax
+        pxor      %xmm1, %xmm1
+        pxor      %xmm0, %xmm0
+        divss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_slog1p_ha_cout_rare_internal,@function
+	.size	__svml_slog1p_ha_cout_rare_internal,.-__svml_slog1p_ha_cout_rare_internal
+..LN__svml_slog1p_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_slog1p_ha_data_internal_avx512:
+	.long	1060205056
+	.long	1059688960
+	.long	1059187712
+	.long	1058701824
+	.long	1058229248
+	.long	1057769472
+	.long	1057321984
+	.long	1056807936
+	.long	1055958016
+	.long	1055129600
+	.long	1054320640
+	.long	1053531136
+	.long	1052760064
+	.long	1052006400
+	.long	1051268096
+	.long	1050547200
+	.long	1049840640
+	.long	1049148416
+	.long	1048365056
+	.long	1047035904
+	.long	1045733376
+	.long	1044455424
+	.long	1043200000
+	.long	1041969152
+	.long	1040760832
+	.long	1038958592
+	.long	1036623872
+	.long	1034330112
+	.long	1032073216
+	.long	1027907584
+	.long	1023541248
+	.long	1015087104
+	.long	901758606
+	.long	3071200204
+	.long	931108809
+	.long	3074069268
+	.long	3077535321
+	.long	3071146094
+	.long	3063010043
+	.long	3072147991
+	.long	908173938
+	.long	3049723733
+	.long	925190435
+	.long	923601997
+	.long	3048768765
+	.long	3076457870
+	.long	926424291
+	.long	3073778483
+	.long	3069146713
+	.long	912794238
+	.long	912483742
+	.long	920635797
+	.long	3054902185
+	.long	3069864633
+	.long	922801832
+	.long	3033791132
+	.long	3076717488
+	.long	3076037756
+	.long	3072434855
+	.long	3077481184
+	.long	3066991812
+	.long	917116064
+	.long	925811956
+	.long	900509991
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3212836864
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.type	__svml_slog1p_ha_data_internal_avx512,@object
+	.size	__svml_slog1p_ha_data_internal_avx512,768
+	.align 32
+__slog1p_ha_CoutTab:
+	.long	1065353216
+	.long	0
+	.long	0
+	.long	1065091072
+	.long	1015087104
+	.long	900509991
+	.long	1064828928
+	.long	1023541248
+	.long	925811956
+	.long	1064566784
+	.long	1027915776
+	.long	3084221144
+	.long	1064304640
+	.long	1032073216
+	.long	3066991812
+	.long	1064173568
+	.long	1033195520
+	.long	882149603
+	.long	1063911424
+	.long	1035468800
+	.long	928189163
+	.long	1063649280
+	.long	1037783040
+	.long	927501741
+	.long	1063518208
+	.long	1038958592
+	.long	3076037756
+	.long	1063256064
+	.long	1040759808
+	.long	904405630
+	.long	1063124992
+	.long	1041361920
+	.long	3052231524
+	.long	1062862848
+	.long	1042581504
+	.long	922094799
+	.long	1062731776
+	.long	1043201024
+	.long	3070120623
+	.long	1062469632
+	.long	1044455424
+	.long	3069864633
+	.long	1062338560
+	.long	1045091328
+	.long	3063188516
+	.long	1062207488
+	.long	1045733376
+	.long	3054902185
+	.long	1061945344
+	.long	1047035904
+	.long	920635797
+	.long	1061814272
+	.long	1047697408
+	.long	904920689
+	.long	1061683200
+	.long	1048365056
+	.long	912483742
+	.long	1061552128
+	.long	1048807936
+	.long	3052664405
+	.long	1061421056
+	.long	1049148416
+	.long	912794238
+	.long	1061158912
+	.long	1049840384
+	.long	889474359
+	.long	1061027840
+	.long	1050191872
+	.long	3059868362
+	.long	1060896768
+	.long	1050546944
+	.long	3059256525
+	.long	1060765696
+	.long	1050905600
+	.long	912008988
+	.long	1060634624
+	.long	1051268352
+	.long	912290698
+	.long	1060503552
+	.long	1051635200
+	.long	3037211048
+	.long	1060372480
+	.long	1052005888
+	.long	906226119
+	.long	1060241408
+	.long	1052380928
+	.long	3052480305
+	.long	1060110336
+	.long	1052760064
+	.long	3048768765
+	.long	1059979264
+	.long	1053143552
+	.long	3049975450
+	.long	1059848192
+	.long	1053531392
+	.long	894485718
+	.long	1059717120
+	.long	1053923840
+	.long	897598623
+	.long	1059586048
+	.long	1054320896
+	.long	907355277
+	.long	1059586048
+	.long	1054320896
+	.long	907355277
+	.long	1059454976
+	.long	1054722816
+	.long	881705073
+	.long	1059323904
+	.long	1055129600
+	.long	3049723733
+	.long	1059192832
+	.long	1055541248
+	.long	890353599
+	.long	1059061760
+	.long	1055958016
+	.long	908173938
+	.long	1059061760
+	.long	1055958016
+	.long	908173938
+	.long	1058930688
+	.long	1056380160
+	.long	883644938
+	.long	1058799616
+	.long	1056807680
+	.long	3052015799
+	.long	1058668544
+	.long	1057102592
+	.long	884897284
+	.long	1058668544
+	.long	1057102592
+	.long	884897284
+	.long	1058537472
+	.long	1057321920
+	.long	3037632470
+	.long	1058406400
+	.long	1057544128
+	.long	865017195
+	.long	1058275328
+	.long	1057769344
+	.long	3042936546
+	.long	1058275328
+	.long	1057769344
+	.long	3042936546
+	.long	1058144256
+	.long	1057997568
+	.long	903344518
+	.long	1058013184
+	.long	1058228992
+	.long	897862967
+	.long	1058013184
+	.long	1058228992
+	.long	897862967
+	.long	1057882112
+	.long	1058463680
+	.long	3047822280
+	.long	1057882112
+	.long	1058463680
+	.long	3047822280
+	.long	1057751040
+	.long	1058701632
+	.long	883793293
+	.long	1057619968
+	.long	1058943040
+	.long	851667963
+	.long	1057619968
+	.long	1058943040
+	.long	851667963
+	.long	1057488896
+	.long	1059187968
+	.long	3000004036
+	.long	1057488896
+	.long	1059187968
+	.long	3000004036
+	.long	1057357824
+	.long	1059436544
+	.long	3047430717
+	.long	1057357824
+	.long	1059436544
+	.long	3047430717
+	.long	1057226752
+	.long	1059688832
+	.long	3043802308
+	.long	1057226752
+	.long	1059688832
+	.long	3043802308
+	.long	1057095680
+	.long	1059944960
+	.long	876113044
+	.long	1057095680
+	.long	1059944960
+	.long	876113044
+	.long	1056964608
+	.long	1060205056
+	.long	901758606
+	.long	1060205056
+	.long	901758606
+	.long	1207959616
+	.long	1174405120
+	.long	1008730112
+	.long	1400897536
+	.long	0
+	.long	1065353216
+	.long	3204448256
+	.long	1051372203
+	.long	3196059648
+	.long	1045220557
+	.long	3190467243
+	.long	1041387009
+	.long	3187672480
+	.type	__slog1p_ha_CoutTab,@object
+	.size	__slog1p_ha_CoutTab,840
+	.align 4
+.L_2il0floatpacket.78:
+	.long	0x53800000
+	.type	.L_2il0floatpacket.78,@object
+	.size	.L_2il0floatpacket.78,4
+	.align 4
+.L_2il0floatpacket.79:
+	.long	0x3c200000
+	.type	.L_2il0floatpacket.79,@object
+	.size	.L_2il0floatpacket.79,4
+	.align 4
+.L_2il0floatpacket.80:
+	.long	0xbf000000
+	.type	.L_2il0floatpacket.80,@object
+	.size	.L_2il0floatpacket.80,4
+	.align 4
+.L_2il0floatpacket.81:
+	.long	0x3eaaaaab
+	.type	.L_2il0floatpacket.81,@object
+	.size	.L_2il0floatpacket.81,4
+	.align 4
+.L_2il0floatpacket.82:
+	.long	0xbe800000
+	.type	.L_2il0floatpacket.82,@object
+	.size	.L_2il0floatpacket.82,4
+	.align 4
+.L_2il0floatpacket.83:
+	.long	0x3e4ccccd
+	.type	.L_2il0floatpacket.83,@object
+	.size	.L_2il0floatpacket.83,4
+	.align 4
+.L_2il0floatpacket.84:
+	.long	0xbe2aaaab
+	.type	.L_2il0floatpacket.84,@object
+	.size	.L_2il0floatpacket.84,4
+	.align 4
+.L_2il0floatpacket.85:
+	.long	0x3e124e01
+	.type	.L_2il0floatpacket.85,@object
+	.size	.L_2il0floatpacket.85,4
+	.align 4
+.L_2il0floatpacket.86:
+	.long	0xbe0005a0
+	.type	.L_2il0floatpacket.86,@object
+	.size	.L_2il0floatpacket.86,4
+	.align 4
+.L_2il0floatpacket.87:
+	.long	0xbf800000
+	.type	.L_2il0floatpacket.87,@object
+	.size	.L_2il0floatpacket.87,4
+	.align 4
+.L_2il0floatpacket.88:
+	.long	0x3f317218
+	.type	.L_2il0floatpacket.88,@object
+	.size	.L_2il0floatpacket.88,4
+	.align 4
+.L_2il0floatpacket.89:
+	.long	0x3f317200
+	.type	.L_2il0floatpacket.89,@object
+	.size	.L_2il0floatpacket.89,4
+	.align 4
+.L_2il0floatpacket.90:
+	.long	0x35bfbe8e
+	.type	.L_2il0floatpacket.90,@object
+	.size	.L_2il0floatpacket.90,4
+	.align 4
+.L_2il0floatpacket.91:
+	.long	0x48000040
+	.type	.L_2il0floatpacket.91,@object
+	.size	.L_2il0floatpacket.91,4
+	.align 4
+.L_2il0floatpacket.92:
+	.long	0x46000000
+	.type	.L_2il0floatpacket.92,@object
+	.size	.L_2il0floatpacket.92,4
+	.align 4
+.L_2il0floatpacket.93:
+	.long	0x3f800000
+	.type	.L_2il0floatpacket.93,@object
+	.size	.L_2il0floatpacket.93,4
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_s_la.s
index 5d1d577177d..ed7a6af0077 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log1p_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_d_ha.s
new file mode 100644
index 00000000000..602a9630f9f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_d_ha.s
@@ -0,0 +1,1793 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  log2(x) = VGETEXP(x) + log2(VGETMANT(x))
+ *  *       VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *  *   mx = VGETMANT(x) is in [1,2) for all x>=0
+ *  *   log2(mx) = -log2(RCP(mx)) + log2(1 +(mx*RCP(mx)-1))
+ *  *      RCP(mx) is rounded to 4 fractional bits,
+ *  *      and the table lookup for log(RCP(mx)) is based on a small permute instruction
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_log28_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_log28_ha
+
+__svml_log28_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm5
+
+/* GetMant(x), normalized to [1,2) for x>=0, NaN for x<0 */
+        vgetmantpd $8, {sae}, %zmm5, %zmm4
+        vmovups   256+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm9
+
+/* Prepare exponent correction: DblRcp<0.75? */
+        vmovups   320+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm8
+
+/* Start polynomial evaluation */
+        vmovups   384+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   448+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm2
+
+/* GetExp(x) */
+        vgetexppd {sae}, %zmm5, %zmm13
+
+/* Table lookup */
+        vmovups   __svml_dlog2_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   512+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm12
+        vmovups   640+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm15
+        vmovups   704+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm0
+        vmovups   128+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm3
+
+/* DblRcp ~ 1/Mantissa */
+        vrcp14pd  %zmm4, %zmm6
+
+/* x<=0? */
+        vfpclasspd $94, %zmm5, %k2
+
+/* round DblRcp to 4 fractional bits (RN mode, no Precision exception) */
+        vrndscalepd $88, {sae}, %zmm6, %zmm7
+        vmovups   576+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm6
+        kmovw     %k2, %edx
+
+/* Reduced argument: R = DblRcp*Mantissa - 1 */
+        vfmsub213pd {rn-sae}, %zmm9, %zmm7, %zmm4
+        vcmppd    $17, {sae}, %zmm8, %zmm7, %k1
+        vfmadd231pd {rn-sae}, %zmm4, %zmm11, %zmm2
+
+/* K+Th+(c0h*R)_h */
+        vmovups   1024+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm11
+        vfmadd231pd {rn-sae}, %zmm4, %zmm12, %zmm6
+        vmovups   768+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm12
+        vfmadd231pd {rn-sae}, %zmm4, %zmm15, %zmm0
+        vmovups   896+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm15
+
+/* R^2 */
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm1
+
+/* Prepare table index */
+        vpsrlq    $48, %zmm7, %zmm10
+        vmovups   832+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm7
+        vfmadd213pd {rn-sae}, %zmm6, %zmm1, %zmm2
+
+/* add 1 to Expon if DblRcp<0.75 */
+        vaddpd    {rn-sae}, %zmm9, %zmm13, %zmm13{%k1}
+        vpermt2pd 64+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm10, %zmm14
+        vpermt2pd 192+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm10, %zmm3
+        vmulpd    {rn-sae}, %zmm1, %zmm1, %zmm9
+        vfmadd231pd {rn-sae}, %zmm4, %zmm12, %zmm7
+        vmovups   960+__svml_dlog2_ha_data_internal_avx512(%rip), %zmm10
+
+/* K+Th */
+        vaddpd    {rn-sae}, %zmm14, %zmm13, %zmm8
+        vfmadd213pd {rn-sae}, %zmm7, %zmm1, %zmm0
+        vfmadd231pd {rn-sae}, %zmm4, %zmm15, %zmm10
+
+/* polynomial */
+        vfmadd213pd {rn-sae}, %zmm0, %zmm9, %zmm2
+        vmovaps   %zmm11, %zmm14
+        vfmadd213pd {rn-sae}, %zmm8, %zmm4, %zmm14
+        vfmadd213pd {rn-sae}, %zmm10, %zmm1, %zmm2
+
+/* (c0h*R)_h */
+        vsubpd    {rn-sae}, %zmm8, %zmm14, %zmm13
+        vxorpd    %zmm13, %zmm13, %zmm13{%k2}
+
+/* c0hR_low */
+        vfmsub231pd {rn-sae}, %zmm11, %zmm4, %zmm13
+        vaddpd    {rn-sae}, %zmm13, %zmm3, %zmm0
+        vfmadd213pd {rn-sae}, %zmm0, %zmm4, %zmm2
+        vaddpd    {rn-sae}, %zmm2, %zmm14, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm5, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dlog2_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_log28_ha,@function
+	.size	__svml_log28_ha,.-__svml_log28_ha
+..LN__svml_log28_ha.0:
+
+.L_2__routine_start___svml_dlog2_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dlog2_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movq      $0, -64(%rsp)
+        movsd     -64(%rsp), %xmm0
+        movsd     (%rdi), %xmm1
+        movups    _zeros.402.0.0.1(%rip), %xmm2
+        subsd     %xmm0, %xmm1
+        movsd     %xmm1, -8(%rsp)
+        movups    %xmm2, -56(%rsp)
+
+
+        movups    _ones.402.0.0.1(%rip), %xmm0
+        movups    %xmm0, -40(%rsp)
+
+
+        movups    _infs.402.0.0.1(%rip), %xmm0
+        movups    %xmm0, -24(%rsp)
+
+
+        movl      -4(%rsp), %eax
+        cmpl      $2146435072, %eax
+        jae       .LBL_2_19
+
+
+        lea       -1072660480(%rax), %edx
+        cmpl      $53248, %edx
+        jae       .LBL_2_14
+
+
+        lea       -1072693232(%rax), %edx
+        cmpl      $24, %edx
+        jae       .LBL_2_13
+
+
+        movl      -8(%rsp), %edi
+        movl      %eax, %ecx
+        movl      %edi, %edx
+        shll      $11, %ecx
+        shrl      $21, %edx
+        orl       %edx, %ecx
+        addl      $-2147483646, %ecx
+        cmpl      $3, %ecx
+        jae       .LBL_2_12
+
+
+        addl      $-1072693248, %eax
+        orl       %edi, %eax
+        jne       .LBL_2_10
+
+
+        movq      -56(%rsp), %rax
+        movq      %rax, (%rsi)
+        jmp       .LBL_2_11
+
+.LBL_2_10:
+
+        movsd     -8(%rsp), %xmm0
+        movsd     16+__dlog2_ha__Q3(%rip), %xmm2
+        movsd     24+__dlog2_ha__Q3(%rip), %xmm1
+        addsd     -32(%rsp), %xmm0
+        mulsd     %xmm0, %xmm2
+        mulsd     %xmm0, %xmm1
+        addsd     8+__dlog2_ha__Q3(%rip), %xmm2
+        mulsd     %xmm0, %xmm2
+        movsd     %xmm0, -8(%rsp)
+        addsd     __dlog2_ha__Q3(%rip), %xmm2
+        mulsd     %xmm0, %xmm2
+        addsd     %xmm1, %xmm2
+        movsd     %xmm2, (%rsi)
+
+.LBL_2_11:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_12:
+
+        movsd     -8(%rsp), %xmm2
+        xorl      %eax, %eax
+        movsd     16+__dlog2_ha__Q2(%rip), %xmm1
+        movsd     24+__dlog2_ha__Q2(%rip), %xmm3
+        addsd     -32(%rsp), %xmm2
+        movaps    %xmm2, %xmm0
+        mulsd     %xmm2, %xmm0
+        mulsd     %xmm0, %xmm1
+        mulsd     %xmm0, %xmm3
+        addsd     __dlog2_ha__Q2(%rip), %xmm1
+        addsd     8+__dlog2_ha__Q2(%rip), %xmm3
+        mulsd     %xmm2, %xmm1
+        mulsd     %xmm0, %xmm3
+        movsd     %xmm2, -8(%rsp)
+        addsd     %xmm1, %xmm3
+        movl      $0, -8(%rsp)
+        movsd     -8(%rsp), %xmm4
+        subsd     %xmm4, %xmm2
+        addsd     %xmm2, %xmm4
+        mulsd     32+__dlog2_ha__Q2(%rip), %xmm4
+        addsd     %xmm3, %xmm4
+        movsd     %xmm4, (%rsi)
+        ret
+
+.LBL_2_13:
+
+        movsd     -8(%rsp), %xmm2
+        xorl      %eax, %eax
+        movsd     72+__dlog2_ha__Q1(%rip), %xmm10
+        movsd     64+__dlog2_ha__Q1(%rip), %xmm4
+        movsd     __dlog2_ha__TWO_32P(%rip), %xmm5
+        movsd     __dlog2_ha__TWO_32P(%rip), %xmm7
+        movsd     88+__dlog2_ha__Q1(%rip), %xmm14
+        movsd     __dlog2_ha__TWO_32P(%rip), %xmm11
+        movsd     __dlog2_ha__TWO_32P(%rip), %xmm15
+        addsd     -32(%rsp), %xmm2
+        movaps    %xmm2, %xmm3
+        movaps    %xmm2, %xmm6
+        mulsd     %xmm2, %xmm3
+        movaps    %xmm2, %xmm0
+        mulsd     %xmm2, %xmm5
+        mulsd     __dlog2_ha__TWO_32(%rip), %xmm6
+        mulsd     %xmm3, %xmm10
+        mulsd     %xmm3, %xmm4
+        addsd     56+__dlog2_ha__Q1(%rip), %xmm10
+        addsd     48+__dlog2_ha__Q1(%rip), %xmm4
+        mulsd     %xmm3, %xmm10
+        mulsd     %xmm3, %xmm4
+        addsd     40+__dlog2_ha__Q1(%rip), %xmm10
+        addsd     32+__dlog2_ha__Q1(%rip), %xmm4
+        mulsd     %xmm3, %xmm10
+        mulsd     %xmm3, %xmm4
+        addsd     24+__dlog2_ha__Q1(%rip), %xmm10
+        addsd     16+__dlog2_ha__Q1(%rip), %xmm4
+        mulsd     %xmm3, %xmm10
+        mulsd     %xmm2, %xmm4
+        movsd     __dlog2_ha__TWO_32(%rip), %xmm3
+        addsd     %xmm4, %xmm10
+        mulsd     %xmm10, %xmm7
+        movaps    %xmm3, %xmm8
+        mulsd     %xmm10, %xmm8
+        movsd     %xmm5, -72(%rsp)
+        movsd     -72(%rsp), %xmm1
+        movsd     %xmm7, -72(%rsp)
+        subsd     %xmm6, %xmm1
+        movsd     -72(%rsp), %xmm9
+        subsd     %xmm1, %xmm0
+        subsd     %xmm8, %xmm9
+        movsd     %xmm1, -8(%rsp)
+        subsd     %xmm9, %xmm10
+        addsd     %xmm9, %xmm14
+        addsd     8+__dlog2_ha__Q1(%rip), %xmm10
+        movaps    %xmm14, %xmm4
+        mulsd     %xmm0, %xmm4
+        mulsd     %xmm2, %xmm10
+        mulsd     %xmm1, %xmm14
+        addsd     %xmm0, %xmm1
+        addsd     %xmm10, %xmm4
+        movaps    %xmm4, %xmm12
+        movsd     80+__dlog2_ha__Q1(%rip), %xmm9
+        addsd     %xmm14, %xmm12
+        mulsd     %xmm12, %xmm11
+        mulsd     %xmm3, %xmm12
+        movsd     %xmm11, -72(%rsp)
+        movsd     -72(%rsp), %xmm13
+        subsd     %xmm12, %xmm13
+        subsd     %xmm13, %xmm4
+        addsd     %xmm13, %xmm9
+        addsd     %xmm14, %xmm4
+        movaps    %xmm9, %xmm13
+        addsd     __dlog2_ha__Q1(%rip), %xmm4
+        addsd     %xmm4, %xmm13
+        mulsd     %xmm13, %xmm15
+        mulsd     %xmm3, %xmm13
+        movsd     %xmm15, -72(%rsp)
+        movsd     -72(%rsp), %xmm14
+        subsd     %xmm13, %xmm14
+        mulsd     %xmm14, %xmm1
+        subsd     %xmm14, %xmm9
+        addsd     %xmm9, %xmm4
+        mulsd     %xmm2, %xmm4
+        addsd     %xmm4, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        movl      %eax, %ecx
+        movl      %eax, %edx
+        shrl      $20, %ecx
+        andl      $1048575, %edx
+        addl      $-1023, %ecx
+        cmpl      $1048576, %eax
+        jae       .LBL_2_18
+
+
+        movl      %edx, -4(%rsp)
+        movl      -8(%rsp), %edx
+        orl       %edx, %eax
+        jne       .LBL_2_17
+
+
+        movsd     -32(%rsp), %xmm0
+        movl      $2, %eax
+        divsd     -56(%rsp), %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_17:
+
+        movsd     -8(%rsp), %xmm0
+        mulsd     __dlog2_ha__TWO_55(%rip), %xmm0
+        movsd     %xmm0, -8(%rsp)
+        movl      -4(%rsp), %edx
+        movl      %edx, %ecx
+        shrl      $20, %ecx
+        movl      %edx, %eax
+        addl      $-1078, %ecx
+
+.LBL_2_18:
+
+        shrl      $12, %eax
+        orl       $1072693248, %edx
+        movzbl    %al, %edi
+        pxor      %xmm7, %xmm7
+        movl      %edx, -4(%rsp)
+        lea       __dlog2_ha___libm_rcp_table_256(%rip), %rdx
+        movsd     -8(%rsp), %xmm10
+        pxor      %xmm8, %xmm8
+        movl      $0, -8(%rsp)
+        lea       __dlog2_ha___libm_log2_table_256(%rip), %rax
+        movsd     -8(%rsp), %xmm0
+        cvtss2sd  (%rdx,%rdi,4), %xmm7
+        cvtsi2sd  %ecx, %xmm8
+        subsd     %xmm0, %xmm10
+        mulsd     %xmm7, %xmm10
+        mulsd     %xmm0, %xmm7
+        movsd     40+__dlog2_ha__P(%rip), %xmm4
+        movsd     32+__dlog2_ha__P(%rip), %xmm3
+        movsd     48+__dlog2_ha__P(%rip), %xmm5
+        movsd     __dlog2_ha__TWO_32(%rip), %xmm6
+        shlq      $4, %rdi
+        addsd     -32(%rsp), %xmm7
+        movaps    %xmm7, %xmm2
+        mulsd     %xmm5, %xmm7
+        addsd     %xmm10, %xmm2
+        mulsd     %xmm5, %xmm10
+        addsd     %xmm7, %xmm6
+        movaps    %xmm2, %xmm1
+        mulsd     %xmm2, %xmm1
+        mulsd     %xmm1, %xmm4
+        mulsd     %xmm1, %xmm3
+        addsd     24+__dlog2_ha__P(%rip), %xmm4
+        addsd     16+__dlog2_ha__P(%rip), %xmm3
+        mulsd     %xmm1, %xmm4
+        mulsd     %xmm1, %xmm3
+        addsd     8+__dlog2_ha__P(%rip), %xmm4
+        addsd     __dlog2_ha__P(%rip), %xmm3
+        mulsd     %xmm1, %xmm4
+        mulsd     %xmm2, %xmm3
+        movsd     %xmm6, -72(%rsp)
+        addsd     %xmm3, %xmm4
+        addsd     8(%rax,%rdi), %xmm8
+        addsd     %xmm4, %xmm10
+        movsd     -72(%rsp), %xmm9
+        subsd     __dlog2_ha__TWO_32(%rip), %xmm9
+        subsd     %xmm9, %xmm7
+        addsd     %xmm8, %xmm9
+        addsd     (%rax,%rdi), %xmm7
+        movsd     %xmm9, -8(%rsp)
+        xorl      %eax, %eax
+        addsd     %xmm7, %xmm10
+        addsd     %xmm9, %xmm10
+        movsd     %xmm10, (%rsi)
+        ret
+
+.LBL_2_19:
+
+        movl      %eax, %edx
+        andl      $2147483647, %edx
+        cmpl      $2146435072, %edx
+        ja        .LBL_2_28
+
+
+        jne       .LBL_2_22
+
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_28
+
+.LBL_2_22:
+
+        testl     $-2147483648, %eax
+        je        .LBL_2_27
+
+
+        movl      -8(%rsp), %eax
+        orl       %eax, %edx
+        movsd     -56(%rsp), %xmm1
+        jne       .LBL_2_25
+
+
+        movsd     -32(%rsp), %xmm0
+        movl      $2, %eax
+        divsd     %xmm1, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_25:
+
+        movsd     -24(%rsp), %xmm0
+        movl      $1, %eax
+        mulsd     %xmm1, %xmm0
+        movsd     %xmm0, (%rsi)
+
+
+        ret
+
+.LBL_2_27:
+
+        movq      -8(%rsp), %rax
+        movq      %rax, (%rsi)
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_28:
+
+        movsd     -8(%rsp), %xmm0
+        xorl      %eax, %eax
+        mulsd     -40(%rsp), %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dlog2_ha_cout_rare_internal,@function
+	.size	__svml_dlog2_ha_cout_rare_internal,.-__svml_dlog2_ha_cout_rare_internal
+..LN__svml_dlog2_ha_cout_rare_internal.1:
+	.section .rodata, "a"
+	.align 64
+	.align 16
+_zeros.402.0.0.1:
+	.long	0
+	.long	0
+	.long	0
+	.long	2147483648
+	.align 16
+_ones.402.0.0.1:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3220176896
+	.align 16
+_infs.402.0.0.1:
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	4293918720
+
+	.section .rodata, "a"
+	.space 16, 0x00 	
+	.align 64
+__svml_dlog2_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	4207476736
+	.long	3216401398
+	.long	972816384
+	.long	3217408026
+	.long	3103948800
+	.long	3218062358
+	.long	1271726080
+	.long	3218381432
+	.long	2300510208
+	.long	3218676666
+	.long	3761438720
+	.long	3218958163
+	.long	14041088
+	.long	3219177733
+	.long	1904279552
+	.long	1071288313
+	.long	1751498752
+	.long	1071041295
+	.long	3815833600
+	.long	1070803966
+	.long	2835742720
+	.long	1070555096
+	.long	180322304
+	.long	1070114968
+	.long	3265986560
+	.long	1069690285
+	.long	2530213888
+	.long	1069012484
+	.long	3386507264
+	.long	1067938708
+	.long	0
+	.long	0
+	.long	3428024929
+	.long	3174241916
+	.long	804943611
+	.long	1028963376
+	.long	1462134616
+	.long	1028126172
+	.long	1227618047
+	.long	3176893182
+	.long	2998207852
+	.long	3176597684
+	.long	3242321520
+	.long	1029042433
+	.long	4137858450
+	.long	1027958429
+	.long	804943611
+	.long	1027914800
+	.long	3679462403
+	.long	1027724294
+	.long	1572243234
+	.long	3176241050
+	.long	1207415416
+	.long	1029642824
+	.long	894287639
+	.long	1029609779
+	.long	1763539348
+	.long	1029327721
+	.long	825146242
+	.long	3176213734
+	.long	1128763360
+	.long	3176457556
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	1264813549
+	.long	3217195783
+	.long	1264813549
+	.long	3217195783
+	.long	1264813549
+	.long	3217195783
+	.long	1264813549
+	.long	3217195783
+	.long	1264813549
+	.long	3217195783
+	.long	1264813549
+	.long	3217195783
+	.long	1264813549
+	.long	3217195783
+	.long	1264813549
+	.long	3217195783
+	.long	2036456900
+	.long	1069846333
+	.long	2036456900
+	.long	1069846333
+	.long	2036456900
+	.long	1069846333
+	.long	2036456900
+	.long	1069846333
+	.long	2036456900
+	.long	1069846333
+	.long	2036456900
+	.long	1069846333
+	.long	2036456900
+	.long	1069846333
+	.long	2036456900
+	.long	1069846333
+	.long	761786641
+	.long	3217495364
+	.long	761786641
+	.long	3217495364
+	.long	761786641
+	.long	3217495364
+	.long	761786641
+	.long	3217495364
+	.long	761786641
+	.long	3217495364
+	.long	761786641
+	.long	3217495364
+	.long	761786641
+	.long	3217495364
+	.long	761786641
+	.long	3217495364
+	.long	3644404840
+	.long	1070227827
+	.long	3644404840
+	.long	1070227827
+	.long	3644404840
+	.long	1070227827
+	.long	3644404840
+	.long	1070227827
+	.long	3644404840
+	.long	1070227827
+	.long	3644404840
+	.long	1070227827
+	.long	3644404840
+	.long	1070227827
+	.long	3644404840
+	.long	1070227827
+	.long	3701438261
+	.long	3217999625
+	.long	3701438261
+	.long	3217999625
+	.long	3701438261
+	.long	3217999625
+	.long	3701438261
+	.long	3217999625
+	.long	3701438261
+	.long	3217999625
+	.long	3701438261
+	.long	3217999625
+	.long	3701438261
+	.long	3217999625
+	.long	3701438261
+	.long	3217999625
+	.long	1359623202
+	.long	1070757740
+	.long	1359623202
+	.long	1070757740
+	.long	1359623202
+	.long	1070757740
+	.long	1359623202
+	.long	1070757740
+	.long	1359623202
+	.long	1070757740
+	.long	1359623202
+	.long	1070757740
+	.long	1359623202
+	.long	1070757740
+	.long	1359623202
+	.long	1070757740
+	.long	1697349732
+	.long	3218543943
+	.long	1697349732
+	.long	3218543943
+	.long	1697349732
+	.long	3218543943
+	.long	1697349732
+	.long	3218543943
+	.long	1697349732
+	.long	3218543943
+	.long	1697349732
+	.long	3218543943
+	.long	1697349732
+	.long	3218543943
+	.long	1697349732
+	.long	3218543943
+	.long	3694789416
+	.long	1071564553
+	.long	3694789416
+	.long	1071564553
+	.long	3694789416
+	.long	1071564553
+	.long	3694789416
+	.long	1071564553
+	.long	3694789416
+	.long	1071564553
+	.long	3694789416
+	.long	1071564553
+	.long	3694789416
+	.long	1071564553
+	.long	3694789416
+	.long	1071564553
+	.long	1697350398
+	.long	3219592519
+	.long	1697350398
+	.long	3219592519
+	.long	1697350398
+	.long	3219592519
+	.long	1697350398
+	.long	3219592519
+	.long	1697350398
+	.long	3219592519
+	.long	1697350398
+	.long	3219592519
+	.long	1697350398
+	.long	3219592519
+	.long	1697350398
+	.long	3219592519
+	.long	1806988078
+	.long	1014479986
+	.long	1806988078
+	.long	1014479986
+	.long	1806988078
+	.long	1014479986
+	.long	1806988078
+	.long	1014479986
+	.long	1806988078
+	.long	1014479986
+	.long	1806988078
+	.long	1014479986
+	.long	1806988078
+	.long	1014479986
+	.long	1806988078
+	.long	1014479986
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	1697350398
+	.long	1073157447
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.type	__svml_dlog2_ha_data_internal_avx512,@object
+	.size	__svml_dlog2_ha_data_internal_avx512,1280
+	.align 64
+__dlog2_ha__Q3:
+	.long	3213221364
+	.long	1050233568
+	.long	1697350398
+	.long	3219592519
+	.long	3694789629
+	.long	1071564553
+	.long	0
+	.long	1073157447
+	.type	__dlog2_ha__Q3,@object
+	.size	__dlog2_ha__Q3,32
+	.space 32, 0x00 	
+	.align 64
+__dlog2_ha__Q2:
+	.long	3213221358
+	.long	1050233568
+	.long	1697350398
+	.long	3219592519
+	.long	3695167823
+	.long	1071564553
+	.long	1697791998
+	.long	3218543943
+	.long	0
+	.long	1073157447
+	.type	__dlog2_ha__Q2,@object
+	.size	__dlog2_ha__Q2,40
+	.space 24, 0x00 	
+	.align 64
+__dlog2_ha__Q1:
+	.long	3213221370
+	.long	1050233568
+	.long	3213207734
+	.long	3196668640
+	.long	3694789628
+	.long	1071564553
+	.long	1697350743
+	.long	3218543943
+	.long	1357908666
+	.long	1070757740
+	.long	3685007067
+	.long	3217999625
+	.long	96832070
+	.long	1070227830
+	.long	1140452344
+	.long	3217495379
+	.long	2606274745
+	.long	1069844296
+	.long	1933654871
+	.long	3217172449
+	.long	0
+	.long	1073157447
+	.long	0
+	.long	3219592519
+	.type	__dlog2_ha__Q1,@object
+	.size	__dlog2_ha__Q1,96
+	.space 32, 0x00 	
+	.align 64
+__dlog2_ha__TWO_32P:
+	.long	1048576
+	.long	1106247680
+	.type	__dlog2_ha__TWO_32P,@object
+	.size	__dlog2_ha__TWO_32P,8
+	.space 56, 0x00 	
+	.align 64
+__dlog2_ha__TWO_32:
+	.long	0
+	.long	1106247680
+	.type	__dlog2_ha__TWO_32,@object
+	.size	__dlog2_ha__TWO_32,8
+	.space 56, 0x00 	
+	.align 64
+__dlog2_ha__TWO_55:
+	.long	0
+	.long	1130364928
+	.type	__dlog2_ha__TWO_55,@object
+	.size	__dlog2_ha__TWO_55,8
+	.space 56, 0x00 	
+	.align 64
+__dlog2_ha___libm_rcp_table_256:
+	.long	0x3f7f8000
+	.long	0x3f7e8000
+	.long	0x3f7d8000
+	.long	0x3f7c8000
+	.long	0x3f7ba000
+	.long	0x3f7aa000
+	.long	0x3f79a000
+	.long	0x3f78c000
+	.long	0x3f77c000
+	.long	0x3f76e000
+	.long	0x3f75e000
+	.long	0x3f750000
+	.long	0x3f742000
+	.long	0x3f732000
+	.long	0x3f724000
+	.long	0x3f716000
+	.long	0x3f708000
+	.long	0x3f6fa000
+	.long	0x3f6ec000
+	.long	0x3f6de000
+	.long	0x3f6d0000
+	.long	0x3f6c2000
+	.long	0x3f6b6000
+	.long	0x3f6a8000
+	.long	0x3f69a000
+	.long	0x3f68c000
+	.long	0x3f680000
+	.long	0x3f672000
+	.long	0x3f666000
+	.long	0x3f658000
+	.long	0x3f64c000
+	.long	0x3f640000
+	.long	0x3f632000
+	.long	0x3f626000
+	.long	0x3f61a000
+	.long	0x3f60e000
+	.long	0x3f600000
+	.long	0x3f5f4000
+	.long	0x3f5e8000
+	.long	0x3f5dc000
+	.long	0x3f5d0000
+	.long	0x3f5c4000
+	.long	0x3f5b8000
+	.long	0x3f5ae000
+	.long	0x3f5a2000
+	.long	0x3f596000
+	.long	0x3f58a000
+	.long	0x3f57e000
+	.long	0x3f574000
+	.long	0x3f568000
+	.long	0x3f55e000
+	.long	0x3f552000
+	.long	0x3f546000
+	.long	0x3f53c000
+	.long	0x3f532000
+	.long	0x3f526000
+	.long	0x3f51c000
+	.long	0x3f510000
+	.long	0x3f506000
+	.long	0x3f4fc000
+	.long	0x3f4f2000
+	.long	0x3f4e6000
+	.long	0x3f4dc000
+	.long	0x3f4d2000
+	.long	0x3f4c8000
+	.long	0x3f4be000
+	.long	0x3f4b4000
+	.long	0x3f4aa000
+	.long	0x3f4a0000
+	.long	0x3f496000
+	.long	0x3f48c000
+	.long	0x3f482000
+	.long	0x3f478000
+	.long	0x3f46e000
+	.long	0x3f464000
+	.long	0x3f45c000
+	.long	0x3f452000
+	.long	0x3f448000
+	.long	0x3f43e000
+	.long	0x3f436000
+	.long	0x3f42c000
+	.long	0x3f422000
+	.long	0x3f41a000
+	.long	0x3f410000
+	.long	0x3f408000
+	.long	0x3f3fe000
+	.long	0x3f3f6000
+	.long	0x3f3ec000
+	.long	0x3f3e4000
+	.long	0x3f3da000
+	.long	0x3f3d2000
+	.long	0x3f3ca000
+	.long	0x3f3c0000
+	.long	0x3f3b8000
+	.long	0x3f3b0000
+	.long	0x3f3a8000
+	.long	0x3f39e000
+	.long	0x3f396000
+	.long	0x3f38e000
+	.long	0x3f386000
+	.long	0x3f37e000
+	.long	0x3f376000
+	.long	0x3f36c000
+	.long	0x3f364000
+	.long	0x3f35c000
+	.long	0x3f354000
+	.long	0x3f34c000
+	.long	0x3f344000
+	.long	0x3f33c000
+	.long	0x3f334000
+	.long	0x3f32e000
+	.long	0x3f326000
+	.long	0x3f31e000
+	.long	0x3f316000
+	.long	0x3f30e000
+	.long	0x3f306000
+	.long	0x3f2fe000
+	.long	0x3f2f8000
+	.long	0x3f2f0000
+	.long	0x3f2e8000
+	.long	0x3f2e2000
+	.long	0x3f2da000
+	.long	0x3f2d2000
+	.long	0x3f2cc000
+	.long	0x3f2c4000
+	.long	0x3f2bc000
+	.long	0x3f2b6000
+	.long	0x3f2ae000
+	.long	0x3f2a8000
+	.long	0x3f2a0000
+	.long	0x3f29a000
+	.long	0x3f292000
+	.long	0x3f28c000
+	.long	0x3f284000
+	.long	0x3f27e000
+	.long	0x3f276000
+	.long	0x3f270000
+	.long	0x3f268000
+	.long	0x3f262000
+	.long	0x3f25c000
+	.long	0x3f254000
+	.long	0x3f24e000
+	.long	0x3f248000
+	.long	0x3f240000
+	.long	0x3f23a000
+	.long	0x3f234000
+	.long	0x3f22e000
+	.long	0x3f226000
+	.long	0x3f220000
+	.long	0x3f21a000
+	.long	0x3f214000
+	.long	0x3f20e000
+	.long	0x3f206000
+	.long	0x3f200000
+	.long	0x3f1fa000
+	.long	0x3f1f4000
+	.long	0x3f1ee000
+	.long	0x3f1e8000
+	.long	0x3f1e2000
+	.long	0x3f1dc000
+	.long	0x3f1d6000
+	.long	0x3f1d0000
+	.long	0x3f1ca000
+	.long	0x3f1c4000
+	.long	0x3f1be000
+	.long	0x3f1b8000
+	.long	0x3f1b2000
+	.long	0x3f1ac000
+	.long	0x3f1a6000
+	.long	0x3f1a0000
+	.long	0x3f19a000
+	.long	0x3f194000
+	.long	0x3f190000
+	.long	0x3f18a000
+	.long	0x3f184000
+	.long	0x3f17e000
+	.long	0x3f178000
+	.long	0x3f172000
+	.long	0x3f16e000
+	.long	0x3f168000
+	.long	0x3f162000
+	.long	0x3f15c000
+	.long	0x3f158000
+	.long	0x3f152000
+	.long	0x3f14c000
+	.long	0x3f148000
+	.long	0x3f142000
+	.long	0x3f13c000
+	.long	0x3f138000
+	.long	0x3f132000
+	.long	0x3f12c000
+	.long	0x3f128000
+	.long	0x3f122000
+	.long	0x3f11c000
+	.long	0x3f118000
+	.long	0x3f112000
+	.long	0x3f10e000
+	.long	0x3f108000
+	.long	0x3f104000
+	.long	0x3f0fe000
+	.long	0x3f0f8000
+	.long	0x3f0f4000
+	.long	0x3f0ee000
+	.long	0x3f0ea000
+	.long	0x3f0e6000
+	.long	0x3f0e0000
+	.long	0x3f0dc000
+	.long	0x3f0d6000
+	.long	0x3f0d2000
+	.long	0x3f0cc000
+	.long	0x3f0c8000
+	.long	0x3f0c2000
+	.long	0x3f0be000
+	.long	0x3f0ba000
+	.long	0x3f0b4000
+	.long	0x3f0b0000
+	.long	0x3f0ac000
+	.long	0x3f0a6000
+	.long	0x3f0a2000
+	.long	0x3f09e000
+	.long	0x3f098000
+	.long	0x3f094000
+	.long	0x3f090000
+	.long	0x3f08a000
+	.long	0x3f086000
+	.long	0x3f082000
+	.long	0x3f07e000
+	.long	0x3f078000
+	.long	0x3f074000
+	.long	0x3f070000
+	.long	0x3f06c000
+	.long	0x3f066000
+	.long	0x3f062000
+	.long	0x3f05e000
+	.long	0x3f05a000
+	.long	0x3f056000
+	.long	0x3f052000
+	.long	0x3f04c000
+	.long	0x3f048000
+	.long	0x3f044000
+	.long	0x3f040000
+	.long	0x3f03c000
+	.long	0x3f038000
+	.long	0x3f034000
+	.long	0x3f030000
+	.long	0x3f02a000
+	.long	0x3f026000
+	.long	0x3f022000
+	.long	0x3f01e000
+	.long	0x3f01a000
+	.long	0x3f016000
+	.long	0x3f012000
+	.long	0x3f00e000
+	.long	0x3f00a000
+	.long	0x3f006000
+	.long	0x3f002000
+	.type	__dlog2_ha___libm_rcp_table_256,@object
+	.size	__dlog2_ha___libm_rcp_table_256,1024
+	.align 64
+__dlog2_ha___libm_log2_table_256:
+	.long	0xfb44c3b7,0x3e1485cb
+	.long	0x00000000,0x3f671b0e
+	.long	0x06028ac0,0x3e31d5d9
+	.long	0x00000000,0x3f815cfe
+	.long	0xb8d7240b,0x3df8b9cb
+	.long	0x00000000,0x3f8cfee7
+	.long	0x0d179106,0x3e38864a
+	.long	0x00000000,0x3f94564a
+	.long	0xecba1593,0x3e459c6a
+	.long	0x00000000,0x3f997723
+	.long	0x94120c14,0x3e48d36a
+	.long	0x00000000,0x3f9f5923
+	.long	0xd2571490,0x3e5410ba
+	.long	0x00000000,0x3fa2a094
+	.long	0x1dc036a2,0x3e2776b0
+	.long	0x00000000,0x3fa53894
+	.long	0x78efe2b1,0x3e537229
+	.long	0x00000000,0x3fa8324c
+	.long	0xfd29dc75,0x3e59c0fa
+	.long	0x00000000,0x3faacf54
+	.long	0x046734f7,0x3e4636b7
+	.long	0x00000000,0x3fadced9
+	.long	0xd3b410b8,0x3e6f7950
+	.long	0x00000000,0x3fb0387e
+	.long	0x00f2200a,0x3e19d1e7
+	.long	0x00000000,0x3fb18ac6
+	.long	0x8661ba82,0x3e4f09a9
+	.long	0x00000000,0x3fb30edd
+	.long	0x9367107c,0x3e564d91
+	.long	0x00000000,0x3fb463c1
+	.long	0x0e4a4ce8,0x3e4e1fd1
+	.long	0x00000000,0x3fb5b9e1
+	.long	0x3cdb6374,0x3e492cf0
+	.long	0x00000000,0x3fb7113f
+	.long	0x069c4f7f,0x3e61a364
+	.long	0x00000000,0x3fb869dd
+	.long	0x427b631b,0x3e6493a6
+	.long	0x00000000,0x3fb9c3be
+	.long	0xe02b3e8b,0x3e6af2c2
+	.long	0x00000000,0x3fbb1ee4
+	.long	0x389f4365,0x3e616e1e
+	.long	0x00000000,0x3fbc7b52
+	.long	0x6a31fd96,0x3e4633b7
+	.long	0x00000000,0x3fbdd90a
+	.long	0x0508664d,0x3e62ed84
+	.long	0x00000000,0x3fbf05d4
+	.long	0xaca1905c,0x3e775dcd
+	.long	0x00000000,0x3fc032fb
+	.long	0x094fbeeb,0x3e753e65
+	.long	0x00000000,0x3fc0e3b5
+	.long	0x96aa4b17,0x3e671f44
+	.long	0x00000000,0x3fc19519
+	.long	0x92da5a47,0x3e785566
+	.long	0x00000000,0x3fc22dad
+	.long	0xbeb7d722,0x3e518efa
+	.long	0x00000000,0x3fc2e050
+	.long	0xab57551c,0x3e738564
+	.long	0x00000000,0x3fc379f7
+	.long	0x54a914e3,0x3e55d0da
+	.long	0x00000000,0x3fc42ddd
+	.long	0xfe974017,0x3e73cd00
+	.long	0x00000000,0x3fc4c89b
+	.long	0x59064390,0x3e54ffd6
+	.long	0x00000000,0x3fc563dc
+	.long	0x633ab50f,0x3e67d75e
+	.long	0x00000000,0x3fc619a2
+	.long	0xc8877e8a,0x3e77e6ce
+	.long	0x00000000,0x3fc6b5ff
+	.long	0x1ab7837f,0x3e7ecc1f
+	.long	0x00000000,0x3fc752e1
+	.long	0xf9d5827a,0x3e7cea7c
+	.long	0x00000000,0x3fc7f049
+	.long	0x64ccd537,0x3e357f7a
+	.long	0x00000000,0x3fc8a898
+	.long	0xf7c9b05b,0x3e7994ca
+	.long	0x00000000,0x3fc94724
+	.long	0xa2f56536,0x3e524b8f
+	.long	0x00000000,0x3fc9e63a
+	.long	0x5edaab42,0x3e7fd640
+	.long	0x00000000,0x3fca85d8
+	.long	0xd163379a,0x3e625f54
+	.long	0x00000000,0x3fcb2602
+	.long	0x936acd51,0x3e7ebdc3
+	.long	0x00000000,0x3fcbc6b6
+	.long	0xcfbc0aa0,0x3e7eee14
+	.long	0x00000000,0x3fcc67f7
+	.long	0xf73bcdad,0x3e764469
+	.long	0x00000000,0x3fcceec4
+	.long	0x60971b86,0x3e6eb44e
+	.long	0x00000000,0x3fcd9109
+	.long	0xcd2052a5,0x3e65fcf4
+	.long	0x00000000,0x3fce33dd
+	.long	0xc402867b,0x3e61af1e
+	.long	0x00000000,0x3fced741
+	.long	0xa0c956e4,0x3e61bfbd
+	.long	0x00000000,0x3fcf7b36
+	.long	0x831e77ff,0x3e85287b
+	.long	0x00000000,0x3fd00223
+	.long	0xaddfdee2,0x3e7d2fc3
+	.long	0x00000000,0x3fd054a4
+	.long	0x342052c1,0x3e83724b
+	.long	0x00000000,0x3fd0999d
+	.long	0x602bcd34,0x3e7b4ec9
+	.long	0x00000000,0x3fd0eca6
+	.long	0x3aa20ead,0x3e6742da
+	.long	0x00000000,0x3fd13ffa
+	.long	0x9ecdadf4,0x3e713e82
+	.long	0x00000000,0x3fd185a4
+	.long	0xfef3031b,0x3e52f27e
+	.long	0x00000000,0x3fd1cb83
+	.long	0x79e4af8a,0x3e710739
+	.long	0x00000000,0x3fd21fa1
+	.long	0xe59ad84a,0x3e637301
+	.long	0x00000000,0x3fd265f5
+	.long	0x3d7dfd9b,0x3e88697c
+	.long	0x00000000,0x3fd2baa0
+	.long	0x738117b0,0x3e717788
+	.long	0x00000000,0x3fd3016b
+	.long	0xd3c26a97,0x3e6c5514
+	.long	0x00000000,0x3fd3486c
+	.long	0x4c4ff246,0x3e8df550
+	.long	0x00000000,0x3fd38fa3
+	.long	0x40340fa6,0x3e88102d
+	.long	0x00000000,0x3fd3e562
+	.long	0x4592f4c3,0x3e5f53b6
+	.long	0x00000000,0x3fd42d14
+	.long	0x8b149a00,0x3e750fc8
+	.long	0x00000000,0x3fd474fd
+	.long	0xa8f50e5f,0x3e86d01c
+	.long	0x00000000,0x3fd4bd1e
+	.long	0x7a22a88a,0x3e83c469
+	.long	0x00000000,0x3fd50578
+	.long	0xdc18b6d2,0x3e79000e
+	.long	0x00000000,0x3fd54e0b
+	.long	0x7c00250b,0x3e7870f0
+	.long	0x00000000,0x3fd596d7
+	.long	0xc1c885ae,0x3e8e3dd5
+	.long	0x00000000,0x3fd5dfdc
+	.long	0xa6ecc47e,0x3e7bf64c
+	.long	0x00000000,0x3fd6291c
+	.long	0x0bc16c18,0x3e6bdaca
+	.long	0x00000000,0x3fd67296
+	.long	0x1f925729,0x3e84d25c
+	.long	0x00000000,0x3fd6bc4a
+	.long	0xf23978de,0x3e5a5fb4
+	.long	0x00000000,0x3fd7063a
+	.long	0xe2ac7f60,0x3e8e3da0
+	.long	0x00000000,0x3fd75064
+	.long	0x71f51a7b,0x3e819e20
+	.long	0x00000000,0x3fd79acb
+	.long	0x5b924234,0x3e5e43ae
+	.long	0x00000000,0x3fd7d67c
+	.long	0xe9aedf37,0x3e701a2b
+	.long	0x00000000,0x3fd8214f
+	.long	0xeddd33ea,0x3e6b6f51
+	.long	0x00000000,0x3fd86c5f
+	.long	0xe732b3c4,0x3e79375f
+	.long	0x00000000,0x3fd8b7ac
+	.long	0xe47cb9df,0x3e7e831b
+	.long	0x00000000,0x3fd8f416
+	.long	0xe392d3c8,0x3e8abc37
+	.long	0x00000000,0x3fd93fd2
+	.long	0x28b35c77,0x3e80852d
+	.long	0x00000000,0x3fd98bcd
+	.long	0xd29cea36,0x3e69f374
+	.long	0x00000000,0x3fd9c8c3
+	.long	0xb3d7b0e6,0x3e542981
+	.long	0x00000000,0x3fda152f
+	.long	0x1fe6d5ac,0x3e8b2bfb
+	.long	0x00000000,0x3fda527f
+	.long	0xf32e5dda,0x3e6f6f5c
+	.long	0x00000000,0x3fda9f5e
+	.long	0xd34d0d30,0x3e65abaa
+	.long	0x00000000,0x3fdadd0b
+	.long	0xe0ac9602,0x3e7bd47f
+	.long	0x00000000,0x3fdb2a5d
+	.long	0x7c5c784b,0x3e833601
+	.long	0x00000000,0x3fdb6867
+	.long	0xd7b0821f,0x3e5b887c
+	.long	0x00000000,0x3fdbb62f
+	.long	0xcd0a8f6a,0x3e83eccc
+	.long	0x00000000,0x3fdbf497
+	.long	0x7500a4e8,0x3e7ba0e6
+	.long	0x00000000,0x3fdc332a
+	.long	0xfc8712bb,0x3e885a8b
+	.long	0x00000000,0x3fdc819d
+	.long	0xf7a3a78f,0x3e81c336
+	.long	0x00000000,0x3fdcc090
+	.long	0x4ad8a38b,0x3e7846b4
+	.long	0x00000000,0x3fdcffae
+	.long	0xfcfc3a99,0x3e7db50f
+	.long	0x00000000,0x3fdd3ef7
+	.long	0x433cccd2,0x3e238809
+	.long	0x00000000,0x3fdd8e50
+	.long	0x6828fa82,0x3e721b2e
+	.long	0x00000000,0x3fddcdfb
+	.long	0xfc7c49c1,0x3e82848b
+	.long	0x00000000,0x3fde0dd2
+	.long	0x1fce4d49,0x3e615144
+	.long	0x00000000,0x3fde4dd6
+	.long	0x77e2e8fd,0x3e68b88a
+	.long	0x00000000,0x3fde8e06
+	.long	0x22a961b0,0x3e8fd353
+	.long	0x00000000,0x3fdece62
+	.long	0x266f2e1f,0x3e72854e
+	.long	0x00000000,0x3fdf1f16
+	.long	0xc69211fe,0x3e8520c7
+	.long	0x00000000,0x3fdf5fd8
+	.long	0xaba878d5,0x3e826fcf
+	.long	0x00000000,0x3fdfa0c8
+	.long	0xcd5b35b1,0x3e726ed8
+	.long	0x00000000,0x3fdfe1e6
+	.long	0x678a4f1c,0x3e49054d
+	.long	0x00000000,0x3fe01199
+	.long	0x3bc19f18,0x3e5d23cb
+	.long	0x00000000,0x3fe03256
+	.long	0x12772acb,0x3e87af35
+	.long	0x00000000,0x3fe0532a
+	.long	0x2849c88a,0x3e67f5fc
+	.long	0x00000000,0x3fe07416
+	.long	0x0e574fa1,0x3e84fce2
+	.long	0x00000000,0x3fe08cd6
+	.long	0x0ec2b5fa,0x3e857044
+	.long	0x00000000,0x3fe0adeb
+	.long	0xd031f353,0x3e6d5d1d
+	.long	0x00000000,0x3fe0cf18
+	.long	0xdae75c4d,0x3e99a159
+	.long	0x00000000,0x3fe0f05c
+	.long	0x1553afb9,0x3e90eaf1
+	.long	0x00000000,0x3fe111b9
+	.long	0xdcc5c3c7,0x3e8bc6f3
+	.long	0x00000000,0x3fe1332e
+	.long	0x1109e597,0x3e94ef84
+	.long	0x00000000,0x3fe154bb
+	.long	0xd82adea4,0x3e937f40
+	.long	0x00000000,0x3fe16df5
+	.long	0x78336a2f,0x3e96dc5a
+	.long	0x00000000,0x3fe18fad
+	.long	0x84c2c804,0x3e90935b
+	.long	0x00000000,0x3fe1b17e
+	.long	0x04fd30aa,0x3e8a837a
+	.long	0x00000000,0x3fe1caeb
+	.long	0xd924b0ac,0x3e99061d
+	.long	0x00000000,0x3fe1ece7
+	.long	0xef9b9d01,0x3e7ed700
+	.long	0x00000000,0x3fe20efd
+	.long	0x202c81ec,0x3e9c6ebb
+	.long	0x00000000,0x3fe2289d
+	.long	0xfc0d7d76,0x3e936d4d
+	.long	0x00000000,0x3fe24adf
+	.long	0x9f8756ba,0x3e9a35d7
+	.long	0x00000000,0x3fe26d3a
+	.long	0xe7c79d83,0x3e584ee3
+	.long	0x00000000,0x3fe28710
+	.long	0x942065a0,0x3e7d9e0d
+	.long	0x00000000,0x3fe2a998
+	.long	0x2175afbf,0x3e872108
+	.long	0x00000000,0x3fe2c38f
+	.long	0xfaf6283c,0x3e9f5809
+	.long	0x00000000,0x3fe2e644
+	.long	0x411d0507,0x3e871209
+	.long	0x00000000,0x3fe3005e
+	.long	0x370c24bf,0x3e9c3b11
+	.long	0x00000000,0x3fe32341
+	.long	0x8044bb5a,0x3e9bce8e
+	.long	0x00000000,0x3fe33d7d
+	.long	0x698ea854,0x3e98aca0
+	.long	0x00000000,0x3fe3608f
+	.long	0xd4d873bb,0x3e972a8e
+	.long	0x00000000,0x3fe37aee
+	.long	0x615e8182,0x3e8f669b
+	.long	0x00000000,0x3fe39e2f
+	.long	0xdda5b49a,0x3e98d1f4
+	.long	0x00000000,0x3fe3b8b1
+	.long	0xe72383f7,0x3e9cc279
+	.long	0x00000000,0x3fe3dc21
+	.long	0x497497f1,0x3e9c6774
+	.long	0x00000000,0x3fe3f6c7
+	.long	0x801bd0e6,0x3e82c7f6
+	.long	0x00000000,0x3fe4117d
+	.long	0xd0ee28b4,0x3e9dfdd6
+	.long	0x00000000,0x3fe43531
+	.long	0xb70d3761,0x3e92d3a2
+	.long	0x00000000,0x3fe4500b
+	.long	0xe7d6bcb2,0x3e9c8343
+	.long	0x00000000,0x3fe46af4
+	.long	0x90d43957,0x3e693179
+	.long	0x00000000,0x3fe48eef
+	.long	0xf12570df,0x3e799eab
+	.long	0x00000000,0x3fe4a9fd
+	.long	0x1b88755d,0x3e78c5f8
+	.long	0x00000000,0x3fe4c51b
+	.long	0xdf99a22c,0x3e7369be
+	.long	0x00000000,0x3fe4e049
+	.long	0x6ed50f62,0x3e88fd93
+	.long	0x00000000,0x3fe5049f
+	.long	0x978605ff,0x3e9c6042
+	.long	0x00000000,0x3fe51ff2
+	.long	0xe6c85f4c,0x3e930ae6
+	.long	0x00000000,0x3fe53b56
+	.long	0xc3275ba6,0x3e92e7b6
+	.long	0x00000000,0x3fe556ca
+	.long	0x91597938,0x3e9e6401
+	.long	0x00000000,0x3fe5724e
+	.long	0x448ebb62,0x3e3d2dee
+	.long	0x00000000,0x3fe59719
+	.long	0x47501b6d,0x3e9b432e
+	.long	0x00000000,0x3fe5b2c3
+	.long	0x571fa7cd,0x3e83cf9b
+	.long	0x00000000,0x3fe5ce7f
+	.long	0x7359819d,0x3e8dc615
+	.long	0x00000000,0x3fe5ea4b
+	.long	0xc78a85ed,0x3e8973c3
+	.long	0x00000000,0x3fe60628
+	.long	0xc15a9f3d,0x3e77d788
+	.long	0x00000000,0x3fe62216
+	.long	0x51952736,0x3e9d2538
+	.long	0x00000000,0x3fe63e14
+	.long	0xde792c07,0x3e94dab9
+	.long	0x00000000,0x3fe65a24
+	.long	0x5bfa4318,0x3e8f5be1
+	.long	0x00000000,0x3fe67645
+	.long	0x55090ec8,0x3e903b26
+	.long	0x00000000,0x3fe69277
+	.long	0xc78b6175,0x3e99236f
+	.long	0x00000000,0x3fe6aeba
+	.long	0x3a80db6a,0x3e8a1972
+	.long	0x00000000,0x3fe6cb0f
+	.long	0xf558aa96,0x3e8d43a2
+	.long	0x00000000,0x3fe6e775
+	.long	0xd9a82f2e,0x3e424ee3
+	.long	0x00000000,0x3fe703ed
+	.long	0x583878f6,0x3e764d8e
+	.long	0x00000000,0x3fe72076
+	.long	0xc1150a3e,0x3e379604
+	.long	0x00000000,0x3fe73d11
+	.long	0xed85584b,0x3e93b229
+	.long	0x00000000,0x3fe759bd
+	.long	0x451a7b48,0x3e62967a
+	.long	0x00000000,0x3fe7767c
+	.long	0xc044e72d,0x3e8e12d7
+	.long	0x00000000,0x3fe7934c
+	.long	0xfdfb6949,0x3e9ca45d
+	.long	0x00000000,0x3fe7b02e
+	.long	0xff690fce,0x3e9244fc
+	.long	0x00000000,0x3fe7c37a
+	.long	0x81487a2c,0x3e7e9cea
+	.long	0x00000000,0x3fe7e07b
+	.long	0xd0ad2d9a,0x3e760da0
+	.long	0x00000000,0x3fe7fd8e
+	.long	0x096f45d9,0x3e8d7703
+	.long	0x00000000,0x3fe81ab3
+	.long	0x1b17115b,0x3e78dbee
+	.long	0x00000000,0x3fe837eb
+	.long	0x7c252ee0,0x3e8dc5a4
+	.long	0x00000000,0x3fe85535
+	.long	0x42d5123f,0x3e950116
+	.long	0x00000000,0x3fe868c6
+	.long	0xf11e41be,0x3e4c4eb7
+	.long	0x00000000,0x3fe88630
+	.long	0xdb2890b4,0x3e7773b8
+	.long	0x00000000,0x3fe8a3ac
+	.long	0x7ffb4479,0x3e7bc8e7
+	.long	0x00000000,0x3fe8c13b
+	.long	0x237693b3,0x3e8c388f
+	.long	0x00000000,0x3fe8d4fa
+	.long	0x45fcf1a0,0x3e731cd4
+	.long	0x00000000,0x3fe8f2a9
+	.long	0xe4895b91,0x3e9fae07
+	.long	0x00000000,0x3fe9106a
+	.long	0x42d2824e,0x3e7d140d
+	.long	0x00000000,0x3fe9244c
+	.long	0x39900f67,0x3e75c3c8
+	.long	0x00000000,0x3fe9422e
+	.long	0xa314252b,0x3e902422
+	.long	0x00000000,0x3fe96023
+	.long	0xf2a6b8ef,0x3e7b9be8
+	.long	0x00000000,0x3fe97427
+	.long	0x1476f5e9,0x3e66b188
+	.long	0x00000000,0x3fe9923d
+	.long	0xdde10a6f,0x3e93c377
+	.long	0x00000000,0x3fe9b066
+	.long	0x4944a32c,0x3e817cad
+	.long	0x00000000,0x3fe9c48d
+	.long	0xc738e7ef,0x3e9927f2
+	.long	0x00000000,0x3fe9e2d7
+	.long	0xd25cfd94,0x3e7ccc41
+	.long	0x00000000,0x3fea0136
+	.long	0x2210e81b,0x3e8382fa
+	.long	0x00000000,0x3fea1580
+	.long	0x1e690ce2,0x3e7752a7
+	.long	0x00000000,0x3fea3400
+	.long	0x122315d2,0x3e94cfee
+	.long	0x00000000,0x3fea4860
+	.long	0xce98333b,0x3e80536e
+	.long	0x00000000,0x3fea6702
+	.long	0xc30f00e9,0x3e838b7e
+	.long	0x00000000,0x3fea7b79
+	.long	0x05b0c779,0x3e9dc380
+	.long	0x00000000,0x3fea9a3c
+	.long	0xdd6dd3fe,0x3e6be168
+	.long	0x00000000,0x3feab915
+	.long	0x26e0d276,0x3e966757
+	.long	0x00000000,0x3feacdb0
+	.long	0x0aad615c,0x3e880252
+	.long	0x00000000,0x3feaecab
+	.long	0x7927096a,0x3e607c31
+	.long	0x00000000,0x3feb015e
+	.long	0x53b3d90e,0x3e596513
+	.long	0x00000000,0x3feb161a
+	.long	0x8f2f0570,0x3e90ec3a
+	.long	0x00000000,0x3feb3545
+	.long	0x81193954,0x3e9cb640
+	.long	0x00000000,0x3feb4a18
+	.long	0x311e7236,0x3e936479
+	.long	0x00000000,0x3feb6967
+	.long	0x3a42a413,0x3e9210e8
+	.long	0x00000000,0x3feb7e52
+	.long	0x4a0daeb2,0x3e9a1717
+	.long	0x00000000,0x3feb9dc4
+	.long	0xce900653,0x3e925bb7
+	.long	0x00000000,0x3febb2c7
+	.long	0xb5087588,0x3e95dbb8
+	.long	0x00000000,0x3febd25d
+	.long	0x4a41204c,0x3e8d0aa9
+	.long	0x00000000,0x3febe778
+	.long	0x69a0d774,0x3e9c772f
+	.long	0x00000000,0x3febfc9c
+	.long	0x79d0a9a5,0x3e97b6a0
+	.long	0x00000000,0x3fec1c65
+	.long	0xd26f1a12,0x3e8f7402
+	.long	0x00000000,0x3fec31a2
+	.long	0x1243bc84,0x3e4db2f1
+	.long	0x00000000,0x3fec46e9
+	.long	0x477e1755,0x3e80dcc2
+	.long	0x00000000,0x3fec66e5
+	.long	0xc2f904c1,0x3e8b1e31
+	.long	0x00000000,0x3fec7c44
+	.long	0x1785b0c4,0x3e8fb619
+	.long	0x00000000,0x3fec91ad
+	.long	0xedb052ef,0x3e98832d
+	.long	0x00000000,0x3fecb1dd
+	.long	0x9e373618,0x3e98c822
+	.long	0x00000000,0x3fecc75f
+	.long	0x32954637,0x3e9a46e7
+	.long	0x00000000,0x3fecdceb
+	.long	0xf0e6b2a9,0x3e996305
+	.long	0x00000000,0x3fecfd50
+	.long	0x1a6614ee,0x3e68c160
+	.long	0x00000000,0x3fed12f6
+	.long	0x09e33b28,0x3e9229c4
+	.long	0x00000000,0x3fed28a5
+	.long	0xe53b994c,0x3e7f281b
+	.long	0x00000000,0x3fed3e5f
+	.long	0x1124ac35,0x3e9f27f3
+	.long	0x00000000,0x3fed5f08
+	.long	0x3d2fdc03,0x3e84e779
+	.long	0x00000000,0x3fed74dc
+	.long	0x1e93fd97,0x3e416c07
+	.long	0x00000000,0x3fed8aba
+	.long	0x91b415ef,0x3e654669
+	.long	0x00000000,0x3feda0a2
+	.long	0x6495f594,0x3e9712d4
+	.long	0x00000000,0x3fedc191
+	.long	0x40171789,0x3e9ca7b2
+	.long	0x00000000,0x3fedd793
+	.long	0xe8fc4323,0x3e929afa
+	.long	0x00000000,0x3fededa0
+	.long	0xeb03bd09,0x3e9a2e96
+	.long	0x00000000,0x3fee03b7
+	.long	0xca370ea2,0x3e94a63d
+	.long	0x00000000,0x3fee19d9
+	.long	0xe30512ec,0x3e6a3bcb
+	.long	0x00000000,0x3fee3006
+	.long	0x411c95ce,0x3e99d3ed
+	.long	0x00000000,0x3fee515c
+	.long	0x35cfaf8e,0x3e3e5b57
+	.long	0x00000000,0x3fee67a4
+	.long	0x5669df6a,0x3e9fca71
+	.long	0x00000000,0x3fee7df5
+	.long	0x04f19d94,0x3e9914e2
+	.long	0x00000000,0x3fee9452
+	.long	0xcaa19134,0x3e8b511c
+	.long	0x00000000,0x3feeaaba
+	.long	0x4bb3bfb1,0x3e9ed72f
+	.long	0x00000000,0x3feec12c
+	.long	0x3c29d75e,0x3e8be8d6
+	.long	0x00000000,0x3feed7aa
+	.long	0x97da24fd,0x3e9c55d9
+	.long	0x00000000,0x3feeee32
+	.long	0x983c68ea,0x3e7ddfb1
+	.long	0x00000000,0x3fef1014
+	.long	0xf4425883,0x3e83ce66
+	.long	0x00000000,0x3fef26b8
+	.long	0xf7857f23,0x3e9055c3
+	.long	0x00000000,0x3fef3d67
+	.long	0x2805b525,0x3e9c2223
+	.long	0x00000000,0x3fef5421
+	.long	0x0c347fcf,0x3e8d59ba
+	.long	0x00000000,0x3fef6ae7
+	.long	0x7c901c44,0x3e82b110
+	.long	0x00000000,0x3fef81b8
+	.long	0x74d1b482,0x3e8b1394
+	.long	0x00000000,0x3fef9894
+	.long	0x36fb9eb2,0x3e9ca75b
+	.long	0x00000000,0x3fefaf7b
+	.long	0x629b1b7e,0x3e981a0a
+	.long	0x00000000,0x3fefc66e
+	.long	0x7b8c1116,0x3e54cc20
+	.long	0x00000000,0x3fefdd6d
+	.long	0xc0babe05,0x3e99ac8b
+	.long	0x00000000,0x3feff476
+	.type	__dlog2_ha___libm_log2_table_256,@object
+	.size	__dlog2_ha___libm_log2_table_256,4096
+	.space 512, 0x00 	
+	.align 64
+__dlog2_ha__P:
+	.long	3213235158
+	.long	1050233568
+	.long	1697350398
+	.long	3219592519
+	.long	3694740707
+	.long	1071564553
+	.long	1697260025
+	.long	3218543943
+	.long	2542794428
+	.long	1070757746
+	.long	2165113687
+	.long	3217999640
+	.long	0
+	.long	1073157447
+	.type	__dlog2_ha__P,@object
+	.size	__dlog2_ha__P,56
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_d_la.s
index 6ba58fab921..53e9c1cc1b1 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_h_la.s
new file mode 100644
index 00000000000..d7e776c27d7
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_h_la.s
@@ -0,0 +1,153 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *    log2(x) = VGETEXP(x) + log2(VGETMANT(x))
+ *    VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *    mx = VGETMANT(x) is in [1,2) for all x>=0
+ *    log2(mx) = -log2(RCP(mx)) + log2(1 +(mx*RCP(mx)-1))
+ *    and the table lookup for log2(RCP(mx))
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_log2s32
+
+__svml_log2s32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hlog2_data_internal(%rip), %zmm1
+        vmovdqu16 64+__svml_hlog2_data_internal(%rip), %zmm31
+        vmovdqu16 128+__svml_hlog2_data_internal(%rip), %zmm3
+        vmovdqu16 192+__svml_hlog2_data_internal(%rip), %zmm6
+        vmovdqu16 256+__svml_hlog2_data_internal(%rip), %zmm7
+        vmovdqu16 320+__svml_hlog2_data_internal(%rip), %zmm8
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__log2_h:
+        cmpq    $31, %rdx
+        ja .loaddata__log2_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $15360, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_log2_h
+.loaddata__log2_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_log2_h:
+
+/* No callout */
+
+/* exponent */
+        vgetexpph {sae}, %zmm0, %zmm4
+
+/* reduce mantissa to [.75, 1.5) */
+        vgetmantph $11, {sae}, %zmm0, %zmm2
+
+/* reduced argument */
+        vsubph    {rn-sae}, %zmm1, %zmm2, %zmm9
+
+/* exponent correction */
+        vgetexpph {sae}, %zmm2, %zmm5
+
+/* start polynomial */
+        vmovdqu16 %zmm31, %zmm0
+        vfmadd213ph {rn-sae}, %zmm3, %zmm9, %zmm0
+
+/* exponent */
+        vsubph    {rn-sae}, %zmm5, %zmm4, %zmm10
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm6, %zmm9, %zmm0
+        vfmadd213ph {rn-sae}, %zmm7, %zmm9, %zmm0
+        vfmadd213ph {rn-sae}, %zmm8, %zmm9, %zmm0
+
+/* Poly*R+expon */
+        vfmadd213ph {rn-sae}, %zmm10, %zmm9, %zmm0
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__log2_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_log2s32,@function
+        .size	__svml_log2s32,.-__svml_log2s32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hlog2_data_internal:
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x328a
+	.endr
+	.rept	32
+        .word	0xb5ff
+	.endr
+	.rept	32
+        .word	0x37cf
+	.endr
+	.rept	32
+        .word	0xb9c5
+	.endr
+	.rept	32
+        .word	0x3dc5
+	.endr
+	.rept	32
+        .word	0x001f
+	.endr
+	.rept	32
+        .word	0x0000
+	.endr
+	.rept	16
+        .long	0x00000001
+	.endr
+	.rept	16
+        .long	0x0000007f
+	.endr
+	.rept	16
+        .long	0x3f800000
+	.endr
+	.rept	16
+        .long	0x3e51367b
+	.endr
+	.rept	16
+        .long	0xbebfd356
+	.endr
+	.rept	16
+        .long	0x3ef9e953
+	.endr
+	.rept	16
+        .long	0xbf389f48
+	.endr
+	.rept	16
+        .long	0x3fb8a7e4
+	.endr
+	.rept	32
+        .word	0xfc00
+	.endr
+        .type	__svml_hlog2_data_internal,@object
+        .size	__svml_hlog2_data_internal,1088
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_s_ha.s
new file mode 100644
index 00000000000..1f58f794649
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_s_ha.s
@@ -0,0 +1,907 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *  log2(x) = VGETEXP(x) + log2(VGETMANT(x))
+ *  *       VGETEXP, VGETMANT will correctly treat special cases too (including denormals)
+ *  *   mx = VGETMANT(x) is in [1,2) for all x>=0
+ *  *   log2(mx) = -log2(RCP(mx)) + log2(1 +(mx*RCP(mx)-1))
+ *  *      RCP(mx) is rounded to 4 fractional bits,
+ *  *      and the table lookup for log2(RCP(mx)) is based on a small permute instruction
+ *  *
+ *  *   LA, EP versions use interval interpolation (16 intervals)
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_log2f16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_log2f16_ha
+
+__svml_log2f16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm2
+
+/* GetMant(x), normalized to [1,2) for x>=0, NaN for x<0 */
+        vgetmantps $10, {sae}, %zmm2, %zmm4
+
+/* Reduced argument: R = SglRcp*Mantissa - 1 */
+        vmovups   256+__svml_slog2_ha_data_internal_avx512(%rip), %zmm1
+
+/* GetExp(x) */
+        vgetexpps {sae}, %zmm2, %zmm7
+
+/* Table lookup */
+        vmovups   __svml_slog2_ha_data_internal_avx512(%rip), %zmm10
+
+/* Start polynomial evaluation */
+        vmovups   320+__svml_slog2_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   448+__svml_slog2_ha_data_internal_avx512(%rip), %zmm12
+        vmovups   512+__svml_slog2_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   128+__svml_slog2_ha_data_internal_avx512(%rip), %zmm0
+
+/* SglRcp ~ 1/Mantissa */
+        vrcp14ps  %zmm4, %zmm3
+
+/* x<=0? */
+        vfpclassps $94, %zmm2, %k1
+
+/* round SglRcp to 5 fractional bits (RN mode, no Precision exception) */
+        vrndscaleps $88, {sae}, %zmm3, %zmm5
+        vmovups   384+__svml_slog2_ha_data_internal_avx512(%rip), %zmm3
+        kmovw     %k1, %edx
+        vgetexpps {sae}, %zmm5, %zmm8
+        vfmsub231ps {rn-sae}, %zmm5, %zmm4, %zmm1
+
+/* Prepare table index */
+        vpsrld    $18, %zmm5, %zmm6
+
+/* K*log(2)_high+Th */
+        vsubps    {rn-sae}, %zmm8, %zmm7, %zmm9
+        vmulps    {rn-sae}, %zmm1, %zmm1, %zmm13
+        vfmadd231ps {rn-sae}, %zmm1, %zmm11, %zmm3
+
+/* K+Th+(R*c1)_h */
+        vmovups   576+__svml_slog2_ha_data_internal_avx512(%rip), %zmm11
+        vfmadd231ps {rn-sae}, %zmm1, %zmm12, %zmm14
+        vpermt2ps 64+__svml_slog2_ha_data_internal_avx512(%rip), %zmm6, %zmm10
+        vpermt2ps 192+__svml_slog2_ha_data_internal_avx512(%rip), %zmm6, %zmm0
+
+/* poly */
+        vfmadd213ps {rn-sae}, %zmm14, %zmm13, %zmm3
+        vaddps    {rn-sae}, %zmm10, %zmm9, %zmm15
+        vfmadd213ps {rn-sae}, %zmm0, %zmm1, %zmm3
+        vmovaps   %zmm15, %zmm4
+        vfmadd231ps {rn-sae}, %zmm1, %zmm11, %zmm4
+
+/* (R*c1)_h */
+        vsubps    {rn-sae}, %zmm15, %zmm4, %zmm13
+        vxorps    %zmm13, %zmm13, %zmm13{%k1}
+
+/* (R*c1h)_l */
+        vfmsub213ps {rn-sae}, %zmm13, %zmm11, %zmm1
+        vaddps    {rn-sae}, %zmm1, %zmm3, %zmm0
+        vaddps    {rn-sae}, %zmm0, %zmm4, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm2, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_slog2_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_log2f16_ha,@function
+	.size	__svml_log2f16_ha,.-__svml_log2f16_ha
+..LN__svml_log2f16_ha.0:
+
+.L_2__routine_start___svml_slog2_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_slog2_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    2(%rdi), %edx
+        andl      $32640, %edx
+        cmpl      $32640, %edx
+        je        .LBL_2_13
+
+
+        movss     (%rdi), %xmm2
+        xorl      %ecx, %ecx
+        pxor      %xmm1, %xmm1
+        movss     %xmm2, -16(%rsp)
+        ucomiss   %xmm1, %xmm2
+        jp        .LBL_2_3
+        je        .LBL_2_5
+
+.LBL_2_3:
+
+        movzwl    -14(%rsp), %edx
+        testl     $32640, %edx
+        jne       .LBL_2_5
+
+
+        movss     .L_2il0floatpacket.81(%rip), %xmm0
+        movl      $-27, %ecx
+        mulss     %xmm0, %xmm2
+        movss     %xmm2, -16(%rsp)
+
+.LBL_2_5:
+
+        comiss    %xmm1, %xmm2
+        jbe       .LBL_2_9
+
+
+        movaps    %xmm2, %xmm1
+        subss     .L_2il0floatpacket.95(%rip), %xmm1
+        movss     %xmm1, -20(%rsp)
+        andb      $127, -17(%rsp)
+        movss     -20(%rsp), %xmm0
+        comiss    .L_2il0floatpacket.82(%rip), %xmm0
+        jbe       .LBL_2_8
+
+
+        movzwl    -14(%rsp), %edx
+        pxor      %xmm8, %xmm8
+        andl      $32640, %edx
+        lea       __slog2_ha_CoutTab(%rip), %r10
+        shrl      $7, %edx
+        movss     %xmm2, -20(%rsp)
+        movss     .L_2il0floatpacket.84(%rip), %xmm2
+        movaps    %xmm2, %xmm1
+        movss     .L_2il0floatpacket.93(%rip), %xmm6
+        lea       -127(%rcx,%rdx), %r9d
+        movzwl    -18(%rsp), %ecx
+        andl      $-32641, %ecx
+        addl      $16256, %ecx
+        movw      %cx, -18(%rsp)
+        movss     -20(%rsp), %xmm3
+        movaps    %xmm3, %xmm0
+        addss     %xmm3, %xmm1
+        addss     .L_2il0floatpacket.83(%rip), %xmm0
+        cvtsi2ss  %r9d, %xmm8
+        movss     %xmm0, -24(%rsp)
+        movl      -24(%rsp), %edi
+        movss     %xmm1, -24(%rsp)
+        andl      $127, %edi
+        movss     -24(%rsp), %xmm7
+        subss     %xmm2, %xmm7
+        lea       (%rdi,%rdi,2), %r8d
+        movss     (%r10,%r8,4), %xmm5
+        subss     %xmm7, %xmm3
+        addss     4(%r10,%r8,4), %xmm8
+        mulss     %xmm5, %xmm7
+        mulss     %xmm3, %xmm5
+        subss     .L_2il0floatpacket.85(%rip), %xmm7
+        movaps    %xmm7, %xmm4
+        addss     %xmm7, %xmm8
+        addss     %xmm5, %xmm4
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.92(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.91(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.90(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.89(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.88(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.87(%rip), %xmm6
+        mulss     %xmm4, %xmm6
+        addss     .L_2il0floatpacket.86(%rip), %xmm6
+        mulss     %xmm6, %xmm7
+        mulss     %xmm5, %xmm6
+        addss     8(%r10,%r8,4), %xmm6
+        addss     %xmm5, %xmm6
+        addss     %xmm6, %xmm7
+        addss     %xmm7, %xmm8
+        movss     %xmm8, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        movss     .L_2il0floatpacket.85(%rip), %xmm0
+        mulss     %xmm0, %xmm1
+        movss     .L_2il0floatpacket.93(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.92(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.91(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.90(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.89(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.88(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.87(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     .L_2il0floatpacket.86(%rip), %xmm2
+        mulss     %xmm1, %xmm2
+        addss     %xmm1, %xmm2
+        movss     %xmm2, (%rsi)
+        ret
+
+.LBL_2_9:
+
+        ucomiss   %xmm1, %xmm2
+        jp        .LBL_2_10
+        je        .LBL_2_12
+
+.LBL_2_10:
+
+        divss     %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        movl      $1, %eax
+
+
+        ret
+
+.LBL_2_12:
+
+        movss     .L_2il0floatpacket.94(%rip), %xmm0
+        movl      $2, %eax
+        divss     %xmm1, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_13:
+
+        movb      3(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_15
+
+.LBL_2_14:
+
+        movss     (%rdi), %xmm0
+        mulss     %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_15:
+
+        testl     $8388607, (%rdi)
+        jne       .LBL_2_14
+
+
+        movl      $1, %eax
+        pxor      %xmm1, %xmm1
+        pxor      %xmm0, %xmm0
+        divss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_slog2_ha_cout_rare_internal,@function
+	.size	__svml_slog2_ha_cout_rare_internal,.-__svml_slog2_ha_cout_rare_internal
+..LN__svml_slog2_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_slog2_ha_data_internal_avx512:
+	.long	1065353216
+	.long	1064608768
+	.long	1063885824
+	.long	1063184384
+	.long	1062502400
+	.long	1061838848
+	.long	1061193728
+	.long	1060564992
+	.long	1059952640
+	.long	1059354624
+	.long	1058770944
+	.long	1058201600
+	.long	1057645568
+	.long	1057100800
+	.long	1056174080
+	.long	1055133696
+	.long	1054113792
+	.long	1053116416
+	.long	1052137472
+	.long	1051179008
+	.long	1050238976
+	.long	1049317376
+	.long	1048248320
+	.long	1046470656
+	.long	1044725760
+	.long	1043013632
+	.long	1041330176
+	.long	1039163392
+	.long	1035911168
+	.long	1032708096
+	.long	1027309568
+	.long	1018822656
+	.long	0
+	.long	3082083684
+	.long	890262383
+	.long	3073448373
+	.long	3058814943
+	.long	933352011
+	.long	3056977939
+	.long	3052757441
+	.long	3085997978
+	.long	3070703220
+	.long	931851714
+	.long	916473404
+	.long	3081224294
+	.long	938815082
+	.long	3055557319
+	.long	3083106903
+	.long	3050426335
+	.long	3083856513
+	.long	913737309
+	.long	3045697063
+	.long	3029223305
+	.long	3078533744
+	.long	3063765991
+	.long	927944704
+	.long	932711104
+	.long	3062847489
+	.long	3072621226
+	.long	3076855565
+	.long	3086857368
+	.long	3075299840
+	.long	937767542
+	.long	930849160
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	3199776222
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	1056326045
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	3208161851
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	848473495
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	1069066811
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.type	__svml_slog2_ha_data_internal_avx512,@object
+	.size	__svml_slog2_ha_data_internal_avx512,832
+	.align 32
+__slog2_ha_CoutTab:
+	.long	1136175680
+	.long	0
+	.long	0
+	.long	1135986583
+	.long	1018822656
+	.long	930849160
+	.long	1135809305
+	.long	1026916352
+	.long	941737263
+	.long	1135632026
+	.long	1032306688
+	.long	936581683
+	.long	1135466566
+	.long	1035100160
+	.long	929197062
+	.long	1135301106
+	.long	1037934592
+	.long	897678483
+	.long	1135135647
+	.long	1040498688
+	.long	3059980496
+	.long	1134982005
+	.long	1041852416
+	.long	908010313
+	.long	1134828364
+	.long	1043226624
+	.long	3073739761
+	.long	1134686541
+	.long	1044510720
+	.long	918631281
+	.long	1134538809
+	.long	1045868544
+	.long	3062817788
+	.long	1134402896
+	.long	1047134208
+	.long	3064656237
+	.long	1134266982
+	.long	1048416256
+	.long	3029590737
+	.long	1134131069
+	.long	1049145856
+	.long	903671587
+	.long	1134001065
+	.long	1049775616
+	.long	911388989
+	.long	1133876970
+	.long	1050384896
+	.long	3069885983
+	.long	1133752875
+	.long	1051001344
+	.long	3037530952
+	.long	1133634689
+	.long	1051596288
+	.long	3069922038
+	.long	1133516503
+	.long	1052198400
+	.long	3070222063
+	.long	1133404227
+	.long	1052776960
+	.long	919559368
+	.long	1133291951
+	.long	1053363200
+	.long	840060372
+	.long	1133185584
+	.long	1053924864
+	.long	915603033
+	.long	1133079217
+	.long	1054493184
+	.long	921334924
+	.long	1132978759
+	.long	1055036416
+	.long	896601826
+	.long	1132872392
+	.long	1055618048
+	.long	908913293
+	.long	1132777843
+	.long	1056141312
+	.long	3065728751
+	.long	1132677386
+	.long	1056702976
+	.long	909020429
+	.long	1132582837
+	.long	1057101312
+	.long	3048020321
+	.long	1132494198
+	.long	1057354752
+	.long	3038815896
+	.long	1132337219
+	.long	1057628160
+	.long	3068137421
+	.long	1132159940
+	.long	1057887232
+	.long	3069993595
+	.long	1131994480
+	.long	1058131456
+	.long	3054354312
+	.long	1131817202
+	.long	1058395904
+	.long	910223436
+	.long	1131651742
+	.long	1058645504
+	.long	3046952660
+	.long	1131486282
+	.long	1058897664
+	.long	3057670844
+	.long	1131332641
+	.long	1059133952
+	.long	924929721
+	.long	1131178999
+	.long	1059373056
+	.long	3068093797
+	.long	1131025358
+	.long	1059614208
+	.long	3058851683
+	.long	1130871717
+	.long	1059857920
+	.long	3069897752
+	.long	1130729894
+	.long	1060084736
+	.long	924446297
+	.long	1130576253
+	.long	1060333312
+	.long	903058075
+	.long	1130434430
+	.long	1060564992
+	.long	3052757441
+	.long	1130304426
+	.long	1060779264
+	.long	3045479197
+	.long	1130162603
+	.long	1061015040
+	.long	924699798
+	.long	1130032599
+	.long	1061233664
+	.long	3070937808
+	.long	1129890776
+	.long	1061473792
+	.long	925912756
+	.long	1129772591
+	.long	1061676032
+	.long	923952205
+	.long	1129642586
+	.long	1061900544
+	.long	906547304
+	.long	1129512582
+	.long	1062127104
+	.long	3050351427
+	.long	1129394397
+	.long	1062334976
+	.long	3070601694
+	.long	1129276211
+	.long	1062544384
+	.long	900519722
+	.long	1129158025
+	.long	1062755840
+	.long	3055774932
+	.long	1129039840
+	.long	1062969088
+	.long	3053661845
+	.long	1128921654
+	.long	1063184384
+	.long	3073448373
+	.long	1128815287
+	.long	1063379456
+	.long	907090876
+	.long	1128697101
+	.long	1063598336
+	.long	881051555
+	.long	1128590734
+	.long	1063796992
+	.long	898320955
+	.long	1128484367
+	.long	1063997440
+	.long	3068804107
+	.long	1128378000
+	.long	1064199168
+	.long	923531617
+	.long	1128283452
+	.long	1064380416
+	.long	3070994608
+	.long	1128177085
+	.long	1064585472
+	.long	901920533
+	.long	1128082536
+	.long	1064769536
+	.long	3071653428
+	.long	1127976169
+	.long	1064977920
+	.long	903017594
+	.long	1127881621
+	.long	1065164800
+	.long	911713416
+	.long	1127787072
+	.long	1065353216
+	.long	0
+	.long	1065353216
+	.long	0
+	.long	1207959616
+	.long	1174405120
+	.long	1002438656
+	.long	1291845632
+	.long	0
+	.long	1065353216
+	.long	1136175680
+	.long	3212771328
+	.long	3065082383
+	.long	841219731
+	.long	2913632803
+	.long	691870088
+	.long	2765780188
+	.long	545377693
+	.long	2619180638
+	.type	__slog2_ha_CoutTab,@object
+	.size	__slog2_ha_CoutTab,848
+	.align 4
+.L_2il0floatpacket.81:
+	.long	0x4d000000
+	.type	.L_2il0floatpacket.81,@object
+	.size	.L_2il0floatpacket.81,4
+	.align 4
+.L_2il0floatpacket.82:
+	.long	0x3bc00000
+	.type	.L_2il0floatpacket.82,@object
+	.size	.L_2il0floatpacket.82,4
+	.align 4
+.L_2il0floatpacket.83:
+	.long	0x48000040
+	.type	.L_2il0floatpacket.83,@object
+	.size	.L_2il0floatpacket.83,4
+	.align 4
+.L_2il0floatpacket.84:
+	.long	0x46000000
+	.type	.L_2il0floatpacket.84,@object
+	.size	.L_2il0floatpacket.84,4
+	.align 4
+.L_2il0floatpacket.85:
+	.long	0x43b8aa40
+	.type	.L_2il0floatpacket.85,@object
+	.size	.L_2il0floatpacket.85,4
+	.align 4
+.L_2il0floatpacket.86:
+	.long	0xbf7f0000
+	.type	.L_2il0floatpacket.86,@object
+	.size	.L_2il0floatpacket.86,4
+	.align 4
+.L_2il0floatpacket.87:
+	.long	0xb6b1720f
+	.type	.L_2il0floatpacket.87,@object
+	.size	.L_2il0floatpacket.87,4
+	.align 4
+.L_2il0floatpacket.88:
+	.long	0x3223fe93
+	.type	.L_2il0floatpacket.88,@object
+	.size	.L_2il0floatpacket.88,4
+	.align 4
+.L_2il0floatpacket.89:
+	.long	0xadaa8223
+	.type	.L_2il0floatpacket.89,@object
+	.size	.L_2il0floatpacket.89,4
+	.align 4
+.L_2il0floatpacket.90:
+	.long	0x293d1988
+	.type	.L_2il0floatpacket.90,@object
+	.size	.L_2il0floatpacket.90,4
+	.align 4
+.L_2il0floatpacket.91:
+	.long	0xa4da74dc
+	.type	.L_2il0floatpacket.91,@object
+	.size	.L_2il0floatpacket.91,4
+	.align 4
+.L_2il0floatpacket.92:
+	.long	0x2081cd9d
+	.type	.L_2il0floatpacket.92,@object
+	.size	.L_2il0floatpacket.92,4
+	.align 4
+.L_2il0floatpacket.93:
+	.long	0x9c1d865e
+	.type	.L_2il0floatpacket.93,@object
+	.size	.L_2il0floatpacket.93,4
+	.align 4
+.L_2il0floatpacket.94:
+	.long	0xbf800000
+	.type	.L_2il0floatpacket.94,@object
+	.size	.L_2il0floatpacket.94,4
+	.align 4
+.L_2il0floatpacket.95:
+	.long	0x3f800000
+	.type	.L_2il0floatpacket.95,@object
+	.size	.L_2il0floatpacket.95,4
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_s_la.s
index 6ae3389d292..ac713d8f958 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log2_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_d_ha.s
new file mode 100644
index 00000000000..a7b621246a5
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_d_ha.s
@@ -0,0 +1,1165 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *  log(x) = -log(Rcp) + log(Rcp*x),
+ *  *    where Rcp ~ 1/x (accuracy ~9 bits, obtained by rounding HW approximation to 1+9 mantissa bits)
+ *  *
+ *  *   Reduced argument R=Rcp*x-1 is used to approximate log(1+R) as polynomial
+ *  *
+ *  *   log(Rcp) = exponent_Rcp*log(2) + log(mantissa_Rcp)
+ *  *     -log(mantissa_Rcp) is obtained from a lookup table, accessed by a 9-bit index
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_log8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_log8_ha
+
+__svml_log8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm6
+        vgetmantpd $8, {sae}, %zmm6, %zmm5
+        vmovups   256+__svml_dlog_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   320+__svml_dlog_ha_data_internal_avx512(%rip), %zmm9
+
+/* GetExp(x) */
+        vgetexppd {sae}, %zmm6, %zmm4
+
+/* Table lookup */
+        vmovups   __svml_dlog_ha_data_internal_avx512(%rip), %zmm2
+        vmovups   512+__svml_dlog_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   896+__svml_dlog_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   640+__svml_dlog_ha_data_internal_avx512(%rip), %zmm15
+        vmovups   384+__svml_dlog_ha_data_internal_avx512(%rip), %zmm12
+
+/* Start polynomial evaluation */
+        vmovups   448+__svml_dlog_ha_data_internal_avx512(%rip), %zmm1
+        vmovups   128+__svml_dlog_ha_data_internal_avx512(%rip), %zmm3
+
+/* DblRcp ~ 1/Mantissa */
+        vrcp14pd  %zmm5, %zmm7
+
+/* x<=0? */
+        vfpclasspd $223, %zmm6, %k2
+
+/* round DblRcp to 4 fractional bits (RN mode, no Precision exception) */
+        vrndscalepd $88, {sae}, %zmm7, %zmm8
+        vmovups   576+__svml_dlog_ha_data_internal_avx512(%rip), %zmm7
+        kmovw     %k2, %edx
+
+/* Reduced argument: R = DblRcp*Mantissa - 1 */
+        vfmsub213pd {rn-sae}, %zmm10, %zmm8, %zmm5
+
+/* Prepare exponent correction: DblRcp<0.75? */
+        vcmppd    $17, {sae}, %zmm9, %zmm8, %k1
+        vmovups   704+__svml_dlog_ha_data_internal_avx512(%rip), %zmm9
+        vfmadd231pd {rn-sae}, %zmm5, %zmm13, %zmm7
+        vmovups   768+__svml_dlog_ha_data_internal_avx512(%rip), %zmm13
+        vfmadd231pd {rn-sae}, %zmm5, %zmm12, %zmm1
+
+/* R^2 */
+        vmulpd    {rn-sae}, %zmm5, %zmm5, %zmm0
+        vfmadd231pd {rn-sae}, %zmm5, %zmm15, %zmm9
+
+/* Prepare table index */
+        vpsrlq    $48, %zmm8, %zmm11
+        vmovups   832+__svml_dlog_ha_data_internal_avx512(%rip), %zmm8
+        vfmadd213pd {rn-sae}, %zmm7, %zmm0, %zmm1
+        vmulpd    {rn-sae}, %zmm0, %zmm0, %zmm15
+        vfmadd231pd {rn-sae}, %zmm5, %zmm13, %zmm8
+
+/* add 1 to Expon if DblRcp<0.75 */
+        vaddpd    {rn-sae}, %zmm10, %zmm4, %zmm4{%k1}
+        vpermt2pd 64+__svml_dlog_ha_data_internal_avx512(%rip), %zmm11, %zmm2
+        vpermt2pd 192+__svml_dlog_ha_data_internal_avx512(%rip), %zmm11, %zmm3
+        vfmadd213pd {rn-sae}, %zmm8, %zmm0, %zmm9
+
+/* K*log(2)_high+Th */
+        vfmadd231pd {rn-sae}, %zmm4, %zmm14, %zmm2
+
+/* polynomial */
+        vfmadd213pd {rn-sae}, %zmm9, %zmm15, %zmm1
+
+/* K*log(2)_high+Th+Rh */
+        vaddpd    {rn-sae}, %zmm5, %zmm2, %zmm11
+
+/*
+ * Here we should use a zeroing mask (when available): VSUBMASKZ(D, Rh, GuardMask, High, Th)
+ * Rh
+ */
+        vsubpd    {rn-sae}, %zmm2, %zmm11, %zmm2
+        vxorpd    %zmm2, %zmm2, %zmm2{%k2}
+
+/*
+ * Rl
+ * When VSUBMASKZ is available, simply use VSUB(D, R, R, Rh) here
+ * VMSUB(D, R, GuardMask, R, Th);
+ */
+        vsubpd    {rn-sae}, %zmm2, %zmm5, %zmm10
+        vmovups   960+__svml_dlog_ha_data_internal_avx512(%rip), %zmm5
+        vfmadd213pd {rn-sae}, %zmm10, %zmm0, %zmm1
+
+/* K*log(2)_low + Tl */
+        vfmadd213pd {rn-sae}, %zmm3, %zmm5, %zmm4
+        vaddpd    {rn-sae}, %zmm4, %zmm1, %zmm0
+        vaddpd    {rn-sae}, %zmm0, %zmm11, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dlog_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_log8_ha,@function
+	.size	__svml_log8_ha,.-__svml_log8_ha
+..LN__svml_log8_ha.0:
+
+.L_2__routine_start___svml_dlog_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dlog_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        xorl      %eax, %eax
+        movzwl    6(%rdi), %edx
+        andl      $32752, %edx
+        cmpl      $32752, %edx
+        je        .LBL_2_12
+
+
+        movsd     (%rdi), %xmm2
+        xorl      %ecx, %ecx
+        movsd     %xmm2, -8(%rsp)
+        movzwl    -2(%rsp), %edx
+        testl     $32752, %edx
+        jne       .LBL_2_4
+
+
+        mulsd     1600+__dlog_ha_CoutTab(%rip), %xmm2
+        movl      $-60, %ecx
+        movsd     %xmm2, -8(%rsp)
+
+.LBL_2_4:
+
+        movsd     1608+__dlog_ha_CoutTab(%rip), %xmm0
+        comisd    %xmm0, %xmm2
+        jbe       .LBL_2_8
+
+
+        movsd     .L_2il0floatpacket.81(%rip), %xmm3
+        movaps    %xmm2, %xmm1
+        subsd     %xmm3, %xmm1
+        movsd     %xmm1, -16(%rsp)
+        andb      $127, -9(%rsp)
+        movsd     -16(%rsp), %xmm0
+        comisd    1592+__dlog_ha_CoutTab(%rip), %xmm0
+        jbe       .LBL_2_7
+
+
+        movsd     %xmm2, -16(%rsp)
+        pxor      %xmm7, %xmm7
+        movzwl    -10(%rsp), %edi
+        lea       __dlog_ha_CoutTab(%rip), %r10
+        andl      $-32753, %edi
+        addl      $16368, %edi
+        movw      %di, -10(%rsp)
+        movsd     -16(%rsp), %xmm4
+        movaps    %xmm4, %xmm1
+        movaps    %xmm4, %xmm2
+        movsd     1672+__dlog_ha_CoutTab(%rip), %xmm8
+        movzwl    -2(%rsp), %edx
+        andl      $32752, %edx
+        addsd     1576+__dlog_ha_CoutTab(%rip), %xmm1
+        addsd     1584+__dlog_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm1, -24(%rsp)
+        movl      -24(%rsp), %r8d
+        movsd     %xmm2, -24(%rsp)
+        andl      $127, %r8d
+        movsd     -24(%rsp), %xmm5
+        movsd     1560+__dlog_ha_CoutTab(%rip), %xmm9
+        movsd     1568+__dlog_ha_CoutTab(%rip), %xmm0
+        shrl      $4, %edx
+        subsd     1584+__dlog_ha_CoutTab(%rip), %xmm5
+        lea       (%r8,%r8,2), %r9d
+        movsd     (%r10,%r9,8), %xmm6
+        lea       -1023(%rcx,%rdx), %ecx
+        cvtsi2sd  %ecx, %xmm7
+        subsd     %xmm5, %xmm4
+        mulsd     %xmm6, %xmm5
+        mulsd     %xmm7, %xmm9
+        subsd     %xmm3, %xmm5
+        mulsd     %xmm4, %xmm6
+        mulsd     %xmm0, %xmm7
+        addsd     8(%r10,%r9,8), %xmm9
+        addsd     16(%r10,%r9,8), %xmm7
+        addsd     %xmm5, %xmm9
+        addsd     %xmm6, %xmm7
+        movaps    %xmm5, %xmm3
+        addsd     %xmm6, %xmm3
+        mulsd     %xmm3, %xmm8
+        addsd     1664+__dlog_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1656+__dlog_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1648+__dlog_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1640+__dlog_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1632+__dlog_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     1624+__dlog_ha_CoutTab(%rip), %xmm8
+        mulsd     %xmm3, %xmm8
+        mulsd     %xmm3, %xmm8
+        addsd     %xmm7, %xmm8
+        addsd     %xmm8, %xmm9
+        movsd     %xmm9, (%rsi)
+        ret
+
+.LBL_2_7:
+
+        movsd     1672+__dlog_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1664+__dlog_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1656+__dlog_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1648+__dlog_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1640+__dlog_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1632+__dlog_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     1624+__dlog_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm1, %xmm0
+        mulsd     %xmm1, %xmm0
+        addsd     %xmm1, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        ucomisd   %xmm0, %xmm2
+        jp        .LBL_2_9
+        je        .LBL_2_11
+
+.LBL_2_9:
+
+        divsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        movl      $1, %eax
+
+
+        ret
+
+.LBL_2_11:
+
+        movsd     1616+__dlog_ha_CoutTab(%rip), %xmm1
+        movl      $2, %eax
+        xorps     .L_2il0floatpacket.80(%rip), %xmm1
+        divsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_12:
+
+        movb      7(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_14
+
+.LBL_2_13:
+
+        movsd     (%rdi), %xmm0
+        mulsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_13
+
+
+        cmpl      $0, (%rdi)
+        jne       .LBL_2_13
+
+
+        movsd     1608+__dlog_ha_CoutTab(%rip), %xmm0
+        movl      $1, %eax
+        divsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dlog_ha_cout_rare_internal,@function
+	.size	__svml_dlog_ha_cout_rare_internal,.-__svml_dlog_ha_cout_rare_internal
+..LN__svml_dlog_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dlog_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	3222405120
+	.long	3215919664
+	.long	1848311808
+	.long	3216910087
+	.long	1890025472
+	.long	3217424176
+	.long	3348791296
+	.long	3217854455
+	.long	2880159744
+	.long	3218171740
+	.long	3256631296
+	.long	3218366859
+	.long	4139499520
+	.long	3218553303
+	.long	288669696
+	.long	1070754146
+	.long	1823703040
+	.long	1070569756
+	.long	1015742464
+	.long	1070240749
+	.long	1800667136
+	.long	1069924160
+	.long	2183659520
+	.long	1069619086
+	.long	1566113792
+	.long	1069101918
+	.long	3047030784
+	.long	1068533144
+	.long	2313682944
+	.long	1067467101
+	.long	0
+	.long	0
+	.long	3496399314
+	.long	1028893491
+	.long	720371772
+	.long	1026176044
+	.long	1944193543
+	.long	3175338952
+	.long	634920691
+	.long	3175752108
+	.long	1664625295
+	.long	1029304828
+	.long	192624563
+	.long	3177103997
+	.long	3796653051
+	.long	3176138396
+	.long	1005287031
+	.long	1029411448
+	.long	3433090547
+	.long	1029291198
+	.long	1779090477
+	.long	3176336968
+	.long	3242817150
+	.long	1029626109
+	.long	3430202884
+	.long	3175842849
+	.long	2270669051
+	.long	1028809259
+	.long	1375653371
+	.long	1028457284
+	.long	4246237509
+	.long	3176626033
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	0
+	.long	1072168960
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	815627376
+	.long	1069318605
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2123607906
+	.long	3217033013
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	2632876527
+	.long	1069697314
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	4213758726
+	.long	3217380691
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	2580324188
+	.long	1070176665
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	787901
+	.long	3218079744
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	1431655526
+	.long	1070945621
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4294967238
+	.long	3219128319
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	4277796864
+	.long	1072049730
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	3164471296
+	.long	1031600026
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	0
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	4294967295
+	.long	2146435071
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.type	__svml_dlog_ha_data_internal_avx512,@object
+	.size	__svml_dlog_ha_data_internal_avx512,1216
+	.align 32
+__dlog_ha_CoutTab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1072660480
+	.long	1486880768
+	.long	1066410070
+	.long	1813744607
+	.long	3179892593
+	.long	0
+	.long	1072629760
+	.long	377487360
+	.long	1067416219
+	.long	919019713
+	.long	3179241129
+	.long	0
+	.long	1072599040
+	.long	1513619456
+	.long	1067944025
+	.long	874573033
+	.long	3178512940
+	.long	0
+	.long	1072570368
+	.long	3221749760
+	.long	1068427825
+	.long	4181665006
+	.long	3177478212
+	.long	0
+	.long	1072541696
+	.long	4162322432
+	.long	1068708823
+	.long	627020255
+	.long	1028629941
+	.long	0
+	.long	1072513024
+	.long	183107584
+	.long	1068957907
+	.long	2376703469
+	.long	1030233118
+	.long	0
+	.long	1072486400
+	.long	1053425664
+	.long	1069192557
+	.long	696277142
+	.long	1030474863
+	.long	0
+	.long	1072459776
+	.long	3996123136
+	.long	1069430535
+	.long	2630798680
+	.long	1028792016
+	.long	0
+	.long	1072435200
+	.long	3452764160
+	.long	1069600382
+	.long	624954044
+	.long	3177101741
+	.long	0
+	.long	1072409600
+	.long	207650816
+	.long	1069717971
+	.long	3272735636
+	.long	3175176575
+	.long	0
+	.long	1072386048
+	.long	2647228416
+	.long	1069827627
+	.long	3594228712
+	.long	1029303785
+	.long	0
+	.long	1072362496
+	.long	2712010752
+	.long	1069938736
+	.long	3653242769
+	.long	3176839013
+	.long	0
+	.long	1072338944
+	.long	374439936
+	.long	1070051337
+	.long	4072775574
+	.long	3176577495
+	.long	0
+	.long	1072316416
+	.long	3707174912
+	.long	1070160474
+	.long	1486946159
+	.long	1023930920
+	.long	0
+	.long	1072294912
+	.long	1443954688
+	.long	1070265993
+	.long	293532967
+	.long	3176278277
+	.long	0
+	.long	1072273408
+	.long	127762432
+	.long	1070372856
+	.long	3404145447
+	.long	3177023955
+	.long	0
+	.long	1072252928
+	.long	2053832704
+	.long	1070475911
+	.long	1575076358
+	.long	1029048544
+	.long	0
+	.long	1072232448
+	.long	3194093568
+	.long	1070580248
+	.long	1864169120
+	.long	1026866084
+	.long	0
+	.long	1072212992
+	.long	3917201408
+	.long	1070638340
+	.long	2362145246
+	.long	3175606197
+	.long	0
+	.long	1072193536
+	.long	3417112576
+	.long	1070689116
+	.long	70087871
+	.long	3174183577
+	.long	0
+	.long	1072175104
+	.long	4226777088
+	.long	1070737793
+	.long	1620410586
+	.long	3174700065
+	.long	0
+	.long	1072156672
+	.long	3168870400
+	.long	1070787042
+	.long	311238082
+	.long	1025781772
+	.long	0
+	.long	1072139264
+	.long	2150580224
+	.long	1070834092
+	.long	1664262457
+	.long	3175299224
+	.long	0
+	.long	1072120832
+	.long	4095672320
+	.long	1070884491
+	.long	1657121015
+	.long	3174674199
+	.long	0
+	.long	1072104448
+	.long	2595577856
+	.long	1070929805
+	.long	2014006823
+	.long	3175423830
+	.long	0
+	.long	1072087040
+	.long	3747176448
+	.long	1070978493
+	.long	144991708
+	.long	3171552042
+	.long	0
+	.long	1072070656
+	.long	1050435584
+	.long	1071024840
+	.long	3386227432
+	.long	1027876916
+	.long	0
+	.long	1072055296
+	.long	255516672
+	.long	1071068760
+	.long	2637594316
+	.long	1028049573
+	.long	0
+	.long	1072038912
+	.long	1640783872
+	.long	1071116120
+	.long	893247007
+	.long	1028452162
+	.long	0
+	.long	1072023552
+	.long	2940411904
+	.long	1071161011
+	.long	813240633
+	.long	1027664048
+	.long	0
+	.long	1072009216
+	.long	882917376
+	.long	1071203348
+	.long	2376597551
+	.long	3175828767
+	.long	0
+	.long	1071993856
+	.long	213966848
+	.long	1071249188
+	.long	2977204125
+	.long	1028350609
+	.long	0
+	.long	1071979520
+	.long	2921504768
+	.long	1071292428
+	.long	523218347
+	.long	1028007004
+	.long	0
+	.long	1071965184
+	.long	3186655232
+	.long	1071336119
+	.long	2352907891
+	.long	1026967097
+	.long	0
+	.long	1071951872
+	.long	2653364224
+	.long	1071377101
+	.long	2453418583
+	.long	3174349512
+	.long	0
+	.long	1071938560
+	.long	3759783936
+	.long	1071418487
+	.long	3685870403
+	.long	3175415611
+	.long	0
+	.long	1071925248
+	.long	2468364288
+	.long	1071460286
+	.long	1578908842
+	.long	3175510517
+	.long	0
+	.long	1071911936
+	.long	81903616
+	.long	1071502506
+	.long	770710269
+	.long	1026742353
+	.long	0
+	.long	1071899648
+	.long	2799321088
+	.long	1071541858
+	.long	3822266185
+	.long	1028434427
+	.long	0
+	.long	1071886336
+	.long	2142265344
+	.long	1071584911
+	.long	175901806
+	.long	3173871540
+	.long	0
+	.long	1071874048
+	.long	2944024576
+	.long	1071625048
+	.long	2747360403
+	.long	1027672159
+	.long	0
+	.long	1071862784
+	.long	3434301440
+	.long	1071653426
+	.long	4194662196
+	.long	3173893003
+	.long	0
+	.long	1071850496
+	.long	1547755520
+	.long	1071673870
+	.long	4248764681
+	.long	3172759087
+	.long	0
+	.long	1071839232
+	.long	4246986752
+	.long	1071692786
+	.long	2840205638
+	.long	3174430911
+	.long	0
+	.long	1071826944
+	.long	3418390528
+	.long	1071713619
+	.long	3041880823
+	.long	1025440860
+	.long	0
+	.long	1071816704
+	.long	4143093760
+	.long	1071731139
+	.long	2727587401
+	.long	3173965207
+	.long	0
+	.long	1071805440
+	.long	3121326080
+	.long	1071750582
+	.long	3173887692
+	.long	3174190163
+	.long	0
+	.long	1071794176
+	.long	1852893184
+	.long	1071770207
+	.long	3951060252
+	.long	1027348295
+	.long	0
+	.long	1071783936
+	.long	3636379648
+	.long	1071788208
+	.long	1684924001
+	.long	3174777086
+	.long	0
+	.long	1071773696
+	.long	516505600
+	.long	1071806366
+	.long	429181199
+	.long	3173211033
+	.long	0
+	.long	1071763456
+	.long	4186185728
+	.long	1071824681
+	.long	2044904577
+	.long	3174967132
+	.long	0
+	.long	1071753216
+	.long	877596672
+	.long	1071843159
+	.long	1396318105
+	.long	3173959727
+	.long	0
+	.long	1071742976
+	.long	2912784384
+	.long	1071861800
+	.long	448136789
+	.long	3174814192
+	.long	0
+	.long	1071733760
+	.long	3722825728
+	.long	1071878720
+	.long	714165913
+	.long	3173439560
+	.long	0
+	.long	1071723520
+	.long	2522374144
+	.long	1071897682
+	.long	3227240353
+	.long	3173394323
+	.long	0
+	.long	1071714304
+	.long	4165410816
+	.long	1071914895
+	.long	1365684961
+	.long	3174365060
+	.long	0
+	.long	1071705088
+	.long	3477135360
+	.long	1071932251
+	.long	368482985
+	.long	3174140821
+	.long	0
+	.long	1071695872
+	.long	2079455232
+	.long	1071949752
+	.long	1320576317
+	.long	1026822714
+	.long	0
+	.long	1071687680
+	.long	851795968
+	.long	1071965432
+	.long	3702467026
+	.long	1025224125
+	.long	0
+	.long	1071678464
+	.long	647743488
+	.long	1071983213
+	.long	772992109
+	.long	3174038459
+	.long	0
+	.long	1071670272
+	.long	26537984
+	.long	1071999146
+	.long	2360214276
+	.long	3174861275
+	.long	0
+	.long	1071661056
+	.long	1547061248
+	.long	1072017216
+	.long	2886781435
+	.long	1026423395
+	.long	0
+	.long	1071652864
+	.long	2854492160
+	.long	1072033410
+	.long	215631550
+	.long	1025638968
+	.long	0
+	.long	1071644672
+	.long	4277811200
+	.long	1072049730
+	.long	2479318832
+	.long	1026487127
+	.long	4277811200
+	.long	1072049730
+	.long	2479318832
+	.long	1026487127
+	.long	64
+	.long	1120927744
+	.long	0
+	.long	1094713344
+	.long	0
+	.long	1065615360
+	.long	0
+	.long	1135607808
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3219128320
+	.long	1431655955
+	.long	1070945621
+	.long	610
+	.long	3218079744
+	.long	2545118337
+	.long	1070176665
+	.long	1378399119
+	.long	3217380693
+	.long	612435357
+	.long	1069697472
+	.long	94536557
+	.long	3217031348
+	.type	__dlog_ha_CoutTab,@object
+	.size	__dlog_ha_CoutTab,1680
+	.align 16
+.L_2il0floatpacket.80:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.80,@object
+	.size	.L_2il0floatpacket.80,16
+	.align 8
+.L_2il0floatpacket.81:
+	.long	0x00000000,0x3ff00000
+	.type	.L_2il0floatpacket.81,@object
+	.size	.L_2il0floatpacket.81,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_d_la.s
index 86074e6033f..2d200843c78 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_s_ha.s
new file mode 100644
index 00000000000..05695a7e125
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_s_ha.s
@@ -0,0 +1,1397 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *
+ *  *   log(x) = exponent_x*log(2) + log(mantissa_x),         if mantissa_x<4/3
+ *  *   log(x) = (exponent_x+1)*log(2) + log(0.5*mantissa_x), if mantissa_x>4/3
+ *  *
+ *  *    R = mantissa_x - 1,     if mantissa_x<4/3
+ *  *    R = 0.5*mantissa_x - 1, if mantissa_x>4/3
+ *  *    |R|< 1/3
+ *  *
+ *  *    log(1+R) is approximated as a polynomial: degree 9 for 1-ulp, degree 7 for 4-ulp,
+ *  *    degree 3 for half-precision
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_logf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_logf16_ha
+
+__svml_logf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+
+/* branch-free */
+        xorl      %edx, %edx
+        vmovaps   %zmm0, %zmm6
+        vrcp14ps  %zmm6, %zmm1
+
+/* Table lookup */
+        vmovups   128+__svml_slog_ha_data_internal_avx512(%rip), %zmm2
+        vmovups   256+__svml_slog_ha_data_internal_avx512(%rip), %zmm3
+
+/* Start polynomial evaluation */
+        vmovups   768+__svml_slog_ha_data_internal_avx512(%rip), %zmm11
+        vpaddd    __svml_slog_ha_data_internal_avx512(%rip), %zmm1, %zmm4
+        vmovups   640+__svml_slog_ha_data_internal_avx512(%rip), %zmm1
+        vpandd    64+__svml_slog_ha_data_internal_avx512(%rip), %zmm4, %zmm7
+        vpsrld    $18, %zmm7, %zmm5
+        vgetexpps {sae}, %zmm7, %zmm10
+        vmovups   960+__svml_slog_ha_data_internal_avx512(%rip), %zmm4
+        vpermt2ps 192+__svml_slog_ha_data_internal_avx512(%rip), %zmm5, %zmm2
+        vpermt2ps 320+__svml_slog_ha_data_internal_avx512(%rip), %zmm5, %zmm3
+
+/* SglRcp is +/-0, +/-Inf, denormal or <0? */
+        vfpclassps $255, %zmm7, %k2
+        vmovups   896+__svml_slog_ha_data_internal_avx512(%rip), %zmm5
+
+/* Reduced argument: R = SglRcp*Mantissa - 1 */
+        vfmsub213ps {rn-sae}, %zmm1, %zmm6, %zmm7
+
+/* branch to long path if MRangeMask is set */
+        kortestw  %k2, %k2
+
+/* -K*log(2)_high+Th */
+        vfnmadd231ps {rn-sae}, %zmm10, %zmm5, %zmm2
+
+/* -K*log(2)_low + Tl */
+        vfnmadd213ps {rn-sae}, %zmm3, %zmm4, %zmm10
+        vmovups   704+__svml_slog_ha_data_internal_avx512(%rip), %zmm3
+        vmulps    {rn-sae}, %zmm7, %zmm7, %zmm9
+        vfmadd231ps {rn-sae}, %zmm7, %zmm3, %zmm11
+
+/*
+ * Set RangeMask if long path is needed for any elements
+ * K*log(2)_high+Th+Rh
+ */
+        vaddps    {rn-sae}, %zmm7, %zmm2, %zmm13
+
+/* Rh */
+        vsubps    {rn-sae}, %zmm2, %zmm13, %zmm8
+        vmovups   832+__svml_slog_ha_data_internal_avx512(%rip), %zmm2
+
+/* Rl */
+        vsubps    {rn-sae}, %zmm8, %zmm7, %zmm12
+        vfmadd213ps {rn-sae}, %zmm2, %zmm7, %zmm11
+
+/* P + Tl */
+        vfmadd213ps {rn-sae}, %zmm10, %zmm9, %zmm11
+        vaddps    {rn-sae}, %zmm12, %zmm11, %zmm14
+        vaddps    {rn-sae}, %zmm14, %zmm13, %zmm0
+        jne       .LBL_1_12
+
+.LBL_1_2:
+
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_4:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_slog_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+
+/* GetMant(x), normalized to [1,2) for x>=0, NaN for x<0 */
+        vgetmantps $10, {sae}, %zmm6, %zmm8
+
+/* GetExp(x) */
+        vgetexpps {sae}, %zmm6, %zmm11
+
+/* Table lookup */
+        vmovups   384+__svml_slog_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   512+__svml_slog_ha_data_internal_avx512(%rip), %zmm13
+
+/* SglRcp ~ 1/Mantissa */
+        vrcp14ps  %zmm8, %zmm7
+
+/* x<=0? */
+        vfpclassps $223, %zmm6, %k1
+
+/* round SglRcp to 5 fractional bits (RN mode, no Precision exception) */
+        vrndscaleps $88, {sae}, %zmm7, %zmm9
+        kmovw     %k1, %edx
+        vgetexpps {sae}, %zmm9, %zmm12
+
+/* Reduced argument: R = SglRcp*Mantissa - 1 */
+        vfmsub231ps {rn-sae}, %zmm9, %zmm8, %zmm1
+
+/* Prepare table index */
+        vpsrld    $18, %zmm9, %zmm10
+
+/* K*log(2)_high+Th */
+        vsubps    {rn-sae}, %zmm12, %zmm11, %zmm15
+        vpermt2ps 448+__svml_slog_ha_data_internal_avx512(%rip), %zmm10, %zmm14
+        vpermt2ps 576+__svml_slog_ha_data_internal_avx512(%rip), %zmm10, %zmm13
+        vfmadd231ps {rn-sae}, %zmm15, %zmm5, %zmm14
+
+/* Start polynomial evaluation */
+        vmovups   768+__svml_slog_ha_data_internal_avx512(%rip), %zmm5
+
+/* K*log(2)_low + Tl */
+        vfmadd213ps {rn-sae}, %zmm13, %zmm4, %zmm15
+
+/* K*log(2)_high+Th+Rh */
+        vaddps    {rn-sae}, %zmm1, %zmm14, %zmm7
+        vfmadd231ps {rn-sae}, %zmm1, %zmm3, %zmm5
+        vmulps    {rn-sae}, %zmm1, %zmm1, %zmm3
+        vfmadd213ps {rn-sae}, %zmm2, %zmm1, %zmm5
+
+/*
+ * Here we should use a zeroing mask (when available): VSUBZM(S, Rh, GuardMask, High, Th)
+ * Rh
+ */
+        vsubps    {rn-sae}, %zmm14, %zmm7, %zmm4
+        vfmadd213ps {rn-sae}, %zmm15, %zmm3, %zmm5
+        vxorps    %zmm4, %zmm4, %zmm4{%k1}
+
+/*
+ * Rl
+ * When VSUBZM is available, simply use VSUB(S, R, R, Rh) here
+ */
+        vsubps    {rn-sae}, %zmm4, %zmm1, %zmm2
+        vaddps    {rn-sae}, %zmm2, %zmm5, %zmm1
+
+/* Store long path result under mask to avoid inconsistent results */
+        vaddps    {rn-sae}, %zmm1, %zmm7, %zmm0{%k2}
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_logf16_ha,@function
+	.size	__svml_logf16_ha,.-__svml_logf16_ha
+..LN__svml_logf16_ha.0:
+
+.L_2__routine_start___svml_slog_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_slog_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L61:
+
+        xorl      %eax, %eax
+        movzwl    2(%rdi), %edx
+        andl      $32640, %edx
+        cmpl      $32640, %edx
+        je        .LBL_2_12
+
+
+        pxor      %xmm2, %xmm2
+        xorl      %ecx, %ecx
+        cvtss2sd  (%rdi), %xmm2
+        movsd     %xmm2, -8(%rsp)
+        movzwl    -2(%rsp), %edx
+        testl     $32752, %edx
+        jne       .LBL_2_4
+
+
+        mulsd     1600+_imlsLnHATab(%rip), %xmm2
+        movl      $-60, %ecx
+        movsd     %xmm2, -8(%rsp)
+
+.LBL_2_4:
+
+        movsd     1608+_imlsLnHATab(%rip), %xmm0
+        comisd    %xmm0, %xmm2
+        jbe       .LBL_2_8
+
+
+        movsd     .L_2il0floatpacket.77(%rip), %xmm3
+        movaps    %xmm2, %xmm1
+        subsd     %xmm3, %xmm1
+        movsd     %xmm1, -16(%rsp)
+        andb      $127, -9(%rsp)
+        movsd     -16(%rsp), %xmm0
+        comisd    1592+_imlsLnHATab(%rip), %xmm0
+        jbe       .LBL_2_7
+
+
+        movsd     %xmm2, -16(%rsp)
+        pxor      %xmm6, %xmm6
+        movzwl    -10(%rsp), %edi
+        lea       _imlsLnHATab(%rip), %r10
+        andl      $-32753, %edi
+        addl      $16368, %edi
+        movw      %di, -10(%rsp)
+        movsd     -16(%rsp), %xmm4
+        movaps    %xmm4, %xmm1
+        movaps    %xmm4, %xmm2
+        movsd     1672+_imlsLnHATab(%rip), %xmm9
+        movzwl    -2(%rsp), %edx
+        andl      $32752, %edx
+        addsd     1576+_imlsLnHATab(%rip), %xmm1
+        addsd     1584+_imlsLnHATab(%rip), %xmm2
+        movsd     %xmm1, -24(%rsp)
+        movl      -24(%rsp), %r8d
+        movsd     %xmm2, -24(%rsp)
+        andl      $127, %r8d
+        movsd     -24(%rsp), %xmm7
+        movsd     1560+_imlsLnHATab(%rip), %xmm5
+        movsd     1568+_imlsLnHATab(%rip), %xmm0
+        shrl      $4, %edx
+        subsd     1584+_imlsLnHATab(%rip), %xmm7
+        lea       (%r8,%r8,2), %r9d
+        movsd     (%r10,%r9,8), %xmm8
+        lea       -1023(%rcx,%rdx), %ecx
+        cvtsi2sd  %ecx, %xmm6
+        subsd     %xmm7, %xmm4
+        mulsd     %xmm8, %xmm7
+        mulsd     %xmm6, %xmm5
+        subsd     %xmm3, %xmm7
+        mulsd     %xmm4, %xmm8
+        mulsd     %xmm0, %xmm6
+        addsd     8(%r10,%r9,8), %xmm5
+        addsd     16(%r10,%r9,8), %xmm6
+        movaps    %xmm7, %xmm3
+        addsd     %xmm8, %xmm3
+        mulsd     %xmm3, %xmm9
+        addsd     1664+_imlsLnHATab(%rip), %xmm9
+        mulsd     %xmm3, %xmm9
+        addsd     1656+_imlsLnHATab(%rip), %xmm9
+        mulsd     %xmm3, %xmm9
+        addsd     1648+_imlsLnHATab(%rip), %xmm9
+        mulsd     %xmm3, %xmm9
+        addsd     1640+_imlsLnHATab(%rip), %xmm9
+        mulsd     %xmm3, %xmm9
+        addsd     1632+_imlsLnHATab(%rip), %xmm9
+        mulsd     %xmm3, %xmm9
+        mulsd     %xmm3, %xmm3
+        addsd     1624+_imlsLnHATab(%rip), %xmm9
+        mulsd     %xmm3, %xmm9
+        addsd     %xmm5, %xmm9
+        addsd     %xmm6, %xmm9
+        addsd     %xmm7, %xmm9
+        addsd     %xmm8, %xmm9
+        cvtsd2ss  %xmm9, %xmm9
+        movss     %xmm9, (%rsi)
+        ret
+
+.LBL_2_7:
+
+        movsd     1672+_imlsLnHATab(%rip), %xmm2
+        movaps    %xmm1, %xmm0
+        mulsd     %xmm1, %xmm2
+        mulsd     %xmm1, %xmm0
+        addsd     1664+_imlsLnHATab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1656+_imlsLnHATab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1648+_imlsLnHATab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1640+_imlsLnHATab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1632+_imlsLnHATab(%rip), %xmm2
+        mulsd     %xmm1, %xmm2
+        addsd     1624+_imlsLnHATab(%rip), %xmm2
+        mulsd     %xmm0, %xmm2
+        addsd     %xmm1, %xmm2
+        cvtsd2ss  %xmm2, %xmm2
+        movss     %xmm2, (%rsi)
+        ret
+
+.LBL_2_8:
+
+        ucomisd   %xmm0, %xmm2
+        jp        .LBL_2_9
+        je        .LBL_2_11
+
+.LBL_2_9:
+
+        divsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        movl      $1, %eax
+
+
+        ret
+
+.LBL_2_11:
+
+        movsd     1616+_imlsLnHATab(%rip), %xmm1
+        movl      $2, %eax
+        xorps     .L_2il0floatpacket.76(%rip), %xmm1
+        divsd     %xmm0, %xmm1
+        cvtsd2ss  %xmm1, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_12:
+
+        movb      3(%rdi), %dl
+        andb      $-128, %dl
+        cmpb      $-128, %dl
+        je        .LBL_2_14
+
+.LBL_2_13:
+
+        movss     (%rdi), %xmm0
+        mulss     %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_14:
+
+        testl     $8388607, (%rdi)
+        jne       .LBL_2_13
+
+
+        movsd     1608+_imlsLnHATab(%rip), %xmm0
+        movl      $1, %eax
+        divsd     %xmm0, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_slog_ha_cout_rare_internal,@function
+	.size	__svml_slog_ha_cout_rare_internal,.-__svml_slog_ha_cout_rare_internal
+..LN__svml_slog_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_slog_ha_data_internal_avx512:
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	131072
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	4294705152
+	.long	0
+	.long	3170631680
+	.long	3178782720
+	.long	3182919680
+	.long	3186704384
+	.long	3189024768
+	.long	3190814720
+	.long	3192557568
+	.long	3194257408
+	.long	3195914240
+	.long	3196795904
+	.long	3197585408
+	.long	3198356480
+	.long	3199110144
+	.long	3199848448
+	.long	3200569344
+	.long	3201275904
+	.long	3201968128
+	.long	3202646016
+	.long	3203310592
+	.long	3203961856
+	.long	3204524544
+	.long	3204838400
+	.long	3205146112
+	.long	3205448192
+	.long	3205745152
+	.long	3206036992
+	.long	3206324224
+	.long	3206605824
+	.long	3206883328
+	.long	3207156224
+	.long	3207424512
+	.long	0
+	.long	3072770974
+	.long	929538039
+	.long	3075640037
+	.long	930648533
+	.long	3072716864
+	.long	3066151582
+	.long	3073718761
+	.long	897812054
+	.long	3057871602
+	.long	923619665
+	.long	921315575
+	.long	3057394118
+	.long	3078028640
+	.long	924853521
+	.long	3075349253
+	.long	3071259390
+	.long	906511159
+	.long	906200662
+	.long	917494258
+	.long	3061185264
+	.long	3071618351
+	.long	919715245
+	.long	3052715317
+	.long	3078288258
+	.long	3077608526
+	.long	3074005625
+	.long	930702671
+	.long	3070133351
+	.long	913590776
+	.long	924241186
+	.long	3021499198
+	.long	1060205056
+	.long	1059688960
+	.long	1059187712
+	.long	1058701824
+	.long	1058229248
+	.long	1057769472
+	.long	1057321984
+	.long	1056807936
+	.long	1055958016
+	.long	1055129600
+	.long	1054320640
+	.long	1053531136
+	.long	1052760064
+	.long	1052006400
+	.long	1051268096
+	.long	1050547200
+	.long	1049840640
+	.long	1049148416
+	.long	1048365056
+	.long	1047035904
+	.long	1045733376
+	.long	1044455424
+	.long	1043200000
+	.long	1041969152
+	.long	1040760832
+	.long	1038958592
+	.long	1036623872
+	.long	1034330112
+	.long	1032073216
+	.long	1027907584
+	.long	1023541248
+	.long	1015087104
+	.long	901758606
+	.long	3071200204
+	.long	931108809
+	.long	3074069268
+	.long	3077535321
+	.long	3071146094
+	.long	3063010043
+	.long	3072147991
+	.long	908173938
+	.long	3049723733
+	.long	925190435
+	.long	923601997
+	.long	3048768765
+	.long	3076457870
+	.long	926424291
+	.long	3073778483
+	.long	3069146713
+	.long	912794238
+	.long	912483742
+	.long	920635797
+	.long	3054902185
+	.long	3069864633
+	.long	922801832
+	.long	3033791132
+	.long	3076717488
+	.long	3076037756
+	.long	3072434855
+	.long	3077481184
+	.long	3066991812
+	.long	917116064
+	.long	925811956
+	.long	900509991
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	3196061712
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	1051373854
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	3204448256
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	8388608
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	2139095039
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	124
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	262144
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	4294443008
+	.long	0
+	.long	3178782720
+	.long	3186704384
+	.long	3190814720
+	.long	3194257408
+	.long	3196795904
+	.long	3198356480
+	.long	3199848448
+	.long	3201275904
+	.long	3202646016
+	.long	3203961856
+	.long	3204838400
+	.long	3205448192
+	.long	3206036992
+	.long	3206605824
+	.long	3207156224
+	.long	0
+	.long	929538039
+	.long	930648533
+	.long	3066151582
+	.long	897812054
+	.long	923619665
+	.long	3057394118
+	.long	924853521
+	.long	3071259390
+	.long	906200662
+	.long	3061185264
+	.long	919715245
+	.long	3078288258
+	.long	3074005625
+	.long	3070133351
+	.long	924241186
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	1060205056
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	901758606
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	1045236958
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	3196066480
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	1051372199
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.long	3204448254
+	.type	__svml_slog_ha_data_internal_avx512,@object
+	.size	__svml_slog_ha_data_internal_avx512,1920
+	.align 32
+_imlsLnHATab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1072660480
+	.long	1486880768
+	.long	1066410070
+	.long	1813744607
+	.long	3179892593
+	.long	0
+	.long	1072629760
+	.long	377487360
+	.long	1067416219
+	.long	919019713
+	.long	3179241129
+	.long	0
+	.long	1072599040
+	.long	1513619456
+	.long	1067944025
+	.long	874573033
+	.long	3178512940
+	.long	0
+	.long	1072570368
+	.long	3221749760
+	.long	1068427825
+	.long	4181665006
+	.long	3177478212
+	.long	0
+	.long	1072541696
+	.long	4162322432
+	.long	1068708823
+	.long	627020255
+	.long	1028629941
+	.long	0
+	.long	1072513024
+	.long	183107584
+	.long	1068957907
+	.long	2376703469
+	.long	1030233118
+	.long	0
+	.long	1072486400
+	.long	1053425664
+	.long	1069192557
+	.long	696277142
+	.long	1030474863
+	.long	0
+	.long	1072459776
+	.long	3996123136
+	.long	1069430535
+	.long	2630798680
+	.long	1028792016
+	.long	0
+	.long	1072435200
+	.long	3452764160
+	.long	1069600382
+	.long	624954044
+	.long	3177101741
+	.long	0
+	.long	1072409600
+	.long	207650816
+	.long	1069717971
+	.long	3272735636
+	.long	3175176575
+	.long	0
+	.long	1072386048
+	.long	2647228416
+	.long	1069827627
+	.long	3594228712
+	.long	1029303785
+	.long	0
+	.long	1072362496
+	.long	2712010752
+	.long	1069938736
+	.long	3653242769
+	.long	3176839013
+	.long	0
+	.long	1072338944
+	.long	374439936
+	.long	1070051337
+	.long	4072775574
+	.long	3176577495
+	.long	0
+	.long	1072316416
+	.long	3707174912
+	.long	1070160474
+	.long	1486946159
+	.long	1023930920
+	.long	0
+	.long	1072294912
+	.long	1443954688
+	.long	1070265993
+	.long	293532967
+	.long	3176278277
+	.long	0
+	.long	1072273408
+	.long	127762432
+	.long	1070372856
+	.long	3404145447
+	.long	3177023955
+	.long	0
+	.long	1072252928
+	.long	2053832704
+	.long	1070475911
+	.long	1575076358
+	.long	1029048544
+	.long	0
+	.long	1072232448
+	.long	3194093568
+	.long	1070580248
+	.long	1864169120
+	.long	1026866084
+	.long	0
+	.long	1072212992
+	.long	3917201408
+	.long	1070638340
+	.long	2362145246
+	.long	3175606197
+	.long	0
+	.long	1072193536
+	.long	3417112576
+	.long	1070689116
+	.long	70087871
+	.long	3174183577
+	.long	0
+	.long	1072175104
+	.long	4226777088
+	.long	1070737793
+	.long	1620410586
+	.long	3174700065
+	.long	0
+	.long	1072156672
+	.long	3168870400
+	.long	1070787042
+	.long	311238082
+	.long	1025781772
+	.long	0
+	.long	1072139264
+	.long	2150580224
+	.long	1070834092
+	.long	1664262457
+	.long	3175299224
+	.long	0
+	.long	1072120832
+	.long	4095672320
+	.long	1070884491
+	.long	1657121015
+	.long	3174674199
+	.long	0
+	.long	1072104448
+	.long	2595577856
+	.long	1070929805
+	.long	2014006823
+	.long	3175423830
+	.long	0
+	.long	1072087040
+	.long	3747176448
+	.long	1070978493
+	.long	144991708
+	.long	3171552042
+	.long	0
+	.long	1072070656
+	.long	1050435584
+	.long	1071024840
+	.long	3386227432
+	.long	1027876916
+	.long	0
+	.long	1072055296
+	.long	255516672
+	.long	1071068760
+	.long	2637594316
+	.long	1028049573
+	.long	0
+	.long	1072038912
+	.long	1640783872
+	.long	1071116120
+	.long	893247007
+	.long	1028452162
+	.long	0
+	.long	1072023552
+	.long	2940411904
+	.long	1071161011
+	.long	813240633
+	.long	1027664048
+	.long	0
+	.long	1072009216
+	.long	882917376
+	.long	1071203348
+	.long	2376597551
+	.long	3175828767
+	.long	0
+	.long	1071993856
+	.long	213966848
+	.long	1071249188
+	.long	2977204125
+	.long	1028350609
+	.long	0
+	.long	1071979520
+	.long	2921504768
+	.long	1071292428
+	.long	523218347
+	.long	1028007004
+	.long	0
+	.long	1071965184
+	.long	3186655232
+	.long	1071336119
+	.long	2352907891
+	.long	1026967097
+	.long	0
+	.long	1071951872
+	.long	2653364224
+	.long	1071377101
+	.long	2453418583
+	.long	3174349512
+	.long	0
+	.long	1071938560
+	.long	3759783936
+	.long	1071418487
+	.long	3685870403
+	.long	3175415611
+	.long	0
+	.long	1071925248
+	.long	2468364288
+	.long	1071460286
+	.long	1578908842
+	.long	3175510517
+	.long	0
+	.long	1071911936
+	.long	81903616
+	.long	1071502506
+	.long	770710269
+	.long	1026742353
+	.long	0
+	.long	1071899648
+	.long	2799321088
+	.long	1071541858
+	.long	3822266185
+	.long	1028434427
+	.long	0
+	.long	1071886336
+	.long	2142265344
+	.long	1071584911
+	.long	175901806
+	.long	3173871540
+	.long	0
+	.long	1071874048
+	.long	2944024576
+	.long	1071625048
+	.long	2747360403
+	.long	1027672159
+	.long	0
+	.long	1071862784
+	.long	3434301440
+	.long	1071653426
+	.long	4194662196
+	.long	3173893003
+	.long	0
+	.long	1071850496
+	.long	1547755520
+	.long	1071673870
+	.long	4248764681
+	.long	3172759087
+	.long	0
+	.long	1071839232
+	.long	4246986752
+	.long	1071692786
+	.long	2840205638
+	.long	3174430911
+	.long	0
+	.long	1071826944
+	.long	3418390528
+	.long	1071713619
+	.long	3041880823
+	.long	1025440860
+	.long	0
+	.long	1071816704
+	.long	4143093760
+	.long	1071731139
+	.long	2727587401
+	.long	3173965207
+	.long	0
+	.long	1071805440
+	.long	3121326080
+	.long	1071750582
+	.long	3173887692
+	.long	3174190163
+	.long	0
+	.long	1071794176
+	.long	1852893184
+	.long	1071770207
+	.long	3951060252
+	.long	1027348295
+	.long	0
+	.long	1071783936
+	.long	3636379648
+	.long	1071788208
+	.long	1684924001
+	.long	3174777086
+	.long	0
+	.long	1071773696
+	.long	516505600
+	.long	1071806366
+	.long	429181199
+	.long	3173211033
+	.long	0
+	.long	1071763456
+	.long	4186185728
+	.long	1071824681
+	.long	2044904577
+	.long	3174967132
+	.long	0
+	.long	1071753216
+	.long	877596672
+	.long	1071843159
+	.long	1396318105
+	.long	3173959727
+	.long	0
+	.long	1071742976
+	.long	2912784384
+	.long	1071861800
+	.long	448136789
+	.long	3174814192
+	.long	0
+	.long	1071733760
+	.long	3722825728
+	.long	1071878720
+	.long	714165913
+	.long	3173439560
+	.long	0
+	.long	1071723520
+	.long	2522374144
+	.long	1071897682
+	.long	3227240353
+	.long	3173394323
+	.long	0
+	.long	1071714304
+	.long	4165410816
+	.long	1071914895
+	.long	1365684961
+	.long	3174365060
+	.long	0
+	.long	1071705088
+	.long	3477135360
+	.long	1071932251
+	.long	368482985
+	.long	3174140821
+	.long	0
+	.long	1071695872
+	.long	2079455232
+	.long	1071949752
+	.long	1320576317
+	.long	1026822714
+	.long	0
+	.long	1071687680
+	.long	851795968
+	.long	1071965432
+	.long	3702467026
+	.long	1025224125
+	.long	0
+	.long	1071678464
+	.long	647743488
+	.long	1071983213
+	.long	772992109
+	.long	3174038459
+	.long	0
+	.long	1071670272
+	.long	26537984
+	.long	1071999146
+	.long	2360214276
+	.long	3174861275
+	.long	0
+	.long	1071661056
+	.long	1547061248
+	.long	1072017216
+	.long	2886781435
+	.long	1026423395
+	.long	0
+	.long	1071652864
+	.long	2854492160
+	.long	1072033410
+	.long	215631550
+	.long	1025638968
+	.long	0
+	.long	1071644672
+	.long	4277811200
+	.long	1072049730
+	.long	2479318832
+	.long	1026487127
+	.long	4277811200
+	.long	1072049730
+	.long	2479318832
+	.long	1026487127
+	.long	64
+	.long	1120927744
+	.long	0
+	.long	1094713344
+	.long	0
+	.long	1065615360
+	.long	0
+	.long	1135607808
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3219128320
+	.long	1431655955
+	.long	1070945621
+	.long	610
+	.long	3218079744
+	.long	2545118337
+	.long	1070176665
+	.long	1378399119
+	.long	3217380693
+	.long	612435357
+	.long	1069697472
+	.long	94536557
+	.long	3217031348
+	.type	_imlsLnHATab,@object
+	.size	_imlsLnHATab,1680
+	.align 16
+.L_2il0floatpacket.76:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.76,@object
+	.size	.L_2il0floatpacket.76,16
+	.align 8
+.L_2il0floatpacket.77:
+	.long	0x00000000,0x3ff00000
+	.type	.L_2il0floatpacket.77,@object
+	.size	.L_2il0floatpacket.77,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_s_la.s
index 771d7b72535..b3999503c86 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_log_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_d_ha.s
new file mode 100644
index 00000000000..69d887b0d0e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_d_ha.s
@@ -0,0 +1,3595 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *       1) Calculating log2|x|
+ * 
+ *           Here we use the following formula.
+ *           Let |x|=2^k1*X1, where k1 is integer, 1<=X1<2.
+ *           Let C ~= 1/ln(2),
+ *           Rcp1 ~= 1/X1,   X2=Rcp1*X1,
+ *           Rcp2 ~= 1/X2,   X3=Rcp2*X2,
+ *           Rcp3 ~= 1/X3,   Rcp3C ~= C/X3.
+ *           Then
+ * 
+ *               log2|x| = k1 + log2(1/Rcp1) + log2(1/Rcp2) + log2(C/Rcp3C) +
+ *                       + log2(X1*Rcp1*Rcp2*Rcp3C/C),
+ * 
+ *           where X1*Rcp1*Rcp2*Rcp3C = C*(1+q), q is very small.
+ * 
+ *           The values of Rcp1, log2(1/Rcp1), Rcp2, log2(1/Rcp2),
+ *           Rcp3C, log2(C/Rcp3C) are taken from tables.
+ *           Values of Rcp1, Rcp2, Rcp3C are such that RcpC=Rcp1*Rcp2*Rcp3C
+ *           is exactly represented in target precision.
+ * 
+ *           log2(X1*Rcp1*Rcp2*Rcp3C/C) = log2(1+q) = ln(1+q)/ln2 =
+ *               = 1/(ln2)*q - 1/(2ln2)*q^2 + 1/(3ln2)*q^3 - ... =
+ *               = 1/(C*ln2)*cq - 1/(2*C^2*ln2)*cq^2 + 1/(3*C^3*ln2)*cq^3 - ... =
+ *               = (1 + a1)*cq + a2*cq^2 + a3*cq^3 + ...,
+ *           where
+ *               cq=X1*Rcp1*Rcp2*Rcp3C-C,
+ *               a1=1/(C*ln(2))-1 is small,
+ *               a2=1/(2*C^2*ln2),
+ *               a3=1/(3*C^3*ln2),
+ *               ...
+ *               We get 3 parts of log2 result: HH+HL+HLL ~= log2|x|.
+ * 
+ *           2)  Calculation of y*(HH+HL+HLL).
+ *               Split y into YHi+YLo.
+ *               Get high PH and medium PL parts of y*log2|x|.
+ *               Get low PLL part of y*log2|x|.
+ *               Now we have PH+PL+PLL ~= y*log2|x|.
+ * 
+ *           3) Calculation of 2^(PH+PL+PLL).
+ * 
+ *               Mathematical idea of computing 2^(PH+PL+PLL) is the following.
+ *               Let's represent PH+PL+PLL in the form N + j/2^expK + Z,
+ *               where expK=7 in this implementation, N and j are integers,
+ *               0<=j<=2^expK-1, |Z|<2^(-expK-1). Hence
+ * 
+ *                   2^(PH+PL+PLL) ~= 2^N * 2^(j/2^expK) * 2^Z,
+ * 
+ *               where 2^(j/2^expK) is stored in a table, and
+ * 
+ *                   2^Z ~= 1 + B1*Z + B2*Z^2 ... + B5*Z^5.
+ * 
+ *               We compute 2^(PH+PL+PLL) as follows.
+ * 
+ *               Break PH into PHH + PHL, where PHH = N + j/2^expK.
+ *               Z = PHL + PL + PLL
+ *               Exp2Poly = B1*Z + B2*Z^2 ... + B5*Z^5
+ *               Get 2^(j/2^expK) from table in the form THI+TLO.
+ *               Now we have 2^(PH+PL+PLL) ~= 2^N * (THI + TLO) * (1 + Exp2Poly).
+ * 
+ *               Get significand of 2^(PH+PL+PLL) in the form ResHi+ResLo:
+ *               ResHi := THI
+ *               ResLo := THI * Exp2Poly + TLO
+ * 
+ *               Get exponent ERes of the result:
+ *               Res := ResHi + ResLo:
+ *               Result := ex(Res) + N
+ * 
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_pow8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_pow8_ha
+
+__svml_pow8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $256, %rsp
+        vgetmantpd $10, {sae}, %zmm0, %zmm14
+        vmovups   768+__svml_dpow_ha_data_internal_avx512(%rip), %zmm12
+
+/* Prepare exponent correction: DblRcp<1.5?  -- was 0.75 in initial AVX3 version, which used Mantissa in [1,2) */
+        vmovups   832+__svml_dpow_ha_data_internal_avx512(%rip), %zmm3
+
+/* Table lookup */
+        vmovups   __svml_dpow_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   128+__svml_dpow_ha_data_internal_avx512(%rip), %zmm8
+
+/* GetExp(x) */
+        vgetexppd {sae}, %zmm0, %zmm11
+        vmovups   896+__svml_dpow_ha_data_internal_avx512(%rip), %zmm4
+        vmovups   256+__svml_dpow_ha_data_internal_avx512(%rip), %zmm7
+        vmovups   384+__svml_dpow_ha_data_internal_avx512(%rip), %zmm6
+
+/* DblRcp ~ 1/Mantissa */
+        vrcp14pd  %zmm14, %zmm15
+
+/* x<=0 or Inf/NaN? */
+        vfpclasspd $223, %zmm0, %k0
+
+/* round DblRcp to 5 fractional bits (RN mode, no Precision exception) */
+        vrndscalepd $88, {sae}, %zmm15, %zmm2
+        vmovups   1152+__svml_dpow_ha_data_internal_avx512(%rip), %zmm15
+        kmovw     %k0, %edx
+
+/* Prepare table index */
+        vpsrlq    $47, %zmm2, %zmm5
+        vcmppd    $17, {sae}, %zmm3, %zmm2, %k1
+
+/* P3_4 */
+        vmovups   1472+__svml_dpow_ha_data_internal_avx512(%rip), %zmm3
+        vpsllq    $59, %zmm5, %zmm9
+        vpermt2pd 64+__svml_dpow_ha_data_internal_avx512(%rip), %zmm5, %zmm10
+        vpermt2pd 192+__svml_dpow_ha_data_internal_avx512(%rip), %zmm5, %zmm8
+        vpmovq2m  %zmm9, %k2
+
+/* add 1 to Expon if DblRcp<1.5 */
+        vaddpd    {rn-sae}, %zmm4, %zmm11, %zmm11{%k1}
+        vpermt2pd 320+__svml_dpow_ha_data_internal_avx512(%rip), %zmm5, %zmm7
+        vpermt2pd 448+__svml_dpow_ha_data_internal_avx512(%rip), %zmm5, %zmm6
+
+/* P9_10 */
+        vmovups   1088+__svml_dpow_ha_data_internal_avx512(%rip), %zmm9
+        vmovups   1536+__svml_dpow_ha_data_internal_avx512(%rip), %zmm4
+        vmovaps   %zmm1, %zmm13
+
+/* Mantissa/2 */
+        vmulpd    {rn-sae}, %zmm12, %zmm14, %zmm1
+        vmovups   1024+__svml_dpow_ha_data_internal_avx512(%rip), %zmm14
+
+/* y Inf/NaN? */
+        vfpclasspd $153, %zmm13, %k3
+
+/* Reduced argument: R = (DblRcp*Mantissa - 1)*0.5 */
+        vfmsub231pd {rn-sae}, %zmm2, %zmm1, %zmm12
+
+/* P7_8 */
+        vmovups   1216+__svml_dpow_ha_data_internal_avx512(%rip), %zmm2
+        vblendmpd %zmm8, %zmm10, %zmm1{%k2}
+        vblendmpd %zmm6, %zmm7, %zmm10{%k2}
+
+/* R^2 */
+        vmulpd    {rn-sae}, %zmm12, %zmm12, %zmm8
+
+/* P5_6 */
+        vmovups   1344+__svml_dpow_ha_data_internal_avx512(%rip), %zmm7
+        vfmadd231pd {rn-sae}, %zmm12, %zmm14, %zmm9
+        vmovups   960+__svml_dpow_ha_data_internal_avx512(%rip), %zmm14
+        vfmadd231pd {rn-sae}, %zmm12, %zmm15, %zmm2
+
+/* P1_2 */
+        vmovups   1600+__svml_dpow_ha_data_internal_avx512(%rip), %zmm15
+
+/* Expon + Th */
+        vaddpd    {rn-sae}, %zmm11, %zmm1, %zmm6
+        vmovups   1280+__svml_dpow_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   1408+__svml_dpow_ha_data_internal_avx512(%rip), %zmm1
+
+/* P7_10 */
+        vfmadd213pd {rn-sae}, %zmm2, %zmm8, %zmm9
+        vfmadd231pd {rn-sae}, %zmm12, %zmm4, %zmm15
+        vfmadd231pd {rn-sae}, %zmm12, %zmm11, %zmm7
+        vfmadd231pd {rn-sae}, %zmm12, %zmm1, %zmm3
+        vmovups   1664+__svml_dpow_ha_data_internal_avx512(%rip), %zmm1
+
+/* R^4 */
+        vmulpd    {rn-sae}, %zmm8, %zmm8, %zmm11
+
+/* P3_6 */
+        vfmadd213pd {rn-sae}, %zmm3, %zmm8, %zmm7
+        kmovw     %k3, %eax
+
+/* P3_10 */
+        vfmadd213pd {rn-sae}, %zmm7, %zmm11, %zmm9
+
+/* P3_4 */
+        vmovups   1984+__svml_dpow_ha_data_internal_avx512(%rip), %zmm11
+
+/* P1_10 */
+        vfmadd213pd {rn-sae}, %zmm15, %zmm8, %zmm9
+
+/* P1_2 */
+        vmovups   2112+__svml_dpow_ha_data_internal_avx512(%rip), %zmm15
+
+/* Tl + R*P1_10 */
+        vfmadd213pd {rn-sae}, %zmm10, %zmm12, %zmm9
+
+/* Expon + Th + C1H*R_high */
+        vmovaps   %zmm6, %zmm5
+        orl       %eax, %edx
+        vfmadd231pd {rn-sae}, %zmm12, %zmm14, %zmm5
+
+/* Rh = (c1h*R)_high */
+        vsubpd    {rn-sae}, %zmm6, %zmm5, %zmm6
+
+/* Expon + Th + Rh_high - (R*Rh)_high */
+        vmovaps   %zmm6, %zmm4
+        vfnmadd213pd {rn-sae}, %zmm5, %zmm12, %zmm4
+
+/* Rl = (C1H*R)_low */
+        vfmsub213pd {rn-sae}, %zmm6, %zmm12, %zmm14
+
+/* (R*Rh)_high */
+        vsubpd    {rn-sae}, %zmm4, %zmm5, %zmm5
+
+/* Rl - R*Rl */
+        vfnmadd231pd {rn-sae}, %zmm12, %zmm14, %zmm14
+
+/* (R*Rh)_low */
+        vfmsub213pd {rn-sae}, %zmm5, %zmm6, %zmm12
+
+/* Rl - R*Rl - (R*Rh)_low */
+        vsubpd    {rn-sae}, %zmm12, %zmm14, %zmm7
+        vaddpd    {rn-sae}, %zmm7, %zmm9, %zmm8
+
+/* high part of log2(input1) */
+        vaddpd    {rn-sae}, %zmm8, %zmm4, %zmm10
+
+/* (y*High)_high */
+        vmulpd    {rz-sae}, %zmm13, %zmm10, %zmm12
+
+/* Tlh */
+        vsubpd    {rn-sae}, %zmm4, %zmm10, %zmm9
+
+/* Zl = (y*_HIGH)_low */
+        vfmsub213pd {rz-sae}, %zmm12, %zmm13, %zmm10
+
+/* Tll */
+        vsubpd    {rn-sae}, %zmm9, %zmm8, %zmm4
+        vmovups   1792+__svml_dpow_ha_data_internal_avx512(%rip), %zmm8
+        vmovups   1920+__svml_dpow_ha_data_internal_avx512(%rip), %zmm9
+
+/* Zl = y*Tll + Zl */
+        vfmadd213pd {rz-sae}, %zmm10, %zmm13, %zmm4
+        vmovups   2048+__svml_dpow_ha_data_internal_avx512(%rip), %zmm10
+        vaddpd    {rz-sae}, %zmm4, %zmm12, %zmm3
+        vsubpd    {rn-sae}, %zmm12, %zmm3, %zmm12
+        vaddpd    {rd-sae}, %zmm1, %zmm3, %zmm2
+
+/*
+ * /
+ * exp2 computation starts here
+ */
+        vreducepd $65, {sae}, %zmm3, %zmm5
+
+/* P5_6 */
+        vmovups   1856+__svml_dpow_ha_data_internal_avx512(%rip), %zmm1
+        vsubpd    {rn-sae}, %zmm12, %zmm4, %zmm6
+        vandpd    2368+__svml_dpow_ha_data_internal_avx512(%rip), %zmm3, %zmm3
+        vmovups   640+__svml_dpow_ha_data_internal_avx512(%rip), %zmm4
+        vaddpd    {rn-sae}, %zmm6, %zmm5, %zmm7
+
+/*
+ * scaled result
+ * Filter very large |y*log2(x)| and scale final result for LRB2
+ */
+        vmovups   2432+__svml_dpow_ha_data_internal_avx512(%rip), %zmm6
+
+/* Table lookup: The, Tle/The */
+        vmovups   512+__svml_dpow_ha_data_internal_avx512(%rip), %zmm5
+
+/* ensure |R|<2 even for special cases */
+        vandpd    1728+__svml_dpow_ha_data_internal_avx512(%rip), %zmm7, %zmm7
+        vpermt2pd 704+__svml_dpow_ha_data_internal_avx512(%rip), %zmm2, %zmm4
+        vpermt2pd 576+__svml_dpow_ha_data_internal_avx512(%rip), %zmm2, %zmm5
+
+/* Re^2 */
+        vmulpd    {rn-sae}, %zmm7, %zmm7, %zmm14
+        vcmppd    $22, {sae}, %zmm6, %zmm3, %k0
+        vfmadd231pd {rn-sae}, %zmm7, %zmm8, %zmm1
+        vfmadd231pd {rn-sae}, %zmm7, %zmm9, %zmm11
+        vfmadd231pd {rn-sae}, %zmm7, %zmm10, %zmm15
+        vpsllq    $48, %zmm2, %zmm2
+        vfmadd213pd {rn-sae}, %zmm11, %zmm14, %zmm1
+        vandpd    2496+__svml_dpow_ha_data_internal_avx512(%rip), %zmm2, %zmm2
+        kmovw     %k0, %ecx
+        vfmadd213pd {rn-sae}, %zmm15, %zmm14, %zmm1
+
+/* R*poly + The/Tle */
+        vfmadd213pd {rn-sae}, %zmm4, %zmm7, %zmm1
+        orl       %ecx, %edx
+
+/* The + The*(R*poly + The/Tle) */
+        vfmadd213pd {rn-sae}, %zmm5, %zmm5, %zmm1
+        vmulpd    {rn-sae}, %zmm2, %zmm1, %zmm1
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        vmovaps   %zmm1, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm13, 128(%rsp)
+        vmovups   %zmm1, 192(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   192(%rsp), %zmm1
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+        lea       192(%rsp,%r12,8), %rdx
+
+        call      __svml_dpow_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_pow8_ha,@function
+	.size	__svml_pow8_ha,.-__svml_pow8_ha
+..LN__svml_pow8_ha.0:
+
+.L_2__routine_start___svml_dpow_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dpow_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        pushq     %r12
+	.cfi_def_cfa_offset 16
+	.cfi_offset 12, -16
+        pushq     %r13
+	.cfi_def_cfa_offset 24
+	.cfi_offset 13, -24
+        pushq     %r15
+	.cfi_def_cfa_offset 32
+	.cfi_offset 15, -32
+        pushq     %rbx
+	.cfi_def_cfa_offset 40
+	.cfi_offset 3, -40
+        movq      %rdx, %r8
+        movsd     6824+__dpow_ha_CoutTab(%rip), %xmm1
+        xorl      %eax, %eax
+        movsd     (%rdi), %xmm3
+        movsd     (%rsi), %xmm0
+        mulsd     %xmm1, %xmm3
+        mulsd     %xmm1, %xmm0
+        movsd     %xmm3, -72(%rsp)
+        movsd     %xmm0, -16(%rsp)
+        movzwl    -66(%rsp), %r13d
+        movzwl    -10(%rsp), %edx
+        andl      $32752, %r13d
+        movzbl    -65(%rsp), %r12d
+        andl      $32752, %edx
+        movb      -9(%rsp), %bl
+        andl      $128, %r12d
+        andb      $-128, %bl
+        shrl      $4, %r13d
+        shrl      $4, %edx
+        shrl      $7, %r12d
+        shrb      $7, %bl
+        testl     $1048575, -68(%rsp)
+        jne       .LBL_2_4
+
+
+        cmpl      $0, -72(%rsp)
+        jne       .LBL_2_4
+
+
+        movb      $1, %r11b
+        jmp       .LBL_2_5
+
+.LBL_2_4:
+
+        xorb      %r11b, %r11b
+
+.LBL_2_5:
+
+        movl      -12(%rsp), %ecx
+        movl      -16(%rsp), %edi
+        andl      $1048575, %ecx
+        jne       .LBL_2_8
+
+
+        testl     %edi, %edi
+        jne       .LBL_2_8
+
+
+        movl      $1, %r10d
+        jmp       .LBL_2_9
+
+.LBL_2_8:
+
+        xorl      %r10d, %r10d
+
+.LBL_2_9:
+
+        movl      %edx, %r9d
+        xorl      %esi, %esi
+        cmpl      $2047, %edx
+        setne     %sil
+        shll      $20, %r9d
+        orl       %ecx, %r9d
+        movl      %edi, %ecx
+        orl       %r9d, %ecx
+        je        .LBL_2_24
+
+
+        lea       -1023(%rdx), %ecx
+        cmpl      $1023, %edx
+        jl        .LBL_2_23
+
+
+        testl     %esi, %esi
+        je        .LBL_2_23
+
+
+        cmpl      $20, %ecx
+        jg        .LBL_2_16
+
+
+        movl      %r9d, %r15d
+        shll      %cl, %r15d
+        shll      $12, %r15d
+        orl       %edi, %r15d
+        je        .LBL_2_15
+
+.LBL_2_14:
+
+        xorl      %r9d, %r9d
+        jmp       .LBL_2_21
+
+.LBL_2_15:
+
+        lea       -1012(%rdx), %ecx
+        shll      %cl, %r9d
+        notl      %r9d
+        shrl      $31, %r9d
+        incl      %r9d
+        jmp       .LBL_2_21
+
+.LBL_2_16:
+
+        cmpl      $53, %ecx
+        jge       .LBL_2_20
+
+
+        lea       -1012(%rdx), %ecx
+        shll      %cl, %edi
+        testl     $2147483647, %edi
+        jne       .LBL_2_14
+
+
+        notl      %edi
+        shrl      $31, %edi
+        lea       1(%rdi), %r9d
+        jmp       .LBL_2_21
+
+.LBL_2_20:
+
+        movl      $2, %r9d
+
+.LBL_2_21:
+
+        testl     %r12d, %r12d
+        jne       .LBL_2_29
+
+
+        cmpl      $1023, %r13d
+        jne       .LBL_2_29
+        jmp       .LBL_2_57
+
+.LBL_2_23:
+
+        xorl      %r9d, %r9d
+        jmp       .LBL_2_25
+
+.LBL_2_24:
+
+        movl      $2, %r9d
+
+.LBL_2_25:
+
+        testl     %r12d, %r12d
+        jne       .LBL_2_27
+
+
+        cmpl      $1023, %r13d
+        je        .LBL_2_74
+
+.LBL_2_27:
+
+        testl     %edx, %edx
+        jne       .LBL_2_29
+
+
+        testl     %r10d, %r10d
+        jne       .LBL_2_55
+
+.LBL_2_29:
+
+        cmpl      $2047, %r13d
+        je        .LBL_2_31
+
+.LBL_2_30:
+
+        movb      $1, %cl
+        jmp       .LBL_2_32
+
+.LBL_2_31:
+
+        xorb      %cl, %cl
+
+.LBL_2_32:
+
+        orb       %cl, %r11b
+        je        .LBL_2_54
+
+
+        orl       %esi, %r10d
+        je        .LBL_2_54
+
+
+        movsd     6816+__dpow_ha_CoutTab(%rip), %xmm2
+        ucomisd   %xmm2, %xmm3
+        jp        .LBL_2_35
+        je        .LBL_2_71
+
+.LBL_2_35:
+
+        ucomisd   6832+__dpow_ha_CoutTab(%rip), %xmm3
+        jp        .LBL_2_36
+        je        .LBL_2_68
+
+.LBL_2_36:
+
+        testb     %cl, %cl
+        je        .LBL_2_47
+
+
+        testl     %esi, %esi
+        je        .LBL_2_47
+
+
+        comisd    %xmm2, %xmm3
+        ja        .LBL_2_40
+
+
+        testl     %r9d, %r9d
+        je        .LBL_2_46
+
+.LBL_2_40:
+
+        lea       6824+__dpow_ha_CoutTab(%rip), %rcx
+        andl      %r12d, %r9d
+        movsd     %xmm3, -72(%rsp)
+        andb      $127, -65(%rsp)
+        movsd     (%rcx,%r9,8), %xmm0
+        xorl      %ecx, %ecx
+        testl     %r13d, %r13d
+        jne       .LBL_2_42
+
+
+        movsd     -72(%rsp), %xmm2
+        movl      $-200, %ecx
+        mulsd     6864+__dpow_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm2, -72(%rsp)
+        jmp       .LBL_2_43
+
+.LBL_2_42:
+
+        movsd     -72(%rsp), %xmm2
+
+.LBL_2_43:
+
+        movzwl    -66(%rsp), %esi
+        pxor      %xmm7, %xmm7
+        andl      $32752, %esi
+        shrl      $4, %esi
+        movl      -68(%rsp), %r9d
+        shll      $20, %esi
+        andl      $1048575, %r9d
+        movsd     %xmm2, -56(%rsp)
+        orl       %r9d, %esi
+        movzwl    -50(%rsp), %edi
+        addl      $-1072152576, %esi
+        andl      $-32753, %edi
+        addl      $16368, %edi
+        movw      %di, -50(%rsp)
+        sarl      $20, %esi
+        movl      -52(%rsp), %r10d
+        addl      %ecx, %esi
+        lea       __dpow_ha_CoutTab(%rip), %rcx
+        andl      $1032192, %r10d
+        addl      $16384, %r10d
+        shrl      $15, %r10d
+        movsd     -56(%rsp), %xmm2
+        movsd     (%rcx,%r10,8), %xmm5
+        addl      %r10d, %r10d
+        movaps    %xmm5, %xmm6
+        movsd     6856+__dpow_ha_CoutTab(%rip), %xmm14
+        mulsd     %xmm2, %xmm6
+        cvtsi2sd  %esi, %xmm7
+        mulsd     %xmm2, %xmm14
+        addsd     264(%rcx,%r10,8), %xmm7
+        movsd     %xmm6, -48(%rsp)
+        movsd     %xmm14, -32(%rsp)
+        movl      -44(%rsp), %r11d
+        andl      $64512, %r11d
+        movsd     -32(%rsp), %xmm15
+        addl      $1024, %r11d
+        shrl      $11, %r11d
+        subsd     -56(%rsp), %xmm15
+        movsd     792(%rcx,%r11,8), %xmm12
+        addl      %r11d, %r11d
+        mulsd     %xmm12, %xmm6
+        addsd     1056(%rcx,%r11,8), %xmm7
+        mulsd     %xmm12, %xmm5
+        movsd     %xmm15, -24(%rsp)
+        movsd     -32(%rsp), %xmm4
+        movsd     -24(%rsp), %xmm3
+        movsd     %xmm6, -40(%rsp)
+        subsd     %xmm3, %xmm4
+        movl      -36(%rsp), %r12d
+        andl      $4080, %r12d
+        addl      $16, %r12d
+        movsd     %xmm4, -32(%rsp)
+        shrl      $5, %r12d
+        movsd     -32(%rsp), %xmm12
+        movsd     1584(%rcx,%r12,8), %xmm13
+        addl      %r12d, %r12d
+        mulsd     %xmm13, %xmm5
+        subsd     %xmm12, %xmm2
+        addsd     2616(%rcx,%r12,8), %xmm7
+        mulsd     %xmm13, %xmm6
+        movsd     %xmm2, -24(%rsp)
+        movaps    %xmm6, %xmm8
+        movsd     6856+__dpow_ha_CoutTab(%rip), %xmm2
+        mulsd     %xmm5, %xmm2
+        subsd     6848+__dpow_ha_CoutTab(%rip), %xmm8
+        movsd     -32(%rsp), %xmm3
+        movsd     -24(%rsp), %xmm4
+        movsd     %xmm2, -32(%rsp)
+        movsd     -32(%rsp), %xmm13
+        movsd     272(%rcx,%r10,8), %xmm11
+        subsd     %xmm5, %xmm13
+        movsd     %xmm13, -24(%rsp)
+        movsd     -32(%rsp), %xmm2
+        movsd     -24(%rsp), %xmm14
+        movsd     1064(%rcx,%r11,8), %xmm10
+        subsd     %xmm14, %xmm2
+        movsd     %xmm2, -32(%rsp)
+        movaps    %xmm3, %xmm2
+        movsd     -32(%rsp), %xmm15
+        movsd     2624(%rcx,%r12,8), %xmm9
+        subsd     %xmm15, %xmm5
+        movsd     %xmm5, -24(%rsp)
+        movsd     -32(%rsp), %xmm5
+        mulsd     %xmm5, %xmm2
+        mulsd     %xmm4, %xmm5
+        subsd     %xmm6, %xmm2
+        movaps    %xmm7, %xmm6
+        addsd     %xmm5, %xmm2
+        addsd     %xmm8, %xmm6
+        movsd     -24(%rsp), %xmm12
+        mulsd     %xmm12, %xmm3
+        mulsd     %xmm12, %xmm4
+        addsd     %xmm3, %xmm2
+        movsd     %xmm6, -32(%rsp)
+        addsd     %xmm4, %xmm2
+        movsd     -32(%rsp), %xmm3
+        subsd     %xmm3, %xmm7
+        addsd     %xmm8, %xmm7
+        movsd     %xmm7, -24(%rsp)
+        movsd     -32(%rsp), %xmm4
+        movsd     %xmm4, -64(%rsp)
+        movzwl    -58(%rsp), %ecx
+        andl      $32752, %ecx
+        shrl      $4, %ecx
+        addl      %edx, %ecx
+        movsd     -24(%rsp), %xmm3
+        cmpl      $2057, %ecx
+        jge       .LBL_2_67
+
+
+        cmpl      $1984, %ecx
+        jg        .LBL_2_58
+
+
+        movsd     %xmm1, -32(%rsp)
+        movsd     -32(%rsp), %xmm1
+        addsd     6808+__dpow_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -32(%rsp)
+        movsd     -32(%rsp), %xmm2
+        mulsd     %xmm0, %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_46:
+
+        movsd     %xmm2, -32(%rsp)
+        movl      $1, %eax
+        movsd     -32(%rsp), %xmm1
+        movsd     -32(%rsp), %xmm0
+        divsd     %xmm0, %xmm1
+        movsd     %xmm1, -32(%rsp)
+        movsd     -32(%rsp), %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_47:
+
+        cmpl      $1023, %r13d
+        jge       .LBL_2_51
+
+
+        testb     %bl, %bl
+        je        .LBL_2_56
+
+
+        movaps    %xmm0, %xmm2
+        mulsd     %xmm0, %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_51:
+
+        testb     %bl, %bl
+        je        .LBL_2_53
+
+
+        lea       6824+__dpow_ha_CoutTab(%rip), %rdx
+        andl      %r12d, %r9d
+        mulsd     (%rdx,%r9,8), %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_53:
+
+        mulsd     %xmm3, %xmm3
+        lea       6824+__dpow_ha_CoutTab(%rip), %rdx
+        mulsd     %xmm0, %xmm3
+        andl      %r12d, %r9d
+        movaps    %xmm3, %xmm2
+        mulsd     (%rdx,%r9,8), %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_54:
+
+        movaps    %xmm3, %xmm2
+        addsd     %xmm0, %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_55:
+
+        movq      6824+__dpow_ha_CoutTab(%rip), %rdx
+        addsd     %xmm0, %xmm3
+        movsd     %xmm3, -32(%rsp)
+        movq      %rdx, -24(%rsp)
+        movb      -25(%rsp), %cl
+        movb      -17(%rsp), %bl
+        andb      $-128, %cl
+        andb      $127, %bl
+        orb       %cl, %bl
+        movb      %bl, -17(%rsp)
+        movsd     -24(%rsp), %xmm2
+        movsd     -24(%rsp), %xmm0
+        mulsd     %xmm0, %xmm2
+
+.LBL_2_56:
+
+        movsd     %xmm2, (%r8)
+	.cfi_restore 3
+        popq      %rbx
+	.cfi_def_cfa_offset 32
+	.cfi_restore 15
+        popq      %r15
+	.cfi_def_cfa_offset 24
+	.cfi_restore 13
+        popq      %r13
+	.cfi_def_cfa_offset 16
+	.cfi_restore 12
+        popq      %r12
+	.cfi_def_cfa_offset 8
+        ret
+	.cfi_def_cfa_offset 40
+	.cfi_offset 3, -40
+	.cfi_offset 12, -16
+	.cfi_offset 13, -24
+	.cfi_offset 15, -32
+
+.LBL_2_57:
+
+        testb     %r11b, %r11b
+        je        .LBL_2_30
+        jmp       .LBL_2_55
+
+.LBL_2_58:
+
+        lea       6752+__dpow_ha_CoutTab(%rip), %rdx
+        movsd     (%rdx), %xmm6
+        lea       4688+__dpow_ha_CoutTab(%rip), %rcx
+        movsd     -64(%rsp), %xmm7
+        addsd     %xmm2, %xmm8
+        addsd     %xmm9, %xmm10
+        addsd     %xmm3, %xmm2
+        mulsd     %xmm8, %xmm6
+        addsd     %xmm10, %xmm11
+        addsd     -8(%rdx), %xmm6
+        addsd     %xmm2, %xmm11
+        mulsd     %xmm8, %xmm6
+        movaps    %xmm11, %xmm9
+        addsd     -16(%rdx), %xmm6
+        mulsd     %xmm8, %xmm6
+        addsd     -24(%rdx), %xmm6
+        mulsd     %xmm8, %xmm6
+        addsd     %xmm6, %xmm4
+        lea       -32(%rsp), %rbx
+        movsd     %xmm4, (%rbx)
+        movsd     (%rbx), %xmm8
+        subsd     %xmm8, %xmm7
+        addsd     %xmm6, %xmm7
+        lea       -24(%rsp), %rsi
+        movsd     %xmm7, (%rsi)
+        movsd     (%rbx), %xmm12
+        movsd     (%rsi), %xmm5
+        addsd     %xmm12, %xmm9
+        movsd     %xmm9, (%rbx)
+        movsd     (%rbx), %xmm10
+        subsd     %xmm10, %xmm12
+        addsd     %xmm11, %xmm12
+        movsd     104(%rdx), %xmm11
+        movsd     %xmm12, (%rsi)
+        movsd     (%rbx), %xmm3
+        mulsd     %xmm3, %xmm11
+        movsd     (%rsi), %xmm4
+        movsd     %xmm11, (%rbx)
+        addsd     %xmm5, %xmm4
+        movsd     (%rbx), %xmm13
+        lea       -16(%rsp), %rdi
+        movsd     (%rdi), %xmm11
+        subsd     %xmm3, %xmm13
+        mulsd     (%rdi), %xmm4
+        movsd     %xmm13, (%rsi)
+        movaps    %xmm11, %xmm6
+        movsd     (%rbx), %xmm15
+        movsd     (%rsi), %xmm14
+        mulsd     104(%rdx), %xmm6
+        subsd     %xmm14, %xmm15
+        movsd     %xmm15, (%rbx)
+        movsd     (%rbx), %xmm2
+        movsd     88(%rdx), %xmm5
+        subsd     %xmm2, %xmm3
+        movsd     %xmm3, (%rsi)
+        movsd     (%rbx), %xmm3
+        movsd     (%rsi), %xmm2
+        movsd     %xmm6, (%rbx)
+        movsd     (%rbx), %xmm7
+        subsd     (%rdi), %xmm7
+        movsd     %xmm7, (%rsi)
+        movsd     (%rbx), %xmm9
+        movsd     (%rsi), %xmm8
+        subsd     %xmm8, %xmm9
+        movsd     %xmm9, (%rbx)
+        movsd     (%rbx), %xmm10
+        subsd     %xmm10, %xmm11
+        movsd     %xmm11, (%rsi)
+        movsd     (%rbx), %xmm6
+        movaps    %xmm6, %xmm14
+        mulsd     %xmm3, %xmm14
+        mulsd     %xmm2, %xmm6
+        addsd     %xmm14, %xmm5
+        movsd     (%rsi), %xmm15
+        mulsd     %xmm15, %xmm2
+        mulsd     %xmm15, %xmm3
+        addsd     %xmm2, %xmm6
+        movsd     %xmm5, (%rbx)
+        addsd     %xmm3, %xmm6
+        movsd     (%rbx), %xmm12
+        addsd     %xmm4, %xmm6
+        subsd     88(%rdx), %xmm12
+        movsd     %xmm12, -88(%rsp)
+        movsd     -88(%rsp), %xmm13
+        movsd     40(%rdx), %xmm3
+        subsd     %xmm13, %xmm14
+        movsd     %xmm14, -80(%rsp)
+        movsd     -80(%rsp), %xmm5
+        movl      (%rbx), %edx
+        movl      %edx, %esi
+        andl      $127, %edx
+        addsd     %xmm6, %xmm5
+        mulsd     %xmm5, %xmm3
+        addl      %edx, %edx
+        movsd     -8(%rcx,%rdx,8), %xmm2
+        sarl      $7, %esi
+        lea       6784+__dpow_ha_CoutTab(%rip), %r9
+        addsd     (%r9), %xmm3
+        mulsd     %xmm5, %xmm3
+        addsd     -8(%r9), %xmm3
+        mulsd     %xmm5, %xmm3
+        addsd     -16(%r9), %xmm3
+        mulsd     %xmm5, %xmm3
+        addsd     -24(%r9), %xmm3
+        mulsd     %xmm5, %xmm3
+        mulsd     %xmm2, %xmm3
+        addsd     (%rcx,%rdx,8), %xmm3
+        movaps    %xmm3, %xmm4
+        addsd     %xmm2, %xmm4
+        movsd     %xmm4, -72(%rsp)
+        movzwl    -66(%rsp), %ecx
+        movl      %ecx, %ebx
+        andl      $32752, %ebx
+        shrl      $4, %ebx
+        lea       -1023(%rsi,%rbx), %edx
+        cmpl      $1024, %edx
+        jge       .LBL_2_66
+
+
+        cmpl      $-1022, %edx
+        jl        .LBL_2_61
+
+
+        andl      $-32753, %ecx
+        lea       1023(%rdx), %edx
+        andl      $2047, %edx
+        shll      $4, %edx
+        orl       %edx, %ecx
+        movw      %cx, -66(%rsp)
+        movsd     -72(%rsp), %xmm2
+        mulsd     %xmm0, %xmm2
+        movsd     %xmm2, -72(%rsp)
+        jmp       .LBL_2_56
+
+.LBL_2_61:
+
+        cmpl      $-1032, %edx
+        jl        .LBL_2_63
+
+
+        lea       -32(%rsp), %rcx
+        movsd     %xmm4, (%rcx)
+        addl      $1223, %esi
+        movsd     (%rcx), %xmm1
+        andl      $2047, %esi
+        lea       6824+__dpow_ha_CoutTab(%rip), %rbx
+        movq      (%rbx), %rdx
+        subsd     %xmm1, %xmm2
+        movq      %rdx, -64(%rsp)
+        addsd     %xmm2, %xmm3
+        lea       -24(%rsp), %rdi
+        movsd     %xmm3, (%rdi)
+        movsd     (%rcx), %xmm7
+        movsd     32(%rbx), %xmm2
+        mulsd     %xmm7, %xmm2
+        movsd     (%rdi), %xmm9
+        movsd     %xmm2, (%rcx)
+        movsd     (%rcx), %xmm3
+        shrq      $48, %rdx
+        subsd     %xmm7, %xmm3
+        movsd     %xmm3, (%rdi)
+        andl      $-32753, %edx
+        movsd     (%rcx), %xmm5
+        movsd     (%rdi), %xmm4
+        shll      $4, %esi
+        subsd     %xmm4, %xmm5
+        movsd     %xmm5, (%rcx)
+        orl       %esi, %edx
+        lea       -32(%rsp), %rsi
+        movsd     (%rsi), %xmm6
+        movw      %dx, -58(%rsp)
+        subsd     %xmm6, %xmm7
+        movsd     %xmm7, (%rdi)
+        movsd     (%rsi), %xmm11
+        movsd     (%rdi), %xmm12
+        movsd     -64(%rsp), %xmm10
+        addsd     %xmm9, %xmm12
+        mulsd     %xmm10, %xmm11
+        mulsd     %xmm10, %xmm12
+        movsd     48(%rbx), %xmm8
+        addsd     %xmm11, %xmm12
+        mulsd     %xmm8, %xmm0
+        movq      -16(%rbx), %rcx
+        movq      %rcx, (%rsi)
+        lea       -32(%rsp), %rcx
+        movsd     (%rcx), %xmm14
+        movsd     (%rcx), %xmm13
+        mulsd     %xmm13, %xmm14
+        mulsd     %xmm12, %xmm0
+        movsd     %xmm14, (%rcx)
+        movsd     (%rcx), %xmm15
+        addsd     %xmm15, %xmm0
+        movaps    %xmm0, %xmm2
+        movsd     %xmm2, -72(%rsp)
+        jmp       .LBL_2_56
+
+.LBL_2_63:
+
+        cmpl      $-1084, %edx
+        jl        .LBL_2_65
+
+
+        addl      $1223, %esi
+        andl      $2047, %esi
+        lea       6830+__dpow_ha_CoutTab(%rip), %rcx
+        movzwl    (%rcx), %edx
+        shll      $4, %esi
+        andl      $-32753, %edx
+        movsd     %xmm1, -64(%rsp)
+        orl       %esi, %edx
+        movw      %dx, -58(%rsp)
+        movsd     42(%rcx), %xmm2
+        movsd     -64(%rsp), %xmm1
+        mulsd     %xmm2, %xmm0
+        mulsd     %xmm1, %xmm4
+        movq      -22(%rcx), %rcx
+        movq      %rcx, -32(%rsp)
+        mulsd     %xmm4, %xmm0
+        lea       -32(%rsp), %rcx
+        movsd     (%rcx), %xmm4
+        movsd     (%rcx), %xmm3
+        mulsd     %xmm3, %xmm4
+        movsd     %xmm4, (%rcx)
+        movsd     (%rcx), %xmm5
+        subsd     %xmm5, %xmm0
+        movaps    %xmm0, %xmm2
+        movsd     %xmm2, -72(%rsp)
+        jmp       .LBL_2_56
+
+.LBL_2_65:
+
+        movq      6808+__dpow_ha_CoutTab(%rip), %rdx
+        movq      %rdx, -32(%rsp)
+        lea       -32(%rsp), %rdx
+        movsd     (%rdx), %xmm2
+        movsd     (%rdx), %xmm1
+        mulsd     %xmm1, %xmm2
+        movsd     %xmm2, (%rdx)
+        movsd     (%rdx), %xmm3
+        mulsd     %xmm3, %xmm0
+        movaps    %xmm0, %xmm2
+        movsd     %xmm2, -72(%rsp)
+        jmp       .LBL_2_56
+
+.LBL_2_66:
+
+        movq      6800+__dpow_ha_CoutTab(%rip), %rdx
+        movq      %rdx, -32(%rsp)
+        lea       -32(%rsp), %rdx
+        movsd     (%rdx), %xmm2
+        movsd     (%rdx), %xmm1
+        mulsd     %xmm1, %xmm2
+        movsd     %xmm2, (%rdx)
+        movsd     (%rdx), %xmm3
+        mulsd     %xmm3, %xmm0
+        movaps    %xmm0, %xmm2
+        movsd     %xmm2, -72(%rsp)
+        jmp       .LBL_2_56
+
+.LBL_2_67:
+
+        movb      -57(%rsp), %dl
+        lea       6800+__dpow_ha_CoutTab(%rip), %rcx
+        andb      $-128, %dl
+        shrb      $7, %dl
+        xorb      %dl, %bl
+        movzbl    %bl, %ebx
+        movsd     (%rcx,%rbx,8), %xmm2
+        mulsd     %xmm2, %xmm2
+        mulsd     %xmm0, %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_68:
+
+        testl     %r9d, %r9d
+        jne       .LBL_2_70
+
+
+        testl     %esi, %esi
+        jne       .LBL_2_36
+
+.LBL_2_70:
+
+        lea       6824+__dpow_ha_CoutTab(%rip), %rdx
+        andl      $1, %r9d
+        movsd     (%rdx,%r9,8), %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_71:
+
+        mulsd     %xmm3, %xmm3
+        testb     %bl, %bl
+        je        .LBL_2_73
+
+
+        lea       6824+__dpow_ha_CoutTab(%rip), %rax
+        andl      %r12d, %r9d
+        movsd     (%rax,%r9,8), %xmm2
+        movl      $1, %eax
+        divsd     %xmm3, %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_73:
+
+        lea       6824+__dpow_ha_CoutTab(%rip), %rdx
+        andl      %r12d, %r9d
+        movsd     (%rdx,%r9,8), %xmm2
+        mulsd     %xmm3, %xmm2
+        jmp       .LBL_2_56
+
+.LBL_2_74:
+
+        testb     %r11b, %r11b
+        jne       .LBL_2_55
+
+
+        testl     %edx, %edx
+        jne       .LBL_2_30
+
+
+        testl     %r10d, %r10d
+        je        .LBL_2_30
+        jmp       .LBL_2_55
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dpow_ha_cout_rare_internal,@function
+	.size	__svml_dpow_ha_cout_rare_internal,.-__svml_dpow_ha_cout_rare_internal
+..LN__svml_dpow_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dpow_ha_data_internal_avx512:
+	.long	0
+	.long	0
+	.long	1972240384
+	.long	3215375059
+	.long	4207476736
+	.long	3216401398
+	.long	2363129856
+	.long	3217067096
+	.long	972816384
+	.long	3217408026
+	.long	766836736
+	.long	3217739614
+	.long	3103948800
+	.long	3218062358
+	.long	2869821440
+	.long	3218228231
+	.long	1271726080
+	.long	3218381432
+	.long	3449618432
+	.long	3218530849
+	.long	2300510208
+	.long	3218676666
+	.long	4147675136
+	.long	3218819051
+	.long	3761438720
+	.long	3218958163
+	.long	1758134272
+	.long	3219094149
+	.long	14041088
+	.long	3219177733
+	.long	513138688
+	.long	3219242801
+	.long	1904279552
+	.long	1071288313
+	.long	180338688
+	.long	1071163544
+	.long	1751498752
+	.long	1071041295
+	.long	2999894016
+	.long	1070921467
+	.long	3815833600
+	.long	1070803966
+	.long	1399062528
+	.long	1070688704
+	.long	2835742720
+	.long	1070555096
+	.long	2818572288
+	.long	1070333031
+	.long	180322304
+	.long	1070114968
+	.long	704610304
+	.long	1069900764
+	.long	3265986560
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+	.long	1073652911
+	.long	396109971
+	.long	3164511267
+	.long	764307441
+	.long	1073663816
+	.long	3021057420
+	.long	3164378099
+	.long	3999357479
+	.long	1073674779
+	.long	2258941616
+	.long	1016973300
+	.long	929806999
+	.long	1073685803
+	.long	3205336643
+	.long	1016308133
+	.long	1533953344
+	.long	1073696886
+	.long	769171850
+	.long	1016714209
+	.long	2912730644
+	.long	1073708029
+	.long	3490067721
+	.long	3164453650
+	.long	2174652632
+	.long	1073719233
+	.long	4087714590
+	.long	1015498835
+	.long	730821105
+	.long	1073730498
+	.long	2523232743
+	.long	1013115764
+	.long	2523158504
+	.long	1048167334
+	.long	1181303047
+	.long	3218484803
+	.long	1656151777
+	.long	1069842388
+	.long	714085080
+	.long	3216330823
+	.long	4277811695
+	.long	1072049730
+	.long	4286760335
+	.long	1070514109
+	.long	3607404736
+	.long	1068264200
+	.long	1874480759
+	.long	1065595563
+	.long	3884607281
+	.long	1062590591
+	.long	0
+	.long	2145386496
+	.long	0
+	.long	1048576
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3220176896
+	.long	0
+	.long	1120403456
+	.long	1073741824
+	.long	1073157447
+	.long	33554432
+	.long	1101004800
+	.long	0
+	.long	1282408448
+	.long	0
+	.long	862978048
+	.type	__dpow_ha_CoutTab,@object
+	.size	__dpow_ha_CoutTab,6880
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_d_la.s
index a0f51e11225..774942adf8f 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_h_la.s
new file mode 100644
index 00000000000..ecefbc47e97
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_h_la.s
@@ -0,0 +1,281 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *    For pow computation sequences for log2() and exp2() are done with small tables
+ *    The log2() part uses VGETEXP/VGETMANT (which treat denormals correctly)
+ *    Branches are not needed for overflow/underflow:
+ *    RZ mode used to prevent overflow to +/-Inf in intermediate computations
+ *    Final VSCALEF properly handles overflow and underflow cases
+ *    Callout is used for Inf/NaNs or x<=0
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_pows32
+
+__svml_pows32:
+
+        .cfi_startproc
+
+        pushq     %rbp
+        .cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+        .cfi_def_cfa 6, 16
+        .cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $256, %rsp
+
+/* x<=0 or Inf/NaN? */
+        vfpclassph $223, %zmm0, %k0
+
+/* y is Inf/NaN? */
+        vfpclassph $153, %zmm1, %k1
+
+/* mx - 1 */
+        vmovups   __svml_hpow_data_internal(%rip), %zmm8
+
+/* log polynomial */
+        vmovups   64+__svml_hpow_data_internal(%rip), %zmm4
+        vmovups   320+__svml_hpow_data_internal(%rip), %zmm7
+        kmovd     %k0, %edx
+        kmovd     %k1, %eax
+
+/* set range mask */
+        orl       %eax, %edx
+        vextractf32x8 $1, %zmm0, %ymm14
+        vcvtph2psx %ymm0, %zmm12
+        vcvtph2psx %ymm14, %zmm10
+
+/* GetMant(x), range [0.75, 1.5) */
+        vgetmantps $11, {sae}, %zmm12, %zmm5
+        vgetmantps $11, {sae}, %zmm10, %zmm6
+
+/* GetExp(x) */
+        vgetexpps {sae}, %zmm12, %zmm13
+        vgetexpps {sae}, %zmm10, %zmm11
+
+/* exponent correction */
+        vgetexpps {sae}, %zmm5, %zmm9
+        vgetexpps {sae}, %zmm6, %zmm3
+        vsubps    {rn-sae}, %zmm8, %zmm5, %zmm12
+        vsubps    {rn-sae}, %zmm8, %zmm6, %zmm10
+
+/* apply exponent correction */
+        vsubps    {rn-sae}, %zmm3, %zmm11, %zmm3
+        vmovups   192+__svml_hpow_data_internal(%rip), %zmm5
+        vmovups   256+__svml_hpow_data_internal(%rip), %zmm6
+        vextractf32x8 $1, %zmm1, %ymm2
+        vcvtph2psx %ymm2, %zmm14
+        vmovups   128+__svml_hpow_data_internal(%rip), %zmm2
+        vmovaps   %zmm2, %zmm8
+        vfmadd231ps {rn-sae}, %zmm12, %zmm4, %zmm8
+        vfmadd231ps {rn-sae}, %zmm10, %zmm4, %zmm2
+        vsubps    {rn-sae}, %zmm9, %zmm13, %zmm4
+
+/* poly */
+        vfmadd213ps {rn-sae}, %zmm5, %zmm12, %zmm8
+        vfmadd213ps {rn-sae}, %zmm5, %zmm10, %zmm2
+        vmovdqu16 512+__svml_hpow_data_internal(%rip), %zmm5
+        vfmadd213ps {rn-sae}, %zmm6, %zmm12, %zmm8
+        vfmadd213ps {rn-sae}, %zmm6, %zmm10, %zmm2
+        vmovdqu16 576+__svml_hpow_data_internal(%rip), %zmm6
+        vfmadd213ps {rn-sae}, %zmm7, %zmm12, %zmm8
+        vfmadd213ps {rn-sae}, %zmm7, %zmm10, %zmm2
+        vmovups   384+__svml_hpow_data_internal(%rip), %zmm7
+        vfmadd213ps {rn-sae}, %zmm7, %zmm12, %zmm8
+        vfmadd213ps {rn-sae}, %zmm7, %zmm10, %zmm2
+
+/* log2(x) */
+        vfmadd213ps {rn-sae}, %zmm4, %zmm12, %zmm8
+        vfmadd213ps {rn-sae}, %zmm3, %zmm10, %zmm2
+
+/* polynomial */
+        vmovdqu16 448+__svml_hpow_data_internal(%rip), %zmm10
+
+/* y*log2(x) */
+        vmulps    {rn-sae}, %zmm2, %zmm14, %zmm13
+        vcvtph2psx %ymm1, %zmm15
+        vmulps    {rn-sae}, %zmm8, %zmm15, %zmm9
+
+/* reduced argument for exp2() computation */
+        vreduceps $9, {sae}, %zmm13, %zmm3
+        vmovdqu16 640+__svml_hpow_data_internal(%rip), %zmm8
+        vreduceps $9, {sae}, %zmm9, %zmm2
+
+/* y*log2(x) in FP16 */
+        vcvtps2phx {rd-sae}, %zmm9, %ymm11
+
+/* reduced argument in FP16 format */
+        vcvtps2phx %zmm2, %ymm9
+        vcvtps2phx %zmm3, %ymm4
+        vcvtps2phx {rd-sae}, %zmm13, %ymm15
+        vinsertf32x8 $1, %ymm4, %zmm9, %zmm7
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm5, %zmm7, %zmm10
+        vfmadd213ph {rn-sae}, %zmm6, %zmm7, %zmm10
+        vfmadd213ph {rn-sae}, %zmm8, %zmm7, %zmm10
+        vinsertf32x8 $1, %ymm15, %zmm11, %zmm11
+
+/* Poly*2^floor(xe) */
+        vscalefph {rn-sae}, %zmm11, %zmm10, %zmm2
+
+/* Go to special inputs processing branch */
+        jne       .LBL_SPECIAL_VALUES_BRANCH
+                                # LOE rbx r12 r13 r14 r15 edx zmm0 zmm1 zmm2
+
+/* Restore registers
+ * and exit the function
+ */
+
+.LBL_EXIT:
+        vmovaps   %zmm2, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+        .cfi_def_cfa 7, 8
+        .cfi_restore 6
+        ret
+        .cfi_def_cfa 6, 16
+        .cfi_offset 6, -16
+
+/* Branch to process
+ * special inputs
+ */
+
+.LBL_SPECIAL_VALUES_BRANCH:
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm1, 128(%rsp)
+        vmovups   %zmm2, 192(%rsp)
+                                # LOE rbx r12 r13 r14 r15 edx zmm2
+
+        xorl      %eax, %eax
+        movq      %r12, 16(%rsp)
+        /*  DW_CFA_expression: r12 (r12) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -240; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 8(%rsp)
+        /*  DW_CFA_expression: r13 (r13) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -248; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+        movq      %r14, (%rsp)
+        /*  DW_CFA_expression: r14 (r14) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -256; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0e, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+                                # LOE rbx r15 r12d r13d
+
+/* Range mask
+ * bits check
+ */
+
+.LBL_RANGEMASK_CHECK:
+        btl       %r12d, %r13d
+
+/* Call scalar math function */
+        jc        .LBL_SCALAR_MATH_CALL
+                                # LOE rbx r15 r12d r13d
+
+/* Special inputs
+ * processing loop
+ */
+
+.LBL_SPECIAL_VALUES_LOOP:
+        incl      %r12d
+        cmpl      $32, %r12d
+
+/* Check bits in range mask */
+        jl        .LBL_RANGEMASK_CHECK
+                                # LOE rbx r15 r12d r13d
+
+        movq      16(%rsp), %r12
+        .cfi_restore 12
+        movq      8(%rsp), %r13
+        .cfi_restore 13
+        movq      (%rsp), %r14
+        .cfi_restore 14
+        vmovups   192(%rsp), %zmm2
+
+/* Go to exit */
+        jmp       .LBL_EXIT
+        /*  DW_CFA_expression: r12 (r12) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -240; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+        /*  DW_CFA_expression: r13 (r13) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -248; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+        /*  DW_CFA_expression: r14 (r14) (DW_OP_lit8; DW_OP_minus; DW_OP_const4s: -64; DW_OP_and; DW_OP_const4s: -256; DW_OP_plus)  */
+        .cfi_escape 0x10, 0x0e, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+                                # LOE rbx r12 r13 r14 r15 zmm2
+
+/* Scalar math fucntion call
+ * to process special input
+ */
+
+.LBL_SCALAR_MATH_CALL:
+        movl      %r12d, %r14d
+        vmovsh    64(%rsp,%r14,2), %xmm1
+        vmovsh    128(%rsp,%r14,2), %xmm2
+        vcvtsh2ss %xmm1, %xmm1, %xmm0
+        vcvtsh2ss %xmm2, %xmm2, %xmm1
+        vzeroupper
+        call      powf@PLT
+                                # LOE rbx r14 r15 r12d r13d xmm0
+
+        vmovaps   %xmm0, %xmm1
+        vxorps    %xmm0, %xmm0, %xmm0
+        vmovss    %xmm1, %xmm0, %xmm2
+        vcvtss2sh %xmm2, %xmm2, %xmm3
+        vmovsh    %xmm3, 192(%rsp,%r14,2)
+
+/* Process special inputs in loop */
+        jmp       .LBL_SPECIAL_VALUES_LOOP
+                                # LOE rbx r15 r12d r13d
+        .cfi_endproc
+
+        .type	__svml_pows32,@function
+        .size	__svml_pows32,.-__svml_pows32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hpow_data_internal:
+	.rept	16
+        .long	0x3f800000
+	.endr
+	.rept	16
+        .long	0xbe1ffcac
+	.endr
+	.rept	16
+        .long	0x3e9864bc
+	.endr
+	.rept	16
+        .long	0xbebd2851
+	.endr
+	.rept	16
+        .long	0x3ef64dc7
+	.endr
+	.rept	16
+        .long	0xbf389e6d
+	.endr
+	.rept	16
+        .long	0x3fb8aa2c
+	.endr
+	.rept	32
+        .word	0x2d12
+	.endr
+	.rept	32
+        .word	0x332e
+	.endr
+	.rept	32
+        .word	0x3992
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+        .type	__svml_hpow_data_internal,@object
+        .size	__svml_hpow_data_internal,704
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_s_ha.s
new file mode 100644
index 00000000000..5b706bb28df
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_s_ha.s
@@ -0,0 +1,2114 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *  *   Typical computation sequences for log2() and exp2(),
+ *  *     with smaller tables (32- and 16-element tables)
+ *  *    The log2() part uses VGETEXP/VGETMANT (which treat denormals correctly),
+ *  *         similar to DP ln() algorithm
+ *  *    Branches are not needed for overflow/underflow:
+ *  *     - RZ mode used to prevent overflow to +/-Inf in intermediate computations
+ *  *     - final VSCALEF properly handles overflow and underflow cases
+ *  *    Callout is still used for Inf/NaNs or x<=0
+ *  *
+ *  
+ */
+
+
+	.text
+.L_2__routine_start___svml_powf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_powf16_ha
+
+__svml_powf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $256, %rsp
+
+/* GetMant(x), normalized to [0.5,1) for x>=0, NaN for x<0 */
+        vgetmantps $10, {sae}, %zmm0, %zmm6
+        vmovups   576+__svml_spow_ha_data_internal_avx512(%rip), %zmm11
+        vmovups   512+__svml_spow_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   640+__svml_spow_ha_data_internal_avx512(%rip), %zmm13
+        vmovups   704+__svml_spow_ha_data_internal_avx512(%rip), %zmm14
+        vmovups   768+__svml_spow_ha_data_internal_avx512(%rip), %zmm2
+
+/* Table lookup */
+        vmovups   __svml_spow_ha_data_internal_avx512(%rip), %zmm15
+        vmovups   128+__svml_spow_ha_data_internal_avx512(%rip), %zmm5
+
+/* SglRcp ~ 1/Mantissa */
+        vrcp14ps  %zmm6, %zmm8
+
+/* x<=0 or Inf/NaN? */
+        vfpclassps $223, %zmm0, %k0
+
+/* round SglRcp to 5 fractional bits (RN mode, no Precision exception) */
+        vrndscaleps $88, {sae}, %zmm8, %zmm9
+        vmovups   832+__svml_spow_ha_data_internal_avx512(%rip), %zmm8
+        kmovw     %k0, %edx
+
+/* Reduced argument: R = (SglRcp*Mantissa - 1) */
+        vfmsub213ps {rn-sae}, %zmm11, %zmm9, %zmm6
+
+/* Prepare exponent correction: SglRcp<1.5? */
+        vcmpps    $17, {sae}, %zmm10, %zmm9, %k1
+
+/* start polynomial evaluation */
+        vfmadd231ps {rn-sae}, %zmm6, %zmm13, %zmm14
+
+/* Poly_low */
+        vmovups   896+__svml_spow_ha_data_internal_avx512(%rip), %zmm10
+        vmovups   1024+__svml_spow_ha_data_internal_avx512(%rip), %zmm13
+
+/* R^2 */
+        vmulps    {rn-sae}, %zmm6, %zmm6, %zmm4
+        vfmadd231ps {rn-sae}, %zmm6, %zmm14, %zmm2
+
+/* Prepare table index */
+        vpsrld    $18, %zmm9, %zmm12
+        vfmadd231ps {rn-sae}, %zmm6, %zmm2, %zmm8
+        vpermt2ps 64+__svml_spow_ha_data_internal_avx512(%rip), %zmm12, %zmm15
+        vpermt2ps 192+__svml_spow_ha_data_internal_avx512(%rip), %zmm12, %zmm5
+        vfmadd231ps {rn-sae}, %zmm6, %zmm8, %zmm10
+        vmovaps   %zmm1, %zmm7
+
+/* GetExp(x) */
+        vgetexpps {sae}, %zmm0, %zmm1
+
+/* y Inf/NaN? */
+        vfpclassps $153, %zmm7, %k2
+
+/* add 1 to Expon if SglRcp<1.5 */
+        vaddps    {rn-sae}, %zmm11, %zmm1, %zmm1{%k1}
+        vmovups   960+__svml_spow_ha_data_internal_avx512(%rip), %zmm11
+
+/* Th+Expon */
+        vaddps    {rn-sae}, %zmm1, %zmm15, %zmm9
+        kmovw     %k2, %eax
+
+/* R2l */
+        vmovaps   %zmm6, %zmm3
+        orl       %eax, %edx
+        vfmsub213ps {rn-sae}, %zmm4, %zmm6, %zmm3
+
+/* Th+Expon+R*c1h */
+        vmovaps   %zmm9, %zmm12
+        vfmadd231ps {rn-sae}, %zmm6, %zmm11, %zmm12
+
+/* Tl+R2l*c2h */
+        vfmadd213ps {rn-sae}, %zmm5, %zmm13, %zmm3
+
+/* (R*c1h)_high */
+        vsubps    {rn-sae}, %zmm9, %zmm12, %zmm5
+
+/* Tl+R2l*c2h + R*Poly_low */
+        vfmadd231ps {rn-sae}, %zmm6, %zmm10, %zmm3
+        vmovups   1216+__svml_spow_ha_data_internal_avx512(%rip), %zmm9
+
+/* (R*c1h)_low */
+        vfmsub213ps {rn-sae}, %zmm5, %zmm11, %zmm6
+        vmovups   1344+__svml_spow_ha_data_internal_avx512(%rip), %zmm10
+
+/* K+Expon+R*c1h+R2*c2h */
+        vmovaps   %zmm12, %zmm1
+        vfmadd231ps {rn-sae}, %zmm4, %zmm13, %zmm1
+
+/* High1 + Tl */
+        vaddps    {rn-sae}, %zmm3, %zmm1, %zmm2
+
+/* (R2*c2h)_high */
+        vsubps    {rn-sae}, %zmm12, %zmm1, %zmm14
+        vmovups   384+__svml_spow_ha_data_internal_avx512(%rip), %zmm12
+
+/* (R2*c2h)_low */
+        vfmsub213ps {rn-sae}, %zmm14, %zmm13, %zmm4
+
+/*
+ * scaled result
+ * Filter very large |y*log2(x)| and scale final result for LRB2
+ */
+        vmovups   1664+__svml_spow_ha_data_internal_avx512(%rip), %zmm14
+
+/* Table lookup: The, Tle/The */
+        vmovups   256+__svml_spow_ha_data_internal_avx512(%rip), %zmm13
+
+/* Tlh */
+        vsubps    {rn-sae}, %zmm1, %zmm2, %zmm15
+
+/* (R2_c2h)_low + (R*c1h)_low */
+        vaddps    {rn-sae}, %zmm4, %zmm6, %zmm4
+
+/* y*High */
+        vmulps    {rz-sae}, %zmm7, %zmm2, %zmm1
+        vmovups   1088+__svml_spow_ha_data_internal_avx512(%rip), %zmm6
+
+/* Tll */
+        vsubps    {rn-sae}, %zmm15, %zmm3, %zmm3
+
+/* (y*High)_low */
+        vfmsub213ps {rz-sae}, %zmm1, %zmm7, %zmm2
+        vmovups   1280+__svml_spow_ha_data_internal_avx512(%rip), %zmm15
+
+/* Tll+(R2_c2h)_low + (R*c1h)_low */
+        vaddps    {rn-sae}, %zmm4, %zmm3, %zmm3
+
+/* Zl = y*Tll + Zl */
+        vfmadd213ps {rz-sae}, %zmm2, %zmm7, %zmm3
+        vaddps    {rz-sae}, %zmm3, %zmm1, %zmm2
+        vsubps    {rn-sae}, %zmm1, %zmm2, %zmm4
+        vaddps    {rd-sae}, %zmm6, %zmm2, %zmm1
+
+/*
+ * /
+ * exp2 computation starts here
+ */
+        vreduceps $81, {sae}, %zmm2, %zmm5
+        vsubps    {rn-sae}, %zmm4, %zmm3, %zmm6
+        vandps    1600+__svml_spow_ha_data_internal_avx512(%rip), %zmm2, %zmm2
+        vpermt2ps 448+__svml_spow_ha_data_internal_avx512(%rip), %zmm1, %zmm12
+        vaddps    {rn-sae}, %zmm6, %zmm5, %zmm8
+        vpermt2ps 320+__svml_spow_ha_data_internal_avx512(%rip), %zmm1, %zmm13
+        vcmpps    $22, {sae}, %zmm14, %zmm2, %k3
+        vpslld    $18, %zmm1, %zmm1
+
+/* ensure |R|<2 even for special cases */
+        vandps    1152+__svml_spow_ha_data_internal_avx512(%rip), %zmm8, %zmm11
+        kmovw     %k3, %ecx
+
+/* polynomial */
+        vfmadd231ps {rn-sae}, %zmm11, %zmm9, %zmm15
+        vandps    1728+__svml_spow_ha_data_internal_avx512(%rip), %zmm1, %zmm1
+        vfmadd213ps {rn-sae}, %zmm10, %zmm11, %zmm15
+
+/* R*poly + Tle/The */
+        vfmadd213ps {rn-sae}, %zmm12, %zmm11, %zmm15
+        orl       %ecx, %edx
+
+/* The + The*(R*poly + Tle/The) */
+        vfmadd213ps {rn-sae}, %zmm13, %zmm13, %zmm15
+        vmulps    {rn-sae}, %zmm1, %zmm15, %zmm1
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        vmovaps   %zmm1, %zmm0
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm0, 64(%rsp)
+        vmovups   %zmm7, 128(%rsp)
+        vmovups   %zmm1, 192(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   192(%rsp), %zmm1
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x28, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x20, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x38, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x30, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x18, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x10, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x08, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x00, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+        lea       192(%rsp,%r12,4), %rdx
+
+        call      __svml_spow_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_powf16_ha,@function
+	.size	__svml_powf16_ha,.-__svml_powf16_ha
+..LN__svml_powf16_ha.0:
+
+.L_2__routine_start___spow_ha_TestIntFunc_1:
+
+	.align    16,0x90
+
+__spow_ha_TestIntFunc:
+
+
+	.cfi_startproc
+..L53:
+
+        movd      %xmm0, %edx
+        andl      $2147483647, %edx
+        lea       -1065353216(%rdx), %eax
+        cmpl      $1073741824, %eax
+        jae       .LBL_2_5
+
+
+        cmpl      $1266679808, %edx
+        jge       .LBL_2_7
+
+
+        movl      %edx, %ecx
+        andl      $-8388608, %ecx
+        addl      $8388608, %ecx
+        shrl      $23, %ecx
+        shll      %cl, %edx
+        testl     $8388607, %edx
+        jne       .LBL_2_5
+
+
+        andl      $16777215, %edx
+        xorl      %eax, %eax
+        cmpl      $8388608, %edx
+        setne     %al
+        incl      %eax
+        ret
+
+.LBL_2_5:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_7:
+
+        movl      $2, %eax
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__spow_ha_TestIntFunc,@function
+	.size	__spow_ha_TestIntFunc,.-__spow_ha_TestIntFunc
+..LN__spow_ha_TestIntFunc.1:
+
+.L_2__routine_start___svml_spow_ha_cout_rare_internal_2:
+
+	.align    16,0x90
+
+__svml_spow_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L56:
+
+        pushq     %r12
+	.cfi_def_cfa_offset 16
+	.cfi_offset 12, -16
+        pushq     %r13
+	.cfi_def_cfa_offset 24
+	.cfi_offset 13, -24
+        pushq     %r14
+	.cfi_def_cfa_offset 32
+	.cfi_offset 14, -32
+        pushq     %r15
+	.cfi_def_cfa_offset 40
+	.cfi_offset 15, -40
+        pushq     %rbx
+	.cfi_def_cfa_offset 48
+	.cfi_offset 3, -48
+        pushq     %rbp
+	.cfi_def_cfa_offset 56
+	.cfi_offset 6, -56
+        subq      $88, %rsp
+	.cfi_def_cfa_offset 144
+        movq      %rdx, %r15
+        movss     (%rdi), %xmm4
+        pxor      %xmm1, %xmm1
+        movss     (%rsi), %xmm3
+        movaps    %xmm4, %xmm2
+        movl      $0, 64(%rsp)
+        movaps    %xmm3, %xmm0
+        ucomiss   %xmm1, %xmm4
+        jp        .LBL_3_2
+        je        .LBL_3_3
+
+.LBL_3_2:
+
+        movss     %xmm4, 8(%rsp)
+        jmp       .LBL_3_4
+
+.LBL_3_3:
+
+        movaps    %xmm4, %xmm2
+        addss     %xmm4, %xmm2
+        movss     %xmm2, 8(%rsp)
+
+.LBL_3_4:
+
+        pxor      %xmm1, %xmm1
+        ucomiss   %xmm1, %xmm3
+        jp        .LBL_3_5
+        je        .LBL_3_6
+
+.LBL_3_5:
+
+        movss     %xmm3, 28(%rsp)
+        jmp       .LBL_3_7
+
+.LBL_3_6:
+
+        movaps    %xmm3, %xmm0
+        addss     %xmm3, %xmm0
+        movss     %xmm0, 28(%rsp)
+
+.LBL_3_7:
+
+        movb      31(%rsp), %al
+        xorl      %ebx, %ebx
+        andb      $-128, %al
+        xorl      %ebp, %ebp
+        shrb      $7, %al
+        testl     $8388607, 8(%rsp)
+        movzwl    30(%rsp), %r13d
+        sete      %bl
+        andl      $32640, %r13d
+        testl     $8388607, 28(%rsp)
+        movzwl    10(%rsp), %r14d
+        movzbl    11(%rsp), %r12d
+        sete      %bpl
+        andl      $32640, %r14d
+        andl      $128, %r12d
+        shrl      $7, %r13d
+        shrl      $7, %r14d
+        shrl      $7, %r12d
+        movb      %al, 72(%rsp)
+        cmpl      $255, %r13d
+        je        .LBL_3_59
+
+
+        movl      $1, 8(%rsp)
+        movss     %xmm2, 32(%rsp)
+        movss     %xmm0, 40(%rsp)
+        movss     %xmm3, 48(%rsp)
+        movss     %xmm4, 56(%rsp)
+
+        call      __spow_ha_TestIntFunc
+
+
+        movss     56(%rsp), %xmm4
+        movl      %eax, %edx
+        movss     48(%rsp), %xmm3
+        movss     40(%rsp), %xmm0
+        movss     32(%rsp), %xmm2
+        testl     %r12d, %r12d
+        jne       .LBL_3_10
+
+
+        cmpl      $127, %r14d
+        je        .LBL_3_56
+
+.LBL_3_10:
+
+        testl     %r13d, %r13d
+        jne       .LBL_3_12
+
+.LBL_3_11:
+
+        testl     %ebp, %ebp
+        jne       .LBL_3_38
+
+.LBL_3_12:
+
+        cmpl      $255, %r14d
+        je        .LBL_3_14
+
+.LBL_3_13:
+
+        movl      $1, %eax
+        jmp       .LBL_3_15
+
+.LBL_3_14:
+
+        xorl      %eax, %eax
+
+.LBL_3_15:
+
+        orl       %eax, %ebx
+        je        .LBL_3_37
+
+
+        orl       8(%rsp), %ebp
+        je        .LBL_3_37
+
+
+        pxor      %xmm1, %xmm1
+        ucomiss   %xmm1, %xmm2
+        jp        .LBL_3_18
+        je        .LBL_3_53
+
+.LBL_3_18:
+
+        ucomiss   .L_2il0floatpacket.122(%rip), %xmm2
+        jp        .LBL_3_19
+        je        .LBL_3_50
+
+.LBL_3_19:
+
+        testl     %eax, %eax
+        je        .LBL_3_30
+
+
+        cmpl      $0, 8(%rsp)
+        je        .LBL_3_30
+
+
+        pxor      %xmm0, %xmm0
+        comiss    %xmm0, %xmm2
+        ja        .LBL_3_23
+
+
+        testl     %edx, %edx
+        je        .LBL_3_29
+
+.LBL_3_23:
+
+        lea       1484+__spow_ha_CoutTab(%rip), %rax
+        andl      %r12d, %edx
+        movss     %xmm2, 12(%rsp)
+        xorl      %ecx, %ecx
+        andb      $127, 15(%rsp)
+        movss     (%rax,%rdx,4), %xmm12
+        testl     %r14d, %r14d
+        jne       .LBL_3_25
+
+
+        movss     12(%rsp), %xmm0
+        movl      $-64, %ecx
+        mulss     .L_2il0floatpacket.123(%rip), %xmm0
+        movss     %xmm0, 12(%rsp)
+        jmp       .LBL_3_26
+
+.LBL_3_25:
+
+        movss     12(%rsp), %xmm0
+
+.LBL_3_26:
+
+        movzwl    14(%rsp), %edi
+        lea       __spow_ha_CoutTab(%rip), %rsi
+        andl      $32640, %edi
+        pxor      %xmm1, %xmm1
+        shrl      $7, %edi
+        movl      12(%rsp), %edx
+        shll      $23, %edi
+        andl      $8388607, %edx
+        movss     %xmm0, 16(%rsp)
+        orl       %edx, %edi
+        movzwl    18(%rsp), %eax
+        addl      $-1060634624, %edi
+        andl      $-32641, %eax
+        addl      $16256, %eax
+        movw      %ax, 18(%rsp)
+        sarl      $23, %edi
+        addl      %ecx, %edi
+        movl      16(%rsp), %ecx
+        andl      $7864320, %ecx
+        addl      $524288, %ecx
+        cvtsi2ss  %edi, %xmm1
+        shrl      $20, %ecx
+        movss     (%rsi,%rcx,4), %xmm4
+        addl      %ecx, %ecx
+        movss     36(%rsi,%rcx,4), %xmm13
+        movaps    %xmm13, %xmm7
+        movss     16(%rsp), %xmm0
+        addss     %xmm1, %xmm7
+        movaps    %xmm0, %xmm6
+        mulss     %xmm4, %xmm6
+        movss     %xmm7, 20(%rsp)
+        movss     20(%rsp), %xmm3
+        movd      %xmm6, %ebx
+        subss     %xmm3, %xmm1
+        andl      $1966080, %ebx
+        addss     %xmm1, %xmm13
+        addl      $131072, %ebx
+        shrl      $18, %ebx
+        movss     108(%rsi,%rbx,4), %xmm11
+        addl      %ebx, %ebx
+        movss     144(%rsi,%rbx,4), %xmm5
+        movss     %xmm13, 24(%rsp)
+        movaps    %xmm5, %xmm14
+        movss     20(%rsp), %xmm1
+        addss     %xmm1, %xmm14
+        mulss     %xmm11, %xmm6
+        mulss     %xmm11, %xmm4
+        movd      %xmm6, %ebp
+        movss     24(%rsp), %xmm7
+        movss     %xmm14, 20(%rsp)
+        movss     20(%rsp), %xmm3
+        andl      $507904, %ebp
+        addl      $16384, %ebp
+        subss     %xmm3, %xmm1
+        shrl      $15, %ebp
+        addss     %xmm1, %xmm5
+        movss     216(%rsi,%rbp,4), %xmm15
+        addl      %ebp, %ebp
+        movss     284(%rsi,%rbp,4), %xmm2
+        movss     %xmm5, 24(%rsp)
+        movaps    %xmm2, %xmm13
+        movss     20(%rsp), %xmm1
+        movss     24(%rsp), %xmm5
+        addss     %xmm1, %xmm13
+        mulss     %xmm15, %xmm4
+        movss     %xmm13, 20(%rsp)
+        movss     20(%rsp), %xmm3
+        movss     .L_2il0floatpacket.125(%rip), %xmm11
+        subss     %xmm3, %xmm1
+        addss     %xmm1, %xmm2
+        mulss     %xmm15, %xmm6
+        movaps    %xmm11, %xmm15
+        movaps    %xmm6, %xmm3
+        mulss     %xmm0, %xmm15
+        subss     .L_2il0floatpacket.124(%rip), %xmm3
+        movss     %xmm2, 24(%rsp)
+        movss     20(%rsp), %xmm1
+        movss     24(%rsp), %xmm2
+        movss     %xmm15, 20(%rsp)
+        movss     20(%rsp), %xmm13
+        movss     40(%rsi,%rcx,4), %xmm9
+        movss     148(%rsi,%rbx,4), %xmm8
+        movss     288(%rsi,%rbp,4), %xmm10
+        subss     16(%rsp), %xmm13
+        movss     %xmm13, 24(%rsp)
+        movss     20(%rsp), %xmm13
+        movss     24(%rsp), %xmm14
+        subss     %xmm14, %xmm13
+        movss     %xmm13, 20(%rsp)
+        movss     20(%rsp), %xmm15
+        subss     %xmm15, %xmm0
+        movss     %xmm0, 24(%rsp)
+        movaps    %xmm4, %xmm0
+        mulss     %xmm11, %xmm0
+        movss     20(%rsp), %xmm13
+        movss     24(%rsp), %xmm14
+        movss     %xmm0, 20(%rsp)
+        movss     20(%rsp), %xmm15
+        subss     %xmm4, %xmm15
+        movss     %xmm15, 24(%rsp)
+        movss     20(%rsp), %xmm15
+        movss     24(%rsp), %xmm0
+        subss     %xmm0, %xmm15
+        movss     %xmm15, 20(%rsp)
+        movss     20(%rsp), %xmm0
+        subss     %xmm0, %xmm4
+        movaps    %xmm13, %xmm0
+        movss     %xmm4, 24(%rsp)
+        movss     20(%rsp), %xmm4
+        mulss     %xmm4, %xmm0
+        mulss     %xmm14, %xmm4
+        subss     %xmm6, %xmm0
+        movaps    %xmm3, %xmm6
+        addss     %xmm4, %xmm0
+        addss     %xmm1, %xmm6
+        movss     24(%rsp), %xmm15
+        movss     %xmm6, 20(%rsp)
+        movss     20(%rsp), %xmm4
+        mulss     %xmm15, %xmm13
+        subss     %xmm4, %xmm1
+        mulss     %xmm15, %xmm14
+        addss     %xmm13, %xmm0
+        addss     %xmm3, %xmm1
+        addss     %xmm14, %xmm0
+        movss     %xmm1, 24(%rsp)
+        movss     20(%rsp), %xmm6
+        movss     %xmm6, 8(%rsp)
+        movzwl    10(%rsp), %eax
+        andl      $32640, %eax
+        shrl      $7, %eax
+        addl      %r13d, %eax
+        movss     24(%rsp), %xmm4
+        cmpl      $265, %eax
+        jge       .LBL_3_49
+
+
+        cmpl      $192, %eax
+        jg        .LBL_3_40
+
+
+        movl      $1065353216, 20(%rsp)
+        movss     20(%rsp), %xmm0
+        addss     .L_2il0floatpacket.134(%rip), %xmm0
+        movss     %xmm0, 20(%rsp)
+        movss     20(%rsp), %xmm1
+        mulss     %xmm12, %xmm1
+        movss     %xmm1, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_29:
+
+        movl      $1, 64(%rsp)
+        pxor      %xmm0, %xmm0
+        movss     %xmm0, 20(%rsp)
+        movss     20(%rsp), %xmm2
+        movss     20(%rsp), %xmm1
+        divss     %xmm1, %xmm2
+        movss     %xmm2, 20(%rsp)
+        movl      20(%rsp), %eax
+        movl      %eax, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_30:
+
+        cmpl      $127, %r14d
+        jge       .LBL_3_34
+
+
+        movb      72(%rsp), %al
+        testb     %al, %al
+        je        .LBL_3_33
+
+
+        mulss     %xmm0, %xmm0
+        movss     %xmm0, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_33:
+
+        pxor      %xmm0, %xmm0
+        movss     %xmm0, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_34:
+
+        movb      72(%rsp), %al
+        testb     %al, %al
+        je        .LBL_3_36
+
+
+        lea       1512+__spow_ha_CoutTab(%rip), %rax
+        andl      %r12d, %edx
+        movl      (%rax,%rdx,4), %ecx
+        movl      %ecx, 12(%rsp)
+        movl      %ecx, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_36:
+
+        mulss     %xmm2, %xmm2
+        lea       1484+__spow_ha_CoutTab(%rip), %rax
+        mulss     %xmm0, %xmm2
+        andl      %r12d, %edx
+        mulss     (%rax,%rdx,4), %xmm2
+        movss     %xmm2, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_37:
+
+        addss     %xmm3, %xmm4
+        movss     %xmm4, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_38:
+
+        addss     %xmm0, %xmm2
+        movss     %xmm2, 20(%rsp)
+        movl      $1065353216, 24(%rsp)
+        movb      23(%rsp), %al
+        movb      27(%rsp), %dl
+        andb      $-128, %al
+        andb      $127, %dl
+        orb       %al, %dl
+        movb      %dl, 27(%rsp)
+        movss     24(%rsp), %xmm1
+        movss     24(%rsp), %xmm0
+        mulss     %xmm0, %xmm1
+        movss     %xmm1, (%r15)
+
+.LBL_3_39:
+
+        movl      64(%rsp), %eax
+        addq      $88, %rsp
+	.cfi_def_cfa_offset 56
+	.cfi_restore 6
+        popq      %rbp
+	.cfi_def_cfa_offset 48
+	.cfi_restore 3
+        popq      %rbx
+	.cfi_def_cfa_offset 40
+	.cfi_restore 15
+        popq      %r15
+	.cfi_def_cfa_offset 32
+	.cfi_restore 14
+        popq      %r14
+	.cfi_def_cfa_offset 24
+	.cfi_restore 13
+        popq      %r13
+	.cfi_def_cfa_offset 16
+	.cfi_restore 12
+        popq      %r12
+	.cfi_def_cfa_offset 8
+        ret
+	.cfi_def_cfa_offset 144
+	.cfi_offset 3, -48
+	.cfi_offset 6, -56
+	.cfi_offset 12, -16
+	.cfi_offset 13, -24
+	.cfi_offset 14, -32
+	.cfi_offset 15, -40
+
+.LBL_3_40:
+
+        movss     .L_2il0floatpacket.129(%rip), %xmm1
+        lea       424+__spow_ha_CoutTab(%rip), %rdx
+        addss     %xmm0, %xmm3
+        addss     %xmm5, %xmm7
+        addss     %xmm4, %xmm0
+        mulss     %xmm3, %xmm1
+        addss     %xmm7, %xmm2
+        addss     .L_2il0floatpacket.128(%rip), %xmm1
+        addss     %xmm2, %xmm9
+        mulss     %xmm3, %xmm1
+        addss     %xmm9, %xmm8
+        addss     .L_2il0floatpacket.127(%rip), %xmm1
+        addss     %xmm8, %xmm10
+        mulss     %xmm3, %xmm1
+        addss     %xmm0, %xmm10
+        addss     .L_2il0floatpacket.126(%rip), %xmm1
+        mulss     %xmm3, %xmm1
+        movaps    %xmm10, %xmm5
+        movss     8(%rsp), %xmm4
+        movaps    %xmm11, %xmm8
+        addss     %xmm1, %xmm6
+        lea       20(%rsp), %rax
+        movss     %xmm6, (%rax)
+        movss     (%rax), %xmm0
+        subss     %xmm0, %xmm4
+        movaps    %xmm11, %xmm0
+        addss     %xmm1, %xmm4
+        lea       24(%rsp), %rcx
+        movss     %xmm4, (%rcx)
+        movss     (%rax), %xmm7
+        movss     (%rcx), %xmm3
+        addss     %xmm7, %xmm5
+        movss     %xmm5, (%rax)
+        movss     (%rax), %xmm6
+        subss     %xmm6, %xmm7
+        addss     %xmm10, %xmm7
+        movss     %xmm7, (%rcx)
+        movss     (%rax), %xmm10
+        mulss     %xmm10, %xmm8
+        movss     (%rcx), %xmm2
+        movss     %xmm8, (%rax)
+        addss     %xmm3, %xmm2
+        movss     (%rax), %xmm9
+        lea       28(%rsp), %rbx
+        movss     (%rbx), %xmm7
+        subss     %xmm10, %xmm9
+        mulss     %xmm7, %xmm0
+        mulss     (%rbx), %xmm2
+        movss     %xmm9, (%rcx)
+        movss     (%rax), %xmm14
+        movss     (%rcx), %xmm13
+        movss     .L_2il0floatpacket.130(%rip), %xmm9
+        subss     %xmm13, %xmm14
+        movss     %xmm14, (%rax)
+        movss     (%rax), %xmm15
+        subss     %xmm15, %xmm10
+        movss     %xmm10, (%rcx)
+        movss     (%rax), %xmm8
+        movss     (%rcx), %xmm10
+        movss     %xmm0, (%rax)
+        movss     (%rax), %xmm1
+        subss     (%rbx), %xmm1
+        movss     %xmm1, (%rcx)
+        movss     (%rax), %xmm5
+        movss     (%rcx), %xmm4
+        subss     %xmm4, %xmm5
+        movss     %xmm5, (%rax)
+        movss     (%rax), %xmm6
+        subss     %xmm6, %xmm7
+        movss     %xmm7, (%rcx)
+        movss     (%rax), %xmm1
+        movss     (%rcx), %xmm15
+        movaps    %xmm1, %xmm0
+        mulss     %xmm8, %xmm0
+        mulss     %xmm10, %xmm1
+        mulss     %xmm15, %xmm8
+        mulss     %xmm15, %xmm10
+        addss     %xmm8, %xmm1
+        movaps    %xmm0, %xmm8
+        addss     %xmm1, %xmm8
+        movaps    %xmm8, %xmm3
+        subss     %xmm8, %xmm0
+        addss     %xmm9, %xmm3
+        addss     %xmm0, %xmm1
+        movss     %xmm3, (%rax)
+        movaps    %xmm8, %xmm3
+        movss     (%rax), %xmm13
+        addss     %xmm1, %xmm10
+        subss     %xmm9, %xmm13
+        addss     %xmm2, %xmm10
+        movss     %xmm13, (%rsp)
+        movss     (%rsp), %xmm14
+        movss     .L_2il0floatpacket.133(%rip), %xmm2
+        subss     %xmm14, %xmm3
+        movss     %xmm3, 4(%rsp)
+        movss     4(%rsp), %xmm4
+        movl      (%rax), %eax
+        movl      %eax, %ebx
+        andl      $127, %eax
+        addss     %xmm10, %xmm4
+        mulss     %xmm4, %xmm2
+        addl      %eax, %eax
+        movss     -4(%rdx,%rax,4), %xmm1
+        shll      $10, %ebx
+        addss     .L_2il0floatpacket.132(%rip), %xmm2
+        mulss     %xmm4, %xmm2
+        sarl      $17, %ebx
+        addss     .L_2il0floatpacket.131(%rip), %xmm2
+        mulss     %xmm4, %xmm2
+        mulss     %xmm1, %xmm2
+        addss     (%rdx,%rax,4), %xmm2
+        movaps    %xmm2, %xmm5
+        addss     %xmm1, %xmm5
+        movss     %xmm5, 12(%rsp)
+        movzwl    14(%rsp), %edx
+        movl      %edx, %ecx
+        andl      $32640, %ecx
+        shrl      $7, %ecx
+        lea       -127(%rbx,%rcx), %eax
+        cmpl      $128, %eax
+        jge       .LBL_3_48
+
+
+        cmpl      $-126, %eax
+        jl        .LBL_3_43
+
+
+        andl      $-32641, %edx
+        lea       127(%rax), %eax
+        movzbl    %al, %eax
+        shll      $7, %eax
+        orl       %eax, %edx
+        movw      %dx, 14(%rsp)
+        movss     12(%rsp), %xmm0
+        mulss     %xmm12, %xmm0
+        movss     %xmm0, 12(%rsp)
+        movss     %xmm0, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_43:
+
+        cmpl      $-136, %eax
+        jl        .LBL_3_45
+
+
+        lea       20(%rsp), %rdx
+        movss     %xmm5, (%rdx)
+        movl      $1065353216, %eax
+        movss     (%rdx), %xmm0
+        addl      $191, %ebx
+        movl      %eax, 8(%rsp)
+        subss     %xmm0, %xmm1
+        shrl      $16, %eax
+        addss     %xmm1, %xmm2
+        movss     %xmm2, 24(%rsp)
+        movss     (%rdx), %xmm5
+        mulss     %xmm5, %xmm11
+        movss     24(%rsp), %xmm6
+        movss     %xmm11, (%rdx)
+        movss     (%rdx), %xmm1
+        movzwl    %ax, %edx
+        subss     %xmm5, %xmm1
+        lea       24(%rsp), %rax
+        movss     %xmm1, (%rax)
+        andl      $-32641, %edx
+        lea       20(%rsp), %rcx
+        movss     (%rcx), %xmm3
+        movss     (%rax), %xmm2
+        movzbl    %bl, %ebx
+        subss     %xmm2, %xmm3
+        movss     %xmm3, (%rcx)
+        movss     (%rcx), %xmm4
+        shll      $7, %ebx
+        subss     %xmm4, %xmm5
+        movss     %xmm5, (%rax)
+        orl       %ebx, %edx
+        movss     (%rcx), %xmm8
+        movss     (%rax), %xmm14
+        movw      %dx, 10(%rsp)
+        addss     %xmm6, %xmm14
+        movss     8(%rsp), %xmm7
+        mulss     %xmm7, %xmm14
+        mulss     %xmm7, %xmm8
+        lea       20(%rsp), %rdx
+        movl      $8388608, (%rdx)
+        addss     %xmm8, %xmm14
+        movss     (%rdx), %xmm10
+        movss     (%rdx), %xmm9
+        mulss     %xmm9, %xmm10
+        mulss     .L_2il0floatpacket.136(%rip), %xmm14
+        movss     %xmm10, (%rdx)
+        movss     (%rdx), %xmm13
+        addss     %xmm13, %xmm14
+        mulss     %xmm14, %xmm12
+        movss     %xmm14, 12(%rsp)
+        movss     %xmm12, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_45:
+
+        cmpl      $-159, %eax
+        jl        .LBL_3_47
+
+
+        movl      $1065353216, %eax
+        addl      $191, %ebx
+        movl      %eax, 8(%rsp)
+        shrl      $16, %eax
+        movzwl    %ax, %edx
+        movzbl    %bl, %ebx
+        andl      $-32641, %edx
+        shll      $7, %ebx
+        orl       %ebx, %edx
+        movw      %dx, 10(%rsp)
+        movss     8(%rsp), %xmm0
+        movss     .L_2il0floatpacket.136(%rip), %xmm1
+        mulss     %xmm0, %xmm5
+        mulss     %xmm1, %xmm12
+        lea       20(%rsp), %rdx
+        movl      $8388608, (%rdx)
+        movss     (%rdx), %xmm3
+        movss     (%rdx), %xmm2
+        mulss     %xmm2, %xmm3
+        mulss     %xmm12, %xmm5
+        movss     %xmm3, (%rdx)
+        movss     (%rdx), %xmm4
+        subss     %xmm4, %xmm5
+        movss     %xmm5, 12(%rsp)
+        movss     %xmm5, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_47:
+
+        lea       20(%rsp), %rax
+        movl      $8388608, (%rax)
+        movss     (%rax), %xmm1
+        movss     (%rax), %xmm0
+        mulss     %xmm0, %xmm1
+        movss     %xmm1, (%rax)
+        movss     (%rax), %xmm2
+        mulss     %xmm2, %xmm12
+        movss     %xmm12, 12(%rsp)
+        movss     %xmm12, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_48:
+
+        lea       20(%rsp), %rax
+        movl      $2130706432, (%rax)
+        movss     (%rax), %xmm1
+        movss     (%rax), %xmm0
+        mulss     %xmm0, %xmm1
+        movss     %xmm1, (%rax)
+        movss     (%rax), %xmm2
+        mulss     %xmm2, %xmm12
+        movss     %xmm12, 12(%rsp)
+        movss     %xmm12, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_49:
+
+        movb      11(%rsp), %al
+        lea       1472+__spow_ha_CoutTab(%rip), %rcx
+        andb      $-128, %al
+        movb      72(%rsp), %dl
+        shrb      $7, %al
+        xorb      %al, %dl
+        movzbl    %dl, %ebx
+        movss     (%rcx,%rbx,4), %xmm0
+        mulss     %xmm0, %xmm0
+        mulss     %xmm12, %xmm0
+        movss     %xmm0, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_50:
+
+        testl     %edx, %edx
+        jne       .LBL_3_52
+
+
+        cmpl      $0, 8(%rsp)
+        jne       .LBL_3_19
+
+.LBL_3_52:
+
+        lea       1484+__spow_ha_CoutTab(%rip), %rax
+        andl      $1, %edx
+        movl      (%rax,%rdx,4), %ecx
+        movl      %ecx, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_53:
+
+        movb      72(%rsp), %al
+        mulss     %xmm2, %xmm2
+        testb     %al, %al
+        je        .LBL_3_55
+
+
+        lea       1484+__spow_ha_CoutTab(%rip), %rax
+        andl      %r12d, %edx
+        movl      $1, 64(%rsp)
+        movss     (%rax,%rdx,4), %xmm0
+        divss     %xmm2, %xmm0
+        movss     %xmm0, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_55:
+
+        lea       1484+__spow_ha_CoutTab(%rip), %rax
+        andl      %r12d, %edx
+        movss     (%rax,%rdx,4), %xmm0
+        mulss     %xmm2, %xmm0
+        movss     %xmm0, (%r15)
+        jmp       .LBL_3_39
+
+.LBL_3_56:
+
+        testl     %ebx, %ebx
+        jne       .LBL_3_38
+
+
+        testl     %r13d, %r13d
+        jne       .LBL_3_13
+        jmp       .LBL_3_11
+
+.LBL_3_59:
+
+        movl      $0, 8(%rsp)
+        movss     %xmm2, 32(%rsp)
+        movss     %xmm0, 40(%rsp)
+        movss     %xmm3, 48(%rsp)
+        movss     %xmm4, 56(%rsp)
+
+        call      __spow_ha_TestIntFunc
+
+
+        movss     56(%rsp), %xmm4
+        movl      %eax, %edx
+        movss     48(%rsp), %xmm3
+        movss     40(%rsp), %xmm0
+        movss     32(%rsp), %xmm2
+        testl     %r12d, %r12d
+        jne       .LBL_3_12
+
+
+        cmpl      $127, %r14d
+        jne       .LBL_3_12
+
+
+        testl     %ebx, %ebx
+        je        .LBL_3_13
+        jmp       .LBL_3_38
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_spow_ha_cout_rare_internal,@function
+	.size	__svml_spow_ha_cout_rare_internal,.-__svml_spow_ha_cout_rare_internal
+..LN__svml_spow_ha_cout_rare_internal.2:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_spow_ha_data_internal_avx512:
+	.long	0
+	.long	3174420480
+	.long	3182632960
+	.long	3187958784
+	.long	3190685696
+	.long	3193338880
+	.long	3195920384
+	.long	3197247488
+	.long	3198473216
+	.long	3199668736
+	.long	3200835072
+	.long	3201974272
+	.long	3203086848
+	.long	3204174848
+	.long	3204843520
+	.long	3205364224
+	.long	1054113792
+	.long	1053115392
+	.long	1052137472
+	.long	1051179008
+	.long	1050238976
+	.long	1049316864
+	.long	1048248320
+	.long	1046471680
+	.long	1044726784
+	.long	1043013632
+	.long	1041329152
+	.long	1039161344
+	.long	1035907072
+	.long	1032706048
+	.long	1027317760
+	.long	1018830848
+	.long	0
+	.long	3067311503
+	.long	890262383
+	.long	916311190
+	.long	3058814943
+	.long	914835756
+	.long	3056977939
+	.long	3052757441
+	.long	905348701
+	.long	921801496
+	.long	900652061
+	.long	916473404
+	.long	3063873943
+	.long	3048020321
+	.long	3055557319
+	.long	921573027
+	.long	3050426335
+	.long	918574590
+	.long	913737309
+	.long	3045697063
+	.long	3029223305
+	.long	866568163
+	.long	3063765991
+	.long	3057827840
+	.long	910185982
+	.long	3062847489
+	.long	917965485
+	.long	903301016
+	.long	882039287
+	.long	910858241
+	.long	3059117133
+	.long	3029061382
+	.long	1065353216
+	.long	1065536903
+	.long	1065724611
+	.long	1065916431
+	.long	1066112450
+	.long	1066312762
+	.long	1066517459
+	.long	1066726640
+	.long	1066940400
+	.long	1067158842
+	.long	1067382066
+	.long	1067610179
+	.long	1067843287
+	.long	1068081499
+	.long	1068324927
+	.long	1068573686
+	.long	1068827891
+	.long	1069087663
+	.long	1069353124
+	.long	1069624397
+	.long	1069901610
+	.long	1070184894
+	.long	1070474380
+	.long	1070770206
+	.long	1071072509
+	.long	1071381432
+	.long	1071697119
+	.long	1072019719
+	.long	1072349383
+	.long	1072686266
+	.long	1073030525
+	.long	1073382323
+	.long	0
+	.long	3007986186
+	.long	860277610
+	.long	3010384254
+	.long	2991457809
+	.long	3008462297
+	.long	860562562
+	.long	3004532446
+	.long	856238081
+	.long	3001480295
+	.long	857441778
+	.long	815380209
+	.long	3003456168
+	.long	3001196762
+	.long	2986372182
+	.long	3006683458
+	.long	848495278
+	.long	851809756
+	.long	3003311522
+	.long	2995654817
+	.long	833868005
+	.long	3004843819
+	.long	835836658
+	.long	3003498340
+	.long	2994528642
+	.long	3002229827
+	.long	2981408986
+	.long	2983889551
+	.long	2983366846
+	.long	3000350873
+	.long	833659207
+	.long	2987748092
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1069547520
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1049872133
+	.long	1049872133
+	.long	1049872133
+	.long	1049872133
+	.long	1049872133
+	.long	1049872133
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+	.type	.L_2il0floatpacket.132,@object
+	.size	.L_2il0floatpacket.132,4
+	.align 4
+.L_2il0floatpacket.133:
+	.long	0x3d635847
+	.type	.L_2il0floatpacket.133,@object
+	.size	.L_2il0floatpacket.133,4
+	.align 4
+.L_2il0floatpacket.134:
+	.long	0x00800000
+	.type	.L_2il0floatpacket.134,@object
+	.size	.L_2il0floatpacket.134,4
+	.align 4
+.L_2il0floatpacket.135:
+	.long	0x7f000000
+	.type	.L_2il0floatpacket.135,@object
+	.size	.L_2il0floatpacket.135,4
+	.align 4
+.L_2il0floatpacket.136:
+	.long	0x1f800000
+	.type	.L_2il0floatpacket.136,@object
+	.size	.L_2il0floatpacket.136,4
+	.align 4
+.L_2il0floatpacket.137:
+	.long	0x3f800000
+	.type	.L_2il0floatpacket.137,@object
+	.size	.L_2il0floatpacket.137,4
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_s_la.s
index cc303bd16a0..31900b83d8e 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_pow_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_d_ha.s
new file mode 100644
index 00000000000..027c694fb24
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_d_ha.s
@@ -0,0 +1,17700 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *     (high accuracy implementation: < 1 ulp)
+ * 
+ *     Argument representation:
+ *     arg = N*Pi/2^k + r = B + (z+e)
+ *             B = N*Pi/2^k
+ *             z+e = r
+ * 
+ *     Result calculation:
+ *     sin(arg) = sin(B+z+e) =
+ *     = [ (SHi+Sigma*z)+CHL*z ] + [ (CHL+Sigma)*PS + PC*SHi + e*(CHL+Sigma-SHi*z) + SLo ]
+ *             Sigma = 2^round(log[2](cosB))
+ *             CHL = cosB - Sigma
+ *             SHi+SLo = sinB
+ *             PS = sin(z) - z
+ *             PC = cos(z)- 1
+ *     Sigma, CHL, SHi, SLo are stored in table
+ *     PS, PC are approximated by polynomials
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_sin8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_sin8_ha
+
+__svml_sin8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        xorl      %edx, %edx
+
+/* start arg. reduction */
+        vmovups   192+__svml_dsin_ha_data_internal(%rip), %zmm9
+        vmovups   128+__svml_dsin_ha_data_internal(%rip), %zmm7
+        vmovups   256+__svml_dsin_ha_data_internal(%rip), %zmm10
+        vmovups   320+__svml_dsin_ha_data_internal(%rip), %zmm1
+        vmovups   384+__svml_dsin_ha_data_internal(%rip), %zmm15
+        vmovups   64+__svml_dsin_ha_data_internal(%rip), %zmm5
+        vmovups   640+__svml_dsin_ha_data_internal(%rip), %zmm2
+
+/* Table lookup: Th, Tl/Th */
+        vmovups   512+__svml_dsin_ha_data_internal(%rip), %zmm3
+        vmovups   768+__svml_dsin_ha_data_internal(%rip), %zmm6
+        vmovaps   %zmm0, %zmm8
+        vfmadd213pd {rn-sae}, %zmm9, %zmm8, %zmm7
+
+/* start polynomial evaluation */
+        vmovups   1024+__svml_dsin_ha_data_internal(%rip), %zmm0
+        vpcmpeqq  896+__svml_dsin_ha_data_internal(%rip), %zmm8, %k2
+
+/*
+ * mask will be used to decide whether long arg. reduction is needed
+ * dN
+ */
+        vsubpd    {rn-sae}, %zmm9, %zmm7, %zmm14
+        vandpd    __svml_dsin_ha_data_internal(%rip), %zmm8, %zmm11
+        vpermt2pd 704+__svml_dsin_ha_data_internal(%rip), %zmm7, %zmm2
+        vpermt2pd 576+__svml_dsin_ha_data_internal(%rip), %zmm7, %zmm3
+        vpermt2pd 832+__svml_dsin_ha_data_internal(%rip), %zmm7, %zmm6
+
+/* set sign of zero */
+        vxorpd    960+__svml_dsin_ha_data_internal(%rip), %zmm7, %zmm7{%k2}
+        vfnmadd213pd {rn-sae}, %zmm8, %zmm14, %zmm10
+        vcmppd    $22, {sae}, %zmm5, %zmm11, %k1
+        vpsrlq    $4, %zmm7, %zmm7
+
+/* will branch if long arg. reduction needed */
+        kortestw  %k1, %k1
+
+/* continue argument reduction */
+        vmovaps   %zmm1, %zmm13
+        vfnmadd213pd {rn-sae}, %zmm10, %zmm14, %zmm13
+        vmovaps   %zmm15, %zmm4
+        vfnmadd213pd {rn-sae}, %zmm13, %zmm14, %zmm4
+
+/* (dN*dPI2)_h */
+        vsubpd    {rn-sae}, %zmm13, %zmm10, %zmm12
+        vmovups   1088+__svml_dsin_ha_data_internal(%rip), %zmm10
+
+/* dR^2 */
+        vmulpd    {rn-sae}, %zmm4, %zmm4, %zmm5
+
+/* -(dN*dPI2)_l */
+        vfnmadd231pd {rn-sae}, %zmm14, %zmm1, %zmm12
+
+/* -(dN*dPI3)_h */
+        vsubpd    {rn-sae}, %zmm13, %zmm4, %zmm13
+        vfmadd231pd {rn-sae}, %zmm5, %zmm0, %zmm10
+        vmovups   1216+__svml_dsin_ha_data_internal(%rip), %zmm0
+
+/* (dN*dPI3)_l */
+        vfmadd213pd {rn-sae}, %zmm13, %zmm15, %zmm14
+        vmovups   1152+__svml_dsin_ha_data_internal(%rip), %zmm15
+        vmovups   1280+__svml_dsin_ha_data_internal(%rip), %zmm13
+
+/* R_low */
+        vsubpd    {rn-sae}, %zmm14, %zmm12, %zmm14
+        vfmadd231pd {rn-sae}, %zmm5, %zmm15, %zmm0
+        vfmadd213pd {rn-sae}, %zmm13, %zmm5, %zmm10
+        vmovups   1408+__svml_dsin_ha_data_internal(%rip), %zmm15
+        vmovups   1344+__svml_dsin_ha_data_internal(%rip), %zmm12
+
+/* Sl */
+        vpsllq    $32, %zmm6, %zmm13
+        vfmadd213pd {rn-sae}, %zmm15, %zmm5, %zmm10
+        vfmadd213pd {rn-sae}, %zmm12, %zmm5, %zmm0
+        vmovups   1472+__svml_dsin_ha_data_internal(%rip), %zmm15
+
+/* Sh + R*Ch */
+        vmovaps   %zmm2, %zmm9
+        vfmadd213pd {rn-sae}, %zmm3, %zmm4, %zmm9
+        vfmadd213pd {rn-sae}, %zmm15, %zmm5, %zmm0
+
+/* (R*Ch)_high */
+        vsubpd    {rn-sae}, %zmm3, %zmm9, %zmm1
+
+/* Ch - Sh*R */
+        vmovaps   %zmm3, %zmm12
+
+/* (R*Ch_low) */
+        vfmsub231pd {rn-sae}, %zmm2, %zmm4, %zmm1
+        vfnmadd213pd {rn-sae}, %zmm2, %zmm4, %zmm12
+
+/* R*Ch */
+        vmulpd    {rn-sae}, %zmm4, %zmm2, %zmm2
+
+/* Sh*dCPoly */
+        vmulpd    {rn-sae}, %zmm10, %zmm3, %zmm3
+
+/* (R*Ch_low) + R*Cl */
+        vfmadd213pd {rn-sae}, %zmm1, %zmm4, %zmm6
+
+/* Sl + R_low*(Ch-Sh*R) */
+        vfmadd213pd {rn-sae}, %zmm13, %zmm14, %zmm12
+        vpsllq    $63, %zmm7, %zmm1
+
+/* Sh*dCPoly + R*Ch*dSPoly */
+        vfmadd213pd {rn-sae}, %zmm3, %zmm2, %zmm0
+
+/* dLow = Sl + R_low*(Ch-Sh*R) + (R*Ch_low) + R*Cl */
+        vaddpd    {rn-sae}, %zmm12, %zmm6, %zmm4
+
+/* R^2*(Sh*dCPoly + R*Ch*dSPoly) + dLow */
+        vfmadd213pd {rn-sae}, %zmm4, %zmm5, %zmm0
+        vaddpd    {rn-sae}, %zmm0, %zmm9, %zmm6
+        vxorpd    %zmm1, %zmm6, %zmm0
+        jne       .LBL_1_12
+
+.LBL_1_2:
+
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_4:
+
+        vmovups   %zmm8, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dsin_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+
+/* ............Check for Inf and NaNs................. */
+        vmovups   16512+__svml_dsin_ha_data_internal_ha(%rip), %zmm1
+
+/*
+ * Get the (2^a / 2pi) mod 1 values from the table.
+ * Because VLANG doesn't have L-type gather, we need a trivial cast
+ */
+        lea       __svml_dsin_ha_reduction_data_internal(%rip), %rax
+        vmovups   %zmm8, 64(%rsp)
+        vmovups   %zmm0, (%rsp)
+        vandpd    %zmm11, %zmm1, %zmm6
+        vxorpd    %zmm8, %zmm11, %zmm8
+        vpbroadcastq .L_2il0floatpacket.27(%rip), %zmm10
+        vcmppd    $4, {sae}, %zmm1, %zmm6, %k2
+        vmovups   %zmm8, 128(%rsp)
+        vmovaps   %zmm10, %zmm5
+
+/*
+ * The output is _VRES_Z (high) + _VRES_E (low), and the integer part is _VRES_IND
+ * Now resume the original code.
+ * ..................... Table look-up ........................
+ */
+        lea       __svml_dsin_ha_data_internal_ha(%rip), %rcx
+        vpandq    .L_2il0floatpacket.28(%rip){1to8}, %zmm11, %zmm4
+        vpandnq   %zmm6, %zmm6, %zmm5{%k2}
+        vpsrlq    $52, %zmm4, %zmm3
+        vcmppd    $3, {sae}, %zmm5, %zmm5, %k0
+        vpsllq    $1, %zmm3, %zmm15
+
+/*
+ * Break the P_xxx and m into 32-bit chunks ready for
+ * the long multiplication via 32x32->64 multiplications
+ */
+        vpbroadcastq .L_2il0floatpacket.31(%rip), %zmm6
+        kxnorw    %k0, %k0, %k3
+        kxnorw    %k0, %k0, %k2
+        kmovw     %k0, %edx
+        vpaddq    %zmm3, %zmm15, %zmm2
+        vpsllq    $3, %zmm2, %zmm14
+        vpmovqd   %zmm14, %ymm12
+        vpxord    %zmm1, %zmm1, %zmm1
+        vgatherdpd (%rax,%ymm12), %zmm1{%k3}
+        kxnorw    %k0, %k0, %k3
+        vpsrlq    $32, %zmm1, %zmm7
+        vpxord    %zmm5, %zmm5, %zmm5
+        vpxord    %zmm4, %zmm4, %zmm4
+        vgatherdpd 8(%rax,%ymm12), %zmm5{%k2}
+        vgatherdpd 16(%rax,%ymm12), %zmm4{%k3}
+        vpsrlq    $32, %zmm5, %zmm9
+        vpsrlq    $32, %zmm4, %zmm13
+
+/*
+ * Also get the significand as an integer
+ * NB: adding in the integer bit is wrong for denorms!
+ * To make this work for denorms we should do something slightly different
+ */
+        vpandq    .L_2il0floatpacket.29(%rip){1to8}, %zmm11, %zmm8
+        vpaddq    .L_2il0floatpacket.30(%rip){1to8}, %zmm8, %zmm15
+        vpsrlq    $32, %zmm15, %zmm8
+        vpmullq   %zmm9, %zmm8, %zmm3
+        vpandq    %zmm6, %zmm1, %zmm0
+        vpandq    %zmm6, %zmm5, %zmm12
+        vpandq    %zmm6, %zmm4, %zmm5
+        vpandq    %zmm6, %zmm15, %zmm14
+
+/* Now do the big multiplication and carry propagation */
+        vpmullq   %zmm0, %zmm8, %zmm4
+        vpmullq   %zmm12, %zmm8, %zmm2
+        vpmullq   %zmm13, %zmm8, %zmm1
+        vpmullq   %zmm5, %zmm8, %zmm8
+        vpmullq   %zmm0, %zmm14, %zmm5
+        vpmullq   %zmm12, %zmm14, %zmm0
+        vpmullq   %zmm13, %zmm14, %zmm13
+        vpmullq   %zmm7, %zmm14, %zmm7
+        vpmullq   %zmm9, %zmm14, %zmm9
+        vpsrlq    $32, %zmm13, %zmm14
+        vpsrlq    $32, %zmm0, %zmm13
+        vpsrlq    $32, %zmm5, %zmm12
+        vpsrlq    $32, %zmm9, %zmm15
+        vpaddq    %zmm14, %zmm1, %zmm1
+        vpaddq    %zmm13, %zmm2, %zmm14
+        vpaddq    %zmm12, %zmm4, %zmm2
+        vpaddq    %zmm15, %zmm3, %zmm13
+        vpandq    %zmm6, %zmm7, %zmm7
+        vpaddq    %zmm2, %zmm7, %zmm4
+        vpsrlq    $32, %zmm8, %zmm2
+        vpandq    %zmm6, %zmm0, %zmm0
+        vpaddq    %zmm1, %zmm0, %zmm1
+        vpaddq    %zmm1, %zmm2, %zmm15
+        vpsrlq    $32, %zmm15, %zmm8
+        vpandq    %zmm6, %zmm9, %zmm9
+        vpaddq    %zmm14, %zmm9, %zmm0
+        vpaddq    %zmm0, %zmm8, %zmm12
+
+/*
+ * Now round at the 2^-9 bit position for reduction mod pi/2^8
+ * instead of the original 2pi (but still with the same 2pi scaling).
+ * Use a shifter of 2^43 + 2^42.
+ * The N we get is our final version; it has an offset of
+ * 2^9 because of the implicit integer bit, and anyway for negative
+ * starting value it's a 2s complement thing. But we need to mask
+ * off the exponent part anyway so it's fine.
+ */
+        vpbroadcastq .L_2il0floatpacket.34(%rip), %zmm0
+        vpsrlq    $32, %zmm12, %zmm7
+        vpandq    %zmm6, %zmm5, %zmm3
+        vpaddq    %zmm13, %zmm3, %zmm5
+        vpaddq    %zmm5, %zmm7, %zmm3
+        vpsllq    $32, %zmm12, %zmm7
+        vpsrlq    $32, %zmm3, %zmm9
+        vpaddq    %zmm4, %zmm9, %zmm2
+
+/* Assemble reduced argument from the pieces */
+        vpandq    %zmm6, %zmm15, %zmm13
+        vpandq    %zmm6, %zmm3, %zmm8
+        vpsllq    $32, %zmm2, %zmm6
+        vpaddq    %zmm13, %zmm7, %zmm4
+
+/*
+ * If the magnitude of the input is <= 2^-20, then
+ * just pass through the input, since no reduction will be needed and
+ * the main path will only work accurately if the reduced argument is
+ * about >= 2^-70 (which it is for all large pi multiples)
+ */
+        vpbroadcastq .L_2il0floatpacket.43(%rip), %zmm13
+        vpaddq    %zmm8, %zmm6, %zmm15
+        vpsrlq    $12, %zmm15, %zmm1
+
+/*
+ * We want to incorporate the original sign now too.
+ * Do it here for convenience in getting the right N value,
+ * though we could wait right to the end if we were prepared
+ * to modify the sign of N later too.
+ * So get the appropriate sign mask now (or sooner).
+ */
+        vpandq    .L_2il0floatpacket.32(%rip){1to8}, %zmm11, %zmm3
+
+/*
+ * Create floating-point high part, implicitly adding integer bit 1
+ * Incorporate overall sign at this stage too.
+ */
+        vpxorq    .L_2il0floatpacket.33(%rip){1to8}, %zmm3, %zmm6
+        vporq     %zmm6, %zmm1, %zmm5
+        vaddpd    {rn-sae}, %zmm5, %zmm0, %zmm2
+        vsubpd    {rn-sae}, %zmm0, %zmm2, %zmm9
+        vsubpd    {rn-sae}, %zmm9, %zmm5, %zmm9
+        vpandq    .L_2il0floatpacket.37(%rip){1to8}, %zmm4, %zmm12
+        vpsllq    $28, %zmm12, %zmm14
+        vpsrlq    $24, %zmm4, %zmm4
+        vpandq    .L_2il0floatpacket.39(%rip){1to8}, %zmm15, %zmm15
+
+/*
+ * Create floating-point low and medium parts, respectively
+ * lo_23, ... lo_0, 0, ..., 0
+ * hi_11, ... hi_0, lo_63, ..., lo_24
+ * then subtract off the implicitly added integer bits,
+ * 2^-104 and 2^-52, respectively.
+ * Put the original sign into all of them at this stage.
+ */
+        vpxorq    .L_2il0floatpacket.36(%rip){1to8}, %zmm3, %zmm7
+        vpxorq    .L_2il0floatpacket.38(%rip){1to8}, %zmm3, %zmm1
+        vpsllq    $40, %zmm15, %zmm3
+        vporq     %zmm7, %zmm14, %zmm8
+        vsubpd    {rn-sae}, %zmm7, %zmm8, %zmm5
+        vandpd    .L_2il0floatpacket.42(%rip){1to8}, %zmm11, %zmm14
+        vcmppd    $22, {sae}, %zmm13, %zmm14, %k3
+        vcmppd    $26, {sae}, %zmm13, %zmm14, %k2
+        vporq     %zmm4, %zmm3, %zmm8
+        vporq     %zmm1, %zmm8, %zmm7
+        vsubpd    {rn-sae}, %zmm1, %zmm7, %zmm6
+
+/*
+ * Now multiply those numbers all by 2 pi, reasonably accurately.
+ * (RHi + RLo) * (pi_lead + pi_trail) ~=
+ * RHi * pi_lead + (RHi * pi_trail + RLo * pi_lead)
+ */
+        vpbroadcastq .L_2il0floatpacket.40(%rip), %zmm4
+
+/* Now add them up into 2 reasonably aligned pieces */
+        vaddpd    {rn-sae}, %zmm6, %zmm9, %zmm12
+        vmulpd    {rn-sae}, %zmm4, %zmm12, %zmm15
+        vsubpd    {rn-sae}, %zmm12, %zmm9, %zmm9
+        vmovaps   %zmm4, %zmm3
+        vfmsub213pd {rn-sae}, %zmm15, %zmm12, %zmm3
+        vmovaps   %zmm10, %zmm1
+
+/* Grab our final N value as an integer, appropriately masked mod 2^9 */
+        vpandq    .L_2il0floatpacket.35(%rip){1to8}, %zmm2, %zmm0
+        vaddpd    {rn-sae}, %zmm9, %zmm6, %zmm2
+        vaddpd    {rn-sae}, %zmm5, %zmm2, %zmm6
+        vpbroadcastq .L_2il0floatpacket.41(%rip), %zmm5
+        vfmadd213pd {rn-sae}, %zmm3, %zmm5, %zmm12
+        vfmadd213pd {rn-sae}, %zmm12, %zmm4, %zmm6
+        vmovups   16960+__svml_dsin_ha_data_internal_ha(%rip), %zmm4
+        vpandnq   %zmm14, %zmm14, %zmm10{%k3}
+        vandpd    %zmm11, %zmm10, %zmm11
+        vpsllq    $5, %zmm0, %zmm10
+        kxnorw    %k0, %k0, %k3
+        vpandnq   %zmm14, %zmm14, %zmm1{%k2}
+        vandpd    %zmm15, %zmm1, %zmm8
+        vandpd    %zmm6, %zmm1, %zmm1
+        vpmovqd   %zmm10, %ymm6
+        vorpd     %zmm8, %zmm11, %zmm7
+        vmovups   17216+__svml_dsin_ha_data_internal_ha(%rip), %zmm14
+        vmovups   17088+__svml_dsin_ha_data_internal_ha(%rip), %zmm15
+
+/* ............... Polynomial approximation ................... */
+        vmulpd    {rn-sae}, %zmm7, %zmm7, %zmm11
+        kxnorw    %k0, %k0, %k2
+
+/* ................. Reconstruction: res_hi ................... */
+        vmovaps   %zmm7, %zmm0
+        vmovaps   %zmm7, %zmm8
+        vpxord    %zmm9, %zmm9, %zmm9
+        vgatherdpd (%rcx,%ymm6), %zmm9{%k2}
+        kxnorw    %k0, %k0, %k2
+        vpxord    %zmm12, %zmm12, %zmm12
+        vpxord    %zmm10, %zmm10, %zmm10
+        vgatherdpd 8(%rcx,%ymm6), %zmm12{%k3}
+        vgatherdpd 16(%rcx,%ymm6), %zmm10{%k2}
+        kxnorw    %k0, %k0, %k3
+        vfmadd213pd {rn-sae}, %zmm10, %zmm12, %zmm0
+        vfmadd132pd {rn-sae}, %zmm9, %zmm0, %zmm8
+
+/* ................. Reconstruction: res_lo ................... */
+        vsubpd    {rn-sae}, %zmm0, %zmm10, %zmm3
+        vsubpd    {rn-sae}, %zmm8, %zmm0, %zmm2
+        vmovups   17024+__svml_dsin_ha_data_internal_ha(%rip), %zmm0
+        vfmadd231pd {rn-sae}, %zmm7, %zmm12, %zmm3
+        vfmadd231pd {rn-sae}, %zmm9, %zmm7, %zmm2
+        vfmadd231pd {rn-sae}, %zmm11, %zmm0, %zmm4
+        vaddpd    {rn-sae}, %zmm3, %zmm2, %zmm13
+        vaddpd    {rn-sae}, %zmm12, %zmm9, %zmm2
+        vmulpd    {rn-sae}, %zmm11, %zmm4, %zmm5
+        vmulpd    {rn-sae}, %zmm7, %zmm5, %zmm0
+
+/* ................. Reconstruction: res_lo+corr ................... */
+        vfnmadd213pd {rn-sae}, %zmm2, %zmm10, %zmm7
+        vfmadd213pd {rn-sae}, %zmm13, %zmm2, %zmm0
+        vmovups   17152+__svml_dsin_ha_data_internal_ha(%rip), %zmm13
+        vfmadd231pd {rn-sae}, %zmm11, %zmm14, %zmm13
+        vfmadd213pd {rn-sae}, %zmm15, %zmm11, %zmm13
+        vmulpd    {rn-sae}, %zmm11, %zmm13, %zmm3
+        vfmadd213pd {rn-sae}, %zmm0, %zmm10, %zmm3
+        vmovups   (%rsp), %zmm0
+        vpxord    %zmm10, %zmm10, %zmm10
+        vgatherdpd 24(%rcx,%ymm6), %zmm10{%k3}
+        vfmadd213pd {rn-sae}, %zmm10, %zmm7, %zmm1
+        vaddpd    {rn-sae}, %zmm1, %zmm3, %zmm7
+
+/* .................. Final reconstruction ................... */
+        vaddpd    {rn-sae}, %zmm7, %zmm8, %zmm11
+        vmovups   64(%rsp), %zmm8
+        vxorpd    128(%rsp), %zmm11, %zmm0{%k1}
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sin8_ha,@function
+	.size	__svml_sin8_ha,.-__svml_sin8_ha
+..LN__svml_sin8_ha.0:
+
+.L_2__routine_start___svml_dsin_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dsin_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L67:
+
+        movzwl    6(%rdi), %eax
+        andl      $32752, %eax
+        movsd     (%rdi), %xmm1
+        movb      7(%rdi), %dl
+        andb      $127, %dl
+        movsd     %xmm1, -8(%rsp)
+        cmpl      $32752, %eax
+        jne       .LBL_2_6
+
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_5
+
+
+        movb      %dl, -1(%rsp)
+        cmpl      $2146435072, -4(%rsp)
+        jne       .LBL_2_5
+
+
+        movsd     __dsin_ha_CoutTab(%rip), %xmm0
+        movl      $1, %eax
+        mulsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_5:
+
+        mulsd     %xmm1, %xmm1
+        xorl      %eax, %eax
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_6:
+
+        xorl      %eax, %eax
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dsin_ha_cout_rare_internal,@function
+	.size	__svml_dsin_ha_cout_rare_internal,.-__svml_dsin_ha_cout_rare_internal
+..LN__svml_dsin_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dsin_ha_data_internal:
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	0
+	.long	1097859072
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	120
+	.long	0
+	.long	0
+	.long	0
+	.long	1013556747
+	.long	1070135480
+	.long	2796464483
+	.long	1071152610
+	.long	967731400
+	.long	1071761211
+	.long	1719614413
+	.long	1072079006
+	.long	688824739
+	.long	1072339814
+	.long	3476196678
+	.long	1072533611
+	.long	3489094832
+	.long	1072652951
+	.long	0
+	.long	1072693248
+	.long	3489094832
+	.long	1072652951
+	.long	3476196678
+	.long	1072533611
+	.long	688824739
+	.long	1072339814
+	.long	1719614413
+	.long	1072079006
+	.long	967731400
+	.long	1071761211
+	.long	2796464483
+	.long	1071152610
+	.long	1013556747
+	.long	1070135480
+	.long	0
+	.long	1072693248
+	.long	3489094832
+	.long	1072652951
+	.long	3476196678
+	.long	1072533611
+	.long	688824739
+	.long	1072339814
+	.long	1719614413
+	.long	1072079006
+	.long	967731400
+	.long	1071761211
+	.long	2796464483
+	.long	1071152610
+	.long	1013556747
+	.long	1070135480
+	.long	0
+	.long	0
+	.long	1013556747
+	.long	3217619128
+	.long	2796464483
+	.long	3218636258
+	.long	967731400
+	.long	3219244859
+	.long	1719614413
+	.long	3219562654
+	.long	688824739
+	.long	3219823462
+	.long	3476196678
+	.long	3220017259
+	.long	3489094832
+	.long	3220136599
+	.long	0
+	.long	0
+	.long	3160567065
+	.long	1014325783
+	.long	3160878317
+	.long	1014257638
+	.long	1015752157
+	.long	1010431536
+	.long	3163282740
+	.long	3163282740
+	.long	1010431536
+	.long	1015752157
+	.long	1014257638
+	.long	3160878317
+	.long	1014325783
+	.long	3160567065
+	.long	0
+	.long	0
+	.long	1014325783
+	.long	1013083417
+	.long	1014257638
+	.long	1013394669
+	.long	1010431536
+	.long	3163235805
+	.long	3163282740
+	.long	1015799092
+	.long	1015752157
+	.long	3157915184
+	.long	3160878317
+	.long	3161741286
+	.long	3160567065
+	.long	3161809431
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	16
+	.long	0
+	.long	16
+	.long	0
+	.long	16
+	.long	0
+	.long	16
+	.long	0
+	.long	16
+	.long	0
+	.long	16
+	.long	0
+	.long	16
+	.long	0
+	.long	16
+	.long	0
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	3654764988
+	.long	1056571351
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	123565048
+	.long	3210133868
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	3525997533
+	.long	1053236375
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	248520414
+	.long	3207201184
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	1431627990
+	.long	1067799893
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	286321053
+	.long	1065423121
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	4294967292
+	.long	3219128319
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.long	1431655765
+	.long	3217380693
+	.type	__svml_dsin_ha_data_internal,@object
+	.size	__svml_dsin_ha_data_internal,1536
+	.align 64
+__svml_dsin_ha_data_internal_ha:
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2376373521
+	.long	3205741868
+	.long	0
+	.long	1072693248
+	.long	4242458500
+	.long	1065951697
+	.long	2684354560
+	.long	1009354638
+	.long	3650591271
+	.long	3207838971
+	.long	0
+	.long	1072693248
+	.long	4154680958
+	.long	1067000149
+	.long	0
+	.long	3153796451
+	.long	69373323
+	.long	3209049152
+	.long	0
+	.long	1072693248
+	.long	1972655565
+	.long	1067636837
+	.long	1073741824
+	.long	1009157878
+	.long	220529721
+	.long	3209935929
+	.long	0
+	.long	1072693248
+	.long	4044216340
+	.long	1068048229
+	.long	0
+	.long	3156808381
+	.long	3810305407
+	.long	3210663220
+	.long	0
+	.long	1072693248
+	.long	2046304480
+	.long	1068459374
+	.long	3758096384
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+	.long	4135194227
+	.long	1648302778
+	.long	3570923199
+	.long	531601349
+	.long	3050610411
+	.long	3727247947
+	.long	3975421159
+	.long	3296605556
+	.long	2846879102
+	.long	1063202699
+	.long	1806253526
+	.long	3159528599
+	.long	3655875023
+	.long	2298243816
+	.long	1398790909
+	.long	2126405399
+	.long	3612507052
+	.long	2024089902
+	.long	3016782751
+	.long	301520336
+	.long	2797581819
+	.long	4252810799
+	.long	2930046808
+	.long	4048179805
+	.long	1738598206
+	.long	603040673
+	.long	1300196342
+	.long	4210654302
+	.long	1565126321
+	.long	3801392314
+	.long	3477196413
+	.long	1206081346
+	.long	2600392684
+	.long	4126341309
+	.long	3130252643
+	.long	3307817333
+	.long	2659425531
+	.long	2412162692
+	.long	905818072
+	.long	3957715323
+	.long	1965537991
+	.long	2320667370
+	.long	1023883767
+	.long	529358088
+	.long	1811636145
+	.long	3620463350
+	.long	3931075983
+	.long	346367444
+	.long	2047767535
+	.long	1058716176
+	.long	3623272290
+	.long	2945959404
+	.long	3567184671
+	.long	692734889
+	.long	4095535070
+	.long	2117432353
+	.long	2951577284
+	.long	1596951513
+	.long	2839402047
+	.long	1385469779
+	.long	3896102844
+	.long	4234864706
+	.long	1608187272
+	.long	3193903027
+	.long	1383836798
+	.long	2770939558
+	.long	3497238392
+	.long	4174762117
+	.long	3216374545
+	.long	2092838759
+	.long	2767673597
+	.long	1246911821
+	.long	2699509489
+	.long	4054556938
+	.long	2137781795
+	.long	4185677519
+	.long	1240379898
+	.long	2493823642
+	.long	1104051682
+	.long	3814146581
+	.long	4275563591
+	.long	4076387742
+	.long	2480759797
+	.long	692679989
+	.long	2208103365
+	.long	3333325867
+	.long	4256159887
+	.long	3857808189
+	.long	666552299
+	.long	1385359979
+	.long	121239434
+	.long	2371684438
+	.long	4217352479
+	.long	3420649082
+	.long	1333104599
+	.long	2770719959
+	.long	242478868
+	.long	448401580
+	.long	4139737663
+	.long	2546330868
+	.long	2666209199
+	.long	1246472623
+	.long	484957737
+	.long	896803160
+	.long	3984508030
+	.type	__svml_dsin_ha_reduction_data_internal,@object
+	.size	__svml_dsin_ha_reduction_data_internal,49152
+	.space 512, 0x00 	
+	.align 8
+.L_2il0floatpacket.27:
+	.long	0xffffffff,0xffffffff
+	.type	.L_2il0floatpacket.27,@object
+	.size	.L_2il0floatpacket.27,8
+	.align 8
+.L_2il0floatpacket.28:
+	.long	0x00000000,0x7ff00000
+	.type	.L_2il0floatpacket.28,@object
+	.size	.L_2il0floatpacket.28,8
+	.align 8
+.L_2il0floatpacket.29:
+	.long	0xffffffff,0x000fffff
+	.type	.L_2il0floatpacket.29,@object
+	.size	.L_2il0floatpacket.29,8
+	.align 8
+.L_2il0floatpacket.30:
+	.long	0x00000000,0x00100000
+	.type	.L_2il0floatpacket.30,@object
+	.size	.L_2il0floatpacket.30,8
+	.align 8
+.L_2il0floatpacket.31:
+	.long	0xffffffff,0x00000000
+	.type	.L_2il0floatpacket.31,@object
+	.size	.L_2il0floatpacket.31,8
+	.align 8
+.L_2il0floatpacket.32:
+	.long	0x00000000,0x80000000
+	.type	.L_2il0floatpacket.32,@object
+	.size	.L_2il0floatpacket.32,8
+	.align 8
+.L_2il0floatpacket.33:
+	.long	0x00000000,0x3ff00000
+	.type	.L_2il0floatpacket.33,@object
+	.size	.L_2il0floatpacket.33,8
+	.align 8
+.L_2il0floatpacket.34:
+	.long	0x00000000,0x42a80000
+	.type	.L_2il0floatpacket.34,@object
+	.size	.L_2il0floatpacket.34,8
+	.align 8
+.L_2il0floatpacket.35:
+	.long	0x000001ff,0x00000000
+	.type	.L_2il0floatpacket.35,@object
+	.size	.L_2il0floatpacket.35,8
+	.align 8
+.L_2il0floatpacket.36:
+	.long	0x00000000,0x39700000
+	.type	.L_2il0floatpacket.36,@object
+	.size	.L_2il0floatpacket.36,8
+	.align 8
+.L_2il0floatpacket.37:
+	.long	0x00ffffff,0x00000000
+	.type	.L_2il0floatpacket.37,@object
+	.size	.L_2il0floatpacket.37,8
+	.align 8
+.L_2il0floatpacket.38:
+	.long	0x00000000,0x3cb00000
+	.type	.L_2il0floatpacket.38,@object
+	.size	.L_2il0floatpacket.38,8
+	.align 8
+.L_2il0floatpacket.39:
+	.long	0x00000fff,0x00000000
+	.type	.L_2il0floatpacket.39,@object
+	.size	.L_2il0floatpacket.39,8
+	.align 8
+.L_2il0floatpacket.40:
+	.long	0x54442d18,0x401921fb
+	.type	.L_2il0floatpacket.40,@object
+	.size	.L_2il0floatpacket.40,8
+	.align 8
+.L_2il0floatpacket.41:
+	.long	0x33145c07,0x3cb1a626
+	.type	.L_2il0floatpacket.41,@object
+	.size	.L_2il0floatpacket.41,8
+	.align 8
+.L_2il0floatpacket.42:
+	.long	0xffffffff,0x7fffffff
+	.type	.L_2il0floatpacket.42,@object
+	.size	.L_2il0floatpacket.42,8
+	.align 8
+.L_2il0floatpacket.43:
+	.long	0x00000000,0x3eb00000
+	.type	.L_2il0floatpacket.43,@object
+	.size	.L_2il0floatpacket.43,8
+	.align 8
+__dsin_ha_CoutTab:
+	.long	0
+	.long	0
+	.long	0
+	.long	2146435072
+	.type	__dsin_ha_CoutTab,@object
+	.size	__dsin_ha_CoutTab,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_d_la.s
index 8641c12cc98..8c7f5a66ba9 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_h_la.s
new file mode 100644
index 00000000000..d43bb220999
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_h_la.s
@@ -0,0 +1,252 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *  Computing 2^(-5)*(RShifter0 + x*(1/pi));
+ *  The scaling needed to get around limited exponent range for long Pi representation.
+ *  fN0 = (int)(sX/pi)*(2^(-5))
+ *  Argument reduction:  x + N*(nPi1+nPi2), where nPi1+nPi2 ~ -pi
+ *  The sign bit, will treat hY as integer value to look at last bit
+ *  Then compute polynomial: hR + hR3*fPoly
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_sins32
+
+__svml_sins32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hsin_data_internal(%rip), %zmm25
+        vmovdqu16 64+__svml_hsin_data_internal(%rip), %zmm24
+        vmovdqu16 128+__svml_hsin_data_internal(%rip), %zmm1
+        vmovdqu16 192+__svml_hsin_data_internal(%rip), %zmm23
+        vmovdqu16 256+__svml_hsin_data_internal(%rip), %zmm22
+        vmovdqu16 320+__svml_hsin_data_internal(%rip), %zmm29
+        vmovdqu16 384+__svml_hsin_data_internal(%rip), %zmm7
+        vmovdqu16 448+__svml_hsin_data_internal(%rip), %zmm21
+        vmovdqu16   512+__svml_hsin_data_internal(%rip), %zmm20
+        vmovdqu16   576+__svml_hsin_data_internal(%rip), %zmm16
+        vmovdqu16   640+__svml_hsin_data_internal(%rip), %zmm28
+        vmovdqu16   704+__svml_hsin_data_internal(%rip), %zmm26
+        vmovdqu16   832+__svml_hsin_data_internal(%rip), %zmm18
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__sin_h:
+        cmpq    $31, %rdx
+        ja .loaddata__sin_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_sin_h
+.loaddata__sin_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+
+.funcbegin_sin_h:
+
+/* Check for +/- INF */
+        vfpclassph $24, %zmm0, %k6
+/* Dummy vsubph instruction to raise an invalid */
+        vsubph  %zmm0, %zmm0, %zmm9{%k6}{z}
+
+        vmovdqu16 %zmm23, %zmm5
+        vmovdqu16 %zmm22, %zmm4
+        vmovdqu16 %zmm21, %zmm9
+
+/*
+ * Variables:
+ * H
+ * S
+ * HM
+ * No callout
+ * Copy argument
+ * Needed to set sin(-0)=-0
+ */
+        vpandd %zmm25, %zmm0, %zmm13
+        vfmadd213ph {rn-sae}, %zmm1, %zmm13, %zmm5
+
+/* |sX| > threshold? */
+        vpcmpgtw %zmm24, %zmm13, %k1
+
+/* fN0 = (int)(sX/pi)*(2^(-5)) */
+        vsubph    {rn-sae}, %zmm1, %zmm5, %zmm10
+
+/*
+ * sign bit, will treat hY as integer value to look at last bit
+ * shift to FP16 sign position
+ */
+        vpsllw    $15, %zmm5, %zmm6
+        vfmadd213ph {rn-sae}, %zmm13, %zmm10, %zmm4
+        vfmadd213ph {rn-sae}, %zmm4, %zmm29, %zmm10
+
+/* hR*hR */
+        vmulph    {rn-sae}, %zmm10, %zmm10, %zmm8
+        vfmadd231ph {rn-sae}, %zmm8, %zmm7, %zmm9
+        vmulph    {rn-sae}, %zmm10, %zmm8, %zmm11
+
+/* hR + hR3*fPoly */
+        vfmadd213ph {rn-sae}, %zmm10, %zmm9, %zmm11
+
+/* short path */
+        kortestd  %k1, %k1
+        vpxord    %zmm13, %zmm0, %zmm3
+
+/* add in input sign */
+        vpxord    %zmm3, %zmm6, %zmm12
+
+/* add sign bit to result (logical XOR between fPoly and isgn bits) */
+        vpxord    %zmm12, %zmm11, %zmm0
+
+/* Go to exit */
+        jne       .LBL_EXIT
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__sin_h
+                                # LOE rbx rbp r12 r13 r14 r15 zmm0 zmm3 zmm13 k1
+
+        ret
+
+/* Restore registers
+ * and exit the function
+ */
+
+.LBL_EXIT:
+
+        vmovdqu16   %zmm16, %zmm5
+        vmovaps   %zmm20, %zmm11
+
+/* convert to FP32 */
+        vextractf32x8 $1, %zmm13, %ymm4
+        vcvtph2psx %ymm13, %zmm8
+        vcvtph2psx %ymm4, %zmm10
+        vfmadd231ps {rn-sae}, %zmm8, %zmm5, %zmm11
+        vfmadd213ps {rn-sae}, %zmm20, %zmm10, %zmm5
+
+/* c2*sR2 + c1 */
+        vmovdqu16   768+__svml_hsin_data_internal(%rip), %zmm4
+
+/* sN = (int)(x/pi) = sY - Rshifter */
+        vsubps    {rn-sae}, %zmm20, %zmm11, %zmm30
+        vsubps    {rn-sae}, %zmm20, %zmm5, %zmm31
+
+/* sign bit, will treat sY as integer value to look at last bit */
+        vpslld    $31, %zmm11, %zmm19
+        vfmadd231ps {rn-sae}, %zmm30, %zmm28, %zmm8
+        vfmadd231ps {rn-sae}, %zmm31, %zmm28, %zmm10
+        vpslld    $31, %zmm5, %zmm27
+        vfmadd213ps {rn-sae}, %zmm8, %zmm26, %zmm30
+        vfmadd213ps {rn-sae}, %zmm10, %zmm26, %zmm31
+
+/* sR*sR */
+        vmulps    {rn-sae}, %zmm30, %zmm30, %zmm14
+        vmulps    {rn-sae}, %zmm31, %zmm31, %zmm15
+        vmovaps   %zmm4, %zmm5
+        vfmadd231ps {rn-sae}, %zmm14, %zmm18, %zmm5
+        vfmadd231ps {rn-sae}, %zmm15, %zmm18, %zmm4
+
+/* sR2*sR */
+        vmulps    {rn-sae}, %zmm30, %zmm14, %zmm17
+        vmulps    {rn-sae}, %zmm31, %zmm15, %zmm14
+
+/* sR + sR3*sPoly */
+        vfmadd213ps {rn-sae}, %zmm30, %zmm5, %zmm17
+        vfmadd213ps {rn-sae}, %zmm31, %zmm4, %zmm14
+
+/* add sign bit to result (logical XOR between sPoly and i32_sgn bits) */
+        vxorps    %zmm19, %zmm17, %zmm31
+        vxorps    %zmm27, %zmm14, %zmm8
+        vcvtps2phx %zmm31, %ymm30
+        vcvtps2phx %zmm8, %ymm9
+        vinsertf32x8 $1, %ymm9, %zmm30, %zmm10
+
+/* needed to set sin(-0)=-0 */
+        vpxord    %zmm3, %zmm10, %zmm3
+
+/*
+ * ensure results are always exactly the same for common arguments
+ * (return fast path result for common args)
+ */
+        vpblendmw %zmm3, %zmm0, %zmm0{%k1}
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__sin_h
+        ret
+                                # LOE rbx rbp r12 r13 r14 r15 zmm0
+        .cfi_endproc
+
+        .type	__svml_sins32,@function
+        .size	__svml_sins32,.-__svml_sins32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hsin_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x52ac
+	.endr
+	.rept	32
+        .word	0x5200
+	.endr
+	.rept	32
+        .word	0x2118
+	.endr
+	.rept	32
+        .word	0xd648
+	.endr
+	.rept	32
+        .word	0xa7ed
+	.endr
+	.rept	32
+        .word	0x2007
+	.endr
+	.rept	32
+        .word	0xb155
+	.endr
+	.rept	16
+        .long	0x4b400000
+	.endr
+	.rept	16
+        .long	0x3ea2f983
+	.endr
+	.rept	16
+        .long	0xc0490fdb
+	.endr
+	.rept	16
+        .long	0x33bbbd2e
+	.endr
+	.rept	16
+        .long	0xbe2a026e
+	.endr
+	.rept	16
+        .long	0x3bf9f9b6
+	.endr
+        .type	__svml_hsin_data_internal,@object
+        .size	__svml_hsin_data_internal,896
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_s_ha.s
new file mode 100644
index 00000000000..950dfd1db8a
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_s_ha.s
@@ -0,0 +1,2814 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *  1) Range reduction to [-Pi/2; +Pi/2] interval
+ *     a) Grab sign from source argument and save it.
+ *     b) Remove sign using AND operation
+ *     c) Getting octant Y by 1/Pi multiplication
+ *     d) Add "Right Shifter" value
+ *     e) Treat obtained value as integer for destination sign setting.
+ *        Shift first bit of this value to the last (sign) position
+ *     f) Change destination sign if source sign is negative
+ *        using XOR operation.
+ *     g) Subtract "Right Shifter" value
+ *     h) Subtract Y*PI from X argument, where PI divided to 4 parts:
+ *        X = X - Y*PI1 - Y*PI2 - Y*PI3 - Y*PI4;
+ *  2) Polynomial (minimax for sin within [-Pi/2; +Pi/2] interval)
+ *     a) Calculate X^2 = X * X
+ *     b) Calculate polynomial:
+ *        R = X + X * X^2 * (A3 + x^2 * (A5 + ......
+ *  3) Destination sign setting
+ *     a) Set shifted destination sign using XOR operation:
+ *        R = XOR( R, S );
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_sinf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_sinf16_ha
+
+__svml_sinf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        xorl      %edx, %edx
+        vmovups   4096+__svml_ssin_ha_data_internal(%rip), %zmm3
+        vmovups   5056+__svml_ssin_ha_data_internal(%rip), %zmm6
+        vmovups   4544+__svml_ssin_ha_data_internal(%rip), %zmm7
+        vmovups   5120+__svml_ssin_ha_data_internal(%rip), %zmm10
+        vmovups   5184+__svml_ssin_ha_data_internal(%rip), %zmm13
+        vmovups   5376+__svml_ssin_ha_data_internal(%rip), %zmm15
+        vmovaps   %zmm0, %zmm11
+
+/* b) Remove sign using AND operation */
+        vandps    %zmm3, %zmm11, %zmm4
+
+/*
+ * c) Getting octant Y by 1/Pi multiplication
+ * d) Add "Right Shifter" value
+ */
+        vfmadd213ps {rn-sae}, %zmm7, %zmm4, %zmm6
+
+/* Convert arg to DP */
+        vcvtps2pd {sae}, %ymm4, %zmm12
+        vextractf32x8 $1, %zmm4, %ymm5
+
+/* g) Subtract "Right Shifter" value */
+        vsubps    {rn-sae}, %zmm7, %zmm6, %zmm8
+
+/*
+ * e) Treat obtained value as integer for destination sign setting.
+ * Shift first bit of this value to the last (sign) position
+ */
+        vpslld    $31, %zmm6, %zmm2
+        vcvtps2pd {sae}, %ymm5, %zmm14
+        vmovups   5312+__svml_ssin_ha_data_internal(%rip), %zmm5
+        vmovups   5248+__svml_ssin_ha_data_internal(%rip), %zmm6
+        vmovaps   %zmm15, %zmm7
+
+/* Convert N to DP */
+        vextractf32x8 $1, %zmm8, %ymm9
+        vcvtps2pd {sae}, %ymm8, %zmm1
+        vcvtps2pd {sae}, %ymm9, %zmm0
+
+/*
+ * h) Subtract Y*PI from X argument, where PI divided to 4 parts:
+ * X = X - Y*PI1 - Y*PI2
+ */
+        vfnmadd231pd {rn-sae}, %zmm1, %zmm10, %zmm12
+        vfnmadd231pd {rn-sae}, %zmm0, %zmm10, %zmm14
+        vandnps   %zmm11, %zmm3, %zmm10
+        vfnmadd213pd {rn-sae}, %zmm12, %zmm13, %zmm1
+        vfnmadd213pd {rn-sae}, %zmm14, %zmm13, %zmm0
+        vmovups   5440+__svml_ssin_ha_data_internal(%rip), %zmm3
+
+/*
+ * f) Change destination sign if source sign is negative
+ * using XOR operation.
+ */
+        vxorps    %zmm10, %zmm2, %zmm14
+        vpternlogd $255, %zmm10, %zmm10, %zmm10
+
+/*
+ * 2) Polynomial (minimax for sin within [-Pi/2; +Pi/2] interval)
+ * a) Calculate X^2 = X * X
+ * b) Calculate polynomial:
+ * R = X + X * X^2 * (A3 + x^2 * (A5 + ......
+ */
+        vmulpd    {rn-sae}, %zmm0, %zmm0, %zmm9
+        vmulpd    {rn-sae}, %zmm1, %zmm1, %zmm8
+        vfmadd231pd {rn-sae}, %zmm9, %zmm3, %zmm15
+        vfmadd231pd {rn-sae}, %zmm8, %zmm3, %zmm7
+        vfmadd213pd {rn-sae}, %zmm5, %zmm9, %zmm15
+        vfmadd213pd {rn-sae}, %zmm5, %zmm8, %zmm7
+        vfmadd213pd {rn-sae}, %zmm6, %zmm9, %zmm15
+        vfmadd213pd {rn-sae}, %zmm6, %zmm8, %zmm7
+        vmulpd    {rn-sae}, %zmm9, %zmm15, %zmm13
+        vmulpd    {rn-sae}, %zmm8, %zmm7, %zmm12
+        vfmadd213pd {rn-sae}, %zmm0, %zmm0, %zmm13
+        vfmadd213pd {rn-sae}, %zmm1, %zmm1, %zmm12
+        vcvtpd2ps {rn-sae}, %zmm13, %ymm0
+        vcvtpd2ps {rn-sae}, %zmm12, %ymm1
+        vinsertf32x8 $1, %ymm0, %zmm1, %zmm2
+
+/* Check for large and special values */
+        vmovups   4160+__svml_ssin_ha_data_internal(%rip), %zmm1
+
+/*
+ * 3) Destination sign setting
+ * a) Set shifted destination sign using XOR operation:
+ * R = XOR( R, S );
+ */
+        vxorps    %zmm14, %zmm2, %zmm0
+        vcmpps    $18, {sae}, %zmm1, %zmm4, %k1
+        vpandnd   %zmm4, %zmm4, %zmm10{%k1}
+        vptestmd  %zmm10, %zmm10, %k0
+        kortestw  %k0, %k0
+        jne       .LBL_1_12
+
+.LBL_1_2:
+
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_4:
+
+        vmovups   %zmm11, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_ssin_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+        vmovups   4224+__svml_ssin_ha_data_internal(%rip), %zmm8
+
+/*
+ * Get the (2^a / 2pi) mod 1 values from the table.
+ * Because VLANG doesn't have I-type gather, we need a trivial cast
+ */
+        lea       __svml_ssin_ha_reduction_data_internal(%rip), %rax
+        vmovups   %zmm0, (%rsp)
+        vpternlogd $255, %zmm1, %zmm1, %zmm1
+        vandps    %zmm4, %zmm8, %zmm6
+        vcmpps    $4, {sae}, %zmm8, %zmm6, %k1
+
+/* ..................... Table look-up ........................ */
+        lea       __svml_ssin_ha_data_internal(%rip), %rcx
+        vpandd    .L_2il0floatpacket.20(%rip), %zmm11, %zmm12
+        vpsrld    $23, %zmm12, %zmm13
+        vpslld    $1, %zmm13, %zmm4
+        vpaddd    %zmm13, %zmm4, %zmm9
+        vpslld    $2, %zmm9, %zmm5
+        vpandnd   %zmm6, %zmm6, %zmm1{%k1}
+        vptestmd  %zmm1, %zmm1, %k0
+
+/*
+ * Break the P_xxx and m into 16-bit chunks ready for
+ * the long multiplication via 16x16->32 multiplications
+ */
+        vmovups   .L_2il0floatpacket.23(%rip), %zmm6
+        kxnorw    %k0, %k0, %k3
+        kxnorw    %k0, %k0, %k1
+        kxnorw    %k0, %k0, %k2
+        kmovw     %k0, %edx
+        vpxord    %zmm3, %zmm3, %zmm3
+        vpxord    %zmm14, %zmm14, %zmm14
+        vpxord    %zmm8, %zmm8, %zmm8
+        vgatherdps 4(%rax,%zmm5), %zmm3{%k3}
+        vgatherdps 8(%rax,%zmm5), %zmm14{%k1}
+        vgatherdps (%rax,%zmm5), %zmm8{%k2}
+        vpsrld    $16, %zmm3, %zmm15
+        vpsrld    $16, %zmm14, %zmm13
+        vpsrld    $16, %zmm8, %zmm7
+        kxnorw    %k0, %k0, %k1
+
+/*
+ * Also get the significand as an integer
+ * NB: adding in the integer bit is wrong for denorms!
+ * To make this work for denorms we should do something slightly different
+ */
+        vpandd    .L_2il0floatpacket.21(%rip), %zmm11, %zmm2
+        vpaddd    .L_2il0floatpacket.22(%rip), %zmm2, %zmm1
+        vpsrld    $16, %zmm1, %zmm9
+        vpandd    %zmm6, %zmm8, %zmm0
+        vpandd    %zmm6, %zmm3, %zmm12
+        vpandd    %zmm6, %zmm14, %zmm5
+        vpandd    %zmm6, %zmm1, %zmm14
+
+/* Now do the big multiplication and carry propagation */
+        vpmulld   %zmm0, %zmm9, %zmm4
+        vpmulld   %zmm15, %zmm9, %zmm3
+        vpmulld   %zmm12, %zmm9, %zmm2
+        vpmulld   %zmm13, %zmm9, %zmm1
+        vpmulld   %zmm5, %zmm9, %zmm8
+        vpmulld   %zmm15, %zmm14, %zmm9
+        vpmulld   %zmm13, %zmm14, %zmm15
+        vpmulld   %zmm7, %zmm14, %zmm7
+        vpmulld   %zmm0, %zmm14, %zmm5
+        vpmulld   %zmm12, %zmm14, %zmm0
+        vpsrld    $16, %zmm15, %zmm14
+        vpsrld    $16, %zmm9, %zmm15
+        vpsrld    $16, %zmm0, %zmm13
+        vpsrld    $16, %zmm5, %zmm12
+        vpaddd    %zmm15, %zmm3, %zmm3
+        vpaddd    %zmm14, %zmm1, %zmm1
+        vpaddd    %zmm13, %zmm2, %zmm14
+        vpaddd    %zmm12, %zmm4, %zmm2
+        vpandd    %zmm6, %zmm5, %zmm4
+        vpaddd    %zmm3, %zmm4, %zmm3
+        vpsrld    $16, %zmm8, %zmm4
+        vpandd    %zmm6, %zmm0, %zmm0
+        vpaddd    %zmm1, %zmm0, %zmm13
+        vpandd    %zmm6, %zmm7, %zmm7
+        vpaddd    %zmm2, %zmm7, %zmm0
+        vpaddd    %zmm13, %zmm4, %zmm7
+        vpandd    %zmm6, %zmm9, %zmm12
+        vpsrld    $16, %zmm7, %zmm9
+        vpaddd    %zmm14, %zmm12, %zmm5
+        vpaddd    %zmm5, %zmm9, %zmm1
+        vpsrld    $16, %zmm1, %zmm2
+        vpslld    $16, %zmm1, %zmm13
+        vpaddd    %zmm3, %zmm2, %zmm15
+        vpsrld    $16, %zmm15, %zmm8
+        vpaddd    %zmm0, %zmm8, %zmm14
+
+/* Assemble reduced argument from the pieces */
+        vpandd    %zmm6, %zmm7, %zmm12
+        vpandd    %zmm6, %zmm15, %zmm4
+        vpslld    $16, %zmm14, %zmm6
+        vpaddd    %zmm12, %zmm13, %zmm13
+        vpaddd    %zmm4, %zmm6, %zmm9
+
+/*
+ * Now round at the 2^-8 bit position for reduction mod pi/2^7
+ * instead of the original 2pi (but still with the same 2pi scaling).
+ * Use a shifter of 2^15 + 2^14.
+ * The N we get is our final version; it has an offset of
+ * 2^8 because of the implicit integer bit, and anyway for negative
+ * starting value it's a 2s complement thing. But we need to mask
+ * off the exponent part anyway so it's fine.
+ */
+        vmovups   .L_2il0floatpacket.26(%rip), %zmm6
+        vpsrld    $9, %zmm9, %zmm2
+        vpandd    .L_2il0floatpacket.29(%rip), %zmm13, %zmm15
+        vpslld    $5, %zmm15, %zmm1
+        vpsrld    $18, %zmm13, %zmm13
+
+/*
+ * We want to incorporate the original sign now too.
+ * Do it here for convenience in getting the right N value,
+ * though we could wait right to the end if we were prepared
+ * to modify the sign of N later too.
+ * So get the appropriate sign mask now (or sooner).
+ */
+        vpandd    .L_2il0floatpacket.24(%rip), %zmm11, %zmm5
+
+/*
+ * Create floating-point high part, implicitly adding integer bit 1
+ * Incorporate overall sign at this stage too.
+ */
+        vpxord    .L_2il0floatpacket.25(%rip), %zmm5, %zmm8
+        vpord     %zmm8, %zmm2, %zmm3
+        vaddps    {rn-sae}, %zmm3, %zmm6, %zmm7
+        vsubps    {rn-sae}, %zmm6, %zmm7, %zmm0
+        vsubps    {rn-sae}, %zmm0, %zmm3, %zmm14
+        vandps    .L_2il0floatpacket.34(%rip), %zmm11, %zmm3
+        vpternlogd $255, %zmm0, %zmm0, %zmm0
+
+/*
+ * Create floating-point low and medium parts, respectively
+ * lo_17, ... lo_0, 0, ..., 0
+ * hi_8, ... hi_0, lo_31, ..., lo_18
+ * then subtract off the implicitly added integer bits,
+ * 2^-46 and 2^-23, respectively.
+ * Put the original sign into all of them at this stage.
+ */
+        vpxord    .L_2il0floatpacket.28(%rip), %zmm5, %zmm8
+        vpord     %zmm8, %zmm1, %zmm2
+        vpandd    .L_2il0floatpacket.31(%rip), %zmm9, %zmm9
+        vsubps    {rn-sae}, %zmm8, %zmm2, %zmm15
+        vpxord    .L_2il0floatpacket.30(%rip), %zmm5, %zmm2
+        vpslld    $14, %zmm9, %zmm5
+        vpord     %zmm13, %zmm5, %zmm5
+        vpord     %zmm2, %zmm5, %zmm9
+        vsubps    {rn-sae}, %zmm2, %zmm9, %zmm8
+
+/*
+ * Now multiply those numbers all by 2 pi, reasonably accurately.
+ * (RHi + RLo) * (pi_lead + pi_trail) ~=
+ * RHi * pi_lead + (RHi * pi_trail + RLo * pi_lead)
+ */
+        vmovups   .L_2il0floatpacket.32(%rip), %zmm9
+        vmovups   .L_2il0floatpacket.33(%rip), %zmm2
+
+/* Now add them up into 2 reasonably aligned pieces */
+        vaddps    {rn-sae}, %zmm8, %zmm14, %zmm6
+        vsubps    {rn-sae}, %zmm6, %zmm14, %zmm14
+        vmovaps   %zmm9, %zmm5
+        vaddps    {rn-sae}, %zmm14, %zmm8, %zmm13
+
+/*
+ * If the magnitude of the input is <= 2^-20, then
+ * just pass through the input, since no reduction will be needed and
+ * the main path will only work accurately if the reduced argument is
+ * about >= 2^-40 (which it is for all large pi multiples)
+ */
+        vmovups   .L_2il0floatpacket.35(%rip), %zmm8
+        vaddps    {rn-sae}, %zmm15, %zmm13, %zmm14
+        vpternlogd $255, %zmm15, %zmm15, %zmm15
+        vcmpps    $26, {sae}, %zmm8, %zmm3, %k2
+        vcmpps    $22, {sae}, %zmm8, %zmm3, %k3
+
+/* Grab our final N value as an integer, appropriately masked mod 2^8 */
+        vpandd    .L_2il0floatpacket.27(%rip), %zmm7, %zmm4
+        vmulps    {rn-sae}, %zmm9, %zmm6, %zmm7
+        vfmsub213ps {rn-sae}, %zmm7, %zmm6, %zmm5
+        vfmadd213ps {rn-sae}, %zmm5, %zmm2, %zmm6
+        vfmadd213ps {rn-sae}, %zmm6, %zmm9, %zmm14
+        vpslld    $4, %zmm4, %zmm9
+        vpandnd   %zmm3, %zmm3, %zmm15{%k2}
+        vpandnd   %zmm3, %zmm3, %zmm0{%k3}
+        kxnorw    %k0, %k0, %k2
+        kxnorw    %k0, %k0, %k3
+        vandps    %zmm7, %zmm15, %zmm12
+        vandps    %zmm11, %zmm0, %zmm1
+        vandps    %zmm14, %zmm15, %zmm2
+        vorps     %zmm12, %zmm1, %zmm13
+
+/* ............... Polynomial approximation ................... */
+        vmovups   4352+__svml_ssin_ha_data_internal(%rip), %zmm12
+
+/*
+ * The output is _VRES_R (high) + _VRES_E (low), and the integer part is _VRES_IND
+ * Set sRp2 = _VRES_R^2 and then resume the original code.
+ */
+        vmulps    {rn-sae}, %zmm13, %zmm13, %zmm3
+
+/* ................. Reconstruction: res_hi ................... */
+        vmovaps   %zmm13, %zmm8
+        vmovaps   %zmm13, %zmm4
+        vpxord    %zmm5, %zmm5, %zmm5
+        vpxord    %zmm7, %zmm7, %zmm7
+        vgatherdps 4(%rcx,%zmm9), %zmm5{%k2}
+        vgatherdps 12(%rcx,%zmm9), %zmm7{%k3}
+        vfmadd213ps {rn-sae}, %zmm5, %zmm7, %zmm8
+        vsubps    {rn-sae}, %zmm8, %zmm5, %zmm1
+        vfmadd231ps {rn-sae}, %zmm13, %zmm7, %zmm1
+        vpxord    %zmm0, %zmm0, %zmm0
+        vgatherdps (%rcx,%zmm9), %zmm0{%k1}
+
+/* ................. Reconstruction: res_lo=corr+polS+polC+res_lo0 ................... */
+        kxnorw    %k0, %k0, %k1
+        vfmadd132ps {rn-sae}, %zmm0, %zmm8, %zmm4
+        vsubps    {rn-sae}, %zmm4, %zmm8, %zmm6
+        vfmadd231ps {rn-sae}, %zmm0, %zmm13, %zmm6
+        vaddps    {rn-sae}, %zmm1, %zmm6, %zmm8
+        vaddps    {rn-sae}, %zmm7, %zmm0, %zmm1
+        vmovups   4288+__svml_ssin_ha_data_internal(%rip), %zmm6
+        vmovups   4480+__svml_ssin_ha_data_internal(%rip), %zmm0
+        vmovups   4416+__svml_ssin_ha_data_internal(%rip), %zmm7
+        vfmadd231ps {rn-sae}, %zmm3, %zmm12, %zmm6
+        vfmadd231ps {rn-sae}, %zmm3, %zmm0, %zmm7
+        vmulps    {rn-sae}, %zmm3, %zmm6, %zmm14
+        vmulps    {rn-sae}, %zmm3, %zmm7, %zmm0
+        vmulps    {rn-sae}, %zmm13, %zmm14, %zmm6
+        vfnmadd213ps {rn-sae}, %zmm1, %zmm5, %zmm13
+        vfmadd213ps {rn-sae}, %zmm8, %zmm13, %zmm6
+        vpxord    %zmm3, %zmm3, %zmm3
+        vgatherdps 8(%rcx,%zmm9), %zmm3{%k1}
+        vfmadd213ps {rn-sae}, %zmm3, %zmm13, %zmm2
+        vfmadd213ps {rn-sae}, %zmm2, %zmm5, %zmm0
+        vaddps    {rn-sae}, %zmm6, %zmm0, %zmm2
+
+/* .................. Final reconstruction ................... */
+        vaddps    {rn-sae}, %zmm2, %zmm4, %zmm4
+
+/*
+ * ----------------------------------------------------------
+ * End of large arguments path
+ * ----------------------------------------------------------
+ * Merge results from main and large paths:
+ */
+        vpandnd   (%rsp), %zmm10, %zmm0
+        vpandd    %zmm10, %zmm4, %zmm10
+        vpord     %zmm10, %zmm0, %zmm0
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sinf16_ha,@function
+	.size	__svml_sinf16_ha,.-__svml_sinf16_ha
+..LN__svml_sinf16_ha.0:
+
+.L_2__routine_start___svml_ssin_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_ssin_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L63:
+
+        movl      (%rdi), %edx
+        movzwl    2(%rdi), %eax
+        movl      %edx, -8(%rsp)
+        andl      $32640, %eax
+        shrl      $24, %edx
+        andl      $127, %edx
+        movss     (%rdi), %xmm1
+        cmpl      $32640, %eax
+        jne       .LBL_2_6
+
+
+        movb      %dl, -5(%rsp)
+        cmpl      $2139095040, -8(%rsp)
+        jne       .LBL_2_4
+
+
+        movss     __ssin_ha__vmlsSinHATab(%rip), %xmm0
+        movl      $1, %eax
+        mulss     %xmm0, %xmm1
+        movss     %xmm1, (%rsi)
+        ret
+
+.LBL_2_4:
+
+        mulss     (%rdi), %xmm1
+        xorl      %eax, %eax
+        movss     %xmm1, (%rsi)
+
+
+        ret
+
+.LBL_2_6:
+
+        xorl      %eax, %eax
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_ssin_ha_cout_rare_internal,@function
+	.size	__svml_ssin_ha_cout_rare_internal,.-__svml_ssin_ha_cout_rare_internal
+..LN__svml_ssin_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+.L_2il0floatpacket.20:
+	.long	0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000
+	.type	.L_2il0floatpacket.20,@object
+	.size	.L_2il0floatpacket.20,64
+	.align 64
+.L_2il0floatpacket.21:
+	.long	0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff
+	.type	.L_2il0floatpacket.21,@object
+	.size	.L_2il0floatpacket.21,64
+	.align 64
+.L_2il0floatpacket.22:
+	.long	0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000
+	.type	.L_2il0floatpacket.22,@object
+	.size	.L_2il0floatpacket.22,64
+	.align 64
+.L_2il0floatpacket.23:
+	.long	0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff
+	.type	.L_2il0floatpacket.23,@object
+	.size	.L_2il0floatpacket.23,64
+	.align 64
+.L_2il0floatpacket.24:
+	.long	0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000
+	.type	.L_2il0floatpacket.24,@object
+	.size	.L_2il0floatpacket.24,64
+	.align 64
+.L_2il0floatpacket.25:
+	.long	0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000
+	.type	.L_2il0floatpacket.25,@object
+	.size	.L_2il0floatpacket.25,64
+	.align 64
+.L_2il0floatpacket.26:
+	.long	0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000
+	.type	.L_2il0floatpacket.26,@object
+	.size	.L_2il0floatpacket.26,64
+	.align 64
+.L_2il0floatpacket.27:
+	.long	0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff
+	.type	.L_2il0floatpacket.27,@object
+	.size	.L_2il0floatpacket.27,64
+	.align 64
+.L_2il0floatpacket.28:
+	.long	0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000
+	.type	.L_2il0floatpacket.28,@object
+	.size	.L_2il0floatpacket.28,64
+	.align 64
+.L_2il0floatpacket.29:
+	.long	0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff
+	.type	.L_2il0floatpacket.29,@object
+	.size	.L_2il0floatpacket.29,64
+	.align 64
+.L_2il0floatpacket.30:
+	.long	0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000
+	.type	.L_2il0floatpacket.30,@object
+	.size	.L_2il0floatpacket.30,64
+	.align 64
+.L_2il0floatpacket.31:
+	.long	0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff
+	.type	.L_2il0floatpacket.31,@object
+	.size	.L_2il0floatpacket.31,64
+	.align 64
+.L_2il0floatpacket.32:
+	.long	0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb
+	.type	.L_2il0floatpacket.32,@object
+	.size	.L_2il0floatpacket.32,64
+	.align 64
+.L_2il0floatpacket.33:
+	.long	0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e
+	.type	.L_2il0floatpacket.33,@object
+	.size	.L_2il0floatpacket.33,64
+	.align 64
+.L_2il0floatpacket.34:
+	.long	0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff
+	.type	.L_2il0floatpacket.34,@object
+	.size	.L_2il0floatpacket.34,64
+	.align 64
+.L_2il0floatpacket.35:
+	.long	0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000
+	.type	.L_2il0floatpacket.35,@object
+	.size	.L_2il0floatpacket.35,64
+	.align 64
+__svml_ssin_ha_data_internal:
+	.long	0
+	.long	0
+	.long	0
+	.long	1065353216
+	.long	3114133471
+	.long	1019808432
+	.long	2953169304
+	.long	1065353216
+	.long	3130909128
+	.long	1028193072
+	.long	2968461951
+	.long	1065353216
+	.long	3140588184
+	.long	1033283845
+	.long	2975014497
+	.long	1065353216
+	.long	3147680113
+	.long	1036565814
+	.long	2960495349
+	.long	1065353216
+	.long	3153489468
+	.long	1039839859
+	.long	2970970319
+	.long	1065353216
+	.long	3157349634
+	.long	1041645699
+	.long	837346836
+	.long	1065353216
+	.long	3161536011
+	.long	1043271842
+	.long	823224313
+	.long	1065353216
+	.long	3164432432
+	.long	1044891074
+	.long	2967836285
+	.long	1065353216
+	.long	3167161428
+	.long	1046502419
+	.long	833086710
+	.long	1065353216
+	.long	3170205956
+	.long	1048104908
+	.long	2971391005
+	.long	1065353216
+	.long	3172229004
+	.long	1049136787
+	.long	824999326
+	.long	1065353216
+	.long	3174063957
+	.long	1049927729
+	.long	846027248
+	.long	1065353216
+	.long	3176053642
+	.long	1050712805
+	.long	2990442912
+	.long	1065353216
+	.long	3178196862
+	.long	1051491540
+	.long	2988789250
+	.long	1065353216
+	.long	3179887378
+	.long	1052263466
+	.long	2993707942
+	.long	1065353216
+	.long	3181110540
+	.long	1053028117
+	.long	836097324
+	.long	1065353216
+	.long	3182408396
+	.long	1053785034
+	.long	829045603
+	.long	1065353216
+	.long	3183780163
+	.long	1054533760
+	.long	840832460
+	.long	1065353216
+	.long	3185225016
+	.long	1055273845
+	.long	2983839604
+	.long	1065353216
+	.long	3186742084
+	.long	1056004842
+	.long	2986287417
+	.long	1065353216
+	.long	3188000746
+	.long	1056726311
+	.long	2978016425
+	.long	1065353216
+	.long	3188830103
+	.long	1057201213
+	.long	2992349186
+	.long	1065353216
+	.long	3189694133
+	.long	1057551771
+	.long	2998815566
+	.long	1065353216
+	.long	3190592315
+	.long	1057896922
+	.long	2991207143
+	.long	1065353216
+	.long	3191524108
+	.long	1058236458
+	.long	852349230
+	.long	1065353216
+	.long	3192488951
+	.long	1058570176
+	.long	2982650867
+	.long	1065353216
+	.long	3193486263
+	.long	1058897873
+	.long	848430348
+	.long	1065353216
+	.long	3194515443
+	.long	1059219353
+	.long	841032635
+	.long	1065353216
+	.long	3195575871
+	.long	1059534422
+	.long	2986574659
+	.long	1065353216
+	.long	3196363278
+	.long	1059842890
+	.long	2998350134
+	.long	1065353216
+	.long	3196923773
+	.long	1060144571
+	.long	2997759282
+	.long	1065353216
+	.long	3197498906
+	.long	1060439283
+	.long	844097402
+	.long	1065353216
+	.long	1044518635
+	.long	1060726850
+	.long	2994798599
+	.long	1056964608
+	.long	1043311911
+	.long	1061007097
+	.long	832220140
+	.long	1056964608
+	.long	1042078039
+	.long	1061279856
+	.long	851442039
+	.long	1056964608
+	.long	1040817765
+	.long	1061544963
+	.long	850481524
+	.long	1056964608
+	.long	1038876298
+	.long	1061802258
+	.long	848897600
+	.long	1056964608
+	.long	1036254719
+	.long	1062051586
+	.long	847147240
+	.long	1056964608
+	.long	1033584979
+	.long	1062292797
+	.long	806113028
+	.long	1056964608
+	.long	1029938589
+	.long	1062525745
+	.long	848357914
+	.long	1056964608
+	.long	1024416170
+	.long	1062750291
+	.long	2994560960
+	.long	1056964608
+	.long	1013387058
+	.long	1062966298
+	.long	841166280
+	.long	1056964608
+	.long	3152590408
+	.long	1063173637
+	.long	851900755
+	.long	1056964608
+	.long	3169472868
+	.long	1063372184
+	.long	3001545765
+	.long	1056964608
+	.long	3176031322
+	.long	1063561817
+	.long	823789818
+	.long	1056964608
+	.long	3180617215
+	.long	1063742424
+	.long	2998678409
+	.long	1056964608
+	.long	3183612120
+	.long	1063913895
+	.long	3001754476
+	.long	1056964608
+	.long	3186639787
+	.long	1064076126
+	.long	854796500
+	.long	1056964608
+	.long	3188684717
+	.long	1064229022
+	.long	2995991516
+	.long	1056964608
+	.long	1035072335
+	.long	1064372488
+	.long	840880349
+	.long	1048576000
+	.long	1031957395
+	.long	1064506439
+	.long	851742225
+	.long	1048576000
+	.long	1025835404
+	.long	1064630795
+	.long	2996018466
+	.long	1048576000
+	.long	1015605553
+	.long	1064745479
+	.long	846006572
+	.long	1048576000
+	.long	3152414341
+	.long	1064850424
+	.long	2987244005
+	.long	1048576000
+	.long	3170705253
+	.long	1064945565
+	.long	851856985
+	.long	1048576000
+	.long	3177244920
+	.long	1065030846
+	.long	855602635
+	.long	1048576000
+	.long	1027359369
+	.long	1065106216
+	.long	2989610635
+	.long	1040187392
+	.long	1018299420
+	.long	1065171628
+	.long	2969000681
+	.long	1040187392
+	.long	3140071849
+	.long	1065227044
+	.long	3002197507
+	.long	1040187392
+	.long	3168602920
+	.long	1065272429
+	.long	838093129
+	.long	1040187392
+	.long	1010124837
+	.long	1065307757
+	.long	852498564
+	.long	1031798784
+	.long	3160150850
+	.long	1065333007
+	.long	836655967
+	.long	1031798784
+	.long	3151746369
+	.long	1065348163
+	.long	814009613
+	.long	1023410176
+	.long	0
+	.long	1065353216
+	.long	0
+	.long	0
+	.long	1004262721
+	.long	1065348163
+	.long	814009613
+	.long	3170893824
+	.long	1012667202
+	.long	1065333007
+	.long	836655967
+	.long	3179282432
+	.long	3157608485
+	.long	1065307757
+	.long	852498564
+	.long	3179282432
+	.long	1021119272
+	.long	1065272429
+	.long	838093129
+	.long	3187671040
+	.long	992588201
+	.long	1065227044
+	.long	3002197507
+	.long	3187671040
+	.long	3165783068
+	.long	1065171628
+	.long	2969000681
+	.long	3187671040
+	.long	3174843017
+	.long	1065106216
+	.long	2989610635
+	.long	3187671040
+	.long	1029761272
+	.long	1065030846
+	.long	855602635
+	.long	3196059648
+	.long	1023221605
+	.long	1064945565
+	.long	851856985
+	.long	3196059648
+	.long	1004930693
+	.long	1064850424
+	.long	2987244005
+	.long	3196059648
+	.long	3163089201
+	.long	1064745479
+	.long	846006572
+	.long	3196059648
+	.long	3173319052
+	.long	1064630795
+	.long	2996018466
+	.long	3196059648
+	.long	3179441043
+	.long	1064506439
+	.long	851742225
+	.long	3196059648
+	.long	3182555983
+	.long	1064372488
+	.long	840880349
+	.long	3196059648
+	.long	1041201069
+	.long	1064229022
+	.long	2995991516
+	.long	3204448256
+	.long	1039156139
+	.long	1064076126
+	.long	854796500
+	.long	3204448256
+	.long	1036128472
+	.long	1063913895
+	.long	3001754476
+	.long	3204448256
+	.long	1033133567
+	.long	1063742424
+	.long	2998678409
+	.long	3204448256
+	.long	1028547674
+	.long	1063561817
+	.long	823789818
+	.long	3204448256
+	.long	1021989220
+	.long	1063372184
+	.long	3001545765
+	.long	3204448256
+	.long	1005106760
+	.long	1063173637
+	.long	851900755
+	.long	3204448256
+	.long	3160870706
+	.long	1062966298
+	.long	841166280
+	.long	3204448256
+	.long	3171899818
+	.long	1062750291
+	.long	2994560960
+	.long	3204448256
+	.long	3177422237
+	.long	1062525745
+	.long	848357914
+	.long	3204448256
+	.long	3181068627
+	.long	1062292797
+	.long	806113028
+	.long	3204448256
+	.long	3183738367
+	.long	1062051586
+	.long	847147240
+	.long	3204448256
+	.long	3186359946
+	.long	1061802258
+	.long	848897600
+	.long	3204448256
+	.long	3188301413
+	.long	1061544963
+	.long	850481524
+	.long	3204448256
+	.long	3189561687
+	.long	1061279856
+	.long	851442039
+	.long	3204448256
+	.long	3190795559
+	.long	1061007097
+	.long	832220140
+	.long	3204448256
+	.long	3192002283
+	.long	1060726850
+	.long	2994798599
+	.long	3204448256
+	.long	1050015258
+	.long	1060439283
+	.long	844097402
+	.long	3212836864
+	.long	1049440125
+	.long	1060144571
+	.long	2997759282
+	.long	3212836864
+	.long	1048879630
+	.long	1059842890
+	.long	2998350134
+	.long	3212836864
+	.long	1048092223
+	.long	1059534422
+	.long	2986574659
+	.long	3212836864
+	.long	1047031795
+	.long	1059219353
+	.long	841032635
+	.long	3212836864
+	.long	1046002615
+	.long	1058897873
+	.long	848430348
+	.long	3212836864
+	.long	1045005303
+	.long	1058570176
+	.long	2982650867
+	.long	3212836864
+	.long	1044040460
+	.long	1058236458
+	.long	852349230
+	.long	3212836864
+	.long	1043108667
+	.long	1057896922
+	.long	2991207143
+	.long	3212836864
+	.long	1042210485
+	.long	1057551771
+	.long	2998815566
+	.long	3212836864
+	.long	1041346455
+	.long	1057201213
+	.long	2992349186
+	.long	3212836864
+	.long	1040517098
+	.long	1056726311
+	.long	2978016425
+	.long	3212836864
+	.long	1039258436
+	.long	1056004842
+	.long	2986287417
+	.long	3212836864
+	.long	1037741368
+	.long	1055273845
+	.long	2983839604
+	.long	3212836864
+	.long	1036296515
+	.long	1054533760
+	.long	840832460
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+	.long	3908737646
+	.long	3765285194
+	.long	704390999
+	.long	3522507997
+	.long	3235603093
+	.long	1408781999
+	.long	2750048699
+	.long	2176238891
+	.long	2817563999
+	.long	1205130103
+	.long	57510486
+	.long	1340160702
+	.long	2410260206
+	.long	115020972
+	.long	2680321405
+	.long	525553116
+	.long	230041945
+	.long	1065675514
+	.long	1051106232
+	.long	460083891
+	.long	2131351028
+	.long	2102212464
+	.long	920167782
+	.long	4262702056
+	.long	4204424928
+	.long	1840335564
+	.long	4230436817
+	.long	4113882560
+	.long	3680671129
+	.long	4165906339
+	.long	3932797825
+	.long	3066374962
+	.long	4036845383
+	.long	3570628355
+	.long	1837782628
+	.long	3778723471
+	.long	2846289414
+	.long	3675565257
+	.long	3262479647
+	.long	1397611533
+	.long	3056163219
+	.long	2229991998
+	.long	2795223067
+	.long	1817359143
+	.long	165016701
+	.long	1295478838
+	.long	3634718287
+	.long	330033402
+	.long	2590957677
+	.long	2974469278
+	.long	660066805
+	.long	886948059
+	.long	1653971260
+	.long	1320133610
+	.long	1773896118
+	.long	3307942520
+	.long	2640267220
+	.long	3547792237
+	.long	2320917745
+	.long	985567145
+	.long	2800617179
+	.long	346868194
+	.long	1971134291
+	.long	1306267062
+	.long	693736388
+	.long	3942268582
+	.long	2612534124
+	.long	1387472776
+	.long	3589569869
+	.long	930100952
+	.long	2774945552
+	.long	2884172442
+	.long	1860201905
+	.long	1254923809
+	.long	1473377588
+	.long	3720403810
+	.long	2509847619
+	.long	2946755177
+	.long	3145840325
+	.long	724727943
+	.long	1598543059
+	.long	1996713354
+	.long	1449455886
+	.long	3197086118
+	.long	3993426708
+	.long	2898911772
+	.long	2099204941
+	.long	3691886121
+	.long	1502856249
+	.long	4198409883
+	.long	3088804946
+	.long	3005712498
+	.long	4101852471
+	.long	1882642597
+	.long	1716457700
+	.long	3908737646
+	.long	3765285194
+	.long	3432915400
+	.long	3522507997
+	.long	3235603093
+	.long	2570863504
+	.long	2750048699
+	.long	2176238891
+	.long	846759712
+	.long	1205130103
+	.long	57510486
+	.long	1693519425
+	.long	2410260206
+	.long	115020972
+	.long	3387038850
+	.long	525553116
+	.long	230041945
+	.long	2479110404
+	.long	1051106232
+	.long	460083891
+	.long	663253512
+	.long	2102212464
+	.long	920167782
+	.long	1326507024
+	.long	4204424928
+	.long	1840335564
+	.long	2653014048
+	.long	4113882560
+	.long	3680671129
+	.long	1011060801
+	.long	3932797825
+	.long	3066374962
+	.long	2022121603
+	.long	3570628355
+	.long	1837782628
+	.long	4044243207
+	.long	2846289414
+	.long	3675565257
+	.long	3793519119
+	.long	1397611533
+	.long	3056163219
+	.long	3292070943
+	.long	2795223067
+	.long	1817359143
+	.long	2289174591
+	.long	1295478838
+	.long	3634718287
+	.long	283381887
+	.long	2590957677
+	.long	2974469278
+	.long	566763775
+	.type	__svml_ssin_ha_reduction_data_internal,@object
+	.size	__svml_ssin_ha_reduction_data_internal,3072
+	.align 4
+__ssin_ha__vmlsSinHATab:
+	.long	0
+	.long	2139095040
+	.type	__ssin_ha__vmlsSinHATab,@object
+	.size	__ssin_ha__vmlsSinHATab,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_s_la.s
index cad6a22d83a..05103f539f2 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sin_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_d_ha.s
new file mode 100644
index 00000000000..7eff7f62e0e
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_d_ha.s
@@ -0,0 +1,5532 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_sinh8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_sinh8_ha
+
+__svml_sinh8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm6
+        vmovups   1408+__svml_dsinh_ha_data_internal(%rip), %zmm0
+        vmovups   512+__svml_dsinh_ha_data_internal(%rip), %zmm9
+
+/*
+ * ............... Load argument ............................
+ * dM = x*2^K/log(2) + RShifter
+ */
+        vmovups   1216+__svml_dsinh_ha_data_internal(%rip), %zmm7
+
+/* .............. Index and lookup ......................... */
+        vmovups   576+__svml_dsinh_ha_data_internal(%rip), %zmm14
+        vmovups   __svml_dsinh_ha_data_internal(%rip), %zmm12
+        vmovups   128+__svml_dsinh_ha_data_internal(%rip), %zmm15
+        vmovups   1088+__svml_dsinh_ha_data_internal(%rip), %zmm3
+        vmovups   1152+__svml_dsinh_ha_data_internal(%rip), %zmm4
+        vmovups   832+__svml_dsinh_ha_data_internal(%rip), %zmm1
+        vmovups   896+__svml_dsinh_ha_data_internal(%rip), %zmm2
+
+/* ............... Abs argument ............................ */
+        vandpd    %zmm6, %zmm0, %zmm5
+        vxorpd    %zmm6, %zmm5, %zmm8
+        vfmadd213pd {rn-sae}, %zmm9, %zmm8, %zmm7
+
+/*
+ * ...............Check for overflow\underflow .............
+ * 
+ */
+        vpsrlq    $32, %zmm8, %zmm10
+        vcmppd    $0, {sae}, %zmm7, %zmm14, %k1
+        vpmovqd   %zmm10, %ymm11
+        vpcmpgtd  640+__svml_dsinh_ha_data_internal(%rip), %ymm11, %ymm13
+        vblendmpd %zmm9, %zmm7, %zmm11{%k1}
+        vmovups   384+__svml_dsinh_ha_data_internal(%rip), %zmm7
+        vmovmskps %ymm13, %edx
+        vmovups   256+__svml_dsinh_ha_data_internal(%rip), %zmm13
+
+/* j */
+        vpandq    704+__svml_dsinh_ha_data_internal(%rip), %zmm11, %zmm10
+
+/* split j and N */
+        vpxorq    %zmm10, %zmm11, %zmm14
+        vpermt2pd 64+__svml_dsinh_ha_data_internal(%rip), %zmm10, %zmm12
+        vpermt2pd 320+__svml_dsinh_ha_data_internal(%rip), %zmm10, %zmm13
+        vpermt2pd 192+__svml_dsinh_ha_data_internal(%rip), %zmm10, %zmm15
+        vpermt2pd 448+__svml_dsinh_ha_data_internal(%rip), %zmm10, %zmm7
+
+/*
+ * ................... R ...................................
+ * dN = dM - RShifter
+ */
+        vsubpd    {rn-sae}, %zmm9, %zmm11, %zmm10
+        vmovups   1280+__svml_dsinh_ha_data_internal(%rip), %zmm9
+
+/* if |dTnl| < dM, then scaled dTnl = 0 */
+        vandnpd   %zmm7, %zmm0, %zmm0
+
+/* dR = dX - dN*Log2_hi/2^K */
+        vfnmadd231pd {rn-sae}, %zmm9, %zmm10, %zmm8
+        vmovups   1344+__svml_dsinh_ha_data_internal(%rip), %zmm9
+
+/* dR = (dX - dN*Log2_hi/2^K) - dN*Log2_lo/2^K */
+        vfnmadd231pd {rn-sae}, %zmm9, %zmm10, %zmm8
+
+/*
+ * ............... G1,G2,G3: dTdif,dTn * 2^N,2^(-N) ...........
+ * lM now is an EXP(2^N)
+ */
+        vpsllq    $48, %zmm14, %zmm10
+
+/* dR2 = dR^2 */
+        vmulpd    {rn-sae}, %zmm8, %zmm8, %zmm9
+        vcmppd    $17, {sae}, %zmm10, %zmm0, %k2
+        vpaddq    %zmm10, %zmm12, %zmm11
+        vpsubq    %zmm10, %zmm13, %zmm12
+        vpaddq    %zmm10, %zmm15, %zmm15
+        vpsubq    %zmm10, %zmm7, %zmm7
+        vsubpd    {rn-sae}, %zmm12, %zmm11, %zmm14
+        vaddpd    {rn-sae}, %zmm12, %zmm11, %zmm13
+        vxorpd    %zmm7, %zmm7, %zmm7{%k2}
+        vsubpd    {rn-sae}, %zmm11, %zmm14, %zmm0
+        vaddpd    {rn-sae}, %zmm0, %zmm12, %zmm12
+
+/* poly(r) = G1(1 + a2*r^2 + a4*r^4) + G2*(r+ a3*r^3 +a5*r^5) */
+        vmovups   960+__svml_dsinh_ha_data_internal(%rip), %zmm0
+        vsubpd    {rn-sae}, %zmm12, %zmm15, %zmm15
+        vfmadd231pd {rn-sae}, %zmm9, %zmm3, %zmm0
+        vmovups   1024+__svml_dsinh_ha_data_internal(%rip), %zmm3
+        vsubpd    {rn-sae}, %zmm7, %zmm15, %zmm7
+        vfmadd213pd {rn-sae}, %zmm1, %zmm9, %zmm0
+        vfmadd231pd {rn-sae}, %zmm9, %zmm4, %zmm3
+        vmovups   768+__svml_dsinh_ha_data_internal(%rip), %zmm1
+        vmulpd    {rn-sae}, %zmm9, %zmm0, %zmm4
+        vfmadd213pd {rn-sae}, %zmm2, %zmm9, %zmm3
+        vfmadd213pd {rn-sae}, %zmm8, %zmm8, %zmm4
+        vfmadd213pd {rn-sae}, %zmm1, %zmm9, %zmm3
+        vmulpd    {rn-sae}, %zmm9, %zmm3, %zmm2
+        vfmadd213pd {rn-sae}, %zmm7, %zmm14, %zmm2
+        vfmadd213pd {rn-sae}, %zmm2, %zmm13, %zmm4
+        vaddpd    {rn-sae}, %zmm14, %zmm4, %zmm8
+
+/* ................... Ret H ...................... */
+        vorpd     %zmm8, %zmm5, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm6, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dsinh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sinh8_ha,@function
+	.size	__svml_sinh8_ha,.-__svml_sinh8_ha
+..LN__svml_sinh8_ha.0:
+
+.L_2__routine_start___svml_dsinh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dsinh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movq      %rsi, %r9
+        movzwl    6(%rdi), %ecx
+        xorl      %eax, %eax
+        andl      $32752, %ecx
+        shrl      $4, %ecx
+        movsd     (%rdi), %xmm2
+        movb      7(%rdi), %dl
+        movsd     %xmm2, -8(%rsp)
+        cmpl      $2047, %ecx
+        je        .LBL_2_17
+
+
+        testl     %ecx, %ecx
+        jle       .LBL_2_16
+
+
+        andb      $127, %dl
+        movsd     %xmm2, -32(%rsp)
+        movb      %dl, -25(%rsp)
+        cmpl      $969, %ecx
+        jle       .LBL_2_14
+
+
+        movsd     -32(%rsp), %xmm0
+        movsd     1136+__dsinh_ha_CoutTab(%rip), %xmm1
+        comisd    %xmm0, %xmm1
+        jbe       .LBL_2_13
+
+
+        movsd     1184+__dsinh_ha_CoutTab(%rip), %xmm1
+        comisd    %xmm0, %xmm1
+        jbe       .LBL_2_9
+
+
+        comisd    1176+__dsinh_ha_CoutTab(%rip), %xmm0
+        jb        .LBL_2_8
+
+
+        movsd     1112+__dsinh_ha_CoutTab(%rip), %xmm1
+        lea       __dsinh_ha_CoutTab(%rip), %rcx
+        mulsd     %xmm0, %xmm1
+        movsd     1144+__dsinh_ha_CoutTab(%rip), %xmm4
+        movq      8+__dsinh_ha_CoutTab(%rip), %r10
+        movq      %r10, %rsi
+        shrq      $48, %rsi
+        addsd     1120+__dsinh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -40(%rsp)
+        andl      $-32753, %esi
+        movsd     -40(%rsp), %xmm10
+        movl      -40(%rsp), %r8d
+        movl      %r8d, %r11d
+        shrl      $6, %r11d
+        andl      $63, %r8d
+        movq      %r10, -16(%rsp)
+        subsd     1120+__dsinh_ha_CoutTab(%rip), %xmm10
+        mulsd     %xmm10, %xmm4
+        lea       1023(%r11), %edi
+        xorps     .L_2il0floatpacket.100(%rip), %xmm10
+        addl      $1022, %r11d
+        mulsd     1152+__dsinh_ha_CoutTab(%rip), %xmm10
+        subsd     %xmm4, %xmm0
+        movaps    %xmm10, %xmm2
+        movaps    %xmm0, %xmm8
+        andl      $2047, %r11d
+        lea       (%r8,%r8), %edx
+        negl      %edi
+        lea       1(%r8,%r8), %r8d
+        movsd     (%rcx,%rdx,8), %xmm9
+        negl      %edx
+        shll      $4, %r11d
+        addl      $-4, %edi
+        orl       %r11d, %esi
+        andl      $2047, %edi
+        movw      %si, -10(%rsp)
+        andl      $-32753, %esi
+        shll      $4, %edi
+        addsd     %xmm0, %xmm2
+        movsd     %xmm2, -24(%rsp)
+        orl       %edi, %esi
+        movsd     -24(%rsp), %xmm6
+        movsd     1128+__dsinh_ha_CoutTab(%rip), %xmm2
+        subsd     %xmm6, %xmm8
+        movsd     %xmm8, -56(%rsp)
+        movsd     -24(%rsp), %xmm12
+        movsd     -56(%rsp), %xmm11
+        movsd     (%rcx,%r8,8), %xmm7
+        addsd     %xmm11, %xmm12
+        movsd     %xmm12, -48(%rsp)
+        movsd     -56(%rsp), %xmm5
+        addsd     %xmm5, %xmm10
+        movsd     -16(%rsp), %xmm5
+        mulsd     %xmm5, %xmm9
+        mulsd     %xmm5, %xmm7
+        movsd     %xmm10, -56(%rsp)
+        movaps    %xmm9, %xmm10
+        movsd     -48(%rsp), %xmm13
+        movw      %si, -10(%rsp)
+        lea       128(%rdx), %esi
+        movsd     -16(%rsp), %xmm14
+        addl      $129, %edx
+        subsd     %xmm13, %xmm0
+        movsd     %xmm0, -48(%rsp)
+        movsd     -56(%rsp), %xmm1
+        movsd     -48(%rsp), %xmm15
+        addsd     %xmm15, %xmm1
+        movsd     %xmm1, -48(%rsp)
+        movsd     -24(%rsp), %xmm4
+        mulsd     %xmm4, %xmm2
+        movaps    %xmm4, %xmm3
+        mulsd     %xmm4, %xmm3
+        movsd     -48(%rsp), %xmm1
+        movaps    %xmm4, %xmm0
+        movsd     %xmm2, -24(%rsp)
+        movsd     -24(%rsp), %xmm6
+        movsd     1064+__dsinh_ha_CoutTab(%rip), %xmm2
+        subsd     %xmm4, %xmm6
+        mulsd     %xmm3, %xmm2
+        movsd     %xmm6, -56(%rsp)
+        movsd     -24(%rsp), %xmm11
+        movsd     -56(%rsp), %xmm8
+        subsd     %xmm8, %xmm11
+        addsd     1048+__dsinh_ha_CoutTab(%rip), %xmm2
+        movsd     %xmm11, -24(%rsp)
+        movsd     1072+__dsinh_ha_CoutTab(%rip), %xmm11
+        mulsd     %xmm3, %xmm11
+        mulsd     %xmm3, %xmm2
+        addsd     1056+__dsinh_ha_CoutTab(%rip), %xmm11
+        mulsd     %xmm4, %xmm2
+        mulsd     %xmm3, %xmm11
+        movsd     -24(%rsp), %xmm12
+        addsd     1040+__dsinh_ha_CoutTab(%rip), %xmm11
+        subsd     %xmm12, %xmm0
+        mulsd     %xmm3, %xmm11
+        movsd     (%rcx,%rsi,8), %xmm3
+        movaps    %xmm9, %xmm12
+        mulsd     %xmm14, %xmm3
+        movsd     %xmm0, -56(%rsp)
+        subsd     %xmm3, %xmm10
+        movsd     -24(%rsp), %xmm8
+        movsd     -56(%rsp), %xmm6
+        movsd     %xmm10, -24(%rsp)
+        movsd     -24(%rsp), %xmm13
+        movsd     (%rcx,%rdx,8), %xmm0
+        subsd     %xmm13, %xmm12
+        mulsd     %xmm14, %xmm0
+        subsd     %xmm3, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -24(%rsp), %xmm14
+        movsd     -56(%rsp), %xmm12
+        movaps    %xmm14, %xmm5
+        movb      -1(%rsp), %cl
+        addsd     %xmm7, %xmm12
+        addsd     %xmm0, %xmm7
+        subsd     %xmm0, %xmm12
+        andb      $-128, %cl
+        addsd     %xmm12, %xmm5
+        movsd     %xmm5, -24(%rsp)
+        movaps    %xmm9, %xmm5
+        movsd     -24(%rsp), %xmm15
+        addsd     %xmm3, %xmm5
+        subsd     %xmm15, %xmm14
+        addsd     %xmm14, %xmm12
+        movsd     %xmm12, -56(%rsp)
+        movsd     -24(%rsp), %xmm10
+        movsd     -56(%rsp), %xmm12
+        movsd     %xmm5, -24(%rsp)
+        movsd     -24(%rsp), %xmm13
+        subsd     %xmm13, %xmm9
+        addsd     %xmm9, %xmm3
+        movsd     %xmm3, -56(%rsp)
+        movsd     -24(%rsp), %xmm3
+        movsd     -56(%rsp), %xmm5
+        addsd     %xmm7, %xmm5
+        movaps    %xmm3, %xmm7
+        addsd     %xmm5, %xmm7
+        movsd     %xmm7, -24(%rsp)
+        movsd     -24(%rsp), %xmm9
+        movsd     1128+__dsinh_ha_CoutTab(%rip), %xmm7
+        subsd     %xmm9, %xmm3
+        addsd     %xmm3, %xmm5
+        movsd     %xmm5, -56(%rsp)
+        movsd     -24(%rsp), %xmm0
+        mulsd     %xmm0, %xmm7
+        movsd     -56(%rsp), %xmm3
+        movsd     %xmm7, -24(%rsp)
+        movaps    %xmm0, %xmm7
+        movsd     -24(%rsp), %xmm14
+        mulsd     %xmm3, %xmm4
+        subsd     %xmm0, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movsd     -24(%rsp), %xmm13
+        movsd     -56(%rsp), %xmm9
+        subsd     %xmm9, %xmm13
+        movsd     %xmm13, -24(%rsp)
+        movaps    %xmm0, %xmm13
+        movsd     -24(%rsp), %xmm15
+        mulsd     %xmm2, %xmm13
+        subsd     %xmm15, %xmm7
+        mulsd     %xmm1, %xmm0
+        mulsd     %xmm3, %xmm1
+        mulsd     %xmm3, %xmm2
+        movaps    %xmm10, %xmm15
+        movaps    %xmm13, %xmm5
+        mulsd     %xmm11, %xmm15
+        mulsd     %xmm12, %xmm11
+        addsd     %xmm15, %xmm5
+        addsd     %xmm12, %xmm11
+        movsd     %xmm7, -56(%rsp)
+        addsd     %xmm0, %xmm11
+        movsd     -24(%rsp), %xmm9
+        addsd     %xmm1, %xmm11
+        movsd     -56(%rsp), %xmm7
+        addsd     %xmm2, %xmm11
+        movsd     %xmm5, -24(%rsp)
+        addsd     %xmm4, %xmm11
+        movsd     -24(%rsp), %xmm14
+        subsd     %xmm14, %xmm13
+        addsd     %xmm13, %xmm15
+        movsd     %xmm15, -56(%rsp)
+        movaps    %xmm8, %xmm15
+        mulsd     %xmm9, %xmm15
+        mulsd     %xmm6, %xmm9
+        mulsd     %xmm7, %xmm6
+        mulsd     %xmm7, %xmm8
+        movsd     -24(%rsp), %xmm14
+        movaps    %xmm15, %xmm13
+        movsd     -56(%rsp), %xmm5
+        addsd     %xmm14, %xmm13
+        addsd     %xmm9, %xmm5
+        movsd     %xmm13, -24(%rsp)
+        addsd     %xmm6, %xmm5
+        movsd     -24(%rsp), %xmm13
+        addsd     %xmm8, %xmm5
+        subsd     %xmm13, %xmm15
+        addsd     %xmm5, %xmm11
+        addsd     %xmm15, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movaps    %xmm10, %xmm15
+        movsd     -24(%rsp), %xmm13
+        movsd     -56(%rsp), %xmm14
+        addsd     %xmm13, %xmm15
+        addsd     %xmm14, %xmm11
+        movsd     %xmm15, -24(%rsp)
+        movsd     -24(%rsp), %xmm15
+        subsd     %xmm15, %xmm10
+        addsd     %xmm10, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -24(%rsp), %xmm13
+        movsd     -56(%rsp), %xmm10
+        addsd     %xmm10, %xmm11
+        addsd     %xmm11, %xmm13
+        movsd     %xmm13, -32(%rsp)
+        movb      -25(%rsp), %dil
+        andb      $127, %dil
+        orb       %cl, %dil
+        movb      %dil, -25(%rsp)
+        movq      -32(%rsp), %r10
+        movq      %r10, (%r9)
+        ret
+
+.LBL_2_8:
+
+        movaps    %xmm0, %xmm2
+        mulsd     %xmm0, %xmm2
+        movsd     1104+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm2, %xmm1
+        movb      -1(%rsp), %dl
+        andb      $-128, %dl
+        addsd     1096+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm2, %xmm1
+        addsd     1088+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm2, %xmm1
+        addsd     1080+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm1, %xmm2
+        mulsd     %xmm0, %xmm2
+        addsd     %xmm2, %xmm0
+        movsd     %xmm0, -32(%rsp)
+        movb      -25(%rsp), %cl
+        andb      $127, %cl
+        orb       %dl, %cl
+        movb      %cl, -25(%rsp)
+        movq      -32(%rsp), %rsi
+        movq      %rsi, (%r9)
+        ret
+
+.LBL_2_9:
+
+        movsd     1112+__dsinh_ha_CoutTab(%rip), %xmm1
+        lea       __dsinh_ha_CoutTab(%rip), %r8
+        mulsd     %xmm0, %xmm1
+        movsd     1144+__dsinh_ha_CoutTab(%rip), %xmm2
+        movsd     1152+__dsinh_ha_CoutTab(%rip), %xmm3
+        movq      8+__dsinh_ha_CoutTab(%rip), %rdx
+        movq      %rdx, -16(%rsp)
+        addsd     1120+__dsinh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -40(%rsp)
+        movsd     -40(%rsp), %xmm4
+        movsd     1072+__dsinh_ha_CoutTab(%rip), %xmm1
+        movl      -40(%rsp), %edx
+        movl      %edx, %esi
+        andl      $63, %esi
+        subsd     1120+__dsinh_ha_CoutTab(%rip), %xmm4
+        mulsd     %xmm4, %xmm2
+        lea       (%rsi,%rsi), %ecx
+        mulsd     %xmm3, %xmm4
+        subsd     %xmm2, %xmm0
+        movsd     (%r8,%rcx,8), %xmm5
+        lea       1(%rsi,%rsi), %edi
+        shrl      $6, %edx
+        subsd     %xmm4, %xmm0
+        mulsd     %xmm0, %xmm1
+        addl      $1022, %edx
+        andl      $2047, %edx
+        addsd     1064+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1056+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1048+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1040+__dsinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     %xmm0, %xmm1
+        mulsd     %xmm5, %xmm1
+        addsd     (%r8,%rdi,8), %xmm1
+        addsd     %xmm5, %xmm1
+        cmpl      $2046, %edx
+        ja        .LBL_2_11
+
+
+        movq      8+__dsinh_ha_CoutTab(%rip), %rcx
+        shrq      $48, %rcx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -10(%rsp)
+        movsd     -16(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        movsd     %xmm1, -32(%rsp)
+        jmp       .LBL_2_12
+
+.LBL_2_11:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -10(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -10(%rsp)
+        movsd     -16(%rsp), %xmm0
+        mulsd     %xmm0, %xmm1
+        mulsd     1024+__dsinh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -32(%rsp)
+
+.LBL_2_12:
+
+        movb      -25(%rsp), %cl
+        movb      -1(%rsp), %dl
+        andb      $127, %cl
+        andb      $-128, %dl
+        orb       %dl, %cl
+        movb      %cl, -25(%rsp)
+        movq      -32(%rsp), %rsi
+        movq      %rsi, (%r9)
+        ret
+
+.LBL_2_13:
+
+        movsd     1168+__dsinh_ha_CoutTab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm2, %xmm0
+        movsd     %xmm0, (%r9)
+        ret
+
+.LBL_2_14:
+
+        movsd     __dsinh_ha_CoutTab(%rip), %xmm0
+        addsd     1160+__dsinh_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm2, %xmm0
+        movsd     %xmm0, (%r9)
+
+
+        ret
+
+.LBL_2_16:
+
+        movsd     1160+__dsinh_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm0, %xmm2
+        movsd     %xmm2, -24(%rsp)
+        movsd     -24(%rsp), %xmm1
+        movq      8+__dsinh_ha_CoutTab(%rip), %rdx
+        movq      %rdx, -16(%rsp)
+        addsd     -8(%rsp), %xmm1
+        movsd     %xmm1, (%r9)
+        ret
+
+.LBL_2_17:
+
+        addsd     %xmm2, %xmm2
+        movsd     %xmm2, (%r9)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dsinh_ha_cout_rare_internal,@function
+	.size	__svml_dsinh_ha_cout_rare_internal,.-__svml_dsinh_ha_cout_rare_internal
+..LN__svml_dsinh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dsinh_ha_data_internal:
+	.long	0
+	.long	1071644672
+	.long	1828292879
+	.long	1071691096
+	.long	1014845819
+	.long	1071739576
+	.long	1853186616
+	.long	1071790202
+	.long	171030293
+	.long	1071843070
+	.long	1276261410
+	.long	1071898278
+	.long	3577096743
+	.long	1071955930
+	.long	3712504873
+	.long	1072016135
+	.long	1719614413
+	.long	1072079006
+	.long	1944781191
+	.long	1072144660
+	.long	1110089947
+	.long	1072213221
+	.long	2191782032
+	.long	1072284817
+	.long	2572866477
+	.long	1072359583
+	.long	3716502172
+	.long	1072437659
+	.long	3707479175
+	.long	1072519192
+	.long	2728693978
+	.long	1072604335
+	.long	0
+	.long	0
+	.long	1255956747
+	.long	1015588398
+	.long	3117910646
+	.long	3161559105
+	.long	3066496371
+	.long	1015656574
+	.long	3526460132
+	.long	1014428778
+	.long	300981948
+	.long	1014684169
+	.long	2951496418
+	.long	1013793687
+	.long	88491949
+	.long	1015427660
+	.long	330458198
+	.long	3163282740
+	.long	3993278767
+	.long	3161724279
+	.long	1451641639
+	.long	1015474673
+	.long	2960257726
+	.long	1013742662
+	.long	878562433
+	.long	1015521741
+	.long	2303740125
+	.long	1014042725
+	.long	3613079303
+	.long	1014164738
+	.long	396109971
+	.long	3163462691
+	.long	0
+	.long	1071644672
+	.long	2728693978
+	.long	1071555759
+	.long	3707479175
+	.long	1071470616
+	.long	3716502172
+	.long	1071389083
+	.long	2572866477
+	.long	1071311007
+	.long	2191782032
+	.long	1071236241
+	.long	1110089947
+	.long	1071164645
+	.long	1944781191
+	.long	1071096084
+	.long	1719614413
+	.long	1071030430
+	.long	3712504873
+	.long	1070967559
+	.long	3577096743
+	.long	1070907354
+	.long	1276261410
+	.long	1070849702
+	.long	171030293
+	.long	1070794494
+	.long	1853186616
+	.long	1070741626
+	.long	1014845819
+	.long	1070691000
+	.long	1828292879
+	.long	1070642520
+	.long	0
+	.long	0
+	.long	396109971
+	.long	3162414115
+	.long	3613079303
+	.long	1013116162
+	.long	2303740125
+	.long	1012994149
+	.long	878562433
+	.long	1014473165
+	.long	2960257726
+	.long	1012694086
+	.long	1451641639
+	.long	1014426097
+	.long	3993278767
+	.long	3160675703
+	.long	330458198
+	.long	3162234164
+	.long	88491949
+	.long	1014379084
+	.long	2951496418
+	.long	1012745111
+	.long	300981948
+	.long	1013635593
+	.long	3526460132
+	.long	1013380202
+	.long	3066496371
+	.long	1014607998
+	.long	3117910646
+	.long	3160510529
+	.long	1255956747
+	.long	1014539822
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	0
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1
+	.long	1123549184
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	1082453555
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	15
+	.long	0
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	0
+	.long	1071644672
+	.long	1431657638
+	.long	1069897045
+	.long	1431657638
+	.long	1069897045
+	.long	1431657638
+	.long	1069897045
+	.long	1431657638
+	.long	1069897045
+	.long	1431657638
+	.long	1069897045
+	.long	1431657638
+	.long	1069897045
+	.long	1431657638
+	.long	1069897045
+	.long	1431657638
+	.long	1069897045
+	.long	1431653196
+	.long	1067799893
+	.long	1431653196
+	.long	1067799893
+	.long	1431653196
+	.long	1067799893
+	.long	1431653196
+	.long	1067799893
+	.long	1431653196
+	.long	1067799893
+	.long	1431653196
+	.long	1067799893
+	.long	1431653196
+	.long	1067799893
+	.long	1431653196
+	.long	1067799893
+	.long	249327322
+	.long	1065423121
+	.long	249327322
+	.long	1065423121
+	.long	249327322
+	.long	1065423121
+	.long	249327322
+	.long	1065423121
+	.long	249327322
+	.long	1065423121
+	.long	249327322
+	.long	1065423121
+	.long	249327322
+	.long	1065423121
+	.long	249327322
+	.long	1065423121
+	.long	419584011
+	.long	1062650220
+	.long	419584011
+	.long	1062650220
+	.long	419584011
+	.long	1062650220
+	.long	419584011
+	.long	1062650220
+	.long	419584011
+	.long	1062650220
+	.long	419584011
+	.long	1062650220
+	.long	419584011
+	.long	1062650220
+	.long	419584011
+	.long	1062650220
+	.long	100753094
+	.long	1059717741
+	.long	100753094
+	.long	1059717741
+	.long	100753094
+	.long	1059717741
+	.long	100753094
+	.long	1059717741
+	.long	100753094
+	.long	1059717741
+	.long	100753094
+	.long	1059717741
+	.long	100753094
+	.long	1059717741
+	.long	100753094
+	.long	1059717741
+	.long	461398617
+	.long	1056571820
+	.long	461398617
+	.long	1056571820
+	.long	461398617
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+	.long	1944781191
+	.long	1073193236
+	.long	3993278767
+	.long	3162772855
+	.long	919555682
+	.long	1073210099
+	.long	3121969534
+	.long	1013996802
+	.long	2571947539
+	.long	1073227145
+	.long	3558159064
+	.long	3164425245
+	.long	2604962541
+	.long	1073244377
+	.long	2614425274
+	.long	3164587768
+	.long	1110089947
+	.long	1073261797
+	.long	1451641639
+	.long	1016523249
+	.long	2568320822
+	.long	1073279406
+	.long	2732824428
+	.long	1015401491
+	.long	2966275557
+	.long	1073297207
+	.long	2176155324
+	.long	3160891335
+	.long	2682146384
+	.long	1073315202
+	.long	2082178513
+	.long	3164411995
+	.long	2191782032
+	.long	1073333393
+	.long	2960257726
+	.long	1014791238
+	.long	2069751141
+	.long	1073351782
+	.long	1562170675
+	.long	3163773257
+	.long	2990417245
+	.long	1073370371
+	.long	3683467745
+	.long	3164417902
+	.long	1434058175
+	.long	1073389163
+	.long	251133233
+	.long	1016134345
+	.long	2572866477
+	.long	1073408159
+	.long	878562433
+	.long	1016570317
+	.long	3092190715
+	.long	1073427362
+	.long	814012168
+	.long	3160571998
+	.long	4076559943
+	.long	1073446774
+	.long	2119478331
+	.long	3161806927
+	.long	2420883922
+	.long	1073466398
+	.long	2049810052
+	.long	1015168464
+	.long	3716502172
+	.long	1073486235
+	.long	2303740125
+	.long	1015091301
+	.long	777507147
+	.long	1073506289
+	.long	4282924205
+	.long	1016236109
+	.long	3706687593
+	.long	1073526560
+	.long	3521726939
+	.long	1014301643
+	.long	1242007932
+	.long	1073547053
+	.long	1132034716
+	.long	3164388407
+	.long	3707479175
+	.long	1073567768
+	.long	3613079303
+	.long	1015213314
+	.long	64696965
+	.long	1073588710
+	.long	1768797490
+	.long	1016865536
+	.long	863738719
+	.long	1073609879
+	.long	1326992220
+	.long	3163661773
+	.long	3884662774
+	.long	1073631278
+	.long	2158611599
+	.long	1015258761
+	.long	2728693978
+	.long	1073652911
+	.long	396109971
+	.long	3164511267
+	.long	3999357479
+	.long	1073674779
+	.long	2258941616
+	.long	1016973300
+	.long	1533953344
+	.long	1073696886
+	.long	769171851
+	.long	1016714209
+	.long	2174652632
+	.long	1073719233
+	.long	4087714590
+	.long	1015498835
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	0
+	.long	0
+	.long	1071644672
+	.long	1431652600
+	.long	1069897045
+	.long	1431670732
+	.long	1067799893
+	.long	984555731
+	.long	1065423122
+	.long	472530941
+	.long	1062650218
+	.long	1431655765
+	.long	1069897045
+	.long	286331153
+	.long	1065423121
+	.long	436314138
+	.long	1059717536
+	.long	2773927732
+	.long	1053236707
+	.long	1697350398
+	.long	1079448903
+	.long	0
+	.long	1127743488
+	.long	33554432
+	.long	1101004800
+	.long	2411329662
+	.long	1082536910
+	.long	4277796864
+	.long	1065758274
+	.long	3164486458
+	.long	1025308570
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	3671843104
+	.long	1067178892
+	.long	3875694624
+	.long	1077247184
+	.type	__dsinh_ha_CoutTab,@object
+	.size	__dsinh_ha_CoutTab,1192
+	.space 8, 0x00 	
+	.align 16
+.L_2il0floatpacket.100:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.100,@object
+	.size	.L_2il0floatpacket.100,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_d_la.s
index ff0557c0f99..70ea648b01c 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_h_la.s
new file mode 100644
index 00000000000..fb56a38e56f
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_h_la.s
@@ -0,0 +1,172 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Compute sinh(x) as (exp(x)-exp(-x))/2,
+ *   where exp is calculated as
+ *   exp(M*ln2 + ln2*(j/2^k) + r) = 2^M * 2^(j/2^k) * exp(r)
+ *
+ *   Special cases:
+ *
+ *   sinh(NaN) = quiet NaN, and raise invalid exception
+ *   sinh(INF) = that INF
+ *   sinh(x)   = x for subnormals
+ *   sinh(x) overflows for big x and returns MAXLOG+log(2)
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_sinhs32
+
+__svml_sinhs32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_hsinh_data_internal(%rip), %zmm23
+        vmovdqu16 64+__svml_hsinh_data_internal(%rip), %zmm29
+        vmovdqu16 128+__svml_hsinh_data_internal(%rip), %zmm28
+        vmovdqu16 192+__svml_hsinh_data_internal(%rip), %zmm26
+        vmovdqu16 256+__svml_hsinh_data_internal(%rip), %zmm5
+        vmovdqu16 320+__svml_hsinh_data_internal(%rip), %zmm31
+        vmovdqu16 384+__svml_hsinh_data_internal(%rip), %zmm8
+        vmovdqu16 448+__svml_hsinh_data_internal(%rip), %zmm24
+        vmovdqu16 512+__svml_hsinh_data_internal(%rip), %zmm9
+        vmovdqu16 576+__svml_hsinh_data_internal(%rip), %zmm11
+        vmovdqu16 640+__svml_hsinh_data_internal(%rip), %zmm30
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__sinh_h:
+        cmpq    $31, %rdx
+        ja .loaddata__sinh_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_sinh_h
+.loaddata__sinh_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_sinh_h:
+
+        vmovdqu16 %zmm29, %zmm2
+        vmovdqu16 %zmm24, %zmm7
+        vpandd    %zmm23, %zmm0, %zmm6
+        vfmadd213ph {rz-sae}, %zmm28, %zmm6, %zmm2
+        vsubph    {rn-sae}, %zmm28, %zmm2, %zmm10
+
+/* hN - 1 */
+        vsubph    {rn-sae}, %zmm11, %zmm10, %zmm12
+
+/* save sign */
+        vpxord    %zmm0, %zmm6, %zmm1
+        vfnmadd231ph {rn-sae}, %zmm10, %zmm26, %zmm6
+
+/* 2^(hN-1) */
+        vscalefph {rn-sae}, %zmm12, %zmm11, %zmm25
+        vfnmadd231ph {rn-sae}, %zmm10, %zmm5, %zmm6
+
+/* fixup for Inf results */
+        vfpclassph $8, %zmm25, %k1
+
+/* 2^(-hN)*2 */
+        vrcpph    %zmm25, %zmm27
+
+/* exp(R) -1 */
+        vmovaps   %zmm7, %zmm0
+        vpandd    %zmm31, %zmm6, %zmm13
+
+/* exp(-R) -1 */
+        vfnmadd231ph {rn-sae}, %zmm13, %zmm8, %zmm7
+        vfmadd231ph {rn-sae}, %zmm13, %zmm8, %zmm0
+
+/* 2^(-hN)*R */
+        vmulph    {rn-sae}, %zmm13, %zmm27, %zmm14
+
+/* 2^hN*R */
+        vmulph    {rn-sae}, %zmm13, %zmm25, %zmm2
+        vfnmadd213ph {rn-sae}, %zmm9, %zmm13, %zmm7
+        vfmadd213ph {rn-sae}, %zmm9, %zmm13, %zmm0
+        vfnmadd213ph {rn-sae}, %zmm11, %zmm13, %zmm7
+        vfmadd213ph {rn-sae}, %zmm11, %zmm13, %zmm0
+
+/* -Tm*mpoly*0.25 */
+        vmulph    {rn-sae}, %zmm14, %zmm7, %zmm15
+        vfnmadd213ph {rn-sae}, %zmm25, %zmm30, %zmm27
+        vmulph    {rn-sae}, %zmm30, %zmm15, %zmm7
+
+/* T*poly - Tm*mpoly */
+        vfmadd213ph {rn-sae}, %zmm7, %zmm2, %zmm0
+        vaddph    {rn-sae}, %zmm27, %zmm0, %zmm0
+
+/* fixup */
+        vmovdqu16 %zmm25, %zmm0{%k1}
+
+/* fix sign */
+        vpxord    %zmm1, %zmm0, %zmm0
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__sinh_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_sinhs32,@function
+        .size	__svml_sinhs32,.-__svml_sinhs32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_hsinh_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x3dc5
+	.endr
+	.rept	32
+        .word	0x6600
+	.endr
+	.rept	32
+        .word	0x398c
+	.endr
+	.rept	32
+        .word	0x8af4
+	.endr
+	.rept	32
+        .word	0xbfff
+	.endr
+	.rept	32
+        .word	0x2976
+	.endr
+	.rept	32
+        .word	0x3176
+	.endr
+	.rept	32
+        .word	0x37ff
+	.endr
+	.rept	32
+        .word	0x3c00
+	.endr
+	.rept	32
+        .word	0x3400
+	.endr
+        .type	__svml_hsinh_data_internal,@object
+        .size	__svml_hsinh_data_internal,704
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_s_ha.s
new file mode 100644
index 00000000000..27b1612e707
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_s_ha.s
@@ -0,0 +1,2381 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+
+	.text
+.L_2__routine_start___svml_sinhf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_sinhf16_ha
+
+__svml_sinhf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovups   1152+__svml_ssinh_ha_data_internal(%rip), %zmm10
+
+/*
+ * ............... Load argument ............................
+ * dM = x/log(2) + RShifter
+ */
+        vmovups   960+__svml_ssinh_ha_data_internal(%rip), %zmm9
+        vmovups   576+__svml_ssinh_ha_data_internal(%rip), %zmm5
+        vmovups   1024+__svml_ssinh_ha_data_internal(%rip), %zmm1
+        vmovups   1088+__svml_ssinh_ha_data_internal(%rip), %zmm8
+
+/* ... */
+        vmovups   896+__svml_ssinh_ha_data_internal(%rip), %zmm12
+
+/* x^2 */
+        vmovups   832+__svml_ssinh_ha_data_internal(%rip), %zmm13
+
+/*
+ * ...............Check for overflow\underflow .............
+ * MORE faster than GE?
+ */
+        vpternlogd $255, %zmm4, %zmm4, %zmm4
+        vmovaps   %zmm0, %zmm15
+
+/*
+ * ----------------------------------- Implementation  ---------------------
+ * ............... Abs argument ............................
+ */
+        vandps    %zmm15, %zmm10, %zmm14
+        vmovups   512+__svml_ssinh_ha_data_internal(%rip), %zmm0
+        vxorps    %zmm15, %zmm14, %zmm11
+        vfmadd213ps {rn-sae}, %zmm0, %zmm11, %zmm9
+        vpcmpd    $2, 704+__svml_ssinh_ha_data_internal(%rip), %zmm11, %k1
+        vcmpps    $0, {sae}, %zmm9, %zmm5, %k2
+        vmovups   256+__svml_ssinh_ha_data_internal(%rip), %zmm5
+        vblendmps %zmm0, %zmm9, %zmm6{%k2}
+
+/* sN = sM - RShifter */
+        vsubps    {rn-sae}, %zmm0, %zmm6, %zmm3
+        vmovups   128+__svml_ssinh_ha_data_internal(%rip), %zmm0
+        vpandnd   %zmm11, %zmm11, %zmm4{%k1}
+
+/*
+ * ................... R ...................................
+ * sR = sX - sN*Log2_hi
+ */
+        vfnmadd231ps {rn-sae}, %zmm1, %zmm3, %zmm11
+        vptestmd  %zmm4, %zmm4, %k0
+        vmovups   __svml_ssinh_ha_data_internal(%rip), %zmm4
+
+/* sR = (sX - sN*Log2_hi) - sN*Log2_lo */
+        vfnmadd231ps {rn-sae}, %zmm8, %zmm3, %zmm11
+        kmovw     %k0, %edx
+
+/* sR2 = sR^2 */
+        vmulps    {rn-sae}, %zmm11, %zmm11, %zmm9
+        vmulps    {rn-sae}, %zmm9, %zmm12, %zmm12
+
+/* sSinh_r = r + r*(r^2*(a3)) */
+        vfmadd213ps {rn-sae}, %zmm11, %zmm11, %zmm12
+
+/* sOut = r^2*(a2) */
+        vmulps    {rn-sae}, %zmm9, %zmm13, %zmm11
+
+/* ............... G1,G2 2^N,2^(-N) ........... */
+        vpandd    640+__svml_ssinh_ha_data_internal(%rip), %zmm6, %zmm7
+        vpxord    %zmm7, %zmm6, %zmm2
+        vpslld    $18, %zmm2, %zmm8
+        vmovups   384+__svml_ssinh_ha_data_internal(%rip), %zmm2
+        vpermt2ps 320+__svml_ssinh_ha_data_internal(%rip), %zmm7, %zmm5
+        vpermt2ps 64+__svml_ssinh_ha_data_internal(%rip), %zmm7, %zmm4
+        vpermt2ps 448+__svml_ssinh_ha_data_internal(%rip), %zmm7, %zmm2
+        vpsubd    %zmm8, %zmm5, %zmm3
+        vpaddd    %zmm8, %zmm4, %zmm6
+        vpermt2ps 192+__svml_ssinh_ha_data_internal(%rip), %zmm7, %zmm0
+
+/* if |sTnl| < sM, then scaled sTnl = 0 */
+        vandnps   %zmm2, %zmm10, %zmm7
+        vsubps    {rn-sae}, %zmm3, %zmm6, %zmm5
+        vpaddd    %zmm8, %zmm0, %zmm1
+        vpsubd    %zmm8, %zmm2, %zmm0
+        vsubps    {rn-sae}, %zmm6, %zmm5, %zmm10
+        vaddps    {rn-sae}, %zmm3, %zmm6, %zmm4
+        vcmpps    $17, {sae}, %zmm8, %zmm7, %k3
+        vaddps    {rn-sae}, %zmm10, %zmm3, %zmm2
+        vxorps    %zmm0, %zmm0, %zmm0{%k3}
+        vsubps    {rn-sae}, %zmm2, %zmm1, %zmm1
+        vsubps    {rn-sae}, %zmm0, %zmm1, %zmm0
+
+/* res = sG1*(r + r*(r^2*(a3))) + sG2*(1+r^2*(a2)) */
+        vfmadd213ps {rn-sae}, %zmm0, %zmm5, %zmm11
+        vfmadd213ps {rn-sae}, %zmm11, %zmm12, %zmm4
+        vaddps    {rn-sae}, %zmm5, %zmm4, %zmm13
+
+/* ................... Ret H ...................... */
+        vorps     %zmm13, %zmm14, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm15, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_ssinh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_sinhf16_ha,@function
+	.size	__svml_sinhf16_ha,.-__svml_sinhf16_ha
+..LN__svml_sinhf16_ha.0:
+
+.L_2__routine_start___svml_ssinh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_ssinh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        movq      %rsi, %r9
+        movzwl    2(%rdi), %edx
+        xorl      %eax, %eax
+        andl      $32640, %edx
+        movss     (%rdi), %xmm2
+        cmpl      $32640, %edx
+        je        .LBL_2_17
+
+
+        cvtss2sd  %xmm2, %xmm2
+        movsd     %xmm2, -8(%rsp)
+        movzwl    -2(%rsp), %edx
+        andl      $32752, %edx
+        movsd     %xmm2, -32(%rsp)
+        shrl      $4, %edx
+        andb      $127, -25(%rsp)
+        testl     %edx, %edx
+        jle       .LBL_2_16
+
+
+        cmpl      $969, %edx
+        jle       .LBL_2_14
+
+
+        movsd     -32(%rsp), %xmm0
+        movsd     1136+__ssinh_ha_CoutTab(%rip), %xmm1
+        comisd    %xmm0, %xmm1
+        jbe       .LBL_2_13
+
+
+        movsd     1184+__ssinh_ha_CoutTab(%rip), %xmm1
+        comisd    %xmm0, %xmm1
+        jbe       .LBL_2_9
+
+
+        comisd    1176+__ssinh_ha_CoutTab(%rip), %xmm0
+        jb        .LBL_2_8
+
+
+        movsd     1112+__ssinh_ha_CoutTab(%rip), %xmm3
+        lea       __ssinh_ha_CoutTab(%rip), %rcx
+        mulsd     %xmm0, %xmm3
+        movsd     1144+__ssinh_ha_CoutTab(%rip), %xmm10
+        movq      8+__ssinh_ha_CoutTab(%rip), %r10
+        movq      %r10, %rsi
+        shrq      $48, %rsi
+        addsd     1120+__ssinh_ha_CoutTab(%rip), %xmm3
+        movsd     %xmm3, -40(%rsp)
+        andl      $-32753, %esi
+        movsd     -40(%rsp), %xmm13
+        movl      -40(%rsp), %r8d
+        movl      %r8d, %r11d
+        shrl      $6, %r11d
+        andl      $63, %r8d
+        movq      %r10, -16(%rsp)
+        subsd     1120+__ssinh_ha_CoutTab(%rip), %xmm13
+        mulsd     %xmm13, %xmm10
+        lea       1023(%r11), %edi
+        xorps     .L_2il0floatpacket.96(%rip), %xmm13
+        addl      $1022, %r11d
+        mulsd     1152+__ssinh_ha_CoutTab(%rip), %xmm13
+        subsd     %xmm10, %xmm0
+        movaps    %xmm0, %xmm5
+        movaps    %xmm0, %xmm11
+        andl      $2047, %r11d
+        lea       (%r8,%r8), %edx
+        negl      %edi
+        lea       1(%r8,%r8), %r8d
+        movsd     (%rcx,%rdx,8), %xmm8
+        negl      %edx
+        shll      $4, %r11d
+        addl      $-4, %edi
+        orl       %r11d, %esi
+        andl      $2047, %edi
+        movw      %si, -10(%rsp)
+        andl      $-32753, %esi
+        shll      $4, %edi
+        addsd     %xmm13, %xmm5
+        movsd     %xmm5, -24(%rsp)
+        orl       %edi, %esi
+        movsd     -24(%rsp), %xmm7
+        movsd     1128+__ssinh_ha_CoutTab(%rip), %xmm5
+        subsd     %xmm7, %xmm11
+        movsd     %xmm11, -56(%rsp)
+        movsd     -24(%rsp), %xmm4
+        movsd     -56(%rsp), %xmm12
+        movsd     (%rcx,%r8,8), %xmm6
+        addsd     %xmm12, %xmm4
+        movsd     %xmm4, -48(%rsp)
+        movsd     -56(%rsp), %xmm9
+        movsd     -16(%rsp), %xmm4
+        addsd     %xmm9, %xmm13
+        mulsd     %xmm4, %xmm8
+        mulsd     %xmm4, %xmm6
+        movsd     %xmm13, -56(%rsp)
+        movaps    %xmm8, %xmm9
+        movsd     -48(%rsp), %xmm15
+        movw      %si, -10(%rsp)
+        lea       128(%rdx), %esi
+        movsd     -16(%rsp), %xmm14
+        addl      $129, %edx
+        subsd     %xmm15, %xmm0
+        movaps    %xmm8, %xmm15
+        movsd     %xmm0, -48(%rsp)
+        movsd     -56(%rsp), %xmm3
+        movsd     -48(%rsp), %xmm0
+        addsd     %xmm0, %xmm3
+        movsd     %xmm3, -48(%rsp)
+        movsd     -24(%rsp), %xmm10
+        mulsd     %xmm10, %xmm5
+        movaps    %xmm10, %xmm2
+        mulsd     %xmm10, %xmm2
+        movsd     -48(%rsp), %xmm3
+        movaps    %xmm10, %xmm1
+        movsd     %xmm5, -24(%rsp)
+        movsd     -24(%rsp), %xmm7
+        subsd     %xmm10, %xmm7
+        movsd     %xmm7, -56(%rsp)
+        movsd     -24(%rsp), %xmm12
+        movsd     -56(%rsp), %xmm11
+        subsd     %xmm11, %xmm12
+        movsd     1064+__ssinh_ha_CoutTab(%rip), %xmm11
+        mulsd     %xmm2, %xmm11
+        movsd     %xmm12, -24(%rsp)
+        movsd     1072+__ssinh_ha_CoutTab(%rip), %xmm12
+        mulsd     %xmm2, %xmm12
+        addsd     1048+__ssinh_ha_CoutTab(%rip), %xmm11
+        mulsd     %xmm2, %xmm11
+        addsd     1056+__ssinh_ha_CoutTab(%rip), %xmm12
+        mulsd     %xmm2, %xmm12
+        mulsd     %xmm10, %xmm11
+        addsd     1040+__ssinh_ha_CoutTab(%rip), %xmm12
+        addsd     %xmm11, %xmm10
+        mulsd     %xmm2, %xmm12
+        movsd     (%rcx,%rsi,8), %xmm2
+        mulsd     %xmm14, %xmm2
+        movsd     -24(%rsp), %xmm0
+        subsd     %xmm2, %xmm9
+        subsd     %xmm0, %xmm1
+        movsd     %xmm1, -56(%rsp)
+        movsd     -24(%rsp), %xmm7
+        movsd     -56(%rsp), %xmm5
+        movsd     %xmm9, -24(%rsp)
+        movsd     -24(%rsp), %xmm13
+        movsd     (%rcx,%rdx,8), %xmm1
+        subsd     %xmm13, %xmm15
+        mulsd     %xmm14, %xmm1
+        subsd     %xmm2, %xmm15
+        movsd     %xmm15, -56(%rsp)
+        movaps    %xmm8, %xmm13
+        movsd     -24(%rsp), %xmm14
+        addsd     %xmm2, %xmm13
+        movsd     -56(%rsp), %xmm9
+        movaps    %xmm14, %xmm0
+        movb      -1(%rsp), %cl
+        addsd     %xmm6, %xmm9
+        addsd     %xmm1, %xmm6
+        subsd     %xmm1, %xmm9
+        andb      $-128, %cl
+        addsd     %xmm9, %xmm0
+        movsd     %xmm0, -24(%rsp)
+        movsd     -24(%rsp), %xmm4
+        subsd     %xmm4, %xmm14
+        addsd     %xmm14, %xmm9
+        movsd     %xmm9, -56(%rsp)
+        movsd     -24(%rsp), %xmm9
+        movsd     -56(%rsp), %xmm0
+        movsd     %xmm13, -24(%rsp)
+        movsd     -24(%rsp), %xmm15
+        subsd     %xmm15, %xmm8
+        addsd     %xmm8, %xmm2
+        movsd     %xmm2, -56(%rsp)
+        movsd     -24(%rsp), %xmm2
+        movsd     -56(%rsp), %xmm4
+        addsd     %xmm6, %xmm4
+        movaps    %xmm2, %xmm6
+        addsd     %xmm4, %xmm6
+        movsd     %xmm6, -24(%rsp)
+        movsd     -24(%rsp), %xmm8
+        movsd     1128+__ssinh_ha_CoutTab(%rip), %xmm6
+        subsd     %xmm8, %xmm2
+        addsd     %xmm2, %xmm4
+        movsd     %xmm4, -56(%rsp)
+        movsd     -24(%rsp), %xmm1
+        mulsd     %xmm1, %xmm6
+        movsd     -56(%rsp), %xmm2
+        movsd     %xmm6, -24(%rsp)
+        movaps    %xmm1, %xmm6
+        movsd     -24(%rsp), %xmm14
+        mulsd     %xmm2, %xmm10
+        subsd     %xmm1, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movsd     -24(%rsp), %xmm13
+        movsd     -56(%rsp), %xmm8
+        subsd     %xmm8, %xmm13
+        movsd     %xmm13, -24(%rsp)
+        movaps    %xmm11, %xmm13
+        movsd     -24(%rsp), %xmm15
+        mulsd     %xmm1, %xmm13
+        subsd     %xmm15, %xmm6
+        mulsd     %xmm3, %xmm1
+        mulsd     %xmm2, %xmm3
+        movaps    %xmm12, %xmm15
+        movaps    %xmm13, %xmm4
+        mulsd     %xmm9, %xmm15
+        mulsd     %xmm0, %xmm12
+        addsd     %xmm15, %xmm4
+        addsd     %xmm0, %xmm12
+        movsd     %xmm6, -56(%rsp)
+        addsd     %xmm1, %xmm12
+        movsd     -24(%rsp), %xmm8
+        addsd     %xmm3, %xmm12
+        movsd     -56(%rsp), %xmm6
+        movsd     %xmm4, -24(%rsp)
+        movsd     -24(%rsp), %xmm14
+        subsd     %xmm14, %xmm13
+        addsd     %xmm13, %xmm15
+        movsd     %xmm15, -56(%rsp)
+        movaps    %xmm7, %xmm15
+        mulsd     %xmm8, %xmm15
+        mulsd     %xmm5, %xmm8
+        mulsd     %xmm6, %xmm5
+        mulsd     %xmm6, %xmm7
+        movsd     -24(%rsp), %xmm14
+        movaps    %xmm14, %xmm13
+        movsd     -56(%rsp), %xmm4
+        addsd     %xmm15, %xmm13
+        addsd     %xmm8, %xmm4
+        movsd     %xmm13, -24(%rsp)
+        addsd     %xmm5, %xmm4
+        movsd     -24(%rsp), %xmm13
+        addsd     %xmm7, %xmm4
+        subsd     %xmm13, %xmm15
+        addsd     %xmm4, %xmm12
+        addsd     %xmm15, %xmm14
+        movsd     %xmm14, -56(%rsp)
+        movaps    %xmm9, %xmm15
+        movsd     -24(%rsp), %xmm13
+        movsd     -56(%rsp), %xmm14
+        addsd     %xmm13, %xmm15
+        addsd     %xmm14, %xmm12
+        movsd     %xmm15, -24(%rsp)
+        movsd     -24(%rsp), %xmm15
+        subsd     %xmm15, %xmm9
+        addsd     %xmm9, %xmm13
+        movsd     %xmm13, -56(%rsp)
+        movsd     -24(%rsp), %xmm13
+        movsd     -56(%rsp), %xmm9
+        addsd     %xmm9, %xmm12
+        addsd     %xmm12, %xmm13
+        addsd     %xmm13, %xmm10
+        movsd     %xmm10, -32(%rsp)
+        movb      -25(%rsp), %dil
+        andb      $127, %dil
+        orb       %cl, %dil
+        movb      %dil, -25(%rsp)
+        movsd     -32(%rsp), %xmm10
+        cvtsd2ss  %xmm10, %xmm10
+        movss     %xmm10, (%r9)
+        ret
+
+.LBL_2_8:
+
+        movaps    %xmm0, %xmm2
+        mulsd     %xmm0, %xmm2
+        movsd     1104+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm2, %xmm1
+        movb      -1(%rsp), %dl
+        andb      $-128, %dl
+        addsd     1096+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm2, %xmm1
+        addsd     1088+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm2, %xmm1
+        addsd     1080+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm1, %xmm2
+        mulsd     %xmm0, %xmm2
+        addsd     %xmm2, %xmm0
+        movsd     %xmm0, -32(%rsp)
+        movb      -25(%rsp), %cl
+        andb      $127, %cl
+        orb       %dl, %cl
+        movb      %cl, -25(%rsp)
+        movsd     -32(%rsp), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r9)
+        ret
+
+.LBL_2_9:
+
+        movsd     1112+__ssinh_ha_CoutTab(%rip), %xmm1
+        lea       __ssinh_ha_CoutTab(%rip), %r8
+        mulsd     %xmm0, %xmm1
+        movsd     1144+__ssinh_ha_CoutTab(%rip), %xmm2
+        movsd     1152+__ssinh_ha_CoutTab(%rip), %xmm3
+        movq      8+__ssinh_ha_CoutTab(%rip), %rdx
+        movq      %rdx, -16(%rsp)
+        addsd     1120+__ssinh_ha_CoutTab(%rip), %xmm1
+        movsd     %xmm1, -40(%rsp)
+        movsd     -40(%rsp), %xmm4
+        movsd     1072+__ssinh_ha_CoutTab(%rip), %xmm1
+        movl      -40(%rsp), %edx
+        movl      %edx, %esi
+        andl      $63, %esi
+        subsd     1120+__ssinh_ha_CoutTab(%rip), %xmm4
+        mulsd     %xmm4, %xmm2
+        lea       (%rsi,%rsi), %ecx
+        mulsd     %xmm3, %xmm4
+        subsd     %xmm2, %xmm0
+        movsd     (%r8,%rcx,8), %xmm5
+        lea       1(%rsi,%rsi), %edi
+        shrl      $6, %edx
+        subsd     %xmm4, %xmm0
+        mulsd     %xmm0, %xmm1
+        addl      $1022, %edx
+        andl      $2047, %edx
+        addsd     1064+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1056+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1048+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     1040+__ssinh_ha_CoutTab(%rip), %xmm1
+        mulsd     %xmm0, %xmm1
+        mulsd     %xmm0, %xmm1
+        addsd     %xmm0, %xmm1
+        mulsd     %xmm5, %xmm1
+        addsd     (%r8,%rdi,8), %xmm1
+        addsd     %xmm5, %xmm1
+        cmpl      $2046, %edx
+        ja        .LBL_2_11
+
+
+        movq      8+__ssinh_ha_CoutTab(%rip), %rcx
+        shrq      $48, %rcx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -10(%rsp)
+        movsd     -16(%rsp), %xmm0
+        mulsd     %xmm1, %xmm0
+        movsd     %xmm0, -32(%rsp)
+        jmp       .LBL_2_12
+
+.LBL_2_11:
+
+        decl      %edx
+        andl      $2047, %edx
+        movzwl    -10(%rsp), %ecx
+        shll      $4, %edx
+        andl      $-32753, %ecx
+        orl       %edx, %ecx
+        movw      %cx, -10(%rsp)
+        movsd     -16(%rsp), %xmm0
+        mulsd     %xmm1, %xmm0
+        mulsd     1024+__ssinh_ha_CoutTab(%rip), %xmm0
+        movsd     %xmm0, -32(%rsp)
+
+.LBL_2_12:
+
+        movb      -25(%rsp), %cl
+        movb      -1(%rsp), %dl
+        andb      $127, %cl
+        andb      $-128, %dl
+        orb       %dl, %cl
+        movb      %cl, -25(%rsp)
+        movsd     -32(%rsp), %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r9)
+        ret
+
+.LBL_2_13:
+
+        movsd     1168+__ssinh_ha_CoutTab(%rip), %xmm0
+        movl      $3, %eax
+        mulsd     %xmm2, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r9)
+        ret
+
+.LBL_2_14:
+
+        movsd     __ssinh_ha_CoutTab(%rip), %xmm0
+        addsd     1160+__ssinh_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm2, %xmm0
+        cvtsd2ss  %xmm0, %xmm0
+        movss     %xmm0, (%r9)
+
+
+        ret
+
+.LBL_2_16:
+
+        movsd     1160+__ssinh_ha_CoutTab(%rip), %xmm0
+        mulsd     %xmm0, %xmm2
+        movsd     %xmm2, -24(%rsp)
+        pxor      %xmm2, %xmm2
+        cvtss2sd  (%rdi), %xmm2
+        movsd     -24(%rsp), %xmm1
+        movq      8+__ssinh_ha_CoutTab(%rip), %rdx
+        addsd     %xmm1, %xmm2
+        cvtsd2ss  %xmm2, %xmm2
+        movq      %rdx, -16(%rsp)
+        movss     %xmm2, (%r9)
+        ret
+
+.LBL_2_17:
+
+        addss     %xmm2, %xmm2
+        movss     %xmm2, (%r9)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_ssinh_ha_cout_rare_internal,@function
+	.size	__svml_ssinh_ha_cout_rare_internal,.-__svml_ssinh_ha_cout_rare_internal
+..LN__svml_ssinh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_ssinh_ha_data_internal:
+	.long	1056964608
+	.long	1057148295
+	.long	1057336003
+	.long	1057527823
+	.long	1057723842
+	.long	1057924154
+	.long	1058128851
+	.long	1058338032
+	.long	1058551792
+	.long	1058770234
+	.long	1058993458
+	.long	1059221571
+	.long	1059454679
+	.long	1059692891
+	.long	1059936319
+	.long	1060185078
+	.long	1060439283
+	.long	1060699055
+	.long	1060964516
+	.long	1061235789
+	.long	1061513002
+	.long	1061796286
+	.long	1062085772
+	.long	1062381598
+	.long	1062683901
+	.long	1062992824
+	.long	1063308511
+	.long	1063631111
+	.long	1063960775
+	.long	1064297658
+	.long	1064641917
+	.long	1064993715
+	.long	0
+	.long	2999887785
+	.long	852465809
+	.long	3003046475
+	.long	2984291233
+	.long	3001644133
+	.long	854021668
+	.long	2997748242
+	.long	849550193
+	.long	2995541347
+	.long	851518274
+	.long	809701978
+	.long	2997656926
+	.long	2996185864
+	.long	2980965110
+	.long	3002882728
+	.long	844097402
+	.long	848217591
+	.long	2999013352
+	.long	2992006718
+	.long	831170615
+	.long	3002278818
+	.long	833158180
+	.long	3000769962
+	.long	2991891850
+	.long	2999994908
+	.long	2979965785
+	.long	2982419430
+	.long	2982221534
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+	.long	3162536638
+	.long	917841882
+	.long	1073049415
+	.long	18715565
+	.long	1016707884
+	.long	3712504873
+	.long	1073064711
+	.long	88491949
+	.long	1016476236
+	.long	363667784
+	.long	1073080175
+	.long	813753950
+	.long	1016833785
+	.long	2956612997
+	.long	1073095806
+	.long	2118169751
+	.long	3163784129
+	.long	2186617381
+	.long	1073111608
+	.long	2270764084
+	.long	3164321289
+	.long	1719614413
+	.long	1073127582
+	.long	330458198
+	.long	3164331316
+	.long	1013258799
+	.long	1073143730
+	.long	1748797611
+	.long	3161177658
+	.long	3907805044
+	.long	1073160053
+	.long	2257091225
+	.long	3162598983
+	.long	1447192521
+	.long	1073176555
+	.long	1462857171
+	.long	3163563097
+	.long	1944781191
+	.long	1073193236
+	.long	3993278767
+	.long	3162772855
+	.long	919555682
+	.long	1073210099
+	.long	3121969534
+	.long	1013996802
+	.long	2571947539
+	.long	1073227145
+	.long	3558159064
+	.long	3164425245
+	.long	2604962541
+	.long	1073244377
+	.long	2614425274
+	.long	3164587768
+	.long	1110089947
+	.long	1073261797
+	.long	1451641639
+	.long	1016523249
+	.long	2568320822
+	.long	1073279406
+	.long	2732824428
+	.long	1015401491
+	.long	2966275557
+	.long	1073297207
+	.long	2176155324
+	.long	3160891335
+	.long	2682146384
+	.long	1073315202
+	.long	2082178513
+	.long	3164411995
+	.long	2191782032
+	.long	1073333393
+	.long	2960257726
+	.long	1014791238
+	.long	2069751141
+	.long	1073351782
+	.long	1562170675
+	.long	3163773257
+	.long	2990417245
+	.long	1073370371
+	.long	3683467745
+	.long	3164417902
+	.long	1434058175
+	.long	1073389163
+	.long	251133233
+	.long	1016134345
+	.long	2572866477
+	.long	1073408159
+	.long	878562433
+	.long	1016570317
+	.long	3092190715
+	.long	1073427362
+	.long	814012168
+	.long	3160571998
+	.long	4076559943
+	.long	1073446774
+	.long	2119478331
+	.long	3161806927
+	.long	2420883922
+	.long	1073466398
+	.long	2049810052
+	.long	1015168464
+	.long	3716502172
+	.long	1073486235
+	.long	2303740125
+	.long	1015091301
+	.long	777507147
+	.long	1073506289
+	.long	4282924205
+	.long	1016236109
+	.long	3706687593
+	.long	1073526560
+	.long	3521726939
+	.long	1014301643
+	.long	1242007932
+	.long	1073547053
+	.long	1132034716
+	.long	3164388407
+	.long	3707479175
+	.long	1073567768
+	.long	3613079303
+	.long	1015213314
+	.long	64696965
+	.long	1073588710
+	.long	1768797490
+	.long	1016865536
+	.long	863738719
+	.long	1073609879
+	.long	1326992220
+	.long	3163661773
+	.long	3884662774
+	.long	1073631278
+	.long	2158611599
+	.long	1015258761
+	.long	2728693978
+	.long	1073652911
+	.long	396109971
+	.long	3164511267
+	.long	3999357479
+	.long	1073674779
+	.long	2258941616
+	.long	1016973300
+	.long	1533953344
+	.long	1073696886
+	.long	769171851
+	.long	1016714209
+	.long	2174652632
+	.long	1073719233
+	.long	4087714590
+	.long	1015498835
+	.long	0
+	.long	1073741824
+	.long	0
+	.long	0
+	.long	0
+	.long	1071644672
+	.long	1431652600
+	.long	1069897045
+	.long	1431670732
+	.long	1067799893
+	.long	984555731
+	.long	1065423122
+	.long	472530941
+	.long	1062650218
+	.long	1431655765
+	.long	1069897045
+	.long	286331153
+	.long	1065423121
+	.long	436314138
+	.long	1059717536
+	.long	2773927732
+	.long	1053236707
+	.long	1697350398
+	.long	1079448903
+	.long	0
+	.long	1127743488
+	.long	33554432
+	.long	1101004800
+	.long	2684354560
+	.long	1079401119
+	.long	4277796864
+	.long	1065758274
+	.long	3164486458
+	.long	1025308570
+	.long	1
+	.long	1048576
+	.long	4294967295
+	.long	2146435071
+	.long	3671843104
+	.long	1067178892
+	.long	3875694624
+	.long	1077247184
+	.type	__ssinh_ha_CoutTab,@object
+	.size	__ssinh_ha_CoutTab,1192
+	.space 8, 0x00 	
+	.align 16
+.L_2il0floatpacket.96:
+	.long	0x00000000,0x80000000,0x00000000,0x00000000
+	.type	.L_2il0floatpacket.96,@object
+	.size	.L_2il0floatpacket.96,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_s_la.s
index a5dace24750..11893ce55d1 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_sinh_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_d_ha.s
new file mode 100644
index 00000000000..aeb573345d7
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_d_ha.s
@@ -0,0 +1,20006 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *     
+ *      ( optimized for throughput, with small table lookup, works when HW FMA is available )
+ *     
+ *      Implementation reduces argument x to |R|<pi/32
+ *      16-entry tables used to store high and low parts of tan(x0)
+ *      Argument x = N*pi + x0 + (R);   x0 = k*pi/16, with k in {0, 1, ..., 15}
+ *      (very large arguments reduction resolved in _vdreduction_core.i)
+ *      Compute result as (tan(x0) + tan(R))/(1-tan(x0)*tan(R))
+ *      High accuracy ( < 1 ulp ) version keeps extra precision for numerator, denominator, and during
+ *      final NR-iteration computing quotient.
+ *     
+ *     
+ */
+
+
+	.text
+.L_2__routine_start___svml_tan8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_tan8_ha
+
+__svml_tan8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        xorl      %edx, %edx
+        vmovaps   %zmm0, %zmm11
+
+/*
+ * ----------------------------------------------------------
+ * Main path
+ * ----------------------------------------------------------
+ * start arg. reduction
+ */
+        vmovups   1152+__svml_dtan_ha_data_internal(%rip), %zmm1
+        vmovups   __svml_dtan_ha_data_internal(%rip), %zmm0
+        vmovups   64+__svml_dtan_ha_data_internal(%rip), %zmm2
+        vmovups   256+__svml_dtan_ha_data_internal(%rip), %zmm5
+        vmovups   320+__svml_dtan_ha_data_internal(%rip), %zmm6
+
+/* Large values check */
+        vmovups   28160+__svml_dtan_ha_data_internal(%rip), %zmm3
+        vfmadd213pd {rn-sae}, %zmm1, %zmm11, %zmm0
+        vandpd    1024+__svml_dtan_ha_data_internal(%rip), %zmm11, %zmm10
+        vsubpd    {rn-sae}, %zmm1, %zmm0, %zmm9
+        vcmppd    $22, {sae}, %zmm3, %zmm10, %k1
+        vfnmadd213pd {rn-sae}, %zmm11, %zmm9, %zmm2
+
+/*
+ * ----------------------------------------------------------
+ * End of main path
+ * ----------------------------------------------------------
+ */
+        kortestw  %k1, %k1
+        vmovaps   %zmm5, %zmm4
+        vfnmadd213pd {rn-sae}, %zmm2, %zmm9, %zmm4
+        vmovaps   %zmm6, %zmm3
+        vfnmadd213pd {rn-sae}, %zmm4, %zmm9, %zmm3
+
+/* Calculate the lower part dRl */
+        vsubpd    {rn-sae}, %zmm4, %zmm2, %zmm8
+        vsubpd    {rn-sae}, %zmm4, %zmm3, %zmm7
+        vfnmadd231pd {rn-sae}, %zmm9, %zmm5, %zmm8
+        vfmadd213pd {rn-sae}, %zmm7, %zmm6, %zmm9
+        vsubpd    {rn-sae}, %zmm9, %zmm8, %zmm12
+        jne       .LBL_1_12
+
+.LBL_1_2:
+
+        vmulpd    {rn-sae}, %zmm3, %zmm3, %zmm8
+        vmovups   384+__svml_dtan_ha_data_internal(%rip), %zmm1
+        vmovups   512+__svml_dtan_ha_data_internal(%rip), %zmm2
+        vmovups   832+__svml_dtan_ha_data_internal(%rip), %zmm7
+        vmovups   768+__svml_dtan_ha_data_internal(%rip), %zmm4
+        vmovups   704+__svml_dtan_ha_data_internal(%rip), %zmm5
+        vmovups   640+__svml_dtan_ha_data_internal(%rip), %zmm6
+        vpermt2pd 576+__svml_dtan_ha_data_internal(%rip), %zmm0, %zmm2
+        vpermt2pd 448+__svml_dtan_ha_data_internal(%rip), %zmm0, %zmm1
+        vmovups   896+__svml_dtan_ha_data_internal(%rip), %zmm0
+        vfmadd231pd {rn-sae}, %zmm8, %zmm0, %zmm7
+        vfmadd213pd {rn-sae}, %zmm4, %zmm8, %zmm7
+        vfmadd213pd {rn-sae}, %zmm5, %zmm8, %zmm7
+        vfmadd213pd {rn-sae}, %zmm6, %zmm8, %zmm7
+        vmulpd    {rn-sae}, %zmm3, %zmm7, %zmm9
+        vfnmsub213pd {rn-sae}, %zmm12, %zmm8, %zmm9
+        vsubpd    {rn-sae}, %zmm9, %zmm3, %zmm0
+        vsubpd    {rn-sae}, %zmm0, %zmm3, %zmm3
+        vsubpd    {rn-sae}, %zmm9, %zmm3, %zmm14
+
+/*
+ * Compute Numerator:
+ * dNumerator + dNlow ~= dTh+dTl+dP+dPlow
+ */
+        vaddpd    {rn-sae}, %zmm1, %zmm0, %zmm3
+        vsubpd    {rn-sae}, %zmm1, %zmm3, %zmm10
+        vsubpd    {rn-sae}, %zmm10, %zmm0, %zmm12
+
+/*
+ * Computer Denominator:
+ * dDenominator - dDlow ~= 1-(dTh+dTl)*(dP+dPlow)
+ */
+        vmovups   960+__svml_dtan_ha_data_internal(%rip), %zmm10
+        vaddpd    {rn-sae}, %zmm2, %zmm12, %zmm13
+        vmovaps   %zmm10, %zmm4
+        vfnmadd231pd {rn-sae}, %zmm1, %zmm0, %zmm4
+        vaddpd    {rn-sae}, %zmm14, %zmm13, %zmm5
+        vsubpd    {rn-sae}, %zmm10, %zmm4, %zmm15
+        vfmadd231pd {rn-sae}, %zmm1, %zmm0, %zmm15
+        vfmadd213pd {rn-sae}, %zmm15, %zmm14, %zmm1
+        vfmadd213pd {rn-sae}, %zmm1, %zmm0, %zmm2
+
+/*
+ * Now computes (dNumerator + dNlow)/(dDenominator - dDlow)
+ * Choose NR iteration instead of hardware division
+ */
+        vrcp14pd  %zmm4, %zmm1
+
+/* One NR iteration to refine dRcp */
+        vfnmadd231pd {rn-sae}, %zmm1, %zmm4, %zmm10
+        vfmadd231pd {rn-sae}, %zmm1, %zmm2, %zmm10
+        vfmadd213pd {rn-sae}, %zmm1, %zmm10, %zmm1
+        vmulpd    {rn-sae}, %zmm3, %zmm1, %zmm6
+
+/* One NR iteration to refine dQuotient */
+        vfmsub213pd {rn-sae}, %zmm3, %zmm6, %zmm4
+        vfnmadd213pd {rn-sae}, %zmm4, %zmm6, %zmm2
+        vsubpd    {rn-sae}, %zmm5, %zmm2, %zmm0
+        vfnmadd213pd {rn-sae}, %zmm6, %zmm1, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_4:
+
+        vmovups   %zmm11, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dtan_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+        vmovups   1088+__svml_dtan_ha_data_internal(%rip), %zmm14
+
+/*
+ * Get the (2^a / 2pi) mod 1 values from the table.
+ * Because VLANG doesn't have L-type gather, we need a trivial cast
+ */
+        lea       __svml_dtan_ha_reduction_data_internal(%rip), %rax
+        vmovups   %zmm0, (%rsp)
+        vmovups   %zmm3, 64(%rsp)
+        vmovups   %zmm12, 128(%rsp)
+        vandpd    %zmm10, %zmm14, %zmm0
+        vpbroadcastq .L_2il0floatpacket.18(%rip), %zmm10
+        vcmppd    $4, {sae}, %zmm14, %zmm0, %k2
+        vmovaps   %zmm10, %zmm3
+        vpandq    .L_2il0floatpacket.19(%rip){1to8}, %zmm11, %zmm7
+        vpsrlq    $52, %zmm7, %zmm8
+        vpsllq    $1, %zmm8, %zmm6
+        vpaddq    %zmm8, %zmm6, %zmm5
+
+/*
+ * Break the P_xxx and m into 32-bit chunks ready for
+ * the long multiplication via 32x32->64 multiplications
+ */
+        vpbroadcastq .L_2il0floatpacket.22(%rip), %zmm6
+        vpsllq    $3, %zmm5, %zmm4
+        vpmovqd   %zmm4, %ymm9
+        vpandnq   %zmm0, %zmm0, %zmm3{%k2}
+        vcmppd    $3, {sae}, %zmm3, %zmm3, %k0
+        kxnorw    %k0, %k0, %k3
+        kxnorw    %k0, %k0, %k2
+        kmovw     %k0, %edx
+        vpxord    %zmm2, %zmm2, %zmm2
+        vgatherdpd (%rax,%ymm9), %zmm2{%k3}
+        kxnorw    %k0, %k0, %k3
+        vpsrlq    $32, %zmm2, %zmm0
+        vpxord    %zmm1, %zmm1, %zmm1
+        vpxord    %zmm3, %zmm3, %zmm3
+        vgatherdpd 8(%rax,%ymm9), %zmm1{%k2}
+        vgatherdpd 16(%rax,%ymm9), %zmm3{%k3}
+        vpsrlq    $32, %zmm1, %zmm9
+        vpsrlq    $32, %zmm3, %zmm13
+
+/*
+ * Also get the significand as an integer
+ * NB: adding in the integer bit is wrong for denorms!
+ * To make this work for denorms we should do something slightly different
+ */
+        vpandq    .L_2il0floatpacket.20(%rip){1to8}, %zmm11, %zmm15
+        vpaddq    .L_2il0floatpacket.21(%rip){1to8}, %zmm15, %zmm14
+        vpsrlq    $32, %zmm14, %zmm7
+        vpandq    %zmm6, %zmm2, %zmm5
+        vpandq    %zmm6, %zmm1, %zmm12
+        vpandq    %zmm6, %zmm3, %zmm8
+        vpandq    %zmm6, %zmm14, %zmm14
+
+/* Now do the big multiplication and carry propagation */
+        vpmullq   %zmm5, %zmm7, %zmm4
+        vpmullq   %zmm9, %zmm7, %zmm3
+        vpmullq   %zmm12, %zmm7, %zmm2
+        vpmullq   %zmm13, %zmm7, %zmm1
+        vpmullq   %zmm8, %zmm7, %zmm8
+        vpmullq   %zmm0, %zmm14, %zmm7
+        vpmullq   %zmm5, %zmm14, %zmm5
+        vpmullq   %zmm9, %zmm14, %zmm9
+        vpmullq   %zmm12, %zmm14, %zmm0
+        vpmullq   %zmm13, %zmm14, %zmm12
+        vpsrlq    $32, %zmm9, %zmm15
+        vpsrlq    $32, %zmm0, %zmm13
+        vpsrlq    $32, %zmm12, %zmm14
+        vpsrlq    $32, %zmm5, %zmm12
+        vpaddq    %zmm15, %zmm3, %zmm15
+        vpaddq    %zmm14, %zmm1, %zmm1
+        vpaddq    %zmm12, %zmm4, %zmm3
+        vpaddq    %zmm13, %zmm2, %zmm2
+        vpandq    %zmm6, %zmm0, %zmm13
+        vpandq    %zmm6, %zmm7, %zmm7
+        vpaddq    %zmm1, %zmm13, %zmm4
+        vpaddq    %zmm3, %zmm7, %zmm13
+        vpsrlq    $32, %zmm8, %zmm3
+        vpaddq    %zmm4, %zmm3, %zmm14
+        vpsrlq    $32, %zmm14, %zmm8
+        vpandq    %zmm6, %zmm9, %zmm0
+        vpaddq    %zmm2, %zmm0, %zmm9
+        vpaddq    %zmm9, %zmm8, %zmm3
+        vpsrlq    $32, %zmm3, %zmm1
+        vpandq    %zmm6, %zmm5, %zmm5
+        vpaddq    %zmm15, %zmm5, %zmm2
+        vpaddq    %zmm2, %zmm1, %zmm15
+
+/*
+ * Now round at the 2^-9 bit position for reduction mod pi/2^8
+ * instead of the original 2pi (but still with the same 2pi scaling).
+ * Use a shifter of 2^43 + 2^42.
+ * The N we get is our final version; it has an offset of
+ * 2^9 because of the implicit integer bit, and anyway for negative
+ * starting value it's a 2s complement thing. But we need to mask
+ * off the exponent part anyway so it's fine.
+ */
+        vpbroadcastq .L_2il0floatpacket.25(%rip), %zmm1
+        vpsrlq    $32, %zmm15, %zmm12
+        vpaddq    %zmm13, %zmm12, %zmm0
+
+/* Assemble reduced argument from the pieces */
+        vpandq    %zmm6, %zmm14, %zmm8
+        vpandq    %zmm6, %zmm15, %zmm7
+        vpsllq    $32, %zmm0, %zmm6
+        vpsllq    $32, %zmm3, %zmm0
+        vpaddq    %zmm7, %zmm6, %zmm4
+        vpaddq    %zmm8, %zmm0, %zmm5
+        vpsrlq    $12, %zmm4, %zmm3
+
+/*
+ * We want to incorporate the original sign now too.
+ * Do it here for convenience in getting the right N value,
+ * though we could wait right to the end if we were prepared
+ * to modify the sign of N later too.
+ * So get the appropriate sign mask now (or sooner).
+ */
+        vpandq    .L_2il0floatpacket.23(%rip){1to8}, %zmm11, %zmm9
+
+/*
+ * Create floating-point high part, implicitly adding integer bit 1
+ * Incorporate overall sign at this stage too.
+ */
+        vpxorq    .L_2il0floatpacket.24(%rip){1to8}, %zmm9, %zmm6
+        vporq     %zmm6, %zmm3, %zmm2
+        vaddpd    {rn-sae}, %zmm2, %zmm1, %zmm13
+        vsubpd    {rn-sae}, %zmm1, %zmm13, %zmm12
+        vsubpd    {rn-sae}, %zmm12, %zmm2, %zmm8
+        vpandq    .L_2il0floatpacket.28(%rip){1to8}, %zmm5, %zmm14
+        vpsllq    $28, %zmm14, %zmm15
+        vpsrlq    $24, %zmm5, %zmm5
+        vandpd    .L_2il0floatpacket.33(%rip){1to8}, %zmm11, %zmm14
+        vpandq    .L_2il0floatpacket.30(%rip){1to8}, %zmm4, %zmm4
+
+/*
+ * Create floating-point low and medium parts, respectively
+ * lo_23, ... lo_0, 0, ..., 0
+ * hi_11, ... hi_0, lo_63, ..., lo_24
+ * then subtract off the implicitly added integer bits,
+ * 2^-104 and 2^-52, respectively.
+ * Put the original sign into all of them at this stage.
+ */
+        vpxorq    .L_2il0floatpacket.27(%rip){1to8}, %zmm9, %zmm3
+        vpxorq    .L_2il0floatpacket.29(%rip){1to8}, %zmm9, %zmm1
+        vpsllq    $40, %zmm4, %zmm9
+        vporq     %zmm3, %zmm15, %zmm0
+        vsubpd    {rn-sae}, %zmm3, %zmm0, %zmm6
+        vporq     %zmm5, %zmm9, %zmm0
+        vporq     %zmm1, %zmm0, %zmm3
+        vsubpd    {rn-sae}, %zmm1, %zmm3, %zmm2
+
+/*
+ * Now multiply those numbers all by 2 pi, reasonably accurately.
+ * (RHi + RLo) * (pi_lead + pi_trail) ~=
+ * RHi * pi_lead + (RHi * pi_trail + RLo * pi_lead)
+ */
+        vpbroadcastq .L_2il0floatpacket.31(%rip), %zmm5
+
+/* Now add them up into 2 reasonably aligned pieces */
+        vaddpd    {rn-sae}, %zmm2, %zmm8, %zmm12
+        vmulpd    {rn-sae}, %zmm5, %zmm12, %zmm15
+        vsubpd    {rn-sae}, %zmm12, %zmm8, %zmm8
+        vaddpd    {rn-sae}, %zmm8, %zmm2, %zmm4
+        vmovaps   %zmm5, %zmm9
+        vfmsub213pd {rn-sae}, %zmm15, %zmm12, %zmm9
+        vaddpd    {rn-sae}, %zmm6, %zmm4, %zmm1
+        vpbroadcastq .L_2il0floatpacket.32(%rip), %zmm6
+        vmovaps   %zmm10, %zmm3
+        vfmadd213pd {rn-sae}, %zmm9, %zmm6, %zmm12
+        vpbroadcastq .L_2il0floatpacket.36(%rip), %zmm9
+        vfmadd213pd {rn-sae}, %zmm12, %zmm5, %zmm1
+        vpbroadcastq .L_2il0floatpacket.37(%rip), %zmm12
+
+/* Grab our final N value as an integer, appropriately masked mod 2^9 */
+        vpandq    .L_2il0floatpacket.26(%rip){1to8}, %zmm13, %zmm7
+
+/*
+ * If the magnitude of the input is <= 2^-20, then
+ * just pass through the input, since no reduction will be needed and
+ * the main path will only work accurately if the reduced argument is
+ * about >= 2^-70 (which it is for all large pi multiples)
+ */
+        vpbroadcastq .L_2il0floatpacket.34(%rip), %zmm13
+
+/* The output is _VRES_Z (high) + _VRES_E (low), and the integer part is _VRES_IND */
+        vpmovqd   %zmm7, %ymm5
+        vcmppd    $26, {sae}, %zmm13, %zmm14, %k2
+        vcmppd    $22, {sae}, %zmm13, %zmm14, %k3
+        vpandnq   %zmm14, %zmm14, %zmm3{%k2}
+        vpandnq   %zmm14, %zmm14, %zmm10{%k3}
+        vandpd    %zmm11, %zmm10, %zmm10
+        vandpd    %zmm15, %zmm3, %zmm0
+        vorpd     %zmm0, %zmm10, %zmm0
+        vandpd    %zmm1, %zmm3, %zmm10
+        vpsrlq    $32, %zmm0, %zmm7
+        vpmovqd   %zmm7, %ymm3
+        vmovdqu   .L_2il0floatpacket.35(%rip), %ymm7
+        vpsrld    $31, %ymm3, %ymm1
+        vmovups   64(%rsp), %zmm3
+        vpsubd    %ymm1, %ymm7, %ymm2
+        vpaddd    %ymm2, %ymm5, %ymm4
+        vpsrld    $4, %ymm4, %ymm13
+        vpslld    $4, %ymm13, %ymm6
+        vpsubd    %ymm6, %ymm5, %ymm8
+        vmovaps   %zmm0, %zmm14
+        vcvtdq2pd %ymm8, %zmm15
+        vfmadd231pd {rn-sae}, %zmm15, %zmm9, %zmm14
+        vfnmadd213pd {rn-sae}, %zmm14, %zmm15, %zmm9
+        vblendmpd %zmm14, %zmm3, %zmm3{%k1}
+        vsubpd    {rn-sae}, %zmm9, %zmm0, %zmm0
+        vfmadd213pd {rn-sae}, %zmm0, %zmm12, %zmm15
+
+/*
+ * ----------------------------------------------------------
+ * End of large arguments path
+ * ----------------------------------------------------------
+ * Merge results from main and large paths:
+ */
+        vmovups   (%rsp), %zmm0
+        vmovups   128(%rsp), %zmm12
+        vpmovzxdq %ymm13, %zmm0{%k1}
+        vaddpd    {rn-sae}, %zmm10, %zmm15, %zmm12{%k1}
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_tan8_ha,@function
+	.size	__svml_tan8_ha,.-__svml_tan8_ha
+..LN__svml_tan8_ha.0:
+
+.L_2__routine_start___svml_dtan_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dtan_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L67:
+
+        xorl      %eax, %eax
+        movzwl    6(%rdi), %ecx
+        movsd     (%rdi), %xmm1
+        andl      $32752, %ecx
+        movb      7(%rdi), %dl
+        andb      $127, %dl
+        movsd     %xmm1, -8(%rsp)
+        movb      %dl, -1(%rsp)
+        cmpl      $32752, %ecx
+        je        .LBL_2_3
+
+
+        ret
+
+.LBL_2_3:
+
+        cmpl      $0, -8(%rsp)
+        jne       .LBL_2_6
+
+
+        cmpl      $2146435072, -4(%rsp)
+        jne       .LBL_2_6
+
+
+        movsd     __dtan_ha_Tab(%rip), %xmm0
+        movl      $1, %eax
+        mulsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+
+.LBL_2_6:
+
+        mulsd     %xmm1, %xmm1
+        movsd     %xmm1, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dtan_ha_cout_rare_internal,@function
+	.size	__svml_dtan_ha_cout_rare_internal,.-__svml_dtan_ha_cout_rare_internal
+..LN__svml_dtan_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dtan_ha_data_internal:
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1841940611
+	.long	1075076912
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	1413754136
+	.long	1070146043
+	.long	856972294
+	.long	1013032486
+	.long	856972294
+	.long	1013032486
+	.long	856972294
+	.long	1013032486
+	.long	856972294
+	.long	1013032486
+	.long	856972294
+	.long	1013032486
+	.long	856972294
+	.long	1013032486
+	.long	856972294
+	.long	1013032486
+	.long	856972294
+	.long	1013032486
+	.long	688016905
+	.long	958143697
+	.long	688016905
+	.long	958143697
+	.long	688016905
+	.long	958143697
+	.long	688016905
+	.long	958143697
+	.long	688016905
+	.long	958143697
+	.long	688016905
+	.long	958143697
+	.long	688016905
+	.long	958143697
+	.long	688016905
+	.long	958143697
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	855638016
+	.long	1013032486
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	3773204808
+	.long	979655686
+	.long	0
+	.long	2147483648
+	.long	3763679576
+	.long	1070167541
+	.long	2583490354
+	.long	1071284857
+	.long	716700048
+	.long	1071997368
+	.long	0
+	.long	1072693248
+	.long	3797578849
+	.long	1073213976
+	.long	859807206
+	.long	1073958991
+	.long	3795994481
+	.long	1075059710
+	.long	4294967295
+	.long	4293918719
+	.long	3795994481
+	.long	3222543358
+	.long	859807206
+	.long	3221442639
+	.long	3797578849
+	.long	3220697624
+	.long	0
+	.long	3220176896
+	.long	716700048
+	.long	3219481016
+	.long	2583490354
+	.long	3218768505
+	.long	3763679576
+	.long	3217651189
+	.long	0
+	.long	2147483648
+	.long	1732516166
+	.long	1009710547
+	.long	2973134505
+	.long	1014008623
+	.long	561165941
+	.long	1015523525
+	.long	0
+	.long	0
+	.long	1767150223
+	.long	1016338938
+	.long	4129738197
+	.long	1017254245
+	.long	4275273486
+	.long	1015088902
+	.long	0
+	.long	4238344192
+	.long	4275273486
+	.long	3162572550
+	.long	4129738197
+	.long	3164737893
+	.long	1767150223
+	.long	3163822586
+	.long	0
+	.long	0
+	.long	561165941
+	.long	3163007173
+	.long	2973134505
+	.long	3161492271
+	.long	1732516166
+	.long	3157194195
+	.long	1431655900
+	.long	1070945621
+	.long	1431655900
+	.long	1070945621
+	.long	1431655900
+	.long	1070945621
+	.long	1431655900
+	.long	1070945621
+	.long	1431655900
+	.long	1070945621
+	.long	1431655900
+	.long	1070945621
+	.long	1431655900
+	.long	1070945621
+	.long	1431655900
+	.long	1070945621
+	.long	285935618
+	.long	1069617425
+	.long	285935618
+	.long	1069617425
+	.long	285935618
+	.long	1069617425
+	.long	285935618
+	.long	1069617425
+	.long	285935618
+	.long	1069617425
+	.long	285935618
+	.long	1069617425
+	.long	285935618
+	.long	1069617425
+	.long	285935618
+	.long	1069617425
+	.long	1218258378
+	.long	1068212666
+	.long	1218258378
+	.long	1068212666
+	.long	1218258378
+	.long	1068212666
+	.long	1218258378
+	.long	1068212666
+	.long	1218258378
+	.long	1068212666
+	.long	1218258378
+	.long	1068212666
+	.long	1218258378
+	.long	1068212666
+	.long	1218258378
+	.long	1068212666
+	.long	1716452265
+	.long	1066820779
+	.long	1716452265
+	.long	1066820779
+	.long	1716452265
+	.long	1066820779
+	.long	1716452265
+	.long	1066820779
+	.long	1716452265
+	.long	1066820779
+	.long	1716452265
+	.long	1066820779
+	.long	1716452265
+	.long	1066820779
+	.long	1716452265
+	.long	1066820779
+	.long	3351902813
+	.long	1065508044
+	.long	3351902813
+	.long	1065508044
+	.long	3351902813
+	.long	1065508044
+	.long	3351902813
+	.long	1065508044
+	.long	3351902813
+	.long	1065508044
+	.long	3351902813
+	.long	1065508044
+	.long	3351902813
+	.long	1065508044
+	.long	3351902813
+	.long	1065508044
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	2146435072
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	0
+	.long	1127743488
+	.long	1413480448
+	.long	1065951739
+	.long	1413480448
+	.long	1065951739
+	.long	1413480448
+	.long	1065951739
+	.long	1413480448
+	.long	1065951739
+	.long	1413480448
+	.long	1065951739
+	.long	1413480448
+	.long	1065951739
+	.long	1413480448
+	.long	1065951739
+	.long	1413480448
+	.long	1065951739
+	.long	442499072
+	.long	1029747809
+	.long	442499072
+	.long	1029747809
+	.long	442499072
+	.long	1029747809
+	.long	442499072
+	.long	1029747809
+	.long	442499072
+	.long	1029747809
+	.long	442499072
+	.long	1029747809
+	.long	442499072
+	.long	1029747809
+	.long	442499072
+	.long	1029747809
+	.long	771977331
+	.long	993204618
+	.long	771977331
+	.long	993204618
+	.long	771977331
+	.long	993204618
+	.long	771977331
+	.long	993204618
+	.long	771977331
+	.long	993204618
+	.long	771977331
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+	.long	1565359979
+	.long	1056000594
+	.long	2338146798
+	.long	3651639290
+	.long	1590641102
+	.long	2986015100
+	.long	3130719959
+	.long	2112001188
+	.long	381326301
+	.long	3008311285
+	.long	3181282204
+	.long	1677062904
+	.long	1966472623
+	.long	4224002377
+	.long	762652602
+	.long	1721655274
+	.long	2067597113
+	.long	3354125809
+	.long	3932945247
+	.long	4153037458
+	.long	1525305205
+	.long	3443310548
+	.long	4135194227
+	.long	2413284322
+	.long	3570923199
+	.long	4011107621
+	.long	3050610411
+	.long	2591653800
+	.long	3975421159
+	.long	531601349
+	.long	2846879102
+	.long	3727247947
+	.long	1806253526
+	.long	888340305
+	.long	3655875023
+	.long	1063202699
+	.long	1398790909
+	.long	3159528599
+	.long	3612507052
+	.long	1776680610
+	.long	3016782751
+	.long	2126405399
+	.long	2797581819
+	.long	2024089902
+	.long	2930046808
+	.long	3553361221
+	.long	1738598206
+	.long	4252810799
+	.long	1300196342
+	.long	4048179805
+	.long	1565126321
+	.long	2811755147
+	.long	3477196413
+	.long	4210654302
+	.long	2600392684
+	.long	3801392314
+	.long	3130252643
+	.long	1328542998
+	.long	2659425531
+	.long	4126341309
+	.long	905818072
+	.long	3307817333
+	.long	1965537991
+	.long	2657085997
+	.long	1023883767
+	.long	3957715323
+	.long	1811636145
+	.long	2320667370
+	.long	3931075983
+	.long	1019204698
+	.long	2047767535
+	.long	3620463350
+	.long	3623272290
+	.long	346367444
+	.long	3567184671
+	.long	2038409397
+	.long	4095535070
+	.long	2945959404
+	.long	2951577284
+	.long	692734889
+	.long	2839402047
+	.long	4076818795
+	.long	3896102844
+	.long	1596951513
+	.long	1608187272
+	.long	1385469779
+	.long	1383836798
+	.long	3858670295
+	.long	3497238392
+	.long	3193903027
+	.long	3216374545
+	.long	2770939558
+	.long	2767673597
+	.long	3422373294
+	.long	2699509489
+	.long	2092838759
+	.long	2137781795
+	.long	1246911821
+	.long	1240379898
+	.long	2549779293
+	.long	1104051682
+	.long	4185677519
+	.long	4275563591
+	.long	2493823642
+	.long	2480759797
+	.long	804591290
+	.long	2208103365
+	.long	4076387742
+	.long	4256159887
+	.long	692679989
+	.long	666552299
+	.long	1609182581
+	.long	121239434
+	.long	3857808189
+	.long	4217352479
+	.long	1385359979
+	.long	1333104599
+	.long	3218365162
+	.long	242478868
+	.long	3420649082
+	.long	4139737663
+	.long	2770719959
+	.long	2666209199
+	.long	2141763028
+	.long	484957737
+	.long	2546330868
+	.long	3984508030
+	.long	1246472623
+	.long	1037451103
+	.long	4283526057
+	.long	969915474
+	.long	797694440
+	.long	3674048764
+	.long	2492945247
+	.long	2074902206
+	.long	4272084818
+	.long	1939830949
+	.long	1595388880
+	.long	3053130232
+	.long	690923199
+	.long	4149804412
+	.long	4249202340
+	.long	3879661898
+	.long	3190777760
+	.long	1811293169
+	.long	1381846399
+	.long	4004641529
+	.long	4203437385
+	.long	3464356500
+	.long	2086588225
+	.long	3622586339
+	.long	2763692798
+	.long	3714315762
+	.long	4111907475
+	.long	2633745705
+	.long	4173176451
+	.long	2950205382
+	.long	1232418301
+	.long	3133664229
+	.long	3928847655
+	.long	972524114
+	.long	4051385607
+	.long	1605443469
+	.long	2464836603
+	.long	1972361163
+	.long	3562728015
+	.long	1945048229
+	.long	3807803918
+	.long	3210886938
+	.long	634705910
+	.long	3944722327
+	.long	2830488734
+	.long	3890096458
+	.long	3320640540
+	.long	2126806581
+	.long	1269411821
+	.long	3594477359
+	.long	1366010173
+	.long	3485225620
+	.long	2346313785
+	.long	4253613162
+	.long	2538823642
+	.long	2893987423
+	.long	2732020347
+	.long	2675483945
+	.long	397660275
+	.long	4212259029
+	.long	782679989
+	.long	1493007550
+	.long	1169073399
+	.long	1056000594
+	.long	795320551
+	.long	4129550763
+	.long	1565359979
+	.long	2986015100
+	.long	2338146798
+	.long	2112001188
+	.long	1590641102
+	.long	3964134231
+	.long	3130719959
+	.long	1677062904
+	.long	381326301
+	.long	4224002377
+	.long	3181282204
+	.long	3633301166
+	.long	1966472623
+	.long	3354125809
+	.long	762652602
+	.long	4153037458
+	.long	2067597113
+	.long	2971635037
+	.long	3932945247
+	.long	2413284322
+	.long	1525305205
+	.long	4011107621
+	.long	4135194227
+	.long	1648302778
+	.long	3570923199
+	.long	531601349
+	.long	3050610411
+	.long	3727247947
+	.long	3975421159
+	.long	3296605556
+	.long	2846879102
+	.long	1063202699
+	.long	1806253526
+	.long	3159528599
+	.long	3655875023
+	.long	2298243816
+	.long	1398790909
+	.long	2126405399
+	.long	3612507052
+	.long	2024089902
+	.long	3016782751
+	.long	301520336
+	.long	2797581819
+	.long	4252810799
+	.long	2930046808
+	.long	4048179805
+	.long	1738598206
+	.long	603040673
+	.long	1300196342
+	.long	4210654302
+	.long	1565126321
+	.long	3801392314
+	.long	3477196413
+	.long	1206081346
+	.long	2600392684
+	.long	4126341309
+	.long	3130252643
+	.long	3307817333
+	.long	2659425531
+	.long	2412162692
+	.long	905818072
+	.long	3957715323
+	.long	1965537991
+	.long	2320667370
+	.long	1023883767
+	.long	529358088
+	.long	1811636145
+	.long	3620463350
+	.long	3931075983
+	.long	346367444
+	.long	2047767535
+	.long	1058716176
+	.long	3623272290
+	.long	2945959404
+	.long	3567184671
+	.long	692734889
+	.long	4095535070
+	.long	2117432353
+	.long	2951577284
+	.long	1596951513
+	.long	2839402047
+	.long	1385469779
+	.long	3896102844
+	.long	4234864706
+	.long	1608187272
+	.long	3193903027
+	.long	1383836798
+	.long	2770939558
+	.long	3497238392
+	.long	4174762117
+	.long	3216374545
+	.long	2092838759
+	.long	2767673597
+	.long	1246911821
+	.long	2699509489
+	.long	4054556938
+	.long	2137781795
+	.long	4185677519
+	.long	1240379898
+	.long	2493823642
+	.long	1104051682
+	.long	3814146581
+	.long	4275563591
+	.long	4076387742
+	.long	2480759797
+	.long	692679989
+	.long	2208103365
+	.long	3333325867
+	.long	4256159887
+	.long	3857808189
+	.long	666552299
+	.long	1385359979
+	.long	121239434
+	.long	2371684438
+	.long	4217352479
+	.long	3420649082
+	.long	1333104599
+	.long	2770719959
+	.long	242478868
+	.long	448401580
+	.long	4139737663
+	.long	2546330868
+	.long	2666209199
+	.long	1246472623
+	.long	484957737
+	.long	896803160
+	.long	3984508030
+	.type	__svml_dtan_ha_reduction_data_internal,@object
+	.size	__svml_dtan_ha_reduction_data_internal,49152
+	.space 512, 0x00 	
+	.align 32
+.L_2il0floatpacket.35:
+	.long	0x00000008,0x00000008,0x00000008,0x00000008,0x00000008,0x00000008,0x00000008,0x00000008
+	.type	.L_2il0floatpacket.35,@object
+	.size	.L_2il0floatpacket.35,32
+	.align 8
+.L_2il0floatpacket.18:
+	.long	0xffffffff,0xffffffff
+	.type	.L_2il0floatpacket.18,@object
+	.size	.L_2il0floatpacket.18,8
+	.align 8
+.L_2il0floatpacket.19:
+	.long	0x00000000,0x7ff00000
+	.type	.L_2il0floatpacket.19,@object
+	.size	.L_2il0floatpacket.19,8
+	.align 8
+.L_2il0floatpacket.20:
+	.long	0xffffffff,0x000fffff
+	.type	.L_2il0floatpacket.20,@object
+	.size	.L_2il0floatpacket.20,8
+	.align 8
+.L_2il0floatpacket.21:
+	.long	0x00000000,0x00100000
+	.type	.L_2il0floatpacket.21,@object
+	.size	.L_2il0floatpacket.21,8
+	.align 8
+.L_2il0floatpacket.22:
+	.long	0xffffffff,0x00000000
+	.type	.L_2il0floatpacket.22,@object
+	.size	.L_2il0floatpacket.22,8
+	.align 8
+.L_2il0floatpacket.23:
+	.long	0x00000000,0x80000000
+	.type	.L_2il0floatpacket.23,@object
+	.size	.L_2il0floatpacket.23,8
+	.align 8
+.L_2il0floatpacket.24:
+	.long	0x00000000,0x3ff00000
+	.type	.L_2il0floatpacket.24,@object
+	.size	.L_2il0floatpacket.24,8
+	.align 8
+.L_2il0floatpacket.25:
+	.long	0x00000000,0x42a80000
+	.type	.L_2il0floatpacket.25,@object
+	.size	.L_2il0floatpacket.25,8
+	.align 8
+.L_2il0floatpacket.26:
+	.long	0x000001ff,0x00000000
+	.type	.L_2il0floatpacket.26,@object
+	.size	.L_2il0floatpacket.26,8
+	.align 8
+.L_2il0floatpacket.27:
+	.long	0x00000000,0x39700000
+	.type	.L_2il0floatpacket.27,@object
+	.size	.L_2il0floatpacket.27,8
+	.align 8
+.L_2il0floatpacket.28:
+	.long	0x00ffffff,0x00000000
+	.type	.L_2il0floatpacket.28,@object
+	.size	.L_2il0floatpacket.28,8
+	.align 8
+.L_2il0floatpacket.29:
+	.long	0x00000000,0x3cb00000
+	.type	.L_2il0floatpacket.29,@object
+	.size	.L_2il0floatpacket.29,8
+	.align 8
+.L_2il0floatpacket.30:
+	.long	0x00000fff,0x00000000
+	.type	.L_2il0floatpacket.30,@object
+	.size	.L_2il0floatpacket.30,8
+	.align 8
+.L_2il0floatpacket.31:
+	.long	0x54442d18,0x401921fb
+	.type	.L_2il0floatpacket.31,@object
+	.size	.L_2il0floatpacket.31,8
+	.align 8
+.L_2il0floatpacket.32:
+	.long	0x33145c07,0x3cb1a626
+	.type	.L_2il0floatpacket.32,@object
+	.size	.L_2il0floatpacket.32,8
+	.align 8
+.L_2il0floatpacket.33:
+	.long	0xffffffff,0x7fffffff
+	.type	.L_2il0floatpacket.33,@object
+	.size	.L_2il0floatpacket.33,8
+	.align 8
+.L_2il0floatpacket.34:
+	.long	0x00000000,0x3eb00000
+	.type	.L_2il0floatpacket.34,@object
+	.size	.L_2il0floatpacket.34,8
+	.align 8
+.L_2il0floatpacket.36:
+	.long	0x54442d18,0x3f8921fb
+	.type	.L_2il0floatpacket.36,@object
+	.size	.L_2il0floatpacket.36,8
+	.align 8
+.L_2il0floatpacket.37:
+	.long	0x33145c07,0x3c21a626
+	.type	.L_2il0floatpacket.37,@object
+	.size	.L_2il0floatpacket.37,8
+	.align 8
+__dtan_ha_Tab:
+	.long	0
+	.long	0
+	.long	0
+	.long	2146435072
+	.type	__dtan_ha_Tab,@object
+	.size	__dtan_ha_Tab,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_d_la.s
index 377f144e0f3..e1cea7b9491 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_h_la.s
new file mode 100644
index 00000000000..52bee9a3980
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_h_la.s
@@ -0,0 +1,161 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   Implementation reduces argument as:
+ *   sX + N*(sNPi1+sNPi2), where sNPi1+sNPi2 ~ -pi/2
+ *   RShifter + x*(2/pi) will round to RShifter+N, where N=(int)(x/pi)
+ *   To get sign bit we treat sY as integer value to look at last bit
+ *   Compute polynomial ~ tan(R)/R
+ *   Result = 1/tan(R) when sN = (int)(x/(Pi/2)) is odd
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_tans32
+
+__svml_tans32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovups   __svml_htan_data_internal(%rip), %zmm25
+        vmovups   64+__svml_htan_data_internal(%rip), %zmm4
+        vmovups   128+__svml_htan_data_internal(%rip), %zmm29
+        vmovups   192+__svml_htan_data_internal(%rip), %zmm5
+        vmovups   256+__svml_htan_data_internal(%rip), %zmm7
+        vmovdqu16 320+__svml_htan_data_internal(%rip), %zmm30
+        vmovdqu16 384+__svml_htan_data_internal(%rip), %zmm23
+        vmovdqu16 448+__svml_htan_data_internal(%rip), %zmm31
+        vmovdqu16 512+__svml_htan_data_internal(%rip), %zmm26
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__tan_h:
+        cmpq    $31, %rdx
+        ja .loaddata__tan_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_tan_h
+.loaddata__tan_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_tan_h:
+
+        vmovups   %zmm29, %zmm3
+        vmovaps   %zmm4, %zmm9
+
+/*
+ * No callout
+ * Copy argument
+ * Needed to set sin(-0)=-0
+ */
+        vpandd    %zmm25, %zmm0, %zmm1
+        vpxord    %zmm1, %zmm0, %zmm0
+
+/* convert to FP32 */
+        vextractf32x8 $1, %zmm1, %ymm2
+        vcvtph2psx %ymm1, %zmm6
+        vcvtph2psx %ymm2, %zmm8
+        vfmadd231ps {rn-sae}, %zmm6, %zmm3, %zmm9
+        vfmadd213ps {rn-sae}, %zmm4, %zmm8, %zmm3
+
+/* sN = (int)(x/pi) = sY - Rshifter */
+        vsubps    {rn-sae}, %zmm4, %zmm9, %zmm10
+        vsubps    {rn-sae}, %zmm4, %zmm3, %zmm12
+
+/* sign bit, will treat sY as integer value to look at last bit */
+        vpslld    $31, %zmm9, %zmm14
+        vpslld    $31, %zmm3, %zmm1
+        vfmadd231ps {rn-sae}, %zmm10, %zmm5, %zmm6
+        vfmadd231ps {rn-sae}, %zmm12, %zmm5, %zmm8
+
+/* polynomial ~ tan(R)/R */
+        vmovdqu16 %zmm23, %zmm9
+        vfmadd213ps {rn-sae}, %zmm6, %zmm7, %zmm10
+        vfmadd213ps {rn-sae}, %zmm8, %zmm7, %zmm12
+        vcvtps2phx %zmm10, %ymm11
+        vcvtps2phx %zmm12, %ymm13
+        vcvtps2phx %zmm14, %ymm15
+        vcvtps2phx %zmm1, %ymm14
+        vinsertf32x8 $1, %ymm13, %zmm11, %zmm2
+
+/* hR*hR */
+        vmulph    {rn-sae}, %zmm2, %zmm2, %zmm28
+        vfmadd231ph {rn-sae}, %zmm28, %zmm30, %zmm9
+        vfmadd213ph {rn-sae}, %zmm31, %zmm28, %zmm9
+        vfmadd213ph {rn-sae}, %zmm26, %zmm28, %zmm9
+        vinsertf32x8 $1, %ymm14, %zmm15, %zmm8
+
+/* result = 1/tan(R) when hN = (int)(x/(Pi/2)) is odd */
+        vpmovw2m  %zmm8, %k1
+
+/* add sign to hR */
+        vpxord    %zmm8, %zmm2, %zmm27
+        vfmadd213ph {rn-sae}, %zmm27, %zmm27, %zmm9
+        vrcpph    %zmm9, %zmm9{%k1}
+
+/* needed to set sin(-0)=-0 */
+        vpxord    %zmm0, %zmm9, %zmm0
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__tan_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_tans32,@function
+        .size	__svml_tans32,.-__svml_tans32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_htan_data_internal:
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	16
+        .long	0x4b000000
+	.endr
+	.rept	16
+        .long	0x3f22f983
+	.endr
+	.rept	16
+        .long	0xbfc90fdb
+	.endr
+	.rept	16
+        .long	0x333bbd2e
+	.endr
+	.rept	32
+        .word	0x2e06
+	.endr
+	.rept	32
+        .word	0x2f6c
+	.endr
+	.rept	32
+        .word	0x355f
+	.endr
+	.rept	32
+        .word	0x82e5
+	.endr
+        .type	__svml_htan_data_internal,@object
+        .size	__svml_htan_data_internal,576
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_s_ha.s
new file mode 100644
index 00000000000..a0d87fa10ee
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_s_ha.s
@@ -0,0 +1,3118 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *     
+ *      ( optimized for throughput, with small table lookup, works when HW FMA is available )
+ *     
+ *       Implementation reduces argument x to |R|<pi/64
+ *       32-entry tables used to store high and low parts of tan(x0)
+ *       Argument x = N*pi + x0 + (R);   x0 = k*pi/32, with k in {0, 1, ..., 31}
+ *       (very large arguments reduction resolved in _vsreduction_core.i)
+ *       Compute result as (tan(x0) + tan(R))/(1-tan(x0)*tan(R))
+ *       _HA_ version keeps extra precision for numerator, denominator, and during
+ *       final NR-iteration computing quotient.
+ *     
+ *     
+ */
+
+
+	.text
+.L_2__routine_start___svml_tanf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_tanf16_ha
+
+__svml_tanf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        xorl      %edx, %edx
+
+/* Large values check */
+        vmovups   768+__svml_stan_ha_data_internal(%rip), %zmm9
+
+/*
+ * ----------------------------------------------------------
+ * Main path
+ * ----------------------------------------------------------
+ * start arg. reduction
+ */
+        vmovups   1088+__svml_stan_ha_data_internal(%rip), %zmm1
+        vmovups   64+__svml_stan_ha_data_internal(%rip), %zmm2
+        vmovups   256+__svml_stan_ha_data_internal(%rip), %zmm4
+        vmovups   320+__svml_stan_ha_data_internal(%rip), %zmm5
+        vmovaps   %zmm0, %zmm11
+        vandps    960+__svml_stan_ha_data_internal(%rip), %zmm11, %zmm10
+        vcmpps    $22, {sae}, %zmm9, %zmm10, %k1
+        vmovups   __svml_stan_ha_data_internal(%rip), %zmm9
+
+/*
+ * ----------------------------------------------------------
+ * End of main path
+ * ----------------------------------------------------------
+ */
+        kortestw  %k1, %k1
+        vfmadd213ps {rn-sae}, %zmm1, %zmm11, %zmm9
+        vsubps    {rn-sae}, %zmm1, %zmm9, %zmm8
+        vfnmadd213ps {rn-sae}, %zmm11, %zmm8, %zmm2
+        vmovaps   %zmm4, %zmm3
+        vfnmadd213ps {rn-sae}, %zmm2, %zmm8, %zmm3
+        vmovaps   %zmm5, %zmm0
+        vfnmadd213ps {rn-sae}, %zmm3, %zmm8, %zmm0
+
+/* Calculate the lower part sRl */
+        vsubps    {rn-sae}, %zmm3, %zmm2, %zmm7
+        vsubps    {rn-sae}, %zmm3, %zmm0, %zmm6
+        vfnmadd231ps {rn-sae}, %zmm8, %zmm4, %zmm7
+        vfmadd213ps {rn-sae}, %zmm6, %zmm5, %zmm8
+        vsubps    {rn-sae}, %zmm8, %zmm7, %zmm1
+        jne       .LBL_1_12
+
+.LBL_1_2:
+
+        vmulps    {rn-sae}, %zmm0, %zmm0, %zmm6
+        vmovups   704+__svml_stan_ha_data_internal(%rip), %zmm4
+        vmovups   640+__svml_stan_ha_data_internal(%rip), %zmm5
+
+/* Table lookup */
+        vmovups   384+__svml_stan_ha_data_internal(%rip), %zmm3
+
+/*
+ * Computer Denominator:
+ * sDenominator - sDlow ~= 1-(sTh+sTl)*(sP+sPlow)
+ */
+        vmovups   1152+__svml_stan_ha_data_internal(%rip), %zmm14
+        vmovups   512+__svml_stan_ha_data_internal(%rip), %zmm2
+        vfmadd231ps {rn-sae}, %zmm6, %zmm4, %zmm5
+        vpermt2ps 448+__svml_stan_ha_data_internal(%rip), %zmm9, %zmm3
+        vpermt2ps 576+__svml_stan_ha_data_internal(%rip), %zmm9, %zmm2
+        vmulps    {rn-sae}, %zmm0, %zmm5, %zmm8
+        vfnmsub213ps {rn-sae}, %zmm1, %zmm6, %zmm8
+        vsubps    {rn-sae}, %zmm8, %zmm0, %zmm1
+        vmulps    {rn-sae}, %zmm1, %zmm3, %zmm4
+        vsubps    {rn-sae}, %zmm1, %zmm0, %zmm7
+
+/*
+ * Compute Numerator:
+ * sNumerator + sNlow ~= sTh+sTl+sP+sPlow
+ */
+        vaddps    {rn-sae}, %zmm3, %zmm1, %zmm0
+        vsubps    {rn-sae}, %zmm4, %zmm14, %zmm6
+        vsubps    {rn-sae}, %zmm8, %zmm7, %zmm9
+        vsubps    {rn-sae}, %zmm3, %zmm0, %zmm10
+        vsubps    {rn-sae}, %zmm14, %zmm6, %zmm15
+        vsubps    {rn-sae}, %zmm10, %zmm1, %zmm12
+        vaddps    {rn-sae}, %zmm4, %zmm15, %zmm14
+        vaddps    {rn-sae}, %zmm2, %zmm12, %zmm13
+        vmovaps   %zmm3, %zmm5
+        vfnmadd213ps {rn-sae}, %zmm4, %zmm1, %zmm5
+        vaddps    {rn-sae}, %zmm9, %zmm13, %zmm7
+        vsubps    {rn-sae}, %zmm5, %zmm14, %zmm15
+        vfmadd213ps {rn-sae}, %zmm15, %zmm9, %zmm3
+        vfmadd213ps {rn-sae}, %zmm3, %zmm1, %zmm2
+
+/*
+ * Now computes (sNumerator + sNlow)/(sDenominator - sDlow)
+ * Choose NR iteration instead of hardware division
+ */
+        vrcp14ps  %zmm6, %zmm1
+        vmulps    {rn-sae}, %zmm0, %zmm1, %zmm3
+
+/* One NR iteration to refine sQuotient */
+        vfmsub213ps {rn-sae}, %zmm0, %zmm3, %zmm6
+        vfnmadd213ps {rn-sae}, %zmm6, %zmm3, %zmm2
+        vsubps    {rn-sae}, %zmm7, %zmm2, %zmm0
+        vfnmadd213ps {rn-sae}, %zmm3, %zmm1, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_4
+
+.LBL_1_3:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_4:
+
+        vmovups   %zmm11, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_3
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_8:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_11
+
+.LBL_1_9:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_8
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_3
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_11:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_stan_ha_cout_rare_internal
+        jmp       .LBL_1_9
+	.cfi_restore 4
+	.cfi_restore 5
+	.cfi_restore 12
+	.cfi_restore 13
+	.cfi_restore 122
+	.cfi_restore 123
+	.cfi_restore 124
+	.cfi_restore 125
+
+.LBL_1_12:
+
+        vmovups   1024+__svml_stan_ha_data_internal(%rip), %zmm6
+
+/*
+ * Get the (2^a / 2pi) mod 1 values from the table.
+ * Because VLANG doesn't have I-type gather, we need a trivial cast
+ */
+        lea       __svml_stan_ha_reduction_data_internal(%rip), %rax
+        vmovups   %zmm0, (%rsp)
+        vmovups   %zmm1, 64(%rsp)
+        vandps    %zmm10, %zmm6, %zmm8
+        vcmpps    $0, {sae}, %zmm6, %zmm8, %k0
+
+/*
+ * Break the P_xxx and m into 16-bit chunks ready for
+ * the long multiplication via 16x16->32 multiplications
+ */
+        vmovups   .L_2il0floatpacket.18(%rip), %zmm6
+        kxnorw    %k0, %k0, %k2
+        kxnorw    %k0, %k0, %k3
+        kmovw     %k0, %edx
+        vpandd    .L_2il0floatpacket.15(%rip), %zmm11, %zmm7
+        vpsrld    $23, %zmm7, %zmm4
+        vpslld    $1, %zmm4, %zmm3
+        vpaddd    %zmm4, %zmm3, %zmm15
+        vpslld    $2, %zmm15, %zmm2
+        vpxord    %zmm1, %zmm1, %zmm1
+        vgatherdps (%rax,%zmm2), %zmm1{%k2}
+        kxnorw    %k0, %k0, %k2
+        vpsrld    $16, %zmm1, %zmm10
+        vpxord    %zmm8, %zmm8, %zmm8
+        vgatherdps 8(%rax,%zmm2), %zmm8{%k2}
+        vpxord    %zmm12, %zmm12, %zmm12
+        vgatherdps 4(%rax,%zmm2), %zmm12{%k3}
+        vpsrld    $16, %zmm12, %zmm0
+
+/*
+ * Also get the significand as an integer
+ * NB: adding in the integer bit is wrong for denorms!
+ * To make this work for denorms we should do something slightly different
+ */
+        vpandd    .L_2il0floatpacket.16(%rip), %zmm11, %zmm13
+        vpaddd    .L_2il0floatpacket.17(%rip), %zmm13, %zmm14
+        vpsrld    $16, %zmm8, %zmm13
+        vpsrld    $16, %zmm14, %zmm7
+        vpandd    %zmm6, %zmm14, %zmm14
+        vpandd    %zmm6, %zmm1, %zmm5
+        vpmulld   %zmm13, %zmm7, %zmm1
+        vpmulld   %zmm13, %zmm14, %zmm13
+        vpsrld    $16, %zmm13, %zmm13
+        vpaddd    %zmm13, %zmm1, %zmm1
+        vpandd    %zmm6, %zmm12, %zmm12
+        vpandd    %zmm6, %zmm8, %zmm15
+
+/* Now do the big multiplication and carry propagation */
+        vpmulld   %zmm5, %zmm7, %zmm4
+        vpmulld   %zmm0, %zmm7, %zmm3
+        vpmulld   %zmm12, %zmm7, %zmm2
+        vpmulld   %zmm15, %zmm7, %zmm8
+        vpmulld   %zmm10, %zmm14, %zmm7
+        vpmulld   %zmm5, %zmm14, %zmm5
+        vpmulld   %zmm0, %zmm14, %zmm10
+        vpmulld   %zmm12, %zmm14, %zmm0
+        vpsrld    $16, %zmm0, %zmm14
+        vpsrld    $16, %zmm10, %zmm15
+        vpsrld    $16, %zmm5, %zmm12
+        vpsrld    $16, %zmm8, %zmm8
+        vpaddd    %zmm14, %zmm2, %zmm13
+        vpaddd    %zmm15, %zmm3, %zmm2
+        vpaddd    %zmm12, %zmm4, %zmm15
+        vpandd    %zmm6, %zmm0, %zmm0
+        vpaddd    %zmm1, %zmm0, %zmm4
+        vpandd    %zmm6, %zmm10, %zmm10
+        vpaddd    %zmm13, %zmm10, %zmm1
+        vpaddd    %zmm4, %zmm8, %zmm10
+        vpandd    %zmm6, %zmm5, %zmm5
+        vpaddd    %zmm2, %zmm5, %zmm14
+        vpsrld    $16, %zmm10, %zmm2
+        vpandd    %zmm6, %zmm7, %zmm3
+        vpaddd    %zmm15, %zmm3, %zmm0
+        vpaddd    %zmm1, %zmm2, %zmm3
+        vpsrld    $16, %zmm3, %zmm12
+        vpaddd    %zmm14, %zmm12, %zmm7
+        vpsrld    $16, %zmm7, %zmm8
+        vpaddd    %zmm0, %zmm8, %zmm5
+        vpslld    $16, %zmm3, %zmm0
+
+/* Assemble reduced argument from the pieces */
+        vpandd    %zmm6, %zmm10, %zmm4
+        vpandd    %zmm6, %zmm7, %zmm8
+        vpslld    $16, %zmm5, %zmm6
+
+/*
+ * Now round at the 2^-8 bit position for reduction mod pi/2^7
+ * instead of the original 2pi (but still with the same 2pi scaling).
+ * Use a shifter of 2^15 + 2^14.
+ * The N we get is our final version; it has an offset of
+ * 2^8 because of the implicit integer bit, and anyway for negative
+ * starting value it's a 2s complement thing. But we need to mask
+ * off the exponent part anyway so it's fine.
+ */
+        vmovups   .L_2il0floatpacket.21(%rip), %zmm7
+        vpaddd    %zmm8, %zmm6, %zmm3
+        vpaddd    %zmm4, %zmm0, %zmm0
+        vpsrld    $9, %zmm3, %zmm6
+
+/*
+ * We want to incorporate the original sign now too.
+ * Do it here for convenience in getting the right N value,
+ * though we could wait right to the end if we were prepared
+ * to modify the sign of N later too.
+ * So get the appropriate sign mask now (or sooner).
+ */
+        vpandd    .L_2il0floatpacket.19(%rip), %zmm11, %zmm4
+
+/*
+ * Create floating-point high part, implicitly adding integer bit 1
+ * Incorporate overall sign at this stage too.
+ */
+        vpxord    .L_2il0floatpacket.20(%rip), %zmm4, %zmm10
+        vpord     %zmm10, %zmm6, %zmm5
+        vaddps    {rn-sae}, %zmm5, %zmm7, %zmm2
+        vpsrld    $18, %zmm0, %zmm6
+        vsubps    {rn-sae}, %zmm7, %zmm2, %zmm1
+        vsubps    {rn-sae}, %zmm1, %zmm5, %zmm5
+        vpandd    .L_2il0floatpacket.26(%rip), %zmm3, %zmm3
+        vpslld    $14, %zmm3, %zmm8
+        vpandd    .L_2il0floatpacket.24(%rip), %zmm0, %zmm12
+        vpslld    $5, %zmm12, %zmm13
+        vpxord    .L_2il0floatpacket.25(%rip), %zmm4, %zmm1
+        vpord     %zmm6, %zmm8, %zmm0
+        vpord     %zmm1, %zmm0, %zmm3
+
+/* Grab our final N value as an integer, appropriately masked mod 2^8 */
+        vpandd    .L_2il0floatpacket.22(%rip), %zmm2, %zmm10
+        vsubps    {rn-sae}, %zmm1, %zmm3, %zmm2
+        vandps    .L_2il0floatpacket.29(%rip), %zmm11, %zmm8
+        vpternlogd $255, %zmm6, %zmm6, %zmm6
+
+/*
+ * Create floating-point low and medium parts, respectively
+ * lo_17, ... lo_0, 0, ..., 0
+ * hi_8, ... hi_0, lo_31, ..., lo_18
+ * then subtract off the implicitly added integer bits,
+ * 2^-46 and 2^-23, respectively.
+ * Put the original sign into all of them at this stage.
+ */
+        vpxord    .L_2il0floatpacket.23(%rip), %zmm4, %zmm15
+        vpord     %zmm15, %zmm13, %zmm14
+
+/*
+ * Now multiply those numbers all by 2 pi, reasonably accurately.
+ * (RHi + RLo) * (pi_lead + pi_trail) ~=
+ * RHi * pi_lead + (RHi * pi_trail + RLo * pi_lead)
+ */
+        vmovups   .L_2il0floatpacket.27(%rip), %zmm13
+        vsubps    {rn-sae}, %zmm15, %zmm14, %zmm7
+
+/* Now add them up into 2 reasonably aligned pieces */
+        vaddps    {rn-sae}, %zmm2, %zmm5, %zmm14
+
+/*
+ * If the magnitude of the input is <= 2^-20, then
+ * just pass through the input, since no reduction will be needed and
+ * the main path will only work accurately if the reduced argument is
+ * about >= 2^-40 (which it is for all large pi multiples)
+ */
+        vmovups   .L_2il0floatpacket.30(%rip), %zmm15
+        vmulps    {rn-sae}, %zmm13, %zmm14, %zmm0
+        vcmpps    $26, {sae}, %zmm15, %zmm8, %k3
+        vcmpps    $22, {sae}, %zmm15, %zmm8, %k2
+        vsubps    {rn-sae}, %zmm14, %zmm5, %zmm5
+        vaddps    {rn-sae}, %zmm5, %zmm2, %zmm4
+
+/*
+ * The output is _VRES_R (high) + _VRES_E (low), and the integer part is _VRES_IND
+ * Set sRp2 = _VRES_R^2 and then resume the original code.
+ */
+        vmovups   .L_2il0floatpacket.31(%rip), %zmm2
+        vaddps    {rn-sae}, %zmm7, %zmm4, %zmm1
+        vmovups   .L_2il0floatpacket.28(%rip), %zmm7
+        vmovaps   %zmm13, %zmm12
+        vfmsub213ps {rn-sae}, %zmm0, %zmm14, %zmm12
+        vfmadd213ps {rn-sae}, %zmm12, %zmm7, %zmm14
+        vmovaps   %zmm6, %zmm12
+        vfmadd213ps {rn-sae}, %zmm14, %zmm13, %zmm1
+        vpandnd   %zmm8, %zmm8, %zmm12{%k3}
+        vpandnd   %zmm8, %zmm8, %zmm6{%k2}
+        vandps    %zmm11, %zmm6, %zmm15
+        vandps    %zmm0, %zmm12, %zmm7
+        vandps    %zmm1, %zmm12, %zmm8
+        vmovups   .L_2il0floatpacket.32(%rip), %zmm0
+        vorps     %zmm7, %zmm15, %zmm6
+        vpsrld    $31, %zmm6, %zmm3
+        vpsubd    %zmm3, %zmm2, %zmm4
+        vpaddd    %zmm4, %zmm10, %zmm5
+        vpsrld    $2, %zmm5, %zmm14
+        vpslld    $2, %zmm14, %zmm13
+
+/*
+ * ----------------------------------------------------------
+ * End of large arguments path
+ * ----------------------------------------------------------
+ * Merge results from main and large paths:
+ */
+        vblendmps %zmm14, %zmm9, %zmm9{%k1}
+        vpsubd    %zmm13, %zmm10, %zmm10
+        vcvtdq2ps {rn-sae}, %zmm10, %zmm2
+        vmovups   .L_2il0floatpacket.33(%rip), %zmm10
+        vmovaps   %zmm0, %zmm1
+        vfmadd213ps {rn-sae}, %zmm6, %zmm2, %zmm1
+        vfnmadd213ps {rn-sae}, %zmm1, %zmm2, %zmm0
+        vsubps    {rn-sae}, %zmm0, %zmm6, %zmm6
+        vmovups   (%rsp), %zmm0
+        vfmadd213ps {rn-sae}, %zmm6, %zmm10, %zmm2
+        vblendmps %zmm1, %zmm0, %zmm0{%k1}
+        vmovups   64(%rsp), %zmm1
+        vaddps    {rn-sae}, %zmm8, %zmm2, %zmm1{%k1}
+        jmp       .LBL_1_2
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_tanf16_ha,@function
+	.size	__svml_tanf16_ha,.-__svml_tanf16_ha
+..LN__svml_tanf16_ha.0:
+
+.L_2__routine_start___svml_stan_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_stan_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L65:
+
+        xorl      %eax, %eax
+        movl      (%rdi), %edx
+        movzwl    2(%rdi), %ecx
+        movl      %edx, -8(%rsp)
+        andl      $32640, %ecx
+        shrl      $24, %edx
+        andl      $127, %edx
+        movb      %dl, -5(%rsp)
+        cmpl      $32640, %ecx
+        je        .LBL_2_3
+
+
+        ret
+
+.LBL_2_3:
+
+        cmpl      $2139095040, -8(%rsp)
+        jne       .LBL_2_5
+
+
+        movss     (%rdi), %xmm0
+        movl      $1, %eax
+        mulss     __stan_ha__vmlsTanTab(%rip), %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+
+.LBL_2_5:
+
+        movss     (%rdi), %xmm0
+        mulss     (%rdi), %xmm0
+        movss     %xmm0, (%rsi)
+        ret
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_stan_ha_cout_rare_internal,@function
+	.size	__svml_stan_ha_cout_rare_internal,.-__svml_stan_ha_cout_rare_internal
+..LN__svml_stan_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+.L_2il0floatpacket.15:
+	.long	0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000,0x7f800000
+	.type	.L_2il0floatpacket.15,@object
+	.size	.L_2il0floatpacket.15,64
+	.align 64
+.L_2il0floatpacket.16:
+	.long	0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff,0x007fffff
+	.type	.L_2il0floatpacket.16,@object
+	.size	.L_2il0floatpacket.16,64
+	.align 64
+.L_2il0floatpacket.17:
+	.long	0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000,0x00800000
+	.type	.L_2il0floatpacket.17,@object
+	.size	.L_2il0floatpacket.17,64
+	.align 64
+.L_2il0floatpacket.18:
+	.long	0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff,0x0000ffff
+	.type	.L_2il0floatpacket.18,@object
+	.size	.L_2il0floatpacket.18,64
+	.align 64
+.L_2il0floatpacket.19:
+	.long	0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000,0x80000000
+	.type	.L_2il0floatpacket.19,@object
+	.size	.L_2il0floatpacket.19,64
+	.align 64
+.L_2il0floatpacket.20:
+	.long	0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000,0x3f800000
+	.type	.L_2il0floatpacket.20,@object
+	.size	.L_2il0floatpacket.20,64
+	.align 64
+.L_2il0floatpacket.21:
+	.long	0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000,0x47400000
+	.type	.L_2il0floatpacket.21,@object
+	.size	.L_2il0floatpacket.21,64
+	.align 64
+.L_2il0floatpacket.22:
+	.long	0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff,0x000000ff
+	.type	.L_2il0floatpacket.22,@object
+	.size	.L_2il0floatpacket.22,64
+	.align 64
+.L_2il0floatpacket.23:
+	.long	0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000,0x28800000
+	.type	.L_2il0floatpacket.23,@object
+	.size	.L_2il0floatpacket.23,64
+	.align 64
+.L_2il0floatpacket.24:
+	.long	0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff,0x0003ffff
+	.type	.L_2il0floatpacket.24,@object
+	.size	.L_2il0floatpacket.24,64
+	.align 64
+.L_2il0floatpacket.25:
+	.long	0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000,0x34000000
+	.type	.L_2il0floatpacket.25,@object
+	.size	.L_2il0floatpacket.25,64
+	.align 64
+.L_2il0floatpacket.26:
+	.long	0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff,0x000001ff
+	.type	.L_2il0floatpacket.26,@object
+	.size	.L_2il0floatpacket.26,64
+	.align 64
+.L_2il0floatpacket.27:
+	.long	0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb,0x40c90fdb
+	.type	.L_2il0floatpacket.27,@object
+	.size	.L_2il0floatpacket.27,64
+	.align 64
+.L_2il0floatpacket.28:
+	.long	0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e,0xb43bbd2e
+	.type	.L_2il0floatpacket.28,@object
+	.size	.L_2il0floatpacket.28,64
+	.align 64
+.L_2il0floatpacket.29:
+	.long	0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff,0x7fffffff
+	.type	.L_2il0floatpacket.29,@object
+	.size	.L_2il0floatpacket.29,64
+	.align 64
+.L_2il0floatpacket.30:
+	.long	0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000,0x35800000
+	.type	.L_2il0floatpacket.30,@object
+	.size	.L_2il0floatpacket.30,64
+	.align 64
+.L_2il0floatpacket.31:
+	.long	0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002,0x00000002
+	.type	.L_2il0floatpacket.31,@object
+	.size	.L_2il0floatpacket.31,64
+	.align 64
+.L_2il0floatpacket.32:
+	.long	0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb,0x3cc90fdb
+	.type	.L_2il0floatpacket.32,@object
+	.size	.L_2il0floatpacket.32,64
+	.align 64
+.L_2il0floatpacket.33:
+	.long	0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e,0xb03bbd2e
+	.type	.L_2il0floatpacket.33,@object
+	.size	.L_2il0floatpacket.33,64
+	.align 64
+__svml_stan_ha_data_internal:
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1092811139
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	1036586970
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	832708968
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	633484485
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	832708608
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	708075802
+	.long	2147483648
+	.long	1036629468
+	.long	1045147567
+	.long	1050366018
+	.long	1054086093
+	.long	1057543609
+	.long	1059786177
+	.long	1062344705
+	.long	1065353216
+	.long	1067186156
+	.long	1069519047
+	.long	1072658590
+	.long	1075479162
+	.long	1079179983
+	.long	1084284919
+	.long	1092776803
+	.long	4286578687
+	.long	3240260451
+	.long	3231768567
+	.long	3226663631
+	.long	3222962810
+	.long	3220142238
+	.long	3217002695
+	.long	3214669804
+	.long	3212836864
+	.long	3209828353
+	.long	3207269825
+	.long	3205027257
+	.long	3201569741
+	.long	3197849666
+	.long	3192631215
+	.long	3184113116
+	.long	2147483648
+	.long	826651354
+	.long	791306928
+	.long	2989111746
+	.long	2982175258
+	.long	2992568675
+	.long	850100121
+	.long	850281093
+	.long	0
+	.long	861435400
+	.long	840342808
+	.long	3003924160
+	.long	3016492578
+	.long	865099790
+	.long	856723932
+	.long	3025444934
+	.long	4085252096
+	.long	877961286
+	.long	3004207580
+	.long	3012583438
+	.long	869008930
+	.long	856440512
+	.long	2987826456
+	.long	3008919048
+	.long	0
+	.long	2997764741
+	.long	2997583769
+	.long	845085027
+	.long	834691610
+	.long	841628098
+	.long	2938790576
+	.long	2974135002
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1051372198
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1040758920
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1174470656
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	1059256707
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	2139095040
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1262485504
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1065353216
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1176256512
+	.long	1413742592
+	.long	1073291771
+	.long	1413742592
+	.long	1073291771
+	.long	1413742592
+	.long	1073291771
+	.long	1413742592
+	.long	1073291771
+	.long	1413742592
+	.long	1073291771
+	.long	1413742592
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+	.long	2974469278
+	.long	566763775
+	.type	__svml_stan_ha_reduction_data_internal,@object
+	.size	__svml_stan_ha_reduction_data_internal,3072
+	.align 4
+__stan_ha__vmlsTanTab:
+	.long	0
+	.long	2139095040
+	.type	__stan_ha__vmlsTanTab,@object
+	.size	__stan_ha__vmlsTanTab,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_s_la.s
index 478a52a509b..d1547022b42 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tan_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_d_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_d_ha.s
new file mode 100644
index 00000000000..6037979cf66
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_d_ha.s
@@ -0,0 +1,3061 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *   NOTE: Since the hyperbolic tangent function is odd
+ *         (tanh(x) = -tanh(-x)), below algorithm deals with the absolute
+ *         value of the argument |x|: tanh(x) = sign(x) * tanh(|x|)
+ * 
+ *   We use a table lookup method to compute tanh(|x|).
+ *   The basic idea is to split the input range into a number of subintervals
+ *   and to approximate tanh(.) with a polynomial on each of them.
+ * 
+ *   IEEE SPECIAL CONDITIONS:
+ *   x = [+,-]0, r = [+,-]0
+ *   x = +Inf,   r = +1
+ *   x = -Inf,   r = -1
+ *   x = QNaN,   r = QNaN
+ *   x = SNaN,   r = QNaN
+ * 
+ * 
+ *   ALGORITHM DETAILS
+ *   We handle special values in a callout function, aside from main path
+ *   computations. "Special" for this algorithm are:
+ *   INF, NAN, |x| > HUGE_THRESHOLD
+ * 
+ * 
+ *   Main path computations are organized as follows:
+ *   Actually we split the interval [0, SATURATION_THRESHOLD)
+ *   into a number of subintervals.  On each subinterval we approximate tanh(.)
+ *   with a minimax polynomial of pre-defined degree. Polynomial coefficients
+ *   are computed beforehand and stored in table. We also use
+ * 
+ *       y := |x| + B,
+ * 
+ *   here B depends on subinterval and is used to make argument
+ *   closer to zero.
+ *   We also add large fake interval [SATURATION_THRESHOLD, HUGE_THRESHOLD],
+ *   where 1.0 + 0.0*y + 0.0*y^2 ... coefficients are stored - just to
+ *   preserve main path computation logic but return 1.0 for all arguments.
+ * 
+ *   Hence reconstruction looks as follows:
+ *   we extract proper polynomial and range reduction coefficients
+ *        (Pj and B), corresponding to subinterval, to which |x| belongs,
+ *        and return
+ * 
+ *       r := sign(x) * (P0 + P1 * y + ... + Pn * y^n)
+ * 
+ *   NOTE: we use multiprecision technique to multiply and sum the first
+ *         K terms of the polynomial. So Pj, j = 0..K are stored in
+ *         table each as a pair of target precision numbers (Pj and PLj) to
+ *         achieve wider than target precision.
+ * 
+ * --
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_tanh8_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_tanh8_ha
+
+__svml_tanh8_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $320, %rsp
+        vpsrlq    $32, %zmm0, %zmm4
+        vmovups   %zmm0, (%rsp)
+        vmovups   __svml_dtanh_ha_data_internal(%rip), %zmm14
+        vmovups   128+__svml_dtanh_ha_data_internal(%rip), %zmm15
+        vpmovqd   %zmm4, %ymm5
+
+/* -------------------- Constant loading ------------------- */
+        vandpd    10432+__svml_dtanh_ha_data_internal(%rip), %zmm0, %zmm13
+        vandpd    10368+__svml_dtanh_ha_data_internal(%rip), %zmm0, %zmm3
+
+/* Here huge arguments, INF and NaNs are filtered out to callout. */
+        vpand     2432+__svml_dtanh_ha_data_internal(%rip), %ymm5, %ymm7
+        vmovups   384+__svml_dtanh_ha_data_internal(%rip), %zmm0
+        vmovups   2176+__svml_dtanh_ha_data_internal(%rip), %zmm4
+        vmovups   2048+__svml_dtanh_ha_data_internal(%rip), %zmm5
+        vmovups   %zmm3, 64(%rsp)
+        vmovups   512+__svml_dtanh_ha_data_internal(%rip), %zmm3
+        vpsubd    2496+__svml_dtanh_ha_data_internal(%rip), %ymm7, %ymm8
+
+/* if VMIN, VMAX is defined for I type */
+        vxorps    %ymm9, %ymm9, %ymm9
+        vpmaxsd   %ymm9, %ymm8, %ymm10
+        vpminsd   2560+__svml_dtanh_ha_data_internal(%rip), %ymm10, %ymm11
+        vpsrld    $19, %ymm11, %ymm12
+        vmovups   1664+__svml_dtanh_ha_data_internal(%rip), %zmm8
+        vmovups   1536+__svml_dtanh_ha_data_internal(%rip), %zmm9
+        vmovups   1408+__svml_dtanh_ha_data_internal(%rip), %zmm10
+        vmovups   1280+__svml_dtanh_ha_data_internal(%rip), %zmm11
+        vpmovzxdq %ymm12, %zmm2
+        vmovups   1152+__svml_dtanh_ha_data_internal(%rip), %zmm12
+        vpermt2pd 448+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm0
+        vpermt2pd 64+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm14
+        vpermt2pd 2240+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm4
+        vpermt2pd 2112+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm5
+        vsubpd    {rn-sae}, %zmm14, %zmm13, %zmm1
+        vpermt2pd 1728+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm8
+        vpermt2pd 1600+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm9
+        vpermt2pd 1472+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm10
+        vpermt2pd 1344+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm11
+        vpermt2pd 1216+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm12
+        vpermt2pd 576+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm3
+        vpermt2pd 192+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm15
+        vmovups   %zmm0, 192(%rsp)
+        vmovups   2304+__svml_dtanh_ha_data_internal(%rip), %zmm0
+        vmovups   1024+__svml_dtanh_ha_data_internal(%rip), %zmm13
+        vmovups   896+__svml_dtanh_ha_data_internal(%rip), %zmm14
+        vmovups   %zmm3, 256(%rsp)
+        vmovups   768+__svml_dtanh_ha_data_internal(%rip), %zmm3
+        vmovups   %zmm15, 128(%rsp)
+        vmovups   640+__svml_dtanh_ha_data_internal(%rip), %zmm15
+        vpermt2pd 2368+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm0
+        vpermt2pd 1088+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm13
+        vpermt2pd 960+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm14
+        vpermt2pd 832+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm3
+        vpermt2pd 704+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm15
+        vfmadd213pd {rn-sae}, %zmm4, %zmm1, %zmm0
+        vmovups   256+__svml_dtanh_ha_data_internal(%rip), %zmm4
+        vfmadd213pd {rn-sae}, %zmm5, %zmm1, %zmm0
+        vpermt2pd 320+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm4
+        vpcmpgtd  10560+__svml_dtanh_ha_data_internal(%rip), %ymm7, %ymm6
+        vmovmskps %ymm6, %edx
+        vmovups   1920+__svml_dtanh_ha_data_internal(%rip), %zmm6
+        vmovups   1792+__svml_dtanh_ha_data_internal(%rip), %zmm7
+        vpermt2pd 1984+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm6
+        vpermt2pd 1856+__svml_dtanh_ha_data_internal(%rip), %zmm2, %zmm7
+        vfmadd213pd {rn-sae}, %zmm6, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm7, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm8, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm9, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm10, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm11, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm12, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm13, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm14, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm3, %zmm1, %zmm0
+        vmovups   256(%rsp), %zmm3
+        vfmadd213pd {rn-sae}, %zmm15, %zmm1, %zmm0
+        vfmadd213pd {rn-sae}, %zmm3, %zmm1, %zmm0
+        vmulpd    {rn-sae}, %zmm1, %zmm0, %zmm2
+        vmovups   192(%rsp), %zmm0
+        vfmadd213pd {rn-sae}, %zmm4, %zmm1, %zmm2
+        vfmadd213pd {rn-sae}, %zmm2, %zmm1, %zmm0
+        vmovups   128(%rsp), %zmm1
+        vaddpd    {rn-sae}, %zmm1, %zmm0, %zmm0
+        vorpd     64(%rsp), %zmm0, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   (%rsp), %zmm1
+        vmovups   %zmm0, 128(%rsp)
+        vmovups   %zmm1, 64(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xe8, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xe0, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xf8, 0xfe, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xf0, 0xfe, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xd8, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xd0, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xc8, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xc0, 0xfe, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $8, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xe8, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xe0, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xf8, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xf0, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xd8, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xd0, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xc8, 0xfe, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0xc0, 0xfe, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,8), %rdi
+        lea       128(%rsp,%r12,8), %rsi
+
+        call      __svml_dtanh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_tanh8_ha,@function
+	.size	__svml_tanh8_ha,.-__svml_tanh8_ha
+..LN__svml_tanh8_ha.0:
+
+.L_2__routine_start___svml_dtanh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_dtanh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L63:
+
+        lea       __dtanh_ha__imldTanhTab(%rip), %rdx
+        movb      7(%rdi), %al
+        andb      $-128, %al
+        shrb      $7, %al
+        movzbl    %al, %ecx
+        movzwl    6(%rdi), %eax
+        andl      $32752, %eax
+        shrl      $4, %eax
+        movq      (%rdx,%rcx,8), %rdx
+        cmpl      $2047, %eax
+        je        .LBL_2_6
+
+
+        cmpl      $2046, %eax
+        jne       .LBL_2_4
+
+.LBL_2_3:
+
+        movq      %rdx, (%rsi)
+        jmp       .LBL_2_5
+
+.LBL_2_4:
+
+        movsd     (%rdi), %xmm1
+        movsd     __dtanh_ha__imldTanhTab(%rip), %xmm0
+        addsd     %xmm1, %xmm0
+        mulsd     %xmm0, %xmm1
+        movsd     %xmm1, (%rsi)
+
+.LBL_2_5:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_6:
+
+        testl     $1048575, 4(%rdi)
+        jne       .LBL_2_9
+
+
+        cmpl      $0, (%rdi)
+        je        .LBL_2_3
+
+.LBL_2_9:
+
+        movsd     (%rdi), %xmm0
+        addsd     %xmm0, %xmm0
+        movsd     %xmm0, (%rsi)
+        jmp       .LBL_2_5
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_dtanh_ha_cout_rare_internal,@function
+	.size	__svml_dtanh_ha_cout_rare_internal,.-__svml_dtanh_ha_cout_rare_internal
+..LN__svml_dtanh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_dtanh_ha_data_internal:
+	.long	0
+	.long	0
+	.long	0
+	.long	1070333952
+	.long	0
+	.long	1070858240
+	.long	0
+	.long	1071382528
+	.long	0
+	.long	1071906816
+	.long	0
+	.long	1072431104
+	.long	0
+	.long	1072955392
+	.long	0
+	.long	1073479680
+	.long	0
+	.long	1074003968
+	.long	0
+	.long	1074528256
+	.long	0
+	.long	1075052544
+	.long	0
+	.long	1075576832
+	.long	0
+	.long	1076101120
+	.long	0
+	.long	1076625408
+	.long	0
+	.long	1077149696
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	1097497746
+	.long	1070305232
+	.long	2699715854
+	.long	1070817176
+	.long	3997728823
+	.long	1071273769
+	.long	3938160533
+	.long	1071759175
+	.long	193732629
+	.long	1072072293
+	.long	3144363502
+	.long	1072375075
+	.long	2548249895
+	.long	1072570303
+	.long	638218690
+	.long	1072665176
+	.long	3325726839
+	.long	1072689426
+	.long	2523455249
+	.long	1072693057
+	.long	2200391922
+	.long	1072693244
+	.long	4257836853
+	.long	1072693247
+	.long	4294954840
+	.long	1072693247
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	3167993022
+	.long	1013310014
+	.long	2889539328
+	.long	3159121931
+	.long	394645568
+	.long	1013728265
+	.long	2799920971
+	.long	1015161303
+	.long	754366556
+	.long	1013577885
+	.long	3960180508
+	.long	1014215657
+	.long	69774042
+	.long	3162475851
+	.long	2161066944
+	.long	3162264280
+	.long	923362432
+	.long	1014879086
+	.long	2160345802
+	.long	1014714211
+	.long	3729195158
+	.long	3161626453
+	.long	675671997
+	.long	1015336837
+	.long	1141527416
+	.long	1013957883
+	.long	2209699952
+	.long	3160643600
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	2750448946
+	.long	1072596012
+	.long	1257606939
+	.long	1072501079
+	.long	2619663609
+	.long	1072338011
+	.long	863303701
+	.long	1072048204
+	.long	2694810990
+	.long	1071654144
+	.long	837003456
+	.long	1070723665
+	.long	472162617
+	.long	1069359818
+	.long	532307062
+	.long	1067137790
+	.long	431106849
+	.long	1064162173
+	.long	329904022
+	.long	1059572972
+	.long	4080160942
+	.long	1053550275
+	.long	1402271583
+	.long	1044493446
+	.long	467296040
+	.long	1032344560
+	.long	26734424
+	.long	1014207804
+	.long	0
+	.long	0
+	.long	1071622681
+	.long	3153114090
+	.long	3948133409
+	.long	3217705130
+	.long	4096956694
+	.long	3218184481
+	.long	2616164369
+	.long	3218465008
+	.long	2620770434
+	.long	3218641845
+	.long	3075958072
+	.long	3218520700
+	.long	1950184789
+	.long	3217978001
+	.long	2686385024
+	.long	3216731525
+	.long	1489441818
+	.long	3214597550
+	.long	1992796697
+	.long	3211642259
+	.long	1952936976
+	.long	3207056478
+	.long	3904162951
+	.long	3201033920
+	.long	1382745445
+	.long	3191977094
+	.long	468673444
+	.long	3179828208
+	.long	3843669553
+	.long	3161691483
+	.long	0
+	.long	0
+	.long	1431655765
+	.long	3218429269
+	.long	3264395793
+	.long	3218178991
+	.long	60595194
+	.long	3217824331
+	.long	1837960166
+	.long	3217124894
+	.long	1964918946
+	.long	3214033516
+	.long	2998587684
+	.long	1068822866
+	.long	3421467326
+	.long	1069267520
+	.long	952003133
+	.long	1068506016
+	.long	1215426504
+	.long	1066495476
+	.long	2650572912
+	.long	1063503494
+	.long	4178958376
+	.long	1059043758
+	.long	3799805457
+	.long	1052940753
+	.long	3704238924
+	.long	1043831645
+	.long	2076585114
+	.long	1031813109
+	.long	1352847351
+	.long	1013591371
+	.long	0
+	.long	0
+	.long	3830371894
+	.long	3169224254
+	.long	74893150
+	.long	1069567437
+	.long	1235750664
+	.long	1069825332
+	.long	916043751
+	.long	1069924975
+	.long	4138411315
+	.long	1069655686
+	.long	829220656
+	.long	1068441660
+	.long	663246204
+	.long	3213477792
+	.long	652268865
+	.long	3214415230
+	.long	3251098232
+	.long	3212868437
+	.long	1480792335
+	.long	3209929101
+	.long	3365608027
+	.long	3205478075
+	.long	3330582883
+	.long	3199375817
+	.long	3573234945
+	.long	3190266717
+	.long	2061970086
+	.long	3178248181
+	.long	3148320390
+	.long	3160026274
+	.long	0
+	.long	0
+	.long	286337717
+	.long	1069617425
+	.long	4261152941
+	.long	1068876190
+	.long	3694459820
+	.long	1067928728
+	.long	2399079031
+	.long	3212381546
+	.long	394897286
+	.long	3215735810
+	.long	3073534041
+	.long	3215860118
+	.long	301960234
+	.long	3214124960
+	.long	3353887502
+	.long	1064191753
+	.long	4200665425
+	.long	1063741482
+	.long	2003926207
+	.long	1061090030
+	.long	2196865207
+	.long	1056528964
+	.long	2107880963
+	.long	1050526402
+	.long	3656860478
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+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	4294967295
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	0
+	.long	2147483648
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	4294967295
+	.long	2147483647
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2147352576
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	1069416448
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	7733248
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.long	535822336
+	.type	__svml_dtanh_ha_data_internal,@object
+	.size	__svml_dtanh_ha_data_internal,10816
+	.align 8
+__dtanh_ha__imldTanhTab:
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	3220176896
+	.type	__dtanh_ha__imldTanhTab,@object
+	.size	__dtanh_ha__imldTanhTab,16
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_d_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_d_la.s
index 34970c6f3a2..cd0675c5674 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_d_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_d_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_h_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_h_la.s
new file mode 100644
index 00000000000..e075059ed4d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_h_la.s
@@ -0,0 +1,183 @@
+/*******************************************
+* Copyright (C) 2022 Intel Corporation
+* SPDX-License-Identifier: BSD-3-Clause
+*******************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ *
+ *   NOTE: Since the hyperbolic tangent function is odd
+ *         (tanh(x) = -tanh(-x)), below algorithm deals with the absolute
+ *         value of the argument |x|: tanh(x) = sign(x) * tanh(|x|)
+ *
+ *   Get absolute value of argument xa = |x| and sign(x)
+ *   Limit arguments by threshold xa = min(xa, Thres), Thres<4.0
+ *   Get table index as Shifter + xa, RZ mode
+ *   Lookup polynomial coefficients: c1, c2 with the index
+ *   Compute polynomial and add sign x back: x+x*Poly
+ *
+ *   IEEE SPECIAL CONDITIONS:
+ *   x = [+,-]0, r = [+,-]0
+ *   x = +Inf,   r = +1
+ *   x = -Inf,   r = -1
+ *   x = QNaN,   r = QNaN
+ *   x = SNaN,   r = QNaN
+ *
+ *
+ */
+
+        .text
+
+        .align    16,0x90
+        .globl __svml_tanhs32
+
+__svml_tanhs32:
+
+        .cfi_startproc
+        kxnord  %k7, %k7, %k7
+        vmovdqu16 __svml_htanh_data_internal(%rip), %zmm31
+        vmovdqu16 64+__svml_htanh_data_internal(%rip), %zmm30
+        vmovdqu16 128+__svml_htanh_data_internal(%rip), %zmm29
+        vmovdqu16 192+__svml_htanh_data_internal(%rip), %zmm1
+        vmovdqu16 256+__svml_htanh_data_internal(%rip), %zmm3
+
+/* npy_half* in -> %rdi, npy_half* out -> %rsi, size_t N -> %rdx */
+.looparray__tanh_h:
+        cmpq    $31, %rdx
+        ja .loaddata__tanh_h
+/* set up mask %k7 for masked load instruction */
+        movl    $1, %eax
+        movl    %edx, %ecx
+        sall    %cl, %eax
+        subl    $1, %eax
+        kmovd   %eax, %k7
+/* Constant required for masked load */
+        movl    $0, %eax
+        vpbroadcastw    %eax, %zmm0
+        vmovdqu16 (%rdi), %zmm0{%k7}
+        jmp .funcbegin_tanh_h
+.loaddata__tanh_h:
+        vmovdqu16 (%rdi), %zmm0
+        addq $64, %rdi
+        
+.funcbegin_tanh_h:
+
+/*
+ * No callout
+ * xa = |x|
+ */
+        vpandd    %zmm29, %zmm0, %zmm2
+        vminph    {sae}, %zmm2, %zmm1, %zmm6
+
+/* Shifter + xa, RZ mode */
+        vaddph    {rz-sae}, %zmm3, %zmm6, %zmm4
+
+/* look up poly coefficients: c1, c2 */
+        vpermw    %zmm31, %zmm4, %zmm5
+
+/* sign(x) */
+        vpxord    %zmm0, %zmm2, %zmm7
+        vpermw    %zmm30, %zmm4, %zmm0
+
+/* polynomial */
+        vfmadd213ph {rn-sae}, %zmm5, %zmm6, %zmm0
+
+/* add sign back to x */
+        vpxord    %zmm7, %zmm6, %zmm8
+
+/* x+x*Poly */
+        vfmadd213ph {rn-sae}, %zmm8, %zmm8, %zmm0
+
+/* store result to our array and adjust pointers */
+        vmovdqu16 %zmm0, (%rsi){%k7}
+        addq $64, %rsi
+        subq $32, %rdx
+        cmpq $0, %rdx
+        jg .looparray__tanh_h
+        ret
+
+        .cfi_endproc
+
+        .type	__svml_tanhs32,@function
+        .size	__svml_tanhs32,.-__svml_tanhs32
+
+        .section .rodata, "a"
+        .align 64
+
+__svml_htanh_data_internal:
+        .word	0x0c00
+        .word	0x216a
+        .word	0x273e
+        .word	0x2a6c
+        .word	0x2c9c
+        .word	0x2dc3
+        .word	0x2e7c
+        .word	0x2eb1
+        .word	0x2e5a
+        .word	0x2d82
+        .word	0x2c3a
+        .word	0x2932
+        .word	0x21b4
+        .word	0xa561
+        .word	0xab02
+        .word	0xadb5
+        .word	0xafe8
+        .word	0xb109
+        .word	0xb216
+        .word	0xb319
+        .word	0xb408
+        .word	0xb47e
+        .word	0xb4ee
+        .word	0xb558
+        .word	0xb5bc
+        .word	0xb61a
+        .word	0xb673
+        .word	0xb6c6
+        .word	0xb715
+        .word	0xb75f
+        .word	0xb7a5
+        .word	0xb7e7
+        .word	0xa94d
+        .word	0xafc2
+        .word	0xb228
+        .word	0xb404
+        .word	0xb4b8
+        .word	0xb52f
+        .word	0xb56d
+        .word	0xb57d
+        .word	0xb567
+        .word	0xb537
+        .word	0xb4f6
+        .word	0xb4aa
+        .word	0xb45a
+        .word	0xb409
+        .word	0xb373
+        .word	0xb2dd
+        .word	0xb250
+        .word	0xb1ce
+        .word	0xb156
+        .word	0xb0e9
+        .word	0xb086
+        .word	0xb02c
+        .word	0xafb6
+        .word	0xaf22
+        .word	0xae9d
+        .word	0xae25
+        .word	0xadb7
+        .word	0xad54
+        .word	0xacfa
+        .word	0xaca9
+        .word	0xac5e
+        .word	0xac1a
+	.rept	32
+        .word	0x7fff
+	.endr
+	.rept	32
+        .word	0x42e3
+	.endr
+	.rept	32
+        .word	0x5a00
+	.endr
+        .type	__svml_htanh_data_internal,@object
+        .size	__svml_htanh_data_internal,320
+	 .section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_s_ha.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_s_ha.s
new file mode 100644
index 00000000000..6b7384417cb
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_s_ha.s
@@ -0,0 +1,1168 @@
+/*******************************************************************************
+* INTEL CONFIDENTIAL
+* Copyright 1996-2023 Intel Corporation.
+*
+* This software and the related documents are Intel copyrighted  materials,  and
+* your use of  them is  governed by the  express license  under which  they were
+* provided to you (License).  Unless the License provides otherwise, you may not
+* use, modify, copy, publish, distribute,  disclose or transmit this software or
+* the related documents without Intel's prior written permission.
+*
+* This software and the related documents  are provided as  is,  with no express
+* or implied  warranties,  other  than those  that are  expressly stated  in the
+* License.
+*******************************************************************************/
+
+/*
+ * ALGORITHM DESCRIPTION:
+ * 
+ *   NOTE: Since the hyperbolic tangent function is odd
+ *         (tanh(x) = -tanh(-x)), below algorithm deals with the absolute
+ *         value of the argument |x|: tanh(x) = sign(x) * tanh(|x|)
+ * 
+ *   We use a table lookup method to compute tanh(|x|).
+ *   The basic idea is to split the input range into a number of subintervals
+ *   and to approximate tanh(.) with a polynomial on each of them.
+ * 
+ *   IEEE SPECIAL CONDITIONS:
+ *   x = [+,-]0, r = [+,-]0
+ *   x = +Inf,   r = +1
+ *   x = -Inf,   r = -1
+ *   x = QNaN,   r = QNaN
+ *   x = SNaN,   r = QNaN
+ * 
+ * 
+ *   ALGORITHM DETAILS
+ *   We handle special values in a callout function, aside from main path
+ *   computations. "Special" for this algorithm are:
+ *   INF, NAN, |x| > HUGE_THRESHOLD
+ * 
+ * 
+ *   Main path computations are organized as follows:
+ *   Actually we split the interval [0, SATURATION_THRESHOLD)
+ *   into a number of subintervals.  On each subinterval we approximate tanh(.)
+ *   with a minimax polynomial of pre-defined degree. Polynomial coefficients
+ *   are computed beforehand and stored in table. We also use
+ * 
+ *       y := |x| + B,
+ * 
+ *   here B depends on subinterval and is used to make argument
+ *   closer to zero.
+ *   We also add large fake interval [SATURATION_THRESHOLD, HUGE_THRESHOLD],
+ *   where 1.0 + 0.0*y + 0.0*y^2 ... coefficients are stored - just to
+ *   preserve main path computation logic but return 1.0 for all arguments.
+ * 
+ *   Hence reconstruction looks as follows:
+ *   we extract proper polynomial and range reduction coefficients
+ *        (Pj and B), corresponding to subinterval, to which |x| belongs,
+ *        and return
+ * 
+ *       r := sign(x) * (P0 + P1 * y + ... + Pn * y^n)
+ * 
+ *   NOTE: we use multiprecision technique to multiply and sum the first
+ *         K terms of the polynomial. So Pj, j = 0..K are stored in
+ *         table each as a pair of target precision numbers (Pj and PLj) to
+ *         achieve wider than target precision.
+ * 
+ * --
+ * 
+ */
+
+
+	.text
+.L_2__routine_start___svml_tanhf16_ha_z0_0:
+
+	.align    16,0x90
+	.globl __svml_tanhf16_ha
+
+__svml_tanhf16_ha:
+
+
+	.cfi_startproc
+..L2:
+
+        pushq     %rbp
+	.cfi_def_cfa_offset 16
+        movq      %rsp, %rbp
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+        andq      $-64, %rsp
+        subq      $192, %rsp
+        vmovaps   %zmm0, %zmm1
+        vmovups   __svml_stanh_ha_data_internal(%rip), %zmm9
+        vmovups   896+__svml_stanh_ha_data_internal(%rip), %zmm10
+        vmovups   1024+__svml_stanh_ha_data_internal(%rip), %zmm15
+        vmovups   768+__svml_stanh_ha_data_internal(%rip), %zmm11
+        vmovups   640+__svml_stanh_ha_data_internal(%rip), %zmm12
+        vmovups   512+__svml_stanh_ha_data_internal(%rip), %zmm13
+        vandps    3136+__svml_stanh_ha_data_internal(%rip), %zmm1, %zmm8
+        vpternlogd $255, %zmm2, %zmm2, %zmm2
+        vandps    3072+__svml_stanh_ha_data_internal(%rip), %zmm1, %zmm0
+
+/* Here huge arguments, INF and NaNs are filtered out to callout. */
+        vpandd    1152+__svml_stanh_ha_data_internal(%rip), %zmm1, %zmm3
+        vpsubd    1216+__svml_stanh_ha_data_internal(%rip), %zmm3, %zmm4
+        vpcmpd    $2, 3264+__svml_stanh_ha_data_internal(%rip), %zmm3, %k1
+
+/*
+ * * small table specific variables *
+ * **********************************
+ * -------------------- Constant loading -------------------
+ */
+        vpxord    %zmm5, %zmm5, %zmm5
+
+/* if VMIN, VMAX is defined for I type */
+        vpmaxsd   %zmm5, %zmm4, %zmm6
+        vpminsd   1280+__svml_stanh_ha_data_internal(%rip), %zmm6, %zmm7
+        vpsrld    $21, %zmm7, %zmm14
+        vmovups   128+__svml_stanh_ha_data_internal(%rip), %zmm4
+        vpermt2ps 64+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm9
+        vpermt2ps 960+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm10
+        vpermt2ps 1088+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm15
+        vpermt2ps 832+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm11
+        vpermt2ps 704+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm12
+        vpermt2ps 576+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm13
+        vpermt2ps 192+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm4
+        vpandnd   %zmm3, %zmm3, %zmm2{%k1}
+        vsubps    {rn-sae}, %zmm9, %zmm8, %zmm3
+        vptestmd  %zmm2, %zmm2, %k0
+        vmovups   384+__svml_stanh_ha_data_internal(%rip), %zmm2
+        vfmadd213ps {rn-sae}, %zmm10, %zmm3, %zmm15
+
+/* sP[1] is the lower part of constant term sP[0] */
+        vmovups   256+__svml_stanh_ha_data_internal(%rip), %zmm10
+        vpermt2ps 448+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm2
+        vfmadd213ps {rn-sae}, %zmm11, %zmm3, %zmm15
+        vpermt2ps 320+__svml_stanh_ha_data_internal(%rip), %zmm14, %zmm10
+        kmovw     %k0, %edx
+        vfmadd213ps {rn-sae}, %zmm12, %zmm3, %zmm15
+        vfmadd213ps {rn-sae}, %zmm13, %zmm3, %zmm15
+        vmulps    {rn-sae}, %zmm3, %zmm15, %zmm11
+        vfmadd213ps {rn-sae}, %zmm10, %zmm3, %zmm11
+        vfmadd213ps {rn-sae}, %zmm11, %zmm2, %zmm3
+        vaddps    {rn-sae}, %zmm4, %zmm3, %zmm5
+        vorps     %zmm0, %zmm5, %zmm0
+        testl     %edx, %edx
+        jne       .LBL_1_3
+
+.LBL_1_2:
+
+
+/* no invcbrt in libm, so taking it out here */
+        movq      %rbp, %rsp
+        popq      %rbp
+	.cfi_def_cfa 7, 8
+	.cfi_restore 6
+        ret
+	.cfi_def_cfa 6, 16
+	.cfi_offset 6, -16
+
+.LBL_1_3:
+
+        vmovups   %zmm1, 64(%rsp)
+        vmovups   %zmm0, 128(%rsp)
+        je        .LBL_1_2
+
+
+        xorl      %eax, %eax
+
+
+        vzeroupper
+        kmovw     %k4, 24(%rsp)
+        kmovw     %k5, 16(%rsp)
+        kmovw     %k6, 8(%rsp)
+        kmovw     %k7, (%rsp)
+        movq      %rsi, 40(%rsp)
+        movq      %rdi, 32(%rsp)
+        movq      %r12, 56(%rsp)
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+        movl      %eax, %r12d
+        movq      %r13, 48(%rsp)
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+        movl      %edx, %r13d
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_7:
+
+        btl       %r12d, %r13d
+        jc        .LBL_1_10
+
+.LBL_1_8:
+
+        incl      %r12d
+        cmpl      $16, %r12d
+        jl        .LBL_1_7
+
+
+        kmovw     24(%rsp), %k4
+	.cfi_restore 122
+        kmovw     16(%rsp), %k5
+	.cfi_restore 123
+        kmovw     8(%rsp), %k6
+	.cfi_restore 124
+        kmovw     (%rsp), %k7
+	.cfi_restore 125
+        vmovups   128(%rsp), %zmm0
+        movq      40(%rsp), %rsi
+	.cfi_restore 4
+        movq      32(%rsp), %rdi
+	.cfi_restore 5
+        movq      56(%rsp), %r12
+	.cfi_restore 12
+        movq      48(%rsp), %r13
+	.cfi_restore 13
+        jmp       .LBL_1_2
+	.cfi_escape 0x10, 0x04, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x68, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x05, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x60, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0c, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x78, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0x0d, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x70, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfa, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x58, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfb, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x50, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfc, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x48, 0xff, 0xff, 0xff, 0x22
+	.cfi_escape 0x10, 0xfd, 0x00, 0x0e, 0x38, 0x1c, 0x0d, 0xc0, 0xff, 0xff, 0xff, 0x1a, 0x0d, 0x40, 0xff, 0xff, 0xff, 0x22
+
+.LBL_1_10:
+
+        lea       64(%rsp,%r12,4), %rdi
+        lea       128(%rsp,%r12,4), %rsi
+
+        call      __svml_stanh_ha_cout_rare_internal
+        jmp       .LBL_1_8
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_tanhf16_ha,@function
+	.size	__svml_tanhf16_ha,.-__svml_tanhf16_ha
+..LN__svml_tanhf16_ha.0:
+
+.L_2__routine_start___svml_stanh_ha_cout_rare_internal_1:
+
+	.align    16,0x90
+
+__svml_stanh_ha_cout_rare_internal:
+
+
+	.cfi_startproc
+..L53:
+
+        lea       __stanh_ha__imlsTanhTab(%rip), %rdx
+        movb      3(%rdi), %al
+        andb      $-128, %al
+        shrb      $7, %al
+        movzbl    %al, %ecx
+        movzwl    2(%rdi), %r8d
+        andl      $32640, %r8d
+        movl      (%rdx,%rcx,4), %eax
+        cmpl      $32640, %r8d
+        je        .LBL_2_4
+
+.LBL_2_2:
+
+        movl      %eax, (%rsi)
+
+.LBL_2_3:
+
+        xorl      %eax, %eax
+        ret
+
+.LBL_2_4:
+
+        testl     $8388607, (%rdi)
+        je        .LBL_2_2
+
+
+        movss     (%rdi), %xmm0
+        addss     %xmm0, %xmm0
+        movss     %xmm0, (%rsi)
+        jmp       .LBL_2_3
+	.align    16,0x90
+
+	.cfi_endproc
+
+	.type	__svml_stanh_ha_cout_rare_internal,@function
+	.size	__svml_stanh_ha_cout_rare_internal,.-__svml_stanh_ha_cout_rare_internal
+..LN__svml_stanh_ha_cout_rare_internal.1:
+
+	.section .rodata, "a"
+	.align 64
+	.align 64
+__svml_stanh_ha_data_internal:
+	.long	0
+	.long	1030750208
+	.long	1032847360
+	.long	1034944512
+	.long	1037041664
+	.long	1039138816
+	.long	1041235968
+	.long	1043333120
+	.long	1045430272
+	.long	1047527424
+	.long	1049624576
+	.long	1051721728
+	.long	1053818880
+	.long	1055916032
+	.long	1058013184
+	.long	1060110336
+	.long	1062207488
+	.long	1064304640
+	.long	1066401792
+	.long	1068498944
+	.long	1070596096
+	.long	1072693248
+	.long	1074790400
+	.long	1076887552
+	.long	1078984704
+	.long	1081081856
+	.long	1083179008
+	.long	1085276160
+	.long	1087373312
+	.long	1089470464
+	.long	1091567616
+	.long	0
+	.long	0
+	.long	1030732233
+	.long	1032831839
+	.long	1034916201
+	.long	1036994987
+	.long	1039067209
+	.long	1041174248
+	.long	1043220868
+	.long	1045245838
+	.long	1047245614
+	.long	1049383373
+	.long	1051287907
+	.long	1053115377
+	.long	1054857013
+	.long	1057129528
+	.long	1058581488
+	.long	1059832960
+	.long	1060891676
+	.long	1062153819
+	.long	1063337043
+	.long	1064100733
+	.long	1064582223
+	.long	1064984555
+	.long	1065216645
+	.long	1065302845
+	.long	1065334668
+	.long	1065349076
+	.long	1065352656
+	.long	1065353140
+	.long	1065353206
+	.long	1065353215
+	.long	1065353216
+	.long	0
+	.long	2963361822
+	.long	2971470750
+	.long	2945658640
+	.long	821708412
+	.long	824483568
+	.long	824941280
+	.long	2984085072
+	.long	2957298688
+	.long	838449816
+	.long	2966046080
+	.long	2988320324
+	.long	2989804564
+	.long	842626356
+	.long	3000013710
+	.long	2972725824
+	.long	3002017674
+	.long	853753500
+	.long	2987104448
+	.long	3000350914
+	.long	855535800
+	.long	852410906
+	.long	851608946
+	.long	2988641656
+	.long	2997011000
+	.long	2989576736
+	.long	3000884068
+	.long	2999984336
+	.long	840950056
+	.long	2995215280
+	.long	855269702
+	.long	0
+	.long	1065353216
+	.long	1065295748
+	.long	1065270545
+	.long	1065229919
+	.long	1065181343
+	.long	1065124909
+	.long	1065025765
+	.long	1064867200
+	.long	1064679597
+	.long	1064464345
+	.long	1064093083
+	.long	1063517074
+	.long	1062862743
+	.long	1062146519
+	.long	1060992371
+	.long	1059386208
+	.long	1057800167
+	.long	1055660649
+	.long	1051764737
+	.long	1046959010
+	.long	1041444634
+	.long	1035462611
+	.long	1026689093
+	.long	1015337940
+	.long	1002731447
+	.long	990958554
+	.long	973168670
+	.long	948705851
+	.long	924299482
+	.long	899955662
+	.long	864224966
+	.long	0
+	.long	2956213371
+	.long	3178161821
+	.long	3180268967
+	.long	3182315389
+	.long	3184339487
+	.long	3186337805
+	.long	3188474939
+	.long	3190373619
+	.long	3192189570
+	.long	3193910865
+	.long	3196176320
+	.long	3197556682
+	.long	3198679950
+	.long	3199536798
+	.long	3200331518
+	.long	3200564882
+	.long	3200049264
+	.long	3199029518
+	.long	3197040598
+	.long	3192620804
+	.long	3188208183
+	.long	3182392393
+	.long	3173916356
+	.long	3162750726
+	.long	3150176437
+	.long	3138431708
+	.long	3120650203
+	.long	3096189170
+	.long	3071783062
+	.long	3047439278
+	.long	3011707180
+	.long	0
+	.long	3198855845
+	.long	3198879250
+	.long	3198677023
+	.long	3198476576
+	.long	3198388151
+	.long	3198245218
+	.long	3197982711
+	.long	3197594458
+	.long	3197117197
+	.long	3196587519
+	.long	3195304371
+	.long	3192667528
+	.long	3189843074
+	.long	3186330810
+	.long	3177085101
+	.long	1013669486
+	.long	1032032579
+	.long	1036132065
+	.long	1038305199
+	.long	1036774550
+	.long	1033498413
+	.long	1028927137
+	.long	1021175553
+	.long	1009568359
+	.long	998361895
+	.long	985691041
+	.long	967585842
+	.long	943363289
+	.long	919210013
+	.long	895139148
+	.long	858471606
+	.long	0
+	.long	3077428921
+	.long	3189516141
+	.long	1008586543
+	.long	1036101517
+	.long	1033304453
+	.long	1034073627
+	.long	1036071831
+	.long	1037235824
+	.long	1039436298
+	.long	1040631208
+	.long	1041906362
+	.long	1042793477
+	.long	1043232976
+	.long	1043086916
+	.long	1042100375
+	.long	1039444212
+	.long	1034126600
+	.long	1026638186
+	.long	995501655
+	.long	3165579977
+	.long	3167654937
+	.long	3165317828
+	.long	3158960080
+	.long	3148291549
+	.long	3137354510
+	.long	3124730373
+	.long	3106670759
+	.long	3082457650
+	.long	3058305807
+	.long	3034235241
+	.long	2997581996
+	.long	0
+	.long	1040781545
+	.long	1131811139
+	.long	1097198812
+	.long	3247503190
+	.long	3230402941
+	.long	3224086547
+	.long	3212798938
+	.long	1059790272
+	.long	1053691997
+	.long	1061317268
+	.long	3134918084
+	.long	1034173207
+	.long	3176246152
+	.long	3165561405
+	.long	3174788493
+	.long	3178015405
+	.long	3178847213
+	.long	3177176538
+	.long	3171127099
+	.long	3155996003
+	.long	985352038
+	.long	999682315
+	.long	998398067
+	.long	989522534
+	.long	977926264
+	.long	966355955
+	.long	948911724
+	.long	924561635
+	.long	900244966
+	.long	875993879
+	.long	841254832
+	.long	0
+	.long	3155046246
+	.long	1175181842
+	.long	1138112751
+	.long	3286309950
+	.long	3267011817
+	.long	3259619885
+	.long	3246758786
+	.long	1088248663
+	.long	1078543936
+	.long	1086795944
+	.long	3205436942
+	.long	1043392367
+	.long	3198686087
+	.long	3182586396
+	.long	3174374999
+	.long	3142320544
+	.long	1008565243
+	.long	1014115537
+	.long	1016545052
+	.long	1010017051
+	.long	998649588
+	.long	975680464
+	.long	3124451591
+	.long	3121544226
+	.long	3112148751
+	.long	3100159824
+	.long	3082673659
+	.long	3058641232
+	.long	3034613169
+	.long	3010665978
+	.long	2975473412
+	.long	0
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	1027604480
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	65011712
+	.long	0
+	.long	0
+	.long	434973
+	.long	1072693248
+	.long	2462381979
+	.long	3194875870
+	.long	1922363613
+	.long	3218429247
+	.long	3436033793
+	.long	3207031997
+	.long	1164049177
+	.long	1069640674
+	.long	2766840751
+	.long	3214171076
+	.long	0
+	.long	0
+	.long	1640345390
+	.long	3194496887
+	.long	1662417413
+	.long	1072693250
+	.long	3716526453
+	.long	3205098446
+	.long	1853512117
+	.long	3218426760
+	.long	3156517427
+	.long	3211911078
+	.long	3184519871
+	.long	1069779119
+	.long	3280598482
+	.long	3215502338
+	.long	0
+	.long	0
+	.long	1962245523
+	.long	3196442075
+	.long	366239963
+	.long	1072693255
+	.long	3337913224
+	.long	3206521562
+	.long	1328356923
+	.long	3218424220
+	.long	1470245799
+	.long	3212690354
+	.long	922782103
+	.long	1069841533
+	.long	705136934
+	.long	3215769608
+	.long	0
+	.long	0
+	.long	917120191
+	.long	3198018856
+	.long	2270262052
+	.long	1072693264
+	.long	21785897
+	.long	3207561752
+	.long	3853373788
+	.long	3218420654
+	.long	3959915849
+	.long	3213220134
+	.long	839274685
+	.long	1069902529
+	.long	3478609944
+	.long	3215984949
+	.long	0
+	.long	0
+	.long	321264669
+	.long	3199232231
+	.long	3507921756
+	.long	1072693279
+	.long	855596455
+	.long	3208292995
+	.long	4197403487
+	.long	3218416395
+	.long	1260192796
+	.long	3213688235
+	.long	509545499
+	.long	1069956190
+	.long	4001843557
+	.long	3216067072
+	.long	0
+	.long	0
+	.long	2572895834
+	.long	3200373196
+	.long	4238319527
+	.long	1072693307
+	.long	1589084946
+	.long	3209032256
+	.long	323547252
+	.long	3218410632
+	.long	129829396
+	.long	3214058556
+	.long	2665301683
+	.long	1070009663
+	.long	3805267410
+	.long	3216137363
+	.long	0
+	.long	0
+	.long	1373918637
+	.long	3199925337
+	.long	2391440540
+	.long	1072693299
+	.long	3494583150
+	.long	3208925835
+	.long	2192964039
+	.long	3218411256
+	.long	579095213
+	.long	3214044622
+	.long	3432431090
+	.long	1070009041
+	.long	3870858437
+	.long	3216138421
+	.long	0
+	.long	0
+	.long	3062447777
+	.long	1055926683
+	.long	3334650904
+	.long	1072692790
+	.long	3497776375
+	.long	1062371871
+	.long	4014660983
+	.long	3218436927
+	.long	1708666466
+	.long	3212333537
+	.long	648260668
+	.long	1069902577
+	.long	1156520282
+	.long	3216044909
+	.long	0
+	.long	0
+	.long	4186264729
+	.long	1058462985
+	.long	3883474621
+	.long	1072690745
+	.long	4001630278
+	.long	1065042042
+	.long	484659484
+	.long	3218507007
+	.long	301873053
+	.long	1066864880
+	.long	2426783364
+	.long	1069685380
+	.long	3518509994
+	.long	3215777524
+	.long	0
+	.long	0
+	.long	1324317639
+	.long	1061009204
+	.long	1677646538
+	.long	1072681882
+	.long	781584286
+	.long	1067165904
+	.long	3649499968
+	.long	3218726741
+	.long	2264952365
+	.long	1069102871
+	.long	2344790854
+	.long	1068835622
+	.long	4047770869
+	.long	3215138580
+	.long	0
+	.long	0
+	.long	70848422
+	.long	1063287485
+	.long	1930391614
+	.long	1072650795
+	.long	586495590
+	.long	1068891644
+	.long	2415479819
+	.long	3219189888
+	.long	2049892606
+	.long	1070582148
+	.long	1783689851
+	.long	3213584996
+	.long	2396151379
+	.long	3213355995
+	.long	0
+	.long	0
+	.long	2764829776
+	.long	1064683280
+	.long	95861817
+	.long	1072595436
+	.long	350241294
+	.long	1069957747
+	.long	1429983818
+	.long	3219518543
+	.long	2046078110
+	.long	1071248730
+	.long	2818409407
+	.long	3216573116
+	.long	351621961
+	.long	1065184929
+	.long	0
+	.long	0
+	.long	818345493
+	.long	1065579544
+	.long	47166764
+	.long	1072535009
+	.long	2931635641
+	.long	1070624305
+	.long	2472163867
+	.long	3219785146
+	.long	898647657
+	.long	1071677167
+	.long	2840881315
+	.long	3217227676
+	.long	1213275070
+	.long	1066490976
+	.long	0
+	.long	0
+	.long	3770339094
+	.long	1065664250
+	.long	4021094867
+	.long	1072525054
+	.long	3250137669
+	.long	1070683759
+	.long	3067647579
+	.long	3219831010
+	.long	706412794
+	.long	1071716084
+	.long	3457985438
+	.long	3217296958
+	.long	693681995
+	.long	1066592455
+	.long	0
+	.long	0
+	.long	794345931
+	.long	3214229553
+	.long	674007974
+	.long	1072761769
+	.long	1339296402
+	.long	3213866766
+	.long	2063412275
+	.long	3219199437
+	.long	3042293216
+	.long	1071038746
+	.long	1218111703
+	.long	3216613854
+	.long	1828949834
+	.long	1065778789
+	.long	0
+	.long	0
+	.long	3709362262
+	.long	3216572138
+	.long	1704472411
+	.long	1073083731
+	.long	334125080
+	.long	3219185499
+	.long	3643953259
+	.long	3216245823
+	.long	972935809
+	.long	1069563300
+	.long	4262764539
+	.long	3215188513
+	.long	3947124972
+	.long	1064363655
+	.long	0
+	.long	0
+	.long	684725320
+	.long	3217602215
+	.long	2059930851
+	.long	1073428282
+	.long	6923247
+	.long	3220175349
+	.long	1962536238
+	.long	1070738118
+	.long	2626892535
+	.long	3214818472
+	.long	1541908021
+	.long	3211168932
+	.long	1264782098
+	.long	1061514036
+	.long	0
+	.long	0
+	.long	4193183898
+	.long	3218211722
+	.long	2527318106
+	.long	1073704783
+	.long	1779267795
+	.long	3220520390
+	.long	2178062862
+	.long	1071649373
+	.long	2371270354
+	.long	3216802466
+	.long	214503718
+	.long	1066134183
+	.long	2527651537
+	.long	3209129722
+	.long	0
+	.long	0
+	.long	1145099230
+	.long	3217848868
+	.long	1219675578
+	.long	1073564173
+	.long	3377824792
+	.long	3220387400
+	.long	1294161399
+	.long	1071386209
+	.long	535756989
+	.long	3216499614
+	.long	3414431292
+	.long	1065769858
+	.long	3872552752
+	.long	3208765586
+	.long	0
+	.long	0
+	.long	3432152680
+	.long	3212471108
+	.long	3481247728
+	.long	1073111648
+	.long	2087872556
+	.long	3219843286
+	.long	1539630695
+	.long	1070713931
+	.long	2045031161
+	.long	3215666774
+	.long	1438917333
+	.long	1064738520
+	.long	2997200424
+	.long	3207590169
+	.long	0
+	.long	0
+	.long	157024952
+	.long	1070614475
+	.long	1896115811
+	.long	1072588717
+	.long	1533634146
+	.long	3219167457
+	.long	3479089950
+	.long	1069795336
+	.long	294041664
+	.long	3214609167
+	.long	3323703207
+	.long	1063520882
+	.long	1200470279
+	.long	3206092743
+	.long	0
+	.long	0
+	.long	780145450
+	.long	1071804775
+	.long	3436973384
+	.long	1071541223
+	.long	1373298557
+	.long	3217881162
+	.long	616458359
+	.long	1068360186
+	.long	1012488256
+	.long	3212939359
+	.long	3381328826
+	.long	1061569412
+	.long	3619594050
+	.long	3203906531
+	.long	0
+	.long	0
+	.long	3555024088
+	.long	1072352823
+	.long	703965661
+	.long	1069801815
+	.long	68876051
+	.long	3215985072
+	.long	4285546012
+	.long	1066131701
+	.long	1692571309
+	.long	3210444434
+	.long	2250664999
+	.long	1058874117
+	.long	2757518980
+	.long	3200902424
+	.long	0
+	.long	0
+	.long	4088530245
+	.long	1072580854
+	.long	2571880719
+	.long	1067895848
+	.long	4091013897
+	.long	3213873796
+	.long	4246435429
+	.long	1063770948
+	.long	92905889
+	.long	3207872058
+	.long	248987709
+	.long	1056074614
+	.long	2369951583
+	.long	3197898922
+	.long	0
+	.long	0
+	.long	3580076556
+	.long	1072660066
+	.long	1353576036
+	.long	1065860878
+	.long	2410885661
+	.long	3211602990
+	.long	2989427096
+	.long	1061369430
+	.long	3886685439
+	.long	3205273864
+	.long	529712074
+	.long	1053215589
+	.long	3764845364
+	.long	3194905549
+	.long	0
+	.long	0
+	.long	660908647
+	.long	1072688177
+	.long	2675542798
+	.long	1062777930
+	.long	772498083
+	.long	3208233517
+	.long	377295306
+	.long	1057798793
+	.long	162648032
+	.long	3201438006
+	.long	623489458
+	.long	1049119366
+	.long	3651746243
+	.long	3190506519
+	.long	0
+	.long	0
+	.long	0
+	.long	1072693248
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	0
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483648
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2147483647
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2145386496
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	2130706432
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	1038090240
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.long	54525952
+	.type	__svml_stanh_ha_data_internal,@object
+	.size	__svml_stanh_ha_data_internal,3456
+	.align 4
+__stanh_ha__imlsTanhTab:
+	.long	1065353216
+	.long	3212836864
+	.type	__stanh_ha__imlsTanhTab,@object
+	.size	__stanh_ha__imlsTanhTab,8
+      	.section        .note.GNU-stack,"",@progbits
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_s_la.s b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_s_la.s
index 60c76d98513..48b48a5c584 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_s_la.s
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/svml/linux/avx512/svml_z0_tanh_s_la.s
@@ -1,5 +1,5 @@
 /*******************************************
-* Copyright (C) 2021 Intel Corporation
+* Copyright (C) 2023 Intel Corporation
 * SPDX-License-Identifier: BSD-3-Clause
 *******************************************/
 
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.c b/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.c
index 3c977cb1cf1..ef12f43c5c1 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.c
@@ -1011,6 +1011,34 @@ convert_ufunc_arguments(PyUFuncObject *ufunc,
          * necessary to propagate the information to the legacy type resolution.
          */
         if (npy_mark_tmp_array_if_pyscalar(obj, out_op[i], &out_op_DTypes[i])) {
+            if (PyArray_FLAGS(out_op[i]) & NPY_ARRAY_WAS_PYTHON_INT
+                    && PyArray_TYPE(out_op[i]) != NPY_LONG) {
+                /*
+                 * When `np.array(integer)` is not the default integer (mainly
+                 * object dtype), this confuses many type resolvers.  Simply
+                 * forcing a default integer array is unfortunately easiest.
+                 * In this disables the optional NEP 50 warnings, but in
+                 * practice when this happens we should _usually_ pick the
+                 * default integer loop and that raises an error.
+                 * (An exception is `float64(1.) + 10**100` which silently
+                 * will give a float64 result rather than a Python float.)
+                 *
+                 * TODO: Just like the general dual NEP 50/legacy promotion
+                 * support this is meant as a temporary hack for NumPy 1.25.
+                 */
+                static PyArrayObject *zero_arr = NULL;
+                if (NPY_UNLIKELY(zero_arr == NULL)) {
+                    zero_arr = (PyArrayObject *)PyArray_ZEROS(
+                            0, NULL, NPY_LONG, NPY_FALSE);
+                    if (zero_arr == NULL) {
+                        goto fail;
+                    }
+                    ((PyArrayObject_fields *)zero_arr)->flags |= (
+                        NPY_ARRAY_WAS_PYTHON_INT|NPY_ARRAY_WAS_INT_AND_REPLACED);
+                }
+                Py_INCREF(zero_arr);
+                Py_SETREF(out_op[i], zero_arr);
+            }
             *promoting_pyscalars = NPY_TRUE;
         }
     }
@@ -1392,7 +1420,7 @@ try_trivial_single_output_loop(PyArrayMethod_Context *context,
  * or pass in the full cast information.  But this can special case
  * the logical functions and prints a better error message.
  */
-static NPY_INLINE int
+static inline int
 validate_casting(PyArrayMethodObject *method, PyUFuncObject *ufunc,
         PyArrayObject *ops[], PyArray_Descr *descriptors[],
         NPY_CASTING casting)
@@ -1782,6 +1810,8 @@ _check_keepdims_support(PyUFuncObject *ufunc) {
 static int
 _parse_axes_arg(PyUFuncObject *ufunc, int op_core_num_dims[], PyObject *axes,
                 PyArrayObject **op, int broadcast_ndim, int **remap_axis) {
+    static PyObject *AxisError_cls = NULL;
+
     int nin = ufunc->nin;
     int nop = ufunc->nargs;
     int iop, list_size;
@@ -1826,16 +1856,17 @@ _parse_axes_arg(PyUFuncObject *ufunc, int op_core_num_dims[], PyObject *axes,
         op_axes_tuple = PyList_GET_ITEM(axes, iop);
         if (PyTuple_Check(op_axes_tuple)) {
             if (PyTuple_Size(op_axes_tuple) != op_ncore) {
-                if (op_ncore == 1) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "axes item %d should be a tuple with a "
-                                 "single element, or an integer", iop);
-                }
-                else {
-                    PyErr_Format(PyExc_ValueError,
-                                 "axes item %d should be a tuple with %d "
-                                 "elements", iop, op_ncore);
+                /* must have been a tuple with too many entries. */
+                npy_cache_import(
+                        "numpy.exceptions", "AxisError", &AxisError_cls);
+                if (AxisError_cls == NULL) {
+                    return -1;
                 }
+                PyErr_Format(AxisError_cls,
+                        "%s: operand %d has %d core dimensions, "
+                        "but %zd dimensions are specified by axes tuple.",
+                        ufunc_get_name_cstr(ufunc), iop, op_ncore,
+                        PyTuple_Size(op_axes_tuple));
                 return -1;
             }
             Py_INCREF(op_axes_tuple);
@@ -1847,8 +1878,22 @@ _parse_axes_arg(PyUFuncObject *ufunc, int op_core_num_dims[], PyObject *axes,
             }
         }
         else {
-            PyErr_Format(PyExc_TypeError, "axes item %d should be a tuple",
-                         iop);
+            /* If input is not an integer tell user that a tuple is needed */
+            if (error_converting(PyArray_PyIntAsInt(op_axes_tuple))) {
+                PyErr_Format(PyExc_TypeError,
+                        "%s: axes item %d should be a tuple.",
+                        ufunc_get_name_cstr(ufunc), iop);
+                return -1;
+            }
+            /* If it is a single integer, inform user that more are needed */
+            npy_cache_import("numpy.exceptions", "AxisError", &AxisError_cls);
+            if (AxisError_cls == NULL) {
+                return -1;
+            }
+            PyErr_Format(AxisError_cls,
+                    "%s: operand %d has %d core dimensions, "
+                    "but the axes item is a single integer.",
+                    ufunc_get_name_cstr(ufunc), iop, op_ncore);
             return -1;
         }
         /*
@@ -2068,12 +2113,16 @@ _get_coredim_sizes(PyUFuncObject *ufunc, PyArrayObject **op,
 }
 
 /*
- * Returns a new reference
+ * Returns a new reference to the ufunc identity.  Note that this identity
+ * is only a default identity value stored on the ufunc, since the invidiual
+ * ufunc loop (ArrayMethod) is queried for the actual identity.
+ *
  * TODO: store a reference in the ufunc object itself, rather than
  *       constructing one each time
  */
-static PyObject *
-_get_identity(PyUFuncObject *ufunc, npy_bool *reorderable) {
+NPY_NO_EXPORT PyObject *
+PyUFunc_GetDefaultIdentity(PyUFuncObject *ufunc, npy_bool *reorderable)
+{
     switch(ufunc->identity) {
     case PyUFunc_One:
         *reorderable = 1;
@@ -2989,10 +3038,8 @@ PyUFunc_Reduce(PyUFuncObject *ufunc,
         PyObject *initial, PyArrayObject *wheremask)
 {
     int iaxes, ndim;
-    npy_bool reorderable;
     npy_bool axis_flags[NPY_MAXDIMS];
-    PyArrayObject *result = NULL;
-    PyObject *identity;
+
     const char *ufunc_name = ufunc_get_name_cstr(ufunc);
     /* These parameters come from a TLS global */
     int buffersize = 0, errormask = 0;
@@ -3017,9 +3064,6 @@ PyUFunc_Reduce(PyUFuncObject *ufunc,
         return NULL;
     }
 
-    /*
-     * Promote and fetch ufuncimpl (currently needed to fix up the identity).
-     */
     PyArray_Descr *descrs[3];
     PyArrayMethodObject *ufuncimpl = reducelike_promote_and_resolve(ufunc,
             arr, out, signature, NPY_FALSE, descrs, NPY_UNSAFE_CASTING, "reduce");
@@ -3027,62 +3071,19 @@ PyUFunc_Reduce(PyUFuncObject *ufunc,
         return NULL;
     }
 
-    /* Get the identity */
-    /* TODO: Both of these should be provided by the ArrayMethod! */
-    identity = _get_identity(ufunc, &reorderable);
-    if (identity == NULL) {
-        goto finish;
-    }
-
-    /* Get the initial value */
-    if (initial == NULL) {
-        initial = identity;
-
-        /*
-        * The identity for a dynamic dtype like
-        * object arrays can't be used in general
-        */
-        if (initial != Py_None && PyArray_ISOBJECT(arr) && PyArray_SIZE(arr) != 0) {
-            Py_DECREF(initial);
-            initial = Py_None;
-            Py_INCREF(initial);
-        }
-        else if (PyTypeNum_ISUNSIGNED(descrs[2]->type_num)
-                    && PyLong_CheckExact(initial)) {
-            /*
-             * This is a bit of a hack until we have truly loop specific
-             * identities.  Python -1 cannot be cast to unsigned so convert
-             * it to a NumPy scalar, but we use -1 for bitwise functions to
-             * signal all 1s.
-             * (A builtin identity would not overflow here, although we may
-             * unnecessary convert 0 and 1.)
-             */
-            Py_SETREF(initial, PyObject_CallFunctionObjArgs(
-                        (PyObject *)&PyLongArrType_Type, initial, NULL));
-            if (initial == NULL) {
-                goto finish;
-            }
-        }
-    } else {
-        Py_DECREF(identity);
-        Py_INCREF(initial);  /* match the reference count in the if above */
-    }
-
     PyArrayMethod_Context context = {
         .caller = (PyObject *)ufunc,
         .method = ufuncimpl,
         .descriptors = descrs,
     };
 
-    result = PyUFunc_ReduceWrapper(&context,
-            arr, out, wheremask, axis_flags, reorderable, keepdims,
-            initial, reduce_loop, ufunc, buffersize, ufunc_name, errormask);
+    PyArrayObject *result = PyUFunc_ReduceWrapper(&context,
+            arr, out, wheremask, axis_flags, keepdims,
+            initial, reduce_loop, buffersize, ufunc_name, errormask);
 
-  finish:
     for (int i = 0; i < 3; i++) {
         Py_DECREF(descrs[i]);
     }
-    Py_XDECREF(initial);
     return result;
 }
 
@@ -4098,7 +4099,7 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc,
     /* We now have all the information required to check for Overrides */
     PyObject *override = NULL;
     int errval = PyUFunc_CheckOverride(ufunc, _reduce_type[operation],
-            full_args.in, full_args.out, args, len_args, kwnames, &override);
+            full_args.in, full_args.out, wheremask_obj, args, len_args, kwnames, &override);
     if (errval) {
         return NULL;
     }
@@ -4870,7 +4871,7 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc,
     /* We now have all the information required to check for Overrides */
     PyObject *override = NULL;
     errval = PyUFunc_CheckOverride(ufunc, method,
-            full_args.in, full_args.out,
+            full_args.in, full_args.out, where_obj,
             args, len_args, kwnames, &override);
     if (errval) {
         goto fail;
@@ -4956,9 +4957,13 @@ ufunc_generic_fastcall(PyUFuncObject *ufunc,
             if (!(orig_flags & NPY_ARRAY_WAS_PYTHON_LITERAL)) {
                 continue;
             }
-            /* If the descriptor matches, no need to worry about conversion */
-            if (PyArray_EquivTypes(
-                    PyArray_DESCR(operands[i]), operation_descrs[i])) {
+            /*
+             * If descriptor matches, no need to convert, but integers may
+             * have been too large.
+             */
+            if (!(orig_flags & NPY_ARRAY_WAS_INT_AND_REPLACED)
+                    && PyArray_EquivTypes(
+                        PyArray_DESCR(operands[i]), operation_descrs[i])) {
                 continue;
             }
             /* Otherwise, replace the operand with a new array */
@@ -5062,14 +5067,11 @@ ufunc_generic_vectorcall(PyObject *ufunc,
 
 
 NPY_NO_EXPORT PyObject *
-ufunc_geterr(PyObject *NPY_UNUSED(dummy), PyObject *args)
+ufunc_geterr(PyObject *NPY_UNUSED(dummy), PyObject *NPY_UNUSED(arg))
 {
     PyObject *thedict;
     PyObject *res;
 
-    if (!PyArg_ParseTuple(args, "")) {
-        return NULL;
-    }
     thedict = PyThreadState_GetDict();
     if (thedict == NULL) {
         thedict = PyEval_GetBuiltins();
@@ -5095,16 +5097,13 @@ ufunc_geterr(PyObject *NPY_UNUSED(dummy), PyObject *args)
 
 
 NPY_NO_EXPORT PyObject *
-ufunc_seterr(PyObject *NPY_UNUSED(dummy), PyObject *args)
+ufunc_seterr(PyObject *NPY_UNUSED(dummy), PyObject *arg)
 {
     PyObject *thedict;
     int res;
-    PyObject *val;
+    PyObject *val = arg;
     static char *msg = "Error object must be a list of length 3";
 
-    if (!PyArg_ParseTuple(args, "O:seterrobj", &val)) {
-        return NULL;
-    }
     if (!PyList_CheckExact(val) || PyList_GET_SIZE(val) != 3) {
         PyErr_SetString(PyExc_ValueError, msg);
         return NULL;
@@ -5375,7 +5374,7 @@ cmp_arg_types(int *arg1, int *arg2, int n)
  * This frees the linked-list structure when the CObject
  * is destroyed (removed from the internal dictionary)
 */
-static NPY_INLINE void
+static inline void
 _free_loop1d_list(PyUFunc_Loop1d *data)
 {
     int i;
@@ -5916,10 +5915,11 @@ ufunc_reduceat(PyUFuncObject *ufunc,
 }
 
 /* Helper for ufunc_at, below */
-static NPY_INLINE PyArrayObject *
+static inline PyArrayObject *
 new_array_op(PyArrayObject *op_array, char *data)
 {
     npy_intp dims[1] = {1};
+    Py_INCREF(PyArray_DESCR(op_array));  /* NewFromDescr steals a reference */
     PyObject *r = PyArray_NewFromDescr(&PyArray_Type, PyArray_DESCR(op_array),
                                        1, dims, NULL, data,
                                        NPY_ARRAY_WRITEABLE, NULL);
@@ -5927,206 +5927,177 @@ new_array_op(PyArrayObject *op_array, char *data)
 }
 
 /*
- * Call ufunc only on selected array items and store result in first operand.
- * For add ufunc, method call is equivalent to op1[idx] += op2 with no
- * buffering of the first operand.
- * Arguments:
- * op1 - First operand to ufunc
- * idx - Indices that are applied to first operand. Equivalent to op1[idx].
- * op2 - Second operand to ufunc (if needed). Must be able to broadcast
- *       over first operand.
+ * Use an indexed loop to do the work
+ * Returns 0 if successful
  */
-static PyObject *
-ufunc_at(PyUFuncObject *ufunc, PyObject *args)
+static int
+trivial_at_loop(PyArrayMethodObject *ufuncimpl, NPY_ARRAYMETHOD_FLAGS flags,
+                    PyArrayMapIterObject *iter,
+                    PyArrayObject *op1_array, PyArrayObject *op2_array,
+                    PyArrayMethod_Context *context)
 {
-    PyObject *op1 = NULL;
-    PyObject *idx = NULL;
-    PyObject *op2 = NULL;
-    PyArrayObject *op1_array = NULL;
-    PyArrayObject *op2_array = NULL;
-    PyArrayMapIterObject *iter = NULL;
-    PyArrayIterObject *iter2 = NULL;
-    PyArrayObject *operands[3] = {NULL, NULL, NULL};
-    PyArrayObject *array_operands[3] = {NULL, NULL, NULL};
-
-    PyArray_DTypeMeta *signature[3] = {NULL, NULL, NULL};
-    PyArray_DTypeMeta *operand_DTypes[3] = {NULL, NULL, NULL};
-    PyArray_Descr *operation_descrs[3] = {NULL, NULL, NULL};
-
-    int nop;
-
-    /* override vars */
-    int errval;
-    PyObject *override = NULL;
-
-    NpyIter *iter_buffer;
-    NpyIter_IterNextFunc *iternext;
-    npy_uint32 op_flags[NPY_MAXARGS];
-    int buffersize;
-    int errormask = 0;
-    char * err_msg = NULL;
-
-    PyArrayMethod_StridedLoop *strided_loop;
-    NpyAuxData *auxdata = NULL;
-
-    NPY_BEGIN_THREADS_DEF;
-
-    if (ufunc->core_enabled) {
-        PyErr_Format(PyExc_TypeError,
-            "%s.at does not support ufunc with non-trivial signature: %s has signature %s.",
-            ufunc->name, ufunc->name, ufunc->core_signature);
-        return NULL;
-    }
-
-    if (ufunc->nin > 2) {
-        PyErr_SetString(PyExc_ValueError,
-            "Only unary and binary ufuncs supported at this time");
-        return NULL;
+    int buffersize=0, errormask = 0;
+    int res;
+    char *args[3];
+    npy_intp steps[4];
+    args[0] = (char *) iter->baseoffset;
+    steps[0] = iter->fancy_strides[0];
+    if (ufuncimpl->nin == 1) {
+        args[2] = NULL;
+        steps[2] = 0;
+    } else {
+        args[2] = (char *)PyArray_DATA(op2_array);
+        if (PyArray_NDIM(op2_array) == 0
+            || PyArray_DIM(op2_array, 0) <= 1) {
+            steps[2] = 0;
+        } else {
+            steps[2] = PyArray_STRIDE(op2_array, 0);
+        }
     }
 
-    if (ufunc->nout != 1) {
-        PyErr_SetString(PyExc_ValueError,
-            "Only single output ufuncs supported at this time");
-        return NULL;
+    if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) {
+        npy_clear_floatstatus_barrier((char *)context);
     }
 
-    if (!PyArg_ParseTuple(args, "OO|O:at", &op1, &idx, &op2)) {
-        return NULL;
-    }
+    do {
+        npy_intp *inner_size = NpyIter_GetInnerLoopSizePtr(iter->outer);
+        npy_intp * indxP = (npy_intp *)iter->outer_ptrs[0];
+        args[1] = (char *)indxP;
+        steps[1] = iter->outer_strides[0];
+        /* 
+         * The value of iter->fancy_dims[0] is added to negative indexes
+         * inside the inner loop
+         */
+        steps[3] = iter->fancy_dims[0];
 
-    if (ufunc->nin == 2 && op2 == NULL) {
-        PyErr_SetString(PyExc_ValueError,
-                        "second operand needed for ufunc");
-        return NULL;
-    }
-    errval = PyUFunc_CheckOverride(ufunc, "at",
-            args, NULL, NULL, 0, NULL, &override);
+        res = ufuncimpl->contiguous_indexed_loop(
+                context, args, inner_size, steps, NULL);
 
-    if (errval) {
-        return NULL;
-    }
-    else if (override) {
-        return override;
-    }
+        if (args[2] != NULL) {
+            args[2] += (*inner_size) * steps[2];
+        }
+    } while (res == 0 && iter->outer_next(iter->outer));
 
-    if (!PyArray_Check(op1)) {
-        PyErr_SetString(PyExc_TypeError,
-                        "first operand must be array");
-        return NULL;
+    if (res == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) {
+        const char * ufunc_name =
+                        ufunc_get_name_cstr((PyUFuncObject *)context->caller);
+        if (_get_bufsize_errmask(NULL, ufunc_name,
+                                 &buffersize, &errormask) < 0) {
+            return -1;
+        }
+        res = _check_ufunc_fperr(errormask, NULL, ufunc_name);
     }
+    return res;
+}
 
-    op1_array = (PyArrayObject *)op1;
+static int
+ufunc_at__fast_iter(PyUFuncObject *ufunc, NPY_ARRAYMETHOD_FLAGS flags,
+                    PyArrayMapIterObject *iter, PyArrayIterObject *iter2,
+                    PyArrayObject *op1_array, PyArrayObject *op2_array,
+                    PyArrayMethod_StridedLoop *strided_loop,
+                    PyArrayMethod_Context *context,
+                    NpyAuxData *auxdata
+                    )
+{
+    int buffersize;
+    int errormask = 0;
+    int res = 0;
+    NPY_BEGIN_THREADS_DEF;
 
-    /* Create second operand from number array if needed. */
-    if (op2 != NULL) {
-        op2_array = (PyArrayObject *)PyArray_FromAny(op2, NULL,
-                                0, 0, 0, NULL);
-        if (op2_array == NULL) {
-            goto fail;
-        }
+    if (_get_bufsize_errmask(NULL, ufunc->name, &buffersize, &errormask) < 0) {
+        return -1;
+    }
+    int needs_api = (flags & NPY_METH_REQUIRES_PYAPI) != 0;
+    if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) {
+        /* Start with the floating-point exception flags cleared */
+        npy_clear_floatstatus_barrier((char*)&iter);
     }
 
-    /* Create map iterator */
-    iter = (PyArrayMapIterObject *)PyArray_MapIterArrayCopyIfOverlap(
-        op1_array, idx, 1, op2_array);
-    if (iter == NULL) {
-        goto fail;
+    if (!needs_api) {
+        NPY_BEGIN_THREADS;
     }
-    op1_array = iter->array;  /* May be updateifcopied on overlap */
 
-    if (op2 != NULL) {
+    npy_intp strides[3] = {0, 0, 0};
+    /*
+     * Iterate over first and second operands and call ufunc
+     * for each pair of inputs
+     */
+    for (npy_intp i = iter->size; i > 0; i--)
+    {
+        char *dataptr[3];
+        /* one element at a time, no stride required but read by innerloop */
+        npy_intp count = 1;
+
         /*
-         * May need to swap axes so that second operand is
-         * iterated over correctly
+         * Set up data pointers for either one or two input operands.
+         * The output data pointer points to the first operand data.
          */
-        if ((iter->subspace != NULL) && (iter->consec)) {
-            PyArray_MapIterSwapAxes(iter, &op2_array, 0);
-            if (op2_array == NULL) {
-                goto fail;
-            }
+        dataptr[0] = iter->dataptr;
+        if (iter2 != NULL) {
+            dataptr[1] = PyArray_ITER_DATA(iter2);
+            dataptr[2] = iter->dataptr;
+        }
+        else {
+            dataptr[1] = iter->dataptr;
+            dataptr[2] = NULL;
         }
 
-        /*
-         * Create array iter object for second operand that
-         * "matches" the map iter object for the first operand.
-         * Then we can just iterate over the first and second
-         * operands at the same time and not have to worry about
-         * picking the correct elements from each operand to apply
-         * the ufunc to.
-         */
-        if ((iter2 = (PyArrayIterObject *)\
-             PyArray_BroadcastToShape((PyObject *)op2_array,
-                                        iter->dimensions, iter->nd))==NULL) {
-            goto fail;
+        res = strided_loop(context, dataptr, &count, strides, auxdata);
+        if (res != 0) {
+            break;
         }
-    }
 
-    /*
-     * Create dtypes array for either one or two input operands.
-     * Compare to the logic in `convert_ufunc_arguments`.
-     * TODO: It may be good to review some of this behaviour, since the
-     *       operand array is special (it is written to) similar to reductions.
-     *       Using unsafe-casting as done here, is likely not desirable.
-     */
-    operands[0] = op1_array;
-    operand_DTypes[0] = NPY_DTYPE(PyArray_DESCR(op1_array));
-    Py_INCREF(operand_DTypes[0]);
-    int force_legacy_promotion = 0;
-    int allow_legacy_promotion = NPY_DT_is_legacy(operand_DTypes[0]);
+        PyArray_MapIterNext(iter);
+        if (iter2 != NULL) {
+            PyArray_ITER_NEXT(iter2);
+        }
+    }
 
-    if (op2_array != NULL) {
-        operands[1] = op2_array;
-        operand_DTypes[1] = NPY_DTYPE(PyArray_DESCR(op2_array));
-        Py_INCREF(operand_DTypes[1]);
-        allow_legacy_promotion &= NPY_DT_is_legacy(operand_DTypes[1]);
-        operands[2] = operands[0];
-        operand_DTypes[2] = operand_DTypes[0];
-        Py_INCREF(operand_DTypes[2]);
+    NPY_END_THREADS;
 
-        nop = 3;
-        if (allow_legacy_promotion && ((PyArray_NDIM(op1_array) == 0)
-                                       != (PyArray_NDIM(op2_array) == 0))) {
-                /* both are legacy and only one is 0-D: force legacy */
-                force_legacy_promotion = should_use_min_scalar(2, operands, 0, NULL);
-            }
-    }
-    else {
-        operands[1] = operands[0];
-        operand_DTypes[1] = operand_DTypes[0];
-        Py_INCREF(operand_DTypes[1]);
-        operands[2] = NULL;
-        nop = 2;
+    if (res == 0 && !(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) {
+        /* NOTE: We could check float errors even when `res < 0` */
+        res = _check_ufunc_fperr(errormask, NULL, "at");
     }
+    return res;
+}
 
-    PyArrayMethodObject *ufuncimpl = promote_and_get_ufuncimpl(ufunc,
-            operands, signature, operand_DTypes,
-            force_legacy_promotion, allow_legacy_promotion,
-            NPY_FALSE, NPY_FALSE);
-    if (ufuncimpl == NULL) {
-        goto fail;
-    }
+static int
+ufunc_at__slow_iter(PyUFuncObject *ufunc, NPY_ARRAYMETHOD_FLAGS flags,
+                    PyArrayMapIterObject *iter, PyArrayIterObject *iter2,
+                    PyArrayObject *op1_array, PyArrayObject *op2_array,
+                    PyArray_Descr *operation_descrs[3],
+                    PyArrayMethod_StridedLoop *strided_loop,
+                    PyArrayMethod_Context *context,
+                    NpyAuxData *auxdata
+                    )
+{
+    NpyIter *iter_buffer = NULL;
+    PyArrayObject *array_operands[3] = {NULL, NULL, NULL};
+    int buffersize;
+    int errormask = 0;
+    int res = 0;
+    int nop = 0;
+    NpyIter_IterNextFunc *iternext;
+    char * err_msg = NULL;
+    NPY_BEGIN_THREADS_DEF;
 
-    /* Find the correct descriptors for the operation */
-    if (resolve_descriptors(nop, ufunc, ufuncimpl,
-            operands, operation_descrs, signature, NPY_UNSAFE_CASTING) < 0) {
-        goto fail;
+    if (_get_bufsize_errmask(NULL, ufunc->name, &buffersize, &errormask) < 0) {
+        return -1;
     }
-
-    Py_INCREF(PyArray_DESCR(op1_array));
     array_operands[0] = new_array_op(op1_array, iter->dataptr);
     if (iter2 != NULL) {
-        Py_INCREF(PyArray_DESCR(op2_array));
         array_operands[1] = new_array_op(op2_array, PyArray_ITER_DATA(iter2));
-        Py_INCREF(PyArray_DESCR(op1_array));
         array_operands[2] = new_array_op(op1_array, iter->dataptr);
+        nop = 3;
     }
     else {
-        Py_INCREF(PyArray_DESCR(op1_array));
         array_operands[1] = new_array_op(op1_array, iter->dataptr);
         array_operands[2] = NULL;
+        nop = 2;
     }
-
     /* Set up the flags */
+    npy_uint32 op_flags[3];
     op_flags[0] = NPY_ITER_READONLY|
                   NPY_ITER_ALIGNED;
 
@@ -6146,11 +6117,6 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args)
                       NPY_ITER_NO_BROADCAST|
                       NPY_ITER_NO_SUBTYPE;
     }
-
-    if (_get_bufsize_errmask(NULL, ufunc->name, &buffersize, &errormask) < 0) {
-        goto fail;
-    }
-
     /*
      * Create NpyIter object to "iterate" over single element of each input
      * operand. This is an easy way to reuse the NpyIter logic for dealing
@@ -6173,33 +6139,23 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args)
                         -1, NULL, NULL, buffersize);
 
     if (iter_buffer == NULL) {
-        goto fail;
+        /* will fail only on memory allocation errors */
+        for (int i = 0; i < 3; i++) {
+            Py_XDECREF(array_operands[i]);
+        }
+        return -1;
     }
 
     iternext = NpyIter_GetIterNext(iter_buffer, NULL);
     if (iternext == NULL) {
+        /* can not really happen, iter_buffer creation is tightly controlled */
         NpyIter_Deallocate(iter_buffer);
-        goto fail;
-    }
-
-    PyArrayMethod_Context context = {
-            .caller = (PyObject *)ufunc,
-            .method = ufuncimpl,
-            .descriptors = operation_descrs,
-    };
-
-    NPY_ARRAYMETHOD_FLAGS flags;
-    /* Use contiguous strides; if there is such a loop it may be faster */
-    npy_intp strides[3] = {
-            operation_descrs[0]->elsize, operation_descrs[1]->elsize, 0};
-    if (nop == 3) {
-        strides[2] = operation_descrs[2]->elsize;
+        for (int i = 0; i < 3; i++) {
+            Py_XDECREF(array_operands[i]);
+        }
+        return -1;
     }
 
-    if (ufuncimpl->get_strided_loop(&context, 1, 0, strides,
-            &strided_loop, &auxdata, &flags) < 0) {
-        goto fail;
-    }
     int needs_api = (flags & NPY_METH_REQUIRES_PYAPI) != 0;
     needs_api |= NpyIter_IterationNeedsAPI(iter_buffer);
     if (!(flags & NPY_METH_NO_FLOATINGPOINT_ERRORS)) {
@@ -6207,6 +6163,7 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args)
         npy_clear_floatstatus_barrier((char*)&iter);
     }
 
+    npy_intp strides[3] = {0, 0, 0};
     if (!needs_api) {
         NPY_BEGIN_THREADS;
     }
@@ -6215,7 +6172,6 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args)
      * Iterate over first and second operands and call ufunc
      * for each pair of inputs
      */
-    int res = 0;
     for (npy_intp i = iter->size; i > 0; i--)
     {
         char *dataptr[3];
@@ -6246,7 +6202,7 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args)
 
         buffer_dataptr = NpyIter_GetDataPtrArray(iter_buffer);
 
-        res = strided_loop(&context, buffer_dataptr, &count, strides, auxdata);
+        res = strided_loop(context, buffer_dataptr, &count, strides, auxdata);
         if (res != 0) {
             break;
         }
@@ -6272,18 +6228,284 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args)
         /* NOTE: We could check float errors even when `res < 0` */
         res = _check_ufunc_fperr(errormask, NULL, "at");
     }
+    NpyIter_Deallocate(iter_buffer);
+    for (int i = 0; i < 3; i++) {
+        Py_XDECREF(array_operands[i]);
+    }
+    return res;
+}
+
+/*
+ * Call ufunc only on selected array items and store result in first operand.
+ * For add ufunc, method call is equivalent to op1[idx] += op2 with no
+ * buffering of the first operand.
+ * Arguments:
+ * op1 - First operand to ufunc
+ * idx - Indices that are applied to first operand. Equivalent to op1[idx].
+ * op2 - Second operand to ufunc (if needed). Must be able to broadcast
+ *       over first operand.
+ */
+static PyObject *
+ufunc_at(PyUFuncObject *ufunc, PyObject *args)
+{
+    PyObject *op1 = NULL;
+    PyObject *idx = NULL;
+    PyObject *op2 = NULL;
+    PyArrayObject *op1_array = NULL;
+    PyArrayObject *op2_array = NULL;
+    PyArrayMapIterObject *iter = NULL;
+    PyArrayIterObject *iter2 = NULL;
+    PyArray_Descr *operation_descrs[3] = {NULL, NULL, NULL};
+
+    int nop;
+
+    /* override vars */
+    int errval;
+    PyObject *override = NULL;
+    int res = -1;   /* start with fail condition so "goto fail" will error */
+
+    PyArrayMethod_StridedLoop *strided_loop;
+    NpyAuxData *auxdata = NULL;
+
+    if (ufunc->core_enabled) {
+        PyErr_Format(PyExc_TypeError,
+            "%s.at does not support ufunc with non-trivial signature: %s has signature %s.",
+            ufunc->name, ufunc->name, ufunc->core_signature);
+        return NULL;
+    }
+
+    if (ufunc->nin > 2) {
+        PyErr_SetString(PyExc_ValueError,
+            "Only unary and binary ufuncs supported at this time");
+        return NULL;
+    }
+
+    if (ufunc->nout != 1) {
+        PyErr_SetString(PyExc_ValueError,
+            "Only single output ufuncs supported at this time");
+        return NULL;
+    }
+
+    if (!PyArg_ParseTuple(args, "OO|O:at", &op1, &idx, &op2)) {
+        return NULL;
+    }
+
+    if (ufunc->nin == 2 && op2 == NULL) {
+        PyErr_SetString(PyExc_ValueError,
+                        "second operand needed for ufunc");
+        return NULL;
+    }
+
+    if (ufunc->nin == 1 && op2 != NULL) {
+        PyErr_SetString(PyExc_ValueError,
+                        "second operand provided when ufunc is unary");
+        return NULL;
+    }
+    errval = PyUFunc_CheckOverride(ufunc, "at",
+            args, NULL, NULL, NULL, 0, NULL, &override);
+
+    if (errval) {
+        return NULL;
+    }
+    else if (override) {
+        return override;
+    }
 
+    if (!PyArray_Check(op1)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "first operand must be array");
+        return NULL;
+    }
+
+    op1_array = (PyArrayObject *)op1;
+
+    /* Create second operand from number array if needed. */
+    if (op2 == NULL) {
+        nop = 2;
+    }
+    else {
+        nop = 3;
+        op2_array = (PyArrayObject *)PyArray_FromAny(op2, NULL,
+                                0, 0, 0, NULL);
+        if (op2_array == NULL) {
+            goto fail;
+        }
+    }
+
+    PyArrayMethodObject *ufuncimpl = NULL;
+    {
+        /* Do all the dtype handling and find the correct ufuncimpl */
+
+        PyArrayObject *tmp_operands[3] = {NULL, NULL, NULL};
+        PyArray_DTypeMeta *signature[3] = {NULL, NULL, NULL};
+        PyArray_DTypeMeta *operand_DTypes[3] = {NULL, NULL, NULL};
+        /*
+         * Create dtypes array for either one or two input operands.
+         * Compare to the logic in `convert_ufunc_arguments`.
+         * TODO: It may be good to review some of this behaviour, since the
+         *       operand array is special (it is written to) similar to reductions.
+         *       Using unsafe-casting as done here, is likely not desirable.
+         */
+        tmp_operands[0] = op1_array;
+        operand_DTypes[0] = NPY_DTYPE(PyArray_DESCR(op1_array));
+        Py_INCREF(operand_DTypes[0]);
+        int force_legacy_promotion = 0;
+        int allow_legacy_promotion = NPY_DT_is_legacy(operand_DTypes[0]);
+
+        if (op2_array != NULL) {
+            tmp_operands[1] = op2_array;
+            operand_DTypes[1] = NPY_DTYPE(PyArray_DESCR(op2_array));
+            Py_INCREF(operand_DTypes[1]);
+            allow_legacy_promotion &= NPY_DT_is_legacy(operand_DTypes[1]);
+            tmp_operands[2] = tmp_operands[0];
+            operand_DTypes[2] = operand_DTypes[0];
+            Py_INCREF(operand_DTypes[2]);
+
+            if (allow_legacy_promotion && ((PyArray_NDIM(op1_array) == 0)
+                                           != (PyArray_NDIM(op2_array) == 0))) {
+                    /* both are legacy and only one is 0-D: force legacy */
+                    force_legacy_promotion = should_use_min_scalar(2, tmp_operands, 0, NULL);
+                }
+        }
+        else {
+            tmp_operands[1] = tmp_operands[0];
+            operand_DTypes[1] = operand_DTypes[0];
+            Py_INCREF(operand_DTypes[1]);
+            tmp_operands[2] = NULL;
+        }
+
+        ufuncimpl = promote_and_get_ufuncimpl(ufunc, tmp_operands, signature,
+                        operand_DTypes, force_legacy_promotion,
+                        allow_legacy_promotion, NPY_FALSE, NPY_FALSE);
+        if (ufuncimpl == NULL) {
+            for (int i = 0; i < 3; i++) {
+                Py_XDECREF(signature[i]);
+                Py_XDECREF(operand_DTypes[i]);
+            }
+            goto fail;
+        }
+
+        /* Find the correct operation_descrs for the operation */
+        int resolve_result = resolve_descriptors(nop, ufunc, ufuncimpl,
+                tmp_operands, operation_descrs, signature, NPY_UNSAFE_CASTING);
+        for (int i = 0; i < 3; i++) {
+            Py_XDECREF(signature[i]);
+            Py_XDECREF(operand_DTypes[i]);
+        }
+        if (resolve_result < 0) {
+            goto fail;
+        }
+    }
+
+    iter = (PyArrayMapIterObject *)PyArray_MapIterArrayCopyIfOverlap(
+        op1_array, idx, 1, op2_array);
+    if (iter == NULL) {
+        goto fail;
+    }
+    op1_array = iter->array;  /* May be updateifcopied on overlap */
+
+    if (op2_array != NULL) {
+        /*
+         * May need to swap axes so that second operand is
+         * iterated over correctly
+         */
+        if ((iter->subspace != NULL) && (iter->consec)) {
+            PyArray_MapIterSwapAxes(iter, &op2_array, 0);
+            if (op2_array == NULL) {
+                /* only on memory allocation failure */
+                goto fail;
+            }
+        }
+
+        /*
+         * Create array iter object for second operand that
+         * "matches" the map iter object for the first operand.
+         * Then we can just iterate over the first and second
+         * operands at the same time and not have to worry about
+         * picking the correct elements from each operand to apply
+         * the ufunc to.
+         */
+        if ((iter2 = (PyArrayIterObject *)\
+             PyArray_BroadcastToShape((PyObject *)op2_array,
+                                        iter->dimensions, iter->nd))==NULL) {
+            goto fail;
+        }
+    }
+
+    PyArrayMethod_Context context = {
+            .caller = (PyObject *)ufunc,
+            .method = ufuncimpl,
+            .descriptors = operation_descrs,
+    };
+
+    /* Use contiguous strides; if there is such a loop it may be faster */
+    npy_intp strides[3] = {
+            operation_descrs[0]->elsize, operation_descrs[1]->elsize, 0};
+    if (nop == 3) {
+        strides[2] = operation_descrs[2]->elsize;
+    }
+
+    NPY_ARRAYMETHOD_FLAGS flags;
+    if (ufuncimpl->get_strided_loop(&context, 1, 0, strides,
+            &strided_loop, &auxdata, &flags) < 0) {
+        goto fail;
+    }
+    int fast_path = 1;
+    /* check no casting, alignment */
+    if (PyArray_DESCR(op1_array) != operation_descrs[0]) {
+        fast_path = 0;
+    }
+    if (PyArray_DESCR(op1_array) != operation_descrs[nop - 1]) {
+        /* output casting */
+        fast_path = 0;
+    }
+    if (!PyArray_ISALIGNED(op1_array)) {
+        fast_path = 0;
+    }
+    if (nop >2) {
+        if  (PyArray_DESCR(op2_array) != operation_descrs[1]) {
+            fast_path = 0;
+        }
+        if (!PyArray_ISALIGNED(op2_array)) {
+            fast_path = 0;
+        }
+    }
+    if (fast_path == 1) {
+        /*
+         * Try to use trivial loop (1d, no casting, aligned) if
+         * - the matching info has a indexed loop
+         * - idx must be exactly one integer index array
+         * - all operands are 1d
+         * A future enhancement could loosen the restriction on 1d operands
+         * by adding an iteration loop inside trivial_at_loop
+         */
+        if ((ufuncimpl->contiguous_indexed_loop != NULL) &&
+                (PyArray_NDIM(op1_array) == 1)  &&
+                (op2_array == NULL || PyArray_NDIM(op2_array) <= 1) &&
+                (iter->subspace_iter == NULL) && (iter->numiter == 1)) {
+            res = trivial_at_loop(ufuncimpl, flags, iter, op1_array,
+                        op2_array, &context);
+
+        }
+        else {
+            /* Couldn't use the fastest path, use the faster path */
+            res = ufunc_at__fast_iter(ufunc, flags, iter, iter2, op1_array,
+                        op2_array, strided_loop, &context, auxdata);
+        }
+    } else {
+        res = ufunc_at__slow_iter(ufunc, flags, iter, iter2, op1_array,
+                        op2_array, operation_descrs, strided_loop, &context,
+                        auxdata);
+    }
+
+fail:
     NPY_AUXDATA_FREE(auxdata);
-    NpyIter_Deallocate(iter_buffer);
 
     Py_XDECREF(op2_array);
     Py_XDECREF(iter);
     Py_XDECREF(iter2);
     for (int i = 0; i < nop; i++) {
-        Py_DECREF(signature[i]);
-        Py_XDECREF(operand_DTypes[i]);
         Py_XDECREF(operation_descrs[i]);
-        Py_XDECREF(array_operands[i]);
     }
 
     /*
@@ -6292,29 +6514,15 @@ ufunc_at(PyUFuncObject *ufunc, PyObject *args)
      * (e.g. `power` released the GIL but manually set an Exception).
      */
     if (res != 0 || PyErr_Occurred()) {
+        /* iter_buffer has already been deallocated, don't use NpyIter_Dealloc */
+        if (PyArray_FLAGS(op1_array) & NPY_ARRAY_WRITEBACKIFCOPY) {
+            PyArray_DiscardWritebackIfCopy(op1_array);
+        }
         return NULL;
     }
     else {
         Py_RETURN_NONE;
     }
-
-fail:
-    /* iter_buffer has already been deallocated, don't use NpyIter_Dealloc */
-    if (op1_array != (PyArrayObject*)op1) {
-        PyArray_DiscardWritebackIfCopy(op1_array);
-    }
-    Py_XDECREF(op2_array);
-    Py_XDECREF(iter);
-    Py_XDECREF(iter2);
-    for (int i = 0; i < 3; i++) {
-        Py_XDECREF(signature[i]);
-        Py_XDECREF(operand_DTypes[i]);
-        Py_XDECREF(operation_descrs[i]);
-        Py_XDECREF(array_operands[i]);
-    }
-    NPY_AUXDATA_FREE(auxdata);
-
-    return NULL;
 }
 
 
@@ -6437,16 +6645,10 @@ py_resolve_dtypes_generic(PyUFuncObject *ufunc, npy_bool return_context,
             Py_INCREF(descr);
             dummy_arrays[i] = (PyArrayObject *)PyArray_NewFromDescr_int(
                     &PyArray_Type, descr, 0, NULL, NULL, NULL,
-                    0, NULL, NULL, 0, 1);
+                    0, NULL, NULL, _NPY_ARRAY_ENSURE_DTYPE_IDENTITY);
             if (dummy_arrays[i] == NULL) {
                 goto finish;
             }
-            if (PyArray_DESCR(dummy_arrays[i]) != descr) {
-                PyErr_SetString(PyExc_NotImplementedError,
-                    "dtype was replaced during array creation, the dtype is "
-                    "unsupported currently (a subarray dtype?).");
-                goto finish;
-            }
             DTypes[i] = NPY_DTYPE(descr);
             Py_INCREF(DTypes[i]);
             if (!NPY_DT_is_legacy(DTypes[i])) {
@@ -6867,7 +7069,7 @@ static PyObject *
 ufunc_get_identity(PyUFuncObject *ufunc, void *NPY_UNUSED(ignored))
 {
     npy_bool reorderable;
-    return _get_identity(ufunc, &reorderable);
+    return PyUFunc_GetDefaultIdentity(ufunc, &reorderable);
 }
 
 static PyObject *
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.h b/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.h
index 32af6c58ed6..1b80bb2df76 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.h
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_object.h
@@ -4,14 +4,17 @@
 #include <numpy/ufuncobject.h>
 
 NPY_NO_EXPORT PyObject *
-ufunc_geterr(PyObject *NPY_UNUSED(dummy), PyObject *args);
+ufunc_geterr(PyObject *NPY_UNUSED(dummy), PyObject *NPY_UNUSED(arg));
 
 NPY_NO_EXPORT PyObject *
-ufunc_seterr(PyObject *NPY_UNUSED(dummy), PyObject *args);
+ufunc_seterr(PyObject *NPY_UNUSED(dummy), PyObject *arg);
 
 NPY_NO_EXPORT const char*
 ufunc_get_name_cstr(PyUFuncObject *ufunc);
 
+NPY_NO_EXPORT PyObject *
+PyUFunc_GetDefaultIdentity(PyUFuncObject *ufunc, npy_bool *reorderable);
+
 /* strings from umathmodule.c that are interned on umath import */
 NPY_VISIBILITY_HIDDEN extern PyObject *npy_um_str_array_ufunc;
 NPY_VISIBILITY_HIDDEN extern PyObject *npy_um_str_array_prepare;
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_type_resolution.c b/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_type_resolution.c
index 707f39e9451..decd26580b5 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_type_resolution.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/ufunc_type_resolution.c
@@ -358,20 +358,51 @@ PyUFunc_SimpleBinaryComparisonTypeResolver(PyUFuncObject *ufunc,
     }
 
     if (type_tup == NULL) {
+        if (PyArray_ISDATETIME(operands[0])
+                && PyArray_ISDATETIME(operands[1])
+                && type_num1 != type_num2) {
+            /*
+             * Reject mixed datetime and timedelta explicitly, this was always
+             * implicitly rejected because casting fails (except with
+             * `casting="unsafe"` admittedly).
+             * This is required to ensure that `==` and `!=` can correctly
+             * detect that they should return a result array of False/True.
+             */
+            return raise_binary_type_reso_error(ufunc, operands);
+        }
         /*
-         * DEPRECATED NumPy 1.20, 2020-12.
-         * This check is required to avoid the FutureWarning that
-         * ResultType will give for number->string promotions.
+         * This check is required to avoid a potential FutureWarning that
+         * ResultType would give for number->string promotions.
          * (We never supported flexible dtypes here.)
          */
-        if (!PyArray_ISFLEXIBLE(operands[0]) &&
+        else if (!PyArray_ISFLEXIBLE(operands[0]) &&
                 !PyArray_ISFLEXIBLE(operands[1])) {
             out_dtypes[0] = PyArray_ResultType(2, operands, 0, NULL);
             if (out_dtypes[0] == NULL) {
                 return -1;
             }
-            out_dtypes[1] = out_dtypes[0];
-            Py_INCREF(out_dtypes[1]);
+            if (PyArray_ISINTEGER(operands[0])
+                    && PyArray_ISINTEGER(operands[1])
+                    && !PyDataType_ISINTEGER(out_dtypes[0])) {
+                /*
+                 * NumPy promotion allows uint+int to go to float, avoid it
+                 * (input must have been a mix of signed and unsigned)
+                 */
+                if (PyArray_ISSIGNED(operands[0])) {
+                    Py_SETREF(out_dtypes[0], PyArray_DescrFromType(NPY_LONGLONG));
+                    out_dtypes[1] = PyArray_DescrFromType(NPY_ULONGLONG);
+                    Py_INCREF(out_dtypes[1]);
+                }
+                else {
+                    Py_SETREF(out_dtypes[0], PyArray_DescrFromType(NPY_ULONGLONG));
+                    out_dtypes[1] = PyArray_DescrFromType(NPY_LONGLONG);
+                    Py_INCREF(out_dtypes[1]);
+                }
+            }
+            else {
+                out_dtypes[1] = out_dtypes[0];
+                Py_INCREF(out_dtypes[1]);
+            }
         }
         else {
             /* Not doing anything will lead to a loop no found error. */
@@ -387,15 +418,8 @@ PyUFunc_SimpleBinaryComparisonTypeResolver(PyUFuncObject *ufunc,
                 operands, type_tup, out_dtypes);
     }
 
-    /* Output type is always boolean */
+    /* Output type is always boolean (cannot fail for builtins) */
     out_dtypes[2] = PyArray_DescrFromType(NPY_BOOL);
-    if (out_dtypes[2] == NULL) {
-        for (i = 0; i < 2; ++i) {
-            Py_DECREF(out_dtypes[i]);
-            out_dtypes[i] = NULL;
-        }
-        return -1;
-    }
 
     /* Check against the casting rules */
     if (PyUFunc_ValidateCasting(ufunc, casting, operands, out_dtypes) < 0) {
diff --git a/contrib/python/numpy/py3/numpy/core/src/umath/wrapping_array_method.c b/contrib/python/numpy/py3/numpy/core/src/umath/wrapping_array_method.c
index 9f8f036e8a6..64fea7aeb7b 100644
--- a/contrib/python/numpy/py3/numpy/core/src/umath/wrapping_array_method.c
+++ b/contrib/python/numpy/py3/numpy/core/src/umath/wrapping_array_method.c
@@ -177,6 +177,39 @@ wrapping_method_get_loop(
 }
 
 
+/*
+ * Wraps the original identity function, needs to translate the descriptors
+ * back to the original ones and provide an "original" context (identically to
+ * `get_loop`).
+ * We assume again that translating the descriptors is quick.
+ */
+static int
+wrapping_method_get_identity_function(
+        PyArrayMethod_Context *context, npy_bool reduction_is_empty,
+        char *item)
+{
+    /* Copy the context, and replace descriptors: */
+    PyArrayMethod_Context orig_context = *context;
+    PyArray_Descr *orig_descrs[NPY_MAXARGS];
+    orig_context.descriptors = orig_descrs;
+    orig_context.method = context->method->wrapped_meth;
+
+    int nin = context->method->nin, nout = context->method->nout;
+    PyArray_DTypeMeta **dtypes = context->method->wrapped_dtypes;
+
+    if (context->method->translate_given_descrs(
+            nin, nout, dtypes, context->descriptors, orig_descrs) < 0) {
+        return -1;
+    }
+    int res = context->method->wrapped_meth->get_reduction_initial(
+            &orig_context, reduction_is_empty, item);
+    for (int i = 0; i < nin + nout; i++) {
+        Py_DECREF(orig_descrs);
+    }
+    return res;
+}
+
+
 /**
  * Allows creating of a fairly lightweight wrapper around an existing ufunc
  * loop.  The idea is mainly for units, as this is currently slightly limited
@@ -235,14 +268,17 @@ PyUFunc_AddWrappingLoop(PyObject *ufunc_obj,
         break;
     }
     if (wrapped_meth == NULL) {
-        PyErr_SetString(PyExc_TypeError,
-                "Did not find the to-be-wrapped loop in the ufunc.");
+        PyErr_Format(PyExc_TypeError,
+                "Did not find the to-be-wrapped loop in the ufunc with given "
+                "DTypes. Received wrapping types: %S", wrapped_dt_tuple);
         goto finish;
     }
 
     PyType_Slot slots[] = {
         {NPY_METH_resolve_descriptors, &wrapping_method_resolve_descriptors},
-        {NPY_METH_get_loop, &wrapping_method_get_loop},
+        {_NPY_METH_get_loop, &wrapping_method_get_loop},
+        {NPY_METH_get_reduction_initial,
+            &wrapping_method_get_identity_function},
         {0, NULL}
     };
 
diff --git a/contrib/python/numpy/py3/numpy/core/tests/data/generate_umath_validation_data.cpp b/contrib/python/numpy/py3/numpy/core/tests/data/generate_umath_validation_data.cpp
index 51ee12501d8..575eec11882 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/data/generate_umath_validation_data.cpp
+++ b/contrib/python/numpy/py3/numpy/core/tests/data/generate_umath_validation_data.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <fstream>
 #include <iostream>
-#include <cmath>
+#include <math.h>
 #include <random>
 #include <cstdio>
 #include <ctime>
@@ -111,8 +111,8 @@ main()
 {
     srand(42);
     std::vector<struct ufunc> umathfunc = {
-            {"sin", sin, sin, 2.37, 3.3},
-            {"cos", cos, cos, 2.36, 3.38},
+            {"sin", sin, sin, 1.49, 1.00},
+            {"cos", cos, cos, 1.49, 1.00},
             {"tan", tan, tan, 3.91, 3.93},
             {"arcsin", asin, asin, 3.12, 2.55},
             {"arccos", acos, acos, 2.1, 1.67},
diff --git a/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-cos.csv b/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-cos.csv
index e315c28b81f..258ae48ce02 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-cos.csv
+++ b/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-cos.csv
@@ -663,713 +663,713 @@ np.float32,0x474e9420,0x3dff77b2,2
 np.float32,0x45cbdb23,0x3dff7240,2
 np.float32,0x44222747,0x3dffb039,2
 np.float32,0x4772e419,0xbdff74b8,2
-np.float64,0x1,0x3ff0000000000000,4
-np.float64,0x8000000000000001,0x3ff0000000000000,4
-np.float64,0x10000000000000,0x3ff0000000000000,4
-np.float64,0x8010000000000000,0x3ff0000000000000,4
-np.float64,0x7fefffffffffffff,0xbfefffe62ecfab75,4
-np.float64,0xffefffffffffffff,0xbfefffe62ecfab75,4
-np.float64,0x7ff0000000000000,0xfff8000000000000,4
-np.float64,0xfff0000000000000,0xfff8000000000000,4
-np.float64,0x7ff8000000000000,0x7ff8000000000000,4
-np.float64,0x7ff4000000000000,0x7ffc000000000000,4
-np.float64,0xbfc28bd9dd2517b4,0x3fefaa28ba13a702,4
-np.float64,0x3fb673c62e2ce790,0x3fefe083847a717f,4
-np.float64,0xbfe3e1dac7e7c3b6,0x3fea0500ba099f3a,4
-np.float64,0xbfbe462caa3c8c58,0x3fefc6c8b9c1c87c,4
-np.float64,0xbfb9353576326a68,0x3fefd8513e50e6b1,4
-np.float64,0xbfc05e798520bcf4,0x3fefbd1ad81cf089,4
-np.float64,0xbfe3ca3be2e79478,0x3fea12b995ea6574,4
-np.float64,0xbfde875d46bd0eba,0x3fec6d888662a824,4
-np.float64,0x3fafc4e02c3f89c0,0x3feff03c34bffd69,4
-np.float64,0xbf98855848310ac0,0x3feffda6c1588bdb,4
-np.float64,0x3fe66c51186cd8a2,0x3fe875c61c630ecb,4
-np.float64,0xbfedff1c3b7bfe38,0x3fe2f0c8c9e8fa39,4
-np.float64,0x3fd6082267ac1044,0x3fee1f6023695050,4
-np.float64,0xbfe78449b06f0894,0x3fe7bda2b223850e,4
-np.float64,0x3feedb8e63fdb71c,0x3fe23d5dfd2dd33f,4
-np.float64,0xbfc0a9de3d2153bc,0x3fefbaadf5e5285e,4
-np.float64,0x3fc04c67432098d0,0x3fefbdae07b7de8d,4
-np.float64,0xbfeeef84c4fddf0a,0x3fe22cf37f309d88,4
-np.float64,0x3fc04bb025209760,0x3fefbdb3d7d34ecf,4
-np.float64,0x3fd6b84d48ad709c,0x3fee013403da6e2a,4
-np.float64,0x3fec1ae25d7835c4,0x3fe46e62195cf274,4
-np.float64,0xbfdc6fdf9bb8dfc0,0x3fece48dc78bbb2e,4
-np.float64,0x3fb4db2c9229b660,0x3fefe4d42f79bf49,4
-np.float64,0xbfc0ed698521dad4,0x3fefb8785ea658c9,4
-np.float64,0xbfee82772b7d04ee,0x3fe2864a80efe8e9,4
-np.float64,0x3fd575b664aaeb6c,0x3fee37c669a12879,4
-np.float64,0x3fe4afb1c5e95f64,0x3fe98b177194439c,4
-np.float64,0x3fd93962f9b272c4,0x3fed8bef61876294,4
-np.float64,0x3fd97ae025b2f5c0,0x3fed7f4cfbf4d300,4
-np.float64,0xbfd9afdb1bb35fb6,0x3fed74fdc44dabb1,4
-np.float64,0x3f8ae65e3035cc80,0x3fefff4b1a0ea62b,4
-np.float64,0xbfe7e58664efcb0d,0x3fe77c02a1cbb670,4
-np.float64,0x3fe5f68b37ebed16,0x3fe8c10f849a5d4d,4
-np.float64,0x3fd9137d61b226fc,0x3fed9330eb4815a1,4
-np.float64,0x3fc146d019228da0,0x3fefb57e2d4d52f8,4
-np.float64,0xbfda6036edb4c06e,0x3fed521b2b578679,4
-np.float64,0xbfe78ddfb0ef1bc0,0x3fe7b734319a77e4,4
-np.float64,0x3fe0877823610ef0,0x3febd33a993dd786,4
-np.float64,0x3fbc61af2e38c360,0x3fefcdb4f889756d,4
-np.float64,0x3fd4dcdca4a9b9b8,0x3fee50962ffea5ae,4
-np.float64,0xbfe03cb29f607965,0x3febf7dbf640a75a,4
-np.float64,0xbfc81de407303bc8,0x3fef6f066cef64bc,4
-np.float64,0x3fd8dea42db1bd48,0x3fed9d3e00dbe0b3,4
-np.float64,0x3feac75e94f58ebe,0x3fe56f1f47f97896,4
-np.float64,0x3fb3a1ea6e2743d0,0x3fefe7ec1247cdaa,4
-np.float64,0x3fd695c0f4ad2b80,0x3fee0730bd40883d,4
-np.float64,0xbfd2c631f5a58c64,0x3feea20cbd1105d7,4
-np.float64,0xbfe978a8e1f2f152,0x3fe663014d40ad7a,4
-np.float64,0x3fd8b6b76ab16d70,0x3feda4c879aacc19,4
-np.float64,0x3feaafd30e755fa6,0x3fe5809514c28453,4
-np.float64,0x3fe1e37dc263c6fc,0x3feb20f9ad1f3f5c,4
-np.float64,0x3fd0ec7c24a1d8f8,0x3feee34048f43b75,4
-np.float64,0xbfe3881cbf67103a,0x3fea38d7886e6f53,4
-np.float64,0xbfd7023957ae0472,0x3fedf4471c765a1c,4
-np.float64,0xbfebc51c4ef78a38,0x3fe4b01c424e297b,4
-np.float64,0xbfe20a93eae41528,0x3feb0c2aa321d2e0,4
-np.float64,0x3fef39be867e737e,0x3fe1efaba9164d27,4
-np.float64,0x3fe8ea9576f1d52a,0x3fe6c7a8826ce1be,4
-np.float64,0x3fea921d91f5243c,0x3fe5968c6cf78963,4
-np.float64,0x3fd7ee5d31afdcbc,0x3fedc9f19d43fe61,4
-np.float64,0xbfe3ed581767dab0,0x3fe9fe4ee2f2b1cd,4
-np.float64,0xbfc40923d5281248,0x3fef9bd8ee9f6e68,4
-np.float64,0x3fe411a834682350,0x3fe9e9103854f057,4
-np.float64,0xbfedf6ccdf7bed9a,0x3fe2f77ad6543246,4
-np.float64,0xbfe8788a44f0f114,0x3fe7172f3aa0c742,4
-np.float64,0xbfce728f173ce520,0x3fef1954083bea04,4
-np.float64,0xbfd64dd0acac9ba2,0x3fee138c3293c246,4
-np.float64,0xbfe00669f5600cd4,0x3fec121443945350,4
-np.float64,0xbfe7152ba2ee2a58,0x3fe8079465d09846,4
-np.float64,0x3fe8654d8f70ca9c,0x3fe7247c94f09596,4
-np.float64,0x3fea68045cf4d008,0x3fe5b58cfe81a243,4
-np.float64,0xbfcd4779073a8ef4,0x3fef2a9d78153fa5,4
-np.float64,0xbfdb4456e5b688ae,0x3fed23b11614203f,4
-np.float64,0x3fcb5d59cd36bab0,0x3fef45818216a515,4
-np.float64,0xbfd914ff5ab229fe,0x3fed92e73746fea8,4
-np.float64,0x3fe4d211db69a424,0x3fe97653f433d15f,4
-np.float64,0xbfdbbb9224b77724,0x3fed0adb593dde80,4
-np.float64,0x3fd424ceafa8499c,0x3fee6d9124795d33,4
-np.float64,0x3feb5968f976b2d2,0x3fe501d116efbf54,4
-np.float64,0x3fee7d92a2fcfb26,0x3fe28a479b6a9dcf,4
-np.float64,0x3fc308e9972611d0,0x3fefa595f4df0c89,4
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diff --git a/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-sin.csv b/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-sin.csv
index 3b913ccd9eb..03e76ffc2c2 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-sin.csv
+++ b/contrib/python/numpy/py3/numpy/core/tests/data/umath-validation-set-sin.csv
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+np.float64,0xffe1fa4be7a3f498,0xbfef4b18ab4b319e,1
+np.float64,0xffe61887bf2c310f,0x3fe846714826cb32,1
+np.float64,0xffdc3cf77f3879ee,0x3fe033b948a36125,1
+np.float64,0xffcc2b86f238570c,0xbfefdcceac3f220f,1
+np.float64,0xffe1f030c0a3e061,0x3fef502a808c359a,1
+np.float64,0xffb872c4ee30e588,0x3fef66ed8d3e6175,1
+np.float64,0xffeac8fc617591f8,0xbfe5d8448602aac9,1
+np.float64,0xffe5be16afab7c2d,0x3fee75ccde3cd14d,1
+np.float64,0xffae230ad83c4610,0xbfe49bbe6074d459,1
+np.float64,0xffc8fbeff531f7e0,0x3f77201e0c927f97,1
+np.float64,0xffdc314f48b8629e,0x3fef810dfc5db118,1
+np.float64,0xffec1f8970783f12,0x3fe15567102e042a,1
+np.float64,0xffc6995f902d32c0,0xbfecd5d2eedf342c,1
+np.float64,0xffdc7af76b38f5ee,0xbfd6e754476ab320,1
+np.float64,0xffb30cf8682619f0,0x3fd5ac3dfc4048d0,1
+np.float64,0xffd3a77695a74eee,0xbfefb5d6889e36e9,1
+np.float64,0xffd8b971803172e4,0xbfeb7f62f0b6c70b,1
+np.float64,0xffde4c0234bc9804,0xbfed50ba9e16d5e0,1
+np.float64,0xffb62b3f342c5680,0xbfeabc0de4069b84,1
+np.float64,0xff9af5674035eac0,0xbfed6c198b6b1bd8,1
+np.float64,0xffdfe20cb43fc41a,0x3fb11f8238f66306,1
+np.float64,0xffd2ecd7a0a5d9b0,0xbfec17ef1a62b1e3,1
+np.float64,0xffce60f7863cc1f0,0x3fe6dbcad3e3a006,1
+np.float64,0xffbbb8306a377060,0xbfbfd0fbef485c4c,1
+np.float64,0xffd1b2bd2b23657a,0xbfda3e046d987b99,1
+np.float64,0xffc480f4092901e8,0xbfeeff0427f6897b,1
+np.float64,0xffe6e02d926dc05a,0xbfcd59552778890b,1
+np.float64,0xffd302e5b7a605cc,0xbfee7c08641366b0,1
+np.float64,0xffec2eb92f785d72,0xbfef5c9c7f771050,1
+np.float64,0xffea3e31a9747c62,0xbfc49cd54755faf0,1
+np.float64,0xffce0a4e333c149c,0x3feeb9a6d0db4aee,1
+np.float64,0xffdc520a2db8a414,0x3fefc7b72613dcd0,1
+np.float64,0xffe056b968a0ad72,0xbfe47a9fe1f827fb,1
+np.float64,0xffe5a10f4cab421e,0x3fec2b1f74b73dec,1
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_api.py b/contrib/python/numpy/py3/numpy/core/tests/test_api.py
index 5006e77f27a..0d922869873 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_api.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_api.py
@@ -102,6 +102,16 @@ def test_array_array():
     assert_raises(ValueError, np.array, [nested], dtype=np.float64)
 
     # Try with lists...
+    # float32
+    assert_equal(np.array([None] * 10, dtype=np.float32),
+                 np.full((10,), np.nan, dtype=np.float32))
+    assert_equal(np.array([[None]] * 10, dtype=np.float32),
+                 np.full((10, 1), np.nan, dtype=np.float32))
+    assert_equal(np.array([[None] * 10], dtype=np.float32),
+                 np.full((1, 10), np.nan, dtype=np.float32))
+    assert_equal(np.array([[None] * 10] * 10, dtype=np.float32),
+                 np.full((10, 10), np.nan, dtype=np.float32))
+    # float64
     assert_equal(np.array([None] * 10, dtype=np.float64),
                  np.full((10,), np.nan, dtype=np.float64))
     assert_equal(np.array([[None]] * 10, dtype=np.float64),
@@ -274,6 +284,9 @@ def test_array_astype():
     a = np.array(1000, dtype='i4')
     assert_raises(TypeError, a.astype, 'U1', casting='safe')
 
+    # gh-24023
+    assert_raises(TypeError, a.astype)
+
 @pytest.mark.parametrize("dt", ["S", "U"])
 def test_array_astype_to_string_discovery_empty(dt):
     # See also gh-19085
@@ -310,7 +323,7 @@ def test_array_astype_warning(t):
 
 @pytest.mark.parametrize(["dtype", "out_dtype"],
         [(np.bytes_, np.bool_),
-         (np.unicode_, np.bool_),
+         (np.str_, np.bool_),
          (np.dtype("S10,S9"), np.dtype("?,?"))])
 def test_string_to_boolean_cast(dtype, out_dtype):
     """
@@ -324,7 +337,7 @@ def test_string_to_boolean_cast(dtype, out_dtype):
 
 @pytest.mark.parametrize(["dtype", "out_dtype"],
         [(np.bytes_, np.bool_),
-         (np.unicode_, np.bool_),
+         (np.str_, np.bool_),
          (np.dtype("S10,S9"), np.dtype("?,?"))])
 def test_string_to_boolean_cast_errors(dtype, out_dtype):
     """
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_argparse.py b/contrib/python/numpy/py3/numpy/core/tests/test_argparse.py
index 63a01dee404..fae22702710 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_argparse.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_argparse.py
@@ -53,8 +53,8 @@ def test_multiple_values():
 def test_string_fallbacks():
     # We can (currently?) use numpy strings to test the "slow" fallbacks
     # that should normally not be taken due to string interning.
-    arg2 = np.unicode_("arg2")
-    missing_arg = np.unicode_("missing_arg")
+    arg2 = np.str_("arg2")
+    missing_arg = np.str_("missing_arg")
     func(1, **{arg2: 3})
     with pytest.raises(TypeError,
             match="got an unexpected keyword argument 'missing_arg'"):
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_array_coercion.py b/contrib/python/numpy/py3/numpy/core/tests/test_array_coercion.py
index fade572928c..629bfce55e8 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_array_coercion.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_array_coercion.py
@@ -1,6 +1,6 @@
 """
 Tests for array coercion, mainly through testing `np.array` results directly.
-Note that other such tests exist e.g. in `test_api.py` and many corner-cases
+Note that other such tests exist, e.g., in `test_api.py` and many corner-cases
 are tested (sometimes indirectly) elsewhere.
 """
 
@@ -20,8 +20,8 @@ from numpy.testing import (
 def arraylikes():
     """
     Generator for functions converting an array into various array-likes.
-    If full is True (default) includes array-likes not capable of handling
-    all dtypes
+    If full is True (default) it includes array-likes not capable of handling
+    all dtypes.
     """
     # base array:
     def ndarray(a):
@@ -39,9 +39,9 @@ def arraylikes():
     yield subclass
 
     class _SequenceLike():
-        # We are giving a warning that array-like's were also expected to be
-        # sequence-like in `np.array([array_like])`, this can be removed
-        # when the deprecation exired (started NumPy 1.20)
+        # Older NumPy versions, sometimes cared whether a protocol array was
+        # also _SequenceLike.  This shouldn't matter, but keep it for now
+        # for __array__ and not the others.
         def __len__(self):
             raise TypeError
 
@@ -62,7 +62,7 @@ def arraylikes():
     yield param(memoryview, id="memoryview")
 
     # Array-interface
-    class ArrayInterface(_SequenceLike):
+    class ArrayInterface:
         def __init__(self, a):
             self.a = a  # need to hold on to keep interface valid
             self.__array_interface__ = a.__array_interface__
@@ -70,7 +70,7 @@ def arraylikes():
     yield param(ArrayInterface, id="__array_interface__")
 
     # Array-Struct
-    class ArrayStruct(_SequenceLike):
+    class ArrayStruct:
         def __init__(self, a):
             self.a = a  # need to hold on to keep struct valid
             self.__array_struct__ = a.__array_struct__
@@ -130,7 +130,7 @@ def scalar_instances(times=True, extended_precision=True, user_dtype=True):
 
     # Strings and unstructured void:
     yield param(np.bytes_(b"1234"), id="bytes")
-    yield param(np.unicode_("2345"), id="unicode")
+    yield param(np.str_("2345"), id="unicode")
     yield param(np.void(b"4321"), id="unstructured_void")
 
 
@@ -161,8 +161,12 @@ class TestStringDiscovery:
         # A nested array is also discovered correctly
         arr = np.array(obj, dtype="O")
         assert np.array(arr, dtype="S").dtype == expected
+        # Also if we use the dtype class
+        assert np.array(arr, dtype=type(expected)).dtype == expected
         # Check that .astype() behaves identical
         assert arr.astype("S").dtype == expected
+        # The DType class is accepted by `.astype()`
+        assert arr.astype(type(np.dtype("S"))).dtype == expected
 
     @pytest.mark.parametrize("obj",
             [object(), 1.2, 10**43, None, "string"],
@@ -257,7 +261,7 @@ class TestScalarDiscovery:
     @pytest.mark.parametrize("scalar", scalar_instances())
     def test_scalar_coercion(self, scalar):
         # This tests various scalar coercion paths, mainly for the numerical
-        # types.  It includes some paths not directly related to `np.array`
+        # types. It includes some paths not directly related to `np.array`.
         if isinstance(scalar, np.inexact):
             # Ensure we have a full-precision number if available
             scalar = type(scalar)((scalar * 2)**0.5)
@@ -292,7 +296,7 @@ class TestScalarDiscovery:
            * `np.array(scalar, dtype=dtype)`
            * `np.empty((), dtype=dtype)[()] = scalar`
            * `np.array(scalar).astype(dtype)`
-        should behave the same.  The only exceptions are paramteric dtypes
+        should behave the same.  The only exceptions are parametric dtypes
         (mainly datetime/timedelta without unit) and void without fields.
         """
         dtype = cast_to.dtype  # use to parametrize only the target dtype
@@ -384,7 +388,7 @@ class TestScalarDiscovery:
         """
         dtype = np.dtype(dtype)
 
-        # This is a special case using casting logic.  It warns for the NaN
+        # This is a special case using casting logic. It warns for the NaN
         # but allows the cast (giving undefined behaviour).
         with np.errstate(invalid="ignore"):
             coerced = np.array(scalar, dtype=dtype)
@@ -650,6 +654,14 @@ class TestArrayLikes:
         res = np.array([obj], dtype=object)
         assert res[0] is obj
 
+    @pytest.mark.parametrize("arraylike", arraylikes())
+    @pytest.mark.parametrize("arr", [np.array(0.), np.arange(4)])
+    def test_object_assignment_special_case(self, arraylike, arr):
+        obj = arraylike(arr)
+        empty = np.arange(1, dtype=object)
+        empty[:] = [obj]
+        assert empty[0] is obj
+
     def test_0d_generic_special_case(self):
         class ArraySubclass(np.ndarray):
             def __float__(self):
@@ -833,3 +845,54 @@ class TestSpecialAttributeLookupFailure:
             np.array(self.WeirdArrayLike())
         with pytest.raises(RuntimeError):
             np.array(self.WeirdArrayInterface())
+
+
+def test_subarray_from_array_construction():
+    # Arrays are more complex, since they "broadcast" on success:
+    arr = np.array([1, 2])
+
+    res = arr.astype("(2)i,")
+    assert_array_equal(res, [[1, 1], [2, 2]])
+
+    res = np.array(arr, dtype="(2)i,")
+
+    assert_array_equal(res, [[1, 1], [2, 2]])
+
+    res = np.array([[(1,), (2,)], arr], dtype="(2)i,")
+    assert_array_equal(res, [[[1, 1], [2, 2]], [[1, 1], [2, 2]]])
+
+    # Also try a multi-dimensional example:
+    arr = np.arange(5 * 2).reshape(5, 2)
+    expected = np.broadcast_to(arr[:, :, np.newaxis, np.newaxis], (5, 2, 2, 2))
+
+    res = arr.astype("(2,2)f")
+    assert_array_equal(res, expected)
+
+    res = np.array(arr, dtype="(2,2)f")
+    assert_array_equal(res, expected)
+
+
+def test_empty_string():
+    # Empty strings are unfortunately often converted to S1 and we need to
+    # make sure we are filling the S1 and not the (possibly) detected S0
+    # result.  This should likely just return S0 and if not maybe the decision
+    # to return S1 should be moved.
+    res = np.array([""] * 10, dtype="S")
+    assert_array_equal(res, np.array("\0", "S1"))
+    assert res.dtype == "S1"
+
+    arr = np.array([""] * 10, dtype=object)
+
+    res = arr.astype("S")
+    assert_array_equal(res, b"")
+    assert res.dtype == "S1"
+
+    res = np.array(arr, dtype="S")
+    assert_array_equal(res, b"")
+    # TODO: This is arguably weird/wrong, but seems old:
+    assert res.dtype == f"S{np.dtype('O').itemsize}"
+
+    res = np.array([[""] * 10, arr], dtype="S")
+    assert_array_equal(res, b"")
+    assert res.shape == (2, 10)
+    assert res.dtype == "S1"
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_arraymethod.py b/contrib/python/numpy/py3/numpy/core/tests/test_arraymethod.py
index 6b75d192121..4fd4d555845 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_arraymethod.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_arraymethod.py
@@ -64,7 +64,6 @@ class TestSimpleStridedCall:
             self.method._simple_strided_call(*args)
 
 
-@pytest.mark.skipif(sys.version_info < (3, 9), reason="Requires python 3.9")
 @pytest.mark.parametrize(
     "cls", [np.ndarray, np.recarray, np.chararray, np.matrix, np.memmap]
 )
@@ -84,10 +83,3 @@ class TestClassGetItem:
             match = f"Too {'few' if arg_len == 0 else 'many'} arguments"
             with pytest.raises(TypeError, match=match):
                 cls[arg_tup]
-
-
-@pytest.mark.skipif(sys.version_info >= (3, 9), reason="Requires python 3.8")
-def test_class_getitem_38() -> None:
-    match = "Type subscription requires python >= 3.9"
-    with pytest.raises(TypeError, match=match):
-        np.ndarray[Any, Any]
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_arrayprint.py b/contrib/python/numpy/py3/numpy/core/tests/test_arrayprint.py
index f1883f703d4..6796b40777f 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_arrayprint.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_arrayprint.py
@@ -9,6 +9,7 @@ from numpy.testing import (
     assert_, assert_equal, assert_raises, assert_warns, HAS_REFCOUNT,
     assert_raises_regex,
     )
+from numpy.core.arrayprint import _typelessdata
 import textwrap
 
 class TestArrayRepr:
@@ -257,8 +258,7 @@ class TestArray2String:
         assert_(np.array2string(s, formatter={'numpystr':lambda s: s*2}) ==
                 '[abcabc defdef]')
 
-
-    def test_structure_format(self):
+    def test_structure_format_mixed(self):
         dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
         x = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt)
         assert_equal(np.array2string(x),
@@ -300,6 +300,7 @@ class TestArray2String:
              ( 'NaT',) ( 'NaT',) ( 'NaT',)]""")
         )
 
+    def test_structure_format_int(self):
         # See #8160
         struct_int = np.array([([1, -1],), ([123, 1],)], dtype=[('B', 'i4', 2)])
         assert_equal(np.array2string(struct_int),
@@ -309,6 +310,7 @@ class TestArray2String:
         assert_equal(np.array2string(struct_2dint),
                 "[([[ 0,  1], [ 2,  3]],) ([[12,  0], [ 0,  0]],)]")
 
+    def test_structure_format_float(self):
         # See #8172
         array_scalar = np.array(
                 (1., 2.1234567890123456789, 3.), dtype=('f8,f8,f8'))
@@ -352,6 +354,33 @@ class TestArray2String:
                 '       [ 501,  502,  503, ...,  999, 1000, 1001]])'
         assert_equal(repr(A), reprA)
 
+    def test_summarize_structure(self):
+        A = (np.arange(2002, dtype="<i8").reshape(2, 1001)
+             .view([('i', "<i8", (1001,))]))
+        strA = ("[[([   0,    1,    2, ...,  998,  999, 1000],)]\n"
+                " [([1001, 1002, 1003, ..., 1999, 2000, 2001],)]]")
+        assert_equal(str(A), strA)
+
+        reprA = ("array([[([   0,    1,    2, ...,  998,  999, 1000],)],\n"
+                 "       [([1001, 1002, 1003, ..., 1999, 2000, 2001],)]],\n"
+                 "      dtype=[('i', '<i8', (1001,))])")
+        assert_equal(repr(A), reprA)
+
+        B = np.ones(2002, dtype=">i8").view([('i', ">i8", (2, 1001))])
+        strB = "[([[1, 1, 1, ..., 1, 1, 1], [1, 1, 1, ..., 1, 1, 1]],)]"
+        assert_equal(str(B), strB)
+
+        reprB = (
+            "array([([[1, 1, 1, ..., 1, 1, 1], [1, 1, 1, ..., 1, 1, 1]],)],\n"
+            "      dtype=[('i', '>i8', (2, 1001))])"
+        )
+        assert_equal(repr(B), reprB)
+
+        C = (np.arange(22, dtype="<i8").reshape(2, 11)
+             .view([('i1', "<i8"), ('i10', "<i8", (10,))]))
+        strC = "[[( 0, [ 1, ..., 10])]\n [(11, [12, ..., 21])]]"
+        assert_equal(np.array2string(C, threshold=1, edgeitems=1), strC)
+
     def test_linewidth(self):
         a = np.full(6, 1)
 
@@ -796,6 +825,47 @@ class TestPrintOptions:
             array(['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1'],
                   dtype='{}')""".format(styp)))
 
+    @pytest.mark.parametrize(
+        ['native'],
+        [
+            ('bool',),
+            ('uint8',),
+            ('uint16',),
+            ('uint32',),
+            ('uint64',),
+            ('int8',),
+            ('int16',),
+            ('int32',),
+            ('int64',),
+            ('float16',),
+            ('float32',),
+            ('float64',),
+            ('U1',),     # 4-byte width string
+        ],
+    )
+    def test_dtype_endianness_repr(self, native):
+        '''
+        there was an issue where
+        repr(array([0], dtype='<u2')) and repr(array([0], dtype='>u2'))
+        both returned the same thing:
+        array([0], dtype=uint16)
+        even though their dtypes have different endianness.
+        '''
+        native_dtype = np.dtype(native)
+        non_native_dtype = native_dtype.newbyteorder()
+        non_native_repr = repr(np.array([1], non_native_dtype))
+        native_repr = repr(np.array([1], native_dtype))
+        # preserve the sensible default of only showing dtype if nonstandard
+        assert ('dtype' in native_repr) ^ (native_dtype in _typelessdata),\
+                ("an array's repr should show dtype if and only if the type "
+                 'of the array is NOT one of the standard types '
+                 '(e.g., int32, bool, float64).')
+        if non_native_dtype.itemsize > 1:
+            # if the type is >1 byte, the non-native endian version
+            # must show endianness.
+            assert non_native_repr != native_repr
+            assert f"dtype='{non_native_dtype.byteorder}" in non_native_repr
+
     def test_linewidth_repr(self):
         a = np.full(7, fill_value=2)
         np.set_printoptions(linewidth=17)
@@ -921,6 +991,16 @@ class TestPrintOptions:
                     [[ 0.]]]])""")
         )
 
+    def test_edgeitems_structured(self):
+        np.set_printoptions(edgeitems=1, threshold=1)
+        A = np.arange(5*2*3, dtype="<i8").view([('i', "<i8", (5, 2, 3))])
+        reprA = (
+            "array([([[[ 0, ...,  2], [ 3, ...,  5]], ..., "
+            "[[24, ..., 26], [27, ..., 29]]],)],\n"
+            "      dtype=[('i', '<i8', (5, 2, 3))])"
+        )
+        assert_equal(repr(A), reprA)
+
     def test_bad_args(self):
         assert_raises(ValueError, np.set_printoptions, threshold=float('nan'))
         assert_raises(TypeError, np.set_printoptions, threshold='1')
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_cpu_features.py b/contrib/python/numpy/py3/numpy/core/tests/test_cpu_features.py
index 1a76897e206..3a7c7a80fdc 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_cpu_features.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_cpu_features.py
@@ -1,5 +1,14 @@
 import sys, platform, re, pytest
-from numpy.core._multiarray_umath import __cpu_features__
+from numpy.core._multiarray_umath import (
+    __cpu_features__,
+    __cpu_baseline__,
+    __cpu_dispatch__,
+)
+import numpy as np
+import subprocess
+import pathlib
+import os
+import re
 
 def assert_features_equal(actual, desired, fname):
     __tracebackhide__ = True  # Hide traceback for py.test
@@ -8,7 +17,7 @@ def assert_features_equal(actual, desired, fname):
         return
     detected = str(__cpu_features__).replace("'", "")
     try:
-        with open("/proc/cpuinfo", "r") as fd:
+        with open("/proc/cpuinfo") as fd:
             cpuinfo = fd.read(2048)
     except Exception as err:
         cpuinfo = str(err)
@@ -48,6 +57,10 @@ def assert_features_equal(actual, desired, fname):
         "%s"
     ) % (fname, actual, desired, error_report))
 
+def _text_to_list(txt):
+    out = txt.strip("][\n").replace("'", "").split(', ')
+    return None if out[0] == "" else out
+
 class AbstractTest:
     features = []
     features_groups = {}
@@ -92,7 +105,6 @@ class AbstractTest:
         return values
 
     def load_flags_auxv(self):
-        import subprocess
         auxv = subprocess.check_output(['/bin/true'], env=dict(LD_SHOW_AUXV="1"))
         for at in auxv.split(b'\n'):
             if not at.startswith(b"AT_HWCAP"):
@@ -103,6 +115,209 @@ class AbstractTest:
                     hwcap_value[1].upper().decode().split()
                 )
 
+@pytest.mark.skipif(
+    sys.platform == 'emscripten',
+    reason= (
+        "The subprocess module is not available on WASM platforms and"
+        " therefore this test class cannot be properly executed."
+    ),
+)
+@pytest.mark.xfail
+class TestEnvPrivation:
+    cwd = pathlib.Path(__file__).parent.resolve()
+    env = os.environ.copy()
+    _enable = os.environ.pop('NPY_ENABLE_CPU_FEATURES', None)
+    _disable = os.environ.pop('NPY_DISABLE_CPU_FEATURES', None)
+    SUBPROCESS_ARGS = dict(cwd=cwd, capture_output=True, text=True, check=True)
+    unavailable_feats = [
+        feat for feat in __cpu_dispatch__ if not __cpu_features__[feat]
+    ]
+    UNAVAILABLE_FEAT = (
+        None if len(unavailable_feats) == 0
+        else unavailable_feats[0]
+    )
+    BASELINE_FEAT = None if len(__cpu_baseline__) == 0 else __cpu_baseline__[0]
+    SCRIPT = """
+def main():
+    from numpy.core._multiarray_umath import __cpu_features__, __cpu_dispatch__
+
+    detected = [feat for feat in __cpu_dispatch__ if __cpu_features__[feat]]
+    print(detected)
+
+if __name__ == "__main__":
+    main()
+    """
+
+    @pytest.fixture(autouse=True)
+    def setup_class(self, tmp_path_factory):
+        file = tmp_path_factory.mktemp("runtime_test_script")
+        file /= "_runtime_detect.py"
+        file.write_text(self.SCRIPT)
+        self.file = file
+        return
+
+    def _run(self):
+        return subprocess.run(
+            [sys.executable, self.file],
+            env=self.env,
+            **self.SUBPROCESS_ARGS,
+            )
+
+    # Helper function mimicing pytest.raises for subprocess call
+    def _expect_error(
+        self,
+        msg,
+        err_type,
+        no_error_msg="Failed to generate error"
+    ):
+        try:
+            self._run()
+        except subprocess.CalledProcessError as e:
+            assertion_message = f"Expected: {msg}\nGot: {e.stderr}"
+            assert re.search(msg, e.stderr), assertion_message
+
+            assertion_message = (
+                f"Expected error of type: {err_type}; see full "
+                f"error:\n{e.stderr}"
+            )
+            assert re.search(err_type, e.stderr), assertion_message
+        else:
+            assert False, no_error_msg
+
+    def setup_method(self):
+        """Ensure that the environment is reset"""
+        self.env = os.environ.copy()
+        return
+
+    def test_runtime_feature_selection(self):
+        """
+        Ensure that when selecting `NPY_ENABLE_CPU_FEATURES`, only the
+        features exactly specified are dispatched.
+        """
+
+        # Capture runtime-enabled features
+        out = self._run()
+        non_baseline_features = _text_to_list(out.stdout)
+
+        if non_baseline_features is None:
+            pytest.skip(
+                "No dispatchable features outside of baseline detected."
+            )
+        feature = non_baseline_features[0]
+
+        # Capture runtime-enabled features when `NPY_ENABLE_CPU_FEATURES` is
+        # specified
+        self.env['NPY_ENABLE_CPU_FEATURES'] = feature
+        out = self._run()
+        enabled_features = _text_to_list(out.stdout)
+
+        # Ensure that only one feature is enabled, and it is exactly the one
+        # specified by `NPY_ENABLE_CPU_FEATURES`
+        assert set(enabled_features) == {feature}
+
+        if len(non_baseline_features) < 2:
+            pytest.skip("Only one non-baseline feature detected.")
+        # Capture runtime-enabled features when `NPY_ENABLE_CPU_FEATURES` is
+        # specified
+        self.env['NPY_ENABLE_CPU_FEATURES'] = ",".join(non_baseline_features)
+        out = self._run()
+        enabled_features = _text_to_list(out.stdout)
+
+        # Ensure that both features are enabled, and they are exactly the ones
+        # specified by `NPY_ENABLE_CPU_FEATURES`
+        assert set(enabled_features) == set(non_baseline_features)
+        return
+
+    @pytest.mark.parametrize("enabled, disabled",
+    [
+        ("feature", "feature"),
+        ("feature", "same"),
+    ])
+    def test_both_enable_disable_set(self, enabled, disabled):
+        """
+        Ensure that when both environment variables are set then an
+        ImportError is thrown
+        """
+        self.env['NPY_ENABLE_CPU_FEATURES'] = enabled
+        self.env['NPY_DISABLE_CPU_FEATURES'] = disabled
+        msg = "Both NPY_DISABLE_CPU_FEATURES and NPY_ENABLE_CPU_FEATURES"
+        err_type = "ImportError"
+        self._expect_error(msg, err_type)
+
+    @pytest.mark.skipif(
+        not __cpu_dispatch__,
+        reason=(
+            "NPY_*_CPU_FEATURES only parsed if "
+            "`__cpu_dispatch__` is non-empty"
+        )
+    )
+    @pytest.mark.parametrize("action", ["ENABLE", "DISABLE"])
+    def test_variable_too_long(self, action):
+        """
+        Test that an error is thrown if the environment variables are too long
+        to be processed. Current limit is 1024, but this may change later.
+        """
+        MAX_VAR_LENGTH = 1024
+        # Actual length is MAX_VAR_LENGTH + 1 due to null-termination
+        self.env[f'NPY_{action}_CPU_FEATURES'] = "t" * MAX_VAR_LENGTH
+        msg = (
+            f"Length of environment variable 'NPY_{action}_CPU_FEATURES' is "
+            f"{MAX_VAR_LENGTH + 1}, only {MAX_VAR_LENGTH} accepted"
+        )
+        err_type = "RuntimeError"
+        self._expect_error(msg, err_type)
+
+    @pytest.mark.skipif(
+        not __cpu_dispatch__,
+        reason=(
+            "NPY_*_CPU_FEATURES only parsed if "
+            "`__cpu_dispatch__` is non-empty"
+        )
+    )
+    def test_impossible_feature_disable(self):
+        """
+        Test that a RuntimeError is thrown if an impossible feature-disabling
+        request is made. This includes disabling a baseline feature.
+        """
+
+        if self.BASELINE_FEAT is None:
+            pytest.skip("There are no unavailable features to test with")
+        bad_feature = self.BASELINE_FEAT
+        self.env['NPY_DISABLE_CPU_FEATURES'] = bad_feature
+        msg = (
+            f"You cannot disable CPU feature '{bad_feature}', since it is "
+            "part of the baseline optimizations"
+        )
+        err_type = "RuntimeError"
+        self._expect_error(msg, err_type)
+
+    def test_impossible_feature_enable(self):
+        """
+        Test that a RuntimeError is thrown if an impossible feature-enabling
+        request is made. This includes enabling a feature not supported by the
+        machine, or disabling a baseline optimization.
+        """
+
+        if self.UNAVAILABLE_FEAT is None:
+            pytest.skip("There are no unavailable features to test with")
+        bad_feature = self.UNAVAILABLE_FEAT
+        self.env['NPY_ENABLE_CPU_FEATURES'] = bad_feature
+        msg = (
+            f"You cannot enable CPU features \\({bad_feature}\\), since "
+            "they are not supported by your machine."
+        )
+        err_type = "RuntimeError"
+        self._expect_error(msg, err_type)
+
+        # Ensure that only the bad feature gets reported
+        feats = f"{bad_feature}, {self.BASELINE_FEAT}"
+        self.env['NPY_ENABLE_CPU_FEATURES'] = feats
+        msg = (
+            f"You cannot enable CPU features \\({bad_feature}\\), since they "
+            "are not supported by your machine."
+        )
+        self._expect_error(msg, err_type)
+
 is_linux = sys.platform.startswith('linux')
 is_cygwin = sys.platform.startswith('cygwin')
 machine  = platform.machine()
@@ -116,7 +331,7 @@ class Test_X86_Features(AbstractTest):
         "AVX", "F16C", "XOP", "FMA4", "FMA3", "AVX2", "AVX512F", "AVX512CD",
         "AVX512ER", "AVX512PF", "AVX5124FMAPS", "AVX5124VNNIW", "AVX512VPOPCNTDQ",
         "AVX512VL", "AVX512BW", "AVX512DQ", "AVX512VNNI", "AVX512IFMA",
-        "AVX512VBMI", "AVX512VBMI2", "AVX512BITALG",
+        "AVX512VBMI", "AVX512VBMI2", "AVX512BITALG", "AVX512FP16",
     ]
     features_groups = dict(
         AVX512_KNL = ["AVX512F", "AVX512CD", "AVX512ER", "AVX512PF"],
@@ -128,6 +343,10 @@ class Test_X86_Features(AbstractTest):
                       "AVX512VBMI"],
         AVX512_ICL = ["AVX512F", "AVX512CD", "AVX512BW", "AVX512DQ", "AVX512VL", "AVX512IFMA",
                       "AVX512VBMI", "AVX512VNNI", "AVX512VBMI2", "AVX512BITALG", "AVX512VPOPCNTDQ"],
+        AVX512_SPR = ["AVX512F", "AVX512CD", "AVX512BW", "AVX512DQ",
+                      "AVX512VL", "AVX512IFMA", "AVX512VBMI", "AVX512VNNI",
+                      "AVX512VBMI2", "AVX512BITALG", "AVX512VPOPCNTDQ",
+                      "AVX512FP16"],
     )
     features_map = dict(
         SSE3="PNI", SSE41="SSE4_1", SSE42="SSE4_2", FMA3="FMA",
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_custom_dtypes.py b/contrib/python/numpy/py3/numpy/core/tests/test_custom_dtypes.py
index 6bcc45d6b39..da6a4bd5064 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_custom_dtypes.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_custom_dtypes.py
@@ -45,6 +45,9 @@ class TestSFloat:
         # Check the repr, mainly to cover the code paths:
         assert repr(SF(scaling=1.)) == "_ScaledFloatTestDType(scaling=1.0)"
 
+    def test_dtype_name(self):
+        assert SF(1.).name == "_ScaledFloatTestDType64"
+
     @pytest.mark.parametrize("scaling", [1., -1., 2.])
     def test_sfloat_from_float(self, scaling):
         a = np.array([1., 2., 3.]).astype(dtype=SF(scaling))
@@ -199,3 +202,52 @@ class TestSFloat:
         # The output casting does not match the bool, bool -> bool loop:
         with pytest.raises(TypeError):
             ufunc(a, a, out=np.empty(a.shape, dtype=int), casting="equiv")
+
+    def test_wrapped_and_wrapped_reductions(self):
+        a = self._get_array(2.)
+        float_equiv = a.astype(float)
+
+        expected = np.hypot(float_equiv, float_equiv)
+        res = np.hypot(a, a)
+        assert res.dtype == a.dtype
+        res_float = res.view(np.float64) * 2
+        assert_array_equal(res_float, expected)
+
+        # Also check reduction (keepdims, due to incorrect getitem)
+        res = np.hypot.reduce(a, keepdims=True)
+        assert res.dtype == a.dtype
+        expected = np.hypot.reduce(float_equiv, keepdims=True)
+        assert res.view(np.float64) * 2 == expected
+
+    def test_astype_class(self):
+        # Very simple test that we accept `.astype()` also on the class.
+        # ScaledFloat always returns the default descriptor, but it does
+        # check the relevant code paths.
+        arr = np.array([1., 2., 3.], dtype=object)
+
+        res = arr.astype(SF)  # passing the class class
+        expected = arr.astype(SF(1.))  # above will have discovered 1. scaling
+        assert_array_equal(res.view(np.float64), expected.view(np.float64))
+
+    def test_creation_class(self):
+        arr1 = np.array([1., 2., 3.], dtype=SF)
+        assert arr1.dtype == SF(1.)
+        arr2 = np.array([1., 2., 3.], dtype=SF(1.))
+        assert_array_equal(arr1.view(np.float64), arr2.view(np.float64))
+
+
+def test_type_pickle():
+    # can't actually unpickle, but we can pickle (if in namespace)
+    import pickle
+
+    np._ScaledFloatTestDType = SF
+
+    s = pickle.dumps(SF)
+    res = pickle.loads(s)
+    assert res is SF
+
+    del np._ScaledFloatTestDType
+
+
+def test_is_numeric():
+    assert SF._is_numeric
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_cython.py b/contrib/python/numpy/py3/numpy/core/tests/test_cython.py
index f4aac4a36ea..e916adceb11 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_cython.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_cython.py
@@ -14,7 +14,7 @@ try:
 except ImportError:
     cython = None
 else:
-    from numpy.compat import _pep440
+    from numpy._utils import _pep440
 
     # Cython 0.29.30 is required for Python 3.11 and there are
     # other fixes in the 0.29 series that are needed even for earlier
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_datetime.py b/contrib/python/numpy/py3/numpy/core/tests/test_datetime.py
index da4a9b0fd9d..547ebf9d674 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_datetime.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_datetime.py
@@ -719,8 +719,8 @@ class TestDateTime:
         assert_equal(uni_a, uni_b)
 
         # Datetime to long string - gh-9712
-        assert_equal(str_a, dt_a.astype((np.string_, 128)))
-        str_b = np.empty(str_a.shape, dtype=(np.string_, 128))
+        assert_equal(str_a, dt_a.astype((np.bytes_, 128)))
+        str_b = np.empty(str_a.shape, dtype=(np.bytes_, 128))
         str_b[...] = dt_a
         assert_equal(str_a, str_b)
 
@@ -1003,12 +1003,11 @@ class TestDateTime:
                      casting='unsafe'))
 
         # Shouldn't be able to compare datetime and timedelta
-        # TODO: Changing to 'same_kind' or 'safe' casting in the ufuncs by
-        #       default is needed to properly catch this kind of thing...
         a = np.array('2012-12-21', dtype='M8[D]')
         b = np.array(3, dtype='m8[D]')
-        #assert_raises(TypeError, np.less, a, b)
-        assert_raises(TypeError, np.less, a, b, casting='same_kind')
+        assert_raises(TypeError, np.less, a, b)
+        # not even if "unsafe"
+        assert_raises(TypeError, np.less, a, b, casting='unsafe')
 
     def test_datetime_like(self):
         a = np.array([3], dtype='m8[4D]')
@@ -2331,16 +2330,23 @@ class TestDateTime:
         assert_equal(np.busday_count('2011-01-01', dates, busdaycal=bdd),
                      np.arange(366))
         # Returns negative value when reversed
+        # -1 since the '2011-01-01' is not a busday
         assert_equal(np.busday_count(dates, '2011-01-01', busdaycal=bdd),
-                     -np.arange(366))
+                     -np.arange(366) - 1)
 
+        # 2011-12-31 is a saturday
         dates = np.busday_offset('2011-12-31', -np.arange(366),
                         roll='forward', busdaycal=bdd)
+        # only the first generated date is in the future of 2011-12-31
+        expected = np.arange(366)
+        expected[0] = -1
         assert_equal(np.busday_count(dates, '2011-12-31', busdaycal=bdd),
-                     np.arange(366))
+                     expected)
         # Returns negative value when reversed
+        expected = -np.arange(366)+1
+        expected[0] = 0
         assert_equal(np.busday_count('2011-12-31', dates, busdaycal=bdd),
-                     -np.arange(366))
+                     expected)
 
         # Can't supply both a weekmask/holidays and busdaycal
         assert_raises(ValueError, np.busday_offset, '2012-01-03', '2012-02-03',
@@ -2353,6 +2359,17 @@ class TestDateTime:
         # Returns negative value when reversed
         assert_equal(np.busday_count('2011-04', '2011-03', weekmask='Mon'), -4)
 
+        sunday = np.datetime64('2023-03-05')
+        monday = sunday + 1
+        friday = sunday + 5
+        saturday = sunday + 6
+        assert_equal(np.busday_count(sunday, monday), 0)
+        assert_equal(np.busday_count(monday, sunday), -1)
+
+        assert_equal(np.busday_count(friday, saturday), 1)
+        assert_equal(np.busday_count(saturday, friday), 0)
+
+
     def test_datetime_is_busday(self):
         holidays = ['2011-01-01', '2011-10-10', '2011-11-11', '2011-11-24',
                     '2011-12-25', '2011-05-30', '2011-02-21', '2011-01-17',
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_defchararray.py b/contrib/python/numpy/py3/numpy/core/tests/test_defchararray.py
index 22296604ee5..39699f457b9 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_defchararray.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_defchararray.py
@@ -1,3 +1,5 @@
+import pytest
+
 import numpy as np
 from numpy.core.multiarray import _vec_string
 from numpy.testing import (
@@ -29,7 +31,7 @@ class TestBasic:
     def test_from_string_array(self):
         A = np.array([[b'abc', b'foo'],
                       [b'long   ', b'0123456789']])
-        assert_equal(A.dtype.type, np.string_)
+        assert_equal(A.dtype.type, np.bytes_)
         B = np.char.array(A)
         assert_array_equal(B, A)
         assert_equal(B.dtype, A.dtype)
@@ -46,7 +48,7 @@ class TestBasic:
     def test_from_unicode_array(self):
         A = np.array([['abc', 'Sigma \u03a3'],
                       ['long   ', '0123456789']])
-        assert_equal(A.dtype.type, np.unicode_)
+        assert_equal(A.dtype.type, np.str_)
         B = np.char.array(A)
         assert_array_equal(B, A)
         assert_equal(B.dtype, A.dtype)
@@ -64,40 +66,40 @@ class TestBasic:
     def test_unicode_upconvert(self):
         A = np.char.array(['abc'])
         B = np.char.array(['\u03a3'])
-        assert_(issubclass((A + B).dtype.type, np.unicode_))
+        assert_(issubclass((A + B).dtype.type, np.str_))
 
     def test_from_string(self):
         A = np.char.array(b'abc')
         assert_equal(len(A), 1)
         assert_equal(len(A[0]), 3)
-        assert_(issubclass(A.dtype.type, np.string_))
+        assert_(issubclass(A.dtype.type, np.bytes_))
 
     def test_from_unicode(self):
         A = np.char.array('\u03a3')
         assert_equal(len(A), 1)
         assert_equal(len(A[0]), 1)
         assert_equal(A.itemsize, 4)
-        assert_(issubclass(A.dtype.type, np.unicode_))
+        assert_(issubclass(A.dtype.type, np.str_))
 
 class TestVecString:
     def test_non_existent_method(self):
 
         def fail():
-            _vec_string('a', np.string_, 'bogus')
+            _vec_string('a', np.bytes_, 'bogus')
 
         assert_raises(AttributeError, fail)
 
     def test_non_string_array(self):
 
         def fail():
-            _vec_string(1, np.string_, 'strip')
+            _vec_string(1, np.bytes_, 'strip')
 
         assert_raises(TypeError, fail)
 
     def test_invalid_args_tuple(self):
 
         def fail():
-            _vec_string(['a'], np.string_, 'strip', 1)
+            _vec_string(['a'], np.bytes_, 'strip', 1)
 
         assert_raises(TypeError, fail)
 
@@ -111,7 +113,7 @@ class TestVecString:
     def test_invalid_function_args(self):
 
         def fail():
-            _vec_string(['a'], np.string_, 'strip', (1,))
+            _vec_string(['a'], np.bytes_, 'strip', (1,))
 
         assert_raises(TypeError, fail)
 
@@ -190,7 +192,7 @@ class TestComparisonsMixed1(TestComparisons):
     def setup_method(self):
         TestComparisons.setup_method(self)
         self.B = np.array([['efg', '123  '],
-                           ['051', 'tuv']], np.unicode_).view(np.chararray)
+                           ['051', 'tuv']], np.str_).view(np.chararray)
 
 class TestComparisonsMixed2(TestComparisons):
     """Ticket #1276"""
@@ -198,7 +200,7 @@ class TestComparisonsMixed2(TestComparisons):
     def setup_method(self):
         TestComparisons.setup_method(self)
         self.A = np.array([['abc', '123'],
-                           ['789', 'xyz']], np.unicode_).view(np.chararray)
+                           ['789', 'xyz']], np.str_).view(np.chararray)
 
 class TestInformation:
     def setup_method(self):
@@ -320,17 +322,17 @@ class TestMethods:
         tgt = [[b' abc ', b''],
                [b'12345', b'Mixedcase'],
                [b'123 \t 345 \0 ', b'Upper']]
-        assert_(issubclass(self.A.capitalize().dtype.type, np.string_))
+        assert_(issubclass(self.A.capitalize().dtype.type, np.bytes_))
         assert_array_equal(self.A.capitalize(), tgt)
 
         tgt = [[' \u03c3 ', ''],
                ['12345', 'Mixedcase'],
                ['123 \t 345 \0 ', 'Upper']]
-        assert_(issubclass(self.B.capitalize().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.capitalize().dtype.type, np.str_))
         assert_array_equal(self.B.capitalize(), tgt)
 
     def test_center(self):
-        assert_(issubclass(self.A.center(10).dtype.type, np.string_))
+        assert_(issubclass(self.A.center(10).dtype.type, np.bytes_))
         C = self.A.center([10, 20])
         assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
 
@@ -341,7 +343,7 @@ class TestMethods:
         C = np.char.center(b'FOO', [[10, 20], [15, 8]])
         tgt = [[b'   FOO    ', b'        FOO         '],
                [b'      FOO      ', b'  FOO   ']]
-        assert_(issubclass(C.dtype.type, np.string_))
+        assert_(issubclass(C.dtype.type, np.bytes_))
         assert_array_equal(C, tgt)
 
     def test_decode(self):
@@ -362,14 +364,14 @@ class TestMethods:
         A0 = self.A.decode('ascii')
 
         A = np.char.join([',', '#'], A0)
-        assert_(issubclass(A.dtype.type, np.unicode_))
+        assert_(issubclass(A.dtype.type, np.str_))
         tgt = np.array([[' ,a,b,c, ', ''],
                         ['1,2,3,4,5', 'M#i#x#e#d#C#a#s#e'],
                         ['1,2,3, ,\t, ,3,4,5, ,\x00, ', 'U#P#P#E#R']])
         assert_array_equal(np.char.join([',', '#'], A0), tgt)
 
     def test_ljust(self):
-        assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
+        assert_(issubclass(self.A.ljust(10).dtype.type, np.bytes_))
 
         C = self.A.ljust([10, 20])
         assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
@@ -382,27 +384,27 @@ class TestMethods:
         C = np.char.ljust(b'FOO', [[10, 20], [15, 8]])
         tgt = [[b'FOO       ', b'FOO                 '],
                [b'FOO            ', b'FOO     ']]
-        assert_(issubclass(C.dtype.type, np.string_))
+        assert_(issubclass(C.dtype.type, np.bytes_))
         assert_array_equal(C, tgt)
 
     def test_lower(self):
         tgt = [[b' abc ', b''],
                [b'12345', b'mixedcase'],
                [b'123 \t 345 \0 ', b'upper']]
-        assert_(issubclass(self.A.lower().dtype.type, np.string_))
+        assert_(issubclass(self.A.lower().dtype.type, np.bytes_))
         assert_array_equal(self.A.lower(), tgt)
 
         tgt = [[' \u03c3 ', ''],
                ['12345', 'mixedcase'],
                ['123 \t 345 \0 ', 'upper']]
-        assert_(issubclass(self.B.lower().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.lower().dtype.type, np.str_))
         assert_array_equal(self.B.lower(), tgt)
 
     def test_lstrip(self):
         tgt = [[b'abc ', b''],
                [b'12345', b'MixedCase'],
                [b'123 \t 345 \0 ', b'UPPER']]
-        assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
+        assert_(issubclass(self.A.lstrip().dtype.type, np.bytes_))
         assert_array_equal(self.A.lstrip(), tgt)
 
         tgt = [[b' abc', b''],
@@ -413,7 +415,7 @@ class TestMethods:
         tgt = [['\u03a3 ', ''],
                ['12345', 'MixedCase'],
                ['123 \t 345 \0 ', 'UPPER']]
-        assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.lstrip().dtype.type, np.str_))
         assert_array_equal(self.B.lstrip(), tgt)
 
     def test_partition(self):
@@ -421,7 +423,7 @@ class TestMethods:
         tgt = [[(b' abc ', b'', b''), (b'', b'', b'')],
                [(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],
                [(b'12', b'3', b' \t 345 \0 '), (b'UPPER', b'', b'')]]
-        assert_(issubclass(P.dtype.type, np.string_))
+        assert_(issubclass(P.dtype.type, np.bytes_))
         assert_array_equal(P, tgt)
 
     def test_replace(self):
@@ -430,11 +432,11 @@ class TestMethods:
         tgt = [[b' abc ', b''],
                [b'12##########45', b'MixedC@se'],
                [b'12########## \t ##########45 \x00', b'UPPER']]
-        assert_(issubclass(R.dtype.type, np.string_))
+        assert_(issubclass(R.dtype.type, np.bytes_))
         assert_array_equal(R, tgt)
 
     def test_rjust(self):
-        assert_(issubclass(self.A.rjust(10).dtype.type, np.string_))
+        assert_(issubclass(self.A.rjust(10).dtype.type, np.bytes_))
 
         C = self.A.rjust([10, 20])
         assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
@@ -447,7 +449,7 @@ class TestMethods:
         C = np.char.rjust(b'FOO', [[10, 20], [15, 8]])
         tgt = [[b'       FOO', b'                 FOO'],
                [b'            FOO', b'     FOO']]
-        assert_(issubclass(C.dtype.type, np.string_))
+        assert_(issubclass(C.dtype.type, np.bytes_))
         assert_array_equal(C, tgt)
 
     def test_rpartition(self):
@@ -455,7 +457,7 @@ class TestMethods:
         tgt = [[(b'', b'', b' abc '), (b'', b'', b'')],
                [(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],
                [(b'123 \t ', b'3', b'45 \0 '), (b'', b'', b'UPPER')]]
-        assert_(issubclass(P.dtype.type, np.string_))
+        assert_(issubclass(P.dtype.type, np.bytes_))
         assert_array_equal(P, tgt)
 
     def test_rsplit(self):
@@ -467,7 +469,7 @@ class TestMethods:
         assert_equal(A.tolist(), tgt)
 
     def test_rstrip(self):
-        assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
+        assert_(issubclass(self.A.rstrip().dtype.type, np.bytes_))
 
         tgt = [[b' abc', b''],
                [b'12345', b'MixedCase'],
@@ -483,14 +485,14 @@ class TestMethods:
         tgt = [[' \u03a3', ''],
                ['12345', 'MixedCase'],
                ['123 \t 345', 'UPPER']]
-        assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.rstrip().dtype.type, np.str_))
         assert_array_equal(self.B.rstrip(), tgt)
 
     def test_strip(self):
         tgt = [[b'abc', b''],
                [b'12345', b'MixedCase'],
                [b'123 \t 345', b'UPPER']]
-        assert_(issubclass(self.A.strip().dtype.type, np.string_))
+        assert_(issubclass(self.A.strip().dtype.type, np.bytes_))
         assert_array_equal(self.A.strip(), tgt)
 
         tgt = [[b' abc ', b''],
@@ -501,7 +503,7 @@ class TestMethods:
         tgt = [['\u03a3', ''],
                ['12345', 'MixedCase'],
                ['123 \t 345', 'UPPER']]
-        assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.strip().dtype.type, np.str_))
         assert_array_equal(self.B.strip(), tgt)
 
     def test_split(self):
@@ -523,39 +525,39 @@ class TestMethods:
         tgt = [[b' ABC ', b''],
                [b'12345', b'mIXEDcASE'],
                [b'123 \t 345 \0 ', b'upper']]
-        assert_(issubclass(self.A.swapcase().dtype.type, np.string_))
+        assert_(issubclass(self.A.swapcase().dtype.type, np.bytes_))
         assert_array_equal(self.A.swapcase(), tgt)
 
         tgt = [[' \u03c3 ', ''],
                ['12345', 'mIXEDcASE'],
                ['123 \t 345 \0 ', 'upper']]
-        assert_(issubclass(self.B.swapcase().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.swapcase().dtype.type, np.str_))
         assert_array_equal(self.B.swapcase(), tgt)
 
     def test_title(self):
         tgt = [[b' Abc ', b''],
                [b'12345', b'Mixedcase'],
                [b'123 \t 345 \0 ', b'Upper']]
-        assert_(issubclass(self.A.title().dtype.type, np.string_))
+        assert_(issubclass(self.A.title().dtype.type, np.bytes_))
         assert_array_equal(self.A.title(), tgt)
 
         tgt = [[' \u03a3 ', ''],
                ['12345', 'Mixedcase'],
                ['123 \t 345 \0 ', 'Upper']]
-        assert_(issubclass(self.B.title().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.title().dtype.type, np.str_))
         assert_array_equal(self.B.title(), tgt)
 
     def test_upper(self):
         tgt = [[b' ABC ', b''],
                [b'12345', b'MIXEDCASE'],
                [b'123 \t 345 \0 ', b'UPPER']]
-        assert_(issubclass(self.A.upper().dtype.type, np.string_))
+        assert_(issubclass(self.A.upper().dtype.type, np.bytes_))
         assert_array_equal(self.A.upper(), tgt)
 
         tgt = [[' \u03a3 ', ''],
                ['12345', 'MIXEDCASE'],
                ['123 \t 345 \0 ', 'UPPER']]
-        assert_(issubclass(self.B.upper().dtype.type, np.unicode_))
+        assert_(issubclass(self.B.upper().dtype.type, np.str_))
         assert_array_equal(self.B.upper(), tgt)
 
     def test_isnumeric(self):
@@ -670,3 +672,15 @@ def test_empty_indexing():
     # empty chararray instead of a chararray with a single empty string in it.
     s = np.chararray((4,))
     assert_(s[[]].size == 0)
+
+
+@pytest.mark.parametrize(["dt1", "dt2"],
+        [("S", "U"), ("U", "S"), ("S", "O"), ("U", "O"),
+         ("S", "d"), ("S", "V")])
+def test_add_types(dt1, dt2):
+    arr1 = np.array([1234234], dtype=dt1)
+    # If the following fails, e.g. use a number and test "V" explicitly
+    arr2 = np.array([b"423"], dtype=dt2)
+    with pytest.raises(TypeError,
+            match=f".*same dtype kind.*{arr1.dtype}.*{arr2.dtype}"):
+        np.char.add(arr1, arr2)
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_deprecations.py b/contrib/python/numpy/py3/numpy/core/tests/test_deprecations.py
index 033815150b0..3ada39e909a 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_deprecations.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_deprecations.py
@@ -138,80 +138,6 @@ class _VisibleDeprecationTestCase(_DeprecationTestCase):
     warning_cls = np.VisibleDeprecationWarning
 
 
-class TestComparisonDeprecations(_DeprecationTestCase):
-    """This tests the deprecation, for non-element-wise comparison logic.
-    This used to mean that when an error occurred during element-wise comparison
-    (i.e. broadcasting) NotImplemented was returned, but also in the comparison
-    itself, False was given instead of the error.
-
-    Also test FutureWarning for the None comparison.
-    """
-
-    message = "elementwise.* comparison failed; .*"
-
-    def test_normal_types(self):
-        for op in (operator.eq, operator.ne):
-            # Broadcasting errors:
-            self.assert_deprecated(op, args=(np.zeros(3), []))
-            a = np.zeros(3, dtype='i,i')
-            # (warning is issued a couple of times here)
-            self.assert_deprecated(op, args=(a, a[:-1]), num=None)
-
-            # ragged array comparison returns True/False
-            a = np.array([1, np.array([1,2,3])], dtype=object)
-            b = np.array([1, np.array([1,2,3])], dtype=object)
-            self.assert_deprecated(op, args=(a, b), num=None)
-
-    def test_string(self):
-        # For two string arrays, strings always raised the broadcasting error:
-        a = np.array(['a', 'b'])
-        b = np.array(['a', 'b', 'c'])
-        assert_warns(FutureWarning, lambda x, y: x == y, a, b)
-
-        # The empty list is not cast to string, and this used to pass due
-        # to dtype mismatch; now (2018-06-21) it correctly leads to a
-        # FutureWarning.
-        assert_warns(FutureWarning, lambda: a == [])
-
-    def test_void_dtype_equality_failures(self):
-        class NotArray:
-            def __array__(self):
-                raise TypeError
-
-            # Needed so Python 3 does not raise DeprecationWarning twice.
-            def __ne__(self, other):
-                return NotImplemented
-
-        self.assert_deprecated(lambda: np.arange(2) == NotArray())
-        self.assert_deprecated(lambda: np.arange(2) != NotArray())
-
-    def test_array_richcompare_legacy_weirdness(self):
-        # It doesn't really work to use assert_deprecated here, b/c part of
-        # the point of assert_deprecated is to check that when warnings are
-        # set to "error" mode then the error is propagated -- which is good!
-        # But here we are testing a bunch of code that is deprecated *because*
-        # it has the habit of swallowing up errors and converting them into
-        # different warnings. So assert_warns will have to be sufficient.
-        assert_warns(FutureWarning, lambda: np.arange(2) == "a")
-        assert_warns(FutureWarning, lambda: np.arange(2) != "a")
-        # No warning for scalar comparisons
-        with warnings.catch_warnings():
-            warnings.filterwarnings("error")
-            assert_(not (np.array(0) == "a"))
-            assert_(np.array(0) != "a")
-            assert_(not (np.int16(0) == "a"))
-            assert_(np.int16(0) != "a")
-
-        for arg1 in [np.asarray(0), np.int16(0)]:
-            struct = np.zeros(2, dtype="i4,i4")
-            for arg2 in [struct, "a"]:
-                for f in [operator.lt, operator.le, operator.gt, operator.ge]:
-                    with warnings.catch_warnings() as l:
-                        warnings.filterwarnings("always")
-                        assert_raises(TypeError, f, arg1, arg2)
-                        assert_(not l)
-
-
 class TestDatetime64Timezone(_DeprecationTestCase):
     """Parsing of datetime64 with timezones deprecated in 1.11.0, because
     datetime64 is now timezone naive rather than UTC only.
@@ -384,12 +310,6 @@ class TestGeneratorSum(_DeprecationTestCase):
         self.assert_deprecated(np.sum, args=((i for i in range(5)),))
 
 
-class TestPositiveOnNonNumerical(_DeprecationTestCase):
-    # 2018-06-28, 1.16.0
-    def test_positive_on_non_number(self):
-        self.assert_deprecated(operator.pos, args=(np.array('foo'),))
-
-
 class TestFromstring(_DeprecationTestCase):
     # 2017-10-19, 1.14
     def test_fromstring(self):
@@ -618,134 +538,6 @@ class FlatteningConcatenateUnsafeCast(_DeprecationTestCase):
                            casting="same_kind")
 
 
-class TestDeprecateSubarrayDTypeDuringArrayCoercion(_DeprecationTestCase):
-    warning_cls = FutureWarning
-    message = "(creating|casting) an array (with|to) a subarray dtype"
-
-    def test_deprecated_array(self):
-        # Arrays are more complex, since they "broadcast" on success:
-        arr = np.array([1, 2])
-
-        self.assert_deprecated(lambda: arr.astype("(2)i,"))
-        with pytest.warns(FutureWarning):
-            res = arr.astype("(2)i,")
-
-        assert_array_equal(res, [[1, 2], [1, 2]])
-
-        self.assert_deprecated(lambda: np.array(arr, dtype="(2)i,"))
-        with pytest.warns(FutureWarning):
-            res = np.array(arr, dtype="(2)i,")
-
-        assert_array_equal(res, [[1, 2], [1, 2]])
-
-        with pytest.warns(FutureWarning):
-            res = np.array([[(1,), (2,)], arr], dtype="(2)i,")
-
-        assert_array_equal(res, [[[1, 1], [2, 2]], [[1, 2], [1, 2]]])
-
-    def test_deprecated_and_error(self):
-        # These error paths do not give a warning, but will succeed in the
-        # future.
-        arr = np.arange(5 * 2).reshape(5, 2)
-        def check():
-            with pytest.raises(ValueError):
-                arr.astype("(2,2)f")
-
-        self.assert_deprecated(check)
-
-        def check():
-            with pytest.raises(ValueError):
-                np.array(arr, dtype="(2,2)f")
-
-        self.assert_deprecated(check)
-
-
-class TestFutureWarningArrayLikeNotIterable(_DeprecationTestCase):
-    # Deprecated 2020-12-09, NumPy 1.20
-    warning_cls = FutureWarning
-    message = "The input object of type.*but not a sequence"
-
-    @pytest.mark.parametrize("protocol",
-            ["__array__", "__array_interface__", "__array_struct__"])
-    def test_deprecated(self, protocol):
-        """Test that these objects give a warning since they are not 0-D,
-        not coerced at the top level `np.array(obj)`, but nested, and do
-        *not* define the sequence protocol.
-
-        NOTE: Tests for the versions including __len__ and __getitem__ exist
-              in `test_array_coercion.py` and they can be modified or amended
-              when this deprecation expired.
-        """
-        blueprint = np.arange(10)
-        MyArr = type("MyArr", (), {protocol: getattr(blueprint, protocol)})
-        self.assert_deprecated(lambda: np.array([MyArr()], dtype=object))
-
-    @pytest.mark.parametrize("protocol",
-             ["__array__", "__array_interface__", "__array_struct__"])
-    def test_0d_not_deprecated(self, protocol):
-        # 0-D always worked (albeit it would use __float__ or similar for the
-        # conversion, which may not happen anymore)
-        blueprint = np.array(1.)
-        MyArr = type("MyArr", (), {protocol: getattr(blueprint, protocol)})
-        myarr = MyArr()
-
-        self.assert_not_deprecated(lambda: np.array([myarr], dtype=object))
-        res = np.array([myarr], dtype=object)
-        expected = np.empty(1, dtype=object)
-        expected[0] = myarr
-        assert_array_equal(res, expected)
-
-    @pytest.mark.parametrize("protocol",
-             ["__array__", "__array_interface__", "__array_struct__"])
-    def test_unnested_not_deprecated(self, protocol):
-        blueprint = np.arange(10)
-        MyArr = type("MyArr", (), {protocol: getattr(blueprint, protocol)})
-        myarr = MyArr()
-
-        self.assert_not_deprecated(lambda: np.array(myarr))
-        res = np.array(myarr)
-        assert_array_equal(res, blueprint)
-
-    @pytest.mark.parametrize("protocol",
-             ["__array__", "__array_interface__", "__array_struct__"])
-    def test_strange_dtype_handling(self, protocol):
-        """The old code would actually use the dtype from the array, but
-        then end up not using the array (for dimension discovery)
-        """
-        blueprint = np.arange(10).astype("f4")
-        MyArr = type("MyArr", (), {protocol: getattr(blueprint, protocol),
-                                   "__float__": lambda _: 0.5})
-        myarr = MyArr()
-
-        # Make sure we warn (and capture the FutureWarning)
-        with pytest.warns(FutureWarning, match=self.message):
-            res = np.array([[myarr]])
-
-        assert res.shape == (1, 1)
-        assert res.dtype == "f4"
-        assert res[0, 0] == 0.5
-
-    @pytest.mark.parametrize("protocol",
-             ["__array__", "__array_interface__", "__array_struct__"])
-    def test_assignment_not_deprecated(self, protocol):
-        # If the result is dtype=object we do not unpack a nested array or
-        # array-like, if it is nested at exactly the right depth.
-        # NOTE: We actually do still call __array__, etc. but ignore the result
-        #       in the end. For `dtype=object` we could optimize that away.
-        blueprint = np.arange(10).astype("f4")
-        MyArr = type("MyArr", (), {protocol: getattr(blueprint, protocol),
-                                   "__float__": lambda _: 0.5})
-        myarr = MyArr()
-
-        res = np.empty(3, dtype=object)
-        def set():
-            res[:] = [myarr, myarr, myarr]
-        self.assert_not_deprecated(set)
-        assert res[0] is myarr
-        assert res[1] is myarr
-        assert res[2] is myarr
-
-
 class TestDeprecatedUnpickleObjectScalar(_DeprecationTestCase):
     # Deprecated 2020-11-24, NumPy 1.20
     """
@@ -759,218 +551,6 @@ class TestDeprecatedUnpickleObjectScalar(_DeprecationTestCase):
         ctor = np.core.multiarray.scalar
         self.assert_deprecated(lambda: ctor(np.dtype("O"), 1))
 
-try:
-    with warnings.catch_warnings():
-        warnings.simplefilter("always")
-        import nose  # noqa: F401
-except ImportError:
-    HAVE_NOSE = False
-else:
-    HAVE_NOSE = True
-
-
-@pytest.mark.skipif(not HAVE_NOSE, reason="Needs nose")
-class TestNoseDecoratorsDeprecated(_DeprecationTestCase):
-    class DidntSkipException(Exception):
-        pass
-
-    def test_slow(self):
-        def _test_slow():
-            @np.testing.dec.slow
-            def slow_func(x, y, z):
-                pass
-
-            assert_(slow_func.slow)
-        self.assert_deprecated(_test_slow)
-
-    def test_setastest(self):
-        def _test_setastest():
-            @np.testing.dec.setastest()
-            def f_default(a):
-                pass
-
-            @np.testing.dec.setastest(True)
-            def f_istest(a):
-                pass
-
-            @np.testing.dec.setastest(False)
-            def f_isnottest(a):
-                pass
-
-            assert_(f_default.__test__)
-            assert_(f_istest.__test__)
-            assert_(not f_isnottest.__test__)
-        self.assert_deprecated(_test_setastest, num=3)
-
-    def test_skip_functions_hardcoded(self):
-        def _test_skip_functions_hardcoded():
-            @np.testing.dec.skipif(True)
-            def f1(x):
-                raise self.DidntSkipException
-
-            try:
-                f1('a')
-            except self.DidntSkipException:
-                raise Exception('Failed to skip')
-            except SkipTest().__class__:
-                pass
-
-            @np.testing.dec.skipif(False)
-            def f2(x):
-                raise self.DidntSkipException
-
-            try:
-                f2('a')
-            except self.DidntSkipException:
-                pass
-            except SkipTest().__class__:
-                raise Exception('Skipped when not expected to')
-        self.assert_deprecated(_test_skip_functions_hardcoded, num=2)
-
-    def test_skip_functions_callable(self):
-        def _test_skip_functions_callable():
-            def skip_tester():
-                return skip_flag == 'skip me!'
-
-            @np.testing.dec.skipif(skip_tester)
-            def f1(x):
-                raise self.DidntSkipException
-
-            try:
-                skip_flag = 'skip me!'
-                f1('a')
-            except self.DidntSkipException:
-                raise Exception('Failed to skip')
-            except SkipTest().__class__:
-                pass
-
-            @np.testing.dec.skipif(skip_tester)
-            def f2(x):
-                raise self.DidntSkipException
-
-            try:
-                skip_flag = 'five is right out!'
-                f2('a')
-            except self.DidntSkipException:
-                pass
-            except SkipTest().__class__:
-                raise Exception('Skipped when not expected to')
-        self.assert_deprecated(_test_skip_functions_callable, num=2)
-
-    def test_skip_generators_hardcoded(self):
-        def _test_skip_generators_hardcoded():
-            @np.testing.dec.knownfailureif(True, "This test is known to fail")
-            def g1(x):
-                yield from range(x)
-
-            try:
-                for j in g1(10):
-                    pass
-            except KnownFailureException().__class__:
-                pass
-            else:
-                raise Exception('Failed to mark as known failure')
-
-            @np.testing.dec.knownfailureif(False, "This test is NOT known to fail")
-            def g2(x):
-                yield from range(x)
-                raise self.DidntSkipException('FAIL')
-
-            try:
-                for j in g2(10):
-                    pass
-            except KnownFailureException().__class__:
-                raise Exception('Marked incorrectly as known failure')
-            except self.DidntSkipException:
-                pass
-        self.assert_deprecated(_test_skip_generators_hardcoded, num=2)
-
-    def test_skip_generators_callable(self):
-        def _test_skip_generators_callable():
-            def skip_tester():
-                return skip_flag == 'skip me!'
-
-            @np.testing.dec.knownfailureif(skip_tester, "This test is known to fail")
-            def g1(x):
-                yield from range(x)
-
-            try:
-                skip_flag = 'skip me!'
-                for j in g1(10):
-                    pass
-            except KnownFailureException().__class__:
-                pass
-            else:
-                raise Exception('Failed to mark as known failure')
-
-            @np.testing.dec.knownfailureif(skip_tester, "This test is NOT known to fail")
-            def g2(x):
-                yield from range(x)
-                raise self.DidntSkipException('FAIL')
-
-            try:
-                skip_flag = 'do not skip'
-                for j in g2(10):
-                    pass
-            except KnownFailureException().__class__:
-                raise Exception('Marked incorrectly as known failure')
-            except self.DidntSkipException:
-                pass
-        self.assert_deprecated(_test_skip_generators_callable, num=2)
-
-    def test_deprecated(self):
-        def _test_deprecated():
-            @np.testing.dec.deprecated(True)
-            def non_deprecated_func():
-                pass
-
-            @np.testing.dec.deprecated()
-            def deprecated_func():
-                import warnings
-                warnings.warn("TEST: deprecated func", DeprecationWarning, stacklevel=1)
-
-            @np.testing.dec.deprecated()
-            def deprecated_func2():
-                import warnings
-                warnings.warn("AHHHH", stacklevel=1)
-                raise ValueError
-
-            @np.testing.dec.deprecated()
-            def deprecated_func3():
-                import warnings
-                warnings.warn("AHHHH", stacklevel=1)
-
-            # marked as deprecated, but does not raise DeprecationWarning
-            assert_raises(AssertionError, non_deprecated_func)
-            # should be silent
-            deprecated_func()
-            with warnings.catch_warnings(record=True):
-                warnings.simplefilter("always")  # do not propagate unrelated warnings
-                # fails if deprecated decorator just disables test. See #1453.
-                assert_raises(ValueError, deprecated_func2)
-                # warning is not a DeprecationWarning
-                assert_raises(AssertionError, deprecated_func3)
-        self.assert_deprecated(_test_deprecated, num=4)
-
-    def test_parametrize(self):
-        def _test_parametrize():
-            # dec.parametrize assumes that it is being run by nose. Because
-            # we are running under pytest, we need to explicitly check the
-            # results.
-            @np.testing.dec.parametrize('base, power, expected',
-                    [(1, 1, 1),
-                    (2, 1, 2),
-                    (2, 2, 4)])
-            def check_parametrize(base, power, expected):
-                assert_(base**power == expected)
-
-            count = 0
-            for test in check_parametrize():
-                test[0](*test[1:])
-                count += 1
-            assert_(count == 3)
-        self.assert_deprecated(_test_parametrize)
-
 
 class TestSingleElementSignature(_DeprecationTestCase):
     # Deprecated 2021-04-01, NumPy 1.21
@@ -1024,15 +604,11 @@ class TestPartitionBoolIndex(_DeprecationTestCase):
 
 
 class TestMachAr(_DeprecationTestCase):
-    # Deprecated 2021-10-19, NumPy 1.22
+    # Deprecated 2022-11-22, NumPy 1.25
     warning_cls = DeprecationWarning
 
     def test_deprecated_module(self):
-        self.assert_deprecated(lambda: getattr(np.core, "machar"))
-
-    def test_deprecated_attr(self):
-        finfo = np.finfo(float)
-        self.assert_deprecated(lambda: getattr(finfo, "machar"))
+        self.assert_deprecated(lambda: getattr(np.core, "MachAr"))
 
 
 class TestQuantileInterpolationDeprecation(_DeprecationTestCase):
@@ -1118,6 +694,18 @@ class TestLoadtxtParseIntsViaFloat(_DeprecationTestCase):
                 assert isinstance(e.__cause__, DeprecationWarning)
 
 
+class TestScalarConversion(_DeprecationTestCase):
+    # 2023-01-02, 1.25.0
+    def test_float_conversion(self):
+        self.assert_deprecated(float, args=(np.array([3.14]),))
+
+    def test_behaviour(self):
+        b = np.array([[3.14]])
+        c = np.zeros(5)
+        with pytest.warns(DeprecationWarning):
+            c[0] = b
+
+
 class TestPyIntConversion(_DeprecationTestCase):
     message = r".*stop allowing conversion of out-of-bound.*"
 
@@ -1191,3 +779,39 @@ class TestRemovedGlobals:
         msg = f".*\n`np.{name}` was a deprecated alias for the builtin"
         with pytest.raises(AttributeError, match=msg):
             getattr(np, name)
+
+
+class TestDeprecatedFinfo(_DeprecationTestCase):
+    # Deprecated in NumPy 1.25, 2023-01-16
+    def test_deprecated_none(self):
+        self.assert_deprecated(np.finfo, args=(None,))
+
+class TestFromnumeric(_DeprecationTestCase):
+    # 2023-02-28, 1.25.0
+    def test_round_(self):
+        self.assert_deprecated(lambda: np.round_(np.array([1.5, 2.5, 3.5])))
+
+    # 2023-03-02, 1.25.0
+    def test_cumproduct(self):
+        self.assert_deprecated(lambda: np.cumproduct(np.array([1, 2, 3])))
+
+    # 2023-03-02, 1.25.0
+    def test_product(self):
+        self.assert_deprecated(lambda: np.product(np.array([1, 2, 3])))
+
+    # 2023-03-02, 1.25.0
+    def test_sometrue(self):
+        self.assert_deprecated(lambda: np.sometrue(np.array([True, False])))
+
+    # 2023-03-02, 1.25.0
+    def test_alltrue(self):
+        self.assert_deprecated(lambda: np.alltrue(np.array([True, False])))
+
+
+class TestMathAlias(_DeprecationTestCase):
+    # Deprecated in Numpy 1.25, 2023-04-06
+    def test_deprecated_np_math(self):
+        self.assert_deprecated(lambda: np.math)
+
+    def test_deprecated_np_lib_math(self):
+        self.assert_deprecated(lambda: np.lib.math)
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_dlpack.py b/contrib/python/numpy/py3/numpy/core/tests/test_dlpack.py
index 278bdd12de5..49249bc6a8b 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_dlpack.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_dlpack.py
@@ -38,13 +38,14 @@ class TestDLPack:
         assert sys.getrefcount(x) == 2
 
     @pytest.mark.parametrize("dtype", [
+        np.bool_,
         np.int8, np.int16, np.int32, np.int64,
         np.uint8, np.uint16, np.uint32, np.uint64,
         np.float16, np.float32, np.float64,
         np.complex64, np.complex128
     ])
     def test_dtype_passthrough(self, dtype):
-        x = np.arange(5, dtype=dtype)
+        x = np.arange(5).astype(dtype)
         y = np.from_dlpack(x)
 
         assert y.dtype == x.dtype
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_dtype.py b/contrib/python/numpy/py3/numpy/core/tests/test_dtype.py
index 0a56483d64e..57831f46f43 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_dtype.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_dtype.py
@@ -7,6 +7,7 @@ import types
 from typing import Any
 
 import numpy as np
+import numpy.dtypes
 from numpy.core._rational_tests import rational
 from numpy.core._multiarray_tests import create_custom_field_dtype
 from numpy.testing import (
@@ -195,6 +196,34 @@ class TestBuiltin:
         # This is an safe cast (not equiv) due to the different names:
         assert np.can_cast(x, y, casting="safe")
 
+    @pytest.mark.parametrize(
+        ["type_char", "char_size", "scalar_type"],
+        [["U", 4, np.str_],
+         ["S", 1, np.bytes_]])
+    def test_create_string_dtypes_directly(
+            self, type_char, char_size, scalar_type):
+        dtype_class = type(np.dtype(type_char))
+
+        dtype = dtype_class(8)
+        assert dtype.type is scalar_type
+        assert dtype.itemsize == 8*char_size
+
+    def test_create_invalid_string_errors(self):
+        one_too_big = np.iinfo(np.intc).max + 1
+        with pytest.raises(TypeError):
+            type(np.dtype("U"))(one_too_big // 4)
+
+        with pytest.raises(TypeError):
+            # Code coverage for very large numbers:
+            type(np.dtype("U"))(np.iinfo(np.intp).max // 4 + 1)
+
+        if one_too_big < sys.maxsize:
+            with pytest.raises(TypeError):
+                type(np.dtype("S"))(one_too_big)
+
+        with pytest.raises(ValueError):
+            type(np.dtype("U"))(-1)
+
 
 class TestRecord:
     def test_equivalent_record(self):
@@ -523,6 +552,14 @@ class TestRecord:
         assert_equal(np.zeros((1, 2), dtype=[]) == a,
                      np.ones((1, 2), dtype=bool))
 
+    def test_nonstructured_with_object(self):
+        # See gh-23277, the dtype here thinks it contain objects, if the
+        # assert about that fails, the test becomes meaningless (which is OK)
+        arr = np.recarray((0,), dtype="O") 
+        assert arr.dtype.names is None  # no fields
+        assert arr.dtype.hasobject  # but claims to contain objects
+        del arr  # the deletion failed previously.
+
 
 class TestSubarray:
     def test_single_subarray(self):
@@ -1527,8 +1564,21 @@ class TestDTypeClasses:
         dtype = np.dtype(dtype)
         assert isinstance(dtype, np.dtype)
         assert type(dtype) is not np.dtype
-        assert type(dtype).__name__ == f"dtype[{dtype.type.__name__}]"
-        assert type(dtype).__module__ == "numpy"
+        if dtype.type.__name__ != "rational":
+            dt_name = type(dtype).__name__.lower().removesuffix("dtype")
+            if dt_name == "uint" or dt_name == "int":
+                # The scalar names has a `c` attached because "int" is Python
+                # int and that is long...
+                dt_name += "c"
+            sc_name = dtype.type.__name__
+            assert dt_name == sc_name.strip("_")
+            assert type(dtype).__module__ == "numpy.dtypes"
+
+            assert getattr(numpy.dtypes, type(dtype).__name__) is type(dtype)
+        else:
+            assert type(dtype).__name__ == "dtype[rational]"
+            assert type(dtype).__module__ == "numpy"
+
         assert not type(dtype)._abstract
 
         # the flexible dtypes and datetime/timedelta have additional parameters
@@ -1551,6 +1601,32 @@ class TestDTypeClasses:
         assert type(np.dtype).__module__ == "numpy"
         assert np.dtype._abstract
 
+    def test_is_numeric(self):
+        all_codes = set(np.typecodes['All'])
+        numeric_codes = set(np.typecodes['AllInteger'] +
+                            np.typecodes['AllFloat'] + '?')
+        non_numeric_codes = all_codes - numeric_codes
+
+        for code in numeric_codes:
+            assert type(np.dtype(code))._is_numeric
+
+        for code in non_numeric_codes:
+            assert not type(np.dtype(code))._is_numeric
+
+    @pytest.mark.parametrize("int_", ["UInt", "Int"])
+    @pytest.mark.parametrize("size", [8, 16, 32, 64])
+    def test_integer_alias_names(self, int_, size):
+        DType = getattr(numpy.dtypes, f"{int_}{size}DType")
+        sctype = getattr(numpy, f"{int_.lower()}{size}")
+        assert DType.type is sctype
+        assert DType.__name__.lower().removesuffix("dtype") == sctype.__name__
+
+    @pytest.mark.parametrize("name",
+            ["Half", "Float", "Double", "CFloat", "CDouble"])
+    def test_float_alias_names(self, name):
+        with pytest.raises(AttributeError):
+            getattr(numpy.dtypes, name + "DType") is numpy.dtypes.Float16DType
+
 
 class TestFromCTypes:
 
@@ -1785,7 +1861,6 @@ class TestUserDType:
             create_custom_field_dtype(blueprint, mytype, 2)
 
 
-@pytest.mark.skipif(sys.version_info < (3, 9), reason="Requires python 3.9")
 class TestClassGetItem:
     def test_dtype(self) -> None:
         alias = np.dtype[Any]
@@ -1818,10 +1893,3 @@ def test_result_type_integers_and_unitless_timedelta64():
     td = np.timedelta64(4)
     result = np.result_type(0, td)
     assert_dtype_equal(result, td.dtype)
-
-
-@pytest.mark.skipif(sys.version_info >= (3, 9), reason="Requires python 3.8")
-def test_class_getitem_38() -> None:
-    match = "Type subscription requires python >= 3.9"
-    with pytest.raises(TypeError, match=match):
-        np.dtype[Any]
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_einsum.py b/contrib/python/numpy/py3/numpy/core/tests/test_einsum.py
index 7c0e8d97ccc..702be248651 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_einsum.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_einsum.py
@@ -1,4 +1,6 @@
 import itertools
+import sys
+import platform
 
 import pytest
 
@@ -8,6 +10,12 @@ from numpy.testing import (
     assert_raises, suppress_warnings, assert_raises_regex, assert_allclose
     )
 
+try:
+    COMPILERS = np.show_config(mode="dicts")["Compilers"]
+    USING_CLANG_CL = COMPILERS["c"]["name"] == "clang-cl"
+except TypeError:
+    USING_CLANG_CL = False
+
 # Setup for optimize einsum
 chars = 'abcdefghij'
 sizes = np.array([2, 3, 4, 5, 4, 3, 2, 6, 5, 4, 3])
@@ -100,6 +108,69 @@ class TestEinsum:
             assert_raises(ValueError, np.einsum, "i->i", np.arange(6).reshape(-1, 1),
                           optimize=do_opt, order='d')
 
+    def test_einsum_object_errors(self):
+        # Exceptions created by object arithmetic should
+        # successfully propagate
+
+        class CustomException(Exception):
+            pass
+
+        class DestructoBox:
+
+            def __init__(self, value, destruct):
+                self._val = value
+                self._destruct = destruct
+
+            def __add__(self, other):
+                tmp = self._val + other._val
+                if tmp >= self._destruct:
+                    raise CustomException
+                else:
+                    self._val = tmp
+                    return self
+
+            def __radd__(self, other):
+                if other == 0:
+                    return self
+                else:
+                    return self.__add__(other)
+
+            def __mul__(self, other):
+                tmp = self._val * other._val
+                if tmp >= self._destruct:
+                    raise CustomException
+                else:
+                    self._val = tmp
+                    return self
+
+            def __rmul__(self, other):
+                if other == 0:
+                    return self
+                else:
+                    return self.__mul__(other)
+
+        a = np.array([DestructoBox(i, 5) for i in range(1, 10)],
+                     dtype='object').reshape(3, 3)
+
+        # raised from unbuffered_loop_nop1_ndim2
+        assert_raises(CustomException, np.einsum, "ij->i", a)
+
+        # raised from unbuffered_loop_nop1_ndim3
+        b = np.array([DestructoBox(i, 100) for i in range(0, 27)],
+                     dtype='object').reshape(3, 3, 3)
+        assert_raises(CustomException, np.einsum, "i...k->...", b)
+
+        # raised from unbuffered_loop_nop2_ndim2
+        b = np.array([DestructoBox(i, 55) for i in range(1, 4)],
+                     dtype='object')
+        assert_raises(CustomException, np.einsum, "ij, j", a, b)
+
+        # raised from unbuffered_loop_nop2_ndim3
+        assert_raises(CustomException, np.einsum, "ij, jh", a, a)
+
+        # raised from PyArray_EinsteinSum
+        assert_raises(CustomException, np.einsum, "ij->", a)
+
     def test_einsum_views(self):
         # pass-through
         for do_opt in [True, False]:
@@ -247,47 +318,50 @@ class TestEinsum:
         # sum(a, axis=-1)
         for n in range(1, 17):
             a = np.arange(n, dtype=dtype)
-            assert_equal(np.einsum("i->", a, optimize=do_opt),
-                         np.sum(a, axis=-1).astype(dtype))
-            assert_equal(np.einsum(a, [0], [], optimize=do_opt),
-                         np.sum(a, axis=-1).astype(dtype))
+            b = np.sum(a, axis=-1)
+            if hasattr(b, 'astype'):
+                b = b.astype(dtype)
+            assert_equal(np.einsum("i->", a, optimize=do_opt), b)
+            assert_equal(np.einsum(a, [0], [], optimize=do_opt), b)
 
         for n in range(1, 17):
             a = np.arange(2*3*n, dtype=dtype).reshape(2, 3, n)
-            assert_equal(np.einsum("...i->...", a, optimize=do_opt),
-                         np.sum(a, axis=-1).astype(dtype))
-            assert_equal(np.einsum(a, [Ellipsis, 0], [Ellipsis], optimize=do_opt),
-                         np.sum(a, axis=-1).astype(dtype))
+            b = np.sum(a, axis=-1)
+            if hasattr(b, 'astype'):
+                b = b.astype(dtype)
+            assert_equal(np.einsum("...i->...", a, optimize=do_opt), b)
+            assert_equal(np.einsum(a, [Ellipsis, 0], [Ellipsis], optimize=do_opt), b)
 
         # sum(a, axis=0)
         for n in range(1, 17):
             a = np.arange(2*n, dtype=dtype).reshape(2, n)
-            assert_equal(np.einsum("i...->...", a, optimize=do_opt),
-                         np.sum(a, axis=0).astype(dtype))
-            assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt),
-                         np.sum(a, axis=0).astype(dtype))
+            b = np.sum(a, axis=0)
+            if hasattr(b, 'astype'):
+                b = b.astype(dtype)
+            assert_equal(np.einsum("i...->...", a, optimize=do_opt), b)
+            assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt), b)
 
         for n in range(1, 17):
             a = np.arange(2*3*n, dtype=dtype).reshape(2, 3, n)
-            assert_equal(np.einsum("i...->...", a, optimize=do_opt),
-                         np.sum(a, axis=0).astype(dtype))
-            assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt),
-                         np.sum(a, axis=0).astype(dtype))
+            b = np.sum(a, axis=0)
+            if hasattr(b, 'astype'):
+                b = b.astype(dtype)
+            assert_equal(np.einsum("i...->...", a, optimize=do_opt), b)
+            assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt), b)
 
         # trace(a)
         for n in range(1, 17):
             a = np.arange(n*n, dtype=dtype).reshape(n, n)
-            assert_equal(np.einsum("ii", a, optimize=do_opt),
-                         np.trace(a).astype(dtype))
-            assert_equal(np.einsum(a, [0, 0], optimize=do_opt),
-                         np.trace(a).astype(dtype))
+            b = np.trace(a)
+            if hasattr(b, 'astype'):
+                b = b.astype(dtype)
+            assert_equal(np.einsum("ii", a, optimize=do_opt), b)
+            assert_equal(np.einsum(a, [0, 0], optimize=do_opt), b)
 
             # gh-15961: should accept numpy int64 type in subscript list
             np_array = np.asarray([0, 0])
-            assert_equal(np.einsum(a, np_array, optimize=do_opt),
-                         np.trace(a).astype(dtype))
-            assert_equal(np.einsum(a, list(np_array), optimize=do_opt),
-                         np.trace(a).astype(dtype))
+            assert_equal(np.einsum(a, np_array, optimize=do_opt), b)
+            assert_equal(np.einsum(a, list(np_array), optimize=do_opt), b)
 
         # multiply(a, b)
         assert_equal(np.einsum("..., ...", 3, 4), 12)  # scalar case
@@ -489,11 +563,15 @@ class TestEinsum:
 
         b = np.einsum("i->", a, dtype=dtype, casting='unsafe')
         assert_equal(b, np.sum(a))
-        assert_equal(b.dtype, np.dtype(dtype))
+        if hasattr(b, "dtype"):
+            # Can be a python object when dtype is object
+            assert_equal(b.dtype, np.dtype(dtype))
 
         b = np.einsum(a, [0], [], dtype=dtype, casting='unsafe')
         assert_equal(b, np.sum(a))
-        assert_equal(b.dtype, np.dtype(dtype))
+        if hasattr(b, "dtype"):
+            # Can be a python object when dtype is object
+            assert_equal(b.dtype, np.dtype(dtype))
 
         # A case which was failing (ticket #1885)
         p = np.arange(2) + 1
@@ -539,9 +617,23 @@ class TestEinsum:
                            [2.])  # contig_stride0_outstride0_two
 
     def test_einsum_sums_int8(self):
+        if (
+                (sys.platform == 'darwin' and platform.machine() == 'x86_64')
+                or
+                USING_CLANG_CL
+        ):
+            pytest.xfail('Fails on macOS x86-64 and when using clang-cl '
+                         'with Meson, see gh-23838')
         self.check_einsum_sums('i1')
 
     def test_einsum_sums_uint8(self):
+        if (
+                (sys.platform == 'darwin' and platform.machine() == 'x86_64')
+                or
+                USING_CLANG_CL
+        ):
+            pytest.xfail('Fails on macOS x86-64 and when using clang-cl '
+                         'with Meson, see gh-23838')
         self.check_einsum_sums('u1')
 
     def test_einsum_sums_int16(self):
@@ -587,6 +679,10 @@ class TestEinsum:
     def test_einsum_sums_clongdouble(self):
         self.check_einsum_sums(np.clongdouble)
 
+    def test_einsum_sums_object(self):
+        self.check_einsum_sums('object')
+        self.check_einsum_sums('object', True)
+
     def test_einsum_misc(self):
         # This call used to crash because of a bug in
         # PyArray_AssignZero
@@ -625,6 +721,21 @@ class TestEinsum:
         # see issue gh-15776 and issue gh-15256
         assert_equal(np.einsum('i,j', [1], [2], out=None), [[2]])
 
+    def test_object_loop(self):
+
+        class Mult:
+            def __mul__(self, other):
+                return 42
+
+        objMult = np.array([Mult()])
+        objNULL = np.ndarray(buffer = b'\0' * np.intp(0).itemsize, shape=1, dtype=object)
+
+        with pytest.raises(TypeError):
+            np.einsum("i,j", [1], objNULL)
+        with pytest.raises(TypeError):
+            np.einsum("i,j", objNULL, [1])
+        assert np.einsum("i,j", objMult, objMult) == 42
+
     def test_subscript_range(self):
         # Issue #7741, make sure that all letters of Latin alphabet (both uppercase & lowercase) can be used
         # when creating a subscript from arrays
@@ -755,7 +866,7 @@ class TestEinsum:
         # Test originally added to cover broken float16 path: gh-20305
         # Likely most are covered elsewhere, at least partially.
         dtype = np.dtype(dtype)
-        # Simple test, designed to excersize most specialized code paths,
+        # Simple test, designed to exercise most specialized code paths,
         # note the +0.5 for floats.  This makes sure we use a float value
         # where the results must be exact.
         arr = (np.arange(7) + 0.5).astype(dtype)
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_function_base.py b/contrib/python/numpy/py3/numpy/core/tests/test_function_base.py
index dad7a58835f..79f1ecfc9d0 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_function_base.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_function_base.py
@@ -1,3 +1,4 @@
+import pytest
 from numpy import (
     logspace, linspace, geomspace, dtype, array, sctypes, arange, isnan,
     ndarray, sqrt, nextafter, stack, errstate
@@ -65,6 +66,33 @@ class TestLogspace:
         t5 = logspace(start, stop, 6, axis=-1)
         assert_equal(t5, t2.T)
 
+    @pytest.mark.parametrize("axis", [0, 1, -1])
+    def test_base_array(self, axis: int):
+        start = 1
+        stop = 2
+        num = 6
+        base = array([1, 2])
+        t1 = logspace(start, stop, num=num, base=base, axis=axis)
+        t2 = stack(
+            [logspace(start, stop, num=num, base=_base) for _base in base],
+            axis=(axis + 1) % t1.ndim,
+        )
+        assert_equal(t1, t2)
+
+    @pytest.mark.parametrize("axis", [0, 1, -1])
+    def test_stop_base_array(self, axis: int):
+        start = 1
+        stop = array([2, 3])
+        num = 6
+        base = array([1, 2])
+        t1 = logspace(start, stop, num=num, base=base, axis=axis)
+        t2 = stack(
+            [logspace(start, _stop, num=num, base=_base)
+             for _stop, _base in zip(stop, base)],
+            axis=(axis + 1) % t1.ndim,
+        )
+        assert_equal(t1, t2)
+
     def test_dtype(self):
         y = logspace(0, 6, dtype='float32')
         assert_equal(y.dtype, dtype('float32'))
@@ -407,3 +435,12 @@ class TestLinspace:
         y = linspace(-1, 3, num=8, dtype=int)
         t = array([-1, -1, 0, 0, 1, 1, 2, 3], dtype=int)
         assert_array_equal(y, t)
+
+    def test_any_step_zero_and_not_mult_inplace(self):
+        # any_step_zero is True, _mult_inplace is False
+        start = array([0.0, 1.0])
+        stop = array([2.0, 1.0])
+        y = linspace(start, stop, 3)
+        assert_array_equal(y, array([[0.0, 1.0], [1.0, 1.0], [2.0, 1.0]]))
+    
+
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_getlimits.py b/contrib/python/numpy/py3/numpy/core/tests/test_getlimits.py
index b8aaba386e9..f646e2bd798 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_getlimits.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_getlimits.py
@@ -3,6 +3,7 @@
 """
 import warnings
 import numpy as np
+import pytest
 from numpy.core import finfo, iinfo
 from numpy import half, single, double, longdouble
 from numpy.testing import assert_equal, assert_, assert_raises
@@ -40,20 +41,47 @@ class TestLongdouble:
         ftype2 = finfo(longdouble)
         assert_equal(id(ftype), id(ftype2))
 
+def assert_finfo_equal(f1, f2):
+    # assert two finfo instances have the same attributes
+    for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machep',
+                 'max', 'maxexp', 'min', 'minexp', 'negep', 'nexp',
+                 'nmant', 'precision', 'resolution', 'tiny',
+                 'smallest_normal', 'smallest_subnormal'):
+        assert_equal(getattr(f1, attr), getattr(f2, attr),
+                     f'finfo instances {f1} and {f2} differ on {attr}')
+
+def assert_iinfo_equal(i1, i2):
+    # assert two iinfo instances have the same attributes
+    for attr in ('bits', 'min', 'max'):
+        assert_equal(getattr(i1, attr), getattr(i2, attr),
+                     f'iinfo instances {i1} and {i2} differ on {attr}')
+
 class TestFinfo:
     def test_basic(self):
         dts = list(zip(['f2', 'f4', 'f8', 'c8', 'c16'],
                        [np.float16, np.float32, np.float64, np.complex64,
                         np.complex128]))
         for dt1, dt2 in dts:
-            for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machep',
-                         'max', 'maxexp', 'min', 'minexp', 'negep', 'nexp',
-                         'nmant', 'precision', 'resolution', 'tiny',
-                         'smallest_normal', 'smallest_subnormal'):
-                assert_equal(getattr(finfo(dt1), attr),
-                             getattr(finfo(dt2), attr), attr)
+            assert_finfo_equal(finfo(dt1), finfo(dt2))
+
         assert_raises(ValueError, finfo, 'i4')
 
+    def test_regression_gh23108(self):
+        # np.float32(1.0) and np.float64(1.0) have the same hash and are
+        # equal under the == operator
+        f1 = np.finfo(np.float32(1.0))
+        f2 = np.finfo(np.float64(1.0))
+        assert f1 != f2
+
+    def test_regression_gh23867(self):
+        class NonHashableWithDtype:
+            __hash__ = None
+            dtype = np.dtype('float32')
+  
+        x = NonHashableWithDtype()
+        assert np.finfo(x) == np.finfo(x.dtype)
+        
+
 class TestIinfo:
     def test_basic(self):
         dts = list(zip(['i1', 'i2', 'i4', 'i8',
@@ -61,9 +89,8 @@ class TestIinfo:
                   [np.int8, np.int16, np.int32, np.int64,
                    np.uint8, np.uint16, np.uint32, np.uint64]))
         for dt1, dt2 in dts:
-            for attr in ('bits', 'min', 'max'):
-                assert_equal(getattr(iinfo(dt1), attr),
-                             getattr(iinfo(dt2), attr), attr)
+            assert_iinfo_equal(iinfo(dt1), iinfo(dt2))
+
         assert_raises(ValueError, iinfo, 'f4')
 
     def test_unsigned_max(self):
@@ -85,8 +112,28 @@ class TestRepr:
 
 
 def test_instances():
-    iinfo(10)
-    finfo(3.0)
+    # Test the finfo and iinfo results on numeric instances agree with
+    # the results on the corresponding types
+
+    for c in [int, np.int16, np.int32, np.int64]:
+        class_iinfo = iinfo(c)
+        instance_iinfo = iinfo(c(12))
+
+        assert_iinfo_equal(class_iinfo, instance_iinfo)
+
+    for c in [float, np.float16, np.float32, np.float64]:
+        class_finfo = finfo(c)
+        instance_finfo = finfo(c(1.2))
+        assert_finfo_equal(class_finfo, instance_finfo)
+
+    with pytest.raises(ValueError):
+        iinfo(10.)
+
+    with pytest.raises(ValueError):
+        iinfo('hi')
+
+    with pytest.raises(ValueError):
+        finfo(np.int64(1))
 
 
 def assert_ma_equal(discovered, ma_like):
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_indexing.py b/contrib/python/numpy/py3/numpy/core/tests/test_indexing.py
index 74075639cf8..04293670230 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_indexing.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_indexing.py
@@ -1062,7 +1062,7 @@ class TestMultiIndexingAutomated:
                         if np.any(_indx >= _size) or np.any(_indx < -_size):
                                 raise IndexError
                 if len(indx[1:]) == len(orig_slice):
-                    if np.product(orig_slice) == 0:
+                    if np.prod(orig_slice) == 0:
                         # Work around for a crash or IndexError with 'wrap'
                         # in some 0-sized cases.
                         try:
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_item_selection.py b/contrib/python/numpy/py3/numpy/core/tests/test_item_selection.py
index 3c35245a3f4..5660ef583ed 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_item_selection.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_item_selection.py
@@ -1,5 +1,7 @@
 import sys
 
+import pytest
+
 import numpy as np
 from numpy.testing import (
     assert_, assert_raises, assert_array_equal, HAS_REFCOUNT
@@ -84,3 +86,80 @@ class TestTake:
 
         b = np.array([0, 1, 2, 3, 4, 5])
         assert_array_equal(a, b)
+
+
+class TestPutMask:
+    @pytest.mark.parametrize("dtype", list(np.typecodes["All"]) + ["i,O"])
+    def test_simple(self, dtype):
+        if dtype.lower() == "m":
+            dtype += "8[ns]"
+
+        # putmask is weird and doesn't care about value length (even shorter)
+        vals = np.arange(1001).astype(dtype=dtype)
+
+        mask = np.random.randint(2, size=1000).astype(bool)
+        # Use vals.dtype in case of flexible dtype (i.e. string)
+        arr = np.zeros(1000, dtype=vals.dtype)
+        zeros = arr.copy()
+
+        np.putmask(arr, mask, vals)
+        assert_array_equal(arr[mask], vals[:len(mask)][mask])
+        assert_array_equal(arr[~mask], zeros[~mask])
+
+    @pytest.mark.parametrize("dtype", list(np.typecodes["All"])[1:] + ["i,O"])
+    @pytest.mark.parametrize("mode", ["raise", "wrap", "clip"])
+    def test_empty(self, dtype, mode):
+        arr = np.zeros(1000, dtype=dtype)
+        arr_copy = arr.copy()
+        mask = np.random.randint(2, size=1000).astype(bool)
+
+        # Allowing empty values like this is weird...
+        np.put(arr, mask, [])
+        assert_array_equal(arr, arr_copy)
+
+
+class TestPut:
+    @pytest.mark.parametrize("dtype", list(np.typecodes["All"])[1:] + ["i,O"])
+    @pytest.mark.parametrize("mode", ["raise", "wrap", "clip"])
+    def test_simple(self, dtype, mode):
+        if dtype.lower() == "m":
+            dtype += "8[ns]"
+
+        # put is weird and doesn't care about value length (even shorter)
+        vals = np.arange(1001).astype(dtype=dtype)
+
+        # Use vals.dtype in case of flexible dtype (i.e. string)
+        arr = np.zeros(1000, dtype=vals.dtype)
+        zeros = arr.copy()
+
+        if mode == "clip":
+            # Special because 0 and -1 value are "reserved" for clip test
+            indx = np.random.permutation(len(arr) - 2)[:-500] + 1
+
+            indx[-1] = 0
+            indx[-2] = len(arr) - 1
+            indx_put = indx.copy()
+            indx_put[-1] = -1389
+            indx_put[-2] = 1321
+        else:
+            # Avoid duplicates (for simplicity) and fill half only
+            indx = np.random.permutation(len(arr) - 3)[:-500]
+            indx_put = indx
+            if mode == "wrap":
+                indx_put = indx_put + len(arr)
+
+        np.put(arr, indx_put, vals, mode=mode)
+        assert_array_equal(arr[indx], vals[:len(indx)])
+        untouched = np.ones(len(arr), dtype=bool)
+        untouched[indx] = False
+        assert_array_equal(arr[untouched], zeros[:untouched.sum()])
+
+    @pytest.mark.parametrize("dtype", list(np.typecodes["All"])[1:] + ["i,O"])
+    @pytest.mark.parametrize("mode", ["raise", "wrap", "clip"])
+    def test_empty(self, dtype, mode):
+        arr = np.zeros(1000, dtype=dtype)
+        arr_copy = arr.copy()
+
+        # Allowing empty values like this is weird...
+        np.put(arr, [1, 2, 3], [])
+        assert_array_equal(arr, arr_copy)
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_longdouble.py b/contrib/python/numpy/py3/numpy/core/tests/test_longdouble.py
index 8d8ad265861..7b4e6e59cb7 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_longdouble.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_longdouble.py
@@ -1,10 +1,11 @@
 import warnings
+import platform
 import pytest
 
 import numpy as np
 from numpy.testing import (
     assert_, assert_equal, assert_raises, assert_warns, assert_array_equal,
-    temppath,
+    temppath, IS_MUSL
     )
 from __tests__.core.tests._locales import CommaDecimalPointLocale
 
@@ -30,6 +31,10 @@ def test_scalar_extraction():
 # 0.1 not exactly representable in base 2 floating point.
 repr_precision = len(repr(np.longdouble(0.1)))
 # +2 from macro block starting around line 842 in scalartypes.c.src.
+
+
+@pytest.mark.skipif(IS_MUSL,
+                    reason="test flaky on musllinux")
 @pytest.mark.skipif(LD_INFO.precision + 2 >= repr_precision,
                     reason="repr precision not enough to show eps")
 def test_repr_roundtrip():
@@ -142,7 +147,7 @@ class TestFileBased:
 
     def test_fromfile_bogus(self):
         with temppath() as path:
-            with open(path, 'wt') as f:
+            with open(path, 'w') as f:
                 f.write("1. 2. 3. flop 4.\n")
 
             with assert_warns(DeprecationWarning):
@@ -153,7 +158,7 @@ class TestFileBased:
         for ctype in ["complex", "cdouble", "cfloat"]:
             # Check spacing between separator and only real component specified
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1, 2 ,  3  ,4\n")
 
                 res = np.fromfile(path, dtype=ctype, sep=",")
@@ -161,7 +166,7 @@ class TestFileBased:
 
             # Real component not specified
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1j, -2j,  3j, 4e1j\n")
 
                 res = np.fromfile(path, dtype=ctype, sep=",")
@@ -169,7 +174,7 @@ class TestFileBased:
 
             # Both components specified
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1+1j,2-2j, -3+3j,  -4e1+4j\n")
 
                 res = np.fromfile(path, dtype=ctype, sep=",")
@@ -177,7 +182,7 @@ class TestFileBased:
 
             # Spaces at wrong places
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1+2 j,3\n")
 
                 with assert_warns(DeprecationWarning):
@@ -186,7 +191,7 @@ class TestFileBased:
 
             # Spaces at wrong places
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1+ 2j,3\n")
 
                 with assert_warns(DeprecationWarning):
@@ -195,7 +200,7 @@ class TestFileBased:
 
             # Spaces at wrong places
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1 +2j,3\n")
 
                 with assert_warns(DeprecationWarning):
@@ -204,7 +209,7 @@ class TestFileBased:
 
             # Spaces at wrong places
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1+j\n")
 
                 with assert_warns(DeprecationWarning):
@@ -213,7 +218,7 @@ class TestFileBased:
 
             # Spaces at wrong places
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1+\n")
 
                 with assert_warns(DeprecationWarning):
@@ -222,7 +227,7 @@ class TestFileBased:
 
             # Spaces at wrong places
             with temppath() as path:
-                with open(path, 'wt') as f:
+                with open(path, 'w') as f:
                     f.write("1j+1\n")
 
                 with assert_warns(DeprecationWarning):
@@ -235,7 +240,7 @@ class TestFileBased:
                         reason="Need strtold_l")
     def test_fromfile(self):
         with temppath() as path:
-            with open(path, 'wt') as f:
+            with open(path, 'w') as f:
                 f.write(self.out)
             res = np.fromfile(path, dtype=np.longdouble, sep="\n")
         assert_equal(res, self.tgt)
@@ -244,7 +249,7 @@ class TestFileBased:
                         reason="Need strtold_l")
     def test_genfromtxt(self):
         with temppath() as path:
-            with open(path, 'wt') as f:
+            with open(path, 'w') as f:
                 f.write(self.out)
             res = np.genfromtxt(path, dtype=np.longdouble)
         assert_equal(res, self.tgt)
@@ -253,7 +258,7 @@ class TestFileBased:
                         reason="Need strtold_l")
     def test_loadtxt(self):
         with temppath() as path:
-            with open(path, 'wt') as f:
+            with open(path, 'w') as f:
                 f.write(self.out)
             res = np.loadtxt(path, dtype=np.longdouble)
         assert_equal(res, self.tgt)
@@ -368,3 +373,23 @@ def test_longdouble_from_int(int_val):
     True, False])
 def test_longdouble_from_bool(bool_val):
     assert np.longdouble(bool_val) == np.longdouble(int(bool_val))
+
+
+@pytest.mark.skipif(
+    not (IS_MUSL and platform.machine() == "x86_64"),
+    reason="only need to run on musllinux_x86_64"
+)
+def test_musllinux_x86_64_signature():
+    # this test may fail if you're emulating musllinux_x86_64 on a different
+    # architecture, but should pass natively.
+    known_sigs = [b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf']
+    sig = (np.longdouble(-1.0) / np.longdouble(10.0)
+           ).newbyteorder('<').tobytes()[:10]
+    assert sig in known_sigs
+
+
+def test_eps_positive():
+    # np.finfo('g').eps should be positive on all platforms. If this isn't true
+    # then something may have gone wrong with the MachArLike, e.g. if
+    # np.core.getlimits._discovered_machar didn't work properly
+    assert np.finfo(np.longdouble).eps > 0.
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_mem_overlap.py b/contrib/python/numpy/py3/numpy/core/tests/test_mem_overlap.py
index d66decfda07..1fd4c4d4120 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_mem_overlap.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_mem_overlap.py
@@ -55,7 +55,7 @@ def _indices(ndims):
 def _check_assignment(srcidx, dstidx):
     """Check assignment arr[dstidx] = arr[srcidx] works."""
 
-    arr = np.arange(np.product(shape)).reshape(shape)
+    arr = np.arange(np.prod(shape)).reshape(shape)
 
     cpy = arr.copy()
 
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_mem_policy.py b/contrib/python/numpy/py3/numpy/core/tests/test_mem_policy.py
index d5dfbc38b3d..60cdaa8faa2 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_mem_policy.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_mem_policy.py
@@ -89,6 +89,7 @@ def get_module(tmp_path):
          """),
     ]
     prologue = '''
+        #define NPY_TARGET_VERSION NPY_1_22_API_VERSION
         #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
         #include <numpy/arrayobject.h>
         /*
@@ -394,16 +395,19 @@ def test_switch_owner(get_module, policy):
     a = get_module.get_array()
     assert np.core.multiarray.get_handler_name(a) is None
     get_module.set_own(a)
-    oldval = os.environ.get('NUMPY_WARN_IF_NO_MEM_POLICY', None)
+
     if policy is None:
-        if 'NUMPY_WARN_IF_NO_MEM_POLICY' in os.environ:
-            os.environ.pop('NUMPY_WARN_IF_NO_MEM_POLICY')
+        # See what we expect to be set based on the env variable
+        policy = os.getenv("NUMPY_WARN_IF_NO_MEM_POLICY", "0") == "1"
+        oldval = None
     else:
-        os.environ['NUMPY_WARN_IF_NO_MEM_POLICY'] = policy
+        policy = policy == "1"
+        oldval = np.core._multiarray_umath._set_numpy_warn_if_no_mem_policy(
+            policy)
     try:
         # The policy should be NULL, so we have to assume we can call
         # "free".  A warning is given if the policy == "1"
-        if policy == "1":
+        if policy:
             with assert_warns(RuntimeWarning) as w:
                 del a
                 gc.collect()
@@ -412,11 +416,8 @@ def test_switch_owner(get_module, policy):
             gc.collect()
 
     finally:
-        if oldval is None:
-            if 'NUMPY_WARN_IF_NO_MEM_POLICY' in os.environ:
-                os.environ.pop('NUMPY_WARN_IF_NO_MEM_POLICY')
-        else:
-            os.environ['NUMPY_WARN_IF_NO_MEM_POLICY'] = oldval
+        if oldval is not None:
+            np.core._multiarray_umath._set_numpy_warn_if_no_mem_policy(oldval)
 
 def test_owner_is_base(get_module):
     a = get_module.get_array_with_base()
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_memmap.py b/contrib/python/numpy/py3/numpy/core/tests/test_memmap.py
index 914f86f1480..ad074b312d5 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_memmap.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_memmap.py
@@ -6,7 +6,7 @@ from pathlib import Path
 from tempfile import NamedTemporaryFile, TemporaryFile
 
 from numpy import (
-    memmap, sum, average, product, ndarray, isscalar, add, subtract, multiply)
+    memmap, sum, average, prod, ndarray, isscalar, add, subtract, multiply)
 
 from numpy import arange, allclose, asarray
 from numpy.testing import (
@@ -153,7 +153,7 @@ class TestMemmap:
 
         with suppress_warnings() as sup:
             sup.filter(FutureWarning, "np.average currently does not preserve")
-            for unary_op in [sum, average, product]:
+            for unary_op in [sum, average, prod]:
                 result = unary_op(fp)
                 assert_(isscalar(result))
                 assert_(result.__class__ is self.data[0, 0].__class__)
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_multiarray.py b/contrib/python/numpy/py3/numpy/core/tests/test_multiarray.py
index d8044fcbc78..c80d8d3ff6b 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_multiarray.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_multiarray.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import collections.abc
 import tempfile
 import sys
@@ -28,16 +30,23 @@ from numpy.testing import (
     assert_, assert_raises, assert_warns, assert_equal, assert_almost_equal,
     assert_array_equal, assert_raises_regex, assert_array_almost_equal,
     assert_allclose, IS_PYPY, IS_PYSTON, HAS_REFCOUNT, assert_array_less,
-    runstring, temppath, suppress_warnings, break_cycles,
+    runstring, temppath, suppress_warnings, break_cycles, _SUPPORTS_SVE,
     )
 from numpy.testing._private.utils import requires_memory, _no_tracing
 from __tests__.core.tests._locales import CommaDecimalPointLocale
 from numpy.lib.recfunctions import repack_fields
+from numpy.core.multiarray import _get_ndarray_c_version
 
 # Need to test an object that does not fully implement math interface
 from datetime import timedelta, datetime
 
 
+def assert_arg_sorted(arr, arg):
+    # resulting array should be sorted and arg values should be unique
+    assert_equal(arr[arg], np.sort(arr))
+    assert_equal(np.sort(arg), np.arange(len(arg)))
+
+
 def _aligned_zeros(shape, dtype=float, order="C", align=None):
     """
     Allocate a new ndarray with aligned memory.
@@ -1193,6 +1202,17 @@ class TestCreation:
         expected = expected * (arr.nbytes // len(expected))
         assert arr.tobytes() == expected
 
+    @pytest.mark.parametrize("func", [
+        np.array, np.asarray, np.asanyarray, np.ascontiguousarray,
+        np.asfortranarray])
+    def test_creation_from_dtypemeta(self, func):
+        dtype = np.dtype('i')
+        arr1 = func([1, 2, 3], dtype=dtype)
+        arr2 = func([1, 2, 3], dtype=type(dtype))
+        assert_array_equal(arr1, arr2)
+        assert arr2.dtype == dtype
+
+
 class TestStructured:
     def test_subarray_field_access(self):
         a = np.zeros((3, 5), dtype=[('a', ('i4', (2, 2)))])
@@ -1278,6 +1298,13 @@ class TestStructured:
         a = np.zeros((1, 0, 1), [('a', '<f8', (1, 1))])
         assert_equal(a, a)
 
+    @pytest.mark.parametrize("op", [operator.eq, operator.ne])
+    def test_structured_array_comparison_bad_broadcasts(self, op):
+        a = np.zeros(3, dtype='i,i')
+        b = np.array([], dtype="i,i")
+        with pytest.raises(ValueError):
+            op(a, b)
+
     def test_structured_comparisons_with_promotion(self):
         # Check that structured arrays can be compared so long as their
         # dtypes promote fine:
@@ -1298,7 +1325,10 @@ class TestStructured:
         assert_equal(a == b, [False, True])
         assert_equal(a != b, [True, False])
 
-    def test_void_comparison_failures(self):
+    @pytest.mark.parametrize("op", [
+            operator.eq, lambda x, y: operator.eq(y, x),
+            operator.ne, lambda x, y: operator.ne(y, x)])
+    def test_void_comparison_failures(self, op):
         # In principle, one could decide to return an array of False for some
         # if comparisons are impossible.  But right now we return TypeError
         # when "void" dtype are involved.
@@ -1306,18 +1336,18 @@ class TestStructured:
         y = np.zeros(3)
         # Cannot compare non-structured to structured:
         with pytest.raises(TypeError):
-            x == y
+            op(x, y)
 
         # Added title prevents promotion, but casts are OK:
         y = np.zeros(3, dtype=[(('title', 'a'), 'i1')])
         assert np.can_cast(y.dtype, x.dtype)
         with pytest.raises(TypeError):
-            x == y
+            op(x, y)
 
         x = np.zeros(3, dtype="V7")
         y = np.zeros(3, dtype="V8")
         with pytest.raises(TypeError):
-            x == y
+            op(x, y)
 
     def test_casting(self):
         # Check that casting a structured array to change its byte order
@@ -1695,7 +1725,7 @@ class TestBool:
 
     @pytest.mark.xfail(reason="See gh-9847")
     def test_cast_from_unicode(self):
-        self._test_cast_from_flexible(np.unicode_)
+        self._test_cast_from_flexible(np.str_)
 
     @pytest.mark.xfail(reason="See gh-9847")
     def test_cast_from_bytes(self):
@@ -1910,8 +1940,9 @@ class TestMethods:
                 assert_array_equal(a2.prod(axis=-1),
                                    np.array([24, 1890, 600], ctype))
 
-    def test_repeat(self):
-        m = np.array([1, 2, 3, 4, 5, 6])
+    @pytest.mark.parametrize('dtype', [None, object])
+    def test_repeat(self, dtype):
+        m = np.array([1, 2, 3, 4, 5, 6], dtype=dtype)
         m_rect = m.reshape((2, 3))
 
         A = m.repeat([1, 3, 2, 1, 1, 2])
@@ -2077,7 +2108,7 @@ class TestMethods:
                 msg = 'byte-swapped complex sort, dtype={0}'.format(dt)
                 assert_equal(c, arr, msg)
 
-    @pytest.mark.parametrize('dtype', [np.bytes_, np.unicode_])
+    @pytest.mark.parametrize('dtype', [np.bytes_, np.str_])
     def test_sort_string(self, dtype):
         # np.array will perform the encoding to bytes for us in the bytes test
         a = np.array(['aaaaaaaa' + chr(i) for i in range(101)], dtype=dtype)
@@ -2105,19 +2136,26 @@ class TestMethods:
             c.sort(kind=kind)
             assert_equal(c, a, msg)
 
-    def test_sort_structured(self):
+    @pytest.mark.parametrize("dt", [
+            np.dtype([('f', float), ('i', int)]),
+            np.dtype([('f', float), ('i', object)])])
+    @pytest.mark.parametrize("step", [1, 2])
+    def test_sort_structured(self, dt, step):
         # test record array sorts.
-        dt = np.dtype([('f', float), ('i', int)])
-        a = np.array([(i, i) for i in range(101)], dtype=dt)
+        a = np.array([(i, i) for i in range(101*step)], dtype=dt)
         b = a[::-1]
         for kind in ['q', 'h', 'm']:
             msg = "kind=%s" % kind
-            c = a.copy()
+            c = a.copy()[::step]
+            indx = c.argsort(kind=kind)
             c.sort(kind=kind)
-            assert_equal(c, a, msg)
-            c = b.copy()
+            assert_equal(c, a[::step], msg)
+            assert_equal(a[::step][indx], a[::step], msg)
+            c = b.copy()[::step]
+            indx = c.argsort(kind=kind)
             c.sort(kind=kind)
-            assert_equal(c, a, msg)
+            assert_equal(c, a[step-1::step], msg)
+            assert_equal(b[::step][indx], a[step-1::step], msg)
 
     @pytest.mark.parametrize('dtype', ['datetime64[D]', 'timedelta64[D]'])
     def test_sort_time(self, dtype):
@@ -2351,7 +2389,7 @@ class TestMethods:
 
         # test unicode argsorts.
         s = 'aaaaaaaa'
-        a = np.array([s + chr(i) for i in range(101)], dtype=np.unicode_)
+        a = np.array([s + chr(i) for i in range(101)], dtype=np.str_)
         b = a[::-1]
         r = np.arange(101)
         rr = r[::-1]
@@ -2434,7 +2472,7 @@ class TestMethods:
         a = np.array(['aaaaaaaaa' for i in range(100)])
         assert_equal(a.argsort(kind='m'), r)
         # unicode
-        a = np.array(['aaaaaaaaa' for i in range(100)], dtype=np.unicode_)
+        a = np.array(['aaaaaaaaa' for i in range(100)], dtype=np.str_)
         assert_equal(a.argsort(kind='m'), r)
 
     def test_sort_unicode_kind(self):
@@ -2578,7 +2616,7 @@ class TestMethods:
                       'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100197_1',
                       'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100198_1',
                       'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100199_1'],
-                     dtype=np.unicode_)
+                     dtype=np.str_)
         ind = np.arange(len(a))
         assert_equal([a.searchsorted(v, 'left') for v in a], ind)
         assert_equal([a.searchsorted(v, 'right') for v in a], ind + 1)
@@ -3613,9 +3651,13 @@ class TestMethods:
             msg = 'dtype: {0}'.format(dt)
             ap = complex(a)
             assert_equal(ap, a, msg)
-            bp = complex(b)
+
+            with assert_warns(DeprecationWarning):
+                bp = complex(b)
             assert_equal(bp, b, msg)
-            cp = complex(c)
+
+            with assert_warns(DeprecationWarning):
+                cp = complex(c)
             assert_equal(cp, c, msg)
 
     def test__complex__should_not_work(self):
@@ -3638,7 +3680,8 @@ class TestMethods:
         assert_raises(TypeError, complex, d)
 
         e = np.array(['1+1j'], 'U')
-        assert_raises(TypeError, complex, e)
+        with assert_warns(DeprecationWarning):
+            assert_raises(TypeError, complex, e)
 
 class TestCequenceMethods:
     def test_array_contains(self):
@@ -3700,7 +3743,7 @@ class TestBinop:
             'and':      (np.bitwise_and, True, int),
             'xor':      (np.bitwise_xor, True, int),
             'or':       (np.bitwise_or, True, int),
-            'matmul':   (np.matmul, False, float),
+            'matmul':   (np.matmul, True, float),
             # 'ge':       (np.less_equal, False),
             # 'gt':       (np.less, False),
             # 'le':       (np.greater_equal, False),
@@ -4718,23 +4761,23 @@ class TestArgmax:
 
         a = np.array([1, 2**7 - 1, -2**7], dtype=np.int8)
         assert_equal(np.argmax(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmax(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmax(a), 129)
 
         a = np.array([1, 2**15 - 1, -2**15], dtype=np.int16)
         assert_equal(np.argmax(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmax(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmax(a), 129)
 
         a = np.array([1, 2**31 - 1, -2**31], dtype=np.int32)
         assert_equal(np.argmax(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmax(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmax(a), 129)
 
         a = np.array([1, 2**63 - 1, -2**63], dtype=np.int64)
         assert_equal(np.argmax(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmax(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmax(a), 129)
 
 class TestArgmin:
     usg_data = [
@@ -4860,23 +4903,23 @@ class TestArgmin:
 
         a = np.array([1, -2**7, -2**7 + 1, 2**7 - 1], dtype=np.int8)
         assert_equal(np.argmin(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmin(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmin(a), 129)
 
         a = np.array([1, -2**15, -2**15 + 1, 2**15 - 1], dtype=np.int16)
         assert_equal(np.argmin(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmin(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmin(a), 129)
 
         a = np.array([1, -2**31, -2**31 + 1, 2**31 - 1], dtype=np.int32)
         assert_equal(np.argmin(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmin(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmin(a), 129)
 
         a = np.array([1, -2**63, -2**63 + 1, 2**63 - 1], dtype=np.int64)
         assert_equal(np.argmin(a), 1)
-        a.repeat(129)
-        assert_equal(np.argmin(a), 1)
+        a = a.repeat(129)
+        assert_equal(np.argmin(a), 129)
 
 class TestMinMax:
 
@@ -5074,6 +5117,22 @@ class TestPutmask:
         with pytest.raises(ValueError):
             np.putmask(a, a >= 2, 3)
 
+    def test_kwargs(self):
+        x = np.array([0, 0])
+        np.putmask(x, [0, 1], [-1, -2])
+        assert_array_equal(x, [0, -2])
+
+        x = np.array([0, 0])
+        np.putmask(x, mask=[0, 1], values=[-1, -2])
+        assert_array_equal(x, [0, -2])
+
+        x = np.array([0, 0])
+        np.putmask(x, values=[-1, -2],  mask=[0, 1])
+        assert_array_equal(x, [0, -2])
+
+        with pytest.raises(TypeError):
+            np.putmask(a=x, values=[-1, -2],  mask=[0, 1])
+
 
 class TestTake:
     def tst_basic(self, x):
@@ -5525,33 +5584,6 @@ class TestIO:
             tmp_filename,
             dtype='<f4')
 
-    @pytest.mark.slow  # takes > 1 minute on mechanical hard drive
-    def test_big_binary(self):
-        """Test workarounds for 32-bit limit for MSVC fwrite, fseek, and ftell
-
-        These normally would hang doing something like this.
-        See : https://github.com/numpy/numpy/issues/2256
-        """
-        if sys.platform != 'win32' or '[GCC ' in sys.version:
-            return
-        try:
-            # before workarounds, only up to 2**32-1 worked
-            fourgbplus = 2**32 + 2**16
-            testbytes = np.arange(8, dtype=np.int8)
-            n = len(testbytes)
-            flike = tempfile.NamedTemporaryFile()
-            f = flike.file
-            np.tile(testbytes, fourgbplus // testbytes.nbytes).tofile(f)
-            flike.seek(0)
-            a = np.fromfile(f, dtype=np.int8)
-            flike.close()
-            assert_(len(a) == fourgbplus)
-            # check only start and end for speed:
-            assert_((a[:n] == testbytes).all())
-            assert_((a[-n:] == testbytes).all())
-        except (MemoryError, ValueError):
-            pass
-
     def test_string(self, tmp_filename):
         self._check_from(b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, sep=',')
 
@@ -6204,7 +6236,7 @@ class TestStats:
     def test_mean_axis_error(self):
         # Ensure that AxisError is raised instead of IndexError when axis is
         # out of bounds, see gh-15817.
-        with assert_raises(np.core._exceptions.AxisError):
+        with assert_raises(np.exceptions.AxisError):
             np.arange(10).mean(axis=2)
 
     def test_mean_where(self):
@@ -6288,7 +6320,7 @@ class TestStats:
     def test_var_axis_error(self):
         # Ensure that AxisError is raised instead of IndexError when axis is
         # out of bounds, see gh-15817.
-        with assert_raises(np.core._exceptions.AxisError):
+        with assert_raises(np.exceptions.AxisError):
             np.arange(10).var(axis=2)
 
     def test_var_where(self):
@@ -6655,6 +6687,22 @@ class TestDot:
         r = np.empty((1024, 32), dtype=int)
         assert_raises(ValueError, dot, f, v, r)
 
+    def test_dot_out_result(self):
+        x = np.ones((), dtype=np.float16)
+        y = np.ones((5,), dtype=np.float16)
+        z = np.zeros((5,), dtype=np.float16)
+        res = x.dot(y, out=z)
+        assert np.array_equal(res, y)
+        assert np.array_equal(z, y)
+
+    def test_dot_out_aliasing(self):
+        x = np.ones((), dtype=np.float16)
+        y = np.ones((5,), dtype=np.float16)
+        z = np.zeros((5,), dtype=np.float16)
+        res = x.dot(y, out=z)
+        z[0] = 2
+        assert np.array_equal(res, z)
+
     def test_dot_array_order(self):
         a = np.array([[1, 2], [3, 4]], order='C')
         b = np.array([[1, 2], [3, 4]], order='F')
@@ -7162,16 +7210,69 @@ class TestMatmulOperator(MatmulCommon):
         assert_raises(TypeError, self.matmul, np.void(b'abc'), np.void(b'abc'))
         assert_raises(TypeError, self.matmul, np.arange(10), np.void(b'abc'))
 
-def test_matmul_inplace():
-    # It would be nice to support in-place matmul eventually, but for now
-    # we don't have a working implementation, so better just to error out
-    # and nudge people to writing "a = a @ b".
-    a = np.eye(3)
-    b = np.eye(3)
-    assert_raises(TypeError, a.__imatmul__, b)
-    import operator
-    assert_raises(TypeError, operator.imatmul, a, b)
-    assert_raises(TypeError, exec, "a @= b", globals(), locals())
+
+class TestMatmulInplace:
+    DTYPES = {}
+    for i in MatmulCommon.types:
+        for j in MatmulCommon.types:
+            if np.can_cast(j, i):
+                DTYPES[f"{i}-{j}"] = (np.dtype(i), np.dtype(j))
+
+    @pytest.mark.parametrize("dtype1,dtype2", DTYPES.values(), ids=DTYPES)
+    def test_basic(self, dtype1: np.dtype, dtype2: np.dtype) -> None:
+        a = np.arange(10).reshape(5, 2).astype(dtype1)
+        a_id = id(a)
+        b = np.ones((2, 2), dtype=dtype2)
+
+        ref = a @ b
+        a @= b
+
+        assert id(a) == a_id
+        assert a.dtype == dtype1
+        assert a.shape == (5, 2)
+        if dtype1.kind in "fc":
+            np.testing.assert_allclose(a, ref)
+        else:
+            np.testing.assert_array_equal(a, ref)
+
+    SHAPES = {
+        "2d_large": ((10**5, 10), (10, 10)),
+        "3d_large": ((10**4, 10, 10), (1, 10, 10)),
+        "1d": ((3,), (3,)),
+        "2d_1d": ((3, 3), (3,)),
+        "1d_2d": ((3,), (3, 3)),
+        "2d_broadcast": ((3, 3), (3, 1)),
+        "2d_broadcast_reverse": ((1, 3), (3, 3)),
+        "3d_broadcast1": ((3, 3, 3), (1, 3, 1)),
+        "3d_broadcast2": ((3, 3, 3), (1, 3, 3)),
+        "3d_broadcast3": ((3, 3, 3), (3, 3, 1)),
+        "3d_broadcast_reverse1": ((1, 3, 3), (3, 3, 3)),
+        "3d_broadcast_reverse2": ((3, 1, 3), (3, 3, 3)),
+        "3d_broadcast_reverse3": ((1, 1, 3), (3, 3, 3)),
+    }
+
+    @pytest.mark.parametrize("a_shape,b_shape", SHAPES.values(), ids=SHAPES)
+    def test_shapes(self, a_shape: tuple[int, ...], b_shape: tuple[int, ...]):
+        a_size = np.prod(a_shape)
+        a = np.arange(a_size).reshape(a_shape).astype(np.float64)
+        a_id = id(a)
+
+        b_size = np.prod(b_shape)
+        b = np.arange(b_size).reshape(b_shape)
+
+        ref = a @ b
+        if ref.shape != a_shape:
+            with pytest.raises(ValueError):
+                a @= b
+            return
+        else:
+            a @= b
+
+        assert id(a) == a_id
+        assert a.dtype.type == np.float64
+        assert a.shape == a_shape
+        np.testing.assert_allclose(a, ref)
+
 
 def test_matmul_axes():
     a = np.arange(3*4*5).reshape(3, 4, 5)
@@ -8667,14 +8768,16 @@ class TestConversion:
         int_funcs = (int, lambda x: x.__int__())
         for int_func in int_funcs:
             assert_equal(int_func(np.array(0)), 0)
-            assert_equal(int_func(np.array([1])), 1)
-            assert_equal(int_func(np.array([[42]])), 42)
+            with assert_warns(DeprecationWarning):
+                assert_equal(int_func(np.array([1])), 1)
+            with assert_warns(DeprecationWarning):
+                assert_equal(int_func(np.array([[42]])), 42)
             assert_raises(TypeError, int_func, np.array([1, 2]))
 
             # gh-9972
             assert_equal(4, int_func(np.array('4')))
             assert_equal(5, int_func(np.bytes_(b'5')))
-            assert_equal(6, int_func(np.unicode_('6')))
+            assert_equal(6, int_func(np.str_('6')))
 
             # The delegation of int() to __trunc__ was deprecated in
             # Python 3.11.
@@ -8683,7 +8786,8 @@ class TestConversion:
                     def __trunc__(self):
                         return 3
                 assert_equal(3, int_func(np.array(HasTrunc())))
-                assert_equal(3, int_func(np.array([HasTrunc()])))
+                with assert_warns(DeprecationWarning):
+                    assert_equal(3, int_func(np.array([HasTrunc()])))
             else:
                 pass
 
@@ -8692,8 +8796,9 @@ class TestConversion:
                     raise NotImplementedError
             assert_raises(NotImplementedError,
                 int_func, np.array(NotConvertible()))
-            assert_raises(NotImplementedError,
-                int_func, np.array([NotConvertible()]))
+            with assert_warns(DeprecationWarning):
+                assert_raises(NotImplementedError,
+                    int_func, np.array([NotConvertible()]))
 
 
 class TestWhere:
@@ -8860,6 +8965,11 @@ class TestWhere:
             result = array.nonzero()
             assert_array_equal(benchmark, result)
 
+    def test_kwargs(self):
+        a = np.zeros(1)
+        with assert_raises(TypeError):
+            np.where(a, x=a, y=a)
+
 
 if not IS_PYPY:
     # sys.getsizeof() is not valid on PyPy
@@ -9041,33 +9151,33 @@ class TestUnicodeEncoding:
     def test_assign_scalar(self):
         # gh-3258
         l = np.array(['aa', 'bb'])
-        l[:] = np.unicode_('cc')
+        l[:] = np.str_('cc')
         assert_equal(l, ['cc', 'cc'])
 
     def test_fill_scalar(self):
         # gh-7227
         l = np.array(['aa', 'bb'])
-        l.fill(np.unicode_('cc'))
+        l.fill(np.str_('cc'))
         assert_equal(l, ['cc', 'cc'])
 
 
 class TestUnicodeArrayNonzero:
 
     def test_empty_ustring_array_is_falsey(self):
-        assert_(not np.array([''], dtype=np.unicode_))
+        assert_(not np.array([''], dtype=np.str_))
 
     def test_whitespace_ustring_array_is_falsey(self):
-        a = np.array(['eggs'], dtype=np.unicode_)
+        a = np.array(['eggs'], dtype=np.str_)
         a[0] = '  \0\0'
         assert_(not a)
 
     def test_all_null_ustring_array_is_falsey(self):
-        a = np.array(['eggs'], dtype=np.unicode_)
+        a = np.array(['eggs'], dtype=np.str_)
         a[0] = '\0\0\0\0'
         assert_(not a)
 
     def test_null_inside_ustring_array_is_truthy(self):
-        a = np.array(['eggs'], dtype=np.unicode_)
+        a = np.array(['eggs'], dtype=np.str_)
         a[0] = ' \0 \0'
         assert_(a)
 
@@ -9500,6 +9610,105 @@ def test_equal_override():
         assert_equal(array != my_always_equal, 'ne')
 
 
+@pytest.mark.parametrize("op", [operator.eq, operator.ne])
+@pytest.mark.parametrize(["dt1", "dt2"], [
+        ([("f", "i")], [("f", "i")]),  # structured comparison (successful)
+        ("M8", "d"),  # impossible comparison: result is all True or False
+        ("d", "d"),  # valid comparison
+        ])
+def test_equal_subclass_no_override(op, dt1, dt2):
+    # Test how the three different possible code-paths deal with subclasses
+
+    class MyArr(np.ndarray):
+        called_wrap = 0
+
+        def __array_wrap__(self, new):
+            type(self).called_wrap += 1
+            return super().__array_wrap__(new)
+
+    numpy_arr = np.zeros(5, dtype=dt1)
+    my_arr = np.zeros(5, dtype=dt2).view(MyArr)
+
+    assert type(op(numpy_arr, my_arr)) is MyArr
+    assert type(op(my_arr, numpy_arr)) is MyArr
+    # We expect 2 calls (more if there were more fields):
+    assert MyArr.called_wrap == 2
+
+
+@pytest.mark.parametrize(["dt1", "dt2"], [
+        ("M8[ns]", "d"),
+        ("M8[s]", "l"),
+        ("m8[ns]", "d"),
+        # Missing: ("m8[ns]", "l") as timedelta currently promotes ints
+        ("M8[s]", "m8[s]"),
+        ("S5", "U5"),
+        # Structured/void dtypes have explicit paths not tested here.
+])
+def test_no_loop_gives_all_true_or_false(dt1, dt2):
+    # Make sure they broadcast to test result shape, use random values, since
+    # the actual value should be ignored
+    arr1 = np.random.randint(5, size=100).astype(dt1)
+    arr2 = np.random.randint(5, size=99)[:, np.newaxis].astype(dt2)
+
+    res = arr1 == arr2
+    assert res.shape == (99, 100)
+    assert res.dtype == bool
+    assert not res.any()
+
+    res = arr1 != arr2
+    assert res.shape == (99, 100)
+    assert res.dtype == bool
+    assert res.all()
+
+    # incompatible shapes raise though
+    arr2 = np.random.randint(5, size=99).astype(dt2)
+    with pytest.raises(ValueError):
+        arr1 == arr2
+
+    with pytest.raises(ValueError):
+        arr1 != arr2
+
+    # Basic test with another operation:
+    with pytest.raises(np.core._exceptions._UFuncNoLoopError):
+        arr1 > arr2
+
+
+@pytest.mark.parametrize("op", [
+        operator.eq, operator.ne, operator.le, operator.lt, operator.ge,
+        operator.gt])
+def test_comparisons_forwards_error(op):
+    class NotArray:
+        def __array__(self):
+            raise TypeError("run you fools")
+
+    with pytest.raises(TypeError, match="run you fools"):
+        op(np.arange(2), NotArray())
+
+    with pytest.raises(TypeError, match="run you fools"):
+        op(NotArray(), np.arange(2))
+
+
+def test_richcompare_scalar_boolean_singleton_return():
+    # These are currently guaranteed to be the boolean singletons, but maybe
+    # returning NumPy booleans would also be OK:
+    assert (np.array(0) == "a") is False
+    assert (np.array(0) != "a") is True
+    assert (np.int16(0) == "a") is False
+    assert (np.int16(0) != "a") is True
+
+
+@pytest.mark.parametrize("op", [
+        operator.eq, operator.ne, operator.le, operator.lt, operator.ge,
+        operator.gt])
+def test_ragged_comparison_fails(op):
+    # This needs to convert the internal array to True/False, which fails:
+    a = np.array([1, np.array([1, 2, 3])], dtype=object)
+    b = np.array([1, np.array([1, 2, 3])], dtype=object)
+
+    with pytest.raises(ValueError, match="The truth value.*ambiguous"):
+        op(a, b)
+
+
 @pytest.mark.parametrize(
     ["fun", "npfun"],
     [
@@ -9726,40 +9935,50 @@ class TestViewDtype:
         assert_array_equal(x.view('<i2'), expected)
 
 
+@pytest.mark.xfail(_SUPPORTS_SVE, reason="gh-22982")
 # Test various array sizes that hit different code paths in quicksort-avx512
-@pytest.mark.parametrize("N", [8, 16, 24, 32, 48, 64, 96, 128, 151, 191,
-                               256, 383, 512, 1023, 2047])
-def test_sort_float(N):
+@pytest.mark.parametrize("N", np.arange(1, 512))
+@pytest.mark.parametrize("dtype", ['e', 'f', 'd'])
+def test_sort_float(N, dtype):
     # Regular data with nan sprinkled
     np.random.seed(42)
-    arr = -0.5 + np.random.sample(N).astype('f')
+    arr = -0.5 + np.random.sample(N).astype(dtype)
     arr[np.random.choice(arr.shape[0], 3)] = np.nan
     assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))
 
     # (2) with +INF
-    infarr = np.inf*np.ones(N, dtype='f')
+    infarr = np.inf*np.ones(N, dtype=dtype)
     infarr[np.random.choice(infarr.shape[0], 5)] = -1.0
     assert_equal(np.sort(infarr, kind='quick'), np.sort(infarr, kind='heap'))
 
     # (3) with -INF
-    neginfarr = -np.inf*np.ones(N, dtype='f')
+    neginfarr = -np.inf*np.ones(N, dtype=dtype)
     neginfarr[np.random.choice(neginfarr.shape[0], 5)] = 1.0
     assert_equal(np.sort(neginfarr, kind='quick'),
                  np.sort(neginfarr, kind='heap'))
 
     # (4) with +/-INF
-    infarr = np.inf*np.ones(N, dtype='f')
+    infarr = np.inf*np.ones(N, dtype=dtype)
     infarr[np.random.choice(infarr.shape[0], (int)(N/2))] = -np.inf
     assert_equal(np.sort(infarr, kind='quick'), np.sort(infarr, kind='heap'))
 
-
-def test_sort_int():
-    # Random data with NPY_MAX_INT32 and NPY_MIN_INT32 sprinkled
-    rng = np.random.default_rng(42)
-    N = 2047
-    minv = np.iinfo(np.int32).min
-    maxv = np.iinfo(np.int32).max
-    arr = rng.integers(low=minv, high=maxv, size=N).astype('int32')
+def test_sort_float16():
+    arr = np.arange(65536, dtype=np.int16)
+    temp = np.frombuffer(arr.tobytes(), dtype=np.float16)
+    data = np.copy(temp)
+    np.random.shuffle(data)
+    data_backup = data
+    assert_equal(np.sort(data, kind='quick'),
+            np.sort(data_backup, kind='heap'))
+
+
+@pytest.mark.parametrize("N", np.arange(1, 512))
+@pytest.mark.parametrize("dtype", ['h', 'H', 'i', 'I', 'l', 'L'])
+def test_sort_int(N, dtype):
+    # Random data with MAX and MIN sprinkled
+    minv = np.iinfo(dtype).min
+    maxv = np.iinfo(dtype).max
+    arr = np.random.randint(low=minv, high=maxv-1, size=N, dtype=dtype)
     arr[np.random.choice(arr.shape[0], 10)] = minv
     arr[np.random.choice(arr.shape[0], 10)] = maxv
     assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))
@@ -9773,3 +9992,42 @@ def test_sort_uint():
     arr = rng.integers(low=0, high=maxv, size=N).astype('uint32')
     arr[np.random.choice(arr.shape[0], 10)] = maxv
     assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))
+
+def test_private_get_ndarray_c_version():
+    assert isinstance(_get_ndarray_c_version(), int)
+
+
+@pytest.mark.parametrize("N", np.arange(1, 512))
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+def test_argsort_float(N, dtype):
+    rnd = np.random.RandomState(116112)
+    # (1) Regular data with a few nan: doesn't use vectorized sort
+    arr = -0.5 + rnd.random(N).astype(dtype)
+    arr[rnd.choice(arr.shape[0], 3)] = np.nan
+    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
+
+    # (2) Random data with inf at the end of array
+    # See: https://github.com/intel/x86-simd-sort/pull/39
+    arr = -0.5 + rnd.rand(N).astype(dtype)
+    arr[N-1] = np.inf
+    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
+
+
+@pytest.mark.parametrize("N", np.arange(2, 512))
+@pytest.mark.parametrize("dtype", [np.int32, np.uint32, np.int64, np.uint64])
+def test_argsort_int(N, dtype):
+    rnd = np.random.RandomState(1100710816)
+    # (1) random data with min and max values
+    minv = np.iinfo(dtype).min
+    maxv = np.iinfo(dtype).max
+    arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
+    i, j = rnd.choice(N, 2, replace=False)
+    arr[i] = minv
+    arr[j] = maxv
+    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
+
+    # (2) random data with max value at the end of array
+    # See: https://github.com/intel/x86-simd-sort/pull/39
+    arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
+    arr[N-1] = maxv
+    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_nditer.py b/contrib/python/numpy/py3/numpy/core/tests/test_nditer.py
index 395ccad3bdd..35bd6e97e04 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_nditer.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_nditer.py
@@ -1457,6 +1457,38 @@ def test_iter_copy_casts_structured():
         assert_array_equal(res2["b"][field], expected)
 
 
+def test_iter_copy_casts_structured2():
+    # Similar to the above, this is a fairly arcane test to cover internals
+    in_dtype = np.dtype([("a", np.dtype("O,O")),
+                         ("b", np.dtype("(5)O,(3)O,(1,)O,(1,)i,(1,)O"))])
+    out_dtype = np.dtype([("a", np.dtype("O")),
+                          ("b", np.dtype("O,(3)i,(4)O,(4)O,(4)i"))])
+
+    arr = np.ones(1, dtype=in_dtype)
+    it = np.nditer((arr,), ["buffered", "external_loop", "refs_ok"],
+                   op_dtypes=[out_dtype], casting="unsafe")
+    it_copy = it.copy()
+
+    res1 = next(it)
+    del it
+    res2 = next(it_copy)
+    del it_copy
+
+    # Array of two structured scalars:
+    for res in res1, res2:
+        # Cast to tuple by getitem, which may be weird and changable?:
+        assert type(res["a"][0]) == tuple
+        assert res["a"][0] == (1, 1)
+
+    for res in res1, res2:
+        assert_array_equal(res["b"]["f0"][0], np.ones(5, dtype=object))
+        assert_array_equal(res["b"]["f1"], np.ones((1, 3), dtype="i"))
+        assert res["b"]["f2"].shape == (1, 4)
+        assert_array_equal(res["b"]["f2"][0], np.ones(4, dtype=object))
+        assert_array_equal(res["b"]["f3"][0], np.ones(4, dtype=object))
+        assert_array_equal(res["b"]["f3"][0], np.ones(4, dtype="i"))
+
+
 def test_iter_allocate_output_simple():
     # Check that the iterator will properly allocate outputs
 
@@ -2260,7 +2292,7 @@ def test_iter_buffering_string():
     assert_equal(i[0], b'abc')
     assert_equal(i[0].dtype, np.dtype('S6'))
 
-    a = np.array(['abc', 'a', 'abcd'], dtype=np.unicode_)
+    a = np.array(['abc', 'a', 'abcd'], dtype=np.str_)
     assert_equal(a.dtype, np.dtype('U4'))
     assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
                     op_dtypes='U2')
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_nep50_promotions.py b/contrib/python/numpy/py3/numpy/core/tests/test_nep50_promotions.py
index 20c9000f60b..5c587102eae 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_nep50_promotions.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_nep50_promotions.py
@@ -132,7 +132,7 @@ def test_nep50_weak_integers_with_inexact(dtype):
 
 @pytest.mark.parametrize("op", [operator.add, operator.pow, operator.eq])
 def test_weak_promotion_scalar_path(op):
-    # Some additional paths excercising the weak scalars.
+    # Some additional paths exercising the weak scalars.
     np._set_promotion_state("weak")
 
     # Integer path:
@@ -181,3 +181,67 @@ def test_nep50_integer_regression():
     arr = np.array(1)
     assert (arr + 2**63).dtype == np.float64
     assert (arr[()] + 2**63).dtype == np.float64
+
+
+def test_nep50_with_axisconcatenator():
+    # I promised that this will be an error in the future in the 1.25
+    # release notes;  test this (NEP 50 opt-in makes the deprecation an error).
+    np._set_promotion_state("weak")
+
+    with pytest.raises(OverflowError):
+        np.r_[np.arange(5, dtype=np.int8), 255]
+
+
+@pytest.mark.parametrize("ufunc", [np.add, np.power])
+@pytest.mark.parametrize("state", ["weak", "weak_and_warn"])
+def test_nep50_huge_integers(ufunc, state):
+    # Very large integers are complicated, because they go to uint64 or
+    # object dtype.  This tests covers a few possible paths (some of which
+    # cannot give the NEP 50 warnings).
+    np._set_promotion_state(state)
+
+    with pytest.raises(OverflowError):
+        ufunc(np.int64(0), 2**63)  # 2**63 too large for int64
+
+    if state == "weak_and_warn":
+        with pytest.warns(UserWarning,
+                match="result dtype changed.*float64.*uint64"):
+            with pytest.raises(OverflowError):
+                ufunc(np.uint64(0), 2**64)
+    else:
+        with pytest.raises(OverflowError):
+            ufunc(np.uint64(0), 2**64)  # 2**64 cannot be represented by uint64
+
+    # However, 2**63 can be represented by the uint64 (and that is used):
+    if state == "weak_and_warn":
+        with pytest.warns(UserWarning,
+                match="result dtype changed.*float64.*uint64"):
+            res = ufunc(np.uint64(1), 2**63)
+    else:
+        res = ufunc(np.uint64(1), 2**63)
+
+    assert res.dtype == np.uint64
+    assert res == ufunc(1, 2**63, dtype=object)
+
+    # The following paths fail to warn correctly about the change:
+    with pytest.raises(OverflowError):
+        ufunc(np.int64(1), 2**63)  # np.array(2**63) would go to uint
+
+    with pytest.raises(OverflowError):
+        ufunc(np.int64(1), 2**100)  # np.array(2**100) would go to object
+
+    # This would go to object and thus a Python float, not a NumPy one:
+    res = ufunc(1.0, 2**100)
+    assert isinstance(res, np.float64)
+
+
+def test_nep50_in_concat_and_choose():
+    np._set_promotion_state("weak_and_warn")
+
+    with pytest.warns(UserWarning, match="result dtype changed"):
+        res = np.concatenate([np.float32(1), 1.], axis=None)
+    assert res.dtype == "float32"
+
+    with pytest.warns(UserWarning, match="result dtype changed"):
+        res = np.choose(1, [np.float32(1), 1.])
+    assert res.dtype == "float32"
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_numeric.py b/contrib/python/numpy/py3/numpy/core/tests/test_numeric.py
index 3cc168b3429..dd41d6a6234 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_numeric.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_numeric.py
@@ -114,7 +114,9 @@ class TestNonarrayArgs:
 
     def test_cumproduct(self):
         A = [[1, 2, 3], [4, 5, 6]]
-        assert_(np.all(np.cumproduct(A) == np.array([1, 2, 6, 24, 120, 720])))
+        with assert_warns(DeprecationWarning):
+            expected = np.array([1, 2, 6, 24, 120, 720])
+            assert_(np.all(np.cumproduct(A) == expected))
 
     def test_diagonal(self):
         a = [[0, 1, 2, 3],
@@ -1193,8 +1195,8 @@ class TestFromiter:
         expected = np.array(list(self.makegen()))
         a = np.fromiter(self.makegen(), int)
         a20 = np.fromiter(self.makegen(), int, 20)
-        assert_(np.alltrue(a == expected, axis=0))
-        assert_(np.alltrue(a20 == expected[:20], axis=0))
+        assert_(np.all(a == expected, axis=0))
+        assert_(np.all(a20 == expected[:20], axis=0))
 
     def load_data(self, n, eindex):
         # Utility method for the issue 2592 tests.
@@ -1822,20 +1824,11 @@ class TestClip:
         self.nr = 5
         self.nc = 3
 
-    def fastclip(self, a, m, M, out=None, casting=None):
-        if out is None:
-            if casting is None:
-                return a.clip(m, M)
-            else:
-                return a.clip(m, M, casting=casting)
-        else:
-            if casting is None:
-                return a.clip(m, M, out)
-            else:
-                return a.clip(m, M, out, casting=casting)
+    def fastclip(self, a, m, M, out=None, **kwargs):
+        return a.clip(m, M, out=out, **kwargs)
 
     def clip(self, a, m, M, out=None):
-        # use slow-clip
+        # use a.choose to verify fastclip result
         selector = np.less(a, m) + 2*np.greater(a, M)
         return selector.choose((a, m, M), out=out)
 
@@ -1991,14 +1984,13 @@ class TestClip:
         ac = np.zeros(a.shape, dtype=np.int32)
         act = ac.copy()
         if casting is None:
-            with assert_warns(DeprecationWarning):
-                # NumPy 1.17.0, 2018-02-24 - casting is unsafe
+            with pytest.raises(TypeError):
                 self.fastclip(a, m, M, ac, casting=casting)
         else:
             # explicitly passing "unsafe" will silence warning
             self.fastclip(a, m, M, ac, casting=casting)
-        self.clip(a, m, M, act)
-        assert_array_strict_equal(ac, act)
+            self.clip(a, m, M, act)
+            assert_array_strict_equal(ac, act)
 
     def test_simple_int64_out(self):
         # Test native int32 input with int32 scalar min/max and int64 out.
@@ -2018,9 +2010,7 @@ class TestClip:
         M = np.float64(1)
         ac = np.zeros(a.shape, dtype=np.int32)
         act = ac.copy()
-        with assert_warns(DeprecationWarning):
-            # NumPy 1.17.0, 2018-02-24 - casting is unsafe
-            self.fastclip(a, m, M, ac)
+        self.fastclip(a, m, M, out=ac, casting="unsafe")
         self.clip(a, m, M, act)
         assert_array_strict_equal(ac, act)
 
@@ -2031,9 +2021,7 @@ class TestClip:
         M = 2.0
         ac = np.zeros(a.shape, dtype=np.int32)
         act = ac.copy()
-        with assert_warns(DeprecationWarning):
-            # NumPy 1.17.0, 2018-02-24 - casting is unsafe
-            self.fastclip(a, m, M, ac)
+        self.fastclip(a, m, M, out=ac, casting="unsafe")
         self.clip(a, m, M, act)
         assert_array_strict_equal(ac, act)
 
@@ -2209,9 +2197,7 @@ class TestClip:
         M = np.float64(2)
         ac = np.zeros(a.shape, dtype=np.int32)
         act = ac.copy()
-        with assert_warns(DeprecationWarning):
-            # NumPy 1.17.0, 2018-02-24 - casting is unsafe
-            self.fastclip(a, m, M, ac)
+        self.fastclip(a, m, M, out=ac, casting="unsafe")
         self.clip(a, m, M, act)
         assert_array_strict_equal(ac, act)
 
@@ -2233,9 +2219,7 @@ class TestClip:
         M = np.float64(1)
         ac = np.zeros(a.shape, dtype=np.int32)
         act = ac.copy()
-        with assert_warns(DeprecationWarning):
-            # NumPy 1.17.0, 2018-02-24 - casting is unsafe
-            self.fastclip(a, m, M, ac)
+        self.fastclip(a, m, M, out=ac, casting="unsafe")
         self.clip(a, m, M, act)
         assert_array_strict_equal(ac, act)
 
@@ -2246,9 +2230,7 @@ class TestClip:
         M = 2.0
         ac = np.zeros(a.shape, dtype=np.int32)
         act = ac.copy()
-        with assert_warns(DeprecationWarning):
-            # NumPy 1.17.0, 2018-02-24 - casting is unsafe
-            self.fastclip(a, m, M, ac)
+        self.fastclip(a, m, M, out=ac, casting="unsafe")
         self.clip(a, m, M, act)
         assert_array_strict_equal(ac, act)
 
@@ -2301,16 +2283,11 @@ class TestClip:
 
     def test_clip_nan(self):
         d = np.arange(7.)
-        with assert_warns(DeprecationWarning):
-            assert_equal(d.clip(min=np.nan), d)
-        with assert_warns(DeprecationWarning):
-            assert_equal(d.clip(max=np.nan), d)
-        with assert_warns(DeprecationWarning):
-            assert_equal(d.clip(min=np.nan, max=np.nan), d)
-        with assert_warns(DeprecationWarning):
-            assert_equal(d.clip(min=-2, max=np.nan), d)
-        with assert_warns(DeprecationWarning):
-            assert_equal(d.clip(min=np.nan, max=10), d)
+        assert_equal(d.clip(min=np.nan), np.nan)
+        assert_equal(d.clip(max=np.nan), np.nan)
+        assert_equal(d.clip(min=np.nan, max=np.nan), np.nan)
+        assert_equal(d.clip(min=-2, max=np.nan), np.nan)
+        assert_equal(d.clip(min=np.nan, max=10), np.nan)
 
     def test_object_clip(self):
         a = np.arange(10, dtype=object)
@@ -2362,16 +2339,12 @@ class TestClip:
         actual = np.clip(arr, amin, amax)
         assert_equal(actual, exp)
 
-    @pytest.mark.xfail(reason="no scalar nan propagation yet",
-                       raises=AssertionError,
-                       strict=True)
     @pytest.mark.parametrize("arr, amin, amax", [
         # problematic scalar nan case from hypothesis
         (np.zeros(10, dtype=np.int64),
          np.array(np.nan),
          np.zeros(10, dtype=np.int32)),
     ])
-    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
     def test_clip_scalar_nan_propagation(self, arr, amin, amax):
         # enforcement of scalar nan propagation for comparisons
         # called through clip()
@@ -2379,7 +2352,7 @@ class TestClip:
         actual = np.clip(arr, amin, amax)
         assert_equal(actual, expected)
 
-    @pytest.mark.xfail(reason="propagation doesn't match spec")
+    @pytest.mark.skip(reason="propagation doesn't match spec")
     @pytest.mark.parametrize("arr, amin, amax", [
         (np.array([1] * 10, dtype='m8'),
          np.timedelta64('NaT'),
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_numerictypes.py b/contrib/python/numpy/py3/numpy/core/tests/test_numerictypes.py
index 072cd65feaf..bab5bf24666 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_numerictypes.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_numerictypes.py
@@ -339,23 +339,28 @@ class TestEmptyField:
 
 class TestCommonType:
     def test_scalar_loses1(self):
-        res = np.find_common_type(['f4', 'f4', 'i2'], ['f8'])
+        with pytest.warns(DeprecationWarning, match="np.find_common_type"):
+            res = np.find_common_type(['f4', 'f4', 'i2'], ['f8'])
         assert_(res == 'f4')
 
     def test_scalar_loses2(self):
-        res = np.find_common_type(['f4', 'f4'], ['i8'])
+        with pytest.warns(DeprecationWarning, match="np.find_common_type"):
+            res = np.find_common_type(['f4', 'f4'], ['i8'])
         assert_(res == 'f4')
 
     def test_scalar_wins(self):
-        res = np.find_common_type(['f4', 'f4', 'i2'], ['c8'])
+        with pytest.warns(DeprecationWarning, match="np.find_common_type"):
+            res = np.find_common_type(['f4', 'f4', 'i2'], ['c8'])
         assert_(res == 'c8')
 
     def test_scalar_wins2(self):
-        res = np.find_common_type(['u4', 'i4', 'i4'], ['f4'])
+        with pytest.warns(DeprecationWarning, match="np.find_common_type"):
+            res = np.find_common_type(['u4', 'i4', 'i4'], ['f4'])
         assert_(res == 'f8')
 
     def test_scalar_wins3(self):  # doesn't go up to 'f16' on purpose
-        res = np.find_common_type(['u8', 'i8', 'i8'], ['f8'])
+        with pytest.warns(DeprecationWarning, match="np.find_common_type"):
+            res = np.find_common_type(['u8', 'i8', 'i8'], ['f8'])
         assert_(res == 'f8')
 
 class TestMultipleFields:
@@ -473,7 +478,8 @@ class TestMaximumSctype:
     def test_complex(self, t):
         assert_equal(np.maximum_sctype(t), np.sctypes['complex'][-1])
 
-    @pytest.mark.parametrize('t', [np.bool_, np.object_, np.unicode_, np.bytes_, np.void])
+    @pytest.mark.parametrize('t', [np.bool_, np.object_, np.str_, np.bytes_,
+                                   np.void])
     def test_other(self, t):
         assert_equal(np.maximum_sctype(t), t)
 
@@ -485,7 +491,7 @@ class Test_sctype2char:
     def test_scalar_type(self):
         assert_equal(np.sctype2char(np.double), 'd')
         assert_equal(np.sctype2char(np.int_), 'l')
-        assert_equal(np.sctype2char(np.unicode_), 'U')
+        assert_equal(np.sctype2char(np.str_), 'U')
         assert_equal(np.sctype2char(np.bytes_), 'S')
 
     def test_other_type(self):
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_overrides.py b/contrib/python/numpy/py3/numpy/core/tests/test_overrides.py
index 63432ffa70d..5924358eaba 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_overrides.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_overrides.py
@@ -10,16 +10,11 @@ from numpy.testing import (
     assert_, assert_equal, assert_raises, assert_raises_regex)
 from numpy.core.overrides import (
     _get_implementing_args, array_function_dispatch,
-    verify_matching_signatures, ARRAY_FUNCTION_ENABLED)
+    verify_matching_signatures)
 from numpy.compat import pickle
 import pytest
 
 
-requires_array_function = pytest.mark.skipif(
-    not ARRAY_FUNCTION_ENABLED,
-    reason="__array_function__ dispatch not enabled.")
-
-
 def _return_not_implemented(self, *args, **kwargs):
     return NotImplemented
 
@@ -150,7 +145,6 @@ class TestGetImplementingArgs:
 
 class TestNDArrayArrayFunction:
 
-    @requires_array_function
     def test_method(self):
 
         class Other:
@@ -209,7 +203,6 @@ class TestNDArrayArrayFunction:
                                      args=(array,), kwargs={})
 
 
-@requires_array_function
 class TestArrayFunctionDispatch:
 
     def test_pickle(self):
@@ -248,8 +241,20 @@ class TestArrayFunctionDispatch:
         with assert_raises_regex(TypeError, 'no implementation found'):
             dispatched_one_arg(array)
 
+    def test_where_dispatch(self):
+
+        class DuckArray:
+            def __array_function__(self, ufunc, method, *inputs, **kwargs):
+                return "overridden"
+
+        array = np.array(1)
+        duck_array = DuckArray()
+
+        result = np.std(array, where=duck_array)
+
+        assert_equal(result, "overridden")
+
 
-@requires_array_function
 class TestVerifyMatchingSignatures:
 
     def test_verify_matching_signatures(self):
@@ -302,7 +307,6 @@ def _new_duck_type_and_implements():
     return (MyArray, implements)
 
 
-@requires_array_function
 class TestArrayFunctionImplementation:
 
     def test_one_arg(self):
@@ -355,6 +359,17 @@ class TestArrayFunctionImplementation:
                 TypeError, "no implementation found for 'my.func'"):
             func(MyArray())
 
+    @pytest.mark.parametrize("name", ["concatenate", "mean", "asarray"])
+    def test_signature_error_message_simple(self, name):
+        func = getattr(np, name)
+        try:
+            # all of these functions need an argument:
+            func()
+        except TypeError as e:
+            exc = e
+
+        assert exc.args[0].startswith(f"{name}()")
+
     def test_signature_error_message(self):
         # The lambda function will be named "<lambda>", but the TypeError
         # should show the name as "func"
@@ -366,7 +381,7 @@ class TestArrayFunctionImplementation:
             pass
 
         try:
-            func(bad_arg=3)
+            func._implementation(bad_arg=3)
         except TypeError as e:
             expected_exception = e
 
@@ -374,6 +389,12 @@ class TestArrayFunctionImplementation:
             func(bad_arg=3)
             raise AssertionError("must fail")
         except TypeError as exc:
+            if exc.args[0].startswith("_dispatcher"):
+                # We replace the qualname currently, but it used `__name__`
+                # (relevant functions have the same name and qualname anyway)
+                pytest.skip("Python version is not using __qualname__ for "
+                            "TypeError formatting.")
+
             assert exc.args == expected_exception.args
 
     @pytest.mark.parametrize("value", [234, "this func is not replaced"])
@@ -394,6 +415,56 @@ class TestArrayFunctionImplementation:
         except TypeError as exc:
             assert exc is error  # unmodified exception
 
+    def test_properties(self):
+        # Check that str and repr are sensible
+        func = dispatched_two_arg
+        assert str(func) == str(func._implementation)
+        repr_no_id = repr(func).split("at ")[0]
+        repr_no_id_impl = repr(func._implementation).split("at ")[0]
+        assert repr_no_id == repr_no_id_impl
+
+    @pytest.mark.parametrize("func", [
+            lambda x, y: 0,  # no like argument
+            lambda like=None: 0,  # not keyword only
+            lambda *, like=None, a=3: 0,  # not last (not that it matters)
+        ])
+    def test_bad_like_sig(self, func):
+        # We sanity check the signature, and these should fail.
+        with pytest.raises(RuntimeError):
+            array_function_dispatch()(func)
+
+    def test_bad_like_passing(self):
+        # Cover internal sanity check for passing like as first positional arg
+        def func(*, like=None):
+            pass
+
+        func_with_like = array_function_dispatch()(func)
+        with pytest.raises(TypeError):
+            func_with_like()
+        with pytest.raises(TypeError):
+            func_with_like(like=234)
+
+    def test_too_many_args(self):
+        # Mainly a unit-test to increase coverage
+        objs = []
+        for i in range(40):
+            class MyArr:
+                def __array_function__(self, *args, **kwargs):
+                    return NotImplemented
+
+            objs.append(MyArr())
+
+        def _dispatch(*args):
+            return args
+
+        @array_function_dispatch(_dispatch)
+        def func(*args):
+            pass
+
+        with pytest.raises(TypeError, match="maximum number"):
+            func(*objs)
+
+
 
 class TestNDArrayMethods:
 
@@ -422,7 +493,6 @@ class TestNumPyFunctions:
         signature = inspect.signature(np.sum)
         assert_('axis' in signature.parameters)
 
-    @requires_array_function
     def test_override_sum(self):
         MyArray, implements = _new_duck_type_and_implements()
 
@@ -432,7 +502,6 @@ class TestNumPyFunctions:
 
         assert_equal(np.sum(MyArray()), 'yes')
 
-    @requires_array_function
     def test_sum_on_mock_array(self):
 
         # We need a proxy for mocks because __array_function__ is only looked
@@ -453,7 +522,6 @@ class TestNumPyFunctions:
             np.sum, (ArrayProxy,), (proxy,), {})
         proxy.value.__array__.assert_not_called()
 
-    @requires_array_function
     def test_sum_forwarding_implementation(self):
 
         class MyArray(np.ndarray):
@@ -505,7 +573,6 @@ class TestArrayLike:
     def func_args(*args, **kwargs):
         return args, kwargs
 
-    @requires_array_function
     def test_array_like_not_implemented(self):
         self.add_method('array', self.MyArray)
 
@@ -538,7 +605,6 @@ class TestArrayLike:
 
     @pytest.mark.parametrize('function, args, kwargs', _array_tests)
     @pytest.mark.parametrize('numpy_ref', [True, False])
-    @requires_array_function
     def test_array_like(self, function, args, kwargs, numpy_ref):
         self.add_method('array', self.MyArray)
         self.add_method(function, self.MyArray)
@@ -571,7 +637,6 @@ class TestArrayLike:
 
     @pytest.mark.parametrize('function, args, kwargs', _array_tests)
     @pytest.mark.parametrize('ref', [1, [1], "MyNoArrayFunctionArray"])
-    @requires_array_function
     def test_no_array_function_like(self, function, args, kwargs, ref):
         self.add_method('array', self.MyNoArrayFunctionArray)
         self.add_method(function, self.MyNoArrayFunctionArray)
@@ -613,7 +678,6 @@ class TestArrayLike:
                 assert type(array_like) is self.MyArray
                 assert array_like.function is self.MyArray.fromfile
 
-    @requires_array_function
     def test_exception_handling(self):
         self.add_method('array', self.MyArray, enable_value_error=True)
 
@@ -640,3 +704,56 @@ class TestArrayLike:
             array_like.fill(1)
             expected.fill(1)
         assert_equal(array_like, expected)
+
+
+def test_function_like():
+    # We provide a `__get__` implementation, make sure it works
+    assert type(np.mean) is np.core._multiarray_umath._ArrayFunctionDispatcher 
+
+    class MyClass:
+        def __array__(self):
+            # valid argument to mean:
+            return np.arange(3)
+
+        func1 = staticmethod(np.mean)
+        func2 = np.mean
+        func3 = classmethod(np.mean)
+
+    m = MyClass()
+    assert m.func1([10]) == 10
+    assert m.func2() == 1  # mean of the arange
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        # Tries to operate on the class
+        m.func3()
+
+    # Manual binding also works (the above may shortcut):
+    bound = np.mean.__get__(m, MyClass)
+    assert bound() == 1
+
+    bound = np.mean.__get__(None, MyClass)  # unbound actually
+    assert bound([10]) == 10
+
+    bound = np.mean.__get__(MyClass)  # classmethod
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        bound()
+
+
+def test_scipy_trapz_support_shim():
+    # SciPy 1.10 and earlier "clone" trapz in this way, so we have a
+    # support shim in place: https://github.com/scipy/scipy/issues/17811
+    # That should be removed eventually.  This test copies what SciPy does.
+    # Hopefully removable 1 year after SciPy 1.11; shim added to NumPy 1.25.
+    import types
+    import functools
+
+    def _copy_func(f):
+        # Based on http://stackoverflow.com/a/6528148/190597 (Glenn Maynard)
+        g = types.FunctionType(f.__code__, f.__globals__, name=f.__name__,
+                            argdefs=f.__defaults__, closure=f.__closure__)
+        g = functools.update_wrapper(g, f)
+        g.__kwdefaults__ = f.__kwdefaults__
+        return g
+
+    trapezoid = _copy_func(np.trapz)
+
+    assert np.trapz([1, 2]) == trapezoid([1, 2])
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_print.py b/contrib/python/numpy/py3/numpy/core/tests/test_print.py
index 034e9ea4a25..e94dfdbb169 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_print.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_print.py
@@ -3,7 +3,7 @@ import sys
 import pytest
 
 import numpy as np
-from numpy.testing import assert_, assert_equal
+from numpy.testing import assert_, assert_equal, IS_MUSL
 from __tests__.core.tests._locales import CommaDecimalPointLocale
 
 
@@ -196,5 +196,7 @@ class TestCommaDecimalPointLocale(CommaDecimalPointLocale):
     def test_locale_double(self):
         assert_equal(str(np.double(1.2)), str(float(1.2)))
 
+    @pytest.mark.skipif(IS_MUSL,
+                        reason="test flaky on musllinux")
     def test_locale_longdouble(self):
         assert_equal(str(np.longdouble('1.2')), str(float(1.2)))
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_regression.py b/contrib/python/numpy/py3/numpy/core/tests/test_regression.py
index 4a34fad6d29..defe3d6f15e 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_regression.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_regression.py
@@ -130,10 +130,7 @@ class TestRegression:
         assert_(a[1] == 'auto')
         assert_(a[0] != 'auto')
         b = np.linspace(0, 10, 11)
-        # This should return true for now, but will eventually raise an error:
-        with suppress_warnings() as sup:
-            sup.filter(FutureWarning)
-            assert_(b != 'auto')
+        assert_array_equal(b != 'auto', np.ones(11, dtype=bool))
         assert_(b[0] != 'auto')
 
     def test_unicode_swapping(self):
@@ -297,7 +294,7 @@ class TestRegression:
 
     def test_unicode_string_comparison(self):
         # Ticket #190
-        a = np.array('hello', np.unicode_)
+        a = np.array('hello', np.str_)
         b = np.array('world')
         a == b
 
@@ -460,7 +457,7 @@ class TestRegression:
 
         test_data = [
             # (original, py2_pickle)
-            (np.unicode_('\u6f2c'),
+            (np.str_('\u6f2c'),
              b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n"
              b"(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\n"
              b"I0\ntp6\nbS',o\\x00\\x00'\np7\ntp8\nRp9\n."),
@@ -521,22 +518,15 @@ class TestRegression:
     def test_method_args(self):
         # Make sure methods and functions have same default axis
         # keyword and arguments
-        funcs1 = ['argmax', 'argmin', 'sum', ('product', 'prod'),
-                 ('sometrue', 'any'),
-                 ('alltrue', 'all'), 'cumsum', ('cumproduct', 'cumprod'),
-                 'ptp', 'cumprod', 'prod', 'std', 'var', 'mean',
-                 'round', 'min', 'max', 'argsort', 'sort']
+        funcs1 = ['argmax', 'argmin', 'sum', 'any', 'all', 'cumsum',
+                  'ptp', 'cumprod', 'prod', 'std', 'var', 'mean',
+                  'round', 'min', 'max', 'argsort', 'sort']
         funcs2 = ['compress', 'take', 'repeat']
 
         for func in funcs1:
             arr = np.random.rand(8, 7)
             arr2 = arr.copy()
-            if isinstance(func, tuple):
-                func_meth = func[1]
-                func = func[0]
-            else:
-                func_meth = func
-            res1 = getattr(arr, func_meth)()
+            res1 = getattr(arr, func)()
             res2 = getattr(np, func)(arr2)
             if res1 is None:
                 res1 = arr
@@ -1341,8 +1331,8 @@ class TestRegression:
         # Ticket #1058
         a = np.fromiter(list(range(10)), dtype='b')
         b = np.fromiter(list(range(10)), dtype='B')
-        assert_(np.alltrue(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
-        assert_(np.alltrue(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
+        assert_(np.all(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
+        assert_(np.all(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
 
     def test_array_from_sequence_scalar_array(self):
         # Ticket #1078: segfaults when creating an array with a sequence of
@@ -1520,8 +1510,8 @@ class TestRegression:
     def test_fromiter_comparison(self):
         a = np.fromiter(list(range(10)), dtype='b')
         b = np.fromiter(list(range(10)), dtype='B')
-        assert_(np.alltrue(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
-        assert_(np.alltrue(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
+        assert_(np.all(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
+        assert_(np.all(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
 
     def test_fromstring_crash(self):
         # Ticket #1345: the following should not cause a crash
@@ -1540,9 +1530,12 @@ class TestRegression:
             for y in dtypes:
                 c = a.astype(y)
                 try:
-                    np.dot(b, c)
+                    d = np.dot(b, c)
                 except TypeError:
                     failures.append((x, y))
+                else:
+                    if d != 0:
+                        failures.append((x, y))
         if failures:
             raise AssertionError("Failures: %r" % failures)
 
@@ -1676,7 +1669,9 @@ class TestRegression:
 
     def test_find_common_type_boolean(self):
         # Ticket #1695
-        assert_(np.find_common_type([], ['?', '?']) == '?')
+        with pytest.warns(DeprecationWarning, match="np.find_common_type"):
+            res = np.find_common_type([], ['?', '?'])
+        assert res == '?'
 
     def test_empty_mul(self):
         a = np.array([1.])
@@ -1690,7 +1685,7 @@ class TestRegression:
         # number 2, and the exception hung around until something checked
         # PyErr_Occurred() and returned an error.
         assert_equal(np.dtype('S10').itemsize, 10)
-        np.array([['abc', 2], ['long   ', '0123456789']], dtype=np.string_)
+        np.array([['abc', 2], ['long   ', '0123456789']], dtype=np.bytes_)
         assert_equal(np.dtype('S10').itemsize, 10)
 
     def test_any_float(self):
@@ -1959,7 +1954,7 @@ class TestRegression:
         # Python2 output for pickle.dumps(...)
         datas = [
             # (original, python2_pickle, koi8r_validity)
-            (np.unicode_('\u6bd2'),
+            (np.str_('\u6bd2'),
              (b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n"
               b"(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\nI0\n"
               b"tp6\nbS'\\xd2k\\x00\\x00'\np7\ntp8\nRp9\n."),
@@ -2083,7 +2078,7 @@ class TestRegression:
         # Ticket #1578, the mismatch only showed up when running
         # python-debug for python versions >= 2.7, and then as
         # a core dump and error message.
-        a = np.array(['abc'], dtype=np.unicode_)[0]
+        a = np.array(['abc'], dtype=np.str_)[0]
         del a
 
     def test_refcount_error_in_clip(self):
@@ -2230,7 +2225,7 @@ class TestRegression:
     def test_pickle_empty_string(self):
         # gh-3926
         for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
-            test_string = np.string_('')
+            test_string = np.bytes_('')
             assert_equal(pickle.loads(
                 pickle.dumps(test_string, protocol=proto)), test_string)
 
@@ -2351,7 +2346,7 @@ class TestRegression:
         values = {
             np.void: b"a",
             np.bytes_: b"a",
-            np.unicode_: "a",
+            np.str_: "a",
             np.datetime64: "2017-08-25",
         }
         for sctype in scalar_types:
@@ -2558,3 +2553,14 @@ class TestRegression:
                 f"Unexpected types order of ufunc in {operation}"
                 f"for {order}. Possible fix: Use signed before unsigned"
                 "in generate_umath.py")
+
+    def test_nonbool_logical(self):
+        # gh-22845
+        # create two arrays with bit patterns that do not overlap.
+        # needs to be large enough to test both SIMD and scalar paths
+        size = 100
+        a = np.frombuffer(b'\x01' * size, dtype=np.bool_)
+        b = np.frombuffer(b'\x80' * size, dtype=np.bool_)
+        expected = np.ones(size, dtype=np.bool_)
+        assert_array_equal(np.logical_and(a, b), expected)
+
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_scalar_methods.py b/contrib/python/numpy/py3/numpy/core/tests/test_scalar_methods.py
index a53e47b1974..18a7bc828b3 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_scalar_methods.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_scalar_methods.py
@@ -1,7 +1,6 @@
 """
 Test the scalar constructors, which also do type-coercion
 """
-import sys
 import fractions
 import platform
 import types
@@ -10,7 +9,7 @@ from typing import Any, Type
 import pytest
 import numpy as np
 
-from numpy.testing import assert_equal, assert_raises
+from numpy.testing import assert_equal, assert_raises, IS_MUSL
 
 
 class TestAsIntegerRatio:
@@ -99,6 +98,8 @@ class TestAsIntegerRatio:
             try:
                 nf = np.longdouble(n)
                 df = np.longdouble(d)
+                if not np.isfinite(df):
+                    raise OverflowError
             except (OverflowError, RuntimeWarning):
                 # the values may not fit in any float type
                 pytest.skip("longdouble too small on this platform")
@@ -132,7 +133,6 @@ class TestIsInteger:
             assert not value.is_integer()
 
 
-@pytest.mark.skipif(sys.version_info < (3, 9), reason="Requires python 3.9")
 class TestClassGetItem:
     @pytest.mark.parametrize("cls", [
         np.number,
@@ -186,14 +186,6 @@ class TestClassGetItem:
         assert np.number[Any]
 
 
-@pytest.mark.skipif(sys.version_info >= (3, 9), reason="Requires python 3.8")
-@pytest.mark.parametrize("cls", [np.number, np.complexfloating, np.int64])
-def test_class_getitem_38(cls: Type[np.number]) -> None:
-    match = "Type subscription requires python >= 3.9"
-    with pytest.raises(TypeError, match=match):
-        cls[Any]
-
-
 class TestBitCount:
     # derived in part from the cpython test "test_bit_count"
 
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_scalarbuffer.py b/contrib/python/numpy/py3/numpy/core/tests/test_scalarbuffer.py
index 0e6ab1015e1..31b0494cf00 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_scalarbuffer.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_scalarbuffer.py
@@ -68,11 +68,11 @@ class TestScalarPEP3118:
             get_buffer_info(x, ["WRITABLE"])
 
     def test_void_scalar_structured_data(self):
-        dt = np.dtype([('name', np.unicode_, 16), ('grades', np.float64, (2,))])
+        dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
         x = np.array(('ndarray_scalar', (1.2, 3.0)), dtype=dt)[()]
         assert_(isinstance(x, np.void))
         mv_x = memoryview(x)
-        expected_size = 16 * np.dtype((np.unicode_, 1)).itemsize
+        expected_size = 16 * np.dtype((np.str_, 1)).itemsize
         expected_size += 2 * np.dtype(np.float64).itemsize
         assert_equal(mv_x.itemsize, expected_size)
         assert_equal(mv_x.ndim, 0)
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_scalarinherit.py b/contrib/python/numpy/py3/numpy/core/tests/test_scalarinherit.py
index f697c3c817d..f9c574d5798 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_scalarinherit.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_scalarinherit.py
@@ -58,8 +58,8 @@ class TestInherit:
 class TestCharacter:
     def test_char_radd(self):
         # GH issue 9620, reached gentype_add and raise TypeError
-        np_s = np.string_('abc')
-        np_u = np.unicode_('abc')
+        np_s = np.bytes_('abc')
+        np_u = np.str_('abc')
         s = b'def'
         u = 'def'
         assert_(np_s.__radd__(np_s) is NotImplemented)
@@ -90,8 +90,8 @@ class TestCharacter:
         assert ret == b"defabc"
 
     def test_char_repeat(self):
-        np_s = np.string_('abc')
-        np_u = np.unicode_('abc')
+        np_s = np.bytes_('abc')
+        np_u = np.str_('abc')
         res_s = b'abc' * 5
         res_u = 'abc' * 5
         assert_(np_s * 5 == res_s)
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_scalarmath.py b/contrib/python/numpy/py3/numpy/core/tests/test_scalarmath.py
index 65b8121a612..9977c8b1163 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_scalarmath.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_scalarmath.py
@@ -4,7 +4,7 @@ import warnings
 import itertools
 import operator
 import platform
-from numpy.compat import _pep440
+from numpy._utils import _pep440
 import pytest
 from hypothesis import given, settings
 from hypothesis.strategies import sampled_from
@@ -14,9 +14,15 @@ import numpy as np
 from numpy.testing import (
     assert_, assert_equal, assert_raises, assert_almost_equal,
     assert_array_equal, IS_PYPY, suppress_warnings, _gen_alignment_data,
-    assert_warns,
+    assert_warns, _SUPPORTS_SVE,
     )
 
+try:
+    COMPILERS = np.show_config(mode="dicts")["Compilers"]
+    USING_CLANG_CL = COMPILERS["c"]["name"] == "clang-cl"
+except TypeError:
+    USING_CLANG_CL = False
+
 types = [np.bool_, np.byte, np.ubyte, np.short, np.ushort, np.intc, np.uintc,
          np.int_, np.uint, np.longlong, np.ulonglong,
          np.single, np.double, np.longdouble, np.csingle,
@@ -146,6 +152,7 @@ def test_int_float_promotion_truediv(fscalar):
 
 
 class TestBaseMath:
+    @pytest.mark.xfail(_SUPPORTS_SVE, reason="gh-22982")
     def test_blocked(self):
         # test alignments offsets for simd instructions
         # alignments for vz + 2 * (vs - 1) + 1
@@ -797,7 +804,13 @@ class TestBitShifts:
     @pytest.mark.parametrize('op',
         [operator.rshift, operator.lshift], ids=['>>', '<<'])
     def test_shift_all_bits(self, type_code, op):
-        """ Shifts where the shift amount is the width of the type or wider """
+        """Shifts where the shift amount is the width of the type or wider """
+        if (
+                USING_CLANG_CL and
+                type_code in ("l", "L") and
+                op is operator.lshift
+        ):
+            pytest.xfail("Failing on clang-cl builds")
         # gh-2449
         dt = np.dtype(type_code)
         nbits = dt.itemsize * 8
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_scalarprint.py b/contrib/python/numpy/py3/numpy/core/tests/test_scalarprint.py
index 4deb5a0a4c2..98d1f4aa11f 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_scalarprint.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_scalarprint.py
@@ -8,7 +8,7 @@ import sys
 
 from tempfile import TemporaryFile
 import numpy as np
-from numpy.testing import assert_, assert_equal, assert_raises
+from numpy.testing import assert_, assert_equal, assert_raises, IS_MUSL
 
 class TestRealScalars:
     def test_str(self):
@@ -260,10 +260,10 @@ class TestRealScalars:
         assert_equal(fpos64('324', unique=False, precision=5,
                                    fractional=False), "324.00")
 
-
     def test_dragon4_interface(self):
         tps = [np.float16, np.float32, np.float64]
-        if hasattr(np, 'float128'):
+        # test is flaky for musllinux on np.float128
+        if hasattr(np, 'float128') and not IS_MUSL:
             tps.append(np.float128)
 
         fpos = np.format_float_positional
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_shape_base.py b/contrib/python/numpy/py3/numpy/core/tests/test_shape_base.py
index 570d006f5ad..0428b95a945 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_shape_base.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_shape_base.py
@@ -152,9 +152,9 @@ class TestHstack:
         assert_array_equal(res, desired)
 
     def test_generator(self):
-        with assert_warns(FutureWarning):
+        with pytest.raises(TypeError, match="arrays to stack must be"):
             hstack((np.arange(3) for _ in range(2)))
-        with assert_warns(FutureWarning):
+        with pytest.raises(TypeError, match="arrays to stack must be"):
             hstack(map(lambda x: x, np.ones((3, 2))))
 
     def test_casting_and_dtype(self):
@@ -207,7 +207,7 @@ class TestVstack:
         assert_array_equal(res, desired)
 
     def test_generator(self):
-        with assert_warns(FutureWarning):
+        with pytest.raises(TypeError, match="arrays to stack must be"):
             vstack((np.arange(3) for _ in range(2)))
 
     def test_casting_and_dtype(self):
@@ -472,10 +472,11 @@ def test_stack():
                         stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
     assert_raises_regex(ValueError, 'must have the same shape',
                         stack, [np.arange(2), np.arange(3)])
-    # generator is deprecated
-    with assert_warns(FutureWarning):
-        result = stack((x for x in range(3)))
-    assert_array_equal(result, np.array([0, 1, 2]))
+
+    # do not accept generators
+    with pytest.raises(TypeError, match="arrays to stack must be"):
+        stack((x for x in range(3)))
+
     #casting and dtype test
     a = np.array([1, 2, 3])
     b = np.array([2.5, 3.5, 4.5])
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_simd.py b/contrib/python/numpy/py3/numpy/core/tests/test_simd.py
index 2c16243db30..92b567446d9 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_simd.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_simd.py
@@ -35,6 +35,9 @@ class _Test_Utility:
         """
         return getattr(self.npyv, attr + "_" + self.sfx)
 
+    def _x2(self, intrin_name):
+        return getattr(self.npyv, f"{intrin_name}_{self.sfx}x2")
+
     def _data(self, start=None, count=None, reverse=False):
         """
         Create list of consecutive numbers according to number of vector's lanes.
@@ -380,6 +383,11 @@ class _SIMD_FP(_Test_Utility):
         nfms = self.nmulsub(vdata_a, vdata_b, vdata_c)
         data_nfms = self.mul(data_fma, self.setall(-1))
         assert nfms == data_nfms
+        # multiply, add for odd elements and subtract even elements.
+        # (a * b) -+ c
+        fmas = list(self.muladdsub(vdata_a, vdata_b, vdata_c))
+        assert fmas[0::2] == list(data_fms)[0::2]
+        assert fmas[1::2] == list(data_fma)[1::2]
 
     def test_abs(self):
         pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
@@ -678,25 +686,34 @@ class _SIMD_ALL(_Test_Utility):
         assert store_h[:self.nlanes//2] == data[self.nlanes//2:]
         assert store_h != vdata  # detect overflow
 
-    def test_memory_partial_load(self):
-        if self.sfx in ("u8", "s8", "u16", "s16"):
+    @pytest.mark.parametrize("intrin, elsizes, scale, fill", [
+        ("self.load_tillz, self.load_till", (32, 64), 1, [0xffff]),
+        ("self.load2_tillz, self.load2_till", (32, 64), 2, [0xffff, 0x7fff]),
+    ])
+    def test_memory_partial_load(self, intrin, elsizes, scale, fill):
+        if self._scalar_size() not in elsizes:
             return
-
+        npyv_load_tillz, npyv_load_till = eval(intrin)
         data = self._data()
         lanes = list(range(1, self.nlanes + 1))
         lanes += [self.nlanes**2, self.nlanes**4] # test out of range
         for n in lanes:
-            load_till  = self.load_till(data, n, 15)
-            data_till  = data[:n] + [15] * (self.nlanes-n)
+            load_till = npyv_load_till(data, n, *fill)
+            load_tillz = npyv_load_tillz(data, n)
+            n *= scale
+            data_till = data[:n] + fill * ((self.nlanes-n) // scale)
             assert load_till == data_till
-            load_tillz = self.load_tillz(data, n)
             data_tillz = data[:n] + [0] * (self.nlanes-n)
             assert load_tillz == data_tillz
 
-    def test_memory_partial_store(self):
-        if self.sfx in ("u8", "s8", "u16", "s16"):
+    @pytest.mark.parametrize("intrin, elsizes, scale", [
+        ("self.store_till", (32, 64), 1),
+        ("self.store2_till", (32, 64), 2),
+    ])
+    def test_memory_partial_store(self, intrin, elsizes, scale):
+        if self._scalar_size() not in elsizes:
             return
-
+        npyv_store_till = eval(intrin)
         data = self._data()
         data_rev = self._data(reverse=True)
         vdata = self.load(data)
@@ -704,105 +721,159 @@ class _SIMD_ALL(_Test_Utility):
         lanes += [self.nlanes**2, self.nlanes**4]
         for n in lanes:
             data_till = data_rev.copy()
-            data_till[:n] = data[:n]
+            data_till[:n*scale] = data[:n*scale]
             store_till = self._data(reverse=True)
-            self.store_till(store_till, n, vdata)
+            npyv_store_till(store_till, n, vdata)
             assert store_till == data_till
 
-    def test_memory_noncont_load(self):
-        if self.sfx in ("u8", "s8", "u16", "s16"):
+    @pytest.mark.parametrize("intrin, elsizes, scale", [
+        ("self.loadn", (32, 64), 1),
+        ("self.loadn2", (32, 64), 2),
+    ])
+    def test_memory_noncont_load(self, intrin, elsizes, scale):
+        if self._scalar_size() not in elsizes:
             return
-
-        for stride in range(1, 64):
-            data = self._data(count=stride*self.nlanes)
-            data_stride = data[::stride]
-            loadn = self.loadn(data, stride)
-            assert loadn == data_stride
-
-        for stride in range(-64, 0):
-            data = self._data(stride, -stride*self.nlanes)
-            data_stride = self.load(data[::stride]) # cast unsigned
-            loadn = self.loadn(data, stride)
+        npyv_loadn = eval(intrin)
+        for stride in range(-64, 64):
+            if stride < 0:
+                data = self._data(stride, -stride*self.nlanes)
+                data_stride = list(itertools.chain(
+                    *zip(*[data[-i::stride] for i in range(scale, 0, -1)])
+                ))
+            elif stride == 0:
+                data = self._data()
+                data_stride = data[0:scale] * (self.nlanes//scale)
+            else:
+                data = self._data(count=stride*self.nlanes)
+                data_stride = list(itertools.chain(
+                    *zip(*[data[i::stride] for i in range(scale)]))
+                )
+            data_stride = self.load(data_stride)  # cast unsigned
+            loadn = npyv_loadn(data, stride)
             assert loadn == data_stride
 
-    def test_memory_noncont_partial_load(self):
-        if self.sfx in ("u8", "s8", "u16", "s16"):
+    @pytest.mark.parametrize("intrin, elsizes, scale, fill", [
+        ("self.loadn_tillz, self.loadn_till", (32, 64), 1, [0xffff]),
+        ("self.loadn2_tillz, self.loadn2_till", (32, 64), 2, [0xffff, 0x7fff]),
+    ])
+    def test_memory_noncont_partial_load(self, intrin, elsizes, scale, fill):
+        if self._scalar_size() not in elsizes:
             return
-
+        npyv_loadn_tillz, npyv_loadn_till = eval(intrin)
         lanes = list(range(1, self.nlanes + 1))
         lanes += [self.nlanes**2, self.nlanes**4]
-        for stride in range(1, 64):
-            data = self._data(count=stride*self.nlanes)
-            data_stride = data[::stride]
-            for n in lanes:
-                data_stride_till = data_stride[:n] + [15] * (self.nlanes-n)
-                loadn_till = self.loadn_till(data, stride, n, 15)
-                assert loadn_till == data_stride_till
-                data_stride_tillz = data_stride[:n] + [0] * (self.nlanes-n)
-                loadn_tillz = self.loadn_tillz(data, stride, n)
-                assert loadn_tillz == data_stride_tillz
-
-        for stride in range(-64, 0):
-            data = self._data(stride, -stride*self.nlanes)
-            data_stride = list(self.load(data[::stride])) # cast unsigned
+        for stride in range(-64, 64):
+            if stride < 0:
+                data = self._data(stride, -stride*self.nlanes)
+                data_stride = list(itertools.chain(
+                    *zip(*[data[-i::stride] for i in range(scale, 0, -1)])
+                ))
+            elif stride == 0:
+                data = self._data()
+                data_stride = data[0:scale] * (self.nlanes//scale)
+            else:
+                data = self._data(count=stride*self.nlanes)
+                data_stride = list(itertools.chain(
+                    *zip(*[data[i::stride] for i in range(scale)])
+                ))
+            data_stride = list(self.load(data_stride))  # cast unsigned
             for n in lanes:
-                data_stride_till = data_stride[:n] + [15] * (self.nlanes-n)
-                loadn_till = self.loadn_till(data, stride, n, 15)
+                nscale = n * scale
+                llanes = self.nlanes - nscale
+                data_stride_till = (
+                    data_stride[:nscale] + fill * (llanes//scale)
+                )
+                loadn_till = npyv_loadn_till(data, stride, n, *fill)
                 assert loadn_till == data_stride_till
-                data_stride_tillz = data_stride[:n] + [0] * (self.nlanes-n)
-                loadn_tillz = self.loadn_tillz(data, stride, n)
+                data_stride_tillz = data_stride[:nscale] + [0] * llanes
+                loadn_tillz = npyv_loadn_tillz(data, stride, n)
                 assert loadn_tillz == data_stride_tillz
 
-    def test_memory_noncont_store(self):
-        if self.sfx in ("u8", "s8", "u16", "s16"):
+    @pytest.mark.parametrize("intrin, elsizes, scale", [
+        ("self.storen", (32, 64), 1),
+        ("self.storen2", (32, 64), 2),
+    ])
+    def test_memory_noncont_store(self, intrin, elsizes, scale):
+        if self._scalar_size() not in elsizes:
             return
-
-        vdata = self.load(self._data())
+        npyv_storen = eval(intrin)
+        data = self._data()
+        vdata = self.load(data)
+        hlanes = self.nlanes // scale
         for stride in range(1, 64):
-            data = [15] * stride * self.nlanes
-            data[::stride] = vdata
-            storen = [15] * stride * self.nlanes
-            storen += [127]*64
-            self.storen(storen, stride, vdata)
-            assert storen[:-64] == data
-            assert storen[-64:] == [127]*64 # detect overflow
+            data_storen = [0xff] * stride * self.nlanes
+            for s in range(0, hlanes*stride, stride):
+                i = (s//stride)*scale
+                data_storen[s:s+scale] = data[i:i+scale]
+            storen = [0xff] * stride * self.nlanes
+            storen += [0x7f]*64
+            npyv_storen(storen, stride, vdata)
+            assert storen[:-64] == data_storen
+            assert storen[-64:] == [0x7f]*64  # detect overflow
 
         for stride in range(-64, 0):
-            data = [15] * -stride * self.nlanes
-            data[::stride] = vdata
-            storen = [127]*64
-            storen += [15] * -stride * self.nlanes
-            self.storen(storen, stride, vdata)
-            assert storen[64:] == data
-            assert storen[:64] == [127]*64 # detect overflow
-
-    def test_memory_noncont_partial_store(self):
-        if self.sfx in ("u8", "s8", "u16", "s16"):
+            data_storen = [0xff] * -stride * self.nlanes
+            for s in range(0, hlanes*stride, stride):
+                i = (s//stride)*scale
+                data_storen[s-scale:s or None] = data[i:i+scale]
+            storen = [0x7f]*64
+            storen += [0xff] * -stride * self.nlanes
+            npyv_storen(storen, stride, vdata)
+            assert storen[64:] == data_storen
+            assert storen[:64] == [0x7f]*64  # detect overflow
+        # stride 0
+        data_storen = [0x7f] * self.nlanes
+        storen = data_storen.copy()
+        data_storen[0:scale] = data[-scale:]
+        npyv_storen(storen, 0, vdata)
+        assert storen == data_storen
+
+    @pytest.mark.parametrize("intrin, elsizes, scale", [
+        ("self.storen_till", (32, 64), 1),
+        ("self.storen2_till", (32, 64), 2),
+    ])
+    def test_memory_noncont_partial_store(self, intrin, elsizes, scale):
+        if self._scalar_size() not in elsizes:
             return
-
+        npyv_storen_till = eval(intrin)
         data = self._data()
         vdata = self.load(data)
         lanes = list(range(1, self.nlanes + 1))
         lanes += [self.nlanes**2, self.nlanes**4]
+        hlanes = self.nlanes // scale
         for stride in range(1, 64):
             for n in lanes:
-                data_till = [15] * stride * self.nlanes
-                data_till[::stride] = data[:n] + [15] * (self.nlanes-n)
-                storen_till = [15] * stride * self.nlanes
-                storen_till += [127]*64
-                self.storen_till(storen_till, stride, n, vdata)
+                data_till = [0xff] * stride * self.nlanes
+                tdata = data[:n*scale] + [0xff] * (self.nlanes-n*scale)
+                for s in range(0, hlanes*stride, stride)[:n]:
+                    i = (s//stride)*scale
+                    data_till[s:s+scale] = tdata[i:i+scale]
+                storen_till = [0xff] * stride * self.nlanes
+                storen_till += [0x7f]*64
+                npyv_storen_till(storen_till, stride, n, vdata)
                 assert storen_till[:-64] == data_till
-                assert storen_till[-64:] == [127]*64 # detect overflow
+                assert storen_till[-64:] == [0x7f]*64  # detect overflow
 
         for stride in range(-64, 0):
             for n in lanes:
-                data_till = [15] * -stride * self.nlanes
-                data_till[::stride] = data[:n] + [15] * (self.nlanes-n)
-                storen_till = [127]*64
-                storen_till += [15] * -stride * self.nlanes
-                self.storen_till(storen_till, stride, n, vdata)
+                data_till = [0xff] * -stride * self.nlanes
+                tdata = data[:n*scale] + [0xff] * (self.nlanes-n*scale)
+                for s in range(0, hlanes*stride, stride)[:n]:
+                    i = (s//stride)*scale
+                    data_till[s-scale:s or None] = tdata[i:i+scale]
+                storen_till = [0x7f]*64
+                storen_till += [0xff] * -stride * self.nlanes
+                npyv_storen_till(storen_till, stride, n, vdata)
                 assert storen_till[64:] == data_till
-                assert storen_till[:64] == [127]*64 # detect overflow
+                assert storen_till[:64] == [0x7f]*64  # detect overflow
+
+        # stride 0
+        for n in lanes:
+            data_till = [0x7f] * self.nlanes
+            storen_till = data_till.copy()
+            data_till[0:scale] = data[:n*scale][-scale:]
+            npyv_storen_till(storen_till, 0, n, vdata)
+            assert storen_till == data_till
 
     @pytest.mark.parametrize("intrin, table_size, elsize", [
         ("self.lut32", 32, 32),
@@ -886,13 +957,27 @@ class _SIMD_ALL(_Test_Utility):
         combineh = self.combineh(vdata_a, vdata_b)
         assert combineh == data_a_hi + data_b_hi
         # combine x2
-        combine  = self.combine(vdata_a, vdata_b)
+        combine = self.combine(vdata_a, vdata_b)
         assert combine == (data_a_lo + data_b_lo, data_a_hi + data_b_hi)
+
         # zip(interleave)
-        data_zipl = [v for p in zip(data_a_lo, data_b_lo) for v in p]
-        data_ziph = [v for p in zip(data_a_hi, data_b_hi) for v in p]
-        vzip  = self.zip(vdata_a, vdata_b)
+        data_zipl = self.load([
+            v for p in zip(data_a_lo, data_b_lo) for v in p
+        ])
+        data_ziph = self.load([
+            v for p in zip(data_a_hi, data_b_hi) for v in p
+        ])
+        vzip = self.zip(vdata_a, vdata_b)
         assert vzip == (data_zipl, data_ziph)
+        vzip = [0]*self.nlanes*2
+        self._x2("store")(vzip, (vdata_a, vdata_b))
+        assert vzip == list(data_zipl) + list(data_ziph)
+
+        # unzip(deinterleave)
+        unzip = self.unzip(data_zipl, data_ziph)
+        assert unzip == (data_a, data_b)
+        unzip = self._x2("load")(list(data_zipl) + list(data_ziph))
+        assert unzip == (data_a, data_b)
 
     def test_reorder_rev64(self):
         # Reverse elements of each 64-bit lane
@@ -906,6 +991,28 @@ class _SIMD_ALL(_Test_Utility):
         rev64 = self.rev64(self.load(range(self.nlanes)))
         assert rev64 == data_rev64
 
+    def test_reorder_permi128(self):
+        """
+        Test permuting elements for each 128-bit lane.
+        npyv_permi128_##sfx
+        """
+        ssize = self._scalar_size()
+        if ssize < 32:
+            return
+        data = self.load(self._data())
+        permn = 128//ssize
+        permd = permn-1
+        nlane128 = self.nlanes//permn
+        shfl = [0, 1] if ssize == 64 else [0, 2, 4, 6]
+        for i in range(permn):
+            indices = [(i >> shf) & permd for shf in shfl]
+            vperm = self.permi128(data, *indices)
+            data_vperm = [
+                data[j + (e & -permn)]
+                for e, j in enumerate(indices*nlane128)
+            ]
+            assert vperm == data_vperm
+
     @pytest.mark.parametrize('func, intrin', [
         (operator.lt, "cmplt"),
         (operator.le, "cmple"),
@@ -1168,6 +1275,18 @@ class _SIMD_ALL(_Test_Utility):
         ifadd = self.ifadd(false_mask, vdata_a, vdata_b, vdata_b)
         assert ifadd == vdata_b
 
+        if not self._is_fp():
+            return
+        data_div = self.div(vdata_b, vdata_a)
+        ifdiv = self.ifdiv(true_mask, vdata_b, vdata_a, vdata_b)
+        assert ifdiv == data_div
+        ifdivz = self.ifdivz(true_mask, vdata_b, vdata_a)
+        assert ifdivz == data_div
+        ifdiv = self.ifdiv(false_mask, vdata_a, vdata_b, vdata_b)
+        assert ifdiv == vdata_b
+        ifdivz = self.ifdivz(false_mask, vdata_a, vdata_b)
+        assert ifdivz == self.zero()
+
 bool_sfx = ("b8", "b16", "b32", "b64")
 int_sfx = ("u8", "s8", "u16", "s16", "u32", "s32", "u64", "s64")
 fp_sfx  = ("f32", "f64")
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_ufunc.py b/contrib/python/numpy/py3/numpy/core/tests/test_ufunc.py
index cae84669740..02c437021fe 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_ufunc.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_ufunc.py
@@ -14,7 +14,7 @@ import numpy.core._rational_tests as _rational_tests
 from numpy.testing import (
     assert_, assert_equal, assert_raises, assert_array_equal,
     assert_almost_equal, assert_array_almost_equal, assert_no_warnings,
-    assert_allclose, HAS_REFCOUNT, suppress_warnings, IS_WASM
+    assert_allclose, HAS_REFCOUNT, suppress_warnings, IS_WASM, IS_PYPY,
     )
 from numpy.testing._private.utils import requires_memory
 from numpy.compat import pickle
@@ -203,6 +203,7 @@ class TestUfunc:
                    b"(S'numpy.core.umath'\np1\nS'cos'\np2\ntp3\nRp4\n.")
         assert_(pickle.loads(astring) is np.cos)
 
+    @pytest.mark.skipif(IS_PYPY, reason="'is' check does not work on PyPy")
     def test_pickle_name_is_qualname(self):
         # This tests that a simplification of our ufunc pickle code will
         # lead to allowing qualnames as names.  Future ufuncs should
@@ -972,10 +973,10 @@ class TestUfunc:
         assert_raises(TypeError, inner1d, a, b, axes=[None, 1])
         # cannot pass an index unless there is only one dimension
         # (output is wrong in this case)
-        assert_raises(TypeError, inner1d, a, b, axes=[-1, -1, -1])
+        assert_raises(np.AxisError, inner1d, a, b, axes=[-1, -1, -1])
         # or pass in generally the wrong number of axes
-        assert_raises(ValueError, inner1d, a, b, axes=[-1, -1, (-1,)])
-        assert_raises(ValueError, inner1d, a, b, axes=[-1, (-2, -1), ()])
+        assert_raises(np.AxisError, inner1d, a, b, axes=[-1, -1, (-1,)])
+        assert_raises(np.AxisError, inner1d, a, b, axes=[-1, (-2, -1), ()])
         # axes need to have same length.
         assert_raises(ValueError, inner1d, a, b, axes=[0, 1])
 
@@ -1015,14 +1016,16 @@ class TestUfunc:
         # list needs to have right length
         assert_raises(ValueError, mm, a, b, axes=[])
         assert_raises(ValueError, mm, a, b, axes=[(-2, -1)])
-        # list should contain tuples for multiple axes
-        assert_raises(TypeError, mm, a, b, axes=[-1, -1, -1])
-        assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), -1])
+        # list should not contain None, or lists
+        assert_raises(TypeError, mm, a, b, axes=[None, None, None])
         assert_raises(TypeError,
                       mm, a, b, axes=[[-2, -1], [-2, -1], [-2, -1]])
         assert_raises(TypeError,
                       mm, a, b, axes=[(-2, -1), (-2, -1), [-2, -1]])
         assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), None])
+        # single integers are AxisErrors if more are required
+        assert_raises(np.AxisError, mm, a, b, axes=[-1, -1, -1])
+        assert_raises(np.AxisError, mm, a, b, axes=[(-2, -1), (-2, -1), -1])
         # tuples should not have duplicated values
         assert_raises(ValueError, mm, a, b, axes=[(-2, -1), (-2, -1), (-2, -2)])
         # arrays should have enough axes.
@@ -1648,6 +1651,19 @@ class TestUfunc:
         a = a[1:, 1:, 1:]
         self.check_identityless_reduction(a)
 
+    def test_reduce_identity_depends_on_loop(self):
+        """
+        The type of the result should always depend on the selected loop, not
+        necessarily the output (only relevant for object arrays).
+        """
+        # For an object loop, the default value 0 with type int is used:
+        assert type(np.add.reduce([], dtype=object)) is int
+        out = np.array(None, dtype=object)
+        # When the loop is float64 but `out` is object this does not happen,
+        # the result is float64 cast to object (which gives Python `float`).
+        np.add.reduce([], out=out, dtype=np.float64)
+        assert type(out[()]) is float
+
     def test_initial_reduction(self):
         # np.minimum.reduce is an identityless reduction
 
@@ -1668,6 +1684,9 @@ class TestUfunc:
         # Check initial=None raises ValueError for both types of ufunc reductions
         assert_raises(ValueError, np.minimum.reduce, [], initial=None)
         assert_raises(ValueError, np.add.reduce, [], initial=None)
+        # Also in the somewhat special object case:
+        with pytest.raises(ValueError):
+            np.add.reduce([], initial=None, dtype=object)
 
         # Check that np._NoValue gives default behavior.
         assert_equal(np.add.reduce([], initial=np._NoValue), 0)
@@ -1677,6 +1696,23 @@ class TestUfunc:
         res = np.add.reduce(a, initial=5)
         assert_equal(res, 15)
 
+    def test_empty_reduction_and_idenity(self):
+        arr = np.zeros((0, 5))
+        # OK, since the reduction itself is *not* empty, the result is
+        assert np.true_divide.reduce(arr, axis=1).shape == (0,)
+        # Not OK, the reduction itself is empty and we have no idenity
+        with pytest.raises(ValueError):
+            np.true_divide.reduce(arr, axis=0)
+
+        # Test that an empty reduction fails also if the result is empty
+        arr = np.zeros((0, 0, 5))
+        with pytest.raises(ValueError):
+            np.true_divide.reduce(arr, axis=1)
+
+        # Division reduction makes sense with `initial=1` (empty or not):
+        res = np.true_divide.reduce(arr, axis=1, initial=1)
+        assert_array_equal(res, np.ones((0, 5)))
+
     @pytest.mark.parametrize('axis', (0, 1, None))
     @pytest.mark.parametrize('where', (np.array([False, True, True]),
                                        np.array([[True], [False], [True]]),
@@ -1921,17 +1957,133 @@ class TestUfunc:
         assert_(MyThing.rmul_count == 1, MyThing.rmul_count)
         assert_(MyThing.getitem_count <= 2, MyThing.getitem_count)
 
-    def test_inplace_fancy_indexing(self):
+    @pytest.mark.parametrize("a", (
+                             np.arange(10, dtype=int),
+                             np.arange(10, dtype=_rational_tests.rational),
+                             ))
+    def test_ufunc_at_basic(self, a):
 
-        a = np.arange(10)
-        np.add.at(a, [2, 5, 2], 1)
-        assert_equal(a, [0, 1, 4, 3, 4, 6, 6, 7, 8, 9])
+        aa = a.copy()
+        np.add.at(aa, [2, 5, 2], 1)
+        assert_equal(aa, [0, 1, 4, 3, 4, 6, 6, 7, 8, 9])
 
-        a = np.arange(10)
+        with pytest.raises(ValueError):
+            # missing second operand
+            np.add.at(aa, [2, 5, 3])
+
+        aa = a.copy()
+        np.negative.at(aa, [2, 5, 3])
+        assert_equal(aa, [0, 1, -2, -3, 4, -5, 6, 7, 8, 9])
+
+        aa = a.copy()
         b = np.array([100, 100, 100])
-        np.add.at(a, [2, 5, 2], b)
-        assert_equal(a, [0, 1, 202, 3, 4, 105, 6, 7, 8, 9])
+        np.add.at(aa, [2, 5, 2], b)
+        assert_equal(aa, [0, 1, 202, 3, 4, 105, 6, 7, 8, 9])
 
+        with pytest.raises(ValueError):
+            # extraneous second operand
+            np.negative.at(a, [2, 5, 3], [1, 2, 3])
+
+        with pytest.raises(ValueError):
+            # second operand cannot be converted to an array
+            np.add.at(a, [2, 5, 3], [[1, 2], 1])
+
+    # ufuncs with indexed loops for performance in ufunc.at
+    indexed_ufuncs = [np.add, np.subtract, np.multiply, np.floor_divide,
+                      np.maximum, np.minimum, np.fmax, np.fmin]
+
+    @pytest.mark.parametrize(
+                "typecode", np.typecodes['AllInteger'] + np.typecodes['Float'])
+    @pytest.mark.parametrize("ufunc", indexed_ufuncs)
+    def test_ufunc_at_inner_loops(self, typecode, ufunc):
+        if ufunc is np.divide and typecode in np.typecodes['AllInteger']:
+            # Avoid divide-by-zero and inf for integer divide
+            a = np.ones(100, dtype=typecode)
+            indx = np.random.randint(100, size=30, dtype=np.intp)
+            vals = np.arange(1, 31, dtype=typecode)
+        else:
+            a = np.ones(1000, dtype=typecode)
+            indx = np.random.randint(1000, size=3000, dtype=np.intp)
+            vals = np.arange(3000, dtype=typecode)
+        atag = a.copy()
+        # Do the calculation twice and compare the answers
+        with warnings.catch_warnings(record=True) as w_at:
+            warnings.simplefilter('always')
+            ufunc.at(a, indx, vals)
+        with warnings.catch_warnings(record=True) as w_loop:
+            warnings.simplefilter('always')
+            for i, v in zip(indx, vals):
+                # Make sure all the work happens inside the ufunc
+                # in order to duplicate error/warning handling
+                ufunc(atag[i], v, out=atag[i:i+1], casting="unsafe")
+        assert_equal(atag, a)
+        # If w_loop warned, make sure w_at warned as well
+        if len(w_loop) > 0:
+            #
+            assert len(w_at) > 0
+            assert w_at[0].category == w_loop[0].category
+            assert str(w_at[0].message)[:10] == str(w_loop[0].message)[:10]
+
+    @pytest.mark.parametrize("typecode", np.typecodes['Complex'])
+    @pytest.mark.parametrize("ufunc", [np.add, np.subtract, np.multiply])
+    def test_ufunc_at_inner_loops_complex(self, typecode, ufunc):
+        a = np.ones(10, dtype=typecode)
+        indx = np.concatenate([np.ones(6, dtype=np.intp),
+                               np.full(18, 4, dtype=np.intp)])
+        value = a.dtype.type(1j)
+        ufunc.at(a, indx, value)
+        expected = np.ones_like(a)
+        if ufunc is np.multiply:
+            expected[1] = expected[4] = -1
+        else:
+            expected[1] += 6 * (value if ufunc is np.add else -value)
+            expected[4] += 18 * (value if ufunc is np.add else -value)
+
+        assert_array_equal(a, expected)
+
+    def test_ufunc_at_ellipsis(self):
+        # Make sure the indexed loop check does not choke on iters
+        # with subspaces
+        arr = np.zeros(5)
+        np.add.at(arr, slice(None), np.ones(5))
+        assert_array_equal(arr, np.ones(5))
+
+    def test_ufunc_at_negative(self):
+        arr = np.ones(5, dtype=np.int32)
+        indx = np.arange(5)
+        umt.indexed_negative.at(arr, indx)
+        # If it is [-1, -1, -1, -100, 0] then the regular strided loop was used
+        assert np.all(arr == [-1, -1, -1, -200, -1])
+
+    def test_ufunc_at_large(self):
+        # issue gh-23457
+        indices = np.zeros(8195, dtype=np.int16)
+        b = np.zeros(8195, dtype=float)
+        b[0] = 10
+        b[1] = 5
+        b[8192:] = 100
+        a = np.zeros(1, dtype=float)
+        np.add.at(a, indices, b)
+        assert a[0] == b.sum()
+
+    def test_cast_index_fastpath(self):
+        arr = np.zeros(10)
+        values = np.ones(100000)
+        # index must be cast, which may be buffered in chunks:
+        index = np.zeros(len(values), dtype=np.uint8)
+        np.add.at(arr, index, values)
+        assert arr[0] == len(values)
+
+    @pytest.mark.parametrize("value", [
+        np.ones(1), np.ones(()), np.float64(1.), 1.])
+    def test_ufunc_at_scalar_value_fastpath(self, value):
+        arr = np.zeros(1000)
+        # index must be cast, which may be buffered in chunks:
+        index = np.repeat(np.arange(1000), 2)
+        np.add.at(arr, index, value)
+        assert_array_equal(arr, np.full_like(arr, 2 * value))
+
+    def test_ufunc_at_multiD(self):
         a = np.arange(9).reshape(3, 3)
         b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]])
         np.add.at(a, (slice(None), [1, 2, 1]), b)
@@ -2011,11 +2163,7 @@ class TestUfunc:
               [121, 222, 323],
               [124, 225, 326]]])
 
-        a = np.arange(10)
-        np.negative.at(a, [2, 5, 2])
-        assert_equal(a, [0, 1, 2, 3, 4, -5, 6, 7, 8, 9])
-
-        # Test 0-dim array
+    def test_ufunc_at_0D(self):
         a = np.array(0)
         np.add.at(a, (), 1)
         assert_equal(a, 1)
@@ -2023,11 +2171,13 @@ class TestUfunc:
         assert_raises(IndexError, np.add.at, a, 0, 1)
         assert_raises(IndexError, np.add.at, a, [], 1)
 
+    def test_ufunc_at_dtypes(self):
         # Test mixed dtypes
         a = np.arange(10)
         np.power.at(a, [1, 2, 3, 2], 3.5)
         assert_equal(a, np.array([0, 1, 4414, 46, 4, 5, 6, 7, 8, 9]))
 
+    def test_ufunc_at_boolean(self):
         # Test boolean indexing and boolean ufuncs
         a = np.arange(10)
         index = a % 2 == 0
@@ -2039,6 +2189,7 @@ class TestUfunc:
         np.invert.at(a, [2, 5, 2])
         assert_equal(a, [0, 1, 2, 3, 4, 5 ^ 0xffffffff, 6, 7, 8, 9])
 
+    def test_ufunc_at_advanced(self):
         # Test empty subspace
         orig = np.arange(4)
         a = orig[:, None][:, 0:0]
@@ -2062,7 +2213,24 @@ class TestUfunc:
         # Test maximum
         a = np.array([1, 2, 3])
         np.maximum.at(a, [0], 0)
-        assert_equal(np.array([1, 2, 3]), a)
+        assert_equal(a, np.array([1, 2, 3]))
+
+    @pytest.mark.parametrize("dtype",
+            np.typecodes['AllInteger'] + np.typecodes['Float'])
+    @pytest.mark.parametrize("ufunc",
+            [np.add, np.subtract, np.divide, np.minimum, np.maximum])
+    def test_at_negative_indexes(self, dtype, ufunc):
+        a = np.arange(0, 10).astype(dtype)
+        indxs = np.array([-1, 1, -1, 2]).astype(np.intp)
+        vals = np.array([1, 5, 2, 10], dtype=a.dtype)
+
+        expected = a.copy()
+        for i, v in zip(indxs, vals):
+            expected[i] = ufunc(expected[i], v)
+
+        ufunc.at(a, indxs, vals)
+        assert_array_equal(a, expected)
+        assert np.all(indxs == [-1, 1, -1, 2])
 
     def test_at_not_none_signature(self):
         # Test ufuncs with non-trivial signature raise a TypeError
@@ -2073,6 +2241,23 @@ class TestUfunc:
         a = np.array([[[1, 2], [3, 4]]])
         assert_raises(TypeError, np.linalg._umath_linalg.det.at, a, [0])
 
+    def test_at_no_loop_for_op(self):
+        # str dtype does not have a ufunc loop for np.add
+        arr = np.ones(10, dtype=str)
+        with pytest.raises(np.core._exceptions._UFuncNoLoopError):
+            np.add.at(arr, [0, 1], [0, 1])
+
+    def test_at_output_casting(self):
+        arr = np.array([-1])
+        np.equal.at(arr, [0], [0])
+        assert arr[0] == 0
+
+    def test_at_broadcast_failure(self):
+        arr = np.arange(5)
+        with pytest.raises(ValueError):
+            np.add.at(arr, [0, 1], [1, 2, 3])
+
+
     def test_reduce_arguments(self):
         f = np.add.reduce
         d = np.ones((5,2), dtype=int)
@@ -2578,7 +2763,9 @@ def test_reduce_casterrors(offset):
     with pytest.raises(ValueError, match="invalid literal"):
         # This is an unsafe cast, but we currently always allow that.
         # Note that the double loop is picked, but the cast fails.
-        np.add.reduce(arr, dtype=np.intp, out=out)
+        # `initial=None` disables the use of an identity here to test failures
+        # while copying the first values path (not used when identity exists).
+        np.add.reduce(arr, dtype=np.intp, out=out, initial=None)
     assert count == sys.getrefcount(value)
     # If an error occurred during casting, the operation is done at most until
     # the error occurs (the result of which would be `value * offset`) and -1
@@ -2587,6 +2774,15 @@ def test_reduce_casterrors(offset):
     assert out[()] < value * offset
 
 
+def test_object_reduce_cleanup_on_failure():
+    # Test cleanup, including of the initial value (manually provided or not)
+    with pytest.raises(TypeError):
+        np.add.reduce([1, 2, None], initial=4)
+
+    with pytest.raises(TypeError):
+        np.add.reduce([1, 2, None])
+
+
 @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
 @pytest.mark.parametrize("method",
         [np.add.accumulate, np.add.reduce,
@@ -2687,10 +2883,10 @@ class TestLowlevelAPIAccess:
         r = np.equal.resolve_dtypes((S0, S0, None))
         assert r == (S0, S0, np.dtype(bool))
 
-        # Subarray dtypes are weird and only really exist nested, they need
-        # the shift to full NEP 50 to be implemented nicely:
+        # Subarray dtypes are weird and may not work fully, we preserve them
+        # leading to a TypeError (currently no equal loop for void/structured)
         dts = np.dtype("10i")
-        with pytest.raises(NotImplementedError):
+        with pytest.raises(TypeError):
             np.equal.resolve_dtypes((dts, dts, None))
 
     def test_resolve_dtypes_reduction(self):
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_umath.py b/contrib/python/numpy/py3/numpy/core/tests/test_umath.py
index 03bed0aba61..892226f993c 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_umath.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_umath.py
@@ -17,7 +17,7 @@ from numpy.testing import (
     assert_, assert_equal, assert_raises, assert_raises_regex,
     assert_array_equal, assert_almost_equal, assert_array_almost_equal,
     assert_array_max_ulp, assert_allclose, assert_no_warnings, suppress_warnings,
-    _gen_alignment_data, assert_array_almost_equal_nulp, IS_WASM
+    _gen_alignment_data, assert_array_almost_equal_nulp, IS_WASM, IS_MUSL
     )
 from numpy.testing._private.utils import _glibc_older_than
 
@@ -369,6 +369,64 @@ class TestComparisons:
         with pytest.raises(TypeError, match="No loop matching"):
             np.equal(1, 1, sig=(None, None, "l"))
 
+    @pytest.mark.parametrize("dtypes", ["qQ", "Qq"])
+    @pytest.mark.parametrize('py_comp, np_comp', [
+        (operator.lt, np.less),
+        (operator.le, np.less_equal),
+        (operator.gt, np.greater),
+        (operator.ge, np.greater_equal),
+        (operator.eq, np.equal),
+        (operator.ne, np.not_equal)
+    ])
+    @pytest.mark.parametrize("vals", [(2**60, 2**60+1), (2**60+1, 2**60)])
+    def test_large_integer_direct_comparison(
+            self, dtypes, py_comp, np_comp, vals):
+        # Note that float(2**60) + 1 == float(2**60).
+        a1 = np.array([2**60], dtype=dtypes[0])
+        a2 = np.array([2**60 + 1], dtype=dtypes[1])
+        expected = py_comp(2**60, 2**60+1)
+
+        assert py_comp(a1, a2) == expected
+        assert np_comp(a1, a2) == expected
+        # Also check the scalars:
+        s1 = a1[0]
+        s2 = a2[0]
+        assert isinstance(s1, np.integer)
+        assert isinstance(s2, np.integer)
+        # The Python operator here is mainly interesting:
+        assert py_comp(s1, s2) == expected
+        assert np_comp(s1, s2) == expected
+
+    @pytest.mark.parametrize("dtype", np.typecodes['UnsignedInteger'])
+    @pytest.mark.parametrize('py_comp_func, np_comp_func', [
+        (operator.lt, np.less),
+        (operator.le, np.less_equal),
+        (operator.gt, np.greater),
+        (operator.ge, np.greater_equal),
+        (operator.eq, np.equal),
+        (operator.ne, np.not_equal)
+    ])
+    @pytest.mark.parametrize("flip", [True, False])
+    def test_unsigned_signed_direct_comparison(
+            self, dtype, py_comp_func, np_comp_func, flip):
+        if flip:
+            py_comp = lambda x, y: py_comp_func(y, x)
+            np_comp = lambda x, y: np_comp_func(y, x)
+        else:
+            py_comp = py_comp_func
+            np_comp = np_comp_func
+
+        arr = np.array([np.iinfo(dtype).max], dtype=dtype)
+        expected = py_comp(int(arr[0]), -1)
+
+        assert py_comp(arr, -1) == expected
+        assert np_comp(arr, -1) == expected
+        scalar = arr[0]
+        assert isinstance(scalar, np.integer)
+        # The Python operator here is mainly interesting:
+        assert py_comp(scalar, -1) == expected
+        assert np_comp(scalar, -1) == expected
+
 
 class TestAdd:
     def test_reduce_alignment(self):
@@ -424,7 +482,7 @@ class TestDivision:
     def test_division_int_boundary(self, dtype, ex_val):
         fo = np.iinfo(dtype)
         neg = -1 if fo.min < 0 else 1
-        # Large enough to test SIMD loops and remaind elements
+        # Large enough to test SIMD loops and remainder elements
         lsize = 512 + 7
         a, b, divisors = eval(ex_val)
         a_lst, b_lst = a.tolist(), b.tolist()
@@ -589,6 +647,8 @@ class TestDivision:
         assert_equal(np.signbit(x//1), 0)
         assert_equal(np.signbit((-x)//1), 1)
 
+    @pytest.mark.skipif(hasattr(np.__config__, "blas_ssl2_info"),
+            reason="gh-22982")
     @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
     @pytest.mark.parametrize('dtype', np.typecodes['Float'])
     def test_floor_division_errors(self, dtype):
@@ -731,6 +791,8 @@ class TestRemainder:
             # inf / 0 does not set any flags, only the modulo creates a NaN
             np.divmod(finf, fzero)
 
+    @pytest.mark.skipif(hasattr(np.__config__, "blas_ssl2_info"),
+            reason="gh-22982")
     @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
     @pytest.mark.xfail(sys.platform.startswith("darwin"),
            reason="MacOS seems to not give the correct 'invalid' warning for "
@@ -1085,6 +1147,33 @@ class TestPower:
                 assert_complex_equal(np.power(zero, -p), cnan)
             assert_complex_equal(np.power(zero, -1+0.2j), cnan)
 
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    def test_zero_power_nonzero(self):
+        # Testing 0^{Non-zero} issue 18378
+        zero = np.array([0.0+0.0j])
+        cnan = np.array([complex(np.nan, np.nan)])
+
+        def assert_complex_equal(x, y):
+            assert_array_equal(x.real, y.real)
+            assert_array_equal(x.imag, y.imag)
+
+        #Complex powers with positive real part will not generate a warning
+        assert_complex_equal(np.power(zero, 1+4j), zero)
+        assert_complex_equal(np.power(zero, 2-3j), zero)
+        #Testing zero values when real part is greater than zero
+        assert_complex_equal(np.power(zero, 1+1j), zero)
+        assert_complex_equal(np.power(zero, 1+0j), zero)
+        assert_complex_equal(np.power(zero, 1-1j), zero)
+        #Complex powers will negative real part or 0 (provided imaginary
+        # part is not zero) will generate a NAN and hence a RUNTIME warning
+        with pytest.warns(expected_warning=RuntimeWarning) as r:
+            assert_complex_equal(np.power(zero, -1+1j), cnan)
+            assert_complex_equal(np.power(zero, -2-3j), cnan)
+            assert_complex_equal(np.power(zero, -7+0j), cnan)
+            assert_complex_equal(np.power(zero, 0+1j), cnan)
+            assert_complex_equal(np.power(zero, 0-1j), cnan)
+        assert len(r) == 5
+
     def test_fast_power(self):
         x = np.array([1, 2, 3], np.int16)
         res = x**2.0
@@ -1265,9 +1354,20 @@ class TestLog:
 
         # test log() of max for dtype does not raise
         for dt in ['f', 'd', 'g']:
-            with np.errstate(all='raise'):
-                x = np.finfo(dt).max
-                np.log(x)
+            try:
+                with np.errstate(all='raise'):
+                    x = np.finfo(dt).max
+                    np.log(x)
+            except FloatingPointError as exc:
+                if dt == 'g' and IS_MUSL:
+                    # FloatingPointError is known to occur on longdouble
+                    # for musllinux_x86_64 x is very large
+                    pytest.skip(
+                        "Overflow has occurred for"
+                        " np.log(np.finfo(np.longdouble).max)"
+                    )
+                else:
+                    raise exc
 
     def test_log_strides(self):
         np.random.seed(42)
@@ -1384,23 +1484,50 @@ class TestSpecialFloats:
             np.log(a)
 
     @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
-    def test_sincos_values(self):
+    @pytest.mark.parametrize('dtype', ['e', 'f', 'd', 'g'])
+    def test_sincos_values(self, dtype):
         with np.errstate(all='ignore'):
             x = [np.nan, np.nan, np.nan, np.nan]
             y = [np.nan, -np.nan, np.inf, -np.inf]
-            for dt in ['e', 'f', 'd', 'g']:
-                xf = np.array(x, dtype=dt)
-                yf = np.array(y, dtype=dt)
-                assert_equal(np.sin(yf), xf)
-                assert_equal(np.cos(yf), xf)
+            xf = np.array(x, dtype=dtype)
+            yf = np.array(y, dtype=dtype)
+            assert_equal(np.sin(yf), xf)
+            assert_equal(np.cos(yf), xf)
 
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    @pytest.mark.xfail(
+        sys.platform.startswith("darwin"),
+        reason="underflow is triggered for scalar 'sin'"
+    )
+    def test_sincos_underflow(self):
+        with np.errstate(under='raise'):
+            underflow_trigger = np.array(
+                float.fromhex("0x1.f37f47a03f82ap-511"),
+                dtype=np.float64
+            )
+            np.sin(underflow_trigger)
+            np.cos(underflow_trigger)
 
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    @pytest.mark.parametrize('callable', [np.sin, np.cos])
+    @pytest.mark.parametrize('dtype', ['e', 'f', 'd'])
+    @pytest.mark.parametrize('value', [np.inf, -np.inf])
+    def test_sincos_errors(self, callable, dtype, value):
         with np.errstate(invalid='raise'):
-            for callable in [np.sin, np.cos]:
-                for value in [np.inf, -np.inf]:
-                    for dt in ['e', 'f', 'd']:
-                        assert_raises(FloatingPointError, callable,
-                                np.array([value], dtype=dt))
+            assert_raises(FloatingPointError, callable,
+                np.array([value], dtype=dtype))
+
+    @pytest.mark.parametrize('callable', [np.sin, np.cos])
+    @pytest.mark.parametrize('dtype', ['f', 'd'])
+    @pytest.mark.parametrize('stride', [-1, 1, 2, 4, 5])
+    def test_sincos_overlaps(self, callable, dtype, stride):
+        N = 100
+        M = N // abs(stride)
+        rng = np.random.default_rng(42)
+        x = rng.standard_normal(N, dtype)
+        y = callable(x[::stride])
+        callable(x[::stride], out=x[:M])
+        assert_equal(x[:M], y)
 
     @pytest.mark.parametrize('dt', ['e', 'f', 'd', 'g'])
     def test_sqrt_values(self, dt):
@@ -1690,7 +1817,8 @@ class TestSpecialFloats:
             ufunc(array)
 
 class TestFPClass:
-    @pytest.mark.parametrize("stride", [-4,-2,-1,1,2,4])
+    @pytest.mark.parametrize("stride", [-5, -4, -3, -2, -1, 1,
+                                2, 4, 5, 6, 7, 8, 9, 10])
     def test_fpclass(self, stride):
         arr_f64 = np.array([np.nan, -np.nan, np.inf, -np.inf, -1.0, 1.0, -0.0, 0.0, 2.2251e-308, -2.2251e-308], dtype='d')
         arr_f32 = np.array([np.nan, -np.nan, np.inf, -np.inf, -1.0, 1.0, -0.0, 0.0, 1.4013e-045, -1.4013e-045], dtype='f')
@@ -1707,6 +1835,52 @@ class TestFPClass:
         assert_equal(np.isfinite(arr_f32[::stride]), finite[::stride])
         assert_equal(np.isfinite(arr_f64[::stride]), finite[::stride])
 
+    @pytest.mark.parametrize("dtype", ['d', 'f'])
+    def test_fp_noncontiguous(self, dtype):
+        data = np.array([np.nan, -np.nan, np.inf, -np.inf, -1.0,
+                            1.0, -0.0, 0.0, 2.2251e-308,
+                            -2.2251e-308], dtype=dtype)
+        nan = np.array([True, True, False, False, False, False,
+                            False, False, False, False])
+        inf = np.array([False, False, True, True, False, False,
+                            False, False, False, False])
+        sign = np.array([False, True, False, True, True, False,
+                            True, False, False, True])
+        finite = np.array([False, False, False, False, True, True,
+                            True, True, True, True])
+        out = np.ndarray(data.shape, dtype='bool')
+        ncontig_in = data[1::3]
+        ncontig_out = out[1::3]
+        contig_in = np.array(ncontig_in)
+        assert_equal(ncontig_in.flags.c_contiguous, False)
+        assert_equal(ncontig_out.flags.c_contiguous, False)
+        assert_equal(contig_in.flags.c_contiguous, True)
+        # ncontig in, ncontig out
+        assert_equal(np.isnan(ncontig_in, out=ncontig_out), nan[1::3])
+        assert_equal(np.isinf(ncontig_in, out=ncontig_out), inf[1::3])
+        assert_equal(np.signbit(ncontig_in, out=ncontig_out), sign[1::3])
+        assert_equal(np.isfinite(ncontig_in, out=ncontig_out), finite[1::3])
+        # contig in, ncontig out
+        assert_equal(np.isnan(contig_in, out=ncontig_out), nan[1::3])
+        assert_equal(np.isinf(contig_in, out=ncontig_out), inf[1::3])
+        assert_equal(np.signbit(contig_in, out=ncontig_out), sign[1::3])
+        assert_equal(np.isfinite(contig_in, out=ncontig_out), finite[1::3])
+        # ncontig in, contig out
+        assert_equal(np.isnan(ncontig_in), nan[1::3])
+        assert_equal(np.isinf(ncontig_in), inf[1::3])
+        assert_equal(np.signbit(ncontig_in), sign[1::3])
+        assert_equal(np.isfinite(ncontig_in), finite[1::3])
+        # contig in, contig out, nd stride
+        data_split = np.array(np.array_split(data, 2))
+        nan_split = np.array(np.array_split(nan, 2))
+        inf_split = np.array(np.array_split(inf, 2))
+        sign_split = np.array(np.array_split(sign, 2))
+        finite_split = np.array(np.array_split(finite, 2))
+        assert_equal(np.isnan(data_split), nan_split)
+        assert_equal(np.isinf(data_split), inf_split)
+        assert_equal(np.signbit(data_split), sign_split)
+        assert_equal(np.isfinite(data_split), finite_split)
+
 class TestLDExp:
     @pytest.mark.parametrize("stride", [-4,-2,-1,1,2,4])
     @pytest.mark.parametrize("dtype", ['f', 'd'])
@@ -1720,6 +1894,7 @@ class TestLDExp:
 class TestFRExp:
     @pytest.mark.parametrize("stride", [-4,-2,-1,1,2,4])
     @pytest.mark.parametrize("dtype", ['f', 'd'])
+    @pytest.mark.xfail(IS_MUSL, reason="gh23048")
     @pytest.mark.skipif(not sys.platform.startswith('linux'),
                         reason="np.frexp gives different answers for NAN/INF on windows and linux")
     def test_frexp(self, dtype, stride):
@@ -2683,6 +2858,35 @@ class TestAbsoluteNegative:
         np.abs(d, out=d)
         np.abs(np.ones_like(d), out=d)
 
+    @pytest.mark.parametrize("dtype", ['d', 'f', 'int32', 'int64'])
+    @pytest.mark.parametrize("big", [True, False])
+    def test_noncontiguous(self, dtype, big):
+        data = np.array([-1.0, 1.0, -0.0, 0.0, 2.2251e-308, -2.5, 2.5, -6,
+                            6, -2.2251e-308, -8, 10], dtype=dtype)
+        expect = np.array([1.0, -1.0, 0.0, -0.0, -2.2251e-308, 2.5, -2.5, 6,
+                            -6, 2.2251e-308, 8, -10], dtype=dtype)
+        if big:
+            data = np.repeat(data, 10)
+            expect = np.repeat(expect, 10)
+        out = np.ndarray(data.shape, dtype=dtype)
+        ncontig_in = data[1::2]
+        ncontig_out = out[1::2]
+        contig_in = np.array(ncontig_in)
+        # contig in, contig out
+        assert_array_equal(np.negative(contig_in), expect[1::2])
+        # contig in, ncontig out
+        assert_array_equal(np.negative(contig_in, out=ncontig_out),
+                                expect[1::2])
+        # ncontig in, contig out
+        assert_array_equal(np.negative(ncontig_in), expect[1::2])
+        # ncontig in, ncontig out
+        assert_array_equal(np.negative(ncontig_in, out=ncontig_out),
+                                expect[1::2])
+        # contig in, contig out, nd stride
+        data_split = np.array(np.array_split(data, 2))
+        expect_split = np.array(np.array_split(expect, 2))
+        assert_equal(np.negative(data_split), expect_split)
+
 
 class TestPositive:
     def test_valid(self):
@@ -3378,6 +3582,79 @@ class TestSpecialMethods:
         assert_raises(ValueError, np.modf, a, out=('one', 'two', 'three'))
         assert_raises(ValueError, np.modf, a, out=('one',))
 
+    def test_ufunc_override_where(self):
+
+        class OverriddenArrayOld(np.ndarray):
+
+            def _unwrap(self, objs):
+                cls = type(self)
+                result = []
+                for obj in objs:
+                    if isinstance(obj, cls):
+                        obj = np.array(obj)
+                    elif type(obj) != np.ndarray:
+                        return NotImplemented
+                    result.append(obj)
+                return result
+
+            def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+
+                inputs = self._unwrap(inputs)
+                if inputs is NotImplemented:
+                    return NotImplemented
+
+                kwargs = kwargs.copy()
+                if "out" in kwargs:
+                    kwargs["out"] = self._unwrap(kwargs["out"])
+                    if kwargs["out"] is NotImplemented:
+                        return NotImplemented
+
+                r = super().__array_ufunc__(ufunc, method, *inputs, **kwargs)
+                if r is not NotImplemented:
+                    r = r.view(type(self))
+
+                return r
+
+        class OverriddenArrayNew(OverriddenArrayOld):
+            def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+
+                kwargs = kwargs.copy()
+                if "where" in kwargs:
+                    kwargs["where"] = self._unwrap((kwargs["where"], ))
+                    if kwargs["where"] is NotImplemented:
+                        return NotImplemented
+                    else:
+                        kwargs["where"] = kwargs["where"][0]
+
+                r = super().__array_ufunc__(ufunc, method, *inputs, **kwargs)
+                if r is not NotImplemented:
+                    r = r.view(type(self))
+
+                return r
+
+        ufunc = np.negative
+
+        array = np.array([1, 2, 3])
+        where = np.array([True, False, True])
+        expected = ufunc(array, where=where)
+
+        with pytest.raises(TypeError):
+            ufunc(array, where=where.view(OverriddenArrayOld))
+
+        result_1 = ufunc(
+            array,
+            where=where.view(OverriddenArrayNew)
+        )
+        assert isinstance(result_1, OverriddenArrayNew)
+        assert np.all(np.array(result_1) == expected, where=where)
+
+        result_2 = ufunc(
+            array.view(OverriddenArrayNew),
+            where=where.view(OverriddenArrayNew)
+        )
+        assert isinstance(result_2, OverriddenArrayNew)
+        assert np.all(np.array(result_2) == expected, where=where)
+
     def test_ufunc_override_exception(self):
 
         class A:
@@ -3639,6 +3916,19 @@ class TestSpecialMethods:
         assert_equal(a, check)
         assert_(a.info, {'inputs': [0, 2]})
 
+    def test_array_ufunc_direct_call(self):
+        # This is mainly a regression test for gh-24023 (shouldn't segfault)
+        a = np.array(1)
+        with pytest.raises(TypeError):
+            a.__array_ufunc__()
+
+        # No kwargs means kwargs may be NULL on the C-level
+        with pytest.raises(TypeError):
+            a.__array_ufunc__(1, 2)
+
+        # And the same with a valid call:
+        res = a.__array_ufunc__(np.add, "__call__", a, a)
+        assert_array_equal(res, a + a)
 
 class TestChoose:
     def test_mixed(self):
@@ -3799,6 +4089,7 @@ class TestComplexFunctions:
             assert_almost_equal(fz.real, fr, err_msg='real part %s' % f)
             assert_almost_equal(fz.imag, 0., err_msg='imag part %s' % f)
 
+    @pytest.mark.xfail(IS_MUSL, reason="gh23049")
     @pytest.mark.xfail(IS_WASM, reason="doesn't work")
     def test_precisions_consistent(self):
         z = 1 + 1j
@@ -3809,6 +4100,7 @@ class TestComplexFunctions:
             assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s' % f)
             assert_almost_equal(fcl, fcd, decimal=15, err_msg='fch-fcl %s' % f)
 
+    @pytest.mark.xfail(IS_MUSL, reason="gh23049")
     @pytest.mark.xfail(IS_WASM, reason="doesn't work")
     def test_branch_cuts(self):
         # check branch cuts and continuity on them
@@ -3835,6 +4127,7 @@ class TestComplexFunctions:
         _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1,  1,  1j], 1, 1)
         _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1,  1,  1j], 1, 1)
 
+    @pytest.mark.xfail(IS_MUSL, reason="gh23049")
     @pytest.mark.xfail(IS_WASM, reason="doesn't work")
     def test_branch_cuts_complex64(self):
         # check branch cuts and continuity on them
@@ -3880,6 +4173,7 @@ class TestComplexFunctions:
                 b = cfunc(p)
                 assert_(abs(a - b) < atol, "%s %s: %s; cmath: %s" % (fname, p, a, b))
 
+    @pytest.mark.xfail(IS_MUSL, reason="gh23049")
     @pytest.mark.xfail(IS_WASM, reason="doesn't work")
     @pytest.mark.parametrize('dtype', [np.complex64, np.complex_, np.longcomplex])
     def test_loss_of_precision(self, dtype):
@@ -4152,7 +4446,7 @@ def _test_spacing(t):
     nan = t(np.nan)
     inf = t(np.inf)
     with np.errstate(invalid='ignore'):
-        assert_(np.spacing(one) == eps)
+        assert_equal(np.spacing(one), eps)
         assert_(np.isnan(np.spacing(nan)))
         assert_(np.isnan(np.spacing(inf)))
         assert_(np.isnan(np.spacing(-inf)))
diff --git a/contrib/python/numpy/py3/numpy/core/tests/test_unicode.py b/contrib/python/numpy/py3/numpy/core/tests/test_unicode.py
index 2d7c2818e98..e5454bd48df 100644
--- a/contrib/python/numpy/py3/numpy/core/tests/test_unicode.py
+++ b/contrib/python/numpy/py3/numpy/core/tests/test_unicode.py
@@ -36,10 +36,10 @@ def test_string_cast():
     uni_arr1 = str_arr.astype('>U')
     uni_arr2 = str_arr.astype('<U')
 
-    with pytest.warns(FutureWarning):
-        assert str_arr != uni_arr1
-    with pytest.warns(FutureWarning):
-        assert str_arr != uni_arr2
+    assert_array_equal(str_arr != uni_arr1, np.ones(2, dtype=bool))
+    assert_array_equal(uni_arr1 != str_arr, np.ones(2, dtype=bool))
+    assert_array_equal(str_arr == uni_arr1, np.zeros(2, dtype=bool))
+    assert_array_equal(uni_arr1 == str_arr, np.zeros(2, dtype=bool))
 
     assert_array_equal(uni_arr1, uni_arr2)
 
diff --git a/contrib/python/numpy/py3/numpy/ctypeslib.py b/contrib/python/numpy/py3/numpy/ctypeslib.py
index c4bafca1bd1..d9f64fd9e71 100644
--- a/contrib/python/numpy/py3/numpy/ctypeslib.py
+++ b/contrib/python/numpy/py3/numpy/ctypeslib.py
@@ -120,28 +120,26 @@ else:
             If there is no library with the expected extension, or the
             library is defective and cannot be loaded.
         """
-        if ctypes.__version__ < '1.0.1':
-            import warnings
-            warnings.warn("All features of ctypes interface may not work "
-                          "with ctypes < 1.0.1", stacklevel=2)
-
         # Convert path-like objects into strings
         libname = os.fsdecode(libname)
         loader_path = os.fsdecode(loader_path)
 
         ext = os.path.splitext(libname)[1]
         if not ext:
+            import sys
+            import sysconfig
             # Try to load library with platform-specific name, otherwise
-            # default to libname.[so|pyd].  Sometimes, these files are built
-            # erroneously on non-linux platforms.
-            from numpy.distutils.misc_util import get_shared_lib_extension
-            so_ext = get_shared_lib_extension()
-            libname_ext = [libname + so_ext]
-            # mac, windows and linux >= py3.2 shared library and loadable
-            # module have different extensions so try both
-            so_ext2 = get_shared_lib_extension(is_python_ext=True)
-            if not so_ext2 == so_ext:
-                libname_ext.insert(0, libname + so_ext2)
+            # default to libname.[so|dll|dylib].  Sometimes, these files are
+            # built erroneously on non-linux platforms.
+            base_ext = ".so"
+            if sys.platform.startswith("darwin"):
+                base_ext = ".dylib"
+            elif sys.platform.startswith("win"):
+                base_ext = ".dll"
+            libname_ext = [libname + base_ext]
+            so_ext = sysconfig.get_config_var("EXT_SUFFIX")
+            if not so_ext == base_ext:
+                libname_ext.insert(0, libname + so_ext)
         else:
             libname_ext = [libname]
 
diff --git a/contrib/python/numpy/py3/numpy/ctypeslib.pyi b/contrib/python/numpy/py3/numpy/ctypeslib.pyi
index 0313cd82a5a..3edf98e143c 100644
--- a/contrib/python/numpy/py3/numpy/ctypeslib.pyi
+++ b/contrib/python/numpy/py3/numpy/ctypeslib.pyi
@@ -91,10 +91,10 @@ class _ndptr(ctypes.c_void_p, Generic[_DTypeOptional]):
 
     @overload
     @classmethod
-    def from_param(cls: type[_ndptr[None]], obj: ndarray[Any, Any]) -> _ctypes: ...
+    def from_param(cls: type[_ndptr[None]], obj: ndarray[Any, Any]) -> _ctypes[Any]: ...
     @overload
     @classmethod
-    def from_param(cls: type[_ndptr[_DType]], obj: ndarray[Any, _DType]) -> _ctypes: ...
+    def from_param(cls: type[_ndptr[_DType]], obj: ndarray[Any, _DType]) -> _ctypes[Any]: ...
 
 class _concrete_ndptr(_ndptr[_DType]):
     _dtype_: ClassVar[_DType]
diff --git a/contrib/python/numpy/py3/numpy/distutils/ccompiler.py b/contrib/python/numpy/py3/numpy/distutils/ccompiler.py
index f0487cb64fd..40f495fc7aa 100644
--- a/contrib/python/numpy/py3/numpy/distutils/ccompiler.py
+++ b/contrib/python/numpy/py3/numpy/distutils/ccompiler.py
@@ -1,10 +1,12 @@
 import os
 import re
 import sys
+import platform
 import shlex
 import time
 import subprocess
 from copy import copy
+from pathlib import Path
 from distutils import ccompiler
 from distutils.ccompiler import (
     compiler_class, gen_lib_options, get_default_compiler, new_compiler,
@@ -57,7 +59,7 @@ def _needs_build(obj, cc_args, extra_postargs, pp_opts):
     # the last line contains the compiler commandline arguments as some
     # projects may compile an extension multiple times with different
     # arguments
-    with open(dep_file, "r") as f:
+    with open(dep_file) as f:
         lines = f.readlines()
 
     cmdline =_commandline_dep_string(cc_args, extra_postargs, pp_opts)
@@ -279,7 +281,8 @@ def CCompiler_compile(self, sources, output_dir=None, macros=None,
 
     if not sources:
         return []
-    from numpy.distutils.fcompiler import (FCompiler, is_f_file,
+    from numpy.distutils.fcompiler import (FCompiler,
+                                           FORTRAN_COMMON_FIXED_EXTENSIONS,
                                            has_f90_header)
     if isinstance(self, FCompiler):
         display = []
@@ -338,7 +341,8 @@ def CCompiler_compile(self, sources, output_dir=None, macros=None,
                 if self.compiler_type=='absoft':
                     obj = cyg2win32(obj)
                     src = cyg2win32(src)
-                if is_f_file(src) and not has_f90_header(src):
+                if Path(src).suffix.lower() in FORTRAN_COMMON_FIXED_EXTENSIONS \
+                   and not has_f90_header(src):
                     f77_objects.append((obj, (src, ext)))
                 else:
                     other_objects.append((obj, (src, ext)))
@@ -391,8 +395,14 @@ def CCompiler_customize_cmd(self, cmd, ignore=()):
     log.info('customize %s using %s' % (self.__class__.__name__,
                                         cmd.__class__.__name__))
 
-    if hasattr(self, 'compiler') and 'clang' in self.compiler[0]:
+    if (
+        hasattr(self, 'compiler') and
+        'clang' in self.compiler[0] and
+        not (platform.machine() == 'arm64' and sys.platform == 'darwin')
+    ):
         # clang defaults to a non-strict floating error point model.
+        # However, '-ftrapping-math' is not currently supported (2023-04-08)
+        # for macosx_arm64.
         # Since NumPy and most Python libs give warnings for these, override:
         self.compiler.append('-ftrapping-math')
         self.compiler_so.append('-ftrapping-math')
@@ -717,6 +727,8 @@ compiler_class['pathcc'] = ('pathccompiler', 'PathScaleCCompiler',
                             "PathScale Compiler for SiCortex-based applications")
 compiler_class['arm'] = ('armccompiler', 'ArmCCompiler',
                             "Arm C Compiler")
+compiler_class['fujitsu'] = ('fujitsuccompiler', 'FujitsuCCompiler',
+                            "Fujitsu C Compiler")
 
 ccompiler._default_compilers += (('linux.*', 'intel'),
                                  ('linux.*', 'intele'),
diff --git a/contrib/python/numpy/py3/numpy/distutils/ccompiler_opt.py b/contrib/python/numpy/py3/numpy/distutils/ccompiler_opt.py
index da550722cb3..1e9de3c45bc 100644
--- a/contrib/python/numpy/py3/numpy/distutils/ccompiler_opt.py
+++ b/contrib/python/numpy/py3/numpy/distutils/ccompiler_opt.py
@@ -16,15 +16,6 @@ import re
 import subprocess
 import textwrap
 
-# These flags are used to compile any C++ source within Numpy.
-# They are chosen to have very few runtime dependencies.
-NPY_CXX_FLAGS = [
-    '-std=c++11',  # Minimal standard version
-    '-D__STDC_VERSION__=0',  # for compatibility with C headers
-    '-fno-exceptions',  # no exception support
-    '-fno-rtti']  # no runtime type information
-
-
 class _Config:
     """An abstract class holds all configurable attributes of `CCompilerOpt`,
     these class attributes can be used to change the default behavior
@@ -229,6 +220,11 @@ class _Config:
             native = None,
             opt = '/O2',
             werror = '/WX',
+        ),
+        fcc = dict(
+            native = '-mcpu=a64fx',
+            opt = None,
+            werror = None,
         )
     )
     conf_min_features = dict(
@@ -295,6 +291,10 @@ class _Config:
             group="AVX512VBMI2 AVX512BITALG AVX512VPOPCNTDQ",
             detect="AVX512_ICL", implies_detect=False
         ),
+        AVX512_SPR = dict(
+            interest=46, implies="AVX512_ICL", group="AVX512FP16",
+            detect="AVX512_SPR", implies_detect=False
+        ),
         # IBM/Power
         ## Power7/ISA 2.06
         VSX = dict(interest=1, headers="altivec.h", extra_checks="VSX_ASM"),
@@ -338,7 +338,7 @@ class _Config:
             return {}
 
         on_x86 = self.cc_on_x86 or self.cc_on_x64
-        is_unix = self.cc_is_gcc or self.cc_is_clang
+        is_unix = self.cc_is_gcc or self.cc_is_clang or self.cc_is_fcc
 
         if on_x86 and is_unix: return dict(
             SSE    = dict(flags="-msse"),
@@ -365,7 +365,8 @@ class _Config:
             AVX512_CNL = dict(flags="-mavx512ifma -mavx512vbmi"),
             AVX512_ICL = dict(
                 flags="-mavx512vbmi2 -mavx512bitalg -mavx512vpopcntdq"
-            )
+            ),
+            AVX512_SPR = dict(flags="-mavx512fp16"),
         )
         if on_x86 and self.cc_is_icc: return dict(
             SSE    = dict(flags="-msse"),
@@ -397,6 +398,7 @@ class _Config:
             AVX512_CLX = dict(flags="-xCASCADELAKE"),
             AVX512_CNL = dict(flags="-xCANNONLAKE"),
             AVX512_ICL = dict(flags="-xICELAKE-CLIENT"),
+            AVX512_SPR = dict(disable="Not supported yet")
         )
         if on_x86 and self.cc_is_iccw: return dict(
             SSE    = dict(flags="/arch:SSE"),
@@ -429,7 +431,8 @@ class _Config:
             AVX512_SKX = dict(flags="/Qx:SKYLAKE-AVX512"),
             AVX512_CLX = dict(flags="/Qx:CASCADELAKE"),
             AVX512_CNL = dict(flags="/Qx:CANNONLAKE"),
-            AVX512_ICL = dict(flags="/Qx:ICELAKE-CLIENT")
+            AVX512_ICL = dict(flags="/Qx:ICELAKE-CLIENT"),
+            AVX512_SPR = dict(disable="Not supported yet")
         )
         if on_x86 and self.cc_is_msvc: return dict(
             SSE = dict(flags="/arch:SSE") if self.cc_on_x86 else {},
@@ -467,7 +470,10 @@ class _Config:
             AVX512_SKX = dict(flags="/arch:AVX512"),
             AVX512_CLX = {},
             AVX512_CNL = {},
-            AVX512_ICL = {}
+            AVX512_ICL = {},
+            AVX512_SPR= dict(
+                disable="MSVC compiler doesn't support it"
+            )
         )
 
         on_power = self.cc_on_ppc64le or self.cc_on_ppc64
@@ -489,9 +495,9 @@ class _Config:
             )
             if self.cc_is_clang:
                 partial["VSX"]["flags"]  = "-maltivec -mvsx"
-                partial["VSX2"]["flags"] = "-mpower8-vector"
-                partial["VSX3"]["flags"] = "-mpower9-vector"
-                partial["VSX4"]["flags"] = "-mpower10-vector"
+                partial["VSX2"]["flags"] = "-mcpu=power8"
+                partial["VSX3"]["flags"] = "-mcpu=power9"
+                partial["VSX4"]["flags"] = "-mcpu=power10"
 
             return partial
 
@@ -979,12 +985,14 @@ class _CCompiler:
             ("cc_is_iccw",     ".*(intelw|intelemw|iccw).*", ""),
             ("cc_is_icc",      ".*(intel|icc).*", ""),  # intel unix like
             ("cc_is_msvc",     ".*msvc.*", ""),
+            ("cc_is_fcc",     ".*fcc.*", ""),
             # undefined compiler will be treat it as gcc
             ("cc_is_nocc",     "", ""),
         )
         detect_args = (
            ("cc_has_debug",  ".*(O0|Od|ggdb|coverage|debug:full).*", ""),
-           ("cc_has_native", ".*(-march=native|-xHost|/QxHost).*", ""),
+           ("cc_has_native",
+                ".*(-march=native|-xHost|/QxHost|-mcpu=a64fx).*", ""),
            # in case if the class run with -DNPY_DISABLE_OPTIMIZATION
            ("cc_noopt", ".*DISABLE_OPT.*", ""),
         )
@@ -1045,7 +1053,7 @@ class _CCompiler:
                 break
 
         self.cc_name = "unknown"
-        for name in ("gcc", "clang", "iccw", "icc", "msvc"):
+        for name in ("gcc", "clang", "iccw", "icc", "msvc", "fcc"):
             if getattr(self, "cc_is_" + name):
                 self.cc_name = name
                 break
@@ -1167,7 +1175,7 @@ class _CCompiler:
                 continue
             lower_flags = flags[:-(i+1)]
             upper_flags = flags[-i:]
-            filterd = list(filter(
+            filtered = list(filter(
                 self._cc_normalize_unix_frgx.search, lower_flags
             ))
             # gather subflags
@@ -1179,7 +1187,7 @@ class _CCompiler:
                         subflags = xsubflags + subflags
                 cur_flag = arch + '+' + '+'.join(subflags)
 
-            flags = filterd + [cur_flag]
+            flags = filtered + [cur_flag]
             if i > 0:
                 flags += upper_flags
             break
diff --git a/contrib/python/numpy/py3/numpy/distutils/checks/cpu_avx512_spr.c b/contrib/python/numpy/py3/numpy/distutils/checks/cpu_avx512_spr.c
new file mode 100644
index 00000000000..9710d0b2fe2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/distutils/checks/cpu_avx512_spr.c
@@ -0,0 +1,26 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX512FP16__)
+        #error "HOST/ARCH doesn't support Sapphire Rapids AVX512FP16 features"
+    #endif
+#endif
+
+#include <immintrin.h>
+
+int main(int argc, char **argv)
+{
+/* clang has a bug regarding our spr coode, see gh-23730. */
+#if __clang__
+#error
+#endif
+    __m512h a = _mm512_loadu_ph((void*)argv[argc-1]);
+    __m512h temp = _mm512_fmadd_ph(a, a, a);
+    _mm512_storeu_ph((void*)(argv[argc-1]), temp);
+    return 0;
+}
diff --git a/contrib/python/numpy/py3/numpy/distutils/command/build_clib.py b/contrib/python/numpy/py3/numpy/distutils/command/build_clib.py
index 45201f98f85..11999dae232 100644
--- a/contrib/python/numpy/py3/numpy/distutils/command/build_clib.py
+++ b/contrib/python/numpy/py3/numpy/distutils/command/build_clib.py
@@ -307,6 +307,7 @@ class build_clib(old_build_clib):
             # problem, msvc uses its own convention :(
             c_sources += cxx_sources
             cxx_sources = []
+            extra_cflags += extra_cxxflags
 
         # filtering C dispatch-table sources when optimization is not disabled,
         # otherwise treated as normal sources.
diff --git a/contrib/python/numpy/py3/numpy/distutils/command/build_ext.py b/contrib/python/numpy/py3/numpy/distutils/command/build_ext.py
index 6dc6b4265f8..68b13c0dd37 100644
--- a/contrib/python/numpy/py3/numpy/distutils/command/build_ext.py
+++ b/contrib/python/numpy/py3/numpy/distutils/command/build_ext.py
@@ -259,7 +259,7 @@ class build_ext (old_build_ext):
                     log.warn('resetting extension %r language from %r to %r.' %
                              (ext.name, l, ext_language))
 
-                ext.language = ext_language
+            ext.language = ext_language
 
             # global language
             all_languages.update(ext_languages)
@@ -407,6 +407,7 @@ class build_ext (old_build_ext):
             if cxx_sources:
                 # Needed to compile kiva.agg._agg extension.
                 extra_args.append('/Zm1000')
+                extra_cflags += extra_cxxflags
             # this hack works around the msvc compiler attributes
             # problem, msvc uses its own convention :(
             c_sources += cxx_sources
@@ -641,12 +642,12 @@ class build_ext (old_build_ext):
                 if os.path.isfile(fake_lib):
                     # Replace fake static library
                     libraries.remove(lib)
-                    with open(fake_lib, 'r') as f:
+                    with open(fake_lib) as f:
                         unlinkable_fobjects.extend(f.read().splitlines())
 
                     # Expand C objects
                     c_lib = os.path.join(libdir, lib + '.cobjects')
-                    with open(c_lib, 'r') as f:
+                    with open(c_lib) as f:
                         objects.extend(f.read().splitlines())
 
         # Wrap unlinkable objects to a linkable one
diff --git a/contrib/python/numpy/py3/numpy/distutils/command/build_src.py b/contrib/python/numpy/py3/numpy/distutils/command/build_src.py
index 5581011f6f2..bf3d03c70e4 100644
--- a/contrib/python/numpy/py3/numpy/distutils/command/build_src.py
+++ b/contrib/python/numpy/py3/numpy/distutils/command/build_src.py
@@ -725,7 +725,7 @@ _has_c_header = re.compile(r'-\*-\s*c\s*-\*-', re.I).search
 _has_cpp_header = re.compile(r'-\*-\s*c\+\+\s*-\*-', re.I).search
 
 def get_swig_target(source):
-    with open(source, 'r') as f:
+    with open(source) as f:
         result = None
         line = f.readline()
         if _has_cpp_header(line):
@@ -735,7 +735,7 @@ def get_swig_target(source):
     return result
 
 def get_swig_modulename(source):
-    with open(source, 'r') as f:
+    with open(source) as f:
         name = None
         for line in f:
             m = _swig_module_name_match(line)
diff --git a/contrib/python/numpy/py3/numpy/distutils/command/install.py b/contrib/python/numpy/py3/numpy/distutils/command/install.py
index 2eff2d14504..efa9b4740fc 100644
--- a/contrib/python/numpy/py3/numpy/distutils/command/install.py
+++ b/contrib/python/numpy/py3/numpy/distutils/command/install.py
@@ -62,7 +62,7 @@ class install(old_install):
             # bdist_rpm fails when INSTALLED_FILES contains
             # paths with spaces. Such paths must be enclosed
             # with double-quotes.
-            with open(self.record, 'r') as f:
+            with open(self.record) as f:
                 lines = []
                 need_rewrite = False
                 for l in f:
diff --git a/contrib/python/numpy/py3/numpy/distutils/fcompiler/__init__.py b/contrib/python/numpy/py3/numpy/distutils/fcompiler/__init__.py
index ecba3e5d581..5160e2abf54 100644
--- a/contrib/python/numpy/py3/numpy/distutils/fcompiler/__init__.py
+++ b/contrib/python/numpy/py3/numpy/distutils/fcompiler/__init__.py
@@ -19,6 +19,7 @@ __all__ = ['FCompiler', 'new_fcompiler', 'show_fcompilers',
 import os
 import sys
 import re
+from pathlib import Path
 
 from distutils.sysconfig import get_python_lib
 from distutils.fancy_getopt import FancyGetopt
@@ -37,6 +38,10 @@ from .environment import EnvironmentConfig
 
 __metaclass__ = type
 
+
+FORTRAN_COMMON_FIXED_EXTENSIONS = ['.for', '.ftn', '.f77', '.f']
+
+
 class CompilerNotFound(Exception):
     pass
 
@@ -571,7 +576,8 @@ class FCompiler(CCompiler):
     def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
         """Compile 'src' to product 'obj'."""
         src_flags = {}
-        if is_f_file(src) and not has_f90_header(src):
+        if Path(src).suffix.lower() in FORTRAN_COMMON_FIXED_EXTENSIONS \
+           and not has_f90_header(src):
             flavor = ':f77'
             compiler = self.compiler_f77
             src_flags = get_f77flags(src)
@@ -970,7 +976,6 @@ def dummy_fortran_file():
     return name[:-2]
 
 
-is_f_file = re.compile(r'.*\.(for|ftn|f77|f)\Z', re.I).match
 _has_f_header = re.compile(r'-\*-\s*fortran\s*-\*-', re.I).search
 _has_f90_header = re.compile(r'-\*-\s*f90\s*-\*-', re.I).search
 _has_fix_header = re.compile(r'-\*-\s*fix\s*-\*-', re.I).search
diff --git a/contrib/python/numpy/py3/numpy/distutils/fcompiler/absoft.py b/contrib/python/numpy/py3/numpy/distutils/fcompiler/absoft.py
index efe3a4cb55e..68f516b9275 100644
--- a/contrib/python/numpy/py3/numpy/distutils/fcompiler/absoft.py
+++ b/contrib/python/numpy/py3/numpy/distutils/fcompiler/absoft.py
@@ -1,6 +1,6 @@
 
-# http://www.absoft.com/literature/osxuserguide.pdf
-# http://www.absoft.com/documentation.html
+# Absoft Corporation ceased operations on 12/31/2022.
+# Thus, all links to <http://www.absoft.com> are invalid.
 
 # Notes:
 # - when using -g77 then use -DUNDERSCORE_G77 to compile f2py
diff --git a/contrib/python/numpy/py3/numpy/distutils/fcompiler/ibm.py b/contrib/python/numpy/py3/numpy/distutils/fcompiler/ibm.py
index eff24401a1c..29927518c70 100644
--- a/contrib/python/numpy/py3/numpy/distutils/fcompiler/ibm.py
+++ b/contrib/python/numpy/py3/numpy/distutils/fcompiler/ibm.py
@@ -76,7 +76,7 @@ class IBMFCompiler(FCompiler):
                 xlf_cfg = '/etc/opt/ibmcmp/xlf/%s/xlf.cfg' % version
             fo, new_cfg = make_temp_file(suffix='_xlf.cfg')
             log.info('Creating '+new_cfg)
-            with open(xlf_cfg, 'r') as fi:
+            with open(xlf_cfg) as fi:
                 crt1_match = re.compile(r'\s*crt\s*=\s*(?P<path>.*)/crt1.o').match
                 for line in fi:
                     m = crt1_match(line)
diff --git a/contrib/python/numpy/py3/numpy/distutils/fujitsuccompiler.py b/contrib/python/numpy/py3/numpy/distutils/fujitsuccompiler.py
new file mode 100644
index 00000000000..c25900b34f1
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/distutils/fujitsuccompiler.py
@@ -0,0 +1,28 @@
+from distutils.unixccompiler import UnixCCompiler
+
+class FujitsuCCompiler(UnixCCompiler):
+
+    """
+    Fujitsu compiler.
+    """
+
+    compiler_type = 'fujitsu'
+    cc_exe = 'fcc'
+    cxx_exe = 'FCC'
+
+    def __init__(self, verbose=0, dry_run=0, force=0):
+        UnixCCompiler.__init__(self, verbose, dry_run, force)
+        cc_compiler = self.cc_exe
+        cxx_compiler = self.cxx_exe
+        self.set_executables(
+            compiler=cc_compiler +
+            ' -O3 -Nclang -fPIC',
+            compiler_so=cc_compiler +
+            ' -O3 -Nclang -fPIC',
+            compiler_cxx=cxx_compiler +
+            ' -O3 -Nclang -fPIC',
+            linker_exe=cc_compiler +
+            ' -lfj90i -lfj90f -lfjsrcinfo -lelf -shared',
+            linker_so=cc_compiler +
+            ' -lfj90i -lfj90f -lfjsrcinfo -lelf -shared'
+            )
diff --git a/contrib/python/numpy/py3/numpy/distutils/include/npy_cpu_dispatch_config.aarch64.h b/contrib/python/numpy/py3/numpy/distutils/include/npy_cpu_dispatch_config.aarch64.h
index 66c3eefea60..547efed67b5 100644
--- a/contrib/python/numpy/py3/numpy/distutils/include/npy_cpu_dispatch_config.aarch64.h
+++ b/contrib/python/numpy/py3/numpy/distutils/include/npy_cpu_dispatch_config.aarch64.h
@@ -40,4 +40,3 @@
 	/** ASIMDFHM **/
 	#define NPY_HAVE_ASIMDFHM 1
 #endif /*NPY__CPU_TARGET_ASIMDFHM*/
-
diff --git a/contrib/python/numpy/py3/numpy/distutils/mingw32ccompiler.py b/contrib/python/numpy/py3/numpy/distutils/mingw32ccompiler.py
index 5d1c27a651f..4763f41ad32 100644
--- a/contrib/python/numpy/py3/numpy/distutils/mingw32ccompiler.py
+++ b/contrib/python/numpy/py3/numpy/distutils/mingw32ccompiler.py
@@ -8,7 +8,6 @@ Support code for building Python extensions on Windows.
 
 """
 import os
-import platform
 import sys
 import subprocess
 import re
@@ -203,11 +202,11 @@ def find_python_dll():
 
     # search in the file system for possible candidates
     major_version, minor_version = tuple(sys.version_info[:2])
-    implementation = platform.python_implementation()
-    if implementation == 'CPython':
+    implementation = sys.implementation.name
+    if implementation == 'cpython':
         dllname = f'python{major_version}{minor_version}.dll'
-    elif implementation == 'PyPy':
-        dllname = f'libpypy{major_version}-c.dll'
+    elif implementation == 'pypy':
+        dllname = f'libpypy{major_version}.{minor_version}-c.dll'
     else:
         dllname = f'Unknown platform {implementation}' 
     print("Looking for %s" % dllname)
diff --git a/contrib/python/numpy/py3/numpy/distutils/misc_util.py b/contrib/python/numpy/py3/numpy/distutils/misc_util.py
index 79ba0851545..e226b474481 100644
--- a/contrib/python/numpy/py3/numpy/distutils/misc_util.py
+++ b/contrib/python/numpy/py3/numpy/distutils/misc_util.py
@@ -475,7 +475,7 @@ def _get_f90_modules(source):
     if not f90_ext_match(source):
         return []
     modules = []
-    with open(source, 'r') as f:
+    with open(source) as f:
         for line in f:
             m = f90_module_name_match(line)
             if m:
@@ -1932,7 +1932,7 @@ class Configuration:
                 revision0 = f.read().strip()
 
             branch_map = {}
-            with open(branch_cache_fn, 'r') as f:
+            with open(branch_cache_fn) as f:
                 for line in f:
                     branch1, revision1  = line.split()[:2]
                     if revision1==revision0:
diff --git a/contrib/python/numpy/py3/numpy/distutils/system_info.py b/contrib/python/numpy/py3/numpy/distutils/system_info.py
index d5a1687da32..3dca7fb5a77 100644
--- a/contrib/python/numpy/py3/numpy/distutils/system_info.py
+++ b/contrib/python/numpy/py3/numpy/distutils/system_info.py
@@ -62,6 +62,7 @@ Currently, the following classes are available, along with their section names:
     blas_ilp64_plain_opt_info:ALL      # usage recommended (general ILP64 BLAS, no symbol suffix)
     blas_info:blas
     blas_mkl_info:mkl
+    blas_ssl2_info:ssl2
     blas_opt_info:ALL                  # usage recommended
     blas_src_info:blas_src
     blis_info:blis
@@ -93,9 +94,11 @@ Currently, the following classes are available, along with their section names:
     lapack_ilp64_plain_opt_info:ALL    # usage recommended (general ILP64 LAPACK, no symbol suffix)
     lapack_info:lapack
     lapack_mkl_info:mkl
+    lapack_ssl2_info:ssl2
     lapack_opt_info:ALL                # usage recommended
     lapack_src_info:lapack_src
     mkl_info:mkl
+    ssl2_info:ssl2
     numarray_info:numarray
     numerix_info:numerix
     numpy_info:numpy
@@ -519,6 +522,7 @@ def get_info(name, notfound_action=0):
           'lapack_atlas_3_10_threads': lapack_atlas_3_10_threads_info,  # ditto
           'flame': flame_info,          # use lapack_opt instead
           'mkl': mkl_info,
+          'ssl2': ssl2_info,
           # openblas which may or may not have embedded lapack
           'openblas': openblas_info,          # use blas_opt instead
           # openblas with embedded lapack
@@ -527,6 +531,8 @@ def get_info(name, notfound_action=0):
           'blis': blis_info,                  # use blas_opt instead
           'lapack_mkl': lapack_mkl_info,      # use lapack_opt instead
           'blas_mkl': blas_mkl_info,          # use blas_opt instead
+          'lapack_ssl2': lapack_ssl2_info,      
+          'blas_ssl2': blas_ssl2_info,          
           'accelerate': accelerate_info,      # use blas_opt instead
           'openblas64_': openblas64__info,
           'openblas64__lapack': openblas64__lapack_info,
@@ -1260,7 +1266,7 @@ class mkl_info(system_info):
         paths = os.environ.get('LD_LIBRARY_PATH', '').split(os.pathsep)
         ld_so_conf = '/etc/ld.so.conf'
         if os.path.isfile(ld_so_conf):
-            with open(ld_so_conf, 'r') as f:
+            with open(ld_so_conf) as f:
                 for d in f:
                     d = d.strip()
                     if d:
@@ -1325,6 +1331,63 @@ class blas_mkl_info(mkl_info):
     pass
 
 
+class ssl2_info(system_info):
+    section = 'ssl2'
+    dir_env_var = 'SSL2_DIR'
+    # Multi-threaded version. Python itself must be built by Fujitsu compiler.
+    _lib_ssl2 = ['fjlapackexsve']
+    # Single-threaded version
+    #_lib_ssl2 = ['fjlapacksve']
+
+    def get_tcsds_rootdir(self):
+        tcsdsroot = os.environ.get('TCSDS_PATH', None)
+        if tcsdsroot is not None:
+            return tcsdsroot
+        return None
+
+    def __init__(self):
+        tcsdsroot = self.get_tcsds_rootdir()
+        if tcsdsroot is None:
+            system_info.__init__(self)
+        else:
+            system_info.__init__(
+                self,
+                default_lib_dirs=[os.path.join(tcsdsroot, 'lib64')],
+                default_include_dirs=[os.path.join(tcsdsroot,
+                    'clang-comp/include')])
+
+    def calc_info(self):
+        tcsdsroot = self.get_tcsds_rootdir()
+
+        lib_dirs = self.get_lib_dirs()
+        if lib_dirs is None:
+            lib_dirs = os.path.join(tcsdsroot, 'lib64')
+
+        incl_dirs = self.get_include_dirs()
+        if incl_dirs is None:
+            incl_dirs = os.path.join(tcsdsroot, 'clang-comp/include')
+
+        ssl2_libs = self.get_libs('ssl2_libs', self._lib_ssl2)
+
+        info = self.check_libs2(lib_dirs, ssl2_libs)
+        if info is None:
+            return
+        dict_append(info,
+                    define_macros=[('HAVE_CBLAS', None),
+                                   ('HAVE_SSL2', 1)],
+                    include_dirs=incl_dirs,)
+        self.set_info(**info)
+
+
+class lapack_ssl2_info(ssl2_info):
+    pass
+
+
+class blas_ssl2_info(ssl2_info):
+    pass
+
+
+
 class armpl_info(system_info):
     section = 'armpl'
     dir_env_var = 'ARMPL_DIR'
@@ -1787,7 +1850,7 @@ class lapack_opt_info(system_info):
     notfounderror = LapackNotFoundError
 
     # List of all known LAPACK libraries, in the default order
-    lapack_order = ['armpl', 'mkl', 'openblas', 'flame',
+    lapack_order = ['armpl', 'mkl', 'ssl2', 'openblas', 'flame',
                     'accelerate', 'atlas', 'lapack']
     order_env_var_name = 'NPY_LAPACK_ORDER'
     
@@ -1805,6 +1868,13 @@ class lapack_opt_info(system_info):
             return True
         return False
 
+    def _calc_info_ssl2(self):
+        info = get_info('lapack_ssl2')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
     def _calc_info_openblas(self):
         info = get_info('openblas_lapack')
         if info:
@@ -1971,7 +2041,7 @@ class blas_opt_info(system_info):
     notfounderror = BlasNotFoundError
     # List of all known BLAS libraries, in the default order
 
-    blas_order = ['armpl', 'mkl', 'blis', 'openblas',
+    blas_order = ['armpl', 'mkl', 'ssl2', 'blis', 'openblas',
                   'accelerate', 'atlas', 'blas']
     order_env_var_name = 'NPY_BLAS_ORDER'
     
@@ -1989,6 +2059,13 @@ class blas_opt_info(system_info):
             return True
         return False
 
+    def _calc_info_ssl2(self):
+        info = get_info('blas_ssl2')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
     def _calc_info_blis(self):
         info = get_info('blis')
         if info:
@@ -2190,7 +2267,7 @@ class blas_info(system_info):
             }""")
         src = os.path.join(tmpdir, 'source.c')
         try:
-            with open(src, 'wt') as f:
+            with open(src, 'w') as f:
                 f.write(s)
 
             try:
@@ -2345,7 +2422,7 @@ class openblas_info(blas_info):
         except Exception:
             extra_args = []
         try:
-            with open(src, 'wt') as f:
+            with open(src, 'w') as f:
                 f.write(s)
             obj = c.compile([src], output_dir=tmpdir)
             try:
@@ -2456,7 +2533,7 @@ class flame_info(system_info):
         # Add the additional "extra" arguments
         extra_args = info.get('extra_link_args', [])
         try:
-            with open(src, 'wt') as f:
+            with open(src, 'w') as f:
                 f.write(s)
             obj = c.compile([src], output_dir=tmpdir)
             try:
diff --git a/contrib/python/numpy/py3/numpy/distutils/tests/test_ccompiler_opt.py b/contrib/python/numpy/py3/numpy/distutils/tests/test_ccompiler_opt.py
index 657ebdb6851..3714aea0e12 100644
--- a/contrib/python/numpy/py3/numpy/distutils/tests/test_ccompiler_opt.py
+++ b/contrib/python/numpy/py3/numpy/distutils/tests/test_ccompiler_opt.py
@@ -31,7 +31,7 @@ arch_compilers = dict(
     ppc64 = ("gcc", "clang"),
     ppc64le = ("gcc", "clang"),
     armhf = ("gcc", "clang"),
-    aarch64 = ("gcc", "clang"),
+    aarch64 = ("gcc", "clang", "fcc"),
     s390x = ("gcc", "clang"),
     noarch = ("gcc",)
 )
@@ -422,8 +422,8 @@ class _Test_CCompilerOpt:
         # when option "native" is activated through the args
         try:
             self.expect("native",
-                trap_flags=".*(-march=native|-xHost|/QxHost).*",
-                x86=".*", ppc64=".*", armhf=".*", s390x=".*"
+                trap_flags=".*(-march=native|-xHost|/QxHost|-mcpu=a64fx).*",
+                x86=".*", ppc64=".*", armhf=".*", s390x=".*", aarch64=".*",
             )
             if self.march() != "unknown":
                 raise AssertionError(
@@ -440,7 +440,7 @@ class _Test_CCompilerOpt:
             x86_iccw="/arch:SSE2",
             x86_msvc="/arch:SSE2" if self.march() == "x86" else "",
             ppc64_gcc= "-mcpu=power8",
-            ppc64_clang="-maltivec -mvsx -mpower8-vector",
+            ppc64_clang="-mcpu=power8",
             armhf_gcc="-mfpu=neon-fp16 -mfp16-format=ieee",
             aarch64="",
             s390x="-mzvector -march=arch12"
diff --git a/contrib/python/numpy/py3/numpy/distutils/tests/test_system_info.py b/contrib/python/numpy/py3/numpy/distutils/tests/test_system_info.py
index eb7235e0462..66304a5e50f 100644
--- a/contrib/python/numpy/py3/numpy/distutils/tests/test_system_info.py
+++ b/contrib/python/numpy/py3/numpy/distutils/tests/test_system_info.py
@@ -271,7 +271,7 @@ class TestSystemInfoReading:
 
             # But if we copy the values to a '[mkl]' section the value
             # is correct
-            with open(cfg, 'r') as fid:
+            with open(cfg) as fid:
                 mkl = fid.read().replace('[ALL]', '[mkl]', 1)
             with open(cfg, 'w') as fid:
                 fid.write(mkl)
@@ -279,7 +279,7 @@ class TestSystemInfoReading:
             assert info.get_lib_dirs() == lib_dirs
 
             # Also, the values will be taken from a section named '[DEFAULT]'
-            with open(cfg, 'r') as fid:
+            with open(cfg) as fid:
                 dflt = fid.read().replace('[mkl]', '[DEFAULT]', 1)
             with open(cfg, 'w') as fid:
                 fid.write(dflt)
diff --git a/contrib/python/numpy/py3/numpy/dtypes.py b/contrib/python/numpy/py3/numpy/dtypes.py
new file mode 100644
index 00000000000..068a6a1a0f5
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/dtypes.py
@@ -0,0 +1,77 @@
+"""
+DType classes and utility (:mod:`numpy.dtypes`)
+===============================================
+
+This module is home to specific dtypes related functionality and their classes.
+For more general information about dtypes, also see `numpy.dtype` and
+:ref:`arrays.dtypes`.
+
+Similar to the builtin ``types`` module, this submodule defines types (classes)
+that are not widely used directly.
+
+.. versionadded:: NumPy 1.25
+
+    The dtypes module is new in NumPy 1.25.  Previously DType classes were
+    only accessible indirectly.
+
+
+DType classes
+-------------
+
+The following are the classes of the corresponding NumPy dtype instances and
+NumPy scalar types.  The classes can be used in ``isinstance`` checks and can
+also be instantiated or used directly.  Direct use of these classes is not
+typical, since their scalar counterparts (e.g. ``np.float64``) or strings
+like ``"float64"`` can be used.
+
+.. list-table::
+    :header-rows: 1
+
+    * - Group
+      - DType class
+
+    * - Boolean
+      - ``BoolDType``
+
+    * - Bit-sized integers
+      - ``Int8DType``, ``UInt8DType``, ``Int16DType``, ``UInt16DType``,
+        ``Int32DType``, ``UInt32DType``, ``Int64DType``, ``UInt64DType``
+
+    * - C-named integers (may be aliases)
+      - ``ByteDType``, ``UByteDType``, ``ShortDType``, ``UShortDType``,
+        ``IntDType``, ``UIntDType``, ``LongDType``, ``ULongDType``,
+        ``LongLongDType``, ``ULongLongDType``
+
+    * - Floating point
+      - ``Float16DType``, ``Float32DType``, ``Float64DType``,
+        ``LongDoubleDType``
+
+    * - Complex
+      - ``Complex64DType``, ``Complex128DType``, ``CLongDoubleDType``
+
+    * - Strings
+      - ``BytesDType``, ``BytesDType``
+
+    * - Times
+      - ``DateTime64DType``, ``TimeDelta64DType``
+
+    * - Others
+      - ``ObjectDType``, ``VoidDType``
+
+"""
+
+__all__ = []
+
+
+def _add_dtype_helper(DType, alias):
+    # Function to add DTypes a bit more conveniently without channeling them
+    # through `numpy.core._multiarray_umath` namespace or similar.
+    from numpy import dtypes
+
+    setattr(dtypes, DType.__name__, DType)
+    __all__.append(DType.__name__)
+
+    if alias:
+        alias = alias.removeprefix("numpy.dtypes.")
+        setattr(dtypes, alias, DType)
+        __all__.append(alias)
diff --git a/contrib/python/numpy/py3/numpy/dtypes.pyi b/contrib/python/numpy/py3/numpy/dtypes.pyi
new file mode 100644
index 00000000000..2f7e846f23d
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/dtypes.pyi
@@ -0,0 +1,43 @@
+import numpy as np
+
+
+__all__: list[str]
+
+# Boolean:
+BoolDType = np.dtype[np.bool_]
+# Sized integers:
+Int8DType = np.dtype[np.int8]
+UInt8DType = np.dtype[np.uint8]
+Int16DType = np.dtype[np.int16]
+UInt16DType = np.dtype[np.uint16]
+Int32DType = np.dtype[np.int32]
+UInt32DType = np.dtype[np.uint32]
+Int64DType = np.dtype[np.int64]
+UInt64DType = np.dtype[np.uint64]
+# Standard C-named version/alias:
+ByteDType = np.dtype[np.byte]
+UByteDType = np.dtype[np.ubyte]
+ShortDType = np.dtype[np.short]
+UShortDType = np.dtype[np.ushort]
+IntDType = np.dtype[np.intc]
+UIntDType = np.dtype[np.uintc]
+LongDType = np.dtype[np.int_]  # Unfortunately, the correct scalar
+ULongDType = np.dtype[np.uint]  # Unfortunately, the correct scalar
+LongLongDType = np.dtype[np.longlong]
+ULongLongDType = np.dtype[np.ulonglong]
+# Floats
+Float16DType = np.dtype[np.float16]
+Float32DType = np.dtype[np.float32]
+Float64DType = np.dtype[np.float64]
+LongDoubleDType = np.dtype[np.longdouble]
+# Complex:
+Complex64DType = np.dtype[np.complex64]
+Complex128DType = np.dtype[np.complex128]
+CLongDoubleDType = np.dtype[np.clongdouble]
+# Others:
+ObjectDType = np.dtype[np.object_]
+BytesDType = np.dtype[np.bytes_]
+StrDType = np.dtype[np.str_]
+VoidDType = np.dtype[np.void]
+DateTime64DType = np.dtype[np.datetime64]
+TimeDelta64DType = np.dtype[np.timedelta64]
diff --git a/contrib/python/numpy/py3/numpy/dual.py b/contrib/python/numpy/py3/numpy/dual.py
deleted file mode 100644
index eb7e61aac08..00000000000
--- a/contrib/python/numpy/py3/numpy/dual.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""
-.. deprecated:: 1.20
-
-*This module is deprecated.  Instead of importing functions from*
-``numpy.dual``, *the functions should be imported directly from NumPy
-or SciPy*.
-
-Aliases for functions which may be accelerated by SciPy.
-
-SciPy_ can be built to use accelerated or otherwise improved libraries
-for FFTs, linear algebra, and special functions. This module allows
-developers to transparently support these accelerated functions when
-SciPy is available but still support users who have only installed
-NumPy.
-
-.. _SciPy : https://www.scipy.org
-
-"""
-import warnings
-
-
-warnings.warn('The module numpy.dual is deprecated.  Instead of using dual, '
-              'use the functions directly from numpy or scipy.',
-              category=DeprecationWarning,
-              stacklevel=2)
-
-# This module should be used for functions both in numpy and scipy if
-#  you want to use the numpy version if available but the scipy version
-#  otherwise.
-#  Usage  --- from numpy.dual import fft, inv
-
-__all__ = ['fft', 'ifft', 'fftn', 'ifftn', 'fft2', 'ifft2',
-           'norm', 'inv', 'svd', 'solve', 'det', 'eig', 'eigvals',
-           'eigh', 'eigvalsh', 'lstsq', 'pinv', 'cholesky', 'i0']
-
-import numpy.linalg as linpkg
-import numpy.fft as fftpkg
-from numpy.lib import i0
-import sys
-
-
-fft = fftpkg.fft
-ifft = fftpkg.ifft
-fftn = fftpkg.fftn
-ifftn = fftpkg.ifftn
-fft2 = fftpkg.fft2
-ifft2 = fftpkg.ifft2
-
-norm = linpkg.norm
-inv = linpkg.inv
-svd = linpkg.svd
-solve = linpkg.solve
-det = linpkg.det
-eig = linpkg.eig
-eigvals = linpkg.eigvals
-eigh = linpkg.eigh
-eigvalsh = linpkg.eigvalsh
-lstsq = linpkg.lstsq
-pinv = linpkg.pinv
-cholesky = linpkg.cholesky
-
-_restore_dict = {}
-
-def register_func(name, func):
-    if name not in __all__:
-        raise ValueError("{} not a dual function.".format(name))
-    f = sys._getframe(0).f_globals
-    _restore_dict[name] = f[name]
-    f[name] = func
-
-def restore_func(name):
-    if name not in __all__:
-        raise ValueError("{} not a dual function.".format(name))
-    try:
-        val = _restore_dict[name]
-    except KeyError:
-        return
-    else:
-        sys._getframe(0).f_globals[name] = val
-
-def restore_all():
-    for name in _restore_dict.keys():
-        restore_func(name)
diff --git a/contrib/python/numpy/py3/numpy/exceptions.py b/contrib/python/numpy/py3/numpy/exceptions.py
new file mode 100644
index 00000000000..2f843810141
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/exceptions.py
@@ -0,0 +1,231 @@
+"""
+Exceptions and Warnings (:mod:`numpy.exceptions`)
+=================================================
+
+General exceptions used by NumPy.  Note that some exceptions may be module
+specific, such as linear algebra errors.
+
+.. versionadded:: NumPy 1.25
+
+    The exceptions module is new in NumPy 1.25.  Older exceptions remain
+    available through the main NumPy namespace for compatibility.
+
+.. currentmodule:: numpy.exceptions
+
+Warnings
+--------
+.. autosummary::
+   :toctree: generated/
+
+   ComplexWarning             Given when converting complex to real.
+   VisibleDeprecationWarning  Same as a DeprecationWarning, but more visible.
+
+Exceptions
+----------
+.. autosummary::
+   :toctree: generated/
+
+    AxisError          Given when an axis was invalid.
+    DTypePromotionError   Given when no common dtype could be found.
+    TooHardError       Error specific to `numpy.shares_memory`.
+
+"""
+
+
+__all__ = [
+    "ComplexWarning", "VisibleDeprecationWarning", "ModuleDeprecationWarning",
+    "TooHardError", "AxisError", "DTypePromotionError"]
+
+
+# Disallow reloading this module so as to preserve the identities of the
+# classes defined here.
+if '_is_loaded' in globals():
+    raise RuntimeError('Reloading numpy._globals is not allowed')
+_is_loaded = True
+
+
+class ComplexWarning(RuntimeWarning):
+    """
+    The warning raised when casting a complex dtype to a real dtype.
+
+    As implemented, casting a complex number to a real discards its imaginary
+    part, but this behavior may not be what the user actually wants.
+
+    """
+    pass
+
+
+class ModuleDeprecationWarning(DeprecationWarning):
+    """Module deprecation warning.
+
+    .. warning::
+
+        This warning should not be used, since nose testing is not relevant
+        anymore.
+
+    The nose tester turns ordinary Deprecation warnings into test failures.
+    That makes it hard to deprecate whole modules, because they get
+    imported by default. So this is a special Deprecation warning that the
+    nose tester will let pass without making tests fail.
+
+    """
+
+
+class VisibleDeprecationWarning(UserWarning):
+    """Visible deprecation warning.
+
+    By default, python will not show deprecation warnings, so this class
+    can be used when a very visible warning is helpful, for example because
+    the usage is most likely a user bug.
+
+    """
+
+
+# Exception used in shares_memory()
+class TooHardError(RuntimeError):
+    """max_work was exceeded.
+
+    This is raised whenever the maximum number of candidate solutions
+    to consider specified by the ``max_work`` parameter is exceeded.
+    Assigning a finite number to max_work may have caused the operation
+    to fail.
+
+    """
+
+    pass
+
+
+class AxisError(ValueError, IndexError):
+    """Axis supplied was invalid.
+
+    This is raised whenever an ``axis`` parameter is specified that is larger
+    than the number of array dimensions.
+    For compatibility with code written against older numpy versions, which
+    raised a mixture of `ValueError` and `IndexError` for this situation, this
+    exception subclasses both to ensure that ``except ValueError`` and
+    ``except IndexError`` statements continue to catch `AxisError`.
+
+    .. versionadded:: 1.13
+
+    Parameters
+    ----------
+    axis : int or str
+        The out of bounds axis or a custom exception message.
+        If an axis is provided, then `ndim` should be specified as well.
+    ndim : int, optional
+        The number of array dimensions.
+    msg_prefix : str, optional
+        A prefix for the exception message.
+
+    Attributes
+    ----------
+    axis : int, optional
+        The out of bounds axis or ``None`` if a custom exception
+        message was provided. This should be the axis as passed by
+        the user, before any normalization to resolve negative indices.
+
+        .. versionadded:: 1.22
+    ndim : int, optional
+        The number of array dimensions or ``None`` if a custom exception
+        message was provided.
+
+        .. versionadded:: 1.22
+
+
+    Examples
+    --------
+    >>> array_1d = np.arange(10)
+    >>> np.cumsum(array_1d, axis=1)
+    Traceback (most recent call last):
+      ...
+    numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
+
+    Negative axes are preserved:
+
+    >>> np.cumsum(array_1d, axis=-2)
+    Traceback (most recent call last):
+      ...
+    numpy.exceptions.AxisError: axis -2 is out of bounds for array of dimension 1
+
+    The class constructor generally takes the axis and arrays'
+    dimensionality as arguments:
+
+    >>> print(np.AxisError(2, 1, msg_prefix='error'))
+    error: axis 2 is out of bounds for array of dimension 1
+
+    Alternatively, a custom exception message can be passed:
+
+    >>> print(np.AxisError('Custom error message'))
+    Custom error message
+
+    """
+
+    __slots__ = ("axis", "ndim", "_msg")
+
+    def __init__(self, axis, ndim=None, msg_prefix=None):
+        if ndim is msg_prefix is None:
+            # single-argument form: directly set the error message
+            self._msg = axis
+            self.axis = None
+            self.ndim = None
+        else:
+            self._msg = msg_prefix
+            self.axis = axis
+            self.ndim = ndim
+
+    def __str__(self):
+        axis = self.axis
+        ndim = self.ndim
+
+        if axis is ndim is None:
+            return self._msg
+        else:
+            msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
+            if self._msg is not None:
+                msg = f"{self._msg}: {msg}"
+            return msg
+
+
+class DTypePromotionError(TypeError):
+    """Multiple DTypes could not be converted to a common one.
+
+    This exception derives from ``TypeError`` and is raised whenever dtypes
+    cannot be converted to a single common one.  This can be because they
+    are of a different category/class or incompatible instances of the same
+    one (see Examples).
+
+    Notes
+    -----
+    Many functions will use promotion to find the correct result and
+    implementation.  For these functions the error will typically be chained
+    with a more specific error indicating that no implementation was found
+    for the input dtypes.
+
+    Typically promotion should be considered "invalid" between the dtypes of
+    two arrays when `arr1 == arr2` can safely return all ``False`` because the
+    dtypes are fundamentally different.
+
+    Examples
+    --------
+    Datetimes and complex numbers are incompatible classes and cannot be
+    promoted:
+
+    >>> np.result_type(np.dtype("M8[s]"), np.complex128)
+    DTypePromotionError: The DType <class 'numpy.dtype[datetime64]'> could not
+    be promoted by <class 'numpy.dtype[complex128]'>. This means that no common
+    DType exists for the given inputs. For example they cannot be stored in a
+    single array unless the dtype is `object`. The full list of DTypes is:
+    (<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[complex128]'>)
+
+    For example for structured dtypes, the structure can mismatch and the
+    same ``DTypePromotionError`` is given when two structured dtypes with
+    a mismatch in their number of fields is given:
+
+    >>> dtype1 = np.dtype([("field1", np.float64), ("field2", np.int64)])
+    >>> dtype2 = np.dtype([("field1", np.float64)])
+    >>> np.promote_types(dtype1, dtype2)
+    DTypePromotionError: field names `('field1', 'field2')` and `('field1',)`
+    mismatch.
+
+    """
+    pass
diff --git a/contrib/python/numpy/py3/numpy/exceptions.pyi b/contrib/python/numpy/py3/numpy/exceptions.pyi
new file mode 100644
index 00000000000..c76a0946b97
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/exceptions.pyi
@@ -0,0 +1,18 @@
+from typing import overload
+
+__all__: list[str]
+
+class ComplexWarning(RuntimeWarning): ...
+class ModuleDeprecationWarning(DeprecationWarning): ...
+class VisibleDeprecationWarning(UserWarning): ...
+class TooHardError(RuntimeError): ...
+class DTypePromotionError(TypeError): ...
+
+class AxisError(ValueError, IndexError):
+    axis: None | int
+    ndim: None | int
+    @overload
+    def __init__(self, axis: str, ndim: None = ..., msg_prefix: None = ...) -> None: ...
+    @overload
+    def __init__(self, axis: int, ndim: int, msg_prefix: None | str = ...) -> None: ...
+    def __str__(self) -> str: ...
diff --git a/contrib/python/numpy/py3/numpy/f2py/capi_maps.py b/contrib/python/numpy/py3/numpy/f2py/capi_maps.py
index aaae3a8e0b1..f0a7221b716 100644
--- a/contrib/python/numpy/py3/numpy/f2py/capi_maps.py
+++ b/contrib/python/numpy/py3/numpy/f2py/capi_maps.py
@@ -196,7 +196,7 @@ def load_f2cmap_file(f2cmap_file):
     # they use PARAMETERS in type specifications.
     try:
         outmess('Reading f2cmap from {!r} ...\n'.format(f2cmap_file))
-        with open(f2cmap_file, 'r') as f:
+        with open(f2cmap_file) as f:
             d = eval(f.read().lower(), {}, {})
         for k, d1 in d.items():
             for k1 in d1.keys():
diff --git a/contrib/python/numpy/py3/numpy/f2py/cfuncs.py b/contrib/python/numpy/py3/numpy/f2py/cfuncs.py
index 7416925624e..2d27b652432 100644
--- a/contrib/python/numpy/py3/numpy/f2py/cfuncs.py
+++ b/contrib/python/numpy/py3/numpy/f2py/cfuncs.py
@@ -800,16 +800,23 @@ character_from_pyobj(character* v, PyObject *obj, const char *errmess) {
         }
     }
     {
+        /* TODO: This error (and most other) error handling needs cleaning. */
         char mess[F2PY_MESSAGE_BUFFER_SIZE];
         strcpy(mess, errmess);
         PyObject* err = PyErr_Occurred();
         if (err == NULL) {
             err = PyExc_TypeError;
+            Py_INCREF(err);
+        }
+        else {
+            Py_INCREF(err);
+            PyErr_Clear();
         }
         sprintf(mess + strlen(mess),
                 " -- expected str|bytes|sequence-of-str-or-bytes, got ");
         f2py_describe(obj, mess + strlen(mess));
         PyErr_SetString(err, mess);
+        Py_DECREF(err);
     }
     return 0;
 }
@@ -1227,8 +1234,8 @@ static int try_pyarr_from_character(PyObject* obj, character* v) {
             strcpy(mess, "try_pyarr_from_character failed"
                          " -- expected bytes array-scalar|array, got ");
             f2py_describe(obj, mess + strlen(mess));
+            PyErr_SetString(err, mess);
         }
-        PyErr_SetString(err, mess);
     }
     return 0;
 }
diff --git a/contrib/python/numpy/py3/numpy/f2py/crackfortran.py b/contrib/python/numpy/py3/numpy/f2py/crackfortran.py
index 63597cef157..cce93dd7f53 100755
--- a/contrib/python/numpy/py3/numpy/f2py/crackfortran.py
+++ b/contrib/python/numpy/py3/numpy/f2py/crackfortran.py
@@ -147,6 +147,7 @@ import os
 import copy
 import platform
 import codecs
+from pathlib import Path
 try:
     import charset_normalizer
 except ImportError:
@@ -289,22 +290,15 @@ def undo_rmbadname(names):
     return [undo_rmbadname1(_m) for _m in names]
 
 
-def getextension(name):
-    i = name.rfind('.')
-    if i == -1:
-        return ''
-    if '\\' in name[i:]:
-        return ''
-    if '/' in name[i:]:
-        return ''
-    return name[i + 1:]
-
-is_f_file = re.compile(r'.*\.(for|ftn|f77|f)\Z', re.I).match
 _has_f_header = re.compile(r'-\*-\s*fortran\s*-\*-', re.I).search
 _has_f90_header = re.compile(r'-\*-\s*f90\s*-\*-', re.I).search
 _has_fix_header = re.compile(r'-\*-\s*fix\s*-\*-', re.I).search
 _free_f90_start = re.compile(r'[^c*]\s*[^\s\d\t]', re.I).match
 
+# Extensions
+COMMON_FREE_EXTENSIONS = ['.f90', '.f95', '.f03', '.f08']
+COMMON_FIXED_EXTENSIONS = ['.for', '.ftn', '.f77', '.f']
+
 
 def openhook(filename, mode):
     """Ensures that filename is opened with correct encoding parameter.
@@ -337,26 +331,28 @@ def openhook(filename, mode):
     return open(filename, mode, encoding=encoding)
 
 
-def is_free_format(file):
+def is_free_format(fname):
     """Check if file is in free format Fortran."""
     # f90 allows both fixed and free format, assuming fixed unless
     # signs of free format are detected.
-    result = 0
-    with openhook(file, 'r') as f:
-        line = f.readline()
+    result = False
+    if Path(fname).suffix.lower() in COMMON_FREE_EXTENSIONS:
+        result = True
+    with openhook(fname, 'r') as fhandle:
+        line = fhandle.readline()
         n = 15  # the number of non-comment lines to scan for hints
         if _has_f_header(line):
             n = 0
         elif _has_f90_header(line):
             n = 0
-            result = 1
+            result = True
         while n > 0 and line:
             if line[0] != '!' and line.strip():
                 n -= 1
                 if (line[0] != '\t' and _free_f90_start(line[:5])) or line[-2:-1] == '&':
-                    result = 1
+                    result = True
                     break
-            line = f.readline()
+            line = fhandle.readline()
     return result
 
 
@@ -412,7 +408,7 @@ def readfortrancode(ffile, dowithline=show, istop=1):
             strictf77 = 0
             sourcecodeform = 'fix'
             ext = os.path.splitext(currentfilename)[1]
-            if is_f_file(currentfilename) and \
+            if Path(currentfilename).suffix.lower() in COMMON_FIXED_EXTENSIONS and \
                     not (_has_f90_header(l) or _has_fix_header(l)):
                 strictf77 = 1
             elif is_free_format(currentfilename) and not _has_fix_header(l):
@@ -617,15 +613,15 @@ beginpattern90 = re.compile(
 groupends = (r'end|endprogram|endblockdata|endmodule|endpythonmodule|'
              r'endinterface|endsubroutine|endfunction')
 endpattern = re.compile(
-    beforethisafter % ('', groupends, groupends, r'.*'), re.I), 'end'
+    beforethisafter % ('', groupends, groupends, '.*'), re.I), 'end'
 endifs = r'end\s*(if|do|where|select|while|forall|associate|block|' + \
          r'critical|enum|team)'
 endifpattern = re.compile(
-    beforethisafter % (r'[\w]*?', endifs, endifs, r'[\w\s]*'), re.I), 'endif'
+    beforethisafter % (r'[\w]*?', endifs, endifs, '.*'), re.I), 'endif'
 #
 moduleprocedures = r'module\s*procedure'
 moduleprocedurepattern = re.compile(
-    beforethisafter % ('', moduleprocedures, moduleprocedures, r'.*'), re.I), \
+    beforethisafter % ('', moduleprocedures, moduleprocedures, '.*'), re.I), \
     'moduleprocedure'
 implicitpattern = re.compile(
     beforethisafter % ('', 'implicit', 'implicit', '.*'), re.I), 'implicit'
@@ -939,7 +935,7 @@ typedefpattern = re.compile(
     r'(?:,(?P<attributes>[\w(),]+))?(::)?(?P<name>\b[a-z$_][\w$]*\b)'
     r'(?:\((?P<params>[\w,]*)\))?\Z', re.I)
 nameargspattern = re.compile(
-    r'\s*(?P<name>\b[\w$]+\b)\s*(@\(@\s*(?P<args>[\w\s,]*)\s*@\)@|)\s*((result(\s*@\(@\s*(?P<result>\b[\w$]+\b)\s*@\)@|))|(bind\s*@\(@\s*(?P<bind>.*)\s*@\)@))*\s*\Z', re.I)
+    r'\s*(?P<name>\b[\w$]+\b)\s*(@\(@\s*(?P<args>[\w\s,]*)\s*@\)@|)\s*((result(\s*@\(@\s*(?P<result>\b[\w$]+\b)\s*@\)@|))|(bind\s*@\(@\s*(?P<bind>(?:(?!@\)@).)*)\s*@\)@))*\s*\Z', re.I)
 operatorpattern = re.compile(
     r'\s*(?P<scheme>(operator|assignment))'
     r'@\(@\s*(?P<name>[^)]+)\s*@\)@\s*\Z', re.I)
@@ -2183,6 +2179,13 @@ def analyzebody(block, args, tab=''):
     global usermodules, skipfuncs, onlyfuncs, f90modulevars
 
     setmesstext(block)
+
+    maybe_private = {
+        key: value
+        for key, value in block['vars'].items()
+        if 'attrspec' not in value or 'public' not in value['attrspec']
+    }
+
     body = []
     for b in block['body']:
         b['parent_block'] = block
@@ -2191,6 +2194,9 @@ def analyzebody(block, args, tab=''):
                 continue
             else:
                 as_ = b['args']
+            # Add private members to skipfuncs for gh-23879
+            if b['name'] in maybe_private.keys():
+                skipfuncs.append(b['name'])
             if b['name'] in skipfuncs:
                 continue
             if onlyfuncs and b['name'] not in onlyfuncs:
@@ -2397,19 +2403,19 @@ def _selected_int_kind_func(r):
 
 def _selected_real_kind_func(p, r=0, radix=0):
     # XXX: This should be processor dependent
-    # This is only good for 0 <= p <= 20
+    # This is only verified for 0 <= p <= 20, possibly good for p <= 33 and above
     if p < 7:
         return 4
     if p < 16:
         return 8
     machine = platform.machine().lower()
-    if machine.startswith(('aarch64', 'power', 'ppc', 'riscv', 's390x', 'sparc')):
-        if p <= 20:
+    if machine.startswith(('aarch64', 'arm64', 'power', 'ppc', 'riscv', 's390x', 'sparc')):
+        if p <= 33:
             return 16
     else:
         if p < 19:
             return 10
-        elif p <= 20:
+        elif p <= 33:
             return 16
     return -1
 
@@ -2860,6 +2866,11 @@ def analyzevars(block):
                         kindselect, charselect, typename = cracktypespec(
                             typespec, selector)
                         vars[n]['typespec'] = typespec
+                        try:
+                            if block['result']:
+                                vars[block['result']]['typespec'] = typespec
+                        except Exception:
+                            pass
                         if kindselect:
                             if 'kind' in kindselect:
                                 try:
diff --git a/contrib/python/numpy/py3/numpy/f2py/diagnose.py b/contrib/python/numpy/py3/numpy/f2py/diagnose.py
index 21ee399f035..86d7004abad 100644
--- a/contrib/python/numpy/py3/numpy/f2py/diagnose.py
+++ b/contrib/python/numpy/py3/numpy/f2py/diagnose.py
@@ -30,15 +30,15 @@ def run():
     try:
         import numpy
         has_newnumpy = 1
-    except ImportError:
-        print('Failed to import new numpy:', sys.exc_info()[1])
+    except ImportError as e:
+        print('Failed to import new numpy:', e)
         has_newnumpy = 0
 
     try:
         from numpy.f2py import f2py2e
         has_f2py2e = 1
-    except ImportError:
-        print('Failed to import f2py2e:', sys.exc_info()[1])
+    except ImportError as e:
+        print('Failed to import f2py2e:', e)
         has_f2py2e = 0
 
     try:
@@ -48,8 +48,8 @@ def run():
         try:
             import numpy_distutils
             has_numpy_distutils = 1
-        except ImportError:
-            print('Failed to import numpy_distutils:', sys.exc_info()[1])
+        except ImportError as e:
+            print('Failed to import numpy_distutils:', e)
             has_numpy_distutils = 0
 
     if has_newnumpy:
diff --git a/contrib/python/numpy/py3/numpy/f2py/f90mod_rules.py b/contrib/python/numpy/py3/numpy/f2py/f90mod_rules.py
index a3bb6a212a6..1c47bee02b9 100644
--- a/contrib/python/numpy/py3/numpy/f2py/f90mod_rules.py
+++ b/contrib/python/numpy/py3/numpy/f2py/f90mod_rules.py
@@ -161,8 +161,8 @@ def buildhooks(pymod):
                 fargs.append('f2py_%s_getdims_%s' % (m['name'], n))
                 efargs.append(fargs[-1])
                 sargs.append(
-                    'void (*%s)(int*,int*,void(*)(char*,int*),int*)' % (n))
-                sargsp.append('void (*)(int*,int*,void(*)(char*,int*),int*)')
+                    'void (*%s)(int*,npy_intp*,void(*)(char*,npy_intp*),int*)' % (n))
+                sargsp.append('void (*)(int*,npy_intp*,void(*)(char*,npy_intp*),int*)')
                 iadd('\tf2py_%s_def[i_f2py++].func = %s;' % (m['name'], n))
                 fadd('subroutine %s(r,s,f2pysetdata,flag)' % (fargs[-1]))
                 fadd('use %s, only: d => %s\n' %
diff --git a/contrib/python/numpy/py3/numpy/f2py/func2subr.py b/contrib/python/numpy/py3/numpy/f2py/func2subr.py
index 2a05f065b62..cc3cdc5b4f9 100644
--- a/contrib/python/numpy/py3/numpy/f2py/func2subr.py
+++ b/contrib/python/numpy/py3/numpy/f2py/func2subr.py
@@ -119,6 +119,12 @@ def createfuncwrapper(rout, signature=0):
 
     sargs = ', '.join(args)
     if f90mode:
+        # gh-23598 fix warning
+        # Essentially, this gets called again with modules where the name of the
+        # function is added to the arguments, which is not required, and removed
+        sargs = sargs.replace(f"{name}, ", '')
+        args = [arg for arg in args if arg != name]
+        rout['args'] = args
         add('subroutine f2pywrap_%s_%s (%s)' %
             (rout['modulename'], name, sargs))
         if not signature:
diff --git a/contrib/python/numpy/py3/numpy/f2py/symbolic.py b/contrib/python/numpy/py3/numpy/f2py/symbolic.py
index c2ab0f14000..b1b9f5b6a10 100644
--- a/contrib/python/numpy/py3/numpy/f2py/symbolic.py
+++ b/contrib/python/numpy/py3/numpy/f2py/symbolic.py
@@ -801,7 +801,7 @@ def normalize(obj):
             else:
                 _pairs_add(d, t, c)
         if len(d) == 0:
-            # TODO: deterimine correct kind
+            # TODO: determine correct kind
             return as_number(0)
         elif len(d) == 1:
             (t, c), = d.items()
@@ -836,7 +836,7 @@ def normalize(obj):
             else:
                 _pairs_add(d, b, e)
         if len(d) == 0 or coeff == 0:
-            # TODO: deterimine correct kind
+            # TODO: determine correct kind
             assert isinstance(coeff, number_types)
             return as_number(coeff)
         elif len(d) == 1:
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23533.f b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23533.f
new file mode 100644
index 00000000000..db522afa7d2
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23533.f
@@ -0,0 +1,5 @@
+      SUBROUTINE EXAMPLE( )
+        IF( .TRUE. ) THEN
+            CALL DO_SOMETHING()
+        END IF ! ** .TRUE. **
+      END
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23598.f90 b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23598.f90
new file mode 100644
index 00000000000..e0dffb5ef29
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23598.f90
@@ -0,0 +1,4 @@
+integer function intproduct(a, b) result(res)
+  integer, intent(in) :: a, b
+  res = a*b
+end function
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23598Warn.f90 b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23598Warn.f90
new file mode 100644
index 00000000000..3b44efc5ef1
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23598Warn.f90
@@ -0,0 +1,11 @@
+module test_bug
+    implicit none
+    private
+    public :: intproduct
+
+contains
+    integer function intproduct(a, b) result(res)
+    integer, intent(in) :: a, b
+    res = a*b
+    end function
+end module
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23879.f90 b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23879.f90
new file mode 100644
index 00000000000..fac262d53c9
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/src/crackfortran/gh23879.f90
@@ -0,0 +1,20 @@
+module gh23879
+    implicit none
+    private
+    public :: foo
+
+ contains
+
+    subroutine foo(a, b)
+       integer, intent(in) :: a
+       integer, intent(out) :: b
+       b = a
+       call bar(b)
+    end subroutine
+
+    subroutine bar(x)
+        integer, intent(inout) :: x
+        x = 2*x
+     end subroutine
+
+ end module gh23879
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/test_character.py b/contrib/python/numpy/py3/numpy/f2py/tests/test_character.py
index 79998b460ed..0bb0f429027 100644
--- a/contrib/python/numpy/py3/numpy/f2py/tests/test_character.py
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/test_character.py
@@ -457,9 +457,10 @@ class TestMiscCharacter(util.F2PyTest):
          character(len=*), intent(in) :: x(:)
          !f2py intent(out) x
          integer :: i
-         do i=1, size(x)
-           print*, "x(",i,")=", x(i)
-         end do
+         ! Uncomment for debug printing:
+         !do i=1, size(x)
+         !   print*, "x(",i,")=", x(i)
+         !end do
        end subroutine {fprefix}_gh4519
 
        pure function {fprefix}_gh3425(x) result (y)
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/test_crackfortran.py b/contrib/python/numpy/py3/numpy/f2py/tests/test_crackfortran.py
index 73ac4e2760c..c07ae036c0e 100644
--- a/contrib/python/numpy/py3/numpy/f2py/tests/test_crackfortran.py
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/test_crackfortran.py
@@ -1,9 +1,10 @@
 import importlib
 import codecs
+import time
 import unicodedata
 import pytest
 import numpy as np
-from numpy.f2py.crackfortran import markinnerspaces
+from numpy.f2py.crackfortran import markinnerspaces, nameargspattern
 from . import util
 from numpy.f2py import crackfortran
 import textwrap
@@ -56,6 +57,12 @@ class TestPublicPrivate:
         assert set(tt['b_']['attrspec']) == {'public', 'bind(c)'}
         assert set(tt['c']['attrspec']) == {'public'}
 
+    def test_nowrap_private_proceedures(self, tmp_path):
+        fpath = util.getpath("tests", "src", "crackfortran", "gh23879.f90")
+        mod = crackfortran.crackfortran([str(fpath)])
+        assert len(mod) == 1
+        pyf = crackfortran.crack2fortran(mod)
+        assert 'bar' not in pyf
 
 class TestModuleProcedure():
     def test_moduleOperators(self, tmp_path):
@@ -134,6 +141,7 @@ class TestMarkinnerspaces:
         assert markinnerspaces("a 'b c' 'd e'") == "a 'b@_@c' 'd@_@e'"
         assert markinnerspaces(r'a "b c" "d e"') == r'a "b@_@c" "d@_@e"'
 
+
 class TestDimSpec(util.F2PyTest):
     """This test suite tests various expressions that are used as dimension
     specifications.
@@ -180,9 +188,6 @@ class TestDimSpec(util.F2PyTest):
         ! the value of n is computed in f2py wrapper
         !f2py intent(out) n
         integer, dimension({dimspec}), intent(in) :: a
-        if (a({first}).gt.0) then
-          print*, "a=", a
-        endif
       end subroutine
     """)
 
@@ -243,6 +248,7 @@ class TestModuleDeclaration:
         assert len(mod) == 1
         assert mod[0]["vars"]["abar"]["="] == "bar('abar')"
 
+
 class TestEval(util.F2PyTest):
     def test_eval_scalar(self):
         eval_scalar = crackfortran._eval_scalar
@@ -267,6 +273,7 @@ class TestFortranReader(util.F2PyTest):
         mod = crackfortran.crackfortran([str(f_path)])
         assert mod[0]['name'] == 'foo'
 
+
 class TestUnicodeComment(util.F2PyTest):
     sources = [util.getpath("tests", "src", "crackfortran", "unicode_comment.f90")]
 
@@ -276,3 +283,58 @@ class TestUnicodeComment(util.F2PyTest):
     )
     def test_encoding_comment(self):
         self.module.foo(3)
+
+
+class TestNameArgsPatternBacktracking:
+    @pytest.mark.parametrize(
+        ['adversary'],
+        [
+            ('@)@bind@(@',),
+            ('@)@bind                         @(@',),
+            ('@)@bind foo bar baz@(@',)
+        ]
+    )
+    def test_nameargspattern_backtracking(self, adversary):
+        '''address ReDOS vulnerability:
+        https://github.com/numpy/numpy/issues/23338'''
+        trials_per_batch = 12
+        batches_per_regex = 4
+        start_reps, end_reps = 15, 25
+        for ii in range(start_reps, end_reps):
+            repeated_adversary = adversary * ii
+            # test times in small batches.
+            # this gives us more chances to catch a bad regex
+            # while still catching it before too long if it is bad
+            for _ in range(batches_per_regex):
+                times = []
+                for _ in range(trials_per_batch):
+                    t0 = time.perf_counter()
+                    mtch = nameargspattern.search(repeated_adversary)
+                    times.append(time.perf_counter() - t0)
+                # our pattern should be much faster than 0.2s per search
+                # it's unlikely that a bad regex will pass even on fast CPUs
+                assert np.median(times) < 0.2
+            assert not mtch
+            # if the adversary is capped with @)@, it becomes acceptable
+            # according to the old version of the regex.
+            # that should still be true.
+            good_version_of_adversary = repeated_adversary + '@)@'
+            assert nameargspattern.search(good_version_of_adversary)
+
+
+class TestFunctionReturn(util.F2PyTest):
+    sources = [util.getpath("tests", "src", "crackfortran", "gh23598.f90")]
+
+    def test_function_rettype(self):
+        # gh-23598
+        assert self.module.intproduct(3, 4) == 12
+
+
+class TestFortranGroupCounters(util.F2PyTest):
+    def test_end_if_comment(self):
+        # gh-23533
+        fpath = util.getpath("tests", "src", "crackfortran", "gh23533.f")
+        try:
+            crackfortran.crackfortran([str(fpath)])
+        except Exception as exc:
+            assert False, f"'crackfortran.crackfortran' raised an exception {exc}"
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/test_f2py2e.py b/contrib/python/numpy/py3/numpy/f2py/tests/test_f2py2e.py
index 2c10f046f1d..5f7b56a68a9 100644
--- a/contrib/python/numpy/py3/numpy/f2py/tests/test_f2py2e.py
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/test_f2py2e.py
@@ -63,6 +63,15 @@ def hello_world_f90(tmpdir_factory):
 
 
 @pytest.fixture(scope="session")
+def gh23598_warn(tmpdir_factory):
+    """F90 file for testing warnings in gh23598"""
+    fdat = util.getpath("tests", "src", "crackfortran", "gh23598Warn.f90").read_text()
+    fn = tmpdir_factory.getbasetemp() / "gh23598Warn.f90"
+    fn.write_text(fdat, encoding="ascii")
+    return fn
+
+
+@pytest.fixture(scope="session")
 def hello_world_f77(tmpdir_factory):
     """Generates a single f77 file for testing"""
     fdat = util.getpath("tests", "src", "cli", "hi77.f").read_text()
@@ -91,6 +100,19 @@ def f2cmap_f90(tmpdir_factory):
     return fn
 
 
+def test_gh23598_warn(capfd, gh23598_warn, monkeypatch):
+    foutl = get_io_paths(gh23598_warn, mname="test")
+    ipath = foutl.f90inp
+    monkeypatch.setattr(
+        sys, "argv",
+        f'f2py {ipath} -m test'.split())
+
+    with util.switchdir(ipath.parent):
+        f2pycli()  # Generate files
+        wrapper = foutl.wrap90.read_text()
+        assert "intproductf2pywrap, intpr" not in wrapper
+
+
 def test_gen_pyf(capfd, hello_world_f90, monkeypatch):
     """Ensures that a signature file is generated via the CLI
     CLI :: -h
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/test_kind.py b/contrib/python/numpy/py3/numpy/f2py/tests/test_kind.py
index f0cb61fb6ce..69b85aaad21 100644
--- a/contrib/python/numpy/py3/numpy/f2py/tests/test_kind.py
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/test_kind.py
@@ -1,5 +1,6 @@
 import os
 import pytest
+import platform
 
 from numpy.f2py.crackfortran import (
     _selected_int_kind_func as selected_int_kind,
@@ -11,8 +12,8 @@ from . import util
 class TestKind(util.F2PyTest):
     sources = [util.getpath("tests", "src", "kind", "foo.f90")]
 
-    def test_all(self):
-        selectedrealkind = self.module.selectedrealkind
+    def test_int(self):
+        """Test `int` kind_func for integers up to 10**40."""
         selectedintkind = self.module.selectedintkind
 
         for i in range(40):
@@ -20,7 +21,27 @@ class TestKind(util.F2PyTest):
                 i
             ), f"selectedintkind({i}): expected {selected_int_kind(i)!r} but got {selectedintkind(i)!r}"
 
-        for i in range(20):
+    def test_real(self):
+        """
+        Test (processor-dependent) `real` kind_func for real numbers
+        of up to 31 digits precision (extended/quadruple).
+        """
+        selectedrealkind = self.module.selectedrealkind
+
+        for i in range(32):
+            assert selectedrealkind(i) == selected_real_kind(
+                i
+            ), f"selectedrealkind({i}): expected {selected_real_kind(i)!r} but got {selectedrealkind(i)!r}"
+
+    @pytest.mark.xfail(platform.machine().lower().startswith("ppc"),
+                       reason="Some PowerPC may not support full IEEE 754 precision")
+    def test_quad_precision(self):
+        """
+        Test kind_func for quadruple precision [`real(16)`] of 32+ digits .
+        """
+        selectedrealkind = self.module.selectedrealkind
+
+        for i in range(32, 40):
             assert selectedrealkind(i) == selected_real_kind(
                 i
             ), f"selectedrealkind({i}): expected {selected_real_kind(i)!r} but got {selectedrealkind(i)!r}"
diff --git a/contrib/python/numpy/py3/numpy/f2py/tests/util.py b/contrib/python/numpy/py3/numpy/f2py/tests/util.py
index 1534c4e7dfe..26fa7e49d14 100644
--- a/contrib/python/numpy/py3/numpy/f2py/tests/util.py
+++ b/contrib/python/numpy/py3/numpy/f2py/tests/util.py
@@ -6,6 +6,7 @@ Utility functions for
 - determining paths to tests
 
 """
+import glob
 import os
 import sys
 import subprocess
@@ -30,6 +31,10 @@ from importlib import import_module
 _module_dir = None
 _module_num = 5403
 
+if sys.platform == "cygwin":
+    NUMPY_INSTALL_ROOT = Path(__file__).parent.parent.parent
+    _module_list = list(NUMPY_INSTALL_ROOT.glob("**/*.dll"))
+
 
 def _cleanup():
     global _module_dir
@@ -147,6 +152,21 @@ def build_module(source_files, options=[], skip=[], only=[], module_name=None):
         for fn in dst_sources:
             os.unlink(fn)
 
+    # Rebase (Cygwin-only)
+    if sys.platform == "cygwin":
+        # If someone starts deleting modules after import, this will
+        # need to change to record how big each module is, rather than
+        # relying on rebase being able to find that from the files.
+        _module_list.extend(
+            glob.glob(os.path.join(d, "{:s}*".format(module_name)))
+        )
+        subprocess.check_call(
+            ["/usr/bin/rebase", "--database", "--oblivious", "--verbose"]
+            + _module_list
+        )
+
+
+
     # Import
     return import_module(module_name)
 
diff --git a/contrib/python/numpy/py3/numpy/fft/_pocketfft.c b/contrib/python/numpy/py3/numpy/fft/_pocketfft.c
index 86de57bd3e8..37649386fda 100644
--- a/contrib/python/numpy/py3/numpy/fft/_pocketfft.c
+++ b/contrib/python/numpy/py3/numpy/fft/_pocketfft.c
@@ -23,7 +23,7 @@
 #include <stdlib.h>
 
 #define RALLOC(type,num) \
-  ((type *)malloc((num)*sizeof(type)))
+  (assert(num != 0), ((type *)malloc((num)*sizeof(type))))
 #define DEALLOC(ptr) \
   do { free(ptr); (ptr)=NULL; } while(0)
 
@@ -1056,8 +1056,10 @@ static cfftp_plan make_cfftp_plan (size_t length)
   if (length==1) return plan;
   if (cfftp_factorize(plan)!=0) { DEALLOC(plan); return NULL; }
   size_t tws=cfftp_twsize(plan);
-  plan->mem=RALLOC(cmplx,tws);
-  if (!plan->mem) { DEALLOC(plan); return NULL; }
+  if (tws != 0) {
+    plan->mem=RALLOC(cmplx,tws);
+    if (!plan->mem) { DEALLOC(plan); return NULL; }
+  }
   if (cfftp_comp_twiddle(plan)!=0)
     { DEALLOC(plan->mem); DEALLOC(plan); return NULL; }
   return plan;
@@ -1820,7 +1822,6 @@ static size_t rfftp_twsize(rfftp_plan plan)
     l1*=ip;
     }
   return twsize;
-  return 0;
   }
 
 WARN_UNUSED_RESULT NOINLINE static int rfftp_comp_twiddle (rfftp_plan plan)
@@ -1876,8 +1877,10 @@ NOINLINE static rfftp_plan make_rfftp_plan (size_t length)
   if (length==1) return plan;
   if (rfftp_factorize(plan)!=0) { DEALLOC(plan); return NULL; }
   size_t tws=rfftp_twsize(plan);
-  plan->mem=RALLOC(double,tws);
-  if (!plan->mem) { DEALLOC(plan); return NULL; }
+  if (tws != 0) {
+    plan->mem=RALLOC(double,tws);
+    if (!plan->mem) { DEALLOC(plan); return NULL; }
+  }
   if (rfftp_comp_twiddle(plan)!=0)
     { DEALLOC(plan->mem); DEALLOC(plan); return NULL; }
   return plan;
@@ -2229,6 +2232,8 @@ execute_real_forward(PyObject *a1, double fct)
 {
     rfft_plan plan=NULL;
     int fail = 0;
+    npy_intp tdim[NPY_MAXDIMS];
+
     PyArrayObject *data = (PyArrayObject *)PyArray_FromAny(a1,
             PyArray_DescrFromType(NPY_DOUBLE), 1, 0,
             NPY_ARRAY_DEFAULT | NPY_ARRAY_ENSUREARRAY | NPY_ARRAY_FORCECAST,
@@ -2238,15 +2243,11 @@ execute_real_forward(PyObject *a1, double fct)
     int ndim = PyArray_NDIM(data);
     const npy_intp *odim = PyArray_DIMS(data);
     int npts = odim[ndim - 1];
-    npy_intp *tdim=(npy_intp *)malloc(ndim*sizeof(npy_intp));
-    if (!tdim)
-      { Py_XDECREF(data); return NULL; }
     for (int d=0; d<ndim-1; ++d)
       tdim[d] = odim[d];
     tdim[ndim-1] = npts/2 + 1;
     PyArrayObject *ret = (PyArrayObject *)PyArray_Empty(ndim,
             tdim, PyArray_DescrFromType(NPY_CDOUBLE), 0);
-    free(tdim);
     if (!ret) fail=1;
     if (!fail) {
       int rstep = PyArray_DIM(ret, PyArray_NDIM(ret) - 1)*2;
diff --git a/contrib/python/numpy/py3/numpy/fft/meson.build b/contrib/python/numpy/py3/numpy/fft/meson.build
new file mode 100644
index 00000000000..fedbf6349d7
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/fft/meson.build
@@ -0,0 +1,33 @@
+largefile_define = []
+if host_machine.system() == 'aix' or host_machine.system() == 'AIX'
+  largefile_define += '-D_LARGE_FILES'
+endif
+
+py.extension_module('_pocketfft_internal',
+  '_pocketfft.c',
+  c_args: largefile_define,
+  dependencies: np_core_dep,
+  install: true,
+  subdir: 'numpy/fft',
+)
+
+py.install_sources(
+  [
+    '__init__.py',
+    '__init__.pyi',
+    '_pocketfft.py',
+    '_pocketfft.pyi',
+    'helper.py',
+    'helper.pyi',
+  ],
+  subdir: 'numpy/fft'
+)
+
+py.install_sources(
+  [
+    'tests/__init__.py',
+    'tests/test_helper.py',
+    'tests/test_pocketfft.py',
+  ],
+  subdir: 'numpy/fft/tests'
+)
diff --git a/contrib/python/numpy/py3/numpy/lib/__init__.py b/contrib/python/numpy/py3/numpy/lib/__init__.py
index 58166d4b1d0..d3cc9fee4ae 100644
--- a/contrib/python/numpy/py3/numpy/lib/__init__.py
+++ b/contrib/python/numpy/py3/numpy/lib/__init__.py
@@ -11,7 +11,6 @@ Most contains basic functions that are used by several submodules and are
 useful to have in the main name-space.
 
 """
-import math
 
 from numpy.version import version as __version__
 
@@ -58,7 +57,7 @@ from .arraypad import *
 from ._version import *
 from numpy.core._multiarray_umath import tracemalloc_domain
 
-__all__ = ['emath', 'math', 'tracemalloc_domain', 'Arrayterator']
+__all__ = ['emath', 'tracemalloc_domain', 'Arrayterator']
 __all__ += type_check.__all__
 __all__ += index_tricks.__all__
 __all__ += function_base.__all__
@@ -77,3 +76,19 @@ __all__ += histograms.__all__
 from numpy._pytesttester import PytestTester
 test = PytestTester(__name__)
 del PytestTester
+
+def __getattr__(attr):
+    # Warn for reprecated attributes
+    import math
+    import warnings
+
+    if attr == 'math':
+        warnings.warn(
+            "`np.lib.math` is a deprecated alias for the standard library "
+            "`math` module (Deprecated Numpy 1.25). Replace usages of "
+            "`numpy.lib.math` with `math`", DeprecationWarning, stacklevel=2)
+        return math
+    else:
+        raise AttributeError("module {!r} has no attribute "
+                             "{!r}".format(__name__, attr))
+        
diff --git a/contrib/python/numpy/py3/numpy/lib/_datasource.py b/contrib/python/numpy/py3/numpy/lib/_datasource.py
index b7778234e85..613733fa516 100644
--- a/contrib/python/numpy/py3/numpy/lib/_datasource.py
+++ b/contrib/python/numpy/py3/numpy/lib/_datasource.py
@@ -37,7 +37,7 @@ Example::
 import os
 import io
 
-from numpy.core.overrides import set_module
+from .._utils import set_module
 
 
 _open = open
diff --git a/contrib/python/numpy/py3/numpy/lib/_iotools.py b/contrib/python/numpy/py3/numpy/lib/_iotools.py
index 4a5ac1285fa..534d1b3eea6 100644
--- a/contrib/python/numpy/py3/numpy/lib/_iotools.py
+++ b/contrib/python/numpy/py3/numpy/lib/_iotools.py
@@ -513,8 +513,8 @@ class StringConverter:
                     (nx.complexfloating, complex, nx.nan + 0j),
                     # Last, try with the string types (must be last, because
                     # `_mapper[-1]` is used as default in some cases)
-                    (nx.unicode_, asunicode, '???'),
-                    (nx.string_, asbytes, '???'),
+                    (nx.str_, asunicode, '???'),
+                    (nx.bytes_, asbytes, '???'),
                     ])
 
     @classmethod
diff --git a/contrib/python/numpy/py3/numpy/lib/arraypad.py b/contrib/python/numpy/py3/numpy/lib/arraypad.py
index 28123246dc2..b06a645d836 100644
--- a/contrib/python/numpy/py3/numpy/lib/arraypad.py
+++ b/contrib/python/numpy/py3/numpy/lib/arraypad.py
@@ -378,7 +378,7 @@ def _set_reflect_both(padded, axis, width_pair, method, include_edge=False):
     return left_pad, right_pad
 
 
-def _set_wrap_both(padded, axis, width_pair):
+def _set_wrap_both(padded, axis, width_pair, original_period):
     """
     Pad `axis` of `arr` with wrapped values.
 
@@ -391,6 +391,8 @@ def _set_wrap_both(padded, axis, width_pair):
     width_pair : (int, int)
         Pair of widths that mark the pad area on both sides in the given
         dimension.
+    original_period : int
+        Original length of data on `axis` of `arr`.
 
     Returns
     -------
@@ -400,6 +402,9 @@ def _set_wrap_both(padded, axis, width_pair):
     """
     left_pad, right_pad = width_pair
     period = padded.shape[axis] - right_pad - left_pad
+    # Avoid wrapping with only a subset of the original area by ensuring period
+    # can only be a multiple of the original area's length.
+    period = period // original_period * original_period
 
     # If the current dimension of `arr` doesn't contain enough valid values
     # (not part of the undefined pad area) we need to pad multiple times.
@@ -410,14 +415,12 @@ def _set_wrap_both(padded, axis, width_pair):
 
     if left_pad > 0:
         # Pad with wrapped values on left side
-        # First slice chunk from right side of the non-pad area.
+        # First slice chunk from left side of the non-pad area.
         # Use min(period, left_pad) to ensure that chunk is not larger than
-        # pad area
-        right_slice = _slice_at_axis(
-            slice(-right_pad - min(period, left_pad),
-                  -right_pad if right_pad != 0 else None),
-            axis
-        )
+        # pad area.
+        slice_end = left_pad + period
+        slice_start = slice_end - min(period, left_pad)
+        right_slice = _slice_at_axis(slice(slice_start, slice_end), axis)
         right_chunk = padded[right_slice]
 
         if left_pad > period:
@@ -431,11 +434,12 @@ def _set_wrap_both(padded, axis, width_pair):
 
     if right_pad > 0:
         # Pad with wrapped values on right side
-        # First slice chunk from left side of the non-pad area.
+        # First slice chunk from right side of the non-pad area.
         # Use min(period, right_pad) to ensure that chunk is not larger than
-        # pad area
-        left_slice = _slice_at_axis(
-            slice(left_pad, left_pad + min(period, right_pad),), axis)
+        # pad area.
+        slice_start = -right_pad - period
+        slice_end = slice_start + min(period, right_pad)
+        left_slice = _slice_at_axis(slice(slice_start, slice_end), axis)
         left_chunk = padded[left_slice]
 
         if right_pad > period:
@@ -867,11 +871,12 @@ def pad(array, pad_width, mode='constant', **kwargs):
     elif mode == "wrap":
         for axis, (left_index, right_index) in zip(axes, pad_width):
             roi = _view_roi(padded, original_area_slice, axis)
+            original_period = padded.shape[axis] - right_index - left_index
             while left_index > 0 or right_index > 0:
                 # Iteratively pad until dimension is filled with wrapped
                 # values. This is necessary if the pad area is larger than
                 # the length of the original values in the current dimension.
                 left_index, right_index = _set_wrap_both(
-                    roi, axis, (left_index, right_index))
+                    roi, axis, (left_index, right_index), original_period)
 
     return padded
diff --git a/contrib/python/numpy/py3/numpy/lib/format.py b/contrib/python/numpy/py3/numpy/lib/format.py
index 9a19966eb6a..d5b3fbac23a 100644
--- a/contrib/python/numpy/py3/numpy/lib/format.py
+++ b/contrib/python/numpy/py3/numpy/lib/format.py
@@ -163,11 +163,10 @@ evolved with time and this document is more current.
 """
 import numpy
 import warnings
-from numpy.lib.utils import safe_eval
+from numpy.lib.utils import safe_eval, drop_metadata
 from numpy.compat import (
-    os_fspath, pickle
+    isfileobj, os_fspath, pickle
     )
-from numpy.compat.py3k import _isfileobj
 
 
 __all__ = []
@@ -240,15 +239,6 @@ def read_magic(fp):
     major, minor = magic_str[-2:]
     return major, minor
 
-def _has_metadata(dt):
-    if dt.metadata is not None:
-        return True
-    elif dt.names is not None:
-        return any(_has_metadata(dt[k]) for k in dt.names)
-    elif dt.subdtype is not None:
-        return _has_metadata(dt.base)
-    else:
-        return False
 
 def dtype_to_descr(dtype):
     """
@@ -273,9 +263,12 @@ def dtype_to_descr(dtype):
         replicate the input dtype.
 
     """
-    if _has_metadata(dtype):
-        warnings.warn("metadata on a dtype may be saved or ignored, but will "
-                      "raise if saved when read. Use another form of storage.",
+    # NOTE: that drop_metadata may not return the right dtype e.g. for user
+    #       dtypes.  In that case our code below would fail the same, though.
+    new_dtype = drop_metadata(dtype)
+    if new_dtype is not dtype:
+        warnings.warn("metadata on a dtype is not saved to an npy/npz. "
+                      "Use another format (such as pickle) to store it.",
                       UserWarning, stacklevel=2)
     if dtype.names is not None:
         # This is a record array. The .descr is fine.  XXX: parts of the
@@ -438,15 +431,15 @@ def _write_array_header(fp, d, version=None):
         header.append("'%s': %s, " % (key, repr(value)))
     header.append("}")
     header = "".join(header)
-    
+
     # Add some spare space so that the array header can be modified in-place
     # when changing the array size, e.g. when growing it by appending data at
-    # the end. 
+    # the end.
     shape = d['shape']
     header += " " * ((GROWTH_AXIS_MAX_DIGITS - len(repr(
         shape[-1 if d['fortran_order'] else 0]
     ))) if len(shape) > 0 else 0)
-    
+
     if version is None:
         header = _wrap_header_guess_version(header)
     else:
@@ -506,7 +499,7 @@ def read_array_header_1_0(fp, max_header_size=_MAX_HEADER_SIZE):
     max_header_size : int, optional
         Maximum allowed size of the header.  Large headers may not be safe
         to load securely and thus require explicitly passing a larger value.
-        See :py:meth:`ast.literal_eval()` for details.
+        See :py:func:`ast.literal_eval()` for details.
 
     Raises
     ------
@@ -533,7 +526,7 @@ def read_array_header_2_0(fp, max_header_size=_MAX_HEADER_SIZE):
     max_header_size : int, optional
         Maximum allowed size of the header.  Large headers may not be safe
         to load securely and thus require explicitly passing a larger value.
-        See :py:meth:`ast.literal_eval()` for details.
+        See :py:func:`ast.literal_eval()` for details.
 
     Returns
     -------
@@ -624,13 +617,27 @@ def _read_array_header(fp, version, max_header_size=_MAX_HEADER_SIZE):
     #   "descr" : dtype.descr
     # Versions (2, 0) and (1, 0) could have been created by a Python 2
     # implementation before header filtering was implemented.
-    if version <= (2, 0):
-        header = _filter_header(header)
+    #
+    # For performance reasons, we try without _filter_header first though
     try:
         d = safe_eval(header)
     except SyntaxError as e:
-        msg = "Cannot parse header: {!r}"
-        raise ValueError(msg.format(header)) from e
+        if version <= (2, 0):
+            header = _filter_header(header)
+            try:
+                d = safe_eval(header)
+            except SyntaxError as e2:
+                msg = "Cannot parse header: {!r}"
+                raise ValueError(msg.format(header)) from e2
+            else:
+                warnings.warn(
+                    "Reading `.npy` or `.npz` file required additional "
+                    "header parsing as it was created on Python 2. Save the "
+                    "file again to speed up loading and avoid this warning.",
+                    UserWarning, stacklevel=4)
+        else:
+            msg = "Cannot parse header: {!r}"
+            raise ValueError(msg.format(header)) from e
     if not isinstance(d, dict):
         msg = "Header is not a dictionary: {!r}"
         raise ValueError(msg.format(d))
@@ -711,7 +718,7 @@ def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
             pickle_kwargs = {}
         pickle.dump(array, fp, protocol=3, **pickle_kwargs)
     elif array.flags.f_contiguous and not array.flags.c_contiguous:
-        if _isfileobj(fp):
+        if isfileobj(fp):
             array.T.tofile(fp)
         else:
             for chunk in numpy.nditer(
@@ -719,7 +726,7 @@ def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
                     buffersize=buffersize, order='F'):
                 fp.write(chunk.tobytes('C'))
     else:
-        if _isfileobj(fp):
+        if isfileobj(fp):
             array.tofile(fp)
         else:
             for chunk in numpy.nditer(
@@ -751,7 +758,7 @@ def read_array(fp, allow_pickle=False, pickle_kwargs=None, *,
     max_header_size : int, optional
         Maximum allowed size of the header.  Large headers may not be safe
         to load securely and thus require explicitly passing a larger value.
-        See :py:meth:`ast.literal_eval()` for details.
+        See :py:func:`ast.literal_eval()` for details.
         This option is ignored when `allow_pickle` is passed.  In that case
         the file is by definition trusted and the limit is unnecessary.
 
@@ -797,7 +804,7 @@ def read_array(fp, allow_pickle=False, pickle_kwargs=None, *,
                                "You may need to pass the encoding= option "
                                "to numpy.load" % (err,)) from err
     else:
-        if _isfileobj(fp):
+        if isfileobj(fp):
             # We can use the fast fromfile() function.
             array = numpy.fromfile(fp, dtype=dtype, count=count)
         else:
@@ -870,7 +877,7 @@ def open_memmap(filename, mode='r+', dtype=None, shape=None,
     max_header_size : int, optional
         Maximum allowed size of the header.  Large headers may not be safe
         to load securely and thus require explicitly passing a larger value.
-        See :py:meth:`ast.literal_eval()` for details.
+        See :py:func:`ast.literal_eval()` for details.
 
     Returns
     -------
@@ -889,7 +896,7 @@ def open_memmap(filename, mode='r+', dtype=None, shape=None,
     numpy.memmap
 
     """
-    if _isfileobj(filename):
+    if isfileobj(filename):
         raise ValueError("Filename must be a string or a path-like object."
                          "  Memmap cannot use existing file handles.")
 
diff --git a/contrib/python/numpy/py3/numpy/lib/function_base.py b/contrib/python/numpy/py3/numpy/lib/function_base.py
index 84b96d54ec3..e75aca1e58e 100644
--- a/contrib/python/numpy/py3/numpy/lib/function_base.py
+++ b/contrib/python/numpy/py3/numpy/lib/function_base.py
@@ -4,6 +4,7 @@ import re
 import sys
 import warnings
 
+from .._utils import set_module
 import numpy as np
 import numpy.core.numeric as _nx
 from numpy.core import transpose
@@ -19,12 +20,11 @@ from numpy.core.fromnumeric import (
     ravel, nonzero, partition, mean, any, sum
     )
 from numpy.core.numerictypes import typecodes
-from numpy.core.overrides import set_module
 from numpy.core import overrides
 from numpy.core.function_base import add_newdoc
 from numpy.lib.twodim_base import diag
 from numpy.core.multiarray import (
-    _insert, add_docstring, bincount, normalize_axis_index, _monotonicity,
+    _place, add_docstring, bincount, normalize_axis_index, _monotonicity,
     interp as compiled_interp, interp_complex as compiled_interp_complex
     )
 from numpy.core.umath import _add_newdoc_ufunc as add_newdoc_ufunc
@@ -161,6 +161,8 @@ def rot90(m, k=1, axes=(0, 1)):
     Rotate an array by 90 degrees in the plane specified by axes.
 
     Rotation direction is from the first towards the second axis.
+    This means for a 2D array with the default `k` and `axes`, the
+    rotation will be counterclockwise.
 
     Parameters
     ----------
@@ -1309,6 +1311,8 @@ def gradient(f, *varargs, axis=None, edge_order=1):
 
     if len_axes == 1:
         return outvals[0]
+    elif np._using_numpy2_behavior():
+        return tuple(outvals)
     else:
         return outvals
 
@@ -1947,11 +1951,7 @@ def place(arr, mask, vals):
            [44, 55, 44]])
 
     """
-    if not isinstance(arr, np.ndarray):
-        raise TypeError("argument 1 must be numpy.ndarray, "
-                        "not {name}".format(name=type(arr).__name__))
-
-    return _insert(arr, mask, vals)
+    return _place(arr, mask, vals)
 
 
 def disp(mesg, device=None, linefeed=True):
@@ -2117,10 +2117,10 @@ def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes,
 @set_module('numpy')
 class vectorize:
     """
-    vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False,
-              signature=None)
+    vectorize(pyfunc=np._NoValue, otypes=None, doc=None, excluded=None,
+    cache=False, signature=None)
 
-    Generalized function class.
+    Returns an object that acts like pyfunc, but takes arrays as input.
 
     Define a vectorized function which takes a nested sequence of objects or
     numpy arrays as inputs and returns a single numpy array or a tuple of numpy
@@ -2134,8 +2134,9 @@ class vectorize:
 
     Parameters
     ----------
-    pyfunc : callable
+    pyfunc : callable, optional
         A python function or method.
+        Can be omitted to produce a decorator with keyword arguments.
     otypes : str or list of dtypes, optional
         The output data type. It must be specified as either a string of
         typecode characters or a list of data type specifiers. There should
@@ -2167,8 +2168,9 @@ class vectorize:
 
     Returns
     -------
-    vectorized : callable
-        Vectorized function.
+    out : callable
+        A vectorized function if ``pyfunc`` was provided,
+        a decorator otherwise.
 
     See Also
     --------
@@ -2265,18 +2267,44 @@ class vectorize:
            [0., 0., 1., 2., 1., 0.],
            [0., 0., 0., 1., 2., 1.]])
 
+    Decorator syntax is supported.  The decorator can be called as
+    a function to provide keyword arguments.
+    >>>@np.vectorize
+    ...def identity(x):
+    ...    return x
+    ...
+    >>>identity([0, 1, 2])
+    array([0, 1, 2])
+    >>>@np.vectorize(otypes=[float])
+    ...def as_float(x):
+    ...    return x
+    ...
+    >>>as_float([0, 1, 2])
+    array([0., 1., 2.])
     """
-    def __init__(self, pyfunc, otypes=None, doc=None, excluded=None,
-                 cache=False, signature=None):
+    def __init__(self, pyfunc=np._NoValue, otypes=None, doc=None,
+                 excluded=None, cache=False, signature=None):
+
+        if (pyfunc != np._NoValue) and (not callable(pyfunc)):
+            #Splitting the error message to keep
+            #the length below 79 characters.
+            part1 = "When used as a decorator, "
+            part2 = "only accepts keyword arguments."
+            raise TypeError(part1 + part2)
+
         self.pyfunc = pyfunc
         self.cache = cache
         self.signature = signature
-        self._ufunc = {}    # Caching to improve default performance
+        if pyfunc != np._NoValue and hasattr(pyfunc, '__name__'):
+            self.__name__ = pyfunc.__name__
 
-        if doc is None:
+        self._ufunc = {}    # Caching to improve default performance
+        self._doc = None
+        self.__doc__ = doc
+        if doc is None and hasattr(pyfunc, '__doc__'):
             self.__doc__ = pyfunc.__doc__
         else:
-            self.__doc__ = doc
+            self._doc = doc
 
         if isinstance(otypes, str):
             for char in otypes:
@@ -2298,7 +2326,15 @@ class vectorize:
         else:
             self._in_and_out_core_dims = None
 
-    def __call__(self, *args, **kwargs):
+    def _init_stage_2(self, pyfunc, *args, **kwargs):
+        self.__name__ = pyfunc.__name__
+        self.pyfunc = pyfunc
+        if self._doc is None:
+            self.__doc__ = pyfunc.__doc__
+        else:
+            self.__doc__ = self._doc
+
+    def _call_as_normal(self, *args, **kwargs):
         """
         Return arrays with the results of `pyfunc` broadcast (vectorized) over
         `args` and `kwargs` not in `excluded`.
@@ -2328,6 +2364,13 @@ class vectorize:
 
         return self._vectorize_call(func=func, args=vargs)
 
+    def __call__(self, *args, **kwargs):
+        if self.pyfunc is np._NoValue:
+            self._init_stage_2(*args, **kwargs)
+            return self
+
+        return self._call_as_normal(*args, **kwargs)
+
     def _get_ufunc_and_otypes(self, func, args):
         """Return (ufunc, otypes)."""
         # frompyfunc will fail if args is empty
@@ -2693,7 +2736,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
 
     if fact <= 0:
         warnings.warn("Degrees of freedom <= 0 for slice",
-                      RuntimeWarning, stacklevel=3)
+                      RuntimeWarning, stacklevel=2)
         fact = 0.0
 
     X -= avg[:, None]
@@ -2842,7 +2885,7 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *,
     if bias is not np._NoValue or ddof is not np._NoValue:
         # 2015-03-15, 1.10
         warnings.warn('bias and ddof have no effect and are deprecated',
-                      DeprecationWarning, stacklevel=3)
+                      DeprecationWarning, stacklevel=2)
     c = cov(x, y, rowvar, dtype=dtype)
     try:
         d = diag(c)
@@ -2956,10 +2999,15 @@ def blackman(M):
     >>> plt.show()
 
     """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
     if M < 1:
-        return array([], dtype=np.result_type(M, 0.0))
+        return array([], dtype=values.dtype)
     if M == 1:
-        return ones(1, dtype=np.result_type(M, 0.0))
+        return ones(1, dtype=values.dtype)
     n = arange(1-M, M, 2)
     return 0.42 + 0.5*cos(pi*n/(M-1)) + 0.08*cos(2.0*pi*n/(M-1))
 
@@ -3064,10 +3112,15 @@ def bartlett(M):
     >>> plt.show()
 
     """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
     if M < 1:
-        return array([], dtype=np.result_type(M, 0.0))
+        return array([], dtype=values.dtype)
     if M == 1:
-        return ones(1, dtype=np.result_type(M, 0.0))
+        return ones(1, dtype=values.dtype)
     n = arange(1-M, M, 2)
     return where(less_equal(n, 0), 1 + n/(M-1), 1 - n/(M-1))
 
@@ -3168,10 +3221,15 @@ def hanning(M):
     >>> plt.show()
 
     """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
     if M < 1:
-        return array([], dtype=np.result_type(M, 0.0))
+        return array([], dtype=values.dtype)
     if M == 1:
-        return ones(1, dtype=np.result_type(M, 0.0))
+        return ones(1, dtype=values.dtype)
     n = arange(1-M, M, 2)
     return 0.5 + 0.5*cos(pi*n/(M-1))
 
@@ -3268,10 +3326,15 @@ def hamming(M):
     >>> plt.show()
 
     """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
     if M < 1:
-        return array([], dtype=np.result_type(M, 0.0))
+        return array([], dtype=values.dtype)
     if M == 1:
-        return ones(1, dtype=np.result_type(M, 0.0))
+        return ones(1, dtype=values.dtype)
     n = arange(1-M, M, 2)
     return 0.54 + 0.46*cos(pi*n/(M-1))
 
@@ -3547,11 +3610,19 @@ def kaiser(M, beta):
     >>> plt.show()
 
     """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.  (Simplified result_type with 0.0
+    # strongly typed.  result-type is not/less order sensitive, but that mainly
+    # matters for integers anyway.)
+    values = np.array([0.0, M, beta])
+    M = values[1]
+    beta = values[2]
+
     if M == 1:
-        return np.ones(1, dtype=np.result_type(M, 0.0))
+        return np.ones(1, dtype=values.dtype)
     n = arange(0, M)
     alpha = (M-1)/2.0
-    return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(float(beta))
+    return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(beta)
 
 
 def _sinc_dispatcher(x):
@@ -3682,7 +3753,7 @@ def msort(a):
     warnings.warn(
         "msort is deprecated, use np.sort(a, axis=0) instead",
         DeprecationWarning,
-        stacklevel=3,
+        stacklevel=2,
     )
     b = array(a, subok=True, copy=True)
     b.sort(0)
@@ -3872,8 +3943,10 @@ def _median(a, axis=None, out=None, overwrite_input=False):
         kth = [szh - 1, szh]
     else:
         kth = [(sz - 1) // 2]
-    # Check if the array contains any nan's
-    if np.issubdtype(a.dtype, np.inexact):
+
+    # We have to check for NaNs (as of writing 'M' doesn't actually work).
+    supports_nans = np.issubdtype(a.dtype, np.inexact) or a.dtype.kind in 'Mm'
+    if supports_nans:
         kth.append(-1)
 
     if overwrite_input:
@@ -3904,8 +3977,7 @@ def _median(a, axis=None, out=None, overwrite_input=False):
     # Use mean in both odd and even case to coerce data type,
     # using out array if needed.
     rout = mean(part[indexer], axis=axis, out=out)
-    # Check if the array contains any nan's
-    if np.issubdtype(a.dtype, np.inexact) and sz > 0:
+    if supports_nans and sz > 0:
         # If nans are possible, warn and replace by nans like mean would.
         rout = np.lib.utils._median_nancheck(part, rout, axis)
 
@@ -3934,11 +4006,11 @@ def percentile(a,
 
     Parameters
     ----------
-    a : array_like
+    a : array_like of real numbers
         Input array or object that can be converted to an array.
     q : array_like of float
-        Percentile or sequence of percentiles to compute, which must be between
-        0 and 100 inclusive.
+        Percentage or sequence of percentages for the percentiles to compute.
+        Values must be between 0 and 100 inclusive.
     axis : {int, tuple of int, None}, optional
         Axis or axes along which the percentiles are computed. The
         default is to compute the percentile(s) along a flattened
@@ -4035,7 +4107,7 @@ def percentile(a,
     since Python uses 0-based indexing, the code subtracts another 1 from the
     index internally.
 
-    The following formula determines the virtual index ``i + g``, the location 
+    The following formula determines the virtual index ``i + g``, the location
     of the percentile in the sorted sample:
 
     .. math::
@@ -4185,7 +4257,8 @@ def percentile(a,
             xlabel='Percentile',
             ylabel='Estimated percentile value',
             yticks=a)
-        ax.legend()
+        ax.legend(bbox_to_anchor=(1.03, 1))
+        plt.tight_layout()
         plt.show()
 
     References
@@ -4198,6 +4271,11 @@ def percentile(a,
     if interpolation is not None:
         method = _check_interpolation_as_method(
             method, interpolation, "percentile")
+
+    a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
     q = np.true_divide(q, 100)
     q = asanyarray(q)  # undo any decay that the ufunc performed (see gh-13105)
     if not _quantile_is_valid(q):
@@ -4228,11 +4306,11 @@ def quantile(a,
 
     Parameters
     ----------
-    a : array_like
+    a : array_like of real numbers
         Input array or object that can be converted to an array.
     q : array_like of float
-        Quantile or sequence of quantiles to compute, which must be between
-        0 and 1 inclusive.
+        Probability or sequence of probabilities for the quantiles to compute.
+        Values must be between 0 and 1 inclusive.
     axis : {int, tuple of int, None}, optional
         Axis or axes along which the quantiles are computed. The default is
         to compute the quantile(s) along a flattened version of the array.
@@ -4286,8 +4364,8 @@ def quantile(a,
     Returns
     -------
     quantile : scalar or ndarray
-        If `q` is a single quantile and `axis=None`, then the result
-        is a scalar. If multiple quantiles are given, first axis of
+        If `q` is a single probability and `axis=None`, then the result
+        is a scalar. If multiple probabilies levels are given, first axis of
         the result corresponds to the quantiles. The other axes are
         the axes that remain after the reduction of `a`. If the input
         contains integers or floats smaller than ``float64``, the output
@@ -4324,7 +4402,7 @@ def quantile(a,
     since Python uses 0-based indexing, the code subtracts another 1 from the
     index internally.
 
-    The following formula determines the virtual index ``i + g``, the location 
+    The following formula determines the virtual index ``i + g``, the location
     of the quantile in the sorted sample:
 
     .. math::
@@ -4455,6 +4533,10 @@ def quantile(a,
         method = _check_interpolation_as_method(
             method, interpolation, "quantile")
 
+    a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
     q = np.asanyarray(q)
     if not _quantile_is_valid(q):
         raise ValueError("Quantiles must be in the range [0, 1]")
@@ -4703,9 +4785,9 @@ def _quantile(
     values_count = arr.shape[axis]
     # The dimensions of `q` are prepended to the output shape, so we need the
     # axis being sampled from `arr` to be last.
-    DATA_AXIS = 0
-    if axis != DATA_AXIS:  # But moveaxis is slow, so only call it if axis!=0.
-        arr = np.moveaxis(arr, axis, destination=DATA_AXIS)
+
+    if axis != 0:  # But moveaxis is slow, so only call it if necessary.
+        arr = np.moveaxis(arr, axis, destination=0)
     # --- Computation of indexes
     # Index where to find the value in the sorted array.
     # Virtual because it is a floating point value, not an valid index.
@@ -4718,12 +4800,16 @@ def _quantile(
             f"{_QuantileMethods.keys()}") from None
     virtual_indexes = method["get_virtual_index"](values_count, quantiles)
     virtual_indexes = np.asanyarray(virtual_indexes)
+
+    supports_nans = (
+            np.issubdtype(arr.dtype, np.inexact) or arr.dtype.kind in 'Mm')
+
     if np.issubdtype(virtual_indexes.dtype, np.integer):
         # No interpolation needed, take the points along axis
-        if np.issubdtype(arr.dtype, np.inexact):
+        if supports_nans:
             # may contain nan, which would sort to the end
             arr.partition(concatenate((virtual_indexes.ravel(), [-1])), axis=0)
-            slices_having_nans = np.isnan(arr[-1])
+            slices_having_nans = np.isnan(arr[-1, ...])
         else:
             # cannot contain nan
             arr.partition(virtual_indexes.ravel(), axis=0)
@@ -4739,16 +4825,14 @@ def _quantile(
                                       previous_indexes.ravel(),
                                       next_indexes.ravel(),
                                       ))),
-            axis=DATA_AXIS)
-        if np.issubdtype(arr.dtype, np.inexact):
-            slices_having_nans = np.isnan(
-                take(arr, indices=-1, axis=DATA_AXIS)
-            )
+            axis=0)
+        if supports_nans:
+            slices_having_nans = np.isnan(arr[-1, ...])
         else:
             slices_having_nans = None
         # --- Get values from indexes
-        previous = np.take(arr, previous_indexes, axis=DATA_AXIS)
-        next = np.take(arr, next_indexes, axis=DATA_AXIS)
+        previous = arr[previous_indexes]
+        next = arr[next_indexes]
         # --- Linear interpolation
         gamma = _get_gamma(virtual_indexes, previous_indexes, method)
         result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1)
@@ -4759,10 +4843,10 @@ def _quantile(
                        out=out)
     if np.any(slices_having_nans):
         if result.ndim == 0 and out is None:
-            # can't write to a scalar
-            result = arr.dtype.type(np.nan)
+            # can't write to a scalar, but indexing will be correct
+            result = arr[-1]
         else:
-            result[..., slices_having_nans] = np.nan
+            np.copyto(result, arr[-1, ...], where=slices_having_nans)
     return result
 
 
@@ -4827,26 +4911,42 @@ def trapz(y, x=None, dx=1.0, axis=-1):
 
     Examples
     --------
-    >>> np.trapz([1,2,3])
+    Use the trapezoidal rule on evenly spaced points:
+
+    >>> np.trapz([1, 2, 3])
     4.0
-    >>> np.trapz([1,2,3], x=[4,6,8])
+
+    The spacing between sample points can be selected by either the
+    ``x`` or ``dx`` arguments:
+
+    >>> np.trapz([1, 2, 3], x=[4, 6, 8])
     8.0
-    >>> np.trapz([1,2,3], dx=2)
+    >>> np.trapz([1, 2, 3], dx=2)
     8.0
 
-    Using a decreasing `x` corresponds to integrating in reverse:
+    Using a decreasing ``x`` corresponds to integrating in reverse:
 
-    >>> np.trapz([1,2,3], x=[8,6,4])
+    >>> np.trapz([1, 2, 3], x=[8, 6, 4])
     -8.0
 
-    More generally `x` is used to integrate along a parametric curve.
-    This finds the area of a circle, noting we repeat the sample which closes
+    More generally ``x`` is used to integrate along a parametric curve. We can
+    estimate the integral :math:`\int_0^1 x^2 = 1/3` using:
+
+    >>> x = np.linspace(0, 1, num=50)
+    >>> y = x**2
+    >>> np.trapz(y, x)
+    0.33340274885464394
+
+    Or estimate the area of a circle, noting we repeat the sample which closes
     the curve:
 
     >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
     >>> np.trapz(np.cos(theta), x=np.sin(theta))
     3.141571941375841
 
+    ``np.trapz`` can be applied along a specified axis to do multiple
+    computations in one call:
+
     >>> a = np.arange(6).reshape(2, 3)
     >>> a
     array([[0, 1, 2],
@@ -4884,6 +4984,24 @@ def trapz(y, x=None, dx=1.0, axis=-1):
     return ret
 
 
+# __array_function__ has no __code__ or other attributes normal Python funcs we
+# wrap everything into a C callable. SciPy however, tries to "clone" `trapz`
+# into a new Python function which requires `__code__` and a few other
+# attributes. So we create a dummy clone and copy over its attributes allowing
+# SciPy <= 1.10 to work: https://github.com/scipy/scipy/issues/17811
+assert not hasattr(trapz, "__code__")
+
+def _fake_trapz(y, x=None, dx=1.0, axis=-1):
+    return trapz(y, x=x, dx=dx, axis=axis)
+
+
+trapz.__code__ = _fake_trapz.__code__
+trapz.__globals__ = _fake_trapz.__globals__
+trapz.__defaults__ = _fake_trapz.__defaults__
+trapz.__closure__ = _fake_trapz.__closure__
+trapz.__kwdefaults__ = _fake_trapz.__kwdefaults__
+
+
 def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
     return xi
 
@@ -4892,7 +5010,7 @@ def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
 @array_function_dispatch(_meshgrid_dispatcher)
 def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
     """
-    Return coordinate matrices from coordinate vectors.
+    Return a list of coordinate matrices from coordinate vectors.
 
     Make N-D coordinate arrays for vectorized evaluations of
     N-D scalar/vector fields over N-D grids, given
@@ -4933,7 +5051,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
 
     Returns
     -------
-    X1, X2,..., XN : ndarray
+    X1, X2,..., XN : list of ndarrays
         For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
         returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
         or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
@@ -5370,7 +5488,7 @@ def insert(arr, obj, values, axis=None):
             warnings.warn(
                 "in the future insert will treat boolean arrays and "
                 "array-likes as a boolean index instead of casting it to "
-                "integer", FutureWarning, stacklevel=3)
+                "integer", FutureWarning, stacklevel=2)
             indices = indices.astype(intp)
             # Code after warning period:
             #if obj.ndim != 1:
diff --git a/contrib/python/numpy/py3/numpy/lib/histograms.py b/contrib/python/numpy/py3/numpy/lib/histograms.py
index 0dfa7b4c181..6ac65b72692 100644
--- a/contrib/python/numpy/py3/numpy/lib/histograms.py
+++ b/contrib/python/numpy/py3/numpy/lib/histograms.py
@@ -807,7 +807,8 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
         n = np.zeros(n_equal_bins, ntype)
 
         # Pre-compute histogram scaling factor
-        norm = n_equal_bins / _unsigned_subtract(last_edge, first_edge)
+        norm_numerator = n_equal_bins
+        norm_denom = _unsigned_subtract(last_edge, first_edge)
 
         # We iterate over blocks here for two reasons: the first is that for
         # large arrays, it is actually faster (for example for a 10^8 array it
@@ -835,7 +836,8 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
 
             # Compute the bin indices, and for values that lie exactly on
             # last_edge we need to subtract one
-            f_indices = _unsigned_subtract(tmp_a, first_edge) * norm
+            f_indices = ((_unsigned_subtract(tmp_a, first_edge) / norm_denom)
+                         * norm_numerator)
             indices = f_indices.astype(np.intp)
             indices[indices == n_equal_bins] -= 1
 
@@ -981,7 +983,7 @@ def histogramdd(sample, bins=10, range=None, density=None, weights=None):
         if M != D:
             raise ValueError(
                 'The dimension of bins must be equal to the dimension of the '
-                ' sample x.')
+                'sample x.')
     except TypeError:
         # bins is an integer
         bins = D*[bins]
diff --git a/contrib/python/numpy/py3/numpy/lib/index_tricks.py b/contrib/python/numpy/py3/numpy/lib/index_tricks.py
index 95d5e3edeb8..6913d2b95b7 100644
--- a/contrib/python/numpy/py3/numpy/lib/index_tricks.py
+++ b/contrib/python/numpy/py3/numpy/lib/index_tricks.py
@@ -3,16 +3,15 @@ import sys
 import math
 import warnings
 
+import numpy as np
+from .._utils import set_module
 import numpy.core.numeric as _nx
-from numpy.core.numeric import (
-    asarray, ScalarType, array, alltrue, cumprod, arange, ndim
-)
-from numpy.core.numerictypes import find_common_type, issubdtype
+from numpy.core.numeric import ScalarType, array
+from numpy.core.numerictypes import issubdtype
 
 import numpy.matrixlib as matrixlib
 from .function_base import diff
 from numpy.core.multiarray import ravel_multi_index, unravel_index
-from numpy.core.overrides import set_module
 from numpy.core import overrides, linspace
 from numpy.lib.stride_tricks import as_strided
 
@@ -94,7 +93,7 @@ def ix_(*args):
     nd = len(args)
     for k, new in enumerate(args):
         if not isinstance(new, _nx.ndarray):
-            new = asarray(new)
+            new = np.asarray(new)
             if new.size == 0:
                 # Explicitly type empty arrays to avoid float default
                 new = new.astype(_nx.intp)
@@ -211,13 +210,13 @@ class nd_grid:
 
 class MGridClass(nd_grid):
     """
-    `nd_grid` instance which returns a dense multi-dimensional "meshgrid".
+    An instance which returns a dense multi-dimensional "meshgrid".
 
-    An instance of `numpy.lib.index_tricks.nd_grid` which returns an dense
-    (or fleshed out) mesh-grid when indexed, so that each returned argument
-    has the same shape.  The dimensions and number of the output arrays are
-    equal to the number of indexing dimensions.  If the step length is not a
-    complex number, then the stop is not inclusive.
+    An instance which returns a dense (or fleshed out) mesh-grid
+    when indexed, so that each returned argument has the same shape.
+    The dimensions and number of the output arrays are equal to the
+    number of indexing dimensions.  If the step length is not a complex
+    number, then the stop is not inclusive.
 
     However, if the step length is a **complex number** (e.g. 5j), then
     the integer part of its magnitude is interpreted as specifying the
@@ -230,8 +229,7 @@ class MGridClass(nd_grid):
 
     See Also
     --------
-    lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
-    ogrid : like mgrid but returns open (not fleshed out) mesh grids
+    ogrid : like `mgrid` but returns open (not fleshed out) mesh grids
     meshgrid: return coordinate matrices from coordinate vectors
     r_ : array concatenator
     :ref:`how-to-partition`
@@ -263,13 +261,13 @@ mgrid = MGridClass()
 
 class OGridClass(nd_grid):
     """
-    `nd_grid` instance which returns an open multi-dimensional "meshgrid".
+    An instance which returns an open multi-dimensional "meshgrid".
 
-    An instance of `numpy.lib.index_tricks.nd_grid` which returns an open
-    (i.e. not fleshed out) mesh-grid when indexed, so that only one dimension
-    of each returned array is greater than 1.  The dimension and number of the
-    output arrays are equal to the number of indexing dimensions.  If the step
-    length is not a complex number, then the stop is not inclusive.
+    An instance which returns an open (i.e. not fleshed out) mesh-grid
+    when indexed, so that only one dimension of each returned array is
+    greater than 1.  The dimension and number of the output arrays are
+    equal to the number of indexing dimensions.  If the step length is
+    not a complex number, then the stop is not inclusive.
 
     However, if the step length is a **complex number** (e.g. 5j), then
     the integer part of its magnitude is interpreted as specifying the
@@ -283,7 +281,6 @@ class OGridClass(nd_grid):
 
     See Also
     --------
-    np.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
     mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids
     meshgrid: return coordinate matrices from coordinate vectors
     r_ : array concatenator
@@ -343,9 +340,8 @@ class AxisConcatenator:
         axis = self.axis
 
         objs = []
-        scalars = []
-        arraytypes = []
-        scalartypes = []
+        # dtypes or scalars for weak scalar handling in result_type
+        result_type_objs = []
 
         for k, item in enumerate(key):
             scalar = False
@@ -391,12 +387,10 @@ class AxisConcatenator:
                 except (ValueError, TypeError) as e:
                     raise ValueError("unknown special directive") from e
             elif type(item) in ScalarType:
-                newobj = array(item, ndmin=ndmin)
-                scalars.append(len(objs))
                 scalar = True
-                scalartypes.append(newobj.dtype)
+                newobj = item
             else:
-                item_ndim = ndim(item)
+                item_ndim = np.ndim(item)
                 newobj = array(item, copy=False, subok=True, ndmin=ndmin)
                 if trans1d != -1 and item_ndim < ndmin:
                     k2 = ndmin - item_ndim
@@ -406,15 +400,20 @@ class AxisConcatenator:
                     defaxes = list(range(ndmin))
                     axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2]
                     newobj = newobj.transpose(axes)
+
             objs.append(newobj)
-            if not scalar and isinstance(newobj, _nx.ndarray):
-                arraytypes.append(newobj.dtype)
+            if scalar:
+                result_type_objs.append(item)
+            else:
+                result_type_objs.append(newobj.dtype)
 
-        # Ensure that scalars won't up-cast unless warranted
-        final_dtype = find_common_type(arraytypes, scalartypes)
-        if final_dtype is not None:
-            for k in scalars:
-                objs[k] = objs[k].astype(final_dtype)
+        # Ensure that scalars won't up-cast unless warranted, for 0, drops
+        # through to error in concatenate.
+        if len(result_type_objs) != 0:
+            final_dtype = _nx.result_type(*result_type_objs)
+            # concatenate could do cast, but that can be overriden:
+            objs = [array(obj, copy=False, subok=True,
+                          ndmin=ndmin, dtype=final_dtype) for obj in objs]
 
         res = self.concatenate(tuple(objs), axis=axis)
 
@@ -596,7 +595,7 @@ class ndenumerate:
     """
 
     def __init__(self, arr):
-        self.iter = asarray(arr).flat
+        self.iter = np.asarray(arr).flat
 
     def __next__(self):
         """
@@ -909,9 +908,9 @@ def fill_diagonal(a, val, wrap=False):
     else:
         # For more than d=2, the strided formula is only valid for arrays with
         # all dimensions equal, so we check first.
-        if not alltrue(diff(a.shape) == 0):
+        if not np.all(diff(a.shape) == 0):
             raise ValueError("All dimensions of input must be of equal length")
-        step = 1 + (cumprod(a.shape[:-1])).sum()
+        step = 1 + (np.cumprod(a.shape[:-1])).sum()
 
     # Write the value out into the diagonal.
     a.flat[:end:step] = val
@@ -982,7 +981,7 @@ def diag_indices(n, ndim=2):
             [0, 1]]])
 
     """
-    idx = arange(n)
+    idx = np.arange(n)
     return (idx,) * ndim
 
 
@@ -1009,13 +1008,39 @@ def diag_indices_from(arr):
     -----
     .. versionadded:: 1.4.0
 
+    Examples
+    --------
+    
+    Create a 4 by 4 array.
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+    
+    Get the indices of the diagonal elements.
+
+    >>> di = np.diag_indices_from(a)
+    >>> di
+    (array([0, 1, 2, 3]), array([0, 1, 2, 3]))
+
+    >>> a[di]
+    array([ 0,  5, 10, 15])
+
+    This is simply syntactic sugar for diag_indices.
+
+    >>> np.diag_indices(a.shape[0])
+    (array([0, 1, 2, 3]), array([0, 1, 2, 3]))
+
     """
 
     if not arr.ndim >= 2:
         raise ValueError("input array must be at least 2-d")
     # For more than d=2, the strided formula is only valid for arrays with
     # all dimensions equal, so we check first.
-    if not alltrue(diff(arr.shape) == 0):
+    if not np.all(diff(arr.shape) == 0):
         raise ValueError("All dimensions of input must be of equal length")
 
     return diag_indices(arr.shape[0], arr.ndim)
diff --git a/contrib/python/numpy/py3/numpy/lib/index_tricks.pyi b/contrib/python/numpy/py3/numpy/lib/index_tricks.pyi
index c9251abd15b..29a6b9e2b9f 100644
--- a/contrib/python/numpy/py3/numpy/lib/index_tricks.pyi
+++ b/contrib/python/numpy/py3/numpy/lib/index_tricks.pyi
@@ -119,7 +119,7 @@ class AxisConcatenator:
     @staticmethod
     def makemat(
         data: ArrayLike, dtype: DTypeLike = ..., copy: bool = ...
-    ) -> _Matrix: ...
+    ) -> _Matrix[Any, Any]: ...
 
     # TODO: Sort out this `__getitem__` method
     def __getitem__(self, key: Any) -> Any: ...
diff --git a/contrib/python/numpy/py3/numpy/lib/mixins.py b/contrib/python/numpy/py3/numpy/lib/mixins.py
index c81239f6b44..117cc785187 100644
--- a/contrib/python/numpy/py3/numpy/lib/mixins.py
+++ b/contrib/python/numpy/py3/numpy/lib/mixins.py
@@ -133,6 +133,7 @@ class NDArrayOperatorsMixin:
 
     .. versionadded:: 1.13
     """
+    __slots__ = ()
     # Like np.ndarray, this mixin class implements "Option 1" from the ufunc
     # overrides NEP.
 
diff --git a/contrib/python/numpy/py3/numpy/lib/nanfunctions.py b/contrib/python/numpy/py3/numpy/lib/nanfunctions.py
index ae2dfa1657f..b3b570860ff 100644
--- a/contrib/python/numpy/py3/numpy/lib/nanfunctions.py
+++ b/contrib/python/numpy/py3/numpy/lib/nanfunctions.py
@@ -169,7 +169,7 @@ def _remove_nan_1d(arr1d, overwrite_input=False):
     s = np.nonzero(c)[0]
     if s.size == arr1d.size:
         warnings.warn("All-NaN slice encountered", RuntimeWarning,
-                      stacklevel=5)
+                      stacklevel=6)
         return arr1d[:0], True
     elif s.size == 0:
         return arr1d, overwrite_input
@@ -343,7 +343,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
             warnings.warn("All-NaN slice encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, +np.inf)
@@ -357,7 +357,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         if np.any(mask):
             res = _copyto(res, np.nan, mask)
             warnings.warn("All-NaN axis encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     return res
 
 
@@ -476,7 +476,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         res = np.fmax.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
             warnings.warn("All-NaN slice encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, -np.inf)
@@ -490,7 +490,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         if np.any(mask):
             res = _copyto(res, np.nan, mask)
             warnings.warn("All-NaN axis encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     return res
 
 
@@ -1049,7 +1049,7 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
 
     isbad = (cnt == 0)
     if isbad.any():
-        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=3)
+        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2)
         # NaN is the only possible bad value, so no further
         # action is needed to handle bad results.
     return avg
@@ -1109,7 +1109,7 @@ def _nanmedian_small(a, axis=None, out=None, overwrite_input=False):
     m = np.ma.median(a, axis=axis, overwrite_input=overwrite_input)
     for i in range(np.count_nonzero(m.mask.ravel())):
         warnings.warn("All-NaN slice encountered", RuntimeWarning,
-                      stacklevel=4)
+                      stacklevel=5)
 
     fill_value = np.timedelta64("NaT") if m.dtype.kind == "m" else np.nan
     if out is not None:
@@ -1373,6 +1373,9 @@ def nanpercentile(
             method, interpolation, "nanpercentile")
 
     a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
     q = np.true_divide(q, 100.0)
     # undo any decay that the ufunc performed (see gh-13105)
     q = np.asanyarray(q)
@@ -1412,8 +1415,8 @@ def nanquantile(
         Input array or object that can be converted to an array, containing
         nan values to be ignored
     q : array_like of float
-        Quantile or sequence of quantiles to compute, which must be between
-        0 and 1 inclusive.
+        Probability or sequence of probabilities for the quantiles to compute.
+        Values must be between 0 and 1 inclusive.
     axis : {int, tuple of int, None}, optional
         Axis or axes along which the quantiles are computed. The
         default is to compute the quantile(s) along a flattened
@@ -1473,8 +1476,8 @@ def nanquantile(
     Returns
     -------
     quantile : scalar or ndarray
-        If `q` is a single percentile and `axis=None`, then the result
-        is a scalar. If multiple quantiles are given, first axis of
+        If `q` is a single probability and `axis=None`, then the result
+        is a scalar. If multiple probability levels are given, first axis of
         the result corresponds to the quantiles. The other axes are
         the axes that remain after the reduction of `a`. If the input
         contains integers or floats smaller than ``float64``, the output
@@ -1527,11 +1530,15 @@ def nanquantile(
        The American Statistician, 50(4), pp. 361-365, 1996
 
     """
+
     if interpolation is not None:
         method = function_base._check_interpolation_as_method(
             method, interpolation, "nanquantile")
 
     a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
     q = np.asanyarray(q)
     if not function_base._quantile_is_valid(q):
         raise ValueError("Quantiles must be in the range [0, 1]")
@@ -1756,7 +1763,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
     isbad = (dof <= 0)
     if np.any(isbad):
         warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning,
-                      stacklevel=3)
+                      stacklevel=2)
         # NaN, inf, or negative numbers are all possible bad
         # values, so explicitly replace them with NaN.
         var = _copyto(var, np.nan, isbad)
diff --git a/contrib/python/numpy/py3/numpy/lib/npyio.py b/contrib/python/numpy/py3/numpy/lib/npyio.py
index 3d6951416e2..339b1dc6211 100644
--- a/contrib/python/numpy/py3/numpy/lib/npyio.py
+++ b/contrib/python/numpy/py3/numpy/lib/npyio.py
@@ -142,7 +142,7 @@ class NpzFile(Mapping):
     max_header_size : int, optional
         Maximum allowed size of the header.  Large headers may not be safe
         to load securely and thus require explicitly passing a larger value.
-        See :py:meth:`ast.literal_eval()` for details.
+        See :py:func:`ast.literal_eval()` for details.
         This option is ignored when `allow_pickle` is passed.  In that case
         the file is by definition trusted and the limit is unnecessary.
 
@@ -167,6 +167,8 @@ class NpzFile(Mapping):
     >>> npz = np.load(outfile)
     >>> isinstance(npz, np.lib.npyio.NpzFile)
     True
+    >>> npz
+    NpzFile 'object' with keys x, y
     >>> sorted(npz.files)
     ['x', 'y']
     >>> npz['x']  # getitem access
@@ -178,6 +180,7 @@ class NpzFile(Mapping):
     # Make __exit__ safe if zipfile_factory raises an exception
     zip = None
     fid = None
+    _MAX_REPR_ARRAY_COUNT = 5
 
     def __init__(self, fid, own_fid=False, allow_pickle=False,
                  pickle_kwargs=None, *,
@@ -257,7 +260,23 @@ class NpzFile(Mapping):
             else:
                 return self.zip.read(key)
         else:
-            raise KeyError("%s is not a file in the archive" % key)
+            raise KeyError(f"{key} is not a file in the archive")
+
+    def __contains__(self, key):
+        return (key in self._files or key in self.files)
+
+    def __repr__(self):
+        # Get filename or default to `object`
+        if isinstance(self.fid, str):
+            filename = self.fid
+        else:
+            filename = getattr(self.fid, "name", "object")
+
+        # Get the name of arrays
+        array_names = ', '.join(self.files[:self._MAX_REPR_ARRAY_COUNT])
+        if len(self.files) > self._MAX_REPR_ARRAY_COUNT:
+            array_names += "..."
+        return f"NpzFile {filename!r} with keys: {array_names}"
 
 
 @set_module('numpy')
@@ -309,7 +328,7 @@ def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
     max_header_size : int, optional
         Maximum allowed size of the header.  Large headers may not be safe
         to load securely and thus require explicitly passing a larger value.
-        See :py:meth:`ast.literal_eval()` for details.
+        See :py:func:`ast.literal_eval()` for details.
         This option is ignored when `allow_pickle` is passed.  In that case
         the file is by definition trusted and the limit is unnecessary.
 
@@ -327,6 +346,9 @@ def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
         If ``allow_pickle=True``, but the file cannot be loaded as a pickle.
     ValueError
         The file contains an object array, but ``allow_pickle=False`` given.
+    EOFError
+        When calling ``np.load`` multiple times on the same file handle,
+        if all data has already been read
 
     See Also
     --------
@@ -410,6 +432,8 @@ def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
         _ZIP_SUFFIX = b'PK\x05\x06' # empty zip files start with this
         N = len(format.MAGIC_PREFIX)
         magic = fid.read(N)
+        if not magic:
+            raise EOFError("No data left in file")
         # If the file size is less than N, we need to make sure not
         # to seek past the beginning of the file
         fid.seek(-min(N, len(magic)), 1)  # back-up
@@ -760,13 +784,6 @@ def _ensure_ndmin_ndarray(a, *, ndmin: int):
 _loadtxt_chunksize = 50000
 
 
-def _loadtxt_dispatcher(
-        fname, dtype=None, comments=None, delimiter=None,
-        converters=None, skiprows=None, usecols=None, unpack=None,
-        ndmin=None, encoding=None, max_rows=None, *, like=None):
-    return (like,)
-
-
 def _check_nonneg_int(value, name="argument"):
     try:
         operator.index(value)
@@ -1161,10 +1178,10 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
         while such lines are counted in `skiprows`.
 
         .. versionadded:: 1.16.0
-        
+
         .. versionchanged:: 1.23.0
-            Lines containing no data, including comment lines (e.g., lines 
-            starting with '#' or as specified via `comments`) are not counted 
+            Lines containing no data, including comment lines (e.g., lines
+            starting with '#' or as specified via `comments`) are not counted
             towards `max_rows`.
     quotechar : unicode character or None, optional
         The character used to denote the start and end of a quoted item.
@@ -1331,10 +1348,10 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
 
     if like is not None:
         return _loadtxt_with_like(
-            fname, dtype=dtype, comments=comments, delimiter=delimiter,
+            like, fname, dtype=dtype, comments=comments, delimiter=delimiter,
             converters=converters, skiprows=skiprows, usecols=usecols,
             unpack=unpack, ndmin=ndmin, encoding=encoding,
-            max_rows=max_rows, like=like
+            max_rows=max_rows
         )
 
     if isinstance(delimiter, bytes):
@@ -1361,9 +1378,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
     return arr
 
 
-_loadtxt_with_like = array_function_dispatch(
-    _loadtxt_dispatcher, use_like=True
-)(loadtxt)
+_loadtxt_with_like = array_function_dispatch()(loadtxt)
 
 
 def _savetxt_dispatcher(fname, X, fmt=None, delimiter=None, newline=None,
@@ -1724,17 +1739,6 @@ def fromregex(file, regexp, dtype, encoding=None):
 #####--------------------------------------------------------------------------
 
 
-def _genfromtxt_dispatcher(fname, dtype=None, comments=None, delimiter=None,
-                           skip_header=None, skip_footer=None, converters=None,
-                           missing_values=None, filling_values=None, usecols=None,
-                           names=None, excludelist=None, deletechars=None,
-                           replace_space=None, autostrip=None, case_sensitive=None,
-                           defaultfmt=None, unpack=None, usemask=None, loose=None,
-                           invalid_raise=None, max_rows=None, encoding=None,
-                           *, ndmin=None, like=None):
-    return (like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
@@ -1932,7 +1936,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
 
     if like is not None:
         return _genfromtxt_with_like(
-            fname, dtype=dtype, comments=comments, delimiter=delimiter,
+            like, fname, dtype=dtype, comments=comments, delimiter=delimiter,
             skip_header=skip_header, skip_footer=skip_footer,
             converters=converters, missing_values=missing_values,
             filling_values=filling_values, usecols=usecols, names=names,
@@ -1942,7 +1946,6 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
             unpack=unpack, usemask=usemask, loose=loose,
             invalid_raise=invalid_raise, max_rows=max_rows, encoding=encoding,
             ndmin=ndmin,
-            like=like
         )
 
     _ensure_ndmin_ndarray_check_param(ndmin)
@@ -2335,7 +2338,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
         column_types = [conv.type for conv in converters]
         # Find the columns with strings...
         strcolidx = [i for (i, v) in enumerate(column_types)
-                     if v == np.unicode_]
+                     if v == np.str_]
 
         if byte_converters and strcolidx:
             # convert strings back to bytes for backward compatibility
@@ -2471,9 +2474,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
     return output
 
 
-_genfromtxt_with_like = array_function_dispatch(
-    _genfromtxt_dispatcher, use_like=True
-)(genfromtxt)
+_genfromtxt_with_like = array_function_dispatch()(genfromtxt)
 
 
 def recfromtxt(fname, **kwargs):
diff --git a/contrib/python/numpy/py3/numpy/lib/npyio.pyi b/contrib/python/numpy/py3/numpy/lib/npyio.pyi
index 8007b2dc717..ef0f2a5f177 100644
--- a/contrib/python/numpy/py3/numpy/lib/npyio.pyi
+++ b/contrib/python/numpy/py3/numpy/lib/npyio.pyi
@@ -72,6 +72,7 @@ class NpzFile(Mapping[str, NDArray[Any]]):
     files: list[str]
     allow_pickle: bool
     pickle_kwargs: None | Mapping[str, Any]
+    _MAX_REPR_ARRAY_COUNT: int
     # Represent `f` as a mutable property so we can access the type of `self`
     @property
     def f(self: _T) -> BagObj[_T]: ...
@@ -97,6 +98,8 @@ class NpzFile(Mapping[str, NDArray[Any]]):
     def __iter__(self) -> Iterator[str]: ...
     def __len__(self) -> int: ...
     def __getitem__(self, key: str) -> NDArray[Any]: ...
+    def __contains__(self, key: str) -> bool: ...
+    def __repr__(self) -> str: ...
 
 # NOTE: Returns a `NpzFile` if file is a zip file;
 # returns an `ndarray`/`memmap` otherwise
@@ -236,7 +239,7 @@ def genfromtxt(
     *,
     ndmin: L[0, 1, 2] = ...,
     like: None | _SupportsArrayFunc = ...,
-) -> NDArray[float64]: ...
+) -> NDArray[Any]: ...
 @overload
 def genfromtxt(
     fname: str | os.PathLike[str] | Iterable[str] | Iterable[bytes],
diff --git a/contrib/python/numpy/py3/numpy/lib/polynomial.py b/contrib/python/numpy/py3/numpy/lib/polynomial.py
index 6aa7088616b..3b8db2a9512 100644
--- a/contrib/python/numpy/py3/numpy/lib/polynomial.py
+++ b/contrib/python/numpy/py3/numpy/lib/polynomial.py
@@ -9,12 +9,13 @@ __all__ = ['poly', 'roots', 'polyint', 'polyder', 'polyadd',
 import functools
 import re
 import warnings
+
+from .._utils import set_module
 import numpy.core.numeric as NX
 
 from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
                         ones)
 from numpy.core import overrides
-from numpy.core.overrides import set_module
 from numpy.lib.twodim_base import diag, vander
 from numpy.lib.function_base import trim_zeros
 from numpy.lib.type_check import iscomplex, real, imag, mintypecode
@@ -103,7 +104,7 @@ def poly(seq_of_zeros):
 
     References
     ----------
-    .. [1] M. Sullivan and M. Sullivan, III, "Algebra and Trignometry,
+    .. [1] M. Sullivan and M. Sullivan, III, "Algebra and Trigonometry,
        Enhanced With Graphing Utilities," Prentice-Hall, pg. 318, 1996.
 
     .. [2] G. Strang, "Linear Algebra and Its Applications, 2nd Edition,"
@@ -671,7 +672,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
     # warn on rank reduction, which indicates an ill conditioned matrix
     if rank != order and not full:
         msg = "Polyfit may be poorly conditioned"
-        warnings.warn(msg, RankWarning, stacklevel=4)
+        warnings.warn(msg, RankWarning, stacklevel=2)
 
     if full:
         return c, resids, rank, s, rcond
diff --git a/contrib/python/numpy/py3/numpy/lib/recfunctions.py b/contrib/python/numpy/py3/numpy/lib/recfunctions.py
index 6afcf1b7fc5..83ae413c603 100644
--- a/contrib/python/numpy/py3/numpy/lib/recfunctions.py
+++ b/contrib/python/numpy/py3/numpy/lib/recfunctions.py
@@ -885,6 +885,50 @@ def _get_fields_and_offsets(dt, offset=0):
                     fields.extend([(d, c, o + i*size) for d, c, o in subfields])
     return fields
 
+def _common_stride(offsets, counts, itemsize):
+    """
+    Returns the stride between the fields, or None if the stride is not
+    constant. The values in "counts" designate the lengths of
+    subarrays. Subarrays are treated as many contiguous fields, with
+    always positive stride.
+    """
+    if len(offsets) <= 1:
+        return itemsize
+
+    negative = offsets[1] < offsets[0]  # negative stride
+    if negative:
+        # reverse, so offsets will be ascending
+        it = zip(reversed(offsets), reversed(counts))
+    else:
+        it = zip(offsets, counts)
+
+    prev_offset = None
+    stride = None
+    for offset, count in it:
+        if count != 1:  # subarray: always c-contiguous
+            if negative:
+                return None  # subarrays can never have a negative stride
+            if stride is None:
+                stride = itemsize
+            if stride != itemsize:
+                return None
+            end_offset = offset + (count - 1) * itemsize
+        else:
+            end_offset = offset
+
+        if prev_offset is not None:
+            new_stride = offset - prev_offset
+            if stride is None:
+                stride = new_stride
+            if stride != new_stride:
+                return None
+
+        prev_offset = end_offset
+
+    if negative:
+        return -stride
+    return stride
+
 
 def _structured_to_unstructured_dispatcher(arr, dtype=None, copy=None,
                                            casting=None):
@@ -910,9 +954,15 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
     dtype : dtype, optional
        The dtype of the output unstructured array.
     copy : bool, optional
-        See copy argument to `numpy.ndarray.astype`. If true, always return a
-        copy. If false, and `dtype` requirements are satisfied, a view is
-        returned.
+        If true, always return a copy. If false, a view is returned if
+        possible, such as when the `dtype` and strides of the fields are
+        suitable and the array subtype is one of `np.ndarray`, `np.recarray`
+        or `np.memmap`.
+
+        .. versionchanged:: 1.25.0
+            A view can now be returned if the fields are separated by a
+            uniform stride.
+
     casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
         See casting argument of `numpy.ndarray.astype`. Controls what kind of
         data casting may occur.
@@ -960,7 +1010,7 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
     if dtype is None:
         out_dtype = np.result_type(*[dt.base for dt in dts])
     else:
-        out_dtype = dtype
+        out_dtype = np.dtype(dtype)
 
     # Use a series of views and casts to convert to an unstructured array:
 
@@ -972,6 +1022,39 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
                                  'itemsize': arr.dtype.itemsize})
     arr = arr.view(flattened_fields)
 
+    # we only allow a few types to be unstructured by manipulating the
+    # strides, because we know it won't work with, for example, np.matrix nor
+    # np.ma.MaskedArray.
+    can_view = type(arr) in (np.ndarray, np.recarray, np.memmap)
+    if (not copy) and can_view and all(dt.base == out_dtype for dt in dts):
+        # all elements have the right dtype already; if they have a common
+        # stride, we can just return a view
+        common_stride = _common_stride(offsets, counts, out_dtype.itemsize)
+        if common_stride is not None:
+            wrap = arr.__array_wrap__
+
+            new_shape = arr.shape + (sum(counts), out_dtype.itemsize)
+            new_strides = arr.strides + (abs(common_stride), 1)
+
+            arr = arr[..., np.newaxis].view(np.uint8)  # view as bytes
+            arr = arr[..., min(offsets):]  # remove the leading unused data
+            arr = np.lib.stride_tricks.as_strided(arr,
+                                                  new_shape,
+                                                  new_strides,
+                                                  subok=True)
+
+            # cast and drop the last dimension again
+            arr = arr.view(out_dtype)[..., 0]
+
+            if common_stride < 0:
+                arr = arr[..., ::-1]  # reverse, if the stride was negative
+            if type(arr) is not type(wrap.__self__):
+                # Some types (e.g. recarray) turn into an ndarray along the
+                # way, so we have to wrap it again in order to match the
+                # behavior with copy=True.
+                arr = wrap(arr)
+            return arr
+
     # next cast to a packed format with all fields converted to new dtype
     packed_fields = np.dtype({'names': names,
                               'formats': [(out_dtype, dt.shape) for dt in dts]})
diff --git a/contrib/python/numpy/py3/numpy/lib/shape_base.py b/contrib/python/numpy/py3/numpy/lib/shape_base.py
index ab91423d989..5d8a41bfe4a 100644
--- a/contrib/python/numpy/py3/numpy/lib/shape_base.py
+++ b/contrib/python/numpy/py3/numpy/lib/shape_base.py
@@ -1,9 +1,7 @@
 import functools
 
 import numpy.core.numeric as _nx
-from numpy.core.numeric import (
-    asarray, zeros, outer, concatenate, array, asanyarray
-    )
+from numpy.core.numeric import asarray, zeros, array, asanyarray
 from numpy.core.fromnumeric import reshape, transpose
 from numpy.core.multiarray import normalize_axis_index
 from numpy.core import overrides
@@ -124,19 +122,21 @@ def take_along_axis(arr, indices, axis):
     >>> np.sort(a, axis=1)
     array([[10, 20, 30],
            [40, 50, 60]])
-    >>> ai = np.argsort(a, axis=1); ai
+    >>> ai = np.argsort(a, axis=1)
+    >>> ai
     array([[0, 2, 1],
            [1, 2, 0]])
     >>> np.take_along_axis(a, ai, axis=1)
     array([[10, 20, 30],
            [40, 50, 60]])
 
-    The same works for max and min, if you expand the dimensions:
+    The same works for max and min, if you maintain the trivial dimension
+    with ``keepdims``:
 
-    >>> np.expand_dims(np.max(a, axis=1), axis=1)
+    >>> np.max(a, axis=1, keepdims=True)
     array([[30],
            [60]])
-    >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
+    >>> ai = np.argmax(a, axis=1, keepdims=True)
     >>> ai
     array([[1],
            [0]])
@@ -147,8 +147,8 @@ def take_along_axis(arr, indices, axis):
     If we want to get the max and min at the same time, we can stack the
     indices first
 
-    >>> ai_min = np.expand_dims(np.argmin(a, axis=1), axis=1)
-    >>> ai_max = np.expand_dims(np.argmax(a, axis=1), axis=1)
+    >>> ai_min = np.argmin(a, axis=1, keepdims=True)
+    >>> ai_max = np.argmax(a, axis=1, keepdims=True)
     >>> ai = np.concatenate([ai_min, ai_max], axis=1)
     >>> ai
     array([[0, 1],
@@ -237,7 +237,7 @@ def put_along_axis(arr, indices, values, axis):
 
     We can replace the maximum values with:
 
-    >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
+    >>> ai = np.argmax(a, axis=1, keepdims=True)
     >>> ai
     array([[1],
            [0]])
@@ -372,7 +372,7 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs):
     # invoke the function on the first item
     try:
         ind0 = next(inds)
-    except StopIteration as e:
+    except StopIteration:
         raise ValueError(
             'Cannot apply_along_axis when any iteration dimensions are 0'
         ) from None
@@ -643,10 +643,6 @@ def column_stack(tup):
            [3, 4]])
 
     """
-    if not overrides.ARRAY_FUNCTION_ENABLED:
-        # raise warning if necessary
-        _arrays_for_stack_dispatcher(tup, stacklevel=2)
-
     arrays = []
     for v in tup:
         arr = asanyarray(v)
@@ -713,10 +709,6 @@ def dstack(tup):
            [[3, 4]]])
 
     """
-    if not overrides.ARRAY_FUNCTION_ENABLED:
-        # raise warning if necessary
-        _arrays_for_stack_dispatcher(tup, stacklevel=2)
-
     arrs = atleast_3d(*tup)
     if not isinstance(arrs, list):
         arrs = [arrs]
@@ -1041,6 +1033,7 @@ def dsplit(ary, indices_or_sections):
         raise ValueError('dsplit only works on arrays of 3 or more dimensions')
     return split(ary, indices_or_sections, 2)
 
+
 def get_array_prepare(*args):
     """Find the wrapper for the array with the highest priority.
 
@@ -1053,6 +1046,7 @@ def get_array_prepare(*args):
         return wrappers[-1][-1]
     return None
 
+
 def get_array_wrap(*args):
     """Find the wrapper for the array with the highest priority.
 
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_arraypad.py b/contrib/python/numpy/py3/numpy/lib/tests/test_arraypad.py
index a596815730d..0bebe369340 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_arraypad.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_arraypad.py
@@ -1139,6 +1139,23 @@ class TestWrap:
         a = np.arange(5)
         b = np.pad(a, (0, 12), mode="wrap")
         assert_array_equal(np.r_[a, a, a, a][:-3], b)
+    
+    def test_repeated_wrapping_multiple_origin(self):
+        """
+        Assert that 'wrap' pads only with multiples of the original area if
+        the pad width is larger than the original array.
+        """
+        a = np.arange(4).reshape(2, 2)
+        a = np.pad(a, [(1, 3), (3, 1)], mode='wrap')
+        b = np.array(
+            [[3, 2, 3, 2, 3, 2],
+             [1, 0, 1, 0, 1, 0],
+             [3, 2, 3, 2, 3, 2],
+             [1, 0, 1, 0, 1, 0],
+             [3, 2, 3, 2, 3, 2],
+             [1, 0, 1, 0, 1, 0]]
+        )
+        assert_array_equal(a, b)
 
 
 class TestEdge:
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_format.py b/contrib/python/numpy/py3/numpy/lib/tests/test_format.py
index abe1d44aa0a..766c499c284 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_format.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_format.py
@@ -257,7 +257,8 @@ def test_load_padded_dtype(tmpdir, dt):
 def test_python2_python3_interoperability():
     fname = 'win64python2.npy'
     path = os.path.join(yatest.common.source_path('contrib/python/numpy/py3/numpy/lib/tests'), 'data', fname)
-    data = np.load(path)
+    with pytest.warns(UserWarning, match="Reading.*this warning\\."):
+        data = np.load(path)
     assert_array_equal(data, np.ones(2))
 
 def test_pickle_python2_python3():
@@ -746,8 +747,7 @@ def test_metadata_dtype(dt, fail):
     else:
         arr2 = np.load(buf)
         # BUG: assert_array_equal does not check metadata
-        from numpy.lib.format import _has_metadata
+        from numpy.lib.utils import drop_metadata
         assert_array_equal(arr, arr2)
-        assert _has_metadata(arr.dtype)
-        assert not _has_metadata(arr2.dtype)
-
+        assert drop_metadata(arr.dtype) is not arr.dtype
+        assert drop_metadata(arr2.dtype) is arr2.dtype
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_function_base.py b/contrib/python/numpy/py3/numpy/lib/tests/test_function_base.py
index 8017c2cafad..11e44630e79 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_function_base.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_function_base.py
@@ -8,7 +8,7 @@ import pytest
 import hypothesis
 from hypothesis.extra.numpy import arrays
 import hypothesis.strategies as st
-
+from functools import partial
 
 import numpy as np
 from numpy import ma
@@ -229,8 +229,8 @@ class TestAny:
     def test_nd(self):
         y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
         assert_(np.any(y1))
-        assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0])
-        assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1])
+        assert_array_equal(np.any(y1, axis=0), [1, 1, 0])
+        assert_array_equal(np.any(y1, axis=1), [0, 1, 1])
 
 
 class TestAll:
@@ -247,8 +247,8 @@ class TestAll:
     def test_nd(self):
         y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
         assert_(not np.all(y1))
-        assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1])
-        assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1])
+        assert_array_equal(np.all(y1, axis=0), [0, 0, 1])
+        assert_array_equal(np.all(y1, axis=1), [0, 0, 1])
 
 
 class TestCopy:
@@ -1217,6 +1217,13 @@ class TestGradient:
         dfdx = gradient(f, x)
         assert_array_equal(dfdx, [0.5, 0.5])
 
+    def test_return_type(self):
+        res = np.gradient(([1, 2], [2, 3]))
+        if np._using_numpy2_behavior():
+            assert type(res) is tuple
+        else:
+            assert type(res) is list
+
 
 class TestAngle:
 
@@ -1780,6 +1787,70 @@ class TestVectorize:
         assert_equal(type(r), subclass)
         assert_equal(r, m * v)
 
+    def test_name(self):
+        #See gh-23021
+        @np.vectorize
+        def f2(a, b):
+            return a + b
+
+        assert f2.__name__ == 'f2'
+
+    def test_decorator(self):
+        @vectorize
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        r = addsubtract([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_docstring(self):
+        @vectorize
+        def f(x):
+            """Docstring"""
+            return x
+
+        if sys.flags.optimize < 2:
+            assert f.__doc__ == "Docstring"
+
+    def test_partial(self):
+        def foo(x, y):
+            return x + y
+
+        bar = partial(foo, 3)
+        vbar = np.vectorize(bar)
+        assert vbar(1) == 4
+
+    def test_signature_otypes_decorator(self):
+        @vectorize(signature='(n)->(n)', otypes=['float64'])
+        def f(x):
+            return x
+
+        r = f([1, 2, 3])
+        assert_equal(r.dtype, np.dtype('float64'))
+        assert_array_equal(r, [1, 2, 3])
+        assert f.__name__ == 'f'
+
+    def test_bad_input(self):
+        with assert_raises(TypeError):
+            A = np.vectorize(pyfunc = 3)
+
+    def test_no_keywords(self):
+        with assert_raises(TypeError):
+            @np.vectorize("string")
+            def foo():
+                return "bar"
+
+    def test_positional_regression_9477(self):
+        # This supplies the first keyword argument as a positional,
+        # to ensure that they are still properly forwarded after the
+        # enhancement for #9477
+        f = vectorize((lambda x: x), ['float64'])
+        r = f([2])
+        assert_equal(r.dtype, np.dtype('float64'))
+
 
 class TestLeaks:
     class A:
@@ -2973,6 +3044,14 @@ class TestPercentile:
         o = np.ones((1,))
         np.percentile(d, 5, None, o, False, 'linear')
 
+    def test_complex(self):
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+
     def test_2D(self):
         x = np.array([[1, 1, 1],
                       [1, 1, 1],
@@ -2981,7 +3060,7 @@ class TestPercentile:
                       [1, 1, 1]])
         assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1])
 
-    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("dtype", np.typecodes["Float"])
     def test_linear_nan_1D(self, dtype):
         # METHOD 1 of H&F
         arr = np.asarray([15.0, np.NAN, 35.0, 40.0, 50.0], dtype=dtype)
@@ -2998,9 +3077,6 @@ class TestPercentile:
                            (np.float32, np.float32),
                            (np.float64, np.float64),
                            (np.longdouble, np.longdouble),
-                           (np.complex64, np.complex64),
-                           (np.complex128, np.complex128),
-                           (np.clongdouble, np.clongdouble),
                            (np.dtype("O"), np.float64)]
 
     @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES)
@@ -3040,7 +3116,7 @@ class TestPercentile:
             np.testing.assert_equal(np.asarray(actual).dtype,
                                     np.dtype(expected_dtype))
 
-    TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O"
+    TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["Float"] + "O"
 
     @pytest.mark.parametrize("dtype", TYPE_CODES)
     def test_lower_higher(self, dtype):
@@ -3461,10 +3537,40 @@ class TestPercentile:
         with pytest.raises(ValueError, match="Percentiles must be in"):
             np.percentile([1, 2, 3, 4.0], q)
 
+    @pytest.mark.parametrize("dtype", ["m8[D]", "M8[s]"])
+    @pytest.mark.parametrize("pos", [0, 23, 10])
+    def test_nat_basic(self, dtype, pos):
+        # TODO: Note that times have dubious rounding as of fixing NaTs!
+        # NaT and NaN should behave the same, do basic tests for NaT:
+        a = np.arange(0, 24, dtype=dtype)
+        a[pos] = "NaT"
+        res = np.percentile(a, 30)
+        assert res.dtype == dtype
+        assert np.isnat(res)
+        res = np.percentile(a, [30, 60])
+        assert res.dtype == dtype
+        assert np.isnat(res).all()
+
+        a = np.arange(0, 24*3, dtype=dtype).reshape(-1, 3)
+        a[pos, 1] = "NaT"
+        res = np.percentile(a, 30, axis=0)
+        assert_array_equal(np.isnat(res), [False, True, False])
+
+
+quantile_methods = [
+    'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+    'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear',
+    'median_unbiased', 'normal_unbiased', 'nearest', 'lower', 'higher',
+    'midpoint']
+
 
 class TestQuantile:
     # most of this is already tested by TestPercentile
 
+    def V(self, x, y, alpha):
+        # Identification function used in several tests.
+        return (x >= y) - alpha
+
     def test_max_ulp(self):
         x = [0.0, 0.2, 0.4]
         a = np.quantile(x, 0.45)
@@ -3479,8 +3585,6 @@ class TestQuantile:
         assert_equal(np.quantile(x, 1), 3.5)
         assert_equal(np.quantile(x, 0.5), 1.75)
 
-    @pytest.mark.skip
-    @pytest.mark.xfail(reason="See gh-19154")
     def test_correct_quantile_value(self):
         a = np.array([True])
         tf_quant = np.quantile(True, False)
@@ -3518,6 +3622,15 @@ class TestQuantile:
         x = np.arange(8)
         assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2))
 
+    def test_complex(self):
+        #See gh-22652
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+
     def test_no_p_overwrite(self):
         # this is worth retesting, because quantile does not make a copy
         p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
@@ -3536,11 +3649,7 @@ class TestQuantile:
                           method="nearest")
         assert res.dtype == dtype
 
-    @pytest.mark.parametrize("method",
-             ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
-              'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear',
-              'median_unbiased', 'normal_unbiased',
-              'nearest', 'lower', 'higher', 'midpoint'])
+    @pytest.mark.parametrize("method", quantile_methods)
     def test_quantile_monotonic(self, method):
         # GH 14685
         # test that the return value of quantile is monotonic if p0 is ordered
@@ -3571,6 +3680,94 @@ class TestQuantile:
         assert np.isscalar(actual)
         assert_equal(np.quantile(a, 0.5), np.nan)
 
+    @pytest.mark.parametrize("method", quantile_methods)
+    @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+    def test_quantile_identification_equation(self, method, alpha):
+        # Test that the identification equation holds for the empirical
+        # CDF:
+        #   E[V(x, Y)] = 0  <=>  x is quantile
+        # with Y the random variable for which we have observed values and
+        # V(x, y) the canonical identification function for the quantile (at
+        # level alpha), see
+        # https://doi.org/10.48550/arXiv.0912.0902        
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we cover 3 cases:
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        x = np.quantile(y, alpha, method=method)
+        if method in ("higher",):
+            # These methods do not fulfill the identification equation.
+            assert np.abs(np.mean(self.V(x, y, alpha))) > 0.1 / n
+        elif int(n * alpha) == n * alpha:
+            # We can expect exact results, up to machine precision.
+            assert_allclose(np.mean(self.V(x, y, alpha)), 0, atol=1e-14)
+        else:
+            # V = (x >= y) - alpha cannot sum to zero exactly but within
+            # "sample precision".
+            assert_allclose(np.mean(self.V(x, y, alpha)), 0,
+                atol=1 / n / np.amin([alpha, 1 - alpha]))
+
+    @pytest.mark.parametrize("method", quantile_methods)
+    @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+    def test_quantile_add_and_multiply_constant(self, method, alpha):
+        # Test that
+        #  1. quantile(c + x) = c + quantile(x)
+        #  2. quantile(c * x) = c * quantile(x)
+        #  3. quantile(-x) = -quantile(x, 1 - alpha)
+        #     On empirical quantiles, this equation does not hold exactly.
+        # Koenker (2005) "Quantile Regression" Chapter 2.2.3 calls these
+        # properties equivariance.
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we have cases for
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        q = np.quantile(y, alpha, method=method)
+        c = 13.5
+
+        # 1
+        assert_allclose(np.quantile(c + y, alpha, method=method), c + q)
+        # 2
+        assert_allclose(np.quantile(c * y, alpha, method=method), c * q)
+        # 3
+        q = -np.quantile(-y, 1 - alpha, method=method)
+        if method == "inverted_cdf":
+            if (
+                n * alpha == int(n * alpha)
+                or np.round(n * alpha) == int(n * alpha) + 1
+            ):
+                assert_allclose(q, np.quantile(y, alpha, method="higher"))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        elif method == "closest_observation":
+            if n * alpha == int(n * alpha):
+                assert_allclose(q, np.quantile(y, alpha, method="higher"))
+            elif np.round(n * alpha) == int(n * alpha) + 1:
+                assert_allclose(
+                    q, np.quantile(y, alpha + 1/n, method="higher"))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        elif method == "interpolated_inverted_cdf":
+            assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+        elif method == "nearest":
+            if n * alpha == int(n * alpha):
+                assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method=method))
+        elif method == "lower":
+            assert_allclose(q, np.quantile(y, alpha, method="higher"))
+        elif method == "higher":
+            assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        else:
+            # "averaged_inverted_cdf", "hazen", "weibull", "linear",
+            # "median_unbiased", "normal_unbiased", "midpoint"
+            assert_allclose(q, np.quantile(y, alpha, method=method))
+
 
 class TestLerp:
     @hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False,
@@ -3894,6 +4091,25 @@ class TestMedian:
         assert result is out
         assert_equal(result.shape, shape_out)
 
+    @pytest.mark.parametrize("dtype", ["m8[s]"])
+    @pytest.mark.parametrize("pos", [0, 23, 10])
+    def test_nat_behavior(self, dtype, pos):
+        # TODO: Median does not support Datetime, due to `mean`.
+        # NaT and NaN should behave the same, do basic tests for NaT.
+        a = np.arange(0, 24, dtype=dtype)
+        a[pos] = "NaT"
+        res = np.median(a)
+        assert res.dtype == dtype
+        assert np.isnat(res)
+        res = np.percentile(a, [30, 60])
+        assert res.dtype == dtype
+        assert np.isnat(res).all()
+
+        a = np.arange(0, 24*3, dtype=dtype).reshape(-1, 3)
+        a[pos, 1] = "NaT"
+        res = np.median(a, axis=0)
+        assert_array_equal(np.isnat(res), [False, True, False])
+
 
 class TestAdd_newdoc_ufunc:
 
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_histograms.py b/contrib/python/numpy/py3/numpy/lib/tests/test_histograms.py
index 87e6e1d41c4..38b3d3dcbf3 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_histograms.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_histograms.py
@@ -408,6 +408,13 @@ class TestHistogram:
         hist = np.histogramdd(sample=sample, bins=(xbins, ybins, zbins))
         assert_equal(type(hist), type((1, 2)))
 
+    def test_gh_23110(self):
+        hist, e = np.histogram(np.array([-0.9e-308], dtype='>f8'),
+                               bins=2,
+                               range=(-1e-308, -2e-313))
+        expected_hist = np.array([1, 0])
+        assert_array_equal(hist, expected_hist)
+
 
 class TestHistogramOptimBinNums:
     """
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_io.py b/contrib/python/numpy/py3/numpy/lib/tests/test_io.py
index 3af2e6f42cc..c1032df8e1d 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_io.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_io.py
@@ -232,15 +232,16 @@ class TestSavezLoad(RoundtripTest):
         assert_equal(a, l['file_a'])
         assert_equal(b, l['file_b'])
 
-    def test_named_arrays_with_like(self):
-        a = np.array([[1, 2], [3, 4]], float)
-        b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex)
-        c = BytesIO()
-        np.savez(c, file_a=a, like=b)
-        c.seek(0)
-        l = np.load(c)
-        assert_equal(a, l['file_a'])
-        assert_equal(b, l['like'])
+
+    def test_tuple_getitem_raises(self):
+        # gh-23748
+        a = np.array([1, 2, 3])
+        f = BytesIO()
+        np.savez(f, a=a)
+        f.seek(0)
+        l = np.load(f)
+        with pytest.raises(KeyError, match="(1, 2)"):
+            l[1, 2]
 
     def test_BagObj(self):
         a = np.array([[1, 2], [3, 4]], float)
@@ -331,6 +332,21 @@ class TestSavezLoad(RoundtripTest):
             data.close()
             assert_(fp.closed)
 
+    @pytest.mark.parametrize("count, expected_repr", [
+        (1, "NpzFile {fname!r} with keys: arr_0"),
+        (5, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4"),
+        # _MAX_REPR_ARRAY_COUNT is 5, so files with more than 5 keys are
+        # expected to end in '...'
+        (6, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4..."),
+    ])
+    def test_repr_lists_keys(self, count, expected_repr):
+        a = np.array([[1, 2], [3, 4]], float)
+        with temppath(suffix='.npz') as tmp:
+            np.savez(tmp, *[a]*count)
+            l = np.load(tmp)
+            assert repr(l) == expected_repr.format(fname=tmp)
+            l.close()
+
 
 class TestSaveTxt:
     def test_array(self):
@@ -532,7 +548,7 @@ class TestSaveTxt:
 
     def test_unicode(self):
         utf8 = b'\xcf\x96'.decode('UTF-8')
-        a = np.array([utf8], dtype=np.unicode_)
+        a = np.array([utf8], dtype=np.str_)
         with tempdir() as tmpdir:
             # set encoding as on windows it may not be unicode even on py3
             np.savetxt(os.path.join(tmpdir, 'test.csv'), a, fmt=['%s'],
@@ -540,7 +556,7 @@ class TestSaveTxt:
 
     def test_unicode_roundtrip(self):
         utf8 = b'\xcf\x96'.decode('UTF-8')
-        a = np.array([utf8], dtype=np.unicode_)
+        a = np.array([utf8], dtype=np.str_)
         # our gz wrapper support encoding
         suffixes = ['', '.gz']
         if HAS_BZ2:
@@ -552,12 +568,12 @@ class TestSaveTxt:
                 np.savetxt(os.path.join(tmpdir, 'test.csv' + suffix), a,
                            fmt=['%s'], encoding='UTF-16-LE')
                 b = np.loadtxt(os.path.join(tmpdir, 'test.csv' + suffix),
-                               encoding='UTF-16-LE', dtype=np.unicode_)
+                               encoding='UTF-16-LE', dtype=np.str_)
                 assert_array_equal(a, b)
 
     def test_unicode_bytestream(self):
         utf8 = b'\xcf\x96'.decode('UTF-8')
-        a = np.array([utf8], dtype=np.unicode_)
+        a = np.array([utf8], dtype=np.str_)
         s = BytesIO()
         np.savetxt(s, a, fmt=['%s'], encoding='UTF-8')
         s.seek(0)
@@ -565,7 +581,7 @@ class TestSaveTxt:
 
     def test_unicode_stringstream(self):
         utf8 = b'\xcf\x96'.decode('UTF-8')
-        a = np.array([utf8], dtype=np.unicode_)
+        a = np.array([utf8], dtype=np.str_)
         s = StringIO()
         np.savetxt(s, a, fmt=['%s'], encoding='UTF-8')
         s.seek(0)
@@ -607,8 +623,8 @@ class TestSaveTxt:
         # in our process if needed, see gh-16889
         memoryerror_raised = Value(c_bool)
 
-        # Since Python 3.8, the default start method for multiprocessing has 
-        # been changed from 'fork' to 'spawn' on macOS, causing inconsistency 
+        # Since Python 3.8, the default start method for multiprocessing has
+        # been changed from 'fork' to 'spawn' on macOS, causing inconsistency
         # on memory sharing model, lead to failed test for check_large_zip
         ctx = get_context('fork')
         p = ctx.Process(target=check_large_zip, args=(memoryerror_raised,))
@@ -662,12 +678,12 @@ class LoadTxtBase:
         with temppath() as path:
             with open(path, "wb") as f:
                 f.write(nonascii.encode("UTF-16"))
-            x = self.loadfunc(path, encoding="UTF-16", dtype=np.unicode_)
+            x = self.loadfunc(path, encoding="UTF-16", dtype=np.str_)
             assert_array_equal(x, nonascii)
 
     def test_binary_decode(self):
         utf16 = b'\xff\xfeh\x04 \x00i\x04 \x00j\x04'
-        v = self.loadfunc(BytesIO(utf16), dtype=np.unicode_, encoding='UTF-16')
+        v = self.loadfunc(BytesIO(utf16), dtype=np.str_, encoding='UTF-16')
         assert_array_equal(v, np.array(utf16.decode('UTF-16').split()))
 
     def test_converters_decode(self):
@@ -675,7 +691,7 @@ class LoadTxtBase:
         c = TextIO()
         c.write(b'\xcf\x96')
         c.seek(0)
-        x = self.loadfunc(c, dtype=np.unicode_,
+        x = self.loadfunc(c, dtype=np.str_,
                           converters={0: lambda x: x.decode('UTF-8')})
         a = np.array([b'\xcf\x96'.decode('UTF-8')])
         assert_array_equal(x, a)
@@ -686,7 +702,7 @@ class LoadTxtBase:
         with temppath() as path:
             with io.open(path, 'wt', encoding='UTF-8') as f:
                 f.write(utf8)
-            x = self.loadfunc(path, dtype=np.unicode_,
+            x = self.loadfunc(path, dtype=np.str_,
                               converters={0: lambda x: x + 't'},
                               encoding='UTF-8')
             a = np.array([utf8 + 't'])
@@ -1171,7 +1187,7 @@ class TestLoadTxt(LoadTxtBase):
             with open(path, "wb") as f:
                 f.write(butf8)
             with open(path, "rb") as f:
-                x = np.loadtxt(f, encoding="UTF-8", dtype=np.unicode_)
+                x = np.loadtxt(f, encoding="UTF-8", dtype=np.str_)
             assert_array_equal(x, sutf8)
             # test broken latin1 conversion people now rely on
             with open(path, "rb") as f:
@@ -2229,7 +2245,7 @@ M   33  21.99
             ctl = np.array([
                      ["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"],
                      ["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"]],
-                     dtype=np.unicode_)
+                     dtype=np.str_)
             assert_array_equal(test, ctl)
 
             # test a mixed dtype
@@ -2272,7 +2288,7 @@ M   33  21.99
                      ["norm1", "norm2", "norm3"],
                      ["norm1", latin1, "norm3"],
                      ["test1", "testNonethe" + utf8, "test3"]],
-                     dtype=np.unicode_)
+                     dtype=np.str_)
             assert_array_equal(test, ctl)
 
     def test_recfromtxt(self):
@@ -2747,3 +2763,13 @@ def test_load_refcount():
     with assert_no_gc_cycles():
         x = np.loadtxt(TextIO("0 1 2 3"), dtype=dt)
         assert_equal(x, np.array([((0, 1), (2, 3))], dtype=dt))
+
+def test_load_multiple_arrays_until_eof():
+    f = BytesIO()
+    np.save(f, 1)
+    np.save(f, 2)
+    f.seek(0)
+    assert np.load(f) == 1
+    assert np.load(f) == 2
+    with pytest.raises(EOFError):
+        np.load(f)
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_loadtxt.py b/contrib/python/numpy/py3/numpy/lib/tests/test_loadtxt.py
index f9b5a411c5a..b66092e7010 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_loadtxt.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_loadtxt.py
@@ -244,6 +244,14 @@ def test_converters_negative_indices_with_usecols():
                      usecols=[0, -1], converters={-1: (lambda x: -1)})
     assert_array_equal(res, [[0, -1], [0, -1]])
 
+
+def test_ragged_error():
+    rows = ["1,2,3", "1,2,3", "4,3,2,1"]
+    with pytest.raises(ValueError,
+            match="the number of columns changed from 3 to 4 at row 3"):
+        np.loadtxt(rows, delimiter=",")
+
+
 def test_ragged_usecols():
     # usecols, and negative ones, work even with varying number of columns.
     txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n")
@@ -555,7 +563,8 @@ def test_quote_support_default():
     txt = StringIO('"lat,long", 45, 30\n')
     dtype = np.dtype([('f0', 'U24'), ('f1', np.float64), ('f2', np.float64)])
 
-    with pytest.raises(ValueError, match="the number of columns changed"):
+    with pytest.raises(ValueError,
+            match="the dtype passed requires 3 columns but 4 were"):
         np.loadtxt(txt, dtype=dtype, delimiter=",")
 
     # Enable quoting support with non-None value for quotechar param
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_nanfunctions.py b/contrib/python/numpy/py3/numpy/lib/tests/test_nanfunctions.py
index 7cdcff32dc8..257de381b39 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_nanfunctions.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_nanfunctions.py
@@ -404,14 +404,20 @@ class TestNanFunctions_NumberTypes:
     )
     def test_nanfunc_q(self, mat, dtype, nanfunc, func):
         mat = mat.astype(dtype)
-        tgt = func(mat, q=1)
-        out = nanfunc(mat, q=1)
+        if mat.dtype.kind == "c":
+            assert_raises(TypeError, func, mat, q=1)
+            assert_raises(TypeError, nanfunc, mat, q=1)
 
-        assert_almost_equal(out, tgt)
-        if dtype == "O":
-            assert type(out) is type(tgt)
         else:
-            assert out.dtype == tgt.dtype
+            tgt = func(mat, q=1)
+            out = nanfunc(mat, q=1)
+
+            assert_almost_equal(out, tgt)
+
+            if dtype == "O":
+                assert type(out) is type(tgt)
+            else:
+                assert out.dtype == tgt.dtype
 
     @pytest.mark.parametrize(
         "nanfunc,func",
@@ -1060,6 +1066,14 @@ class TestNanFunctions_Percentile:
         assert_almost_equal(res, resout)
         assert_almost_equal(res, tgt)
 
+    def test_complex(self):
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.nanpercentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.nanpercentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.nanpercentile, arr_c, 0.5)
+
     def test_result_values(self):
         tgt = [np.percentile(d, 28) for d in _rdat]
         res = np.nanpercentile(_ndat, 28, axis=1)
@@ -1070,7 +1084,7 @@ class TestNanFunctions_Percentile:
         assert_almost_equal(res, tgt)
 
     @pytest.mark.parametrize("axis", [None, 0, 1])
-    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("dtype", np.typecodes["Float"])
     @pytest.mark.parametrize("array", [
         np.array(np.nan),
         np.full((3, 3), np.nan),
@@ -1164,6 +1178,14 @@ class TestNanFunctions_Quantile:
         assert_equal(np.nanquantile(x, 1), 3.5)
         assert_equal(np.nanquantile(x, 0.5), 1.75)
 
+    def test_complex(self):
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.nanquantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.nanquantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.nanquantile, arr_c, 0.5)
+
     def test_no_p_overwrite(self):
         # this is worth retesting, because quantile does not make a copy
         p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
@@ -1177,7 +1199,7 @@ class TestNanFunctions_Quantile:
         assert_array_equal(p, p0)
 
     @pytest.mark.parametrize("axis", [None, 0, 1])
-    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("dtype", np.typecodes["Float"])
     @pytest.mark.parametrize("array", [
         np.array(np.nan),
         np.full((3, 3), np.nan),
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_recfunctions.py b/contrib/python/numpy/py3/numpy/lib/tests/test_recfunctions.py
index 0c919a53d84..98860dfdab7 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_recfunctions.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_recfunctions.py
@@ -228,7 +228,7 @@ class TestRecFunctions:
         dt = np.dtype((np.record, dt))
         assert_(repack_fields(dt).type is np.record)
 
-    def test_structured_to_unstructured(self):
+    def test_structured_to_unstructured(self, tmp_path):
         a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
         out = structured_to_unstructured(a)
         assert_equal(out, np.zeros((4,5), dtype='f8'))
@@ -263,8 +263,13 @@ class TestRecFunctions:
                      dtype=[('x', 'i4'), ('y', 'i4'), ('z', 'i4')])
         dd = structured_to_unstructured(d)
         ddd = unstructured_to_structured(dd, d.dtype)
-        assert_(dd.base is d)
-        assert_(ddd.base is d)
+        assert_(np.shares_memory(dd, d))
+        assert_(np.shares_memory(ddd, d))
+
+        # check that reversing the order of attributes works
+        dd_attrib_rev = structured_to_unstructured(d[['z', 'x']])
+        assert_equal(dd_attrib_rev, [[5, 1], [7, 4], [11, 7], [12, 10]])
+        assert_(np.shares_memory(dd_attrib_rev, d))
 
         # including uniform fields with subarrays unpacked
         d = np.array([(1, [2,  3], [[ 4,  5], [ 6,  7]]),
@@ -273,8 +278,30 @@ class TestRecFunctions:
                             ('x2', ('i4', (2, 2)))])
         dd = structured_to_unstructured(d)
         ddd = unstructured_to_structured(dd, d.dtype)
-        assert_(dd.base is d)
-        assert_(ddd.base is d)
+        assert_(np.shares_memory(dd, d))
+        assert_(np.shares_memory(ddd, d))
+
+        # check that reversing with sub-arrays works as expected
+        d_rev = d[::-1]
+        dd_rev = structured_to_unstructured(d_rev)
+        assert_equal(dd_rev, [[8, 9, 10, 11, 12, 13, 14],
+                              [1, 2, 3, 4, 5, 6, 7]])
+
+        # check that sub-arrays keep the order of their values
+        d_attrib_rev = d[['x2', 'x1', 'x0']]
+        dd_attrib_rev = structured_to_unstructured(d_attrib_rev)
+        assert_equal(dd_attrib_rev, [[4, 5, 6, 7, 2, 3, 1],
+                                     [11, 12, 13, 14, 9, 10, 8]])
+
+        # with ignored field at the end
+        d = np.array([(1, [2,  3], [[4, 5], [6, 7]], 32),
+                      (8, [9, 10], [[11, 12], [13, 14]], 64)],
+                     dtype=[('x0', 'i4'), ('x1', ('i4', 2)),
+                            ('x2', ('i4', (2, 2))), ('ignored', 'u1')])
+        dd = structured_to_unstructured(d[['x0', 'x1', 'x2']])
+        assert_(np.shares_memory(dd, d))
+        assert_equal(dd, [[1, 2, 3, 4, 5, 6, 7],
+                          [8, 9, 10, 11, 12, 13, 14]])
 
         # test that nested fields with identical names don't break anything
         point = np.dtype([('x', int), ('y', int)])
@@ -318,6 +345,35 @@ class TestRecFunctions:
         assert_raises(NotImplementedError, unstructured_to_structured,
                                            np.zeros((3,0), dtype=np.int32))
 
+        # test supported ndarray subclasses
+        d_plain = np.array([(1, 2), (3, 4)], dtype=[('a', 'i4'), ('b', 'i4')])
+        dd_expected = structured_to_unstructured(d_plain, copy=True)
+
+        # recarray
+        d = d_plain.view(np.recarray)
+
+        dd = structured_to_unstructured(d, copy=False)
+        ddd = structured_to_unstructured(d, copy=True)
+        assert_(np.shares_memory(d, dd))
+        assert_(type(dd) is np.recarray)
+        assert_(type(ddd) is np.recarray)
+        assert_equal(dd, dd_expected)
+        assert_equal(ddd, dd_expected)
+
+        # memmap
+        d = np.memmap(tmp_path / 'memmap',
+                      mode='w+',
+                      dtype=d_plain.dtype,
+                      shape=d_plain.shape)
+        d[:] = d_plain
+        dd = structured_to_unstructured(d, copy=False)
+        ddd = structured_to_unstructured(d, copy=True)
+        assert_(np.shares_memory(d, dd))
+        assert_(type(dd) is np.memmap)
+        assert_(type(ddd) is np.memmap)
+        assert_equal(dd, dd_expected)
+        assert_equal(ddd, dd_expected)
+
     def test_unstructured_to_structured(self):
         # test if dtype is the args of np.dtype
         a = np.zeros((20, 2))
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_shape_base.py b/contrib/python/numpy/py3/numpy/lib/tests/test_shape_base.py
index 76058cf20ff..eb6628904bd 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_shape_base.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_shape_base.py
@@ -492,7 +492,7 @@ class TestColumnStack:
         assert_equal(actual, expected)
 
     def test_generator(self):
-        with assert_warns(FutureWarning):
+        with pytest.raises(TypeError, match="arrays to stack must be"):
             column_stack((np.arange(3) for _ in range(2)))
 
 
@@ -529,7 +529,7 @@ class TestDstack:
         assert_array_equal(res, desired)
 
     def test_generator(self):
-        with assert_warns(FutureWarning):
+        with pytest.raises(TypeError, match="arrays to stack must be"):
             dstack((np.arange(3) for _ in range(2)))
 
 
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_twodim_base.py b/contrib/python/numpy/py3/numpy/lib/tests/test_twodim_base.py
index db02177fa4a..eb008c6002c 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_twodim_base.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_twodim_base.py
@@ -4,20 +4,14 @@
 from numpy.testing import (
     assert_equal, assert_array_equal, assert_array_max_ulp,
     assert_array_almost_equal, assert_raises, assert_
-    )
-
+)
 from numpy import (
     arange, add, fliplr, flipud, zeros, ones, eye, array, diag, histogram2d,
     tri, mask_indices, triu_indices, triu_indices_from, tril_indices,
     tril_indices_from, vander,
-    )
-
+)
 import numpy as np
 
-
-from __tests__.core.tests.test_overrides import requires_array_function
-
-
 import pytest
 
 
@@ -283,7 +277,6 @@ class TestHistogram2d:
         assert_array_equal(H, answer)
         assert_array_equal(xe, array([0., 0.25, 0.5, 0.75, 1]))
 
-    @requires_array_function
     def test_dispatch(self):
         class ShouldDispatch:
             def __array_function__(self, function, types, args, kwargs):
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_type_check.py b/contrib/python/numpy/py3/numpy/lib/tests/test_type_check.py
index 3f4ca630960..ea032613911 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_type_check.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_type_check.py
@@ -155,7 +155,7 @@ class TestIscomplex:
     def test_fail(self):
         z = np.array([-1, 0, 1])
         res = iscomplex(z)
-        assert_(not np.sometrue(res, axis=0))
+        assert_(not np.any(res, axis=0))
 
     def test_pass(self):
         z = np.array([-1j, 1, 0])
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_ufunclike.py b/contrib/python/numpy/py3/numpy/lib/tests/test_ufunclike.py
index c280b696921..fac4f41d091 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_ufunclike.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_ufunclike.py
@@ -80,12 +80,6 @@ class TestUfunclike:
         assert_(isinstance(f0d, MyArray))
         assert_equal(f0d.metadata, 'bar')
 
-    def test_deprecated(self):
-        # NumPy 1.13.0, 2017-04-26
-        assert_warns(DeprecationWarning, ufl.fix, [1, 2], y=nx.empty(2))
-        assert_warns(DeprecationWarning, ufl.isposinf, [1, 2], y=nx.empty(2))
-        assert_warns(DeprecationWarning, ufl.isneginf, [1, 2], y=nx.empty(2))
-
     def test_scalar(self):
         x = np.inf
         actual = np.isposinf(x)
diff --git a/contrib/python/numpy/py3/numpy/lib/tests/test_utils.py b/contrib/python/numpy/py3/numpy/lib/tests/test_utils.py
index 6ad4bfe6d84..45416b05977 100644
--- a/contrib/python/numpy/py3/numpy/lib/tests/test_utils.py
+++ b/contrib/python/numpy/py3/numpy/lib/tests/test_utils.py
@@ -2,6 +2,7 @@ import inspect
 import sys
 import pytest
 
+import numpy as np
 from numpy.core import arange
 from numpy.testing import assert_, assert_equal, assert_raises_regex
 from numpy.lib import deprecate, deprecate_with_doc
@@ -176,3 +177,52 @@ def test_info_method_heading():
 
     assert _has_method_heading(WithPublicMethods)
     assert not _has_method_heading(NoPublicMethods)
+
+
+def test_drop_metadata():
+    def _compare_dtypes(dt1, dt2):
+        return np.can_cast(dt1, dt2, casting='no')
+
+    # structured dtype
+    dt = np.dtype([('l1', [('l2', np.dtype('S8', metadata={'msg': 'toto'}))])],
+                  metadata={'msg': 'titi'})
+    dt_m = utils.drop_metadata(dt)
+    assert _compare_dtypes(dt, dt_m) is True
+    assert dt_m.metadata is None
+    assert dt_m['l1'].metadata is None
+    assert dt_m['l1']['l2'].metadata is None
+    
+    # alignement
+    dt = np.dtype([('x', '<f8'), ('y', '<i4')],
+                  align=True,
+                  metadata={'msg': 'toto'})
+    dt_m = utils.drop_metadata(dt)
+    assert _compare_dtypes(dt, dt_m) is True
+    assert dt_m.metadata is None
+
+    # subdtype
+    dt = np.dtype('8f',
+                  metadata={'msg': 'toto'})
+    dt_m = utils.drop_metadata(dt)
+    assert _compare_dtypes(dt, dt_m) is True
+    assert dt_m.metadata is None
+
+    # scalar
+    dt = np.dtype('uint32',
+                  metadata={'msg': 'toto'})
+    dt_m = utils.drop_metadata(dt)
+    assert _compare_dtypes(dt, dt_m) is True
+    assert dt_m.metadata is None
+
+
+@pytest.mark.parametrize("dtype",
+        [np.dtype("i,i,i,i")[["f1", "f3"]],
+        np.dtype("f8"),
+        np.dtype("10i")])
+def test_drop_metadata_identity_and_copy(dtype):
+    # If there is no metadata, the identity is preserved:
+    assert utils.drop_metadata(dtype) is dtype
+
+    # If there is any, it is dropped (subforms are checked above)
+    dtype = np.dtype(dtype, metadata={1: 2})
+    assert utils.drop_metadata(dtype).metadata is None
diff --git a/contrib/python/numpy/py3/numpy/lib/twodim_base.py b/contrib/python/numpy/py3/numpy/lib/twodim_base.py
index a5f421e5c73..6dcb6565193 100644
--- a/contrib/python/numpy/py3/numpy/lib/twodim_base.py
+++ b/contrib/python/numpy/py3/numpy/lib/twodim_base.py
@@ -155,10 +155,6 @@ def flipud(m):
     return m[::-1, ...]
 
 
-def _eye_dispatcher(N, M=None, k=None, dtype=None, order=None, *, like=None):
-    return (like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def eye(N, M=None, k=0, dtype=float, order='C', *, like=None):
@@ -209,7 +205,7 @@ def eye(N, M=None, k=0, dtype=float, order='C', *, like=None):
 
     """
     if like is not None:
-        return _eye_with_like(N, M=M, k=k, dtype=dtype, order=order, like=like)
+        return _eye_with_like(like, N, M=M, k=k, dtype=dtype, order=order)
     if M is None:
         M = N
     m = zeros((N, M), dtype=dtype, order=order)
@@ -228,9 +224,7 @@ def eye(N, M=None, k=0, dtype=float, order='C', *, like=None):
     return m
 
 
-_eye_with_like = array_function_dispatch(
-    _eye_dispatcher, use_like=True
-)(eye)
+_eye_with_like = array_function_dispatch()(eye)
 
 
 def _diag_dispatcher(v, k=None):
@@ -369,10 +363,6 @@ def diagflat(v, k=0):
     return wrap(res)
 
 
-def _tri_dispatcher(N, M=None, k=None, dtype=None, *, like=None):
-    return (like,)
-
-
 @set_array_function_like_doc
 @set_module('numpy')
 def tri(N, M=None, k=0, dtype=float, *, like=None):
@@ -416,7 +406,7 @@ def tri(N, M=None, k=0, dtype=float, *, like=None):
 
     """
     if like is not None:
-        return _tri_with_like(N, M=M, k=k, dtype=dtype, like=like)
+        return _tri_with_like(like, N, M=M, k=k, dtype=dtype)
 
     if M is None:
         M = N
@@ -430,9 +420,7 @@ def tri(N, M=None, k=0, dtype=float, *, like=None):
     return m
 
 
-_tri_with_like = array_function_dispatch(
-    _tri_dispatcher, use_like=True
-)(tri)
+_tri_with_like = array_function_dispatch()(tri)
 
 
 def _trilu_dispatcher(m, k=None):
@@ -995,9 +983,42 @@ def tril_indices_from(arr, k=0):
     k : int, optional
         Diagonal offset (see `tril` for details).
 
+    Examples
+    --------
+
+    Create a 4 by 4 array.
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Pass the array to get the indices of the lower triangular elements.
+
+    >>> trili = np.tril_indices_from(a)
+    >>> trili
+    (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))
+
+    >>> a[trili]
+    array([ 0,  4,  5,  8,  9, 10, 12, 13, 14, 15])
+
+    This is syntactic sugar for tril_indices().
+
+    >>> np.tril_indices(a.shape[0])
+    (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))
+
+    Use the `k` parameter to return the indices for the lower triangular array
+    up to the k-th diagonal.
+
+    >>> trili1 = np.tril_indices_from(a, k=1)
+    >>> a[trili1]
+    array([ 0,  1,  4,  5,  6,  8,  9, 10, 11, 12, 13, 14, 15])
+
     See Also
     --------
-    tril_indices, tril
+    tril_indices, tril, triu_indices_from
 
     Notes
     -----
@@ -1114,9 +1135,43 @@ def triu_indices_from(arr, k=0):
     triu_indices_from : tuple, shape(2) of ndarray, shape(N)
         Indices for the upper-triangle of `arr`.
 
+    Examples
+    --------
+
+    Create a 4 by 4 array.
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Pass the array to get the indices of the upper triangular elements.
+
+    >>> triui = np.triu_indices_from(a)
+    >>> triui
+    (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
+
+    >>> a[triui]
+    array([ 0,  1,  2,  3,  5,  6,  7, 10, 11, 15])
+
+    This is syntactic sugar for triu_indices().
+
+    >>> np.triu_indices(a.shape[0])
+    (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
+
+    Use the `k` parameter to return the indices for the upper triangular array
+    from the k-th diagonal.
+
+    >>> triuim1 = np.triu_indices_from(a, k=1)
+    >>> a[triuim1]
+    array([ 1,  2,  3,  6,  7, 11])
+
+
     See Also
     --------
-    triu_indices, triu
+    triu_indices, triu, tril_indices_from
 
     Notes
     -----
diff --git a/contrib/python/numpy/py3/numpy/lib/type_check.py b/contrib/python/numpy/py3/numpy/lib/type_check.py
index 94d525f51da..3f84b80e586 100644
--- a/contrib/python/numpy/py3/numpy/lib/type_check.py
+++ b/contrib/python/numpy/py3/numpy/lib/type_check.py
@@ -2,17 +2,16 @@
 
 """
 import functools
-import warnings
 
 __all__ = ['iscomplexobj', 'isrealobj', 'imag', 'iscomplex',
            'isreal', 'nan_to_num', 'real', 'real_if_close',
            'typename', 'asfarray', 'mintypecode',
            'common_type']
 
+from .._utils import set_module
 import numpy.core.numeric as _nx
 from numpy.core.numeric import asarray, asanyarray, isnan, zeros
-from numpy.core.overrides import set_module
-from numpy.core import overrides
+from numpy.core import overrides, getlimits
 from .ufunclike import isneginf, isposinf
 
 
@@ -541,7 +540,8 @@ def real_if_close(a, tol=100):
         Input array.
     tol : float
         Tolerance in machine epsilons for the complex part of the elements
-        in the array.
+        in the array. If the tolerance is <=1, then the absolute tolerance
+        is used.
 
     Returns
     -------
@@ -572,11 +572,11 @@ def real_if_close(a, tol=100):
 
     """
     a = asanyarray(a)
-    if not issubclass(a.dtype.type, _nx.complexfloating):
+    type_ = a.dtype.type
+    if not issubclass(type_, _nx.complexfloating):
         return a
     if tol > 1:
-        from numpy.core import getlimits
-        f = getlimits.finfo(a.dtype.type)
+        f = getlimits.finfo(type_)
         tol = f.eps * tol
     if _nx.all(_nx.absolute(a.imag) < tol):
         a = a.real
diff --git a/contrib/python/numpy/py3/numpy/lib/ufunclike.py b/contrib/python/numpy/py3/numpy/lib/ufunclike.py
index a93c4773bc4..05fe60c5b10 100644
--- a/contrib/python/numpy/py3/numpy/lib/ufunclike.py
+++ b/contrib/python/numpy/py3/numpy/lib/ufunclike.py
@@ -6,72 +6,16 @@ storing results in an output array.
 __all__ = ['fix', 'isneginf', 'isposinf']
 
 import numpy.core.numeric as nx
-from numpy.core.overrides import (
-    array_function_dispatch, ARRAY_FUNCTION_ENABLED,
-)
+from numpy.core.overrides import array_function_dispatch
 import warnings
 import functools
 
 
-def _deprecate_out_named_y(f):
-    """
-    Allow the out argument to be passed as the name `y` (deprecated)
-
-    In future, this decorator should be removed.
-    """
-    @functools.wraps(f)
-    def func(x, out=None, **kwargs):
-        if 'y' in kwargs:
-            if 'out' in kwargs:
-                raise TypeError(
-                    "{} got multiple values for argument 'out'/'y'"
-                    .format(f.__name__)
-                )
-            out = kwargs.pop('y')
-            # NumPy 1.13.0, 2017-04-26
-            warnings.warn(
-                "The name of the out argument to {} has changed from `y` to "
-                "`out`, to match other ufuncs.".format(f.__name__),
-                DeprecationWarning, stacklevel=3)
-        return f(x, out=out, **kwargs)
-
-    return func
-
-
-def _fix_out_named_y(f):
-    """
-    Allow the out argument to be passed as the name `y` (deprecated)
-
-    This decorator should only be used if _deprecate_out_named_y is used on
-    a corresponding dispatcher function.
-    """
-    @functools.wraps(f)
-    def func(x, out=None, **kwargs):
-        if 'y' in kwargs:
-            # we already did error checking in _deprecate_out_named_y
-            out = kwargs.pop('y')
-        return f(x, out=out, **kwargs)
-
-    return func
-
-
-def _fix_and_maybe_deprecate_out_named_y(f):
-    """
-    Use the appropriate decorator, depending upon if dispatching is being used.
-    """
-    if ARRAY_FUNCTION_ENABLED:
-        return _fix_out_named_y(f)
-    else:
-        return _deprecate_out_named_y(f)
-
-
-@_deprecate_out_named_y
 def _dispatcher(x, out=None):
     return (x, out)
 
 
 @array_function_dispatch(_dispatcher, verify=False, module='numpy')
-@_fix_and_maybe_deprecate_out_named_y
 def fix(x, out=None):
     """
     Round to nearest integer towards zero.
@@ -125,7 +69,6 @@ def fix(x, out=None):
 
 
 @array_function_dispatch(_dispatcher, verify=False, module='numpy')
-@_fix_and_maybe_deprecate_out_named_y
 def isposinf(x, out=None):
     """
     Test element-wise for positive infinity, return result as bool array.
@@ -197,7 +140,6 @@ def isposinf(x, out=None):
 
 
 @array_function_dispatch(_dispatcher, verify=False, module='numpy')
-@_fix_and_maybe_deprecate_out_named_y
 def isneginf(x, out=None):
     """
     Test element-wise for negative infinity, return result as bool array.
diff --git a/contrib/python/numpy/py3/numpy/lib/utils.py b/contrib/python/numpy/py3/numpy/lib/utils.py
index afde8cc600a..6174c8d0876 100644
--- a/contrib/python/numpy/py3/numpy/lib/utils.py
+++ b/contrib/python/numpy/py3/numpy/lib/utils.py
@@ -5,9 +5,10 @@ import types
 import re
 import warnings
 import functools
+import platform
 
+from .._utils import set_module
 from numpy.core.numerictypes import issubclass_, issubsctype, issubdtype
-from numpy.core.overrides import set_module
 from numpy.core import ndarray, ufunc, asarray
 import numpy as np
 
@@ -24,6 +25,8 @@ def show_runtime():
     including available intrinsic support and BLAS/LAPACK library
     in use
 
+    .. versionadded:: 1.24.0
+
     See Also
     --------
     show_config : Show libraries in the system on which NumPy was built.
@@ -31,45 +34,20 @@ def show_runtime():
     Notes
     -----
     1. Information is derived with the help of `threadpoolctl <https://pypi.org/project/threadpoolctl/>`_
-       library.
+       library if available.
     2. SIMD related information is derived from ``__cpu_features__``,
        ``__cpu_baseline__`` and ``__cpu_dispatch__``
 
-    Examples
-    --------
-    >>> import numpy as np
-    >>> np.show_runtime()
-    [{'simd_extensions': {'baseline': ['SSE', 'SSE2', 'SSE3'],
-                          'found': ['SSSE3',
-                                    'SSE41',
-                                    'POPCNT',
-                                    'SSE42',
-                                    'AVX',
-                                    'F16C',
-                                    'FMA3',
-                                    'AVX2'],
-                          'not_found': ['AVX512F',
-                                        'AVX512CD',
-                                        'AVX512_KNL',
-                                        'AVX512_KNM',
-                                        'AVX512_SKX',
-                                        'AVX512_CLX',
-                                        'AVX512_CNL',
-                                        'AVX512_ICL']}},
-     {'architecture': 'Zen',
-      'filepath': '/usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so',
-      'internal_api': 'openblas',
-      'num_threads': 12,
-      'prefix': 'libopenblas',
-      'threading_layer': 'pthreads',
-      'user_api': 'blas',
-      'version': '0.3.20'}]
     """
     from numpy.core._multiarray_umath import (
         __cpu_features__, __cpu_baseline__, __cpu_dispatch__
     )
     from pprint import pprint
-    config_found = []
+    config_found = [{
+        "numpy_version": np.__version__,
+        "python": sys.version,
+        "uname": platform.uname(),
+        }]
     features_found, features_not_found = [], []
     for feature in __cpu_dispatch__:
         if __cpu_features__[feature]:
@@ -550,15 +528,16 @@ def _info(obj, output=None):
 @set_module('numpy')
 def info(object=None, maxwidth=76, output=None, toplevel='numpy'):
     """
-    Get help information for a function, class, or module.
+    Get help information for an array, function, class, or module.
 
     Parameters
     ----------
     object : object or str, optional
-        Input object or name to get information about. If `object` is a
-        numpy object, its docstring is given. If it is a string, available
-        modules are searched for matching objects.  If None, information
-        about `info` itself is returned.
+        Input object or name to get information about. If `object` is
+        an `ndarray` instance, information about the array is printed.
+        If `object` is a numpy object, its docstring is given. If it is
+        a string, available modules are searched for matching objects.
+        If None, information about `info` itself is returned.
     maxwidth : int, optional
         Printing width.
     output : file like object, optional
@@ -597,6 +576,22 @@ def info(object=None, maxwidth=76, output=None, toplevel='numpy'):
          *** Repeat reference found in numpy.fft.fftpack ***
          *** Total of 3 references found. ***
 
+    When the argument is an array, information about the array is printed.
+
+    >>> a = np.array([[1 + 2j, 3, -4], [-5j, 6, 0]], dtype=np.complex64)
+    >>> np.info(a)
+    class:  ndarray
+    shape:  (2, 3)
+    strides:  (24, 8)
+    itemsize:  8
+    aligned:  True
+    contiguous:  True
+    fortran:  False
+    data pointer: 0x562b6e0d2860  # may vary
+    byteorder:  little
+    byteswap:  False
+    type: complex64
+
     """
     global _namedict, _dictlist
     # Local import to speed up numpy's import time.
@@ -1106,17 +1101,23 @@ def _median_nancheck(data, result, axis):
     """
     if data.size == 0:
         return result
-    n = np.isnan(data.take(-1, axis=axis))
-    # masked NaN values are ok
+    potential_nans = data.take(-1, axis=axis)
+    n = np.isnan(potential_nans)
+    # masked NaN values are ok, although for masked the copyto may fail for
+    # unmasked ones (this was always broken) when the result is a scalar.
     if np.ma.isMaskedArray(n):
         n = n.filled(False)
-    if np.count_nonzero(n.ravel()) > 0:
-        # Without given output, it is possible that the current result is a
-        # numpy scalar, which is not writeable.  If so, just return nan.
-        if isinstance(result, np.generic):
-            return data.dtype.type(np.nan)
 
-        result[n] = np.nan
+    if not n.any():
+        return result
+
+    # Without given output, it is possible that the current result is a
+    # numpy scalar, which is not writeable.  If so, just return nan.
+    if isinstance(result, np.generic):
+        return potential_nans
+
+    # Otherwise copy NaNs (if there are any)
+    np.copyto(result, potential_nans, where=n)
     return result
 
 def _opt_info():
@@ -1145,4 +1146,66 @@ def _opt_info():
             enabled_features += f" {feature}?"
 
     return enabled_features
-#-----------------------------------------------------------------------------
+
+
+def drop_metadata(dtype, /):
+    """
+    Returns the dtype unchanged if it contained no metadata or a copy of the
+    dtype if it (or any of its structure dtypes) contained metadata.
+
+    This utility is used by `np.save` and `np.savez` to drop metadata before
+    saving.
+
+    .. note::
+
+        Due to its limitation this function may move to a more appropriate
+        home or change in the future and is considered semi-public API only.
+
+    .. warning::
+
+        This function does not preserve more strange things like record dtypes
+        and user dtypes may simply return the wrong thing.  If you need to be
+        sure about the latter, check the result with:
+        ``np.can_cast(new_dtype, dtype, casting="no")``.
+
+    """
+    if dtype.fields is not None:
+        found_metadata = dtype.metadata is not None
+
+        names = []
+        formats = []
+        offsets = []
+        titles = []
+        for name, field in dtype.fields.items():
+            field_dt = drop_metadata(field[0])
+            if field_dt is not field[0]:
+                found_metadata = True
+
+            names.append(name)
+            formats.append(field_dt)
+            offsets.append(field[1])
+            titles.append(None if len(field) < 3 else field[2])
+
+        if not found_metadata:
+            return dtype
+
+        structure = dict(
+            names=names, formats=formats, offsets=offsets, titles=titles,
+            itemsize=dtype.itemsize)
+
+        # NOTE: Could pass (dtype.type, structure) to preserve record dtypes...
+        return np.dtype(structure, align=dtype.isalignedstruct)
+    elif dtype.subdtype is not None:
+        # subarray dtype
+        subdtype, shape = dtype.subdtype
+        new_subdtype = drop_metadata(subdtype)
+        if dtype.metadata is None and new_subdtype is subdtype:
+            return dtype
+
+        return np.dtype((new_subdtype, shape))
+    else:
+        # Normal unstructured dtype
+        if dtype.metadata is None:
+            return dtype
+        # Note that `dt.str` doesn't round-trip e.g. for user-dtypes.
+        return np.dtype(dtype.str)
diff --git a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/clapack_scrub.py b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/clapack_scrub.py
index fffd7091048..d4da9070d26 100644
--- a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/clapack_scrub.py
+++ b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/clapack_scrub.py
@@ -297,7 +297,7 @@ def scrubSource(source, nsteps=None, verbose=False):
 if __name__ == '__main__':
     filename = sys.argv[1]
     outfilename = os.path.join(sys.argv[2], os.path.basename(filename))
-    with open(filename, 'r') as fo:
+    with open(filename) as fo:
         source = fo.read()
 
     if len(sys.argv) > 3:
diff --git a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.c b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.c
index 869fce5d3d9..ddaa8d17278 100644
--- a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.c
+++ b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.c
@@ -7,10 +7,14 @@
   it is available, and shipping a static library isn't portable.
 */
 
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
 #include <math.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
+
 #include "f2c.h"
 
 
diff --git a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.h b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.h
index b44aaac440f..0db1b9ecb54 100644
--- a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.h
+++ b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/f2c.h
@@ -7,6 +7,9 @@
 #ifndef F2C_INCLUDE
 #define F2C_INCLUDE
 
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
 #include <math.h>
 #include "numpy/npy_common.h"
 #include "npy_cblas.h"
diff --git a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/make_lite.py b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/make_lite.py
index ca8d4c62cc5..20b212792eb 100755
--- a/contrib/python/numpy/py3/numpy/linalg/lapack_lite/make_lite.py
+++ b/contrib/python/numpy/py3/numpy/linalg/lapack_lite/make_lite.py
@@ -253,7 +253,7 @@ def dumpRoutineNames(library, output_dir):
 def concatenateRoutines(routines, output_file):
     with open(output_file, 'w') as output_fo:
         for r in routines:
-            with open(r.filename, 'r') as fo:
+            with open(r.filename) as fo:
                 source = fo.read()
             output_fo.write(source)
 
@@ -296,7 +296,7 @@ def create_name_header(output_dir):
         if not fn.endswith('.f'):
             continue
 
-        with open(fn, 'r') as f:
+        with open(fn) as f:
             for line in f:
                 m = routine_re.match(line)
                 if m:
@@ -304,7 +304,7 @@ def create_name_header(output_dir):
 
     # f2c symbols
     f2c_symbols = set()
-    with open('f2c.h', 'r') as f:
+    with open('f2c.h') as f:
         for line in f:
             m = extern_re.match(line)
             if m:
diff --git a/contrib/python/numpy/py3/numpy/linalg/linalg.py b/contrib/python/numpy/py3/numpy/linalg/linalg.py
index 697193321be..b838b939702 100644
--- a/contrib/python/numpy/py3/numpy/linalg/linalg.py
+++ b/contrib/python/numpy/py3/numpy/linalg/linalg.py
@@ -17,22 +17,45 @@ __all__ = ['matrix_power', 'solve', 'tensorsolve', 'tensorinv', 'inv',
 import functools
 import operator
 import warnings
+from typing import NamedTuple, Any
 
+from .._utils import set_module
 from numpy.core import (
     array, asarray, zeros, empty, empty_like, intc, single, double,
     csingle, cdouble, inexact, complexfloating, newaxis, all, Inf, dot,
     add, multiply, sqrt, sum, isfinite,
-    finfo, errstate, geterrobj, moveaxis, amin, amax, product, abs,
+    finfo, errstate, geterrobj, moveaxis, amin, amax, prod, abs,
     atleast_2d, intp, asanyarray, object_, matmul,
     swapaxes, divide, count_nonzero, isnan, sign, argsort, sort,
     reciprocal
 )
 from numpy.core.multiarray import normalize_axis_index
-from numpy.core.overrides import set_module
 from numpy.core import overrides
 from numpy.lib.twodim_base import triu, eye
 from numpy.linalg import _umath_linalg
 
+from numpy._typing import NDArray
+
+class EigResult(NamedTuple):
+    eigenvalues: NDArray[Any]
+    eigenvectors: NDArray[Any]
+
+class EighResult(NamedTuple):
+    eigenvalues: NDArray[Any]
+    eigenvectors: NDArray[Any]
+
+class QRResult(NamedTuple):
+    Q: NDArray[Any]
+    R: NDArray[Any]
+
+class SlogdetResult(NamedTuple):
+    sign: NDArray[Any]
+    logabsdet: NDArray[Any]
+
+class SVDResult(NamedTuple):
+    U: NDArray[Any]
+    S: NDArray[Any]
+    Vh: NDArray[Any]
 
 array_function_dispatch = functools.partial(
     overrides.array_function_dispatch, module='numpy.linalg')
@@ -42,11 +65,11 @@ fortran_int = intc
 
 
 @set_module('numpy.linalg')
-class LinAlgError(Exception):
+class LinAlgError(ValueError):
     """
     Generic Python-exception-derived object raised by linalg functions.
 
-    General purpose exception class, derived from Python's exception.Exception
+    General purpose exception class, derived from Python's ValueError
     class, programmatically raised in linalg functions when a Linear
     Algebra-related condition would prevent further correct execution of the
     function.
@@ -138,24 +161,24 @@ def _commonType(*arrays):
     result_type = single
     is_complex = False
     for a in arrays:
-        if issubclass(a.dtype.type, inexact):
-            if isComplexType(a.dtype.type):
+        type_ = a.dtype.type
+        if issubclass(type_, inexact):
+            if isComplexType(type_):
                 is_complex = True
-            rt = _realType(a.dtype.type, default=None)
-            if rt is None:
+            rt = _realType(type_, default=None)
+            if rt is double:
+                result_type = double
+            elif rt is None:
                 # unsupported inexact scalar
                 raise TypeError("array type %s is unsupported in linalg" %
                         (a.dtype.name,))
         else:
-            rt = double
-        if rt is double:
             result_type = double
     if is_complex:
-        t = cdouble
         result_type = _complex_types_map[result_type]
+        return cdouble, result_type
     else:
-        t = double
-    return t, result_type
+        return double, result_type
 
 
 def _to_native_byte_order(*arrays):
@@ -196,7 +219,7 @@ def _assert_finite(*arrays):
 
 def _is_empty_2d(arr):
     # check size first for efficiency
-    return arr.size == 0 and product(arr.shape[-2:]) == 0
+    return arr.size == 0 and prod(arr.shape[-2:]) == 0
 
 
 def transpose(a):
@@ -778,10 +801,9 @@ def qr(a, mode='reduced'):
     mode : {'reduced', 'complete', 'r', 'raw'}, optional
         If K = min(M, N), then
 
-        * 'reduced'  : returns q, r with dimensions
-                       (..., M, K), (..., K, N) (default)
-        * 'complete' : returns q, r with dimensions (..., M, M), (..., M, N)
-        * 'r'        : returns r only with dimensions (..., K, N)
+        * 'reduced'  : returns Q, R with dimensions (..., M, K), (..., K, N) (default)
+        * 'complete' : returns Q, R with dimensions (..., M, M), (..., M, N)
+        * 'r'        : returns R only with dimensions (..., K, N)
         * 'raw'      : returns h, tau with dimensions (..., N, M), (..., K,)
 
         The options 'reduced', 'complete, and 'raw' are new in numpy 1.8,
@@ -797,14 +819,17 @@ def qr(a, mode='reduced'):
 
     Returns
     -------
-    q : ndarray of float or complex, optional
+    When mode is 'reduced' or 'complete', the result will be a namedtuple with
+    the attributes `Q` and `R`.
+
+    Q : ndarray of float or complex, optional
         A matrix with orthonormal columns. When mode = 'complete' the
         result is an orthogonal/unitary matrix depending on whether or not
         a is real/complex. The determinant may be either +/- 1 in that
         case. In case the number of dimensions in the input array is
         greater than 2 then a stack of the matrices with above properties
         is returned.
-    r : ndarray of float or complex, optional
+    R : ndarray of float or complex, optional
         The upper-triangular matrix or a stack of upper-triangular
         matrices if the number of dimensions in the input array is greater
         than 2.
@@ -849,19 +874,19 @@ def qr(a, mode='reduced'):
     Examples
     --------
     >>> a = np.random.randn(9, 6)
-    >>> q, r = np.linalg.qr(a)
-    >>> np.allclose(a, np.dot(q, r))  # a does equal qr
+    >>> Q, R = np.linalg.qr(a)
+    >>> np.allclose(a, np.dot(Q, R))  # a does equal QR
     True
-    >>> r2 = np.linalg.qr(a, mode='r')
-    >>> np.allclose(r, r2)  # mode='r' returns the same r as mode='full'
+    >>> R2 = np.linalg.qr(a, mode='r')
+    >>> np.allclose(R, R2)  # mode='r' returns the same R as mode='full'
     True
     >>> a = np.random.normal(size=(3, 2, 2)) # Stack of 2 x 2 matrices as input
-    >>> q, r = np.linalg.qr(a)
-    >>> q.shape
+    >>> Q, R = np.linalg.qr(a)
+    >>> Q.shape
     (3, 2, 2)
-    >>> r.shape
+    >>> R.shape
     (3, 2, 2)
-    >>> np.allclose(a, np.matmul(q, r))
+    >>> np.allclose(a, np.matmul(Q, R))
     True
 
     Example illustrating a common use of `qr`: solving of least squares
@@ -876,8 +901,8 @@ def qr(a, mode='reduced'):
       x = array([[y0], [m]])
       b = array([[1], [0], [2], [1]])
 
-    If A = qr such that q is orthonormal (which is always possible via
-    Gram-Schmidt), then ``x = inv(r) * (q.T) * b``.  (In numpy practice,
+    If A = QR such that Q is orthonormal (which is always possible via
+    Gram-Schmidt), then ``x = inv(R) * (Q.T) * b``.  (In numpy practice,
     however, we simply use `lstsq`.)
 
     >>> A = np.array([[0, 1], [1, 1], [1, 1], [2, 1]])
@@ -887,9 +912,9 @@ def qr(a, mode='reduced'):
            [1, 1],
            [2, 1]])
     >>> b = np.array([1, 2, 2, 3])
-    >>> q, r = np.linalg.qr(A)
-    >>> p = np.dot(q.T, b)
-    >>> np.dot(np.linalg.inv(r), p)
+    >>> Q, R = np.linalg.qr(A)
+    >>> p = np.dot(Q.T, b)
+    >>> np.dot(np.linalg.inv(R), p)
     array([  1.,   1.])
 
     """
@@ -899,12 +924,12 @@ def qr(a, mode='reduced'):
             msg = "".join((
                     "The 'full' option is deprecated in favor of 'reduced'.\n",
                     "For backward compatibility let mode default."))
-            warnings.warn(msg, DeprecationWarning, stacklevel=3)
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
             mode = 'reduced'
         elif mode in ('e', 'economic'):
             # 2013-04-01, 1.8
             msg = "The 'economic' option is deprecated."
-            warnings.warn(msg, DeprecationWarning, stacklevel=3)
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
             mode = 'economic'
         else:
             raise ValueError(f"Unrecognized mode '{mode}'")
@@ -943,7 +968,7 @@ def qr(a, mode='reduced'):
         return wrap(a)
 
     # mc is the number of columns in the resulting q
-    # matrix. If the mode is complete then it is 
+    # matrix. If the mode is complete then it is
     # same as number of rows, and if the mode is reduced,
     # then it is the minimum of number of rows and columns.
     if mode == 'complete' and m > n:
@@ -961,7 +986,7 @@ def qr(a, mode='reduced'):
     q = q.astype(result_t, copy=False)
     r = r.astype(result_t, copy=False)
 
-    return wrap(q), wrap(r)
+    return QRResult(wrap(q), wrap(r))
 
 # Eigenvalues
 
@@ -1178,7 +1203,9 @@ def eig(a):
 
     Returns
     -------
-    w : (..., M) array
+    A namedtuple with the following attributes:
+
+    eigenvalues : (..., M) array
         The eigenvalues, each repeated according to its multiplicity.
         The eigenvalues are not necessarily ordered. The resulting
         array will be of complex type, unless the imaginary part is
@@ -1186,10 +1213,10 @@ def eig(a):
         is real the resulting eigenvalues will be real (0 imaginary
         part) or occur in conjugate pairs
 
-    v : (..., M, M) array
+    eigenvectors : (..., M, M) array
         The normalized (unit "length") eigenvectors, such that the
-        column ``v[:,i]`` is the eigenvector corresponding to the
-        eigenvalue ``w[i]``.
+        column ``eigenvectors[:,i]`` is the eigenvector corresponding to the
+        eigenvalue ``eigenvalues[i]``.
 
     Raises
     ------
@@ -1219,30 +1246,30 @@ def eig(a):
     This is implemented using the ``_geev`` LAPACK routines which compute
     the eigenvalues and eigenvectors of general square arrays.
 
-    The number `w` is an eigenvalue of `a` if there exists a vector
-    `v` such that ``a @ v = w * v``. Thus, the arrays `a`, `w`, and
-    `v` satisfy the equations ``a @ v[:,i] = w[i] * v[:,i]``
-    for :math:`i \\in \\{0,...,M-1\\}`.
+    The number `w` is an eigenvalue of `a` if there exists a vector `v` such
+    that ``a @ v = w * v``. Thus, the arrays `a`, `eigenvalues`, and
+    `eigenvectors` satisfy the equations ``a @ eigenvectors[:,i] =
+    eigenvalues[i] * eigenvalues[:,i]`` for :math:`i \\in \\{0,...,M-1\\}`.
 
-    The array `v` of eigenvectors may not be of maximum rank, that is, some
-    of the columns may be linearly dependent, although round-off error may
-    obscure that fact. If the eigenvalues are all different, then theoretically
-    the eigenvectors are linearly independent and `a` can be diagonalized by
-    a similarity transformation using `v`, i.e, ``inv(v) @ a @ v`` is diagonal.
+    The array `eigenvectors` may not be of maximum rank, that is, some of the
+    columns may be linearly dependent, although round-off error may obscure
+    that fact. If the eigenvalues are all different, then theoretically the
+    eigenvectors are linearly independent and `a` can be diagonalized by a
+    similarity transformation using `eigenvectors`, i.e, ``inv(eigenvectors) @
+    a @ eigenvectors`` is diagonal.
 
     For non-Hermitian normal matrices the SciPy function `scipy.linalg.schur`
-    is preferred because the matrix `v` is guaranteed to be unitary, which is
-    not the case when using `eig`. The Schur factorization produces an
-    upper triangular matrix rather than a diagonal matrix, but for normal
-    matrices only the diagonal of the upper triangular matrix is needed, the
-    rest is roundoff error.
-
-    Finally, it is emphasized that `v` consists of the *right* (as in
-    right-hand side) eigenvectors of `a`.  A vector `y` satisfying
-    ``y.T @ a = z * y.T`` for some number `z` is called a *left*
-    eigenvector of `a`, and, in general, the left and right eigenvectors
-    of a matrix are not necessarily the (perhaps conjugate) transposes
-    of each other.
+    is preferred because the matrix `eigenvectors` is guaranteed to be
+    unitary, which is not the case when using `eig`. The Schur factorization
+    produces an upper triangular matrix rather than a diagonal matrix, but for
+    normal matrices only the diagonal of the upper triangular matrix is
+    needed, the rest is roundoff error.
+
+    Finally, it is emphasized that `eigenvectors` consists of the *right* (as
+    in right-hand side) eigenvectors of `a`. A vector `y` satisfying ``y.T @ a
+    = z * y.T`` for some number `z` is called a *left* eigenvector of `a`,
+    and, in general, the left and right eigenvectors of a matrix are not
+    necessarily the (perhaps conjugate) transposes of each other.
 
     References
     ----------
@@ -1253,41 +1280,45 @@ def eig(a):
     --------
     >>> from numpy import linalg as LA
 
-    (Almost) trivial example with real e-values and e-vectors.
+    (Almost) trivial example with real eigenvalues and eigenvectors.
 
-    >>> w, v = LA.eig(np.diag((1, 2, 3)))
-    >>> w; v
+    >>> eigenvalues, eigenvectors = LA.eig(np.diag((1, 2, 3)))
+    >>> eigenvalues
     array([1., 2., 3.])
+    >>> eigenvectors
     array([[1., 0., 0.],
            [0., 1., 0.],
            [0., 0., 1.]])
 
-    Real matrix possessing complex e-values and e-vectors; note that the
-    e-values are complex conjugates of each other.
+    Real matrix possessing complex eigenvalues and eigenvectors; note that the
+    eigenvalues are complex conjugates of each other.
 
-    >>> w, v = LA.eig(np.array([[1, -1], [1, 1]]))
-    >>> w; v
+    >>> eigenvalues, eigenvectors = LA.eig(np.array([[1, -1], [1, 1]]))
+    >>> eigenvalues
     array([1.+1.j, 1.-1.j])
+    >>> eigenvectors
     array([[0.70710678+0.j        , 0.70710678-0.j        ],
            [0.        -0.70710678j, 0.        +0.70710678j]])
 
-    Complex-valued matrix with real e-values (but complex-valued e-vectors);
+    Complex-valued matrix with real eigenvalues (but complex-valued eigenvectors);
     note that ``a.conj().T == a``, i.e., `a` is Hermitian.
 
     >>> a = np.array([[1, 1j], [-1j, 1]])
-    >>> w, v = LA.eig(a)
-    >>> w; v
+    >>> eigenvalues, eigenvectors = LA.eig(a)
+    >>> eigenvalues
     array([2.+0.j, 0.+0.j])
+    >>> eigenvectors
     array([[ 0.        +0.70710678j,  0.70710678+0.j        ], # may vary
            [ 0.70710678+0.j        , -0.        +0.70710678j]])
 
     Be careful about round-off error!
 
     >>> a = np.array([[1 + 1e-9, 0], [0, 1 - 1e-9]])
-    >>> # Theor. e-values are 1 +/- 1e-9
-    >>> w, v = LA.eig(a)
-    >>> w; v
+    >>> # Theor. eigenvalues are 1 +/- 1e-9
+    >>> eigenvalues, eigenvectors = LA.eig(a)
+    >>> eigenvalues
     array([1., 1.])
+    >>> eigenvectors
     array([[1., 0.],
            [0., 1.]])
 
@@ -1311,7 +1342,7 @@ def eig(a):
         result_t = _complexType(result_t)
 
     vt = vt.astype(result_t, copy=False)
-    return w.astype(result_t, copy=False), wrap(vt)
+    return EigResult(w.astype(result_t, copy=False), wrap(vt))
 
 
 @array_function_dispatch(_eigvalsh_dispatcher)
@@ -1339,13 +1370,15 @@ def eigh(a, UPLO='L'):
 
     Returns
     -------
-    w : (..., M) ndarray
+    A namedtuple with the following attributes:
+
+    eigenvalues : (..., M) ndarray
         The eigenvalues in ascending order, each repeated according to
         its multiplicity.
-    v : {(..., M, M) ndarray, (..., M, M) matrix}
-        The column ``v[:, i]`` is the normalized eigenvector corresponding
-        to the eigenvalue ``w[i]``.  Will return a matrix object if `a` is
-        a matrix object.
+    eigenvectors : {(..., M, M) ndarray, (..., M, M) matrix}
+        The column ``eigenvectors[:, i]`` is the normalized eigenvector
+        corresponding to the eigenvalue ``eigenvalues[i]``.  Will return a
+        matrix object if `a` is a matrix object.
 
     Raises
     ------
@@ -1372,10 +1405,10 @@ def eigh(a, UPLO='L'):
     The eigenvalues/eigenvectors are computed using LAPACK routines ``_syevd``,
     ``_heevd``.
 
-    The eigenvalues of real symmetric or complex Hermitian matrices are
-    always real. [1]_ The array `v` of (column) eigenvectors is unitary
-    and `a`, `w`, and `v` satisfy the equations
-    ``dot(a, v[:, i]) = w[i] * v[:, i]``.
+    The eigenvalues of real symmetric or complex Hermitian matrices are always
+    real. [1]_ The array `eigenvalues` of (column) eigenvectors is unitary and
+    `a`, `eigenvalues`, and `eigenvectors` satisfy the equations ``dot(a,
+    eigenvectors[:, i]) = eigenvalues[i] * eigenvectors[:, i]``.
 
     References
     ----------
@@ -1389,24 +1422,26 @@ def eigh(a, UPLO='L'):
     >>> a
     array([[ 1.+0.j, -0.-2.j],
            [ 0.+2.j,  5.+0.j]])
-    >>> w, v = LA.eigh(a)
-    >>> w; v
+    >>> eigenvalues, eigenvectors = LA.eigh(a)
+    >>> eigenvalues
     array([0.17157288, 5.82842712])
+    >>> eigenvectors
     array([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
            [ 0.        +0.38268343j,  0.        -0.92387953j]])
 
-    >>> np.dot(a, v[:, 0]) - w[0] * v[:, 0] # verify 1st e-val/vec pair
+    >>> np.dot(a, eigenvectors[:, 0]) - eigenvalues[0] * eigenvectors[:, 0] # verify 1st eigenval/vec pair
     array([5.55111512e-17+0.0000000e+00j, 0.00000000e+00+1.2490009e-16j])
-    >>> np.dot(a, v[:, 1]) - w[1] * v[:, 1] # verify 2nd e-val/vec pair
+    >>> np.dot(a, eigenvectors[:, 1]) - eigenvalues[1] * eigenvectors[:, 1] # verify 2nd eigenval/vec pair
     array([0.+0.j, 0.+0.j])
 
     >>> A = np.matrix(a) # what happens if input is a matrix object
     >>> A
     matrix([[ 1.+0.j, -0.-2.j],
             [ 0.+2.j,  5.+0.j]])
-    >>> w, v = LA.eigh(A)
-    >>> w; v
+    >>> eigenvalues, eigenvectors = LA.eigh(A)
+    >>> eigenvalues
     array([0.17157288, 5.82842712])
+    >>> eigenvectors
     matrix([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
             [ 0.        +0.38268343j,  0.        -0.92387953j]])
 
@@ -1430,6 +1465,7 @@ def eigh(a, UPLO='L'):
            [ 0.        +0.89442719j,  0.        -0.4472136j ]])
     array([[ 0.89442719+0.j       , -0.        +0.4472136j],
            [-0.        +0.4472136j,  0.89442719+0.j       ]])
+
     """
     UPLO = UPLO.upper()
     if UPLO not in ('L', 'U'):
@@ -1451,7 +1487,7 @@ def eigh(a, UPLO='L'):
     w, vt = gufunc(a, signature=signature, extobj=extobj)
     w = w.astype(_realType(result_t), copy=False)
     vt = vt.astype(result_t, copy=False)
-    return w, wrap(vt)
+    return EighResult(w, wrap(vt))
 
 
 # Singular value decomposition
@@ -1493,16 +1529,19 @@ def svd(a, full_matrices=True, compute_uv=True, hermitian=False):
 
     Returns
     -------
-    u : { (..., M, M), (..., M, K) } array
+    When `compute_uv` is True, the result is a namedtuple with the following
+    attribute names:
+
+    U : { (..., M, M), (..., M, K) } array
         Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
         size as those of the input `a`. The size of the last two dimensions
         depends on the value of `full_matrices`. Only returned when
         `compute_uv` is True.
-    s : (..., K) array
+    S : (..., K) array
         Vector(s) with the singular values, within each vector sorted in
         descending order. The first ``a.ndim - 2`` dimensions have the same
         size as those of the input `a`.
-    vh : { (..., N, N), (..., K, N) } array
+    Vh : { (..., N, N), (..., K, N) } array
         Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
         size as those of the input `a`. The size of the last two dimensions
         depends on the value of `full_matrices`. Only returned when
@@ -1555,45 +1594,45 @@ def svd(a, full_matrices=True, compute_uv=True, hermitian=False):
 
     Reconstruction based on full SVD, 2D case:
 
-    >>> u, s, vh = np.linalg.svd(a, full_matrices=True)
-    >>> u.shape, s.shape, vh.shape
+    >>> U, S, Vh = np.linalg.svd(a, full_matrices=True)
+    >>> U.shape, S.shape, Vh.shape
     ((9, 9), (6,), (6, 6))
-    >>> np.allclose(a, np.dot(u[:, :6] * s, vh))
+    >>> np.allclose(a, np.dot(U[:, :6] * S, Vh))
     True
     >>> smat = np.zeros((9, 6), dtype=complex)
-    >>> smat[:6, :6] = np.diag(s)
-    >>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
+    >>> smat[:6, :6] = np.diag(S)
+    >>> np.allclose(a, np.dot(U, np.dot(smat, Vh)))
     True
 
     Reconstruction based on reduced SVD, 2D case:
 
-    >>> u, s, vh = np.linalg.svd(a, full_matrices=False)
-    >>> u.shape, s.shape, vh.shape
+    >>> U, S, Vh = np.linalg.svd(a, full_matrices=False)
+    >>> U.shape, S.shape, Vh.shape
     ((9, 6), (6,), (6, 6))
-    >>> np.allclose(a, np.dot(u * s, vh))
+    >>> np.allclose(a, np.dot(U * S, Vh))
     True
-    >>> smat = np.diag(s)
-    >>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
+    >>> smat = np.diag(S)
+    >>> np.allclose(a, np.dot(U, np.dot(smat, Vh)))
     True
 
     Reconstruction based on full SVD, 4D case:
 
-    >>> u, s, vh = np.linalg.svd(b, full_matrices=True)
-    >>> u.shape, s.shape, vh.shape
+    >>> U, S, Vh = np.linalg.svd(b, full_matrices=True)
+    >>> U.shape, S.shape, Vh.shape
     ((2, 7, 8, 8), (2, 7, 3), (2, 7, 3, 3))
-    >>> np.allclose(b, np.matmul(u[..., :3] * s[..., None, :], vh))
+    >>> np.allclose(b, np.matmul(U[..., :3] * S[..., None, :], Vh))
     True
-    >>> np.allclose(b, np.matmul(u[..., :3], s[..., None] * vh))
+    >>> np.allclose(b, np.matmul(U[..., :3], S[..., None] * Vh))
     True
 
     Reconstruction based on reduced SVD, 4D case:
 
-    >>> u, s, vh = np.linalg.svd(b, full_matrices=False)
-    >>> u.shape, s.shape, vh.shape
+    >>> U, S, Vh = np.linalg.svd(b, full_matrices=False)
+    >>> U.shape, S.shape, Vh.shape
     ((2, 7, 8, 3), (2, 7, 3), (2, 7, 3, 3))
-    >>> np.allclose(b, np.matmul(u * s[..., None, :], vh))
+    >>> np.allclose(b, np.matmul(U * S[..., None, :], Vh))
     True
-    >>> np.allclose(b, np.matmul(u, s[..., None] * vh))
+    >>> np.allclose(b, np.matmul(U, S[..., None] * Vh))
     True
 
     """
@@ -1614,7 +1653,7 @@ def svd(a, full_matrices=True, compute_uv=True, hermitian=False):
             u = _nx.take_along_axis(u, sidx[..., None, :], axis=-1)
             # singular values are unsigned, move the sign into v
             vt = transpose(u * sgn[..., None, :]).conjugate()
-            return wrap(u), s, wrap(vt)
+            return SVDResult(wrap(u), s, wrap(vt))
         else:
             s = eigvalsh(a)
             s = abs(s)
@@ -1643,7 +1682,7 @@ def svd(a, full_matrices=True, compute_uv=True, hermitian=False):
         u = u.astype(result_t, copy=False)
         s = s.astype(_realType(result_t), copy=False)
         vh = vh.astype(result_t, copy=False)
-        return wrap(u), s, wrap(vh)
+        return SVDResult(wrap(u), s, wrap(vh))
     else:
         if m < n:
             gufunc = _umath_linalg.svd_m
@@ -2012,15 +2051,17 @@ def slogdet(a):
 
     Returns
     -------
+    A namedtuple with the following attributes:
+
     sign : (...) array_like
         A number representing the sign of the determinant. For a real matrix,
         this is 1, 0, or -1. For a complex matrix, this is a complex number
         with absolute value 1 (i.e., it is on the unit circle), or else 0.
-    logdet : (...) array_like
+    logabsdet : (...) array_like
         The natural log of the absolute value of the determinant.
 
-    If the determinant is zero, then `sign` will be 0 and `logdet` will be
-    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
+    If the determinant is zero, then `sign` will be 0 and `logabsdet` will be
+    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logabsdet)``.
 
     See Also
     --------
@@ -2045,10 +2086,10 @@ def slogdet(a):
     The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
 
     >>> a = np.array([[1, 2], [3, 4]])
-    >>> (sign, logdet) = np.linalg.slogdet(a)
-    >>> (sign, logdet)
+    >>> (sign, logabsdet) = np.linalg.slogdet(a)
+    >>> (sign, logabsdet)
     (-1, 0.69314718055994529) # may vary
-    >>> sign * np.exp(logdet)
+    >>> sign * np.exp(logabsdet)
     -2.0
 
     Computing log-determinants for a stack of matrices:
@@ -2056,10 +2097,10 @@ def slogdet(a):
     >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
     >>> a.shape
     (3, 2, 2)
-    >>> sign, logdet = np.linalg.slogdet(a)
-    >>> (sign, logdet)
+    >>> sign, logabsdet = np.linalg.slogdet(a)
+    >>> (sign, logabsdet)
     (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
-    >>> sign * np.exp(logdet)
+    >>> sign * np.exp(logabsdet)
     array([-2., -3., -8.])
 
     This routine succeeds where ordinary `det` does not:
@@ -2079,7 +2120,7 @@ def slogdet(a):
     sign, logdet = _umath_linalg.slogdet(a, signature=signature)
     sign = sign.astype(result_t, copy=False)
     logdet = logdet.astype(real_t, copy=False)
-    return sign, logdet
+    return SlogdetResult(sign, logdet)
 
 
 @array_function_dispatch(_unary_dispatcher)
@@ -2267,7 +2308,7 @@ def lstsq(a, b, rcond="warn"):
                       "To use the future default and silence this warning "
                       "we advise to pass `rcond=None`, to keep using the old, "
                       "explicitly pass `rcond=-1`.",
-                      FutureWarning, stacklevel=3)
+                      FutureWarning, stacklevel=2)
         rcond = -1
     if rcond is None:
         rcond = finfo(t).eps * max(n, m)
diff --git a/contrib/python/numpy/py3/numpy/linalg/linalg.pyi b/contrib/python/numpy/py3/numpy/linalg/linalg.pyi
index 20cdb708b69..c0b2f29b28d 100644
--- a/contrib/python/numpy/py3/numpy/linalg/linalg.pyi
+++ b/contrib/python/numpy/py3/numpy/linalg/linalg.pyi
@@ -6,6 +6,8 @@ from typing import (
     Any,
     SupportsIndex,
     SupportsInt,
+    NamedTuple,
+    Generic,
 )
 
 from numpy import (
@@ -31,12 +33,37 @@ from numpy._typing import (
 
 _T = TypeVar("_T")
 _ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
+_SCT = TypeVar("_SCT", bound=generic, covariant=True)
+_SCT2 = TypeVar("_SCT2", bound=generic, covariant=True)
 
 _2Tuple = tuple[_T, _T]
 _ModeKind = L["reduced", "complete", "r", "raw"]
 
 __all__: list[str]
 
+class EigResult(NamedTuple):
+    eigenvalues: NDArray[Any]
+    eigenvectors: NDArray[Any]
+
+class EighResult(NamedTuple):
+    eigenvalues: NDArray[Any]
+    eigenvectors: NDArray[Any]
+
+class QRResult(NamedTuple):
+    Q: NDArray[Any]
+    R: NDArray[Any]
+
+class SlogdetResult(NamedTuple):
+    # TODO: `sign` and `logabsdet` are scalars for input 2D arrays and
+    # a `(x.ndim - 2)`` dimensionl arrays otherwise
+    sign: Any
+    logabsdet: Any
+
+class SVDResult(NamedTuple):
+    U: NDArray[Any]
+    S: NDArray[Any]
+    Vh: NDArray[Any]
+
 @overload
 def tensorsolve(
     a: _ArrayLikeInt_co,
@@ -110,11 +137,11 @@ def cholesky(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
 def cholesky(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
 
 @overload
-def qr(a: _ArrayLikeInt_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[float64]]: ...
+def qr(a: _ArrayLikeInt_co, mode: _ModeKind = ...) -> QRResult: ...
 @overload
-def qr(a: _ArrayLikeFloat_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[floating[Any]]]: ...
+def qr(a: _ArrayLikeFloat_co, mode: _ModeKind = ...) -> QRResult: ...
 @overload
-def qr(a: _ArrayLikeComplex_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
+def qr(a: _ArrayLikeComplex_co, mode: _ModeKind = ...) -> QRResult: ...
 
 @overload
 def eigvals(a: _ArrayLikeInt_co) -> NDArray[float64] | NDArray[complex128]: ...
@@ -129,27 +156,27 @@ def eigvalsh(a: _ArrayLikeInt_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[
 def eigvalsh(a: _ArrayLikeComplex_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[floating[Any]]: ...
 
 @overload
-def eig(a: _ArrayLikeInt_co) -> _2Tuple[NDArray[float64]] | _2Tuple[NDArray[complex128]]: ...
+def eig(a: _ArrayLikeInt_co) -> EigResult: ...
 @overload
-def eig(a: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]] | _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
+def eig(a: _ArrayLikeFloat_co) -> EigResult: ...
 @overload
-def eig(a: _ArrayLikeComplex_co) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
+def eig(a: _ArrayLikeComplex_co) -> EigResult: ...
 
 @overload
 def eigh(
     a: _ArrayLikeInt_co,
     UPLO: L["L", "U", "l", "u"] = ...,
-) -> tuple[NDArray[float64], NDArray[float64]]: ...
+) -> EighResult: ...
 @overload
 def eigh(
     a: _ArrayLikeFloat_co,
     UPLO: L["L", "U", "l", "u"] = ...,
-) -> tuple[NDArray[floating[Any]], NDArray[floating[Any]]]: ...
+) -> EighResult: ...
 @overload
 def eigh(
     a: _ArrayLikeComplex_co,
     UPLO: L["L", "U", "l", "u"] = ...,
-) -> tuple[NDArray[floating[Any]], NDArray[complexfloating[Any, Any]]]: ...
+) -> EighResult: ...
 
 @overload
 def svd(
@@ -157,33 +184,21 @@ def svd(
     full_matrices: bool = ...,
     compute_uv: L[True] = ...,
     hermitian: bool = ...,
-) -> tuple[
-    NDArray[float64],
-    NDArray[float64],
-    NDArray[float64],
-]: ...
+) -> SVDResult: ...
 @overload
 def svd(
     a: _ArrayLikeFloat_co,
     full_matrices: bool = ...,
     compute_uv: L[True] = ...,
     hermitian: bool = ...,
-) -> tuple[
-    NDArray[floating[Any]],
-    NDArray[floating[Any]],
-    NDArray[floating[Any]],
-]: ...
+) -> SVDResult: ...
 @overload
 def svd(
     a: _ArrayLikeComplex_co,
     full_matrices: bool = ...,
     compute_uv: L[True] = ...,
     hermitian: bool = ...,
-) -> tuple[
-    NDArray[complexfloating[Any, Any]],
-    NDArray[floating[Any]],
-    NDArray[complexfloating[Any, Any]],
-]: ...
+) -> SVDResult: ...
 @overload
 def svd(
     a: _ArrayLikeInt_co,
@@ -231,7 +246,7 @@ def pinv(
 
 # TODO: Returns a 2-tuple of scalars for 2D arrays and
 # a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
-def slogdet(a: _ArrayLikeComplex_co) -> _2Tuple[Any]: ...
+def slogdet(a: _ArrayLikeComplex_co) -> SlogdetResult: ...
 
 # TODO: Returns a 2-tuple of scalars for 2D arrays and
 # a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
diff --git a/contrib/python/numpy/py3/numpy/linalg/meson.build b/contrib/python/numpy/py3/numpy/linalg/meson.build
new file mode 100644
index 00000000000..d290e5b3932
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/linalg/meson.build
@@ -0,0 +1,56 @@
+lapack_lite_sources = [
+  'lapack_lite/f2c.c',
+  'lapack_lite/f2c_c_lapack.c',
+  'lapack_lite/f2c_d_lapack.c',
+  'lapack_lite/f2c_s_lapack.c',
+  'lapack_lite/f2c_z_lapack.c',
+  'lapack_lite/f2c_blas.c',
+  'lapack_lite/f2c_config.c',
+  'lapack_lite/f2c_lapack.c',
+  'lapack_lite/python_xerbla.c',
+]
+
+# TODO: ILP64 support
+
+lapack_lite_module_src = ['lapack_litemodule.c']
+if not have_lapack
+  warning('LAPACK was not found, NumPy is using an unoptimized, naive build from sources!')
+  lapack_lite_module_src += lapack_lite_sources
+endif
+
+py.extension_module('lapack_lite',
+  lapack_lite_module_src,
+  dependencies: [np_core_dep, blas_dep, lapack_dep],
+  install: true,
+  subdir: 'numpy/linalg',
+)
+
+_umath_linalg_src = ['umath_linalg.cpp'] + lapack_lite_sources
+
+py.extension_module('_umath_linalg',
+  _umath_linalg_src,
+  dependencies: np_core_dep,
+  link_with: npymath_lib,
+  install: true,
+  subdir: 'numpy/linalg',
+)
+
+py.install_sources(
+  [
+    '__init__.py',
+    '__init__.pyi',
+    'linalg.py',
+    'linalg.pyi',
+  ],
+  subdir: 'numpy/linalg'
+)
+
+py.install_sources(
+  [
+    'tests/__init__.py',
+    'tests/test_deprecations.py',
+    'tests/test_linalg.py',
+    'tests/test_regression.py',
+  ],
+  subdir: 'numpy/linalg/tests'
+)
diff --git a/contrib/python/numpy/py3/numpy/linalg/setup.py b/contrib/python/numpy/py3/numpy/linalg/setup.py
index 1c4e1295e3c..6f72635ab67 100644
--- a/contrib/python/numpy/py3/numpy/linalg/setup.py
+++ b/contrib/python/numpy/py3/numpy/linalg/setup.py
@@ -4,7 +4,6 @@ import sysconfig
 
 def configuration(parent_package='', top_path=None):
     from numpy.distutils.misc_util import Configuration
-    from numpy.distutils.ccompiler_opt import NPY_CXX_FLAGS
     from numpy.distutils.system_info import get_info, system_info
     config = Configuration('linalg', parent_package, top_path)
 
@@ -81,7 +80,6 @@ def configuration(parent_package='', top_path=None):
         sources=['umath_linalg.cpp', get_lapack_lite_sources],
         depends=['lapack_lite/f2c.h'],
         extra_info=lapack_info,
-        extra_cxx_compile_args=NPY_CXX_FLAGS,
         libraries=['npymath'],
     )
     config.add_data_files('*.pyi')
diff --git a/contrib/python/numpy/py3/numpy/linalg/tests/test_linalg.py b/contrib/python/numpy/py3/numpy/linalg/tests/test_linalg.py
index 121b359aced..8ba6420bdad 100644
--- a/contrib/python/numpy/py3/numpy/linalg/tests/test_linalg.py
+++ b/contrib/python/numpy/py3/numpy/linalg/tests/test_linalg.py
@@ -589,11 +589,12 @@ class TestEigvals(EigvalsCases):
 class EigCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
 
     def do(self, a, b, tags):
-        evalues, evectors = linalg.eig(a)
-        assert_allclose(dot_generalized(a, evectors),
-                        np.asarray(evectors) * np.asarray(evalues)[..., None, :],
-                        rtol=get_rtol(evalues.dtype))
-        assert_(consistent_subclass(evectors, a))
+        res = linalg.eig(a)
+        eigenvalues, eigenvectors = res.eigenvalues, res.eigenvectors
+        assert_allclose(dot_generalized(a, eigenvectors),
+                        np.asarray(eigenvectors) * np.asarray(eigenvalues)[..., None, :],
+                        rtol=get_rtol(eigenvalues.dtype))
+        assert_(consistent_subclass(eigenvectors, a))
 
 
 class TestEig(EigCases):
@@ -638,10 +639,11 @@ class SVDBaseTests:
     @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
     def test_types(self, dtype):
         x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
-        u, s, vh = linalg.svd(x)
-        assert_equal(u.dtype, dtype)
-        assert_equal(s.dtype, get_real_dtype(dtype))
-        assert_equal(vh.dtype, dtype)
+        res = linalg.svd(x)
+        U, S, Vh = res.U, res.S, res.Vh
+        assert_equal(U.dtype, dtype)
+        assert_equal(S.dtype, get_real_dtype(dtype))
+        assert_equal(Vh.dtype, dtype)
         s = linalg.svd(x, compute_uv=False, hermitian=self.hermitian)
         assert_equal(s.dtype, get_real_dtype(dtype))
 
@@ -844,7 +846,8 @@ class DetCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
 
     def do(self, a, b, tags):
         d = linalg.det(a)
-        (s, ld) = linalg.slogdet(a)
+        res = linalg.slogdet(a)
+        s, ld = res.sign, res.logabsdet
         if asarray(a).dtype.type in (single, double):
             ad = asarray(a).astype(double)
         else:
@@ -1144,7 +1147,8 @@ class TestEighCases(HermitianTestCase, HermitianGeneralizedTestCase):
     def do(self, a, b, tags):
         # note that eigenvalue arrays returned by eig must be sorted since
         # their order isn't guaranteed.
-        ev, evc = linalg.eigh(a)
+        res = linalg.eigh(a)
+        ev, evc = res.eigenvalues, res.eigenvectors
         evalues, evectors = linalg.eig(a)
         evalues.sort(axis=-1)
         assert_almost_equal(ev, evalues)
@@ -1632,16 +1636,17 @@ class TestQR:
         k = min(m, n)
 
         # mode == 'complete'
-        q, r = linalg.qr(a, mode='complete')
-        assert_(q.dtype == a_dtype)
-        assert_(r.dtype == a_dtype)
-        assert_(isinstance(q, a_type))
-        assert_(isinstance(r, a_type))
-        assert_(q.shape == (m, m))
-        assert_(r.shape == (m, n))
-        assert_almost_equal(dot(q, r), a)
-        assert_almost_equal(dot(q.T.conj(), q), np.eye(m))
-        assert_almost_equal(np.triu(r), r)
+        res = linalg.qr(a, mode='complete')
+        Q, R = res.Q, res.R
+        assert_(Q.dtype == a_dtype)
+        assert_(R.dtype == a_dtype)
+        assert_(isinstance(Q, a_type))
+        assert_(isinstance(R, a_type))
+        assert_(Q.shape == (m, m))
+        assert_(R.shape == (m, n))
+        assert_almost_equal(dot(Q, R), a)
+        assert_almost_equal(dot(Q.T.conj(), Q), np.eye(m))
+        assert_almost_equal(np.triu(R), R)
 
         # mode == 'reduced'
         q1, r1 = linalg.qr(a, mode='reduced')
@@ -1736,7 +1741,7 @@ class TestQR:
         assert_(r.shape[-2:] == (m, n))
         assert_almost_equal(matmul(q, r), a)
         I_mat = np.identity(q.shape[-1])
-        stack_I_mat = np.broadcast_to(I_mat, 
+        stack_I_mat = np.broadcast_to(I_mat,
                         q.shape[:-2] + (q.shape[-1],)*2)
         assert_almost_equal(matmul(swapaxes(q, -1, -2).conj(), q), stack_I_mat)
         assert_almost_equal(np.triu(r[..., :, :]), r)
@@ -1751,9 +1756,9 @@ class TestQR:
         assert_(r1.shape[-2:] == (k, n))
         assert_almost_equal(matmul(q1, r1), a)
         I_mat = np.identity(q1.shape[-1])
-        stack_I_mat = np.broadcast_to(I_mat, 
+        stack_I_mat = np.broadcast_to(I_mat,
                         q1.shape[:-2] + (q1.shape[-1],)*2)
-        assert_almost_equal(matmul(swapaxes(q1, -1, -2).conj(), q1), 
+        assert_almost_equal(matmul(swapaxes(q1, -1, -2).conj(), q1),
                             stack_I_mat)
         assert_almost_equal(np.triu(r1[..., :, :]), r1)
 
@@ -1765,12 +1770,12 @@ class TestQR:
 
     @pytest.mark.skip
     @pytest.mark.parametrize("size", [
-        (3, 4), (4, 3), (4, 4), 
+        (3, 4), (4, 3), (4, 4),
         (3, 0), (0, 3)])
     @pytest.mark.parametrize("outer_size", [
         (2, 2), (2,), (2, 3, 4)])
     @pytest.mark.parametrize("dt", [
-        np.single, np.double, 
+        np.single, np.double,
         np.csingle, np.cdouble])
     def test_stacked_inputs(self, outer_size, size, dt):
 
diff --git a/contrib/python/numpy/py3/numpy/linalg/tests/test_regression.py b/contrib/python/numpy/py3/numpy/linalg/tests/test_regression.py
index 7ed932bc928..af38443a93c 100644
--- a/contrib/python/numpy/py3/numpy/linalg/tests/test_regression.py
+++ b/contrib/python/numpy/py3/numpy/linalg/tests/test_regression.py
@@ -107,10 +107,7 @@ class TestRegression:
         assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
         assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
         assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
-        with warnings.catch_warnings():
-            warnings.simplefilter("error", DeprecationWarning)
-            assert_raises((AttributeError, DeprecationWarning),
-                              linalg.norm, testvector, ord=0)
+        assert_raises(ValueError, linalg.norm, testvector, ord=0)
         assert_raises(ValueError, linalg.norm, testvector, ord=-1)
         assert_raises(ValueError, linalg.norm, testvector, ord=-2)
 
diff --git a/contrib/python/numpy/py3/numpy/linalg/umath_linalg.cpp b/contrib/python/numpy/py3/numpy/linalg/umath_linalg.cpp
index 2e4a3b5c889..a82cec8f16c 100644
--- a/contrib/python/numpy/py3/numpy/linalg/umath_linalg.cpp
+++ b/contrib/python/numpy/py3/numpy/linalg/umath_linalg.cpp
@@ -1149,7 +1149,7 @@ slogdet(char **args,
                void *NPY_UNUSED(func))
 {
     fortran_int m;
-    npy_uint8 *tmp_buff = NULL;
+    char *tmp_buff = NULL;
     size_t matrix_size;
     size_t pivot_size;
     size_t safe_m;
@@ -1163,10 +1163,11 @@ slogdet(char **args,
      */
     INIT_OUTER_LOOP_3
     m = (fortran_int) dimensions[0];
-    safe_m = m;
+    /* avoid empty malloc (buffers likely unused) and ensure m is `size_t` */
+    safe_m = m != 0 ? m : 1;
     matrix_size = safe_m * safe_m * sizeof(typ);
     pivot_size = safe_m * sizeof(fortran_int);
-    tmp_buff = (npy_uint8 *)malloc(matrix_size + pivot_size);
+    tmp_buff = (char *)malloc(matrix_size + pivot_size);
 
     if (tmp_buff) {
         LINEARIZE_DATA_t lin_data;
@@ -1183,6 +1184,13 @@ slogdet(char **args,
 
         free(tmp_buff);
     }
+    else {
+        /* TODO: Requires use of new ufunc API to indicate error return */
+        NPY_ALLOW_C_API_DEF
+        NPY_ALLOW_C_API;
+        PyErr_NoMemory();
+        NPY_DISABLE_C_API;
+    }
 }
 
 template<typename typ, typename basetyp>
@@ -1193,7 +1201,7 @@ det(char **args,
            void *NPY_UNUSED(func))
 {
     fortran_int m;
-    npy_uint8 *tmp_buff;
+    char *tmp_buff;
     size_t matrix_size;
     size_t pivot_size;
     size_t safe_m;
@@ -1207,10 +1215,11 @@ det(char **args,
      */
     INIT_OUTER_LOOP_2
     m = (fortran_int) dimensions[0];
-    safe_m = m;
+    /* avoid empty malloc (buffers likely unused) and ensure m is `size_t` */
+    safe_m = m != 0 ? m : 1;
     matrix_size = safe_m * safe_m * sizeof(typ);
     pivot_size = safe_m * sizeof(fortran_int);
-    tmp_buff = (npy_uint8 *)malloc(matrix_size + pivot_size);
+    tmp_buff = (char *)malloc(matrix_size + pivot_size);
 
     if (tmp_buff) {
         LINEARIZE_DATA_t lin_data;
@@ -1231,6 +1240,13 @@ det(char **args,
 
         free(tmp_buff);
     }
+    else {
+        /* TODO: Requires use of new ufunc API to indicate error return */
+        NPY_ALLOW_C_API_DEF
+        NPY_ALLOW_C_API;
+        PyErr_NoMemory();
+        NPY_DISABLE_C_API;
+    }
 }
 
 
@@ -3738,16 +3754,16 @@ scalar_trait)
     fortran_int lda = fortran_int_max(1, m);
     fortran_int ldb = fortran_int_max(1, fortran_int_max(m,n));
 
-    mem_buff = (npy_uint8 *)malloc(a_size + b_size + s_size);
-
-    if (!mem_buff)
-        goto error;
+    size_t msize = a_size + b_size + s_size;
+    mem_buff = (npy_uint8 *)malloc(msize != 0 ? msize : 1);
 
+    if (!mem_buff) {
+        goto no_memory;
+    }
     a = mem_buff;
     b = a + a_size;
     s = b + b_size;
 
-
     params->M = m;
     params->N = n;
     params->NRHS = nrhs;
@@ -3767,9 +3783,9 @@ scalar_trait)
         params->RWORK = NULL;
         params->LWORK = -1;
 
-        if (call_gelsd(params) != 0)
+        if (call_gelsd(params) != 0) {
             goto error;
-
+        }
         work_count = (fortran_int)work_size_query;
 
         work_size  = (size_t) work_size_query * sizeof(ftyp);
@@ -3777,9 +3793,9 @@ scalar_trait)
     }
 
     mem_buff2 = (npy_uint8 *)malloc(work_size + iwork_size);
-    if (!mem_buff2)
-        goto error;
-
+    if (!mem_buff2) {
+        goto no_memory;
+    }
     work = mem_buff2;
     iwork = work + work_size;
 
@@ -3789,12 +3805,18 @@ scalar_trait)
     params->LWORK = work_count;
 
     return 1;
+
+ no_memory:
+    NPY_ALLOW_C_API_DEF
+    NPY_ALLOW_C_API;
+    PyErr_NoMemory();
+    NPY_DISABLE_C_API;
+
  error:
     TRACE_TXT("%s failed init\n", __FUNCTION__);
     free(mem_buff);
     free(mem_buff2);
     memset(params, 0, sizeof(*params));
-
     return 0;
 }
 
@@ -3858,16 +3880,17 @@ using frealtyp = basetype_t<ftyp>;
     fortran_int lda = fortran_int_max(1, m);
     fortran_int ldb = fortran_int_max(1, fortran_int_max(m,n));
 
-    mem_buff = (npy_uint8 *)malloc(a_size + b_size + s_size);
+    size_t msize = a_size + b_size + s_size;
+    mem_buff = (npy_uint8 *)malloc(msize != 0 ? msize : 1);
 
-    if (!mem_buff)
-        goto error;
+    if (!mem_buff) {
+        goto no_memory;
+    }
 
     a = mem_buff;
     b = a + a_size;
     s = b + b_size;
 
-
     params->M = m;
     params->N = n;
     params->NRHS = nrhs;
@@ -3888,8 +3911,9 @@ using frealtyp = basetype_t<ftyp>;
         params->RWORK = &rwork_size_query;
         params->LWORK = -1;
 
-        if (call_gelsd(params) != 0)
+        if (call_gelsd(params) != 0) {
             goto error;
+        }
 
         work_count = (fortran_int)work_size_query.r;
 
@@ -3899,8 +3923,9 @@ using frealtyp = basetype_t<ftyp>;
     }
 
     mem_buff2 = (npy_uint8 *)malloc(work_size + rwork_size + iwork_size);
-    if (!mem_buff2)
-        goto error;
+    if (!mem_buff2) {
+        goto no_memory;
+    }
 
     work = mem_buff2;
     rwork = work + work_size;
@@ -3912,6 +3937,13 @@ using frealtyp = basetype_t<ftyp>;
     params->LWORK = work_count;
 
     return 1;
+
+ no_memory:
+    NPY_ALLOW_C_API_DEF
+    NPY_ALLOW_C_API;
+    PyErr_NoMemory();
+    NPY_DISABLE_C_API;
+
  error:
     TRACE_TXT("%s failed init\n", __FUNCTION__);
     free(mem_buff);
diff --git a/contrib/python/numpy/py3/numpy/ma/__init__.pyi b/contrib/python/numpy/py3/numpy/ma/__init__.pyi
index 7f5cb56a80d..ce72383e5ea 100644
--- a/contrib/python/numpy/py3/numpy/ma/__init__.pyi
+++ b/contrib/python/numpy/py3/numpy/ma/__init__.pyi
@@ -155,7 +155,6 @@ from numpy.ma.core import (
     resize as resize,
     right_shift as right_shift,
     round as round,
-    round_ as round_,
     set_fill_value as set_fill_value,
     shape as shape,
     sin as sin,
diff --git a/contrib/python/numpy/py3/numpy/ma/bench.py b/contrib/python/numpy/py3/numpy/ma/bench.py
deleted file mode 100644
index 56865683d9c..00000000000
--- a/contrib/python/numpy/py3/numpy/ma/bench.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#!/usr/bin/env python3
-
-import timeit
-import numpy
-
-
-###############################################################################
-#                               Global variables                              #
-###############################################################################
-
-
-# Small arrays
-xs = numpy.random.uniform(-1, 1, 6).reshape(2, 3)
-ys = numpy.random.uniform(-1, 1, 6).reshape(2, 3)
-zs = xs + 1j * ys
-m1 = [[True, False, False], [False, False, True]]
-m2 = [[True, False, True], [False, False, True]]
-nmxs = numpy.ma.array(xs, mask=m1)
-nmys = numpy.ma.array(ys, mask=m2)
-nmzs = numpy.ma.array(zs, mask=m1)
-
-# Big arrays
-xl = numpy.random.uniform(-1, 1, 100*100).reshape(100, 100)
-yl = numpy.random.uniform(-1, 1, 100*100).reshape(100, 100)
-zl = xl + 1j * yl
-maskx = xl > 0.8
-masky = yl < -0.8
-nmxl = numpy.ma.array(xl, mask=maskx)
-nmyl = numpy.ma.array(yl, mask=masky)
-nmzl = numpy.ma.array(zl, mask=maskx)
-
-
-###############################################################################
-#                                 Functions                                   #
-###############################################################################
-
-
-def timer(s, v='', nloop=500, nrep=3):
-    units = ["s", "ms", "µs", "ns"]
-    scaling = [1, 1e3, 1e6, 1e9]
-    print("%s : %-50s : " % (v, s), end=' ')
-    varnames = ["%ss,nm%ss,%sl,nm%sl" % tuple(x*4) for x in 'xyz']
-    setup = 'from __main__ import numpy, ma, %s' % ','.join(varnames)
-    Timer = timeit.Timer(stmt=s, setup=setup)
-    best = min(Timer.repeat(nrep, nloop)) / nloop
-    if best > 0.0:
-        order = min(-int(numpy.floor(numpy.log10(best)) // 3), 3)
-    else:
-        order = 3
-    print("%d loops, best of %d: %.*g %s per loop" % (nloop, nrep,
-                                                      3,
-                                                      best * scaling[order],
-                                                      units[order]))
-
-
-def compare_functions_1v(func, nloop=500,
-                       xs=xs, nmxs=nmxs, xl=xl, nmxl=nmxl):
-    funcname = func.__name__
-    print("-"*50)
-    print(f'{funcname} on small arrays')
-    module, data = "numpy.ma", "nmxs"
-    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
-
-    print("%s on large arrays" % funcname)
-    module, data = "numpy.ma", "nmxl"
-    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
-    return
-
-def compare_methods(methodname, args, vars='x', nloop=500, test=True,
-                    xs=xs, nmxs=nmxs, xl=xl, nmxl=nmxl):
-    print("-"*50)
-    print(f'{methodname} on small arrays')
-    data, ver = f'nm{vars}l', 'numpy.ma'
-    timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)
-
-    print("%s on large arrays" % methodname)
-    data, ver = "nm%sl" % vars, 'numpy.ma'
-    timer("%(data)s.%(methodname)s(%(args)s)" % locals(), v=ver, nloop=nloop)
-    return
-
-def compare_functions_2v(func, nloop=500, test=True,
-                       xs=xs, nmxs=nmxs,
-                       ys=ys, nmys=nmys,
-                       xl=xl, nmxl=nmxl,
-                       yl=yl, nmyl=nmyl):
-    funcname = func.__name__
-    print("-"*50)
-    print(f'{funcname} on small arrays')
-    module, data = "numpy.ma", "nmxs,nmys"
-    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
-
-    print(f'{funcname} on large arrays')
-    module, data = "numpy.ma", "nmxl,nmyl"
-    timer("%(module)s.%(funcname)s(%(data)s)" % locals(), v="%11s" % module, nloop=nloop)
-    return
-
-
-if __name__ == '__main__':
-    compare_functions_1v(numpy.sin)
-    compare_functions_1v(numpy.log)
-    compare_functions_1v(numpy.sqrt)
-
-    compare_functions_2v(numpy.multiply)
-    compare_functions_2v(numpy.divide)
-    compare_functions_2v(numpy.power)
-
-    compare_methods('ravel', '', nloop=1000)
-    compare_methods('conjugate', '', 'z', nloop=1000)
-    compare_methods('transpose', '', nloop=1000)
-    compare_methods('compressed', '', nloop=1000)
-    compare_methods('__getitem__', '0', nloop=1000)
-    compare_methods('__getitem__', '(0,0)', nloop=1000)
-    compare_methods('__getitem__', '[0,-1]', nloop=1000)
-    compare_methods('__setitem__', '0, 17', nloop=1000, test=False)
-    compare_methods('__setitem__', '(0,0), 17', nloop=1000, test=False)
-
-    print("-"*50)
-    print("__setitem__ on small arrays")
-    timer('nmxs.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma   ', nloop=10000)
-
-    print("-"*50)
-    print("__setitem__ on large arrays")
-    timer('nmxl.__setitem__((-1,0),numpy.ma.masked)', 'numpy.ma   ', nloop=10000)
-
-    print("-"*50)
-    print("where on small arrays")
-    timer('numpy.ma.where(nmxs>2,nmxs,nmys)', 'numpy.ma   ', nloop=1000)
-    print("-"*50)
-    print("where on large arrays")
-    timer('numpy.ma.where(nmxl>2,nmxl,nmyl)', 'numpy.ma   ', nloop=100)
diff --git a/contrib/python/numpy/py3/numpy/ma/core.py b/contrib/python/numpy/py3/numpy/ma/core.py
index 5fb16d0cba6..2fe32688529 100644
--- a/contrib/python/numpy/py3/numpy/ma/core.py
+++ b/contrib/python/numpy/py3/numpy/ma/core.py
@@ -2310,7 +2310,7 @@ def masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True):
                  mask=False,
            fill_value=2.1)
 
-    Unlike `masked_equal`, `masked_values` can perform approximate equalities. 
+    Unlike `masked_equal`, `masked_values` can perform approximate equalities.
 
     >>> ma.masked_values(x, 2.1, atol=1e-1)
     masked_array(data=[1.0, 1.1, --, 1.1, 3.0],
@@ -2840,7 +2840,6 @@ class MaskedArray(ndarray):
         # Process mask.
         # Type of the mask
         mdtype = make_mask_descr(_data.dtype)
-
         if mask is nomask:
             # Case 1. : no mask in input.
             # Erase the current mask ?
@@ -2858,7 +2857,7 @@ class MaskedArray(ndarray):
                     mask = np.array(
                         [getmaskarray(np.asanyarray(m, dtype=_data.dtype))
                          for m in data], dtype=mdtype)
-                except ValueError:
+                except (ValueError, TypeError):
                     # If data is nested
                     mask = nomask
                 # Force shrinking of the mask if needed (and possible)
@@ -2871,10 +2870,22 @@ class MaskedArray(ndarray):
                     _data._mask = _data._mask.copy()
                     # Reset the shape of the original mask
                     if getmask(data) is not nomask:
-                        data._mask.shape = data.shape
+                        # gh-21022 encounters an issue here
+                        # because data._mask.shape is not writeable, but
+                        # the op was also pointless in that case, because
+                        # the shapes were the same, so we can at least
+                        # avoid that path
+                        if data._mask.shape != data.shape:
+                            data._mask.shape = data.shape
         else:
             # Case 2. : With a mask in input.
             # If mask is boolean, create an array of True or False
+
+            # if users pass `mask=None` be forgiving here and cast it False
+            # for speed; although the default is `mask=nomask` and can differ.
+            if mask is None:
+                mask = False
+
             if mask is True and mdtype == MaskType:
                 mask = np.ones(_data.shape, dtype=mdtype)
             elif mask is False and mdtype == MaskType:
@@ -2922,6 +2933,7 @@ class MaskedArray(ndarray):
                     else:
                         _data._mask = np.logical_or(mask, _data._mask)
                     _data._sharedmask = False
+
         # Update fill_value.
         if fill_value is None:
             fill_value = getattr(data, '_fill_value', None)
@@ -6306,6 +6318,12 @@ class MaskedArray(ndarray):
         memo[id(self)] = copied
         for (k, v) in self.__dict__.items():
             copied.__dict__[k] = deepcopy(v, memo)
+        # as clearly documented for np.copy(), you need to use
+        # deepcopy() directly for arrays of object type that may
+        # contain compound types--you cannot depend on normal
+        # copy semantics to do the right thing here
+        if self.dtype.hasobject:
+            copied._data[...] = deepcopy(copied._data)
         return copied
 
 
@@ -7002,6 +7020,21 @@ def sort(a, axis=-1, kind=None, order=None, endwith=True, fill_value=None):
     See Also
     --------
     MaskedArray.sort : equivalent method
+
+    Examples
+    --------
+    >>> import numpy.ma as ma
+    >>> x = [11.2, -3.973, 0.801, -1.41]
+    >>> mask = [0, 0, 0, 1]
+    >>> masked_x = ma.masked_array(x, mask)
+    >>> masked_x
+    masked_array(data=[11.2, -3.973, 0.801, --],
+                 mask=[False, False, False,  True],
+           fill_value=1e+20)
+    >>> ma.sort(masked_x)
+    masked_array(data=[-3.973, 0.801, 11.2, --],
+                 mask=[False, False, False,  True],
+           fill_value=1e+20)
     """
     a = np.array(a, copy=True, subok=True)
     if axis is None:
@@ -7027,6 +7060,29 @@ def compressed(x):
     --------
     ma.MaskedArray.compressed : Equivalent method.
 
+    Examples
+    --------
+    
+    Create an array with negative values masked:
+
+    >>> import numpy as np
+    >>> x = np.array([[1, -1, 0], [2, -1, 3], [7, 4, -1]])
+    >>> masked_x = np.ma.masked_array(x, mask=x < 0)
+    >>> masked_x
+    masked_array(
+      data=[[1, --, 0],
+            [2, --, 3],
+            [7, 4, --]],
+      mask=[[False,  True, False],
+            [False,  True, False],
+            [False, False,  True]],
+      fill_value=999999)
+
+    Compress the masked array into a 1-D array of non-masked values:
+
+    >>> np.ma.compressed(masked_x)
+    array([1, 0, 2, 3, 7, 4])
+
     """
     return asanyarray(x).compressed()
 
@@ -7102,6 +7158,38 @@ def diag(v, k=0):
     --------
     numpy.diag : Equivalent function for ndarrays.
 
+    Examples
+    --------
+
+    Create an array with negative values masked:
+
+    >>> import numpy as np
+    >>> x = np.array([[11.2, -3.973, 18], [0.801, -1.41, 12], [7, 33, -12]])
+    >>> masked_x = np.ma.masked_array(x, mask=x < 0)
+    >>> masked_x
+    masked_array(
+      data=[[11.2, --, 18.0],
+            [0.801, --, 12.0],
+            [7.0, 33.0, --]],
+      mask=[[False,  True, False],
+            [False,  True, False],
+            [False, False,  True]],
+      fill_value=1e+20)
+
+    Isolate the main diagonal from the masked array:
+
+    >>> np.ma.diag(masked_x)
+    masked_array(data=[11.2, --, --],
+                 mask=[False,  True,  True],
+           fill_value=1e+20)
+
+    Isolate the first diagonal below the main diagonal:
+
+    >>> np.ma.diag(masked_x, -1)
+    masked_array(data=[0.801, 33.0],
+                 mask=[False, False],
+           fill_value=1e+20)
+
     """
     output = np.diag(v, k).view(MaskedArray)
     if getmask(v) is not nomask:
@@ -7141,6 +7229,21 @@ def right_shift(a, n):
     --------
     numpy.right_shift
 
+    Examples
+    --------
+    >>> import numpy.ma as ma
+    >>> x = [11, 3, 8, 1]
+    >>> mask = [0, 0, 0, 1]
+    >>> masked_x = ma.masked_array(x, mask)
+    >>> masked_x
+    masked_array(data=[11, 3, 8, --],
+                 mask=[False, False, False,  True],
+           fill_value=999999)
+    >>> ma.right_shift(masked_x,1)
+    masked_array(data=[5, 1, 4, --],
+                 mask=[False, False, False,  True],
+           fill_value=999999)
+
     """
     m = getmask(a)
     if m is nomask:
@@ -7352,6 +7455,141 @@ def size(obj, axis=None):
 size.__doc__ = np.size.__doc__
 
 
+def diff(a, /, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
+    """
+    Calculate the n-th discrete difference along the given axis.
+    The first difference is given by ``out[i] = a[i+1] - a[i]`` along
+    the given axis, higher differences are calculated by using `diff`
+    recursively.
+    Preserves the input mask.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input
+        is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the
+        last axis.
+    prepend, append : array_like, optional
+        Values to prepend or append to `a` along axis prior to
+        performing the difference.  Scalar values are expanded to
+        arrays with length 1 in the direction of axis and the shape
+        of the input array in along all other axes.  Otherwise the
+        dimension and shape must match `a` except along axis.
+
+    Returns
+    -------
+    diff : MaskedArray
+        The n-th differences. The shape of the output is the same as `a`
+        except along `axis` where the dimension is smaller by `n`. The
+        type of the output is the same as the type of the difference
+        between any two elements of `a`. This is the same as the type of
+        `a` in most cases. A notable exception is `datetime64`, which
+        results in a `timedelta64` output array.
+
+    See Also
+    --------
+    numpy.diff : Equivalent function in the top-level NumPy module.
+
+    Notes
+    -----
+    Type is preserved for boolean arrays, so the result will contain
+    `False` when consecutive elements are the same and `True` when they
+    differ.
+
+    For unsigned integer arrays, the results will also be unsigned. This
+    should not be surprising, as the result is consistent with
+    calculating the difference directly:
+
+    >>> u8_arr = np.array([1, 0], dtype=np.uint8)
+    >>> np.ma.diff(u8_arr)
+    masked_array(data=[255],
+                 mask=False,
+           fill_value=999999,
+                dtype=uint8)
+    >>> u8_arr[1,...] - u8_arr[0,...]
+    255
+
+    If this is not desirable, then the array should be cast to a larger
+    integer type first:
+
+    >>> i16_arr = u8_arr.astype(np.int16)
+    >>> np.ma.diff(i16_arr)
+    masked_array(data=[-1],
+                 mask=False,
+           fill_value=999999,
+                dtype=int16)
+
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3, 4, 7, 0, 2, 3])
+    >>> x = np.ma.masked_where(a < 2, a)
+    >>> np.ma.diff(x)
+    masked_array(data=[--, 1, 1, 3, --, --, 1],
+            mask=[ True, False, False, False,  True,  True, False],
+        fill_value=999999)
+
+    >>> np.ma.diff(x, n=2)
+    masked_array(data=[--, 0, 2, --, --, --],
+                mask=[ True, False, False,  True,  True,  True],
+        fill_value=999999)
+
+    >>> a = np.array([[1, 3, 1, 5, 10], [0, 1, 5, 6, 8]])
+    >>> x = np.ma.masked_equal(a, value=1)
+    >>> np.ma.diff(x)
+    masked_array(
+        data=[[--, --, --, 5],
+                [--, --, 1, 2]],
+        mask=[[ True,  True,  True, False],
+                [ True,  True, False, False]],
+        fill_value=1)
+
+    >>> np.ma.diff(x, axis=0)
+    masked_array(data=[[--, --, --, 1, -2]],
+            mask=[[ True,  True,  True, False, False]],
+        fill_value=1)
+
+    """
+    if n == 0:
+        return a
+    if n < 0:
+        raise ValueError("order must be non-negative but got " + repr(n))
+
+    a = np.ma.asanyarray(a)
+    if a.ndim == 0:
+        raise ValueError(
+            "diff requires input that is at least one dimensional"
+            )
+
+    combined = []
+    if prepend is not np._NoValue:
+        prepend = np.ma.asanyarray(prepend)
+        if prepend.ndim == 0:
+            shape = list(a.shape)
+            shape[axis] = 1
+            prepend = np.broadcast_to(prepend, tuple(shape))
+        combined.append(prepend)
+
+    combined.append(a)
+
+    if append is not np._NoValue:
+        append = np.ma.asanyarray(append)
+        if append.ndim == 0:
+            shape = list(a.shape)
+            shape[axis] = 1
+            append = np.broadcast_to(append, tuple(shape))
+        combined.append(append)
+
+    if len(combined) > 1:
+        a = np.ma.concatenate(combined, axis)
+
+    # GH 22465 np.diff without prepend/append preserves the mask
+    return np.diff(a, n, axis)
+
+
 ##############################################################################
 #                            Extra functions                                 #
 ##############################################################################
@@ -7579,94 +7817,18 @@ def round_(a, decimals=0, out=None):
 round = round_
 
 
-# Needed by dot, so move here from extras.py. It will still be exported
-# from extras.py for compatibility.
-def mask_rowcols(a, axis=None):
+def _mask_propagate(a, axis):
     """
-    Mask rows and/or columns of a 2D array that contain masked values.
-
-    Mask whole rows and/or columns of a 2D array that contain
-    masked values.  The masking behavior is selected using the
-    `axis` parameter.
-
-      - If `axis` is None, rows *and* columns are masked.
-      - If `axis` is 0, only rows are masked.
-      - If `axis` is 1 or -1, only columns are masked.
-
-    Parameters
-    ----------
-    a : array_like, MaskedArray
-        The array to mask.  If not a MaskedArray instance (or if no array
-        elements are masked).  The result is a MaskedArray with `mask` set
-        to `nomask` (False). Must be a 2D array.
-    axis : int, optional
-        Axis along which to perform the operation. If None, applies to a
-        flattened version of the array.
-
-    Returns
-    -------
-    a : MaskedArray
-        A modified version of the input array, masked depending on the value
-        of the `axis` parameter.
-
-    Raises
-    ------
-    NotImplementedError
-        If input array `a` is not 2D.
-
-    See Also
-    --------
-    mask_rows : Mask rows of a 2D array that contain masked values.
-    mask_cols : Mask cols of a 2D array that contain masked values.
-    masked_where : Mask where a condition is met.
-
-    Notes
-    -----
-    The input array's mask is modified by this function.
-
-    Examples
-    --------
-    >>> import numpy.ma as ma
-    >>> a = np.zeros((3, 3), dtype=int)
-    >>> a[1, 1] = 1
-    >>> a
-    array([[0, 0, 0],
-           [0, 1, 0],
-           [0, 0, 0]])
-    >>> a = ma.masked_equal(a, 1)
-    >>> a
-    masked_array(
-      data=[[0, 0, 0],
-            [0, --, 0],
-            [0, 0, 0]],
-      mask=[[False, False, False],
-            [False,  True, False],
-            [False, False, False]],
-      fill_value=1)
-    >>> ma.mask_rowcols(a)
-    masked_array(
-      data=[[0, --, 0],
-            [--, --, --],
-            [0, --, 0]],
-      mask=[[False,  True, False],
-            [ True,  True,  True],
-            [False,  True, False]],
-      fill_value=1)
-
+    Mask whole 1-d vectors of an array that contain masked values.
     """
     a = array(a, subok=False)
-    if a.ndim != 2:
-        raise NotImplementedError("mask_rowcols works for 2D arrays only.")
     m = getmask(a)
-    # Nothing is masked: return a
-    if m is nomask or not m.any():
+    if m is nomask or not m.any() or axis is None:
         return a
-    maskedval = m.nonzero()
     a._mask = a._mask.copy()
-    if not axis:
-        a[np.unique(maskedval[0])] = masked
-    if axis in [None, 1, -1]:
-        a[:, np.unique(maskedval[1])] = masked
+    axes = normalize_axis_tuple(axis, a.ndim)
+    for ax in axes:
+        a._mask |= m.any(axis=ax, keepdims=True)
     return a
 
 
@@ -7683,10 +7845,6 @@ def dot(a, b, strict=False, out=None):
     corresponding method, it is recommended that the optional arguments be
     treated as keyword only.  At some point that may be mandatory.
 
-    .. note::
-      Works only with 2-D arrays at the moment.
-
-
     Parameters
     ----------
     a, b : masked_array_like
@@ -7730,18 +7888,22 @@ def dot(a, b, strict=False, out=None):
       fill_value=999999)
 
     """
-    # !!!: Works only with 2D arrays. There should be a way to get it to run
-    # with higher dimension
-    if strict and (a.ndim == 2) and (b.ndim == 2):
-        a = mask_rowcols(a, 0)
-        b = mask_rowcols(b, 1)
+    if strict is True:
+        if np.ndim(a) == 0 or np.ndim(b) == 0:
+            pass
+        elif b.ndim == 1:
+            a = _mask_propagate(a, a.ndim - 1)
+            b = _mask_propagate(b, b.ndim - 1)
+        else:
+            a = _mask_propagate(a, a.ndim - 1)
+            b = _mask_propagate(b, b.ndim - 2)
     am = ~getmaskarray(a)
     bm = ~getmaskarray(b)
 
     if out is None:
         d = np.dot(filled(a, 0), filled(b, 0))
         m = ~np.dot(am, bm)
-        if d.ndim == 0:
+        if np.ndim(d) == 0:
             d = np.asarray(d)
         r = d.view(get_masked_subclass(a, b))
         r.__setmask__(m)
@@ -8292,12 +8454,6 @@ clip = _convert2ma(
     np_ret='clipped_array : ndarray',
     np_ma_ret='clipped_array : MaskedArray',
 )
-diff = _convert2ma(
-    'diff',
-    params=dict(fill_value=None, hardmask=False),
-    np_ret='diff : ndarray',
-    np_ma_ret='diff : MaskedArray',
-)
 empty = _convert2ma(
     'empty',
     params=dict(fill_value=None, hardmask=False),
diff --git a/contrib/python/numpy/py3/numpy/ma/core.pyi b/contrib/python/numpy/py3/numpy/ma/core.pyi
index d5d4f7f3632..e94ebce3c31 100644
--- a/contrib/python/numpy/py3/numpy/ma/core.pyi
+++ b/contrib/python/numpy/py3/numpy/ma/core.pyi
@@ -7,7 +7,6 @@ from numpy import (
     amin as amin,
     bool_ as bool_,
     expand_dims as expand_dims,
-    diff as diff,
     clip as clip,
     indices as indices,
     ones_like as ones_like,
@@ -433,10 +432,10 @@ def resize(x, new_shape): ...
 def ndim(obj): ...
 def shape(obj): ...
 def size(obj, axis=...): ...
+def diff(a, /, n=..., axis=..., prepend=..., append=...): ...
 def where(condition, x=..., y=...): ...
 def choose(indices, choices, out=..., mode=...): ...
-def round_(a, decimals=..., out=...): ...
-round = round_
+def round(a, decimals=..., out=...): ...
 
 def inner(a, b): ...
 innerproduct = inner
diff --git a/contrib/python/numpy/py3/numpy/ma/extras.py b/contrib/python/numpy/py3/numpy/ma/extras.py
index 41bce0f2232..8a6246c3666 100644
--- a/contrib/python/numpy/py3/numpy/ma/extras.py
+++ b/contrib/python/numpy/py3/numpy/ma/extras.py
@@ -27,8 +27,7 @@ from . import core as ma
 from .core import (
     MaskedArray, MAError, add, array, asarray, concatenate, filled, count,
     getmask, getmaskarray, make_mask_descr, masked, masked_array, mask_or,
-    nomask, ones, sort, zeros, getdata, get_masked_subclass, dot,
-    mask_rowcols
+    nomask, ones, sort, zeros, getdata, get_masked_subclass, dot
     )
 
 import numpy as np
@@ -398,7 +397,7 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs):
         dtypes.append(np.asarray(res).dtype)
         outarr = zeros(outshape, object)
         outarr[tuple(ind)] = res
-        Ntot = np.product(outshape)
+        Ntot = np.prod(outshape)
         k = 1
         while k < Ntot:
             # increment the index
@@ -418,7 +417,7 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs):
         j = i.copy()
         j[axis] = ([slice(None, None)] * res.ndim)
         j.put(indlist, ind)
-        Ntot = np.product(outshape)
+        Ntot = np.prod(outshape)
         holdshape = outshape
         outshape = list(arr.shape)
         outshape[axis] = res.shape
@@ -955,6 +954,95 @@ def compress_cols(a):
     return compress_rowcols(a, 1)
 
 
+def mask_rowcols(a, axis=None):
+    """
+    Mask rows and/or columns of a 2D array that contain masked values.
+
+    Mask whole rows and/or columns of a 2D array that contain
+    masked values.  The masking behavior is selected using the
+    `axis` parameter.
+
+      - If `axis` is None, rows *and* columns are masked.
+      - If `axis` is 0, only rows are masked.
+      - If `axis` is 1 or -1, only columns are masked.
+
+    Parameters
+    ----------
+    a : array_like, MaskedArray
+        The array to mask.  If not a MaskedArray instance (or if no array
+        elements are masked), the result is a MaskedArray with `mask` set
+        to `nomask` (False). Must be a 2D array.
+    axis : int, optional
+        Axis along which to perform the operation. If None, applies to a
+        flattened version of the array.
+
+    Returns
+    -------
+    a : MaskedArray
+        A modified version of the input array, masked depending on the value
+        of the `axis` parameter.
+
+    Raises
+    ------
+    NotImplementedError
+        If input array `a` is not 2D.
+
+    See Also
+    --------
+    mask_rows : Mask rows of a 2D array that contain masked values.
+    mask_cols : Mask cols of a 2D array that contain masked values.
+    masked_where : Mask where a condition is met.
+
+    Notes
+    -----
+    The input array's mask is modified by this function.
+
+    Examples
+    --------
+    >>> import numpy.ma as ma
+    >>> a = np.zeros((3, 3), dtype=int)
+    >>> a[1, 1] = 1
+    >>> a
+    array([[0, 0, 0],
+           [0, 1, 0],
+           [0, 0, 0]])
+    >>> a = ma.masked_equal(a, 1)
+    >>> a
+    masked_array(
+      data=[[0, 0, 0],
+            [0, --, 0],
+            [0, 0, 0]],
+      mask=[[False, False, False],
+            [False,  True, False],
+            [False, False, False]],
+      fill_value=1)
+    >>> ma.mask_rowcols(a)
+    masked_array(
+      data=[[0, --, 0],
+            [--, --, --],
+            [0, --, 0]],
+      mask=[[False,  True, False],
+            [ True,  True,  True],
+            [False,  True, False]],
+      fill_value=1)
+
+    """
+    a = array(a, subok=False)
+    if a.ndim != 2:
+        raise NotImplementedError("mask_rowcols works for 2D arrays only.")
+    m = getmask(a)
+    # Nothing is masked: return a
+    if m is nomask or not m.any():
+        return a
+    maskedval = m.nonzero()
+    a._mask = a._mask.copy()
+    if not axis:
+        a[np.unique(maskedval[0])] = masked
+    if axis in [None, 1, -1]:
+        a[:, np.unique(maskedval[1])] = masked
+    return a
+
+
 def mask_rows(a, axis=np._NoValue):
     """
     Mask rows of a 2D array that contain masked values.
diff --git a/contrib/python/numpy/py3/numpy/ma/tests/test_core.py b/contrib/python/numpy/py3/numpy/ma/tests/test_core.py
index 722b3ded4fb..6ab1d7e4f1a 100644
--- a/contrib/python/numpy/py3/numpy/ma/tests/test_core.py
+++ b/contrib/python/numpy/py3/numpy/ma/tests/test_core.py
@@ -8,6 +8,7 @@ __author__ = "Pierre GF Gerard-Marchant"
 
 import sys
 import warnings
+import copy
 import operator
 import itertools
 import textwrap
@@ -215,6 +216,8 @@ class TestMaskedArray:
         assert_(np.may_share_memory(x.mask, y.mask))
         y = array([1, 2, 3], mask=x._mask, copy=True)
         assert_(not np.may_share_memory(x.mask, y.mask))
+        x = array([1, 2, 3], mask=None)
+        assert_equal(x._mask, [False, False, False])
 
     def test_masked_singleton_array_creation_warns(self):
         # The first works, but should not (ideally), there may be no way
@@ -1351,16 +1354,16 @@ class TestMaskedArrayArithmetic:
         assert_equal(np.sum(x, axis=0), sum(x, axis=0))
         assert_equal(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0))
         assert_equal(np.sum(x, 0), sum(x, 0))
-        assert_equal(np.product(x, axis=0), product(x, axis=0))
-        assert_equal(np.product(x, 0), product(x, 0))
-        assert_equal(np.product(filled(xm, 1), axis=0), product(xm, axis=0))
+        assert_equal(np.prod(x, axis=0), product(x, axis=0))
+        assert_equal(np.prod(x, 0), product(x, 0))
+        assert_equal(np.prod(filled(xm, 1), axis=0), product(xm, axis=0))
         s = (3, 4)
         x.shape = y.shape = xm.shape = ym.shape = s
         if len(s) > 1:
             assert_equal(np.concatenate((x, y), 1), concatenate((xm, ym), 1))
             assert_equal(np.add.reduce(x, 1), add.reduce(x, 1))
             assert_equal(np.sum(x, 1), sum(x, 1))
-            assert_equal(np.product(x, 1), product(x, 1))
+            assert_equal(np.prod(x, 1), product(x, 1))
 
     def test_binops_d2D(self):
         # Test binary operations on 2D data
@@ -4127,6 +4130,46 @@ class TestMaskedArrayMathMethods:
                          mask=np.zeros((10000, 10000)))
         assert_equal(a.mean(), 65535.0)
 
+    def test_diff_with_prepend(self):
+        # GH 22465
+        x = np.array([1, 2, 2, 3, 4, 2, 1, 1])
+
+        a = np.ma.masked_equal(x[3:], value=2)
+        a_prep = np.ma.masked_equal(x[:3], value=2)
+        diff1 = np.ma.diff(a, prepend=a_prep, axis=0)
+
+        b = np.ma.masked_equal(x, value=2)
+        diff2 = np.ma.diff(b, axis=0)
+
+        assert_(np.ma.allequal(diff1, diff2))
+
+    def test_diff_with_append(self):
+        # GH 22465
+        x = np.array([1, 2, 2, 3, 4, 2, 1, 1])
+
+        a = np.ma.masked_equal(x[:3], value=2)
+        a_app = np.ma.masked_equal(x[3:], value=2)
+        diff1 = np.ma.diff(a, append=a_app, axis=0)
+
+        b = np.ma.masked_equal(x, value=2)
+        diff2 = np.ma.diff(b, axis=0)
+
+        assert_(np.ma.allequal(diff1, diff2))
+
+    def test_diff_with_dim_0(self):
+        with pytest.raises(
+            ValueError,
+            match="diff requires input that is at least one dimensional"
+            ):
+            np.ma.diff(np.array(1))
+
+    def test_diff_with_n_0(self):
+        a = np.ma.masked_equal([1, 2, 2, 3, 4, 2, 1, 1], value=2)
+        diff = np.ma.diff(a, n=0, axis=0)
+
+        assert_(np.ma.allequal(a, diff))
+
+
 class TestMaskedArrayMathMethodsComplex:
     # Test class for miscellaneous MaskedArrays methods.
     def setup_method(self):
@@ -4500,7 +4543,7 @@ class TestMaskedArrayFunctions:
         x = np.arange(4, dtype=np.int32)
         y = np.arange(4, dtype=np.float32) * 2.2
         test = where(x > 1.5, y, x).dtype
-        control = np.find_common_type([np.int32, np.float32], [])
+        control = np.result_type(np.int32, np.float32)
         assert_equal(test, control)
 
     def test_where_broadcast(self):
@@ -5564,3 +5607,47 @@ note
 original note"""
 
     assert_equal(np.ma.core.doc_note(method.__doc__, "note"), expected_doc)
+
+
+def test_gh_22556():
+    source = np.ma.array([0, [0, 1, 2]], dtype=object)
+    deepcopy = copy.deepcopy(source)
+    deepcopy[1].append('this should not appear in source')
+    assert len(source[1]) == 3
+
+
+def test_gh_21022():
+    # testing for absence of reported error
+    source = np.ma.masked_array(data=[-1, -1], mask=True, dtype=np.float64)
+    axis = np.array(0)
+    result = np.prod(source, axis=axis, keepdims=False)
+    result = np.ma.masked_array(result,
+                                mask=np.ones(result.shape, dtype=np.bool_))
+    array = np.ma.masked_array(data=-1, mask=True, dtype=np.float64)
+    copy.deepcopy(array)
+    copy.deepcopy(result)
+
+
+def test_deepcopy_2d_obj():
+    source = np.ma.array([[0, "dog"],
+                          [1, 1],
+                          [[1, 2], "cat"]],
+                        mask=[[0, 1],
+                              [0, 0],
+                              [0, 0]],
+                        dtype=object)
+    deepcopy = copy.deepcopy(source)
+    deepcopy[2, 0].extend(['this should not appear in source', 3])
+    assert len(source[2, 0]) == 2
+    assert len(deepcopy[2, 0]) == 4
+    assert_equal(deepcopy._mask, source._mask)
+    deepcopy._mask[0, 0] = 1
+    assert source._mask[0, 0] == 0
+
+
+def test_deepcopy_0d_obj():
+    source = np.ma.array(0, mask=[0], dtype=object)
+    deepcopy = copy.deepcopy(source)
+    deepcopy[...] = 17
+    assert_equal(source, 0)
+    assert_equal(deepcopy, 17)
diff --git a/contrib/python/numpy/py3/numpy/ma/tests/test_extras.py b/contrib/python/numpy/py3/numpy/ma/tests/test_extras.py
index 38603fb8433..d09a50fecd4 100644
--- a/contrib/python/numpy/py3/numpy/ma/tests/test_extras.py
+++ b/contrib/python/numpy/py3/numpy/ma/tests/test_extras.py
@@ -387,8 +387,8 @@ class TestConcatenator:
         # Tests mr_ on 2D arrays.
         a_1 = np.random.rand(5, 5)
         a_2 = np.random.rand(5, 5)
-        m_1 = np.round_(np.random.rand(5, 5), 0)
-        m_2 = np.round_(np.random.rand(5, 5), 0)
+        m_1 = np.round(np.random.rand(5, 5), 0)
+        m_2 = np.round(np.random.rand(5, 5), 0)
         b_1 = masked_array(a_1, mask=m_1)
         b_2 = masked_array(a_2, mask=m_2)
         # append columns
@@ -730,6 +730,47 @@ class TestCompressFunctions:
         assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]])
         c = dot(b, a, strict=False)
         assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[0, 0], [0, 0]], [[0, 0], [0, 1]]])
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask,
+                     [[[[1, 1], [1, 1]], [[0, 0], [0, 1]]],
+                      [[[0, 0], [0, 1]], [[0, 0], [0, 1]]]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask,
+                     [[[[0, 0], [0, 1]], [[0, 0], [0, 0]]],
+                      [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask,
+                     [[[[1, 0], [0, 0]], [[1, 0], [0, 0]]],
+                      [[[1, 0], [0, 0]], [[1, 1], [1, 1]]]])
+        c = dot(b, a, strict=False)
+        assert_equal(c.mask,
+                     [[[[0, 0], [0, 0]], [[0, 0], [0, 0]]],
+                      [[[0, 0], [0, 0]], [[1, 0], [0, 0]]]])
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = 5.
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(b, a, strict=False)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = masked_array(np.arange(2), mask=[0, 1])
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 1], [1, 1]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask, [[1, 0], [0, 0]])
 
     def test_dot_returns_maskedarray(self):
         # See gh-6611
diff --git a/contrib/python/numpy/py3/numpy/ma/tests/test_old_ma.py b/contrib/python/numpy/py3/numpy/ma/tests/test_old_ma.py
index 8465b11531d..7b892ad2375 100644
--- a/contrib/python/numpy/py3/numpy/ma/tests/test_old_ma.py
+++ b/contrib/python/numpy/py3/numpy/ma/tests/test_old_ma.py
@@ -194,16 +194,16 @@ class TestMa:
         assert_(eq(np.sum(x, axis=0), sum(x, axis=0)))
         assert_(eq(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0)))
         assert_(eq(np.sum(x, 0), sum(x, 0)))
-        assert_(eq(np.product(x, axis=0), product(x, axis=0)))
-        assert_(eq(np.product(x, 0), product(x, 0)))
-        assert_(eq(np.product(filled(xm, 1), axis=0),
+        assert_(eq(np.prod(x, axis=0), product(x, axis=0)))
+        assert_(eq(np.prod(x, 0), product(x, 0)))
+        assert_(eq(np.prod(filled(xm, 1), axis=0),
                            product(xm, axis=0)))
         if len(s) > 1:
             assert_(eq(np.concatenate((x, y), 1),
                                concatenate((xm, ym), 1)))
             assert_(eq(np.add.reduce(x, 1), add.reduce(x, 1)))
             assert_(eq(np.sum(x, 1), sum(x, 1)))
-            assert_(eq(np.product(x, 1), product(x, 1)))
+            assert_(eq(np.prod(x, 1), product(x, 1)))
 
     def test_testCI(self):
         # Test of conversions and indexing
diff --git a/contrib/python/numpy/py3/numpy/ma/tests/test_regression.py b/contrib/python/numpy/py3/numpy/ma/tests/test_regression.py
index cb3d0349f96..f4f32cc7a98 100644
--- a/contrib/python/numpy/py3/numpy/ma/tests/test_regression.py
+++ b/contrib/python/numpy/py3/numpy/ma/tests/test_regression.py
@@ -89,3 +89,9 @@ class TestRegression:
     def test_masked_array_tobytes_fortran(self):
         ma = np.ma.arange(4).reshape((2,2))
         assert_array_equal(ma.tobytes(order='F'), ma.T.tobytes())
+
+    def test_structured_array(self):
+        # see gh-22041
+        np.ma.array((1, (b"", b"")),
+                    dtype=[("x", np.int_),
+                          ("y", [("i", np.void), ("j", np.void)])])
diff --git a/contrib/python/numpy/py3/numpy/ma/tests/test_subclassing.py b/contrib/python/numpy/py3/numpy/ma/tests/test_subclassing.py
index 64c66eeb9d6..e3c88525371 100644
--- a/contrib/python/numpy/py3/numpy/ma/tests/test_subclassing.py
+++ b/contrib/python/numpy/py3/numpy/ma/tests/test_subclassing.py
@@ -154,6 +154,7 @@ class WrappedArray(NDArrayOperatorsMixin):
     ufunc deferrals are commutative.
     See: https://github.com/numpy/numpy/issues/15200)
     """
+    __slots__ = ('_array', 'attrs')
     __array_priority__ = 20
 
     def __init__(self, array, **attrs):
@@ -448,3 +449,12 @@ class TestClassWrapping:
         assert_(isinstance(np.divide(wm, m2), WrappedArray))
         assert_(isinstance(np.divide(m2, wm), WrappedArray))
         assert_equal(np.divide(m2, wm), np.divide(wm, m2))
+
+    def test_mixins_have_slots(self):
+        mixin = NDArrayOperatorsMixin()
+        # Should raise an error
+        assert_raises(AttributeError, mixin.__setattr__, "not_a_real_attr", 1)
+
+        m = np.ma.masked_array([1, 3, 5], mask=[False, True, False])
+        wm = WrappedArray(m)
+        assert_raises(AttributeError, wm.__setattr__, "not_an_attr", 2)
diff --git a/contrib/python/numpy/py3/numpy/ma/testutils.py b/contrib/python/numpy/py3/numpy/ma/testutils.py
index 2dd479abe48..7a633906bb4 100644
--- a/contrib/python/numpy/py3/numpy/ma/testutils.py
+++ b/contrib/python/numpy/py3/numpy/ma/testutils.py
@@ -233,7 +233,7 @@ def fail_if_array_equal(x, y, err_msg='', verbose=True):
 
     """
     def compare(x, y):
-        return (not np.alltrue(approx(x, y)))
+        return (not np.all(approx(x, y)))
     assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
                          header='Arrays are not equal')
 
diff --git a/contrib/python/numpy/py3/numpy/matrixlib/defmatrix.py b/contrib/python/numpy/py3/numpy/matrixlib/defmatrix.py
index a414ee9bbd5..d029b13fb8b 100644
--- a/contrib/python/numpy/py3/numpy/matrixlib/defmatrix.py
+++ b/contrib/python/numpy/py3/numpy/matrixlib/defmatrix.py
@@ -3,9 +3,10 @@ __all__ = ['matrix', 'bmat', 'mat', 'asmatrix']
 import sys
 import warnings
 import ast
+
+from .._utils import set_module
 import numpy.core.numeric as N
 from numpy.core.numeric import concatenate, isscalar
-from numpy.core.overrides import set_module
 # While not in __all__, matrix_power used to be defined here, so we import
 # it for backward compatibility.
 from numpy.linalg import matrix_power
diff --git a/contrib/python/numpy/py3/numpy/meson.build b/contrib/python/numpy/py3/numpy/meson.build
new file mode 100644
index 00000000000..c9a4970a8a5
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/meson.build
@@ -0,0 +1,291 @@
+# We need -lm for all C code (assuming it uses math functions, which is safe to
+# assume for numpy).
+m_dep = cc.find_library('m', required : false)
+mlib_linkflag = ''
+if m_dep.found()
+  mlib_linkflag = '-lm'
+  add_project_link_arguments(mlib_linkflag, language : 'c')
+endif
+
+# Platform detection
+is_windows = host_machine.system() == 'windows'
+is_mingw = is_windows and cc.get_id() == 'gcc'
+
+if is_mingw
+  # For mingw-w64, link statically against the UCRT.
+  gcc_link_args = ['-lucrt', '-static']
+  add_project_link_arguments(gcc_link_args, language: ['c', 'cpp'])
+  # Force gcc to float64 long doubles for compatibility with MSVC
+  # builds, for C only.
+  add_project_arguments('-mlong-double-64', language: 'c')
+  # Make fprintf("%zd") work (see https://github.com/rgommers/scipy/issues/118)
+  add_project_arguments('-D__USE_MINGW_ANSI_STDIO=1', language: ['c', 'cpp'])
+endif
+
+# We install libnpymath and libnpyrandom; ensure they're using a `.lib` rather
+# than a `.a` file extension in order not to break including them in a
+# distutils-based build (see gh-23981 and
+# https://mesonbuild.com/FAQ.html#why-does-building-my-project-with-msvc-output-static-libraries-called-libfooa)
+if is_windows and cc.get_id() == 'msvc'
+  name_prefix_staticlib = ''
+  name_suffix_staticlib = 'lib'
+else
+  name_prefix_staticlib = []
+  name_suffix_staticlib = []
+endif
+
+# Enable UNIX large file support on 32-bit systems (64 bit off_t,
+# lseek -> lseek64, etc.)
+cflags_large_file_support = []
+if host_machine.system() == 'aix'
+  cflags_large_file_support += '-D_LARGE_FILES'
+else
+  cflags_large_file_support += [
+    '-D_FILE_OFFSET_BITS=64',
+    '-D_LARGEFILE_SOURCE=1',
+    '-D_LARGEFILE64_SOURCE=1',
+  ]
+endif
+
+
+# TODO: 64-bit BLAS and LAPACK
+#
+# Note that this works as long as BLAS and LAPACK are detected properly via
+# pkg-config. By default we look for OpenBLAS, other libraries can be configured via
+# `meson configure -Dblas=blas -Dlapack=lapack` (example to build with Netlib
+# BLAS and LAPACK).
+# For MKL and for auto-detecting one of multiple libs, we'll need a custom
+# dependency in Meson (like is done for scalapack) - see
+# https://github.com/mesonbuild/meson/issues/2835
+blas_name = get_option('blas')
+lapack_name = get_option('lapack')
+# pkg-config uses a lower-case name while CMake uses a capitalized name, so try
+# that too to make the fallback detection with CMake work
+if blas_name == 'openblas'
+  blas = dependency(['openblas', 'OpenBLAS'], required: false)
+else
+  blas = dependency(blas_name, required: false)
+endif
+have_blas = blas.found()
+cblas = []
+if have_blas
+  # Netlib BLAS has a separate `libcblas.so` which we use directly in the g77
+  # ABI wrappers, so detect it and error out if we cannot find it. OpenBLAS can
+  # be built without CBLAS too (see gh-23909, done by Arch Linux until
+  # recently)
+  # In the future, this should be done automatically for:
+  #   `dependency('blas', modules: cblas)`
+  # see https://github.com/mesonbuild/meson/pull/10921.
+  have_cblas = false
+  if cc.links('''
+      #include <cblas.h>
+      int main(int argc, const char *argv[])
+      {
+          double a[4] = {1,2,3,4};
+          double b[4] = {5,6,7,8};
+          return cblas_ddot(4, a, 1, b, 1) > 10;
+      }
+      ''',
+      dependencies: blas,
+      name: 'CBLAS',
+    )
+    have_cblas = true
+  else
+    cblas = dependency('cblas', required: false)
+    if cblas.found()
+      have_cblas = true
+    endif
+  endif
+endif
+
+if lapack_name == 'openblas'
+  lapack_name = ['openblas', 'OpenBLAS']
+endif
+lapack_dep = dependency(lapack_name, required: false)
+have_lapack = lapack_dep.found()
+
+dependency_map = {
+  'BLAS': blas,
+  'LAPACK': lapack_dep,
+}
+
+use_ilp64 = get_option('use-ilp64')
+if not use_ilp64
+  # For now, keep supporting this environment variable too (same as in setup.py)
+  # `false is the default for the CLI flag, so check if env var was set
+  use_ilp64 = run_command(py,
+    [
+      '-c',
+      'import os; print(1) if os.environ.get("NPY_USE_BLAS_ILP64", "0") != "0" else print(0)'
+    ],
+    check: true
+  ).stdout().strip() == '1'
+endif
+
+# BLAS and LAPACK are optional dependencies for NumPy. We can only use a BLAS
+# which provides a CBLAS interface.
+# TODO: add ILP64 support
+if have_blas
+  c_args_blas = []  # note: used for C and C++ via `blas_dep` below
+  if have_cblas
+    c_args_blas += ['-DHAVE_CBLAS']
+  endif
+  if use_ilp64
+    c_args_blas += ['-DHAVE_BLAS_ILP64']
+  endif
+  # This is currently injected directly into CFLAGS/CXXFLAGS for wheel builds
+  # (see cibuildwheel settings in pyproject.toml)
+  blas_symbol_suffix = get_option('blas-symbol-suffix')
+  if blas_symbol_suffix != ''
+    c_args_blas += ['-DBLAS_SYMBOL_SUFFIX=' + blas_symbol_suffix]
+  endif
+  blas_dep = declare_dependency(
+    dependencies: [blas, cblas],
+    compile_args: c_args_blas,
+  )
+else
+  blas_dep = []
+endif
+
+# Copy the main __init__.py|pxd files to the build dir (needed for Cython)
+__init__py = fs.copyfile('__init__.py')
+__init__pxd = fs.copyfile('__init__.pxd')
+__init__pxd30 = fs.copyfile('__init__.cython-30.pxd')
+_cython_tree = [__init__py, __init__pxd, __init__pxd30]
+
+python_sources = [
+  '__init__.cython-30.pxd',
+  '__init__.pxd',
+  '__init__.py',
+  '__init__.pyi',
+  '_distributor_init.py',
+  '_globals.py',
+  '_pytesttester.py',
+  '_pytesttester.pyi',
+  '_version.py',
+  'conftest.py',
+  'ctypeslib.py',
+  'ctypeslib.pyi',
+  'exceptions.py',
+  'exceptions.pyi',
+  'dtypes.py',
+  'dtypes.pyi',
+  'matlib.py',
+  'py.typed',
+  'version.py'
+]
+
+py.install_sources(
+  python_sources,
+  subdir: 'numpy'
+)
+
+src_file_cli = find_program('_build_utils/process_src_template.py')
+src_file = generator(src_file_cli,
+  arguments : ['@INPUT@', '--outfile', '@OUTPUT@'],
+  output : '@BASENAME@'
+)
+
+tempita_cli = find_program('_build_utils/tempita.py')
+tempita = generator(tempita_cli,
+  arguments : ['@INPUT@', '--outfile', '@OUTPUT@'],
+  output : '@BASENAME@'
+)
+
+pure_subdirs = [
+  '_pyinstaller',
+  '_typing',
+  '_utils',
+  'array_api',
+  'compat',
+  'doc',
+  'f2py',
+  'lib',
+  'ma',
+  'matrixlib',
+  'polynomial',
+  'testing',
+  'tests',
+  'typing',
+]
+if py.version().version_compare('<3.12')
+  pure_subdirs += 'distutils'
+endif
+
+np_dir = py.get_install_dir() / 'numpy'
+
+foreach subdir: pure_subdirs
+  install_subdir(subdir, install_dir: np_dir)
+endforeach
+
+compilers = {
+  'C': cc,
+  'CPP': cpp,
+  'CYTHON': meson.get_compiler('cython')
+}
+
+machines = {
+  'HOST': host_machine,
+  'BUILD': build_machine,
+}
+
+conf_data = configuration_data()
+
+# Set compiler information
+foreach name, compiler : compilers
+  conf_data.set(name + '_COMP', compiler.get_id())
+  conf_data.set(name + '_COMP_LINKER_ID', compiler.get_linker_id())
+  conf_data.set(name + '_COMP_VERSION', compiler.version())
+  conf_data.set(name + '_COMP_CMD_ARRAY', ', '.join(compiler.cmd_array()))
+  conf_data.set(name + '_COMP_ARGS', ', '.join(
+      get_option(name.to_lower() + '_args')
+    )
+  )
+  conf_data.set(name + '_COMP_LINK_ARGS', ', '.join(
+      get_option(name.to_lower() + '_link_args')
+    )
+  )
+endforeach
+
+# Machines CPU and system information
+foreach name, machine : machines
+  conf_data.set(name + '_CPU', machine.cpu())
+  conf_data.set(name + '_CPU_FAMILY', machine.cpu_family())
+  conf_data.set(name + '_CPU_ENDIAN', machine.endian())
+  conf_data.set(name + '_CPU_SYSTEM', machine.system())
+endforeach
+
+conf_data.set('CROSS_COMPILED', meson.is_cross_build())
+
+# Python information
+conf_data.set('PYTHON_PATH', py.full_path())
+conf_data.set('PYTHON_VERSION', py.language_version())
+
+# Dependencies information
+foreach name, dep : dependency_map
+  conf_data.set(name + '_NAME', dep.name())
+  conf_data.set(name + '_FOUND', dep.found())
+  if dep.found()
+    conf_data.set(name + '_VERSION', dep.version())
+    conf_data.set(name + '_TYPE_NAME', dep.type_name())
+    if dep.type_name() == 'pkgconfig'
+      # CMake detection yields less info, so we need to leave it blank there
+      conf_data.set(name + '_INCLUDEDIR', dep.get_variable('includedir'))
+      conf_data.set(name + '_LIBDIR', dep.get_variable('libdir'))
+      conf_data.set(name + '_OPENBLAS_CONFIG', dep.get_variable('openblas_config'))
+      conf_data.set(name + '_PCFILEDIR', dep.get_variable('pcfiledir'))
+    endif
+  endif
+endforeach
+
+configure_file(
+  input: '__config__.py.in',
+  output: '__config__.py',
+  configuration : conf_data,
+  install_dir: np_dir,
+)
+
+subdir('core')
+subdir('fft')
+subdir('linalg')
+subdir('random')
diff --git a/contrib/python/numpy/py3/numpy/polynomial/_polybase.py b/contrib/python/numpy/py3/numpy/polynomial/_polybase.py
index 3bea91dd239..9730574cf22 100644
--- a/contrib/python/numpy/py3/numpy/polynomial/_polybase.py
+++ b/contrib/python/numpy/py3/numpy/polynomial/_polybase.py
@@ -682,6 +682,28 @@ class ABCPolyBase(abc.ABC):
         degree : int
             Degree of the series, one less than the number of coefficients.
 
+        Examples
+        --------
+
+        Create a polynomial object for ``1 + 7*x + 4*x**2``:
+
+        >>> poly = np.polynomial.Polynomial([1, 7, 4])
+        >>> print(poly)
+        1.0 + 7.0·x + 4.0·x²
+        >>> poly.degree()
+        2
+
+        Note that this method does not check for non-zero coefficients.
+        You must trim the polynomial to remove any trailing zeroes:
+
+        >>> poly = np.polynomial.Polynomial([1, 7, 0])
+        >>> print(poly)
+        1.0 + 7.0·x + 0.0·x²
+        >>> poly.degree()
+        2
+        >>> poly.trim().degree()
+        1
+
         """
         return len(self) - 1
 
@@ -887,7 +909,7 @@ class ABCPolyBase(abc.ABC):
         """Return the roots of the series polynomial.
 
         Compute the roots for the series. Note that the accuracy of the
-        roots decrease the further outside the domain they lie.
+        roots decreases the further outside the `domain` they lie.
 
         Returns
         -------
diff --git a/contrib/python/numpy/py3/numpy/polynomial/chebyshev.py b/contrib/python/numpy/py3/numpy/polynomial/chebyshev.py
index c663ffab06f..efbe13e0cad 100644
--- a/contrib/python/numpy/py3/numpy/polynomial/chebyshev.py
+++ b/contrib/python/numpy/py3/numpy/polynomial/chebyshev.py
@@ -2012,6 +2012,12 @@ class Chebyshev(ABCPolyBase):
         Window, see `domain` for its use. The default value is [-1, 1].
 
         .. versionadded:: 1.6.0
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
 
     """
     # Virtual Functions
diff --git a/contrib/python/numpy/py3/numpy/polynomial/hermite.py b/contrib/python/numpy/py3/numpy/polynomial/hermite.py
index e2033912188..210df25f5ca 100644
--- a/contrib/python/numpy/py3/numpy/polynomial/hermite.py
+++ b/contrib/python/numpy/py3/numpy/polynomial/hermite.py
@@ -1675,6 +1675,12 @@ class Hermite(ABCPolyBase):
         Window, see `domain` for its use. The default value is [-1, 1].
 
         .. versionadded:: 1.6.0
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
 
     """
     # Virtual Functions
diff --git a/contrib/python/numpy/py3/numpy/polynomial/hermite_e.py b/contrib/python/numpy/py3/numpy/polynomial/hermite_e.py
index 182c562c296..bdf29405bee 100644
--- a/contrib/python/numpy/py3/numpy/polynomial/hermite_e.py
+++ b/contrib/python/numpy/py3/numpy/polynomial/hermite_e.py
@@ -1667,6 +1667,12 @@ class HermiteE(ABCPolyBase):
         Window, see `domain` for its use. The default value is [-1, 1].
 
         .. versionadded:: 1.6.0
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
 
     """
     # Virtual Functions
diff --git a/contrib/python/numpy/py3/numpy/polynomial/laguerre.py b/contrib/python/numpy/py3/numpy/polynomial/laguerre.py
index 2eacceceda1..925d4898ec0 100644
--- a/contrib/python/numpy/py3/numpy/polynomial/laguerre.py
+++ b/contrib/python/numpy/py3/numpy/polynomial/laguerre.py
@@ -1623,6 +1623,12 @@ class Laguerre(ABCPolyBase):
         Window, see `domain` for its use. The default value is [0, 1].
 
         .. versionadded:: 1.6.0
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
 
     """
     # Virtual Functions
diff --git a/contrib/python/numpy/py3/numpy/polynomial/legendre.py b/contrib/python/numpy/py3/numpy/polynomial/legendre.py
index 028e2fe7b36..8e9c19d94ff 100644
--- a/contrib/python/numpy/py3/numpy/polynomial/legendre.py
+++ b/contrib/python/numpy/py3/numpy/polynomial/legendre.py
@@ -1636,6 +1636,12 @@ class Legendre(ABCPolyBase):
         Window, see `domain` for its use. The default value is [-1, 1].
 
         .. versionadded:: 1.6.0
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
 
     """
     # Virtual Functions
diff --git a/contrib/python/numpy/py3/numpy/polynomial/polynomial.py b/contrib/python/numpy/py3/numpy/polynomial/polynomial.py
index d102f5a30e7..ceadff0bf4e 100644
--- a/contrib/python/numpy/py3/numpy/polynomial/polynomial.py
+++ b/contrib/python/numpy/py3/numpy/polynomial/polynomial.py
@@ -1489,6 +1489,12 @@ class Polynomial(ABCPolyBase):
         Window, see `domain` for its use. The default value is [-1, 1].
 
         .. versionadded:: 1.6.0
+    symbol : str, optional
+        Symbol used to represent the independent variable in string
+        representations of the polynomial expression, e.g. for printing.
+        The symbol must be a valid Python identifier. Default value is 'x'.
+
+        .. versionadded:: 1.24
 
     """
     # Virtual Functions
diff --git a/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx
index fe9731c2871..daebd8f5995 100644
--- a/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx
+++ b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx
@@ -89,8 +89,12 @@ cdef object _rand_uint32_broadcast(np.ndarray low, np.ndarray high, object size,
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
+
+    if np.can_cast(low_arr, np.uint32):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.uint32(0))):
         raise ValueError('low is out of bounds for uint32')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -98,8 +102,11 @@ cdef object _rand_uint32_broadcast(np.ndarray low, np.ndarray high, object size,
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, 0X100000000ULL)):
+    if np.can_cast(high_arr, np.uint32):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.uint64(0X100000000ULL))):
         raise ValueError('high is out of bounds for uint32')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -157,8 +164,12 @@ cdef object _rand_uint16_broadcast(np.ndarray low, np.ndarray high, object size,
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
+
+    if np.can_cast(low_arr, np.uint16):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.uint16(0))):
         raise ValueError('low is out of bounds for uint16')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -166,8 +177,11 @@ cdef object _rand_uint16_broadcast(np.ndarray low, np.ndarray high, object size,
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, 0X10000UL)):
+    if np.can_cast(high_arr, np.uint16):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.uint32(0X10000UL))):
         raise ValueError('high is out of bounds for uint16')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -225,8 +239,12 @@ cdef object _rand_uint8_broadcast(np.ndarray low, np.ndarray high, object size,
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
+
+    if np.can_cast(low_arr, np.uint8):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.uint8(0))):
         raise ValueError('low is out of bounds for uint8')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -234,8 +252,11 @@ cdef object _rand_uint8_broadcast(np.ndarray low, np.ndarray high, object size,
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, 0X100UL)):
+    if np.can_cast(high_arr, np.uint8):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.uint16(0X100UL))):
         raise ValueError('high is out of bounds for uint8')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -293,8 +314,12 @@ cdef object _rand_bool_broadcast(np.ndarray low, np.ndarray high, object size,
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, 0)):
+
+    if np.can_cast(low_arr, np.bool_):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.bool_(0))):
         raise ValueError('low is out of bounds for bool')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -302,8 +327,11 @@ cdef object _rand_bool_broadcast(np.ndarray low, np.ndarray high, object size,
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, 0x2UL)):
+    if np.can_cast(high_arr, np.bool_):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.uint8(0x2UL))):
         raise ValueError('high is out of bounds for bool')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -361,8 +389,12 @@ cdef object _rand_int32_broadcast(np.ndarray low, np.ndarray high, object size,
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, -0x80000000LL)):
+
+    if np.can_cast(low_arr, np.int32):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.int32(-0x80000000LL))):
         raise ValueError('low is out of bounds for int32')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -370,8 +402,11 @@ cdef object _rand_int32_broadcast(np.ndarray low, np.ndarray high, object size,
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, 0x80000000LL)):
+    if np.can_cast(high_arr, np.int32):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.uint64(0x80000000LL))):
         raise ValueError('high is out of bounds for int32')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -429,8 +464,12 @@ cdef object _rand_int16_broadcast(np.ndarray low, np.ndarray high, object size,
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, -0x8000LL)):
+
+    if np.can_cast(low_arr, np.int16):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.int16(-0x8000LL))):
         raise ValueError('low is out of bounds for int16')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -438,8 +477,11 @@ cdef object _rand_int16_broadcast(np.ndarray low, np.ndarray high, object size,
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, 0x8000LL)):
+    if np.can_cast(high_arr, np.int16):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.uint32(0x8000LL))):
         raise ValueError('high is out of bounds for int16')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -497,8 +539,12 @@ cdef object _rand_int8_broadcast(np.ndarray low, np.ndarray high, object size,
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, -0x80LL)):
+
+    if np.can_cast(low_arr, np.int8):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.int8(-0x80LL))):
         raise ValueError('low is out of bounds for int8')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -506,8 +552,11 @@ cdef object _rand_int8_broadcast(np.ndarray low, np.ndarray high, object size,
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, 0x80LL)):
+    if np.can_cast(high_arr, np.int8):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.uint16(0x80LL))):
         raise ValueError('high is out of bounds for int8')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -557,50 +606,69 @@ cdef object _rand_uint64_broadcast(object low, object high, object size,
     64 bit integer type.
     """
 
-    cdef np.ndarray low_arr, high_arr, out_arr, highm1_arr
+    cdef np.ndarray low_arr, low_arr_orig, high_arr, high_arr_orig, out_arr
     cdef np.npy_intp i, cnt, n
     cdef np.broadcast it
     cdef object closed_upper
     cdef uint64_t *out_data
     cdef uint64_t *highm1_data
     cdef uint64_t low_v, high_v
-    cdef uint64_t rng, last_rng, val, mask, off, out_val
+    cdef uint64_t rng, last_rng, val, mask, off, out_val, is_open
 
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
+    low_arr_orig = <np.ndarray>low
+    high_arr_orig = <np.ndarray>high
+
+    is_open = not closed
+
+    # The following code tries to cast safely, but failing that goes via
+    # Python `int()` because it is very difficult to cast integers in a
+    # truly safe way (i.e. so it raises on out-of-bound).
+    # We correct if the interval is not closed in this step if we go the long
+    # route.  (Not otherwise, since the -1 could overflow in theory.)
+    if np.can_cast(low_arr_orig, np.uint64):
+        low_arr = <np.ndarray>np.PyArray_FROM_OTF(low_arr_orig, np.NPY_UINT64, np.NPY_ALIGNED)
+    else:
+        low_arr = <np.ndarray>np.empty_like(low_arr_orig, dtype=np.uint64)
+        flat = low_arr_orig.flat
+        low_data = <uint64_t *>np.PyArray_DATA(low_arr)
+        cnt = np.PyArray_SIZE(low_arr)
+        for i in range(cnt):
+            lower = int(flat[i])
+            if lower < 0x0ULL or lower > 0xFFFFFFFFFFFFFFFFULL:
+                raise ValueError('low is out of bounds for uint64')
+            low_data[i] = lower
+
+    del low_arr_orig
 
-    if np.any(np.less(low_arr, 0x0ULL)):
-        raise ValueError('low is out of bounds for uint64')
-    dt = high_arr.dtype
-    if closed or np.issubdtype(dt, np.integer):
-        # Avoid object dtype path if already an integer
-        high_lower_comp = np.less if closed else np.less_equal
-        if np.any(high_lower_comp(high_arr, 0x0ULL)):
-            raise ValueError(format_bounds_error(closed, low_arr))
-        high_m1 = high_arr if closed else high_arr - dt.type(1)
-        if np.any(np.greater(high_m1, 0xFFFFFFFFFFFFFFFFULL)):
-            raise ValueError('high is out of bounds for uint64')
-        highm1_arr = <np.ndarray>np.PyArray_FROM_OTF(high_m1, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    if np.can_cast(high_arr_orig, np.uint64):
+        high_arr = <np.ndarray>np.PyArray_FROM_OTF(high_arr_orig, np.NPY_UINT64, np.NPY_ALIGNED)
     else:
-        # If input is object or a floating type
-        highm1_arr = <np.ndarray>np.empty_like(high_arr, dtype=np.uint64)
-        highm1_data = <uint64_t *>np.PyArray_DATA(highm1_arr)
+        high_arr = np.empty_like(high_arr_orig, dtype=np.uint64)
+        flat = high_arr_orig.flat
+        high_data = <uint64_t *>np.PyArray_DATA(high_arr)
         cnt = np.PyArray_SIZE(high_arr)
-        flat = high_arr.flat
         for i in range(cnt):
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            closed_upper = int(flat[i]) - 1
+            closed_upper = int(flat[i]) - is_open
             if closed_upper > 0xFFFFFFFFFFFFFFFFULL:
                 raise ValueError('high is out of bounds for uint64')
             if closed_upper < 0x0ULL:
                 raise ValueError(format_bounds_error(closed, low_arr))
-            highm1_data[i] = <uint64_t>closed_upper
+            high_data[i] = closed_upper
 
-    if np.any(np.greater(low_arr, highm1_arr)):
-        raise ValueError(format_bounds_error(closed, low_arr))
+        is_open = 0  # we applied is_open in this path already
 
-    high_arr = highm1_arr
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    del high_arr_orig
+
+    # Since we have the largest supported integer dtypes, they must be within
+    # range at this point; otherwise conversion would have failed.  Check that
+    # it is never true that `high <= low`` if closed and `high < low` if not
+    if not is_open:
+        low_high_comp = np.greater
+    else:
+        low_high_comp = np.greater_equal
+
+    if np.any(low_high_comp(low_arr, high_arr)):
+        raise ValueError(format_bounds_error(closed, low_arr))
 
     if size is not None:
         out_arr = <np.ndarray>np.empty(size, np.uint64)
@@ -616,8 +684,8 @@ cdef object _rand_uint64_broadcast(object low, object high, object size,
         for i in range(n):
             low_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
             high_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Generator produces values on the closed int [off, off+rng], -1 subtracted above
-            rng = <uint64_t>(high_v - low_v)
+            # Generator produces values on the closed int [off, off+rng]
+            rng = <uint64_t>((high_v - is_open) - low_v)
             off = <uint64_t>(<uint64_t>low_v)
 
             if rng != last_rng:
@@ -643,50 +711,69 @@ cdef object _rand_int64_broadcast(object low, object high, object size,
     64 bit integer type.
     """
 
-    cdef np.ndarray low_arr, high_arr, out_arr, highm1_arr
+    cdef np.ndarray low_arr, low_arr_orig, high_arr, high_arr_orig, out_arr
     cdef np.npy_intp i, cnt, n
     cdef np.broadcast it
     cdef object closed_upper
     cdef uint64_t *out_data
     cdef int64_t *highm1_data
     cdef int64_t low_v, high_v
-    cdef uint64_t rng, last_rng, val, mask, off, out_val
+    cdef uint64_t rng, last_rng, val, mask, off, out_val, is_open
 
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
+    low_arr_orig = <np.ndarray>low
+    high_arr_orig = <np.ndarray>high
+
+    is_open = not closed
 
-    if np.any(np.less(low_arr, -0x8000000000000000LL)):
-        raise ValueError('low is out of bounds for int64')
-    dt = high_arr.dtype
-    if closed or np.issubdtype(dt, np.integer):
-        # Avoid object dtype path if already an integer
-        high_lower_comp = np.less if closed else np.less_equal
-        if np.any(high_lower_comp(high_arr, -0x8000000000000000LL)):
-            raise ValueError(format_bounds_error(closed, low_arr))
-        high_m1 = high_arr if closed else high_arr - dt.type(1)
-        if np.any(np.greater(high_m1, 0x7FFFFFFFFFFFFFFFLL)):
-            raise ValueError('high is out of bounds for int64')
-        highm1_arr = <np.ndarray>np.PyArray_FROM_OTF(high_m1, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    # The following code tries to cast safely, but failing that goes via
+    # Python `int()` because it is very difficult to cast integers in a
+    # truly safe way (i.e. so it raises on out-of-bound).
+    # We correct if the interval is not closed in this step if we go the long
+    # route.  (Not otherwise, since the -1 could overflow in theory.)
+    if np.can_cast(low_arr_orig, np.int64):
+        low_arr = <np.ndarray>np.PyArray_FROM_OTF(low_arr_orig, np.NPY_INT64, np.NPY_ALIGNED)
     else:
-        # If input is object or a floating type
-        highm1_arr = <np.ndarray>np.empty_like(high_arr, dtype=np.int64)
-        highm1_data = <int64_t *>np.PyArray_DATA(highm1_arr)
+        low_arr = <np.ndarray>np.empty_like(low_arr_orig, dtype=np.int64)
+        flat = low_arr_orig.flat
+        low_data = <int64_t *>np.PyArray_DATA(low_arr)
+        cnt = np.PyArray_SIZE(low_arr)
+        for i in range(cnt):
+            lower = int(flat[i])
+            if lower < -0x8000000000000000LL or lower > 0x7FFFFFFFFFFFFFFFLL:
+                raise ValueError('low is out of bounds for int64')
+            low_data[i] = lower
+
+    del low_arr_orig
+
+    if np.can_cast(high_arr_orig, np.int64):
+        high_arr = <np.ndarray>np.PyArray_FROM_OTF(high_arr_orig, np.NPY_INT64, np.NPY_ALIGNED)
+    else:
+        high_arr = np.empty_like(high_arr_orig, dtype=np.int64)
+        flat = high_arr_orig.flat
+        high_data = <int64_t *>np.PyArray_DATA(high_arr)
         cnt = np.PyArray_SIZE(high_arr)
-        flat = high_arr.flat
         for i in range(cnt):
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            closed_upper = int(flat[i]) - 1
+            closed_upper = int(flat[i]) - is_open
             if closed_upper > 0x7FFFFFFFFFFFFFFFLL:
                 raise ValueError('high is out of bounds for int64')
             if closed_upper < -0x8000000000000000LL:
                 raise ValueError(format_bounds_error(closed, low_arr))
-            highm1_data[i] = <int64_t>closed_upper
+            high_data[i] = closed_upper
 
-    if np.any(np.greater(low_arr, highm1_arr)):
-        raise ValueError(format_bounds_error(closed, low_arr))
+        is_open = 0  # we applied is_open in this path already
 
-    high_arr = highm1_arr
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    del high_arr_orig
+
+    # Since we have the largest supported integer dtypes, they must be within
+    # range at this point; otherwise conversion would have failed.  Check that
+    # it is never true that `high <= low`` if closed and `high < low` if not
+    if not is_open:
+        low_high_comp = np.greater
+    else:
+        low_high_comp = np.greater_equal
+
+    if np.any(low_high_comp(low_arr, high_arr)):
+        raise ValueError(format_bounds_error(closed, low_arr))
 
     if size is not None:
         out_arr = <np.ndarray>np.empty(size, np.int64)
@@ -702,8 +789,8 @@ cdef object _rand_int64_broadcast(object low, object high, object size,
         for i in range(n):
             low_v = (<int64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
             high_v = (<int64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Generator produces values on the closed int [off, off+rng], -1 subtracted above
-            rng = <uint64_t>(high_v - low_v)
+            # Generator produces values on the closed int [off, off+rng]
+            rng = <uint64_t>((high_v - is_open) - low_v)
             off = <uint64_t>(<int64_t>low_v)
 
             if rng != last_rng:
diff --git a/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx.in b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx.in
index 7eb6aff1e9f..6743001d6d5 100644
--- a/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx.in
+++ b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx.in
@@ -99,8 +99,12 @@ cdef object _rand_{{nptype}}_broadcast(np.ndarray low, np.ndarray high, object s
     is_open = not closed
     low_arr = <np.ndarray>low
     high_arr = <np.ndarray>high
-    if np.any(np.less(low_arr, {{lb}})):
+
+    if np.can_cast(low_arr, np.{{otype}}):
+        pass  # cannot be out-of-bounds
+    elif np.any(np.less(low_arr, np.{{otype}}({{lb}}))):
         raise ValueError('low is out of bounds for {{nptype}}')
+
     if closed:
         high_comp = np.greater_equal
         low_high_comp = np.greater
@@ -108,8 +112,11 @@ cdef object _rand_{{nptype}}_broadcast(np.ndarray low, np.ndarray high, object s
         high_comp = np.greater
         low_high_comp = np.greater_equal
 
-    if np.any(high_comp(high_arr, {{ub}})):
+    if np.can_cast(high_arr, np.{{otype}}):
+        pass  # cannot be out-of-bounds
+    elif np.any(high_comp(high_arr, np.{{nptype_up}}({{ub}}))):
         raise ValueError('high is out of bounds for {{nptype}}')
+
     if np.any(low_high_comp(low_arr, high_arr)):
         raise ValueError(format_bounds_error(closed, low_arr))
 
@@ -165,50 +172,69 @@ cdef object _rand_{{nptype}}_broadcast(object low, object high, object size,
     64 bit integer type.
     """
 
-    cdef np.ndarray low_arr, high_arr, out_arr, highm1_arr
+    cdef np.ndarray low_arr, low_arr_orig, high_arr, high_arr_orig, out_arr
     cdef np.npy_intp i, cnt, n
     cdef np.broadcast it
     cdef object closed_upper
     cdef uint64_t *out_data
     cdef {{nptype}}_t *highm1_data
     cdef {{nptype}}_t low_v, high_v
-    cdef uint64_t rng, last_rng, val, mask, off, out_val
+    cdef uint64_t rng, last_rng, val, mask, off, out_val, is_open
 
-    low_arr = <np.ndarray>low
-    high_arr = <np.ndarray>high
+    low_arr_orig = <np.ndarray>low
+    high_arr_orig = <np.ndarray>high
 
-    if np.any(np.less(low_arr, {{lb}})):
-        raise ValueError('low is out of bounds for {{nptype}}')
-    dt = high_arr.dtype
-    if closed or np.issubdtype(dt, np.integer):
-        # Avoid object dtype path if already an integer
-        high_lower_comp = np.less if closed else np.less_equal
-        if np.any(high_lower_comp(high_arr, {{lb}})):
-            raise ValueError(format_bounds_error(closed, low_arr))
-        high_m1 = high_arr if closed else high_arr - dt.type(1)
-        if np.any(np.greater(high_m1, {{ub}})):
-            raise ValueError('high is out of bounds for {{nptype}}')
-        highm1_arr = <np.ndarray>np.PyArray_FROM_OTF(high_m1, np.{{npctype}}, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    is_open = not closed
+
+    # The following code tries to cast safely, but failing that goes via
+    # Python `int()` because it is very difficult to cast integers in a
+    # truly safe way (i.e. so it raises on out-of-bound).
+    # We correct if the interval is not closed in this step if we go the long
+    # route.  (Not otherwise, since the -1 could overflow in theory.)
+    if np.can_cast(low_arr_orig, np.{{otype}}):
+        low_arr = <np.ndarray>np.PyArray_FROM_OTF(low_arr_orig, np.{{npctype}}, np.NPY_ALIGNED)
+    else:
+        low_arr = <np.ndarray>np.empty_like(low_arr_orig, dtype=np.{{otype}})
+        flat = low_arr_orig.flat
+        low_data = <{{nptype}}_t *>np.PyArray_DATA(low_arr)
+        cnt = np.PyArray_SIZE(low_arr)
+        for i in range(cnt):
+            lower = int(flat[i])
+            if lower < {{lb}} or lower > {{ub}}:
+                raise ValueError('low is out of bounds for {{nptype}}')
+            low_data[i] = lower
+
+    del low_arr_orig
+
+    if np.can_cast(high_arr_orig, np.{{otype}}):
+        high_arr = <np.ndarray>np.PyArray_FROM_OTF(high_arr_orig, np.{{npctype}}, np.NPY_ALIGNED)
     else:
-        # If input is object or a floating type
-        highm1_arr = <np.ndarray>np.empty_like(high_arr, dtype=np.{{otype}})
-        highm1_data = <{{nptype}}_t *>np.PyArray_DATA(highm1_arr)
+        high_arr = np.empty_like(high_arr_orig, dtype=np.{{otype}})
+        flat = high_arr_orig.flat
+        high_data = <{{nptype}}_t *>np.PyArray_DATA(high_arr)
         cnt = np.PyArray_SIZE(high_arr)
-        flat = high_arr.flat
         for i in range(cnt):
-            # Subtract 1 since generator produces values on the closed int [off, off+rng]
-            closed_upper = int(flat[i]) - 1
+            closed_upper = int(flat[i]) - is_open
             if closed_upper > {{ub}}:
                 raise ValueError('high is out of bounds for {{nptype}}')
             if closed_upper < {{lb}}:
                 raise ValueError(format_bounds_error(closed, low_arr))
-            highm1_data[i] = <{{nptype}}_t>closed_upper
+            high_data[i] = closed_upper
 
-    if np.any(np.greater(low_arr, highm1_arr)):
-        raise ValueError(format_bounds_error(closed, low_arr))
+        is_open = 0  # we applied is_open in this path already
 
-    high_arr = highm1_arr
-    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.{{npctype}}, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    del high_arr_orig
+
+    # Since we have the largest supported integer dtypes, they must be within
+    # range at this point; otherwise conversion would have failed.  Check that
+    # it is never true that `high <= low`` if closed and `high < low` if not
+    if not is_open:
+        low_high_comp = np.greater
+    else:
+        low_high_comp = np.greater_equal
+
+    if np.any(low_high_comp(low_arr, high_arr)):
+        raise ValueError(format_bounds_error(closed, low_arr))
 
     if size is not None:
         out_arr = <np.ndarray>np.empty(size, np.{{otype}})
@@ -224,8 +250,8 @@ cdef object _rand_{{nptype}}_broadcast(object low, object high, object size,
         for i in range(n):
             low_v = (<{{nptype}}_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
             high_v = (<{{nptype}}_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
-            # Generator produces values on the closed int [off, off+rng], -1 subtracted above
-            rng = <{{utype}}_t>(high_v - low_v)
+            # Generator produces values on the closed int [off, off+rng]
+            rng = <{{utype}}_t>((high_v - is_open) - low_v)
             off = <{{utype}}_t>(<{{nptype}}_t>low_v)
 
             if rng != last_rng:
diff --git a/contrib/python/numpy/py3/numpy/random/_common.pxd b/contrib/python/numpy/py3/numpy/random/_common.pxd
index 3eaf39ddf83..659da0d2daa 100644
--- a/contrib/python/numpy/py3/numpy/random/_common.pxd
+++ b/contrib/python/numpy/py3/numpy/random/_common.pxd
@@ -39,32 +39,32 @@ cdef extern from "include/aligned_malloc.h":
     cdef void *PyArray_calloc_aligned(size_t n, size_t s)
     cdef void PyArray_free_aligned(void *p)
 
-ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) nogil
-ctypedef double (*random_double_0)(void *state) nogil
-ctypedef double (*random_double_1)(void *state, double a) nogil
-ctypedef double (*random_double_2)(void *state, double a, double b) nogil
-ctypedef double (*random_double_3)(void *state, double a, double b, double c) nogil
+ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out)  noexcept nogil
+ctypedef double (*random_double_0)(void *state)  noexcept nogil
+ctypedef double (*random_double_1)(void *state, double a)  noexcept nogil
+ctypedef double (*random_double_2)(void *state, double a, double b)  noexcept nogil
+ctypedef double (*random_double_3)(void *state, double a, double b, double c)  noexcept nogil
 
-ctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out) nogil
-ctypedef float (*random_float_0)(bitgen_t *state) nogil
-ctypedef float (*random_float_1)(bitgen_t *state, float a) nogil
+ctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out)  noexcept nogil
+ctypedef float (*random_float_0)(bitgen_t *state)  noexcept nogil
+ctypedef float (*random_float_1)(bitgen_t *state, float a)  noexcept nogil
 
-ctypedef int64_t (*random_uint_0)(void *state) nogil
-ctypedef int64_t (*random_uint_d)(void *state, double a) nogil
-ctypedef int64_t (*random_uint_dd)(void *state, double a, double b) nogil
-ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) nogil
-ctypedef int64_t (*random_uint_i)(void *state, int64_t a) nogil
-ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c) nogil
+ctypedef int64_t (*random_uint_0)(void *state)  noexcept nogil
+ctypedef int64_t (*random_uint_d)(void *state, double a)  noexcept nogil
+ctypedef int64_t (*random_uint_dd)(void *state, double a, double b)  noexcept nogil
+ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b)  noexcept nogil
+ctypedef int64_t (*random_uint_i)(void *state, int64_t a)  noexcept nogil
+ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c)  noexcept nogil
 
-ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state) nogil
-ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a) nogil
+ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state)  noexcept nogil
+ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a)  noexcept nogil
 
-ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b) nogil
-ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b) nogil
+ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b)  noexcept nogil
+ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b)  noexcept nogil
 
-cdef double kahan_sum(double *darr, np.npy_intp n)
+cdef double kahan_sum(double *darr, np.npy_intp n) noexcept
 
-cdef inline double uint64_to_double(uint64_t rnd) nogil:
+cdef inline double uint64_to_double(uint64_t rnd) noexcept nogil:
     return (rnd >> 11) * (1.0 / 9007199254740992.0)
 
 cdef object double_fill(void *func, bitgen_t *state, object size, object lock, object out)
diff --git a/contrib/python/numpy/py3/numpy/random/_common.pyx b/contrib/python/numpy/py3/numpy/random/_common.pyx
index 7b6f693037f..c5e4e32973e 100644
--- a/contrib/python/numpy/py3/numpy/random/_common.pyx
+++ b/contrib/python/numpy/py3/numpy/random/_common.pyx
@@ -171,7 +171,7 @@ cdef object prepare_ctypes(bitgen_t *bitgen):
                         ctypes.c_void_p(<uintptr_t>bitgen))
     return _ctypes
 
-cdef double kahan_sum(double *darr, np.npy_intp n):
+cdef double kahan_sum(double *darr, np.npy_intp n) noexcept:
     """
     Parameters
     ----------
diff --git a/contrib/python/numpy/py3/numpy/random/_examples/cffi/parse.py b/contrib/python/numpy/py3/numpy/random/_examples/cffi/parse.py
index daff6bdece0..d41c4c2db23 100644
--- a/contrib/python/numpy/py3/numpy/random/_examples/cffi/parse.py
+++ b/contrib/python/numpy/py3/numpy/random/_examples/cffi/parse.py
@@ -36,9 +36,9 @@ def parse_distributions_h(ffi, inc_dir):
                 continue
     
             # skip any inlined function definition
-            # which starts with 'static NPY_INLINE xxx(...) {'
+            # which starts with 'static inline xxx(...) {'
             # and ends with a closing '}'
-            if line.strip().startswith('static NPY_INLINE'):
+            if line.strip().startswith('static inline'):
                 in_skip += line.count('{')
                 continue
             elif in_skip > 0:
@@ -48,7 +48,6 @@ def parse_distributions_h(ffi, inc_dir):
     
             # replace defines with their value or remove them
             line = line.replace('DECLDIR', '')
-            line = line.replace('NPY_INLINE', '')
             line = line.replace('RAND_INT_TYPE', 'int64_t')
             s.append(line)
         ffi.cdef('\n'.join(s))
diff --git a/contrib/python/numpy/py3/numpy/random/_examples/cython/meson.build b/contrib/python/numpy/py3/numpy/random/_examples/cython/meson.build
new file mode 100644
index 00000000000..c00837d49d9
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/random/_examples/cython/meson.build
@@ -0,0 +1,45 @@
+project('random-build-examples', 'c', 'cpp', 'cython')
+
+py_mod = import('python')
+py3 = py_mod.find_installation(pure: false)
+
+cc = meson.get_compiler('c')
+cy = meson.get_compiler('cython')
+
+if not cy.version().version_compare('>=0.29.35')
+  error('tests requires Cython >= 0.29.35')
+endif
+
+_numpy_abs = run_command(py3, ['-c',
+               'import os; os.chdir(".."); import numpy; print(os.path.abspath(numpy.get_include() + "../../.."))'],
+                         check: true).stdout().strip()
+
+npymath_path = _numpy_abs / 'core' / 'lib'
+npy_include_path = _numpy_abs / 'core' / 'include'
+npyrandom_path = _numpy_abs / 'random' / 'lib'
+npymath_lib = cc.find_library('npymath', dirs: npymath_path)
+npyrandom_lib = cc.find_library('npyrandom', dirs: npyrandom_path)
+
+py3.extension_module(
+    'extending_distributions',
+    'extending_distributions.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)
+py3.extension_module(
+    'extending',
+    'extending.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)
+py3.extension_module(
+    'extending_cpp',
+    'extending_distributions.pyx',
+    install: false,
+    override_options : ['cython_language=cpp'],
+    cython_args: ['--module-name', 'extending_cpp'],
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)
diff --git a/contrib/python/numpy/py3/numpy/random/_generator.pyi b/contrib/python/numpy/py3/numpy/random/_generator.pyi
index f0d814fef79..e1cdefb15b7 100644
--- a/contrib/python/numpy/py3/numpy/random/_generator.pyi
+++ b/contrib/python/numpy/py3/numpy/random/_generator.pyi
@@ -29,6 +29,7 @@ from numpy._typing import (
     _DTypeLikeUInt,
     _Float32Codes,
     _Float64Codes,
+    _FloatLike_co,
     _Int8Codes,
     _Int16Codes,
     _Int32Codes,
@@ -72,6 +73,7 @@ class Generator:
     def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]: ...
     @property
     def bit_generator(self) -> BitGenerator: ...
+    def spawn(self, n_children: int) -> list[Generator]: ...
     def bytes(self, length: int) -> bytes: ...
     @overload
     def standard_normal(  # type: ignore[misc]
@@ -187,13 +189,18 @@ class Generator:
         out: None | ndarray[Any, dtype[float64]] = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def beta(self, a: float, b: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def beta(
+        self,
+        a: _FloatLike_co,
+        b: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def beta(
         self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def exponential(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def exponential(
         self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
@@ -370,7 +377,12 @@ class Generator:
         shuffle: bool = ...,
     ) -> ndarray[Any, Any]: ...
     @overload
-    def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def uniform(
+        self,
+        low: _FloatLike_co = ...,
+        high: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def uniform(
         self,
@@ -379,7 +391,12 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def normal(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def normal(
         self,
@@ -390,7 +407,7 @@ class Generator:
     @overload
     def standard_gamma(  # type: ignore[misc]
         self,
-        shape: float,
+        shape: _FloatLike_co,
         size: None = ...,
         dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
         out: None = ...,
@@ -425,7 +442,7 @@ class Generator:
         out: None | ndarray[Any, dtype[float64]] = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def gamma(
         self,
@@ -434,13 +451,13 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def f(
         self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def noncentral_f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def noncentral_f(
         self,
@@ -450,19 +467,19 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def chisquare(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def chisquare(
         self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def noncentral_chisquare(
         self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def standard_t(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def standard_t(
         self, df: _ArrayLikeFloat_co, size: None = ...
@@ -472,25 +489,25 @@ class Generator:
         self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def vonmises(
         self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def pareto(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def pareto(
         self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def weibull(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def weibull(
         self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def power(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def power(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def power(
         self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
@@ -500,7 +517,12 @@ class Generator:
     @overload
     def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def laplace(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def laplace(
         self,
@@ -509,7 +531,12 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def gumbel(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def gumbel(
         self,
@@ -518,7 +545,12 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def logistic(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def logistic(
         self,
@@ -527,7 +559,12 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def lognormal(
+        self,
+        mean: _FloatLike_co = ...,
+        sigma: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def lognormal(
         self,
@@ -536,19 +573,25 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def rayleigh(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def rayleigh(
         self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def wald(self, mean: float, scale: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def wald(self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
     @overload
     def wald(
         self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    def triangular(
+        self,
+        left: _FloatLike_co,
+        mode: _FloatLike_co,
+        right: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
     @overload
     def triangular(
         self,
@@ -558,31 +601,31 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[float64]]: ...
     @overload
-    def binomial(self, n: int, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
     @overload
     def binomial(
         self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[int64]]: ...
     @overload
-    def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    def negative_binomial(self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
     @overload
     def negative_binomial(
         self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[int64]]: ...
     @overload
-    def poisson(self, lam: float = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ...  # type: ignore[misc]
     @overload
     def poisson(
         self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[int64]]: ...
     @overload
-    def zipf(self, a: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
     @overload
     def zipf(
         self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
     ) -> ndarray[Any, dtype[int64]]: ...
     @overload
-    def geometric(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
     @overload
     def geometric(
         self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
@@ -598,7 +641,7 @@ class Generator:
         size: None | _ShapeLike = ...,
     ) -> ndarray[Any, dtype[int64]]: ...
     @overload
-    def logseries(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
     @overload
     def logseries(
         self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
diff --git a/contrib/python/numpy/py3/numpy/random/_generator.pyx b/contrib/python/numpy/py3/numpy/random/_generator.pyx
index 266a0538799..a30d116c212 100644
--- a/contrib/python/numpy/py3/numpy/random/_generator.pyx
+++ b/contrib/python/numpy/py3/numpy/random/_generator.pyx
@@ -238,6 +238,64 @@ cdef class Generator:
         """
         return self._bit_generator
 
+    def spawn(self, int n_children):
+        """
+        spawn(n_children)
+
+        Create new independent child generators.
+
+        See :ref:`seedsequence-spawn` for additional notes on spawning
+        children.
+
+        .. versionadded:: 1.25.0
+
+        Parameters
+        ----------
+        n_children : int
+
+        Returns
+        -------
+        child_generators : list of Generators
+
+        Raises
+        ------
+        TypeError
+            When the underlying SeedSequence does not implement spawning.
+
+        See Also
+        --------
+        random.BitGenerator.spawn, random.SeedSequence.spawn :
+            Equivalent method on the bit generator and seed sequence.
+        bit_generator :
+            The bit generator instance used by the generator.
+
+        Examples
+        --------
+        Starting from a seeded default generator:
+
+        >>> # High quality entropy created with: f"0x{secrets.randbits(128):x}"
+        >>> entropy = 0x3034c61a9ae04ff8cb62ab8ec2c4b501
+        >>> rng = np.random.default_rng(entropy)
+
+        Create two new generators for example for parallel executation:
+
+        >>> child_rng1, child_rng2 = rng.spawn(2)
+
+        Drawn numbers from each are independent but derived from the initial
+        seeding entropy:
+
+        >>> rng.uniform(), child_rng1.uniform(), child_rng2.uniform()
+        (0.19029263503854454, 0.9475673279178444, 0.4702687338396767)
+
+        It is safe to spawn additional children from the original ``rng`` or
+        the children:
+
+        >>> more_child_rngs = rng.spawn(20)
+        >>> nested_spawn = child_rng1.spawn(20)
+
+        """
+        return [type(self)(g) for g in self._bit_generator.spawn(n_children)]
+
     def random(self, size=None, dtype=np.float64, out=None):
         """
         random(size=None, dtype=np.float64, out=None)
@@ -380,6 +438,22 @@ cdef class Generator:
         out : ndarray or scalar
             Drawn samples from the parameterized exponential distribution.
 
+        Examples
+        --------
+        A real world example: Assume a company has 10000 customer support 
+        agents and the average time between customer calls is 4 minutes.
+
+        >>> n = 10000
+        >>> time_between_calls = np.random.default_rng().exponential(scale=4, size=n)
+
+        What is the probability that a customer will call in the next 
+        4 to 5 minutes? 
+        
+        >>> x = ((time_between_calls < 5).sum())/n 
+        >>> y = ((time_between_calls < 4).sum())/n
+        >>> x-y
+        0.08 # may vary
+
         References
         ----------
         .. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and
@@ -2560,7 +2634,7 @@ cdef class Generator:
         >>> b = []
         >>> for i in range(1000):
         ...    a = 10. + rng.standard_normal(100)
-        ...    b.append(np.product(a))
+        ...    b.append(np.prod(a))
 
         >>> b = np.array(b) / np.min(b) # scale values to be positive
         >>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid')
@@ -3533,8 +3607,8 @@ cdef class Generator:
         generalization of the one-dimensional normal distribution to higher
         dimensions.  Such a distribution is specified by its mean and
         covariance matrix.  These parameters are analogous to the mean
-        (average or "center") and variance (standard deviation, or "width,"
-        squared) of the one-dimensional normal distribution.
+        (average or "center") and variance (the squared standard deviation,
+        or "width") of the one-dimensional normal distribution.
 
         Parameters
         ----------
@@ -3611,6 +3685,12 @@ cdef class Generator:
         nonnegative-definite). Otherwise, the behavior of this method is
         undefined and backwards compatibility is not guaranteed.
 
+        This function internally uses linear algebra routines, and thus results
+        may not be identical (even up to precision) across architectures, OSes,
+        or even builds. For example, this is likely if ``cov`` has multiple equal
+        singular values and ``method`` is ``'svd'`` (default). In this case,
+        ``method='cholesky'`` may be more robust.
+
         References
         ----------
         .. [1] Papoulis, A., "Probability, Random Variables, and Stochastic
@@ -4247,7 +4327,7 @@ cdef class Generator:
         Raises
         ------
         ValueError
-            If any value in ``alpha`` is less than or equal to zero
+            If any value in ``alpha`` is less than zero
 
         Notes
         -----
@@ -4326,8 +4406,8 @@ cdef class Generator:
         alpha_arr = <np.ndarray>np.PyArray_FROMANY(
             alpha, np.NPY_DOUBLE, 1, 1,
             np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
-        if np.any(np.less_equal(alpha_arr, 0)):
-            raise ValueError('alpha <= 0')
+        if np.any(np.less(alpha_arr, 0)):
+            raise ValueError('alpha < 0')
         alpha_data = <double*>np.PyArray_DATA(alpha_arr)
 
         if size is None:
@@ -4745,7 +4825,7 @@ cdef class Generator:
         >>> rng.permutation("abc")
         Traceback (most recent call last):
             ...
-        numpy.AxisError: axis 0 is out of bounds for array of dimension 0
+        numpy.exceptions.AxisError: axis 0 is out of bounds for array of dimension 0
 
         >>> arr = np.arange(9).reshape((3, 3))
         >>> rng.permutation(arr, axis=1)
@@ -4803,6 +4883,8 @@ def default_rng(seed=None):
     -----
     If ``seed`` is not a `BitGenerator` or a `Generator`, a new `BitGenerator`
     is instantiated. This function does not manage a default global instance.
+
+    See :ref:`seeding_and_entropy` for more information about seeding.
     
     Examples
     --------
diff --git a/contrib/python/numpy/py3/numpy/random/_mt19937.pyx b/contrib/python/numpy/py3/numpy/random/_mt19937.pyx
index 5a8d52e6bde..8b991254aac 100644
--- a/contrib/python/numpy/py3/numpy/random/_mt19937.pyx
+++ b/contrib/python/numpy/py3/numpy/random/_mt19937.pyx
@@ -28,16 +28,16 @@ cdef extern from "src/mt19937/mt19937.h":
     enum:
         RK_STATE_LEN
 
-cdef uint64_t mt19937_uint64(void *st) nogil:
+cdef uint64_t mt19937_uint64(void *st) noexcept nogil:
     return mt19937_next64(<mt19937_state *> st)
 
-cdef uint32_t mt19937_uint32(void *st) nogil:
+cdef uint32_t mt19937_uint32(void *st) noexcept nogil:
     return mt19937_next32(<mt19937_state *> st)
 
-cdef double mt19937_double(void *st) nogil:
+cdef double mt19937_double(void *st) noexcept nogil:
     return mt19937_next_double(<mt19937_state *> st)
 
-cdef uint64_t mt19937_raw(void *st) nogil:
+cdef uint64_t mt19937_raw(void *st) noexcept nogil:
     return <uint64_t>mt19937_next32(<mt19937_state *> st)
 
 cdef class MT19937(BitGenerator):
diff --git a/contrib/python/numpy/py3/numpy/random/_pcg64.pyx b/contrib/python/numpy/py3/numpy/random/_pcg64.pyx
index c0a10a81252..f7891aa85b9 100644
--- a/contrib/python/numpy/py3/numpy/random/_pcg64.pyx
+++ b/contrib/python/numpy/py3/numpy/random/_pcg64.pyx
@@ -26,26 +26,26 @@ cdef extern from "src/pcg64/pcg64.h":
     void pcg64_get_state(pcg64_state *state, uint64_t *state_arr, int *has_uint32, uint32_t *uinteger)
     void pcg64_set_state(pcg64_state *state, uint64_t *state_arr, int has_uint32, uint32_t uinteger)
 
-    uint64_t pcg64_cm_next64(pcg64_state *state)  nogil
-    uint32_t pcg64_cm_next32(pcg64_state *state)  nogil
+    uint64_t pcg64_cm_next64(pcg64_state *state)  noexcept nogil
+    uint32_t pcg64_cm_next32(pcg64_state *state)  noexcept nogil
     void pcg64_cm_advance(pcg64_state *state, uint64_t *step)
 
-cdef uint64_t pcg64_uint64(void* st) nogil:
+cdef uint64_t pcg64_uint64(void* st) noexcept nogil:
     return pcg64_next64(<pcg64_state *>st)
 
-cdef uint32_t pcg64_uint32(void *st) nogil:
+cdef uint32_t pcg64_uint32(void *st) noexcept nogil:
     return pcg64_next32(<pcg64_state *> st)
 
-cdef double pcg64_double(void* st) nogil:
+cdef double pcg64_double(void* st) noexcept nogil:
     return uint64_to_double(pcg64_next64(<pcg64_state *>st))
 
-cdef uint64_t pcg64_cm_uint64(void* st) nogil:
+cdef uint64_t pcg64_cm_uint64(void* st) noexcept nogil:
     return pcg64_cm_next64(<pcg64_state *>st)
 
-cdef uint32_t pcg64_cm_uint32(void *st) nogil:
+cdef uint32_t pcg64_cm_uint32(void *st) noexcept nogil:
     return pcg64_cm_next32(<pcg64_state *> st)
 
-cdef double pcg64_cm_double(void* st) nogil:
+cdef double pcg64_cm_double(void* st) noexcept nogil:
     return uint64_to_double(pcg64_cm_next64(<pcg64_state *>st))
 
 cdef class PCG64(BitGenerator):
@@ -515,4 +515,3 @@ cdef class PCG64DXSM(BitGenerator):
         pcg64_cm_advance(&self.rng_state, <uint64_t *>np.PyArray_DATA(d))
         self._reset_state_variables()
         return self
-
diff --git a/contrib/python/numpy/py3/numpy/random/_philox.pyx b/contrib/python/numpy/py3/numpy/random/_philox.pyx
index d9a366e8655..e5353460c83 100644
--- a/contrib/python/numpy/py3/numpy/random/_philox.pyx
+++ b/contrib/python/numpy/py3/numpy/random/_philox.pyx
@@ -36,19 +36,19 @@ cdef extern from 'src/philox/philox.h':
 
     ctypedef s_philox_state philox_state
 
-    uint64_t philox_next64(philox_state *state)  nogil
-    uint32_t philox_next32(philox_state *state)  nogil
+    uint64_t philox_next64(philox_state *state)  noexcept nogil
+    uint32_t philox_next32(philox_state *state)  noexcept nogil
     void philox_jump(philox_state *state)
     void philox_advance(uint64_t *step, philox_state *state)
 
 
-cdef uint64_t philox_uint64(void*st) nogil:
+cdef uint64_t philox_uint64(void*st) noexcept nogil:
     return philox_next64(<philox_state *> st)
 
-cdef uint32_t philox_uint32(void *st) nogil:
+cdef uint32_t philox_uint32(void *st) noexcept nogil:
     return philox_next32(<philox_state *> st)
 
-cdef double philox_double(void*st) nogil:
+cdef double philox_double(void*st) noexcept nogil:
     return uint64_to_double(philox_next64(<philox_state *> st))
 
 cdef class Philox(BitGenerator):
diff --git a/contrib/python/numpy/py3/numpy/random/_sfc64.pyx b/contrib/python/numpy/py3/numpy/random/_sfc64.pyx
index 1daee34f863..419045c1d32 100644
--- a/contrib/python/numpy/py3/numpy/random/_sfc64.pyx
+++ b/contrib/python/numpy/py3/numpy/random/_sfc64.pyx
@@ -21,13 +21,13 @@ cdef extern from "src/sfc64/sfc64.h":
     void sfc64_set_state(sfc64_state *state, uint64_t *state_arr, int has_uint32, uint32_t uinteger)
 
 
-cdef uint64_t sfc64_uint64(void* st) nogil:
+cdef uint64_t sfc64_uint64(void* st) noexcept nogil:
     return sfc64_next64(<sfc64_state *>st)
 
-cdef uint32_t sfc64_uint32(void *st) nogil:
+cdef uint32_t sfc64_uint32(void *st) noexcept nogil:
     return sfc64_next32(<sfc64_state *> st)
 
-cdef double sfc64_double(void* st) nogil:
+cdef double sfc64_double(void* st) noexcept nogil:
     return uint64_to_double(sfc64_next64(<sfc64_state *>st))
 
 
diff --git a/contrib/python/numpy/py3/numpy/random/bit_generator.pyi b/contrib/python/numpy/py3/numpy/random/bit_generator.pyi
index e6e3b10cd45..8b9779cade3 100644
--- a/contrib/python/numpy/py3/numpy/random/bit_generator.pyi
+++ b/contrib/python/numpy/py3/numpy/random/bit_generator.pyi
@@ -96,6 +96,9 @@ class BitGenerator(abc.ABC):
     def state(self) -> Mapping[str, Any]: ...
     @state.setter
     def state(self, value: Mapping[str, Any]) -> None: ...
+    @property
+    def seed_seq(self) -> ISeedSequence: ...
+    def spawn(self, n_children: int) -> list[BitGenerator]: ...
     @overload
     def random_raw(self, size: None = ..., output: Literal[True] = ...) -> int: ...  # type: ignore[misc]
     @overload
diff --git a/contrib/python/numpy/py3/numpy/random/bit_generator.pyx b/contrib/python/numpy/py3/numpy/random/bit_generator.pyx
index 3da4fabce88..83441747a31 100644
--- a/contrib/python/numpy/py3/numpy/random/bit_generator.pyx
+++ b/contrib/python/numpy/py3/numpy/random/bit_generator.pyx
@@ -212,6 +212,9 @@ class ISpawnableSeedSequence(ISeedSequence):
         Spawn a number of child `SeedSequence` s by extending the
         `spawn_key`.
 
+        See :ref:`seedsequence-spawn` for additional notes on spawning
+        children.
+
         Parameters
         ----------
         n_children : int
@@ -260,6 +263,7 @@ cdef class SeedSequence():
     ----------
     entropy : {None, int, sequence[int]}, optional
         The entropy for creating a `SeedSequence`.
+        All integer values must be non-negative.
     spawn_key : {(), sequence[int]}, optional
         An additional source of entropy based on the position of this
         `SeedSequence` in the tree of such objects created with the
@@ -450,6 +454,9 @@ cdef class SeedSequence():
         Spawn a number of child `SeedSequence` s by extending the
         `spawn_key`.
 
+        See :ref:`seedsequence-spawn` for additional notes on spawning
+        children.
+
         Parameters
         ----------
         n_children : int
@@ -457,6 +464,12 @@ cdef class SeedSequence():
         Returns
         -------
         seqs : list of `SeedSequence` s
+
+        See Also
+        --------
+        random.Generator.spawn, random.BitGenerator.spawn :
+            Equivalent method on the generator and bit generator.
+
         """
         cdef uint32_t i
 
@@ -490,6 +503,7 @@ cdef class BitGenerator():
         ``array_like[ints]`` is passed, then it will be passed to
         `~numpy.random.SeedSequence` to derive the initial `BitGenerator` state.
         One may also pass in a `SeedSequence` instance.
+        All integer values must be non-negative.
 
     Attributes
     ----------
@@ -549,6 +563,59 @@ cdef class BitGenerator():
     def state(self, value):
         raise NotImplementedError('Not implemented in base BitGenerator')
 
+    @property
+    def seed_seq(self):
+        """
+        Get the seed sequence used to initialize the bit generator.
+
+        .. versionadded:: 1.25.0
+
+        Returns
+        -------
+        seed_seq : ISeedSequence
+            The SeedSequence object used to initialize the BitGenerator.
+            This is normally a `np.random.SeedSequence` instance.
+
+        """
+        return self._seed_seq
+
+    def spawn(self, int n_children):
+        """
+        spawn(n_children)
+
+        Create new independent child bit generators.
+
+        See :ref:`seedsequence-spawn` for additional notes on spawning
+        children.  Some bit generators also implement ``jumped``
+        as a different approach for creating independent streams.
+
+        .. versionadded:: 1.25.0
+
+        Parameters
+        ----------
+        n_children : int
+
+        Returns
+        -------
+        child_bit_generators : list of BitGenerators
+
+        Raises
+        ------
+        TypeError
+            When the underlying SeedSequence does not implement spawning.
+
+        See Also
+        --------
+        random.Generator.spawn, random.SeedSequence.spawn :
+            Equivalent method on the generator and seed sequence.
+
+        """
+        if not isinstance(self._seed_seq, ISpawnableSeedSequence):
+            raise TypeError(
+                "The underlying SeedSequence does not implement spawning.")
+
+        return [type(self)(seed=s) for s in self._seed_seq.spawn(n_children)]
+
     def random_raw(self, size=None, output=True):
         """
         random_raw(self, size=None)
diff --git a/contrib/python/numpy/py3/numpy/random/c_distributions.pxd b/contrib/python/numpy/py3/numpy/random/c_distributions.pxd
index 6f905edc113..b978d13503e 100644
--- a/contrib/python/numpy/py3/numpy/random/c_distributions.pxd
+++ b/contrib/python/numpy/py3/numpy/random/c_distributions.pxd
@@ -28,18 +28,24 @@ cdef extern from "numpy/random/distributions.h":
 
     ctypedef s_binomial_t binomial_t
 
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
     double random_standard_uniform(bitgen_t *bitgen_state) nogil
     void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
     double random_standard_exponential(bitgen_t *bitgen_state) nogil
-    double random_standard_exponential_f(bitgen_t *bitgen_state) nogil
+    float random_standard_exponential_f(bitgen_t *bitgen_state) nogil
     void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
-    void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
     void random_standard_exponential_inv_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
-    void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
     double random_standard_normal(bitgen_t* bitgen_state) nogil
+    float random_standard_normal_f(bitgen_t *bitgen_state) nogil
     void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out) nogil
     void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out) nogil
     double random_standard_gamma(bitgen_t *bitgen_state, double shape) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
 
     float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
     void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out) nogil
diff --git a/contrib/python/numpy/py3/numpy/random/include/aligned_malloc.h b/contrib/python/numpy/py3/numpy/random/include/aligned_malloc.h
index 43f68253d13..0fff57b6c44 100644
--- a/contrib/python/numpy/py3/numpy/random/include/aligned_malloc.h
+++ b/contrib/python/numpy/py3/numpy/random/include/aligned_malloc.h
@@ -6,7 +6,7 @@
 
 #define NPY_MEMALIGN 16 /* 16 for SSE2, 32 for AVX, 64 for Xeon Phi */
 
-static NPY_INLINE void *PyArray_realloc_aligned(void *p, size_t n)
+static inline void *PyArray_realloc_aligned(void *p, size_t n)
 {
     void *p1, **p2, *base;
     size_t old_offs, offs = NPY_MEMALIGN - 1 + sizeof(void *);
@@ -31,12 +31,12 @@ static NPY_INLINE void *PyArray_realloc_aligned(void *p, size_t n)
     return (void *)p2;
 }
 
-static NPY_INLINE void *PyArray_malloc_aligned(size_t n)
+static inline void *PyArray_malloc_aligned(size_t n)
 {
     return PyArray_realloc_aligned(NULL, n);
 }
 
-static NPY_INLINE void *PyArray_calloc_aligned(size_t n, size_t s)
+static inline void *PyArray_calloc_aligned(size_t n, size_t s)
 {
     void *p;
     if (NPY_UNLIKELY((p = PyArray_realloc_aligned(NULL, n * s)) == NULL))
@@ -45,7 +45,7 @@ static NPY_INLINE void *PyArray_calloc_aligned(size_t n, size_t s)
     return p;
 }
 
-static NPY_INLINE void PyArray_free_aligned(void *p)
+static inline void PyArray_free_aligned(void *p)
 {
     void *base = *(((void **)p) - 1);
     PyMem_Free(base);
diff --git a/contrib/python/numpy/py3/numpy/random/meson.build b/contrib/python/numpy/py3/numpy/random/meson.build
new file mode 100644
index 00000000000..c582428f962
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/random/meson.build
@@ -0,0 +1,167 @@
+# Build npyrandom library
+# -----------------------
+npyrandom_sources = [
+  'src/distributions/logfactorial.c',
+  'src/distributions/distributions.c',
+  'src/distributions/random_mvhg_count.c',
+  'src/distributions/random_mvhg_marginals.c',
+  'src/distributions/random_hypergeometric.c',
+]
+
+npyrandom_lib = static_library('npyrandom',
+  npyrandom_sources,
+  c_args: staticlib_cflags,
+  # include_directories: '../core/include',
+  dependencies: [py_dep, np_core_dep],
+  install: true,
+  install_dir: np_dir / 'random/lib',
+  name_prefix: name_prefix_staticlib,
+  name_suffix: name_suffix_staticlib,
+)
+
+# Build Cython extensions for numpy.random
+# ----------------------------------------
+# pyx -> c transpile output depends on copied __init__.py and pxd files
+_cython_tree_random = [
+  fs.copyfile('__init__.py'),
+  fs.copyfile('__init__.pxd'),
+  fs.copyfile('_common.pxd'),
+  fs.copyfile('bit_generator.pxd'),
+  fs.copyfile('c_distributions.pxd'),
+]
+# Need to use `custom_target` because we need to install this .pxd file
+_cython_tree_random += custom_target('_bounded_integer_pxd',
+  output: '_bounded_integers.pxd',
+  input: '_bounded_integers.pxd.in',
+  command: [tempita_cli, '@INPUT@', '-o', '@OUTPUT@'],
+  install: true,
+  install_dir: np_dir / 'random'
+)
+
+_bounded_integers_pyx = custom_target('_bounded_integer_pyx',
+  output: '_bounded_integers.pyx',
+  input: '_bounded_integers.pyx.in',
+  command: [tempita_cli, '@INPUT@', '-o', '@OUTPUT@'],
+)
+
+c_args_random = [
+  cflags_large_file_support,
+  '-DNPY_NO_DEPRECATED_API=0',  # Cython still uses old NumPy C API
+]
+if host_machine.system() == 'cygwin'
+  c_args_random += ['-Wl,--export-all-symbols']
+endif
+
+# name, sources, extra link libs, extra c_args
+random_pyx_sources = [
+  ['_bounded_integers', _bounded_integers_pyx, [], npymath_lib],
+  ['_common', '_common.pyx', [], []],
+  ['_mt19937', ['_mt19937.pyx', 'src/mt19937/mt19937.c', 'src/mt19937/mt19937-jump.c'], [], []],
+  ['_philox', ['_philox.pyx', 'src/philox/philox.c'], [], []],
+  ['_pcg64', ['_pcg64.pyx', 'src/pcg64/pcg64.c'], ['-U__GNUC_GNU_INLINE__'], []],
+  ['_sfc64', ['_sfc64.pyx', 'src/sfc64/sfc64.c'], [], []],
+  ['bit_generator', 'bit_generator.pyx', [], []],
+  # The `fs.copyfile` usage here is needed because these two .pyx files import
+  # from _bounded_integers,and its pxd file is only present in the build directory
+  ['_generator', fs.copyfile('_generator.pyx'), [], npymath_lib],
+  ['mtrand', [
+      fs.copyfile('mtrand.pyx'),
+      'src/distributions/distributions.c',
+      'src/legacy/legacy-distributions.c'
+    ], ['-DNP_RANDOM_LEGACY=1'], npymath_lib,
+  ],
+]
+foreach gen: random_pyx_sources
+  py.extension_module(gen[0],
+    [gen[1], _cython_tree, _cython_tree_random],
+    c_args: [c_args_random, gen[2]],
+    include_directories: 'src',
+    dependencies: np_core_dep,
+    link_with: [npyrandom_lib, gen[3]],
+    install: true,
+    subdir: 'numpy/random',
+  )
+endforeach
+
+# Install Python sources, stub files, tests, examples and license
+# ---------------------------------------------------------------
+py.install_sources(
+  [
+    '__init__.pxd',
+    '__init__.py',
+    '__init__.pyi',
+    '_common.pxd',
+    '_generator.pyi',
+    '_mt19937.pyi',
+    '_pcg64.pyi',
+    '_pickle.py',
+    '_philox.pyi',
+    '_sfc64.pyi',
+    'bit_generator.pxd',
+    'bit_generator.pyi',
+    'c_distributions.pxd',
+    'LICENSE.md',
+    'mtrand.pyi',
+  ],
+  subdir: 'numpy/random'
+)
+
+py.install_sources(
+  [
+    'tests/__init__.py',
+    'tests/test_direct.py',
+    'tests/test_extending.py',
+    'tests/test_generator_mt19937.py',
+    'tests/test_generator_mt19937_regressions.py',
+    'tests/test_random.py',
+    'tests/test_randomstate.py',
+    'tests/test_randomstate_regression.py',
+    'tests/test_regression.py',
+    'tests/test_seed_sequence.py',
+    'tests/test_smoke.py',
+  ],
+  subdir: 'numpy/random/tests'
+)
+
+py.install_sources(
+  [
+    'tests/data/__init__.py',
+    'tests/data/mt19937-testset-1.csv',
+    'tests/data/mt19937-testset-2.csv',
+    'tests/data/pcg64-testset-1.csv',
+    'tests/data/pcg64-testset-2.csv',
+    'tests/data/pcg64dxsm-testset-1.csv',
+    'tests/data/pcg64dxsm-testset-2.csv',
+    'tests/data/philox-testset-1.csv',
+    'tests/data/philox-testset-2.csv',
+    'tests/data/sfc64-testset-1.csv',
+    'tests/data/sfc64-testset-2.csv',
+  ],
+  subdir: 'numpy/random/tests/data'
+)
+
+py.install_sources(
+  [
+  '_examples/cffi/extending.py',
+  '_examples/cffi/parse.py',
+  ],
+  subdir: 'numpy/random/_examples/cffi'
+)
+
+py.install_sources(
+  [
+  '_examples/cython/extending.pyx',
+  '_examples/cython/extending_distributions.pyx',
+  '_examples/cython/meson.build',
+  '_examples/cython/setup.py',
+  ],
+  subdir: 'numpy/random/_examples/cython'
+)
+
+py.install_sources(
+  [
+  '_examples/numba/extending.py',
+  '_examples/numba/extending_distributions.py',
+  ],
+  subdir: 'numpy/random/_examples/numba'
+)
diff --git a/contrib/python/numpy/py3/numpy/random/mtrand.pyx b/contrib/python/numpy/py3/numpy/random/mtrand.pyx
index edf812a4d85..d14d081644a 100644
--- a/contrib/python/numpy/py3/numpy/random/mtrand.pyx
+++ b/contrib/python/numpy/py3/numpy/random/mtrand.pyx
@@ -537,6 +537,22 @@ cdef class RandomState:
         out : ndarray or scalar
             Drawn samples from the parameterized exponential distribution.
 
+        Examples
+        --------
+        A real world example: Assume a company has 10000 customer support 
+        agents and the average time between customer calls is 4 minutes.
+
+        >>> n = 10000
+        >>> time_between_calls = np.random.default_rng().exponential(scale=4, size=n)
+
+        What is the probability that a customer will call in the next 
+        4 to 5 minutes? 
+        
+        >>> x = ((time_between_calls < 5).sum())/n 
+        >>> y = ((time_between_calls < 4).sum())/n
+        >>> x-y
+        0.08 # may vary
+
         See Also
         --------
         random.Generator.exponential: which should be used for new code.
@@ -670,7 +686,7 @@ cdef class RandomState:
         `high` is None (the default), then results are from [0, `low`).
 
         .. note::
-            New code should use the `~numpy.random.Generator.randint`
+            New code should use the `~numpy.random.Generator.integers`
             method of a `~numpy.random.Generator` instance instead;
             please see the :ref:`random-quick-start`.
 
@@ -3050,7 +3066,7 @@ cdef class RandomState:
         >>> b = []
         >>> for i in range(1000):
         ...    a = 10. + np.random.standard_normal(100)
-        ...    b.append(np.product(a))
+        ...    b.append(np.prod(a))
 
         >>> b = np.array(b) / np.min(b) # scale values to be positive
         >>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid')
diff --git a/contrib/python/numpy/py3/numpy/random/src/distributions/distributions.c b/contrib/python/numpy/py3/numpy/random/src/distributions/distributions.c
index bd1e1faa483..cebeb07cf9f 100644
--- a/contrib/python/numpy/py3/numpy/random/src/distributions/distributions.c
+++ b/contrib/python/numpy/py3/numpy/random/src/distributions/distributions.c
@@ -9,14 +9,14 @@
 #include <assert.h>
 
 /* Inline generators for internal use */
-static NPY_INLINE uint32_t next_uint32(bitgen_t *bitgen_state) {
+static inline uint32_t next_uint32(bitgen_t *bitgen_state) {
   return bitgen_state->next_uint32(bitgen_state->state);
 }
-static NPY_INLINE uint64_t next_uint64(bitgen_t *bitgen_state) {
+static inline uint64_t next_uint64(bitgen_t *bitgen_state) {
   return bitgen_state->next_uint64(bitgen_state->state);
 }
 
-static NPY_INLINE float next_float(bitgen_t *bitgen_state) {
+static inline float next_float(bitgen_t *bitgen_state) {
   return (next_uint32(bitgen_state) >> 8) * (1.0f / 16777216.0f);
 }
 
@@ -960,7 +960,15 @@ RAND_INT_TYPE random_geometric_search(bitgen_t *bitgen_state, double p) {
 }
 
 int64_t random_geometric_inversion(bitgen_t *bitgen_state, double p) {
-  return (int64_t)ceil(-random_standard_exponential(bitgen_state) / npy_log1p(-p));
+  double z = ceil(-random_standard_exponential(bitgen_state) / npy_log1p(-p));
+  /*
+   * The constant 9.223372036854776e+18 is the smallest double that is
+   * larger than INT64_MAX.
+   */
+  if (z >= 9.223372036854776e+18) {
+    return INT64_MAX;
+  }
+  return (int64_t) z;
 }
 
 int64_t random_geometric(bitgen_t *bitgen_state, double p) {
@@ -1047,7 +1055,7 @@ uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max) {
 }
 
 /* Bounded generators */
-static NPY_INLINE uint64_t gen_mask(uint64_t max) {
+static inline uint64_t gen_mask(uint64_t max) {
   uint64_t mask = max;
   mask |= mask >> 1;
   mask |= mask >> 2;
@@ -1059,8 +1067,8 @@ static NPY_INLINE uint64_t gen_mask(uint64_t max) {
 }
 
 /* Generate 16 bit random numbers using a 32 bit buffer. */
-static NPY_INLINE uint16_t buffered_uint16(bitgen_t *bitgen_state, int *bcnt,
-                                           uint32_t *buf) {
+static inline uint16_t buffered_uint16(bitgen_t *bitgen_state, int *bcnt,
+                                       uint32_t *buf) {
   if (!(bcnt[0])) {
     buf[0] = next_uint32(bitgen_state);
     bcnt[0] = 1;
@@ -1073,8 +1081,8 @@ static NPY_INLINE uint16_t buffered_uint16(bitgen_t *bitgen_state, int *bcnt,
 }
 
 /* Generate 8 bit random numbers using a 32 bit buffer. */
-static NPY_INLINE uint8_t buffered_uint8(bitgen_t *bitgen_state, int *bcnt,
-                                         uint32_t *buf) {
+static inline uint8_t buffered_uint8(bitgen_t *bitgen_state, int *bcnt,
+                                     uint32_t *buf) {
   if (!(bcnt[0])) {
     buf[0] = next_uint32(bitgen_state);
     bcnt[0] = 3;
@@ -1087,8 +1095,8 @@ static NPY_INLINE uint8_t buffered_uint8(bitgen_t *bitgen_state, int *bcnt,
 }
 
 /* Static `masked rejection` function called by random_bounded_uint64(...) */
-static NPY_INLINE uint64_t bounded_masked_uint64(bitgen_t *bitgen_state,
-                                                 uint64_t rng, uint64_t mask) {
+static inline uint64_t bounded_masked_uint64(bitgen_t *bitgen_state,
+                                             uint64_t rng, uint64_t mask) {
   uint64_t val;
 
   while ((val = (next_uint64(bitgen_state) & mask)) > rng)
@@ -1099,7 +1107,7 @@ static NPY_INLINE uint64_t bounded_masked_uint64(bitgen_t *bitgen_state,
 
 /* Static `masked rejection` function called by
  * random_buffered_bounded_uint32(...) */
-static NPY_INLINE uint32_t
+static inline uint32_t
 buffered_bounded_masked_uint32(bitgen_t *bitgen_state, uint32_t rng,
                                uint32_t mask, int *bcnt, uint32_t *buf) {
   /*
@@ -1118,7 +1126,7 @@ buffered_bounded_masked_uint32(bitgen_t *bitgen_state, uint32_t rng,
 
 /* Static `masked rejection` function called by
  * random_buffered_bounded_uint16(...) */
-static NPY_INLINE uint16_t
+static inline uint16_t
 buffered_bounded_masked_uint16(bitgen_t *bitgen_state, uint16_t rng,
                                uint16_t mask, int *bcnt, uint32_t *buf) {
   uint16_t val;
@@ -1131,10 +1139,11 @@ buffered_bounded_masked_uint16(bitgen_t *bitgen_state, uint16_t rng,
 
 /* Static `masked rejection` function called by
  * random_buffered_bounded_uint8(...) */
-static NPY_INLINE uint8_t buffered_bounded_masked_uint8(bitgen_t *bitgen_state,
-                                                        uint8_t rng,
-                                                        uint8_t mask, int *bcnt,
-                                                        uint32_t *buf) {
+static inline uint8_t buffered_bounded_masked_uint8(bitgen_t *bitgen_state,
+                                                    uint8_t rng,
+                                                    uint8_t mask,
+                                                    int *bcnt,
+                                                    uint32_t *buf) {
   uint8_t val;
 
   while ((val = (buffered_uint8(bitgen_state, bcnt, buf) & mask)) > rng)
@@ -1143,10 +1152,10 @@ static NPY_INLINE uint8_t buffered_bounded_masked_uint8(bitgen_t *bitgen_state,
   return val;
 }
 
-static NPY_INLINE npy_bool buffered_bounded_bool(bitgen_t *bitgen_state,
-                                                 npy_bool off, npy_bool rng,
-                                                 npy_bool mask, int *bcnt,
-                                                 uint32_t *buf) {
+static inline npy_bool buffered_bounded_bool(bitgen_t *bitgen_state,
+                                             npy_bool off, npy_bool rng,
+                                             npy_bool mask, int *bcnt,
+                                             uint32_t *buf) {
   if (rng == 0)
     return off;
   if (!(bcnt[0])) {
@@ -1160,8 +1169,8 @@ static NPY_INLINE npy_bool buffered_bounded_bool(bitgen_t *bitgen_state,
 }
 
 /* Static `Lemire rejection` function called by random_bounded_uint64(...) */
-static NPY_INLINE uint64_t bounded_lemire_uint64(bitgen_t *bitgen_state,
-                                                 uint64_t rng) {
+static inline uint64_t bounded_lemire_uint64(bitgen_t *bitgen_state,
+                                             uint64_t rng) {
   /*
    * Uses Lemire's algorithm - https://arxiv.org/abs/1805.10941
    *
@@ -1245,7 +1254,7 @@ static NPY_INLINE uint64_t bounded_lemire_uint64(bitgen_t *bitgen_state,
 
 /* Static `Lemire rejection` function called by
  * random_buffered_bounded_uint32(...) */
-static NPY_INLINE uint32_t buffered_bounded_lemire_uint32(
+static inline uint32_t buffered_bounded_lemire_uint32(
     bitgen_t *bitgen_state, uint32_t rng, int *bcnt, uint32_t *buf) {
   /*
    * Uses Lemire's algorithm - https://arxiv.org/abs/1805.10941
@@ -1285,7 +1294,7 @@ static NPY_INLINE uint32_t buffered_bounded_lemire_uint32(
 
 /* Static `Lemire rejection` function called by
  * random_buffered_bounded_uint16(...) */
-static NPY_INLINE uint16_t buffered_bounded_lemire_uint16(
+static inline uint16_t buffered_bounded_lemire_uint16(
     bitgen_t *bitgen_state, uint16_t rng, int *bcnt, uint32_t *buf) {
   /*
    * Uses Lemire's algorithm - https://arxiv.org/abs/1805.10941
@@ -1321,9 +1330,9 @@ static NPY_INLINE uint16_t buffered_bounded_lemire_uint16(
 
 /* Static `Lemire rejection` function called by
  * random_buffered_bounded_uint8(...) */
-static NPY_INLINE uint8_t buffered_bounded_lemire_uint8(bitgen_t *bitgen_state,
-                                                        uint8_t rng, int *bcnt,
-                                                        uint32_t *buf) {
+static inline uint8_t buffered_bounded_lemire_uint8(bitgen_t *bitgen_state,
+                                                    uint8_t rng, int *bcnt,
+                                                    uint32_t *buf) {
   /*
    * Uses Lemire's algorithm - https://arxiv.org/abs/1805.10941
    *
diff --git a/contrib/python/numpy/py3/numpy/random/src/legacy/legacy-distributions.c b/contrib/python/numpy/py3/numpy/random/src/legacy/legacy-distributions.c
index 443c1a4bf78..b518b8a0399 100644
--- a/contrib/python/numpy/py3/numpy/random/src/legacy/legacy-distributions.c
+++ b/contrib/python/numpy/py3/numpy/random/src/legacy/legacy-distributions.c
@@ -11,7 +11,7 @@
 #include "include/legacy-distributions.h"
 
 
-static NPY_INLINE double legacy_double(aug_bitgen_t *aug_state) {
+static inline double legacy_double(aug_bitgen_t *aug_state) {
   return aug_state->bit_generator->next_double(aug_state->bit_generator->state);
 }
 
@@ -494,4 +494,4 @@ int64_t legacy_logseries(bitgen_t *bitgen_state, double p) {
     }
     return 2;
   }
-}
\ No newline at end of file
+}
diff --git a/contrib/python/numpy/py3/numpy/random/src/mt19937/randomkit.c b/contrib/python/numpy/py3/numpy/random/src/mt19937/randomkit.c
index f8ed4b49e2f..e718c2d06cc 100644
--- a/contrib/python/numpy/py3/numpy/random/src/mt19937/randomkit.c
+++ b/contrib/python/numpy/py3/numpy/random/src/mt19937/randomkit.c
@@ -247,7 +247,7 @@ unsigned long rk_random(rk_state *state) {
 /*
  * Returns an unsigned 64 bit random integer.
  */
-NPY_INLINE static npy_uint64 rk_uint64(rk_state *state) {
+static inline npy_uint64 rk_uint64(rk_state *state) {
   npy_uint64 upper = (npy_uint64)rk_random(state) << 32;
   npy_uint64 lower = (npy_uint64)rk_random(state);
   return upper | lower;
@@ -256,7 +256,7 @@ NPY_INLINE static npy_uint64 rk_uint64(rk_state *state) {
 /*
  * Returns an unsigned 32 bit random integer.
  */
-NPY_INLINE static npy_uint32 rk_uint32(rk_state *state) {
+static inline npy_uint32 rk_uint32(rk_state *state) {
   return (npy_uint32)rk_random(state);
 }
 
diff --git a/contrib/python/numpy/py3/numpy/random/src/philox/philox.c b/contrib/python/numpy/py3/numpy/random/src/philox/philox.c
index 6f2fad5a438..7909383e71a 100644
--- a/contrib/python/numpy/py3/numpy/random/src/philox/philox.c
+++ b/contrib/python/numpy/py3/numpy/random/src/philox/philox.c
@@ -1,8 +1,8 @@
 #include "philox.h"
 
-extern NPY_INLINE uint64_t philox_next64(philox_state *state);
+extern inline uint64_t philox_next64(philox_state *state);
 
-extern NPY_INLINE uint32_t philox_next32(philox_state *state);
+extern inline uint32_t philox_next32(philox_state *state);
 
 extern void philox_jump(philox_state *state) {
   /* Advances state as-if 2^128 draws were made */
diff --git a/contrib/python/numpy/py3/numpy/random/src/philox/philox.h b/contrib/python/numpy/py3/numpy/random/src/philox/philox.h
index 81e034a4793..ba7a67470a7 100644
--- a/contrib/python/numpy/py3/numpy/random/src/philox/philox.h
+++ b/contrib/python/numpy/py3/numpy/random/src/philox/philox.h
@@ -18,7 +18,7 @@ typedef struct r123array4x64 philox4x64_ctr_t;
 typedef struct r123array2x64 philox4x64_key_t;
 typedef struct r123array2x64 philox4x64_ukey_t;
 
-static NPY_INLINE struct r123array2x64
+static inline struct r123array2x64
 _philox4x64bumpkey(struct r123array2x64 key) {
   key.v[0] += (0x9E3779B97F4A7C15ULL);
   key.v[1] += (0xBB67AE8584CAA73BULL);
@@ -27,7 +27,7 @@ _philox4x64bumpkey(struct r123array2x64 key) {
 
 /* Prefer uint128 if available: GCC, clang, ICC */
 #ifdef __SIZEOF_INT128__
-static NPY_INLINE uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) {
+static inline uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) {
   __uint128_t product = ((__uint128_t)a) * ((__uint128_t)b);
   *hip = product >> 64;
   return (uint64_t)product;
@@ -39,13 +39,13 @@ static NPY_INLINE uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) {
 #pragma intrinsic(_umul128)
 #elif defined(_WIN64) && defined(_M_ARM64)
 #pragma intrinsic(__umulh)
-static NPY_INLINE uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
+static inline uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
   *high = __umulh(a, b);
   return a * b;
 }
 #else
 #pragma intrinsic(__emulu)
-static NPY_INLINE uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
+static inline uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
 
   uint64_t a_lo, a_hi, b_lo, b_hi, a_x_b_hi, a_x_b_mid, a_x_b_lo, b_x_a_mid,
       carry_bit;
@@ -68,11 +68,11 @@ static NPY_INLINE uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
   return a_x_b_lo + ((a_x_b_mid + b_x_a_mid) << 32);
 }
 #endif
-static NPY_INLINE uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) {
+static inline uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) {
   return _umul128(a, b, hip);
 }
 #else
-static NPY_INLINE uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
+static inline uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
 
   uint64_t a_lo, a_hi, b_lo, b_hi, a_x_b_hi, a_x_b_mid, a_x_b_lo, b_x_a_mid,
       carry_bit;
@@ -94,16 +94,16 @@ static NPY_INLINE uint64_t _umul128(uint64_t a, uint64_t b, uint64_t *high) {
 
   return a_x_b_lo + ((a_x_b_mid + b_x_a_mid) << 32);
 }
-static NPY_INLINE uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) {
+static inline uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t *hip) {
   return _umul128(a, b, hip);
 }
 #endif
 #endif
 
-static NPY_INLINE struct r123array4x64 _philox4x64round(struct r123array4x64 ctr,
+static inline struct r123array4x64 _philox4x64round(struct r123array4x64 ctr,
                                                     struct r123array2x64 key);
 
-static NPY_INLINE struct r123array4x64 _philox4x64round(struct r123array4x64 ctr,
+static inline struct r123array4x64 _philox4x64round(struct r123array4x64 ctr,
                                                     struct r123array2x64 key) {
   uint64_t hi0;
   uint64_t hi1;
@@ -114,14 +114,14 @@ static NPY_INLINE struct r123array4x64 _philox4x64round(struct r123array4x64 ctr
   return out;
 }
 
-static NPY_INLINE philox4x64_key_t philox4x64keyinit(philox4x64_ukey_t uk) {
+static inline philox4x64_key_t philox4x64keyinit(philox4x64_ukey_t uk) {
   return uk;
 }
-static NPY_INLINE philox4x64_ctr_t philox4x64_R(unsigned int R,
+static inline philox4x64_ctr_t philox4x64_R(unsigned int R,
                                             philox4x64_ctr_t ctr,
                                             philox4x64_key_t key);
 
-static NPY_INLINE philox4x64_ctr_t philox4x64_R(unsigned int R,
+static inline philox4x64_ctr_t philox4x64_R(unsigned int R,
                                             philox4x64_ctr_t ctr,
                                             philox4x64_key_t key) {
   if (R > 0) {
@@ -199,7 +199,7 @@ typedef struct s_philox_state {
   uint32_t uinteger;
 } philox_state;
 
-static NPY_INLINE uint64_t philox_next(philox_state *state) {
+static inline uint64_t philox_next(philox_state *state) {
   uint64_t out;
   int i;
   philox4x64_ctr_t ct;
@@ -229,11 +229,11 @@ static NPY_INLINE uint64_t philox_next(philox_state *state) {
   return state->buffer[0];
 }
 
-static NPY_INLINE uint64_t philox_next64(philox_state *state) {
+static inline uint64_t philox_next64(philox_state *state) {
   return philox_next(state);
 }
 
-static NPY_INLINE uint32_t philox_next32(philox_state *state) {
+static inline uint32_t philox_next32(philox_state *state) {
   uint64_t next;
 
   if (state->has_uint32) {
diff --git a/contrib/python/numpy/py3/numpy/random/src/sfc64/sfc64.h b/contrib/python/numpy/py3/numpy/random/src/sfc64/sfc64.h
index 75c4118d319..f6526063ec7 100644
--- a/contrib/python/numpy/py3/numpy/random/src/sfc64/sfc64.h
+++ b/contrib/python/numpy/py3/numpy/random/src/sfc64/sfc64.h
@@ -14,7 +14,7 @@ typedef struct s_sfc64_state {
 } sfc64_state;
 
 
-static NPY_INLINE uint64_t rotl(const uint64_t value, unsigned int rot) {
+static inline uint64_t rotl(const uint64_t value, unsigned int rot) {
 #ifdef _WIN32
   return _rotl64(value, rot);
 #else
@@ -22,7 +22,7 @@ static NPY_INLINE uint64_t rotl(const uint64_t value, unsigned int rot) {
 #endif
 }
 
-static NPY_INLINE uint64_t sfc64_next(uint64_t *s) {
+static inline uint64_t sfc64_next(uint64_t *s) {
   const uint64_t tmp = s[0] + s[1] + s[3]++;
 
   s[0] = s[1] ^ (s[1] >> 11);
@@ -33,11 +33,11 @@ static NPY_INLINE uint64_t sfc64_next(uint64_t *s) {
 }
 
 
-static NPY_INLINE uint64_t sfc64_next64(sfc64_state *state) {
+static inline uint64_t sfc64_next64(sfc64_state *state) {
   return sfc64_next(&state->s[0]);
 }
 
-static NPY_INLINE uint32_t sfc64_next32(sfc64_state *state) {
+static inline uint32_t sfc64_next32(sfc64_state *state) {
   uint64_t next;
   if (state->has_uint32) {
     state->has_uint32 = 0;
diff --git a/contrib/python/numpy/py3/numpy/random/tests/test_direct.py b/contrib/python/numpy/py3/numpy/random/tests/test_direct.py
index 2147553ce0c..1b2f681b590 100644
--- a/contrib/python/numpy/py3/numpy/random/tests/test_direct.py
+++ b/contrib/python/numpy/py3/numpy/random/tests/test_direct.py
@@ -149,6 +149,46 @@ def test_seedsequence():
     assert len(dummy.spawn(10)) == 10
 
 
+def test_generator_spawning():
+    """ Test spawning new generators and bit_generators directly.
+    """
+    rng = np.random.default_rng()
+    seq = rng.bit_generator.seed_seq
+    new_ss = seq.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5)]
+    assert [c.spawn_key for c in new_ss] == expected_keys
+
+    new_bgs = rng.bit_generator.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5, 10)]
+    assert [bg.seed_seq.spawn_key for bg in new_bgs] == expected_keys
+
+    new_rngs = rng.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(10, 15)]
+    found_keys = [rng.bit_generator.seed_seq.spawn_key for rng in new_rngs]
+    assert found_keys == expected_keys
+
+    # Sanity check that streams are actually different:
+    assert new_rngs[0].uniform() != new_rngs[1].uniform()
+
+
+def test_non_spawnable():
+    from numpy.random.bit_generator import ISeedSequence
+
+    class FakeSeedSequence:
+        def generate_state(self, n_words, dtype=np.uint32):
+            return np.zeros(n_words, dtype=dtype)
+
+    ISeedSequence.register(FakeSeedSequence)
+
+    rng = np.random.default_rng(FakeSeedSequence())
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.spawn(5)
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.bit_generator.spawn(5)
+
+
 class Base:
     dtype = np.uint64
     data2 = data1 = {}
diff --git a/contrib/python/numpy/py3/numpy/random/tests/test_extending.py b/contrib/python/numpy/py3/numpy/random/tests/test_extending.py
index bc24bd259e8..5ace080f1bc 100644
--- a/contrib/python/numpy/py3/numpy/random/tests/test_extending.py
+++ b/contrib/python/numpy/py3/numpy/random/tests/test_extending.py
@@ -33,7 +33,7 @@ try:
 except ImportError:
     cython = None
 else:
-    from numpy.compat import _pep440
+    from numpy._utils import _pep440
     # Cython 0.29.30 is required for Python 3.11 and there are
     # other fixes in the 0.29 series that are needed even for earlier
     # Python versions.
@@ -62,7 +62,7 @@ def test_cython(tmp_path):
     # gh-16162: make sure numpy's __init__.pxd was used for cython
     # not really part of this test, but it is a convenient place to check
     with open(build_dir / 'extending.c') as fid:
-        txt_to_find = 'NumPy API declarations from "numpy/__init__.pxd"'
+        txt_to_find = 'NumPy API declarations from "numpy/__init__'
         for i, line in enumerate(fid):
             if txt_to_find in line:
                 break
diff --git a/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937.py b/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937.py
index 54a5b73a31f..5c4c2cbf92f 100644
--- a/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937.py
+++ b/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937.py
@@ -345,6 +345,8 @@ class TestIntegers:
                           endpoint=endpoint, dtype=dt)
             assert_raises(ValueError, self.rfunc, 1, [0],
                           endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [ubnd+1], [ubnd],
+                          endpoint=endpoint, dtype=dt)
 
     def test_bounds_checking_array(self, endpoint):
         for dt in self.itype:
diff --git a/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937_regressions.py b/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937_regressions.py
index 0227d6502c8..7c2b6867c0e 100644
--- a/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937_regressions.py
+++ b/contrib/python/numpy/py3/numpy/random/tests/test_generator_mt19937_regressions.py
@@ -3,32 +3,32 @@ import numpy as np
 import pytest
 from numpy.random import Generator, MT19937
 
-mt19937 = Generator(MT19937())
-
 
 class TestRegression:
 
+    def setup_method(self):
+        self.mt19937 = Generator(MT19937(121263137472525314065))
+
     def test_vonmises_range(self):
         # Make sure generated random variables are in [-pi, pi].
         # Regression test for ticket #986.
         for mu in np.linspace(-7., 7., 5):
-            r = mt19937.vonmises(mu, 1, 50)
+            r = self.mt19937.vonmises(mu, 1, 50)
             assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
 
     def test_hypergeometric_range(self):
         # Test for ticket #921
-        assert_(np.all(mt19937.hypergeometric(3, 18, 11, size=10) < 4))
-        assert_(np.all(mt19937.hypergeometric(18, 3, 11, size=10) > 0))
+        assert_(np.all(self.mt19937.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(self.mt19937.hypergeometric(18, 3, 11, size=10) > 0))
 
         # Test for ticket #5623
         args = (2**20 - 2, 2**20 - 2, 2**20 - 2)  # Check for 32-bit systems
-        assert_(mt19937.hypergeometric(*args) > 0)
+        assert_(self.mt19937.hypergeometric(*args) > 0)
 
     def test_logseries_convergence(self):
         # Test for ticket #923
         N = 1000
-        mt19937 = Generator(MT19937(0))
-        rvsn = mt19937.logseries(0.8, size=N)
+        rvsn = self.mt19937.logseries(0.8, size=N)
         # these two frequency counts should be close to theoretical
         # numbers with this large sample
         # theoretical large N result is 0.49706795
@@ -65,41 +65,38 @@ class TestRegression:
         # Test for multivariate_normal issue with 'size' argument.
         # Check that the multivariate_normal size argument can be a
         # numpy integer.
-        mt19937.multivariate_normal([0], [[0]], size=1)
-        mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
-        mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
+        self.mt19937.multivariate_normal([0], [[0]], size=1)
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
 
     def test_beta_small_parameters(self):
         # Test that beta with small a and b parameters does not produce
         # NaNs due to roundoff errors causing 0 / 0, gh-5851
-        mt19937 = Generator(MT19937(1234567890))
-        x = mt19937.beta(0.0001, 0.0001, size=100)
+        x = self.mt19937.beta(0.0001, 0.0001, size=100)
         assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
 
     def test_choice_sum_of_probs_tolerance(self):
         # The sum of probs should be 1.0 with some tolerance.
         # For low precision dtypes the tolerance was too tight.
         # See numpy github issue 6123.
-        mt19937 = Generator(MT19937(1234))
         a = [1, 2, 3]
         counts = [4, 4, 2]
         for dt in np.float16, np.float32, np.float64:
             probs = np.array(counts, dtype=dt) / sum(counts)
-            c = mt19937.choice(a, p=probs)
+            c = self.mt19937.choice(a, p=probs)
             assert_(c in a)
             with pytest.raises(ValueError):
-                mt19937.choice(a, p=probs*0.9)
+                self.mt19937.choice(a, p=probs*0.9)
 
     def test_shuffle_of_array_of_different_length_strings(self):
         # Test that permuting an array of different length strings
         # will not cause a segfault on garbage collection
         # Tests gh-7710
-        mt19937 = Generator(MT19937(1234))
 
         a = np.array(['a', 'a' * 1000])
 
         for _ in range(100):
-            mt19937.shuffle(a)
+            self.mt19937.shuffle(a)
 
         # Force Garbage Collection - should not segfault.
         import gc
@@ -109,17 +106,17 @@ class TestRegression:
         # Test that permuting an array of objects will not cause
         # a segfault on garbage collection.
         # See gh-7719
-        mt19937 = Generator(MT19937(1234))
         a = np.array([np.arange(1), np.arange(4)], dtype=object)
 
         for _ in range(1000):
-            mt19937.shuffle(a)
+            self.mt19937.shuffle(a)
 
         # Force Garbage Collection - should not segfault.
         import gc
         gc.collect()
 
     def test_permutation_subclass(self):
+
         class N(np.ndarray):
             pass
 
@@ -142,9 +139,16 @@ class TestRegression:
         assert_array_equal(m.__array__(), np.arange(5))
 
     def test_gamma_0(self):
-        assert mt19937.standard_gamma(0.0) == 0.0
-        assert_array_equal(mt19937.standard_gamma([0.0]), 0.0)
+        assert self.mt19937.standard_gamma(0.0) == 0.0
+        assert_array_equal(self.mt19937.standard_gamma([0.0]), 0.0)
 
-        actual = mt19937.standard_gamma([0.0], dtype='float')
+        actual = self.mt19937.standard_gamma([0.0], dtype='float')
         expected = np.array([0.], dtype=np.float32)
         assert_array_equal(actual, expected)
+
+    def test_geometric_tiny_prob(self):
+        # Regression test for gh-17007.
+        # When p = 1e-30, the probability that a sample will exceed 2**63-1
+        # is 0.9999999999907766, so we expect the result to be all 2**63-1.
+        assert_array_equal(self.mt19937.geometric(p=1e-30, size=3),
+                           np.iinfo(np.int64).max)
diff --git a/contrib/python/numpy/py3/numpy/random/tests/test_randomstate.py b/contrib/python/numpy/py3/numpy/random/tests/test_randomstate.py
index 8b911cb3a81..3a296109890 100644
--- a/contrib/python/numpy/py3/numpy/random/tests/test_randomstate.py
+++ b/contrib/python/numpy/py3/numpy/random/tests/test_randomstate.py
@@ -812,6 +812,10 @@ class TestRandomDist:
         alpha = np.array([5.4e-01, -1.0e-16])
         assert_raises(ValueError, random.dirichlet, alpha)
 
+    def test_dirichlet_zero_alpha(self):
+        y = random.default_rng().dirichlet([5, 9, 0, 8])
+        assert_equal(y[2], 0)
+
     def test_dirichlet_alpha_non_contiguous(self):
         a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
         alpha = a[::2]
diff --git a/contrib/python/numpy/py3/numpy/setup.py b/contrib/python/numpy/py3/numpy/setup.py
index 28c28d1acff..b6f879bcca8 100644
--- a/contrib/python/numpy/py3/numpy/setup.py
+++ b/contrib/python/numpy/py3/numpy/setup.py
@@ -20,6 +20,7 @@ def configuration(parent_package='',top_path=None):
     config.add_subpackage('testing')
     config.add_subpackage('typing')
     config.add_subpackage('_typing')
+    config.add_subpackage('_utils')
     config.add_data_dir('doc')
     config.add_data_files('py.typed')
     config.add_data_files('*.pyi')
diff --git a/contrib/python/numpy/py3/numpy/testing/__init__.py b/contrib/python/numpy/py3/numpy/testing/__init__.py
index 087527e43eb..8a34221e4dd 100644
--- a/contrib/python/numpy/py3/numpy/testing/__init__.py
+++ b/contrib/python/numpy/py3/numpy/testing/__init__.py
@@ -10,12 +10,12 @@ from unittest import TestCase
 from . import _private
 from ._private.utils import *
 from ._private.utils import (_assert_valid_refcount, _gen_alignment_data)
-from ._private import extbuild, decorators as dec
-from ._private.nosetester import (
-    run_module_suite, NoseTester as Tester
-    )
+from ._private import extbuild
+from . import overrides
 
-__all__ = _private.utils.__all__ + ['TestCase', 'run_module_suite']
+__all__ = (
+    _private.utils.__all__ + ['TestCase', 'overrides']
+)
 
 from numpy._pytesttester import PytestTester
 test = PytestTester(__name__)
diff --git a/contrib/python/numpy/py3/numpy/testing/__init__.pyi b/contrib/python/numpy/py3/numpy/testing/__init__.pyi
index a981d61136d..d65860ccb04 100644
--- a/contrib/python/numpy/py3/numpy/testing/__init__.pyi
+++ b/contrib/python/numpy/py3/numpy/testing/__init__.pyi
@@ -18,7 +18,6 @@ from numpy.testing._private.utils import (
     jiffies as jiffies,
     memusage as memusage,
     print_assert_equal as print_assert_equal,
-    raises as raises,
     rundocs as rundocs,
     runstring as runstring,
     verbose as verbose,
@@ -49,8 +48,3 @@ from numpy.testing._private.utils import (
 __all__: list[str]
 __path__: list[str]
 test: PytestTester
-
-def run_module_suite(
-    file_to_run: None | str = ...,
-    argv: None | list[str] = ...,
-) -> None: ...
diff --git a/contrib/python/numpy/py3/numpy/testing/_private/decorators.py b/contrib/python/numpy/py3/numpy/testing/_private/decorators.py
deleted file mode 100644
index cb49d9a7347..00000000000
--- a/contrib/python/numpy/py3/numpy/testing/_private/decorators.py
+++ /dev/null
@@ -1,331 +0,0 @@
-"""
-Decorators for labeling and modifying behavior of test objects.
-
-Decorators that merely return a modified version of the original
-function object are straightforward. Decorators that return a new
-function object need to use
-::
-
-  nose.tools.make_decorator(original_function)(decorator)
-
-in returning the decorator, in order to preserve meta-data such as
-function name, setup and teardown functions and so on - see
-``nose.tools`` for more information.
-
-"""
-import collections.abc
-import warnings
-
-from .utils import SkipTest, assert_warns, HAS_REFCOUNT
-
-__all__ = ['slow', 'setastest', 'skipif', 'knownfailureif', 'deprecated',
-           'parametrize', '_needs_refcount',]
-
-
-def slow(t):
-    """
-    .. deprecated:: 1.21
-        This decorator is retained for compatibility with the nose testing framework, which is being phased out.
-        Please use the nose2 or pytest frameworks instead.
-
-    Label a test as 'slow'.
-
-    The exact definition of a slow test is obviously both subjective and
-    hardware-dependent, but in general any individual test that requires more
-    than a second or two should be labeled as slow (the whole suite consists of
-    thousands of tests, so even a second is significant).
-
-    Parameters
-    ----------
-    t : callable
-        The test to label as slow.
-
-    Returns
-    -------
-    t : callable
-        The decorated test `t`.
-
-    Examples
-    --------
-    The `numpy.testing` module includes ``import decorators as dec``.
-    A test can be decorated as slow like this::
-
-      from numpy.testing import *
-
-      @dec.slow
-      def test_big(self):
-          print('Big, slow test')
-
-    """
-    # Numpy 1.21, 2020-12-20
-    warnings.warn('the np.testing.dec decorators are included for nose support, and are '
-                'deprecated since NumPy v1.21. Use the nose2 or pytest frameworks instead.', DeprecationWarning, stacklevel=2)
-
-    t.slow = True
-    return t
-
-def setastest(tf=True):
-    """
-    .. deprecated:: 1.21
-        This decorator is retained for compatibility with the nose testing framework, which is being phased out.
-        Please use the nose2 or pytest frameworks instead.
-
-    Signals to nose that this function is or is not a test.
-
-    Parameters
-    ----------
-    tf : bool
-        If True, specifies that the decorated callable is a test.
-        If False, specifies that the decorated callable is not a test.
-        Default is True.
-
-    Notes
-    -----
-    This decorator can't use the nose namespace, because it can be
-    called from a non-test module. See also ``istest`` and ``nottest`` in
-    ``nose.tools``.
-
-    Examples
-    --------
-    `setastest` can be used in the following way::
-
-      from numpy.testing import dec
-
-      @dec.setastest(False)
-      def func_with_test_in_name(arg1, arg2):
-          pass
-
-    """
-    # Numpy 1.21, 2020-12-20
-    warnings.warn('the np.testing.dec decorators are included for nose support, and are '
-            'deprecated since NumPy v1.21. Use the nose2 or pytest frameworks instead.', DeprecationWarning, stacklevel=2)
-    def set_test(t):
-        t.__test__ = tf
-        return t
-    return set_test
-
-def skipif(skip_condition, msg=None):
-    """
-    .. deprecated:: 1.21
-        This decorator is retained for compatibility with the nose testing framework, which is being phased out.
-        Please use the nose2 or pytest frameworks instead.
-
-    Make function raise SkipTest exception if a given condition is true.
-
-    If the condition is a callable, it is used at runtime to dynamically
-    make the decision. This is useful for tests that may require costly
-    imports, to delay the cost until the test suite is actually executed.
-
-    Parameters
-    ----------
-    skip_condition : bool or callable
-        Flag to determine whether to skip the decorated test.
-    msg : str, optional
-        Message to give on raising a SkipTest exception. Default is None.
-
-    Returns
-    -------
-    decorator : function
-        Decorator which, when applied to a function, causes SkipTest
-        to be raised when `skip_condition` is True, and the function
-        to be called normally otherwise.
-
-    Notes
-    -----
-    The decorator itself is decorated with the ``nose.tools.make_decorator``
-    function in order to transmit function name, and various other metadata.
-
-    """
-
-    def skip_decorator(f):
-        # Local import to avoid a hard nose dependency and only incur the
-        # import time overhead at actual test-time.
-        import nose
-
-        # Numpy 1.21, 2020-12-20
-        warnings.warn('the np.testing.dec decorators are included for nose support, and are '
-            'deprecated since NumPy v1.21. Use the nose2 or pytest frameworks instead.', DeprecationWarning, stacklevel=2)
-
-        # Allow for both boolean or callable skip conditions.
-        if isinstance(skip_condition, collections.abc.Callable):
-            skip_val = lambda: skip_condition()
-        else:
-            skip_val = lambda: skip_condition
-
-        def get_msg(func,msg=None):
-            """Skip message with information about function being skipped."""
-            if msg is None:
-                out = 'Test skipped due to test condition'
-            else:
-                out = msg
-
-            return f'Skipping test: {func.__name__}: {out}'
-
-        # We need to define *two* skippers because Python doesn't allow both
-        # return with value and yield inside the same function.
-        def skipper_func(*args, **kwargs):
-            """Skipper for normal test functions."""
-            if skip_val():
-                raise SkipTest(get_msg(f, msg))
-            else:
-                return f(*args, **kwargs)
-
-        def skipper_gen(*args, **kwargs):
-            """Skipper for test generators."""
-            if skip_val():
-                raise SkipTest(get_msg(f, msg))
-            else:
-                yield from f(*args, **kwargs)
-
-        # Choose the right skipper to use when building the actual decorator.
-        if nose.util.isgenerator(f):
-            skipper = skipper_gen
-        else:
-            skipper = skipper_func
-
-        return nose.tools.make_decorator(f)(skipper)
-
-    return skip_decorator
-
-
-def knownfailureif(fail_condition, msg=None):
-    """
-    .. deprecated:: 1.21
-        This decorator is retained for compatibility with the nose testing framework, which is being phased out.
-        Please use the nose2 or pytest frameworks instead.
-
-    Make function raise KnownFailureException exception if given condition is true.
-
-    If the condition is a callable, it is used at runtime to dynamically
-    make the decision. This is useful for tests that may require costly
-    imports, to delay the cost until the test suite is actually executed.
-
-    Parameters
-    ----------
-    fail_condition : bool or callable
-        Flag to determine whether to mark the decorated test as a known
-        failure (if True) or not (if False).
-    msg : str, optional
-        Message to give on raising a KnownFailureException exception.
-        Default is None.
-
-    Returns
-    -------
-    decorator : function
-        Decorator, which, when applied to a function, causes
-        KnownFailureException to be raised when `fail_condition` is True,
-        and the function to be called normally otherwise.
-
-    Notes
-    -----
-    The decorator itself is decorated with the ``nose.tools.make_decorator``
-    function in order to transmit function name, and various other metadata.
-
-    """
-    # Numpy 1.21, 2020-12-20
-    warnings.warn('the np.testing.dec decorators are included for nose support, and are '
-            'deprecated since NumPy v1.21. Use the nose2 or pytest frameworks instead.', DeprecationWarning, stacklevel=2)
-
-    if msg is None:
-        msg = 'Test skipped due to known failure'
-
-    # Allow for both boolean or callable known failure conditions.
-    if isinstance(fail_condition, collections.abc.Callable):
-        fail_val = lambda: fail_condition()
-    else:
-        fail_val = lambda: fail_condition
-
-    def knownfail_decorator(f):
-        # Local import to avoid a hard nose dependency and only incur the
-        # import time overhead at actual test-time.
-        import nose
-        from .noseclasses import KnownFailureException
-
-        def knownfailer(*args, **kwargs):
-            if fail_val():
-                raise KnownFailureException(msg)
-            else:
-                return f(*args, **kwargs)
-        return nose.tools.make_decorator(f)(knownfailer)
-
-    return knownfail_decorator
-
-def deprecated(conditional=True):
-    """
-    .. deprecated:: 1.21
-        This decorator is retained for compatibility with the nose testing framework, which is being phased out.
-        Please use the nose2 or pytest frameworks instead.
-
-    Filter deprecation warnings while running the test suite.
-
-    This decorator can be used to filter DeprecationWarning's, to avoid
-    printing them during the test suite run, while checking that the test
-    actually raises a DeprecationWarning.
-
-    Parameters
-    ----------
-    conditional : bool or callable, optional
-        Flag to determine whether to mark test as deprecated or not. If the
-        condition is a callable, it is used at runtime to dynamically make the
-        decision. Default is True.
-
-    Returns
-    -------
-    decorator : function
-        The `deprecated` decorator itself.
-
-    Notes
-    -----
-    .. versionadded:: 1.4.0
-
-    """
-    def deprecate_decorator(f):
-        # Local import to avoid a hard nose dependency and only incur the
-        # import time overhead at actual test-time.
-        import nose
-
-        # Numpy 1.21, 2020-12-20
-        warnings.warn('the np.testing.dec decorators are included for nose support, and are '
-            'deprecated since NumPy v1.21. Use the nose2 or pytest frameworks instead.', DeprecationWarning, stacklevel=2)
-
-        def _deprecated_imp(*args, **kwargs):
-            # Poor man's replacement for the with statement
-            with assert_warns(DeprecationWarning):
-                f(*args, **kwargs)
-
-        if isinstance(conditional, collections.abc.Callable):
-            cond = conditional()
-        else:
-            cond = conditional
-        if cond:
-            return nose.tools.make_decorator(f)(_deprecated_imp)
-        else:
-            return f
-    return deprecate_decorator
-
-
-def parametrize(vars, input):
-    """
-    .. deprecated:: 1.21
-        This decorator is retained for compatibility with the nose testing framework, which is being phased out.
-        Please use the nose2 or pytest frameworks instead.
-
-    Pytest compatibility class. This implements the simplest level of
-    pytest.mark.parametrize for use in nose as an aid in making the transition
-    to pytest. It achieves that by adding a dummy var parameter and ignoring
-    the doc_func parameter of the base class. It does not support variable
-    substitution by name, nor does it support nesting or classes. See the
-    pytest documentation for usage.
-
-    .. versionadded:: 1.14.0
-
-    """
-    from .parameterized import parameterized
-
-    # Numpy 1.21, 2020-12-20
-    warnings.warn('the np.testing.dec decorators are included for nose support, and are '
-            'deprecated since NumPy v1.21. Use the nose2 or pytest frameworks instead.', DeprecationWarning, stacklevel=2)
-
-    return parameterized(input)
-
-_needs_refcount = skipif(not HAS_REFCOUNT, "python has no sys.getrefcount")
diff --git a/contrib/python/numpy/py3/numpy/testing/_private/noseclasses.py b/contrib/python/numpy/py3/numpy/testing/_private/noseclasses.py
deleted file mode 100644
index 48fa4dc1f5a..00000000000
--- a/contrib/python/numpy/py3/numpy/testing/_private/noseclasses.py
+++ /dev/null
@@ -1,364 +0,0 @@
-# These classes implement a doctest runner plugin for nose, a "known failure"
-# error class, and a customized TestProgram for NumPy.
-
-# Because this module imports nose directly, it should not
-# be used except by nosetester.py to avoid a general NumPy
-# dependency on nose.
-import os
-import sys
-import doctest
-import inspect
-
-import numpy
-import nose
-from nose.plugins import doctests as npd
-from nose.plugins.errorclass import ErrorClass, ErrorClassPlugin
-from nose.plugins.base import Plugin
-from nose.util import src
-from .nosetester import get_package_name
-from .utils import KnownFailureException, KnownFailureTest
-
-
-# Some of the classes in this module begin with 'Numpy' to clearly distinguish
-# them from the plethora of very similar names from nose/unittest/doctest
-
-#-----------------------------------------------------------------------------
-# Modified version of the one in the stdlib, that fixes a python bug (doctests
-# not found in extension modules, https://bugs.python.org/issue3158)
-class NumpyDocTestFinder(doctest.DocTestFinder):
-
-    def _from_module(self, module, object):
-        """
-        Return true if the given object is defined in the given
-        module.
-        """
-        if module is None:
-            return True
-        elif inspect.isfunction(object):
-            return module.__dict__ is object.__globals__
-        elif inspect.isbuiltin(object):
-            return module.__name__ == object.__module__
-        elif inspect.isclass(object):
-            return module.__name__ == object.__module__
-        elif inspect.ismethod(object):
-            # This one may be a bug in cython that fails to correctly set the
-            # __module__ attribute of methods, but since the same error is easy
-            # to make by extension code writers, having this safety in place
-            # isn't such a bad idea
-            return module.__name__ == object.__self__.__class__.__module__
-        elif inspect.getmodule(object) is not None:
-            return module is inspect.getmodule(object)
-        elif hasattr(object, '__module__'):
-            return module.__name__ == object.__module__
-        elif isinstance(object, property):
-            return True  # [XX] no way not be sure.
-        else:
-            raise ValueError("object must be a class or function")
-
-    def _find(self, tests, obj, name, module, source_lines, globs, seen):
-        """
-        Find tests for the given object and any contained objects, and
-        add them to `tests`.
-        """
-
-        doctest.DocTestFinder._find(self, tests, obj, name, module,
-                                    source_lines, globs, seen)
-
-        # Below we re-run pieces of the above method with manual modifications,
-        # because the original code is buggy and fails to correctly identify
-        # doctests in extension modules.
-
-        # Local shorthands
-        from inspect import (
-            isroutine, isclass, ismodule, isfunction, ismethod
-            )
-
-        # Look for tests in a module's contained objects.
-        if ismodule(obj) and self._recurse:
-            for valname, val in obj.__dict__.items():
-                valname1 = f'{name}.{valname}'
-                if ( (isroutine(val) or isclass(val))
-                     and self._from_module(module, val)):
-
-                    self._find(tests, val, valname1, module, source_lines,
-                               globs, seen)
-
-        # Look for tests in a class's contained objects.
-        if isclass(obj) and self._recurse:
-            for valname, val in obj.__dict__.items():
-                # Special handling for staticmethod/classmethod.
-                if isinstance(val, staticmethod):
-                    val = getattr(obj, valname)
-                if isinstance(val, classmethod):
-                    val = getattr(obj, valname).__func__
-
-                # Recurse to methods, properties, and nested classes.
-                if ((isfunction(val) or isclass(val) or
-                     ismethod(val) or isinstance(val, property)) and
-                      self._from_module(module, val)):
-                    valname = f'{name}.{valname}'
-                    self._find(tests, val, valname, module, source_lines,
-                               globs, seen)
-
-
-# second-chance checker; if the default comparison doesn't
-# pass, then see if the expected output string contains flags that
-# tell us to ignore the output
-class NumpyOutputChecker(doctest.OutputChecker):
-    def check_output(self, want, got, optionflags):
-        ret = doctest.OutputChecker.check_output(self, want, got,
-                                                 optionflags)
-        if not ret:
-            if "#random" in want:
-                return True
-
-            # it would be useful to normalize endianness so that
-            # bigendian machines don't fail all the tests (and there are
-            # actually some bigendian examples in the doctests). Let's try
-            # making them all little endian
-            got = got.replace("'>", "'<")
-            want = want.replace("'>", "'<")
-
-            # try to normalize out 32 and 64 bit default int sizes
-            for sz in [4, 8]:
-                got = got.replace("'<i%d'" % sz, "int")
-                want = want.replace("'<i%d'" % sz, "int")
-
-            ret = doctest.OutputChecker.check_output(self, want,
-                    got, optionflags)
-
-        return ret
-
-
-# Subclass nose.plugins.doctests.DocTestCase to work around a bug in
-# its constructor that blocks non-default arguments from being passed
-# down into doctest.DocTestCase
-class NumpyDocTestCase(npd.DocTestCase):
-    def __init__(self, test, optionflags=0, setUp=None, tearDown=None,
-                 checker=None, obj=None, result_var='_'):
-        self._result_var = result_var
-        self._nose_obj = obj
-        doctest.DocTestCase.__init__(self, test,
-                                     optionflags=optionflags,
-                                     setUp=setUp, tearDown=tearDown,
-                                     checker=checker)
-
-
-print_state = numpy.get_printoptions()
-
-class NumpyDoctest(npd.Doctest):
-    name = 'numpydoctest'   # call nosetests with --with-numpydoctest
-    score = 1000  # load late, after doctest builtin
-
-    # always use whitespace and ellipsis options for doctests
-    doctest_optflags = doctest.NORMALIZE_WHITESPACE | doctest.ELLIPSIS
-
-    # files that should be ignored for doctests
-    doctest_ignore = ['generate_numpy_api.py',
-                      'setup.py']
-
-    # Custom classes; class variables to allow subclassing
-    doctest_case_class = NumpyDocTestCase
-    out_check_class = NumpyOutputChecker
-    test_finder_class = NumpyDocTestFinder
-
-    # Don't use the standard doctest option handler; hard-code the option values
-    def options(self, parser, env=os.environ):
-        Plugin.options(self, parser, env)
-        # Test doctests in 'test' files / directories. Standard plugin default
-        # is False
-        self.doctest_tests = True
-        # Variable name; if defined, doctest results stored in this variable in
-        # the top-level namespace.  None is the standard default
-        self.doctest_result_var = None
-
-    def configure(self, options, config):
-        # parent method sets enabled flag from command line --with-numpydoctest
-        Plugin.configure(self, options, config)
-        self.finder = self.test_finder_class()
-        self.parser = doctest.DocTestParser()
-        if self.enabled:
-            # Pull standard doctest out of plugin list; there's no reason to run
-            # both.  In practice the Unplugger plugin above would cover us when
-            # run from a standard numpy.test() call; this is just in case
-            # someone wants to run our plugin outside the numpy.test() machinery
-            config.plugins.plugins = [p for p in config.plugins.plugins
-                                      if p.name != 'doctest']
-
-    def set_test_context(self, test):
-        """ Configure `test` object to set test context
-
-        We set the numpy / scipy standard doctest namespace
-
-        Parameters
-        ----------
-        test : test object
-            with ``globs`` dictionary defining namespace
-
-        Returns
-        -------
-        None
-
-        Notes
-        -----
-        `test` object modified in place
-        """
-        # set the namespace for tests
-        pkg_name = get_package_name(os.path.dirname(test.filename))
-
-        # Each doctest should execute in an environment equivalent to
-        # starting Python and executing "import numpy as np", and,
-        # for SciPy packages, an additional import of the local
-        # package (so that scipy.linalg.basic.py's doctests have an
-        # implicit "from scipy import linalg" as well).
-        #
-        # Note: __file__ allows the doctest in NoseTester to run
-        # without producing an error
-        test.globs = {'__builtins__':__builtins__,
-                      '__file__':'__main__',
-                      '__name__':'__main__',
-                      'np':numpy}
-        # add appropriate scipy import for SciPy tests
-        if 'scipy' in pkg_name:
-            p = pkg_name.split('.')
-            p2 = p[-1]
-            test.globs[p2] = __import__(pkg_name, test.globs, {}, [p2])
-
-    # Override test loading to customize test context (with set_test_context
-    # method), set standard docstring options, and install our own test output
-    # checker
-    def loadTestsFromModule(self, module):
-        if not self.matches(module.__name__):
-            npd.log.debug("Doctest doesn't want module %s", module)
-            return
-        try:
-            tests = self.finder.find(module)
-        except AttributeError:
-            # nose allows module.__test__ = False; doctest does not and
-            # throws AttributeError
-            return
-        if not tests:
-            return
-        tests.sort()
-        module_file = src(module.__file__)
-        for test in tests:
-            if not test.examples:
-                continue
-            if not test.filename:
-                test.filename = module_file
-            # Set test namespace; test altered in place
-            self.set_test_context(test)
-            yield self.doctest_case_class(test,
-                                          optionflags=self.doctest_optflags,
-                                          checker=self.out_check_class(),
-                                          result_var=self.doctest_result_var)
-
-    # Add an afterContext method to nose.plugins.doctests.Doctest in order
-    # to restore print options to the original state after each doctest
-    def afterContext(self):
-        numpy.set_printoptions(**print_state)
-
-    # Ignore NumPy-specific build files that shouldn't be searched for tests
-    def wantFile(self, file):
-        bn = os.path.basename(file)
-        if bn in self.doctest_ignore:
-            return False
-        return npd.Doctest.wantFile(self, file)
-
-
-class Unplugger:
-    """ Nose plugin to remove named plugin late in loading
-
-    By default it removes the "doctest" plugin.
-    """
-    name = 'unplugger'
-    enabled = True  # always enabled
-    score = 4000  # load late in order to be after builtins
-
-    def __init__(self, to_unplug='doctest'):
-        self.to_unplug = to_unplug
-
-    def options(self, parser, env):
-        pass
-
-    def configure(self, options, config):
-        # Pull named plugin out of plugins list
-        config.plugins.plugins = [p for p in config.plugins.plugins
-                                  if p.name != self.to_unplug]
-
-
-class KnownFailurePlugin(ErrorClassPlugin):
-    '''Plugin that installs a KNOWNFAIL error class for the
-    KnownFailureClass exception.  When KnownFailure is raised,
-    the exception will be logged in the knownfail attribute of the
-    result, 'K' or 'KNOWNFAIL' (verbose) will be output, and the
-    exception will not be counted as an error or failure.'''
-    enabled = True
-    knownfail = ErrorClass(KnownFailureException,
-                           label='KNOWNFAIL',
-                           isfailure=False)
-
-    def options(self, parser, env=os.environ):
-        env_opt = 'NOSE_WITHOUT_KNOWNFAIL'
-        parser.add_option('--no-knownfail', action='store_true',
-                          dest='noKnownFail', default=env.get(env_opt, False),
-                          help='Disable special handling of KnownFailure '
-                               'exceptions')
-
-    def configure(self, options, conf):
-        if not self.can_configure:
-            return
-        self.conf = conf
-        disable = getattr(options, 'noKnownFail', False)
-        if disable:
-            self.enabled = False
-
-KnownFailure = KnownFailurePlugin   # backwards compat
-
-
-class FPUModeCheckPlugin(Plugin):
-    """
-    Plugin that checks the FPU mode before and after each test,
-    raising failures if the test changed the mode.
-    """
-
-    def prepareTestCase(self, test):
-        from numpy.core._multiarray_tests import get_fpu_mode
-
-        def run(result):
-            old_mode = get_fpu_mode()
-            test.test(result)
-            new_mode = get_fpu_mode()
-
-            if old_mode != new_mode:
-                try:
-                    raise AssertionError(
-                        "FPU mode changed from {0:#x} to {1:#x} during the "
-                        "test".format(old_mode, new_mode))
-                except AssertionError:
-                    result.addFailure(test, sys.exc_info())
-
-        return run
-
-
-# Class allows us to save the results of the tests in runTests - see runTests
-# method docstring for details
-class NumpyTestProgram(nose.core.TestProgram):
-    def runTests(self):
-        """Run Tests. Returns true on success, false on failure, and
-        sets self.success to the same value.
-
-        Because nose currently discards the test result object, but we need
-        to return it to the user, override TestProgram.runTests to retain
-        the result
-        """
-        if self.testRunner is None:
-            self.testRunner = nose.core.TextTestRunner(stream=self.config.stream,
-                                                       verbosity=self.config.verbosity,
-                                                       config=self.config)
-        plug_runner = self.config.plugins.prepareTestRunner(self.testRunner)
-        if plug_runner is not None:
-            self.testRunner = plug_runner
-        self.result = self.testRunner.run(self.test)
-        self.success = self.result.wasSuccessful()
-        return self.success
diff --git a/contrib/python/numpy/py3/numpy/testing/_private/nosetester.py b/contrib/python/numpy/py3/numpy/testing/_private/nosetester.py
deleted file mode 100644
index bccec823691..00000000000
--- a/contrib/python/numpy/py3/numpy/testing/_private/nosetester.py
+++ /dev/null
@@ -1,545 +0,0 @@
-"""
-Nose test running.
-
-This module implements ``test()`` and ``bench()`` functions for NumPy modules.
-
-"""
-import os
-import sys
-import warnings
-import numpy as np
-
-from .utils import import_nose, suppress_warnings
-
-
-__all__ = ['get_package_name', 'run_module_suite', 'NoseTester',
-           '_numpy_tester', 'get_package_name', 'import_nose',
-           'suppress_warnings']
-
-
-def get_package_name(filepath):
-    """
-    Given a path where a package is installed, determine its name.
-
-    Parameters
-    ----------
-    filepath : str
-        Path to a file. If the determination fails, "numpy" is returned.
-
-    Examples
-    --------
-    >>> np.testing.nosetester.get_package_name('nonsense')
-    'numpy'
-
-    """
-
-    fullpath = filepath[:]
-    pkg_name = []
-    while 'site-packages' in filepath or 'dist-packages' in filepath:
-        filepath, p2 = os.path.split(filepath)
-        if p2 in ('site-packages', 'dist-packages'):
-            break
-        pkg_name.append(p2)
-
-    # if package name determination failed, just default to numpy/scipy
-    if not pkg_name:
-        if 'scipy' in fullpath:
-            return 'scipy'
-        else:
-            return 'numpy'
-
-    # otherwise, reverse to get correct order and return
-    pkg_name.reverse()
-
-    # don't include the outer egg directory
-    if pkg_name[0].endswith('.egg'):
-        pkg_name.pop(0)
-
-    return '.'.join(pkg_name)
-
-
-def run_module_suite(file_to_run=None, argv=None):
-    """
-    Run a test module.
-
-    Equivalent to calling ``$ nosetests <argv> <file_to_run>`` from
-    the command line
-
-    Parameters
-    ----------
-    file_to_run : str, optional
-        Path to test module, or None.
-        By default, run the module from which this function is called.
-    argv : list of strings
-        Arguments to be passed to the nose test runner. ``argv[0]`` is
-        ignored. All command line arguments accepted by ``nosetests``
-        will work. If it is the default value None, sys.argv is used.
-
-        .. versionadded:: 1.9.0
-
-    Examples
-    --------
-    Adding the following::
-
-        if __name__ == "__main__" :
-            run_module_suite(argv=sys.argv)
-
-    at the end of a test module will run the tests when that module is
-    called in the python interpreter.
-
-    Alternatively, calling::
-
-    >>> run_module_suite(file_to_run="numpy/tests/test_matlib.py")  # doctest: +SKIP
-
-    from an interpreter will run all the test routine in 'test_matlib.py'.
-    """
-    if file_to_run is None:
-        f = sys._getframe(1)
-        file_to_run = f.f_locals.get('__file__', None)
-        if file_to_run is None:
-            raise AssertionError
-
-    if argv is None:
-        argv = sys.argv + [file_to_run]
-    else:
-        argv = argv + [file_to_run]
-
-    nose = import_nose()
-    from .noseclasses import KnownFailurePlugin
-    nose.run(argv=argv, addplugins=[KnownFailurePlugin()])
-
-
-class NoseTester:
-    """
-    Nose test runner.
-
-    This class is made available as numpy.testing.Tester, and a test function
-    is typically added to a package's __init__.py like so::
-
-      from numpy.testing import Tester
-      test = Tester().test
-
-    Calling this test function finds and runs all tests associated with the
-    package and all its sub-packages.
-
-    Attributes
-    ----------
-    package_path : str
-        Full path to the package to test.
-    package_name : str
-        Name of the package to test.
-
-    Parameters
-    ----------
-    package : module, str or None, optional
-        The package to test. If a string, this should be the full path to
-        the package. If None (default), `package` is set to the module from
-        which `NoseTester` is initialized.
-    raise_warnings : None, str or sequence of warnings, optional
-        This specifies which warnings to configure as 'raise' instead
-        of being shown once during the test execution.  Valid strings are:
-
-          - "develop" : equals ``(Warning,)``
-          - "release" : equals ``()``, don't raise on any warnings.
-
-        Default is "release".
-    depth : int, optional
-        If `package` is None, then this can be used to initialize from the
-        module of the caller of (the caller of (...)) the code that
-        initializes `NoseTester`. Default of 0 means the module of the
-        immediate caller; higher values are useful for utility routines that
-        want to initialize `NoseTester` objects on behalf of other code.
-
-    """
-    def __init__(self, package=None, raise_warnings="release", depth=0,
-                 check_fpu_mode=False):
-        # Back-compat: 'None' used to mean either "release" or "develop"
-        # depending on whether this was a release or develop version of
-        # numpy. Those semantics were fine for testing numpy, but not so
-        # helpful for downstream projects like scipy that use
-        # numpy.testing. (They want to set this based on whether *they* are a
-        # release or develop version, not whether numpy is.) So we continue to
-        # accept 'None' for back-compat, but it's now just an alias for the
-        # default "release".
-        if raise_warnings is None:
-            raise_warnings = "release"
-
-        package_name = None
-        if package is None:
-            f = sys._getframe(1 + depth)
-            package_path = f.f_locals.get('__file__', None)
-            if package_path is None:
-                raise AssertionError
-            package_path = os.path.dirname(package_path)
-            package_name = f.f_locals.get('__name__', None)
-        elif isinstance(package, type(os)):
-            package_path = os.path.dirname(package.__file__)
-            package_name = getattr(package, '__name__', None)
-        else:
-            package_path = str(package)
-
-        self.package_path = package_path
-
-        # Find the package name under test; this name is used to limit coverage
-        # reporting (if enabled).
-        if package_name is None:
-            package_name = get_package_name(package_path)
-        self.package_name = package_name
-
-        # Set to "release" in constructor in maintenance branches.
-        self.raise_warnings = raise_warnings
-
-        # Whether to check for FPU mode changes
-        self.check_fpu_mode = check_fpu_mode
-
-    def _test_argv(self, label, verbose, extra_argv):
-        ''' Generate argv for nosetest command
-
-        Parameters
-        ----------
-        label : {'fast', 'full', '', attribute identifier}, optional
-            see ``test`` docstring
-        verbose : int, optional
-            Verbosity value for test outputs, in the range 1-10. Default is 1.
-        extra_argv : list, optional
-            List with any extra arguments to pass to nosetests.
-
-        Returns
-        -------
-        argv : list
-            command line arguments that will be passed to nose
-        '''
-        argv = [__file__, self.package_path, '-s']
-        if label and label != 'full':
-            if not isinstance(label, str):
-                raise TypeError('Selection label should be a string')
-            if label == 'fast':
-                label = 'not slow'
-            argv += ['-A', label]
-        argv += ['--verbosity', str(verbose)]
-
-        # When installing with setuptools, and also in some other cases, the
-        # test_*.py files end up marked +x executable. Nose, by default, does
-        # not run files marked with +x as they might be scripts. However, in
-        # our case nose only looks for test_*.py files under the package
-        # directory, which should be safe.
-        argv += ['--exe']
-
-        if extra_argv:
-            argv += extra_argv
-        return argv
-
-    def _show_system_info(self):
-        nose = import_nose()
-
-        import numpy
-        print(f'NumPy version {numpy.__version__}')
-        relaxed_strides = numpy.ones((10, 1), order="C").flags.f_contiguous
-        print("NumPy relaxed strides checking option:", relaxed_strides)
-        npdir = os.path.dirname(numpy.__file__)
-        print(f'NumPy is installed in {npdir}')
-
-        if 'scipy' in self.package_name:
-            import scipy
-            print(f'SciPy version {scipy.__version__}')
-            spdir = os.path.dirname(scipy.__file__)
-            print(f'SciPy is installed in {spdir}')
-
-        pyversion = sys.version.replace('\n', '')
-        print(f'Python version {pyversion}')
-        print("nose version %d.%d.%d" % nose.__versioninfo__)
-
-    def _get_custom_doctester(self):
-        """ Return instantiated plugin for doctests
-
-        Allows subclassing of this class to override doctester
-
-        A return value of None means use the nose builtin doctest plugin
-        """
-        from .noseclasses import NumpyDoctest
-        return NumpyDoctest()
-
-    def prepare_test_args(self, label='fast', verbose=1, extra_argv=None,
-                          doctests=False, coverage=False, timer=False):
-        """
-        Run tests for module using nose.
-
-        This method does the heavy lifting for the `test` method. It takes all
-        the same arguments, for details see `test`.
-
-        See Also
-        --------
-        test
-
-        """
-        # fail with nice error message if nose is not present
-        import_nose()
-        # compile argv
-        argv = self._test_argv(label, verbose, extra_argv)
-        # our way of doing coverage
-        if coverage:
-            argv += [f'--cover-package={self.package_name}', '--with-coverage',
-                   '--cover-tests', '--cover-erase']
-
-        if timer:
-            if timer is True:
-                argv += ['--with-timer']
-            elif isinstance(timer, int):
-                argv += ['--with-timer', '--timer-top-n', str(timer)]
-
-        # construct list of plugins
-        import nose.plugins.builtin
-        from nose.plugins import EntryPointPluginManager
-        from .noseclasses import (KnownFailurePlugin, Unplugger,
-                                  FPUModeCheckPlugin)
-        plugins = [KnownFailurePlugin()]
-        plugins += [p() for p in nose.plugins.builtin.plugins]
-        if self.check_fpu_mode:
-            plugins += [FPUModeCheckPlugin()]
-            argv += ["--with-fpumodecheckplugin"]
-        try:
-            # External plugins (like nose-timer)
-            entrypoint_manager = EntryPointPluginManager()
-            entrypoint_manager.loadPlugins()
-            plugins += [p for p in entrypoint_manager.plugins]
-        except ImportError:
-            # Relies on pkg_resources, not a hard dependency
-            pass
-
-        # add doctesting if required
-        doctest_argv = '--with-doctest' in argv
-        if doctests == False and doctest_argv:
-            doctests = True
-        plug = self._get_custom_doctester()
-        if plug is None:
-            # use standard doctesting
-            if doctests and not doctest_argv:
-                argv += ['--with-doctest']
-        else:  # custom doctesting
-            if doctest_argv:  # in fact the unplugger would take care of this
-                argv.remove('--with-doctest')
-            plugins += [Unplugger('doctest'), plug]
-            if doctests:
-                argv += ['--with-' + plug.name]
-        return argv, plugins
-
-    def test(self, label='fast', verbose=1, extra_argv=None,
-             doctests=False, coverage=False, raise_warnings=None,
-             timer=False):
-        """
-        Run tests for module using nose.
-
-        Parameters
-        ----------
-        label : {'fast', 'full', '', attribute identifier}, optional
-            Identifies the tests to run. This can be a string to pass to
-            the nosetests executable with the '-A' option, or one of several
-            special values.  Special values are:
-
-            * 'fast' - the default - which corresponds to the ``nosetests -A``
-              option of 'not slow'.
-            * 'full' - fast (as above) and slow tests as in the
-              'no -A' option to nosetests - this is the same as ''.
-            * None or '' - run all tests.
-            * attribute_identifier - string passed directly to nosetests as '-A'.
-
-        verbose : int, optional
-            Verbosity value for test outputs, in the range 1-10. Default is 1.
-        extra_argv : list, optional
-            List with any extra arguments to pass to nosetests.
-        doctests : bool, optional
-            If True, run doctests in module. Default is False.
-        coverage : bool, optional
-            If True, report coverage of NumPy code. Default is False.
-            (This requires the
-            `coverage module <https://pypi.org/project/coverage/>`_).
-        raise_warnings : None, str or sequence of warnings, optional
-            This specifies which warnings to configure as 'raise' instead
-            of being shown once during the test execution. Valid strings are:
-
-            * "develop" : equals ``(Warning,)``
-            * "release" : equals ``()``, do not raise on any warnings.
-        timer : bool or int, optional
-            Timing of individual tests with ``nose-timer`` (which needs to be
-            installed).  If True, time tests and report on all of them.
-            If an integer (say ``N``), report timing results for ``N`` slowest
-            tests.
-
-        Returns
-        -------
-        result : object
-            Returns the result of running the tests as a
-            ``nose.result.TextTestResult`` object.
-
-        Notes
-        -----
-        Each NumPy module exposes `test` in its namespace to run all tests for it.
-        For example, to run all tests for numpy.lib:
-
-        >>> np.lib.test() #doctest: +SKIP
-
-        Examples
-        --------
-        >>> result = np.lib.test() #doctest: +SKIP
-        Running unit tests for numpy.lib
-        ...
-        Ran 976 tests in 3.933s
-
-        OK
-
-        >>> result.errors #doctest: +SKIP
-        []
-        >>> result.knownfail #doctest: +SKIP
-        []
-        """
-
-        # cap verbosity at 3 because nose becomes *very* verbose beyond that
-        verbose = min(verbose, 3)
-
-        from . import utils
-        utils.verbose = verbose
-
-        argv, plugins = self.prepare_test_args(
-                label, verbose, extra_argv, doctests, coverage, timer)
-
-        if doctests:
-            print(f'Running unit tests and doctests for {self.package_name}')
-        else:
-            print(f'Running unit tests for {self.package_name}')
-
-        self._show_system_info()
-
-        # reset doctest state on every run
-        import doctest
-        doctest.master = None
-
-        if raise_warnings is None:
-            raise_warnings = self.raise_warnings
-
-        _warn_opts = dict(develop=(Warning,),
-                          release=())
-        if isinstance(raise_warnings, str):
-            raise_warnings = _warn_opts[raise_warnings]
-
-        with suppress_warnings("location") as sup:
-            # Reset the warning filters to the default state,
-            # so that running the tests is more repeatable.
-            warnings.resetwarnings()
-            # Set all warnings to 'warn', this is because the default 'once'
-            # has the bad property of possibly shadowing later warnings.
-            warnings.filterwarnings('always')
-            # Force the requested warnings to raise
-            for warningtype in raise_warnings:
-                warnings.filterwarnings('error', category=warningtype)
-            # Filter out annoying import messages.
-            sup.filter(message='Not importing directory')
-            sup.filter(message="numpy.dtype size changed")
-            sup.filter(message="numpy.ufunc size changed")
-            sup.filter(category=np.ModuleDeprecationWarning)
-            # Filter out boolean '-' deprecation messages. This allows
-            # older versions of scipy to test without a flood of messages.
-            sup.filter(message=".*boolean negative.*")
-            sup.filter(message=".*boolean subtract.*")
-            # Filter out distutils cpu warnings (could be localized to
-            # distutils tests). ASV has problems with top level import,
-            # so fetch module for suppression here.
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                from ...distutils import cpuinfo
-            sup.filter(category=UserWarning, module=cpuinfo)
-            # Filter out some deprecation warnings inside nose 1.3.7 when run
-            # on python 3.5b2. See
-            #     https://github.com/nose-devs/nose/issues/929
-            # Note: it is hard to filter based on module for sup (lineno could
-            #       be implemented).
-            warnings.filterwarnings("ignore", message=".*getargspec.*",
-                                    category=DeprecationWarning,
-                                    module=r"nose\.")
-
-            from .noseclasses import NumpyTestProgram
-
-            t = NumpyTestProgram(argv=argv, exit=False, plugins=plugins)
-
-        return t.result
-
-    def bench(self, label='fast', verbose=1, extra_argv=None):
-        """
-        Run benchmarks for module using nose.
-
-        Parameters
-        ----------
-        label : {'fast', 'full', '', attribute identifier}, optional
-            Identifies the benchmarks to run. This can be a string to pass to
-            the nosetests executable with the '-A' option, or one of several
-            special values.  Special values are:
-
-            * 'fast' - the default - which corresponds to the ``nosetests -A``
-              option of 'not slow'.
-            * 'full' - fast (as above) and slow benchmarks as in the
-              'no -A' option to nosetests - this is the same as ''.
-            * None or '' - run all tests.
-            * attribute_identifier - string passed directly to nosetests as '-A'.
-
-        verbose : int, optional
-            Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
-        extra_argv : list, optional
-            List with any extra arguments to pass to nosetests.
-
-        Returns
-        -------
-        success : bool
-            Returns True if running the benchmarks works, False if an error
-            occurred.
-
-        Notes
-        -----
-        Benchmarks are like tests, but have names starting with "bench" instead
-        of "test", and can be found under the "benchmarks" sub-directory of the
-        module.
-
-        Each NumPy module exposes `bench` in its namespace to run all benchmarks
-        for it.
-
-        Examples
-        --------
-        >>> success = np.lib.bench() #doctest: +SKIP
-        Running benchmarks for numpy.lib
-        ...
-        using 562341 items:
-        unique:
-        0.11
-        unique1d:
-        0.11
-        ratio: 1.0
-        nUnique: 56230 == 56230
-        ...
-        OK
-
-        >>> success #doctest: +SKIP
-        True
-
-        """
-
-        print(f'Running benchmarks for {self.package_name}')
-        self._show_system_info()
-
-        argv = self._test_argv(label, verbose, extra_argv)
-        argv += ['--match', r'(?:^|[\\b_\\.%s-])[Bb]ench' % os.sep]
-
-        # import nose or make informative error
-        nose = import_nose()
-
-        # get plugin to disable doctests
-        from .noseclasses import Unplugger
-        add_plugins = [Unplugger('doctest')]
-
-        return nose.run(argv=argv, addplugins=add_plugins)
-
-
-def _numpy_tester():
-    if hasattr(np, "__version__") and ".dev0" in np.__version__:
-        mode = "develop"
-    else:
-        mode = "release"
-    return NoseTester(raise_warnings=mode, depth=1,
-                      check_fpu_mode=True)
diff --git a/contrib/python/numpy/py3/numpy/testing/_private/parameterized.py b/contrib/python/numpy/py3/numpy/testing/_private/parameterized.py
deleted file mode 100644
index 3a29a1811d3..00000000000
--- a/contrib/python/numpy/py3/numpy/testing/_private/parameterized.py
+++ /dev/null
@@ -1,432 +0,0 @@
-"""
-tl;dr: all code is licensed under simplified BSD, unless stated otherwise.
-
-Unless stated otherwise in the source files, all code is copyright 2010 David
-Wolever <david@wolever.net>. All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-   1. Redistributions of source code must retain the above copyright notice,
-   this list of conditions and the following disclaimer.
-
-   2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-
-THIS SOFTWARE IS PROVIDED BY <COPYRIGHT HOLDER> ``AS IS'' AND ANY EXPRESS OR
-IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
-EVENT SHALL <COPYRIGHT HOLDER> OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
-INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
-OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-The views and conclusions contained in the software and documentation are those
-of the authors and should not be interpreted as representing official policies,
-either expressed or implied, of David Wolever.
-
-"""
-import re
-import inspect
-import warnings
-from functools import wraps
-from types import MethodType
-from collections import namedtuple
-
-from unittest import TestCase
-
-_param = namedtuple("param", "args kwargs")
-
-class param(_param):
-    """ Represents a single parameter to a test case.
-
-        For example::
-
-            >>> p = param("foo", bar=16)
-            >>> p
-            param("foo", bar=16)
-            >>> p.args
-            ('foo', )
-            >>> p.kwargs
-            {'bar': 16}
-
-        Intended to be used as an argument to ``@parameterized``::
-
-            @parameterized([
-                param("foo", bar=16),
-            ])
-            def test_stuff(foo, bar=16):
-                pass
-        """
-
-    def __new__(cls, *args , **kwargs):
-        return _param.__new__(cls, args, kwargs)
-
-    @classmethod
-    def explicit(cls, args=None, kwargs=None):
-        """ Creates a ``param`` by explicitly specifying ``args`` and
-            ``kwargs``::
-
-                >>> param.explicit([1,2,3])
-                param(*(1, 2, 3))
-                >>> param.explicit(kwargs={"foo": 42})
-                param(*(), **{"foo": "42"})
-            """
-        args = args or ()
-        kwargs = kwargs or {}
-        return cls(*args, **kwargs)
-
-    @classmethod
-    def from_decorator(cls, args):
-        """ Returns an instance of ``param()`` for ``@parameterized`` argument
-            ``args``::
-
-                >>> param.from_decorator((42, ))
-                param(args=(42, ), kwargs={})
-                >>> param.from_decorator("foo")
-                param(args=("foo", ), kwargs={})
-            """
-        if isinstance(args, param):
-            return args
-        elif isinstance(args, (str,)):
-            args = (args, )
-        try:
-            return cls(*args)
-        except TypeError as e:
-            if "after * must be" not in str(e):
-                raise
-            raise TypeError(
-                "Parameters must be tuples, but %r is not (hint: use '(%r, )')"
-                %(args, args),
-            )
-
-    def __repr__(self):
-        return "param(*%r, **%r)" %self
-
-
-def parameterized_argument_value_pairs(func, p):
-    """Return tuples of parameterized arguments and their values.
-
-        This is useful if you are writing your own doc_func
-        function and need to know the values for each parameter name::
-
-            >>> def func(a, foo=None, bar=42, **kwargs): pass
-            >>> p = param(1, foo=7, extra=99)
-            >>> parameterized_argument_value_pairs(func, p)
-            [("a", 1), ("foo", 7), ("bar", 42), ("**kwargs", {"extra": 99})]
-
-        If the function's first argument is named ``self`` then it will be
-        ignored::
-
-            >>> def func(self, a): pass
-            >>> p = param(1)
-            >>> parameterized_argument_value_pairs(func, p)
-            [("a", 1)]
-
-        Additionally, empty ``*args`` or ``**kwargs`` will be ignored::
-
-            >>> def func(foo, *args): pass
-            >>> p = param(1)
-            >>> parameterized_argument_value_pairs(func, p)
-            [("foo", 1)]
-            >>> p = param(1, 16)
-            >>> parameterized_argument_value_pairs(func, p)
-            [("foo", 1), ("*args", (16, ))]
-    """
-    argspec = inspect.getargspec(func)
-    arg_offset = 1 if argspec.args[:1] == ["self"] else 0
-
-    named_args = argspec.args[arg_offset:]
-
-    result = list(zip(named_args, p.args))
-    named_args = argspec.args[len(result) + arg_offset:]
-    varargs = p.args[len(result):]
-
-    result.extend([
-        (name, p.kwargs.get(name, default))
-        for (name, default)
-        in zip(named_args, argspec.defaults or [])
-    ])
-
-    seen_arg_names = {n for (n, _) in result}
-    keywords = dict(sorted([
-        (name, p.kwargs[name])
-        for name in p.kwargs
-        if name not in seen_arg_names
-    ]))
-
-    if varargs:
-        result.append(("*%s" %(argspec.varargs, ), tuple(varargs)))
-
-    if keywords:
-        result.append(("**%s" %(argspec.keywords, ), keywords))
-
-    return result
-
-def short_repr(x, n=64):
-    """ A shortened repr of ``x`` which is guaranteed to be ``unicode``::
-
-            >>> short_repr("foo")
-            u"foo"
-            >>> short_repr("123456789", n=4)
-            u"12...89"
-    """
-
-    x_repr = repr(x)
-    if isinstance(x_repr, bytes):
-        try:
-            x_repr = str(x_repr, "utf-8")
-        except UnicodeDecodeError:
-            x_repr = str(x_repr, "latin1")
-    if len(x_repr) > n:
-        x_repr = x_repr[:n//2] + "..." + x_repr[len(x_repr) - n//2:]
-    return x_repr
-
-def default_doc_func(func, num, p):
-    if func.__doc__ is None:
-        return None
-
-    all_args_with_values = parameterized_argument_value_pairs(func, p)
-
-    # Assumes that the function passed is a bound method.
-    descs = [f'{n}={short_repr(v)}' for n, v in all_args_with_values]
-
-    # The documentation might be a multiline string, so split it
-    # and just work with the first string, ignoring the period
-    # at the end if there is one.
-    first, nl, rest = func.__doc__.lstrip().partition("\n")
-    suffix = ""
-    if first.endswith("."):
-        suffix = "."
-        first = first[:-1]
-    args = "%s[with %s]" %(len(first) and " " or "", ", ".join(descs))
-    return "".join([first.rstrip(), args, suffix, nl, rest])
-
-def default_name_func(func, num, p):
-    base_name = func.__name__
-    name_suffix = "_%s" %(num, )
-    if len(p.args) > 0 and isinstance(p.args[0], (str,)):
-        name_suffix += "_" + parameterized.to_safe_name(p.args[0])
-    return base_name + name_suffix
-
-
-# force nose for numpy purposes.
-_test_runner_override = 'nose'
-_test_runner_guess = False
-_test_runners = set(["unittest", "unittest2", "nose", "nose2", "pytest"])
-_test_runner_aliases = {
-    "_pytest": "pytest",
-}
-
-def set_test_runner(name):
-    global _test_runner_override
-    if name not in _test_runners:
-        raise TypeError(
-            "Invalid test runner: %r (must be one of: %s)"
-            %(name, ", ".join(_test_runners)),
-        )
-    _test_runner_override = name
-
-def detect_runner():
-    """ Guess which test runner we're using by traversing the stack and looking
-        for the first matching module. This *should* be reasonably safe, as
-        it's done during test discovery where the test runner should be the
-        stack frame immediately outside. """
-    if _test_runner_override is not None:
-        return _test_runner_override
-    global _test_runner_guess
-    if _test_runner_guess is False:
-        stack = inspect.stack()
-        for record in reversed(stack):
-            frame = record[0]
-            module = frame.f_globals.get("__name__").partition(".")[0]
-            if module in _test_runner_aliases:
-                module = _test_runner_aliases[module]
-            if module in _test_runners:
-                _test_runner_guess = module
-                break
-        else:
-            _test_runner_guess = None
-    return _test_runner_guess
-
-class parameterized:
-    """ Parameterize a test case::
-
-            class TestInt:
-                @parameterized([
-                    ("A", 10),
-                    ("F", 15),
-                    param("10", 42, base=42)
-                ])
-                def test_int(self, input, expected, base=16):
-                    actual = int(input, base=base)
-                    assert_equal(actual, expected)
-
-            @parameterized([
-                (2, 3, 5)
-                (3, 5, 8),
-            ])
-            def test_add(a, b, expected):
-                assert_equal(a + b, expected)
-        """
-
-    def __init__(self, input, doc_func=None):
-        self.get_input = self.input_as_callable(input)
-        self.doc_func = doc_func or default_doc_func
-
-    def __call__(self, test_func):
-        self.assert_not_in_testcase_subclass()
-
-        @wraps(test_func)
-        def wrapper(test_self=None):
-            test_cls = test_self and type(test_self)
-
-            original_doc = wrapper.__doc__
-            for num, args in enumerate(wrapper.parameterized_input):
-                p = param.from_decorator(args)
-                unbound_func, nose_tuple = self.param_as_nose_tuple(test_self, test_func, num, p)
-                try:
-                    wrapper.__doc__ = nose_tuple[0].__doc__
-                    # Nose uses `getattr(instance, test_func.__name__)` to get
-                    # a method bound to the test instance (as opposed to a
-                    # method bound to the instance of the class created when
-                    # tests were being enumerated). Set a value here to make
-                    # sure nose can get the correct test method.
-                    if test_self is not None:
-                        setattr(test_cls, test_func.__name__, unbound_func)
-                    yield nose_tuple
-                finally:
-                    if test_self is not None:
-                        delattr(test_cls, test_func.__name__)
-                    wrapper.__doc__ = original_doc
-        wrapper.parameterized_input = self.get_input()
-        wrapper.parameterized_func = test_func
-        test_func.__name__ = "_parameterized_original_%s" %(test_func.__name__, )
-        return wrapper
-
-    def param_as_nose_tuple(self, test_self, func, num, p):
-        nose_func = wraps(func)(lambda *args: func(*args[:-1], **args[-1]))
-        nose_func.__doc__ = self.doc_func(func, num, p)
-        # Track the unbound function because we need to setattr the unbound
-        # function onto the class for nose to work (see comments above), and
-        # Python 3 doesn't let us pull the function out of a bound method.
-        unbound_func = nose_func
-        if test_self is not None:
-            nose_func = MethodType(nose_func, test_self)
-        return unbound_func, (nose_func, ) + p.args + (p.kwargs or {}, )
-
-    def assert_not_in_testcase_subclass(self):
-        parent_classes = self._terrible_magic_get_defining_classes()
-        if any(issubclass(cls, TestCase) for cls in parent_classes):
-            raise Exception("Warning: '@parameterized' tests won't work "
-                            "inside subclasses of 'TestCase' - use "
-                            "'@parameterized.expand' instead.")
-
-    def _terrible_magic_get_defining_classes(self):
-        """ Returns the list of parent classes of the class currently being defined.
-            Will likely only work if called from the ``parameterized`` decorator.
-            This function is entirely @brandon_rhodes's fault, as he suggested
-            the implementation: http://stackoverflow.com/a/8793684/71522
-            """
-        stack = inspect.stack()
-        if len(stack) <= 4:
-            return []
-        frame = stack[4]
-        code_context = frame[4] and frame[4][0].strip()
-        if not (code_context and code_context.startswith("class ")):
-            return []
-        _, _, parents = code_context.partition("(")
-        parents, _, _ = parents.partition(")")
-        return eval("[" + parents + "]", frame[0].f_globals, frame[0].f_locals)
-
-    @classmethod
-    def input_as_callable(cls, input):
-        if callable(input):
-            return lambda: cls.check_input_values(input())
-        input_values = cls.check_input_values(input)
-        return lambda: input_values
-
-    @classmethod
-    def check_input_values(cls, input_values):
-        # Explicitly convert non-list inputs to a list so that:
-        # 1. A helpful exception will be raised if they aren't iterable, and
-        # 2. Generators are unwrapped exactly once (otherwise `nosetests
-        #    --processes=n` has issues; see:
-        #    https://github.com/wolever/nose-parameterized/pull/31)
-        if not isinstance(input_values, list):
-            input_values = list(input_values)
-        return [ param.from_decorator(p) for p in input_values ]
-
-    @classmethod
-    def expand(cls, input, name_func=None, doc_func=None, **legacy):
-        """ A "brute force" method of parameterizing test cases. Creates new
-            test cases and injects them into the namespace that the wrapped
-            function is being defined in. Useful for parameterizing tests in
-            subclasses of 'UnitTest', where Nose test generators don't work.
-
-            >>> @parameterized.expand([("foo", 1, 2)])
-            ... def test_add1(name, input, expected):
-            ...     actual = add1(input)
-            ...     assert_equal(actual, expected)
-            ...
-            >>> locals()
-            ... 'test_add1_foo_0': <function ...> ...
-            >>>
-            """
-
-        if "testcase_func_name" in legacy:
-            warnings.warn("testcase_func_name= is deprecated; use name_func=",
-                          DeprecationWarning, stacklevel=2)
-            if not name_func:
-                name_func = legacy["testcase_func_name"]
-
-        if "testcase_func_doc" in legacy:
-            warnings.warn("testcase_func_doc= is deprecated; use doc_func=",
-                          DeprecationWarning, stacklevel=2)
-            if not doc_func:
-                doc_func = legacy["testcase_func_doc"]
-
-        doc_func = doc_func or default_doc_func
-        name_func = name_func or default_name_func
-
-        def parameterized_expand_wrapper(f, instance=None):
-            stack = inspect.stack()
-            frame = stack[1]
-            frame_locals = frame[0].f_locals
-
-            parameters = cls.input_as_callable(input)()
-            for num, p in enumerate(parameters):
-                name = name_func(f, num, p)
-                frame_locals[name] = cls.param_as_standalone_func(p, f, name)
-                frame_locals[name].__doc__ = doc_func(f, num, p)
-
-            f.__test__ = False
-        return parameterized_expand_wrapper
-
-    @classmethod
-    def param_as_standalone_func(cls, p, func, name):
-        @wraps(func)
-        def standalone_func(*a):
-            return func(*(a + p.args), **p.kwargs)
-        standalone_func.__name__ = name
-
-        # place_as is used by py.test to determine what source file should be
-        # used for this test.
-        standalone_func.place_as = func
-
-        # Remove __wrapped__ because py.test will try to look at __wrapped__
-        # to determine which parameters should be used with this test case,
-        # and obviously we don't need it to do any parameterization.
-        try:
-            del standalone_func.__wrapped__
-        except AttributeError:
-            pass
-        return standalone_func
-
-    @classmethod
-    def to_safe_name(cls, s):
-        return str(re.sub("[^a-zA-Z0-9_]+", "_", s))
diff --git a/contrib/python/numpy/py3/numpy/testing/_private/utils.py b/contrib/python/numpy/py3/numpy/testing/_private/utils.py
index 2a212650305..3c3d3412a2e 100644
--- a/contrib/python/numpy/py3/numpy/testing/_private/utils.py
+++ b/contrib/python/numpy/py3/numpy/testing/_private/utils.py
@@ -16,10 +16,12 @@ from tempfile import mkdtemp, mkstemp
 from unittest.case import SkipTest
 from warnings import WarningMessage
 import pprint
+import sysconfig
 
 import numpy as np
-from numpy.core import(
+from numpy.core import (
      intp, float32, empty, arange, array_repr, ndarray, isnat, array)
+from numpy import isfinite, isnan, isinf
 import numpy.linalg.lapack_lite
 
 from io import StringIO
@@ -29,14 +31,14 @@ __all__ = [
         'assert_array_equal', 'assert_array_less', 'assert_string_equal',
         'assert_array_almost_equal', 'assert_raises', 'build_err_msg',
         'decorate_methods', 'jiffies', 'memusage', 'print_assert_equal',
-        'raises', 'rundocs', 'runstring', 'verbose', 'measure',
+        'rundocs', 'runstring', 'verbose', 'measure',
         'assert_', 'assert_array_almost_equal_nulp', 'assert_raises_regex',
         'assert_array_max_ulp', 'assert_warns', 'assert_no_warnings',
         'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings',
         'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY',
         'HAS_REFCOUNT', "IS_WASM", 'suppress_warnings', 'assert_array_compare',
         'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON',
-        '_OLD_PROMOTION'
+        '_OLD_PROMOTION', 'IS_MUSL', '_SUPPORTS_SVE'
         ]
 
 
@@ -56,27 +58,14 @@ HAS_LAPACK64 = numpy.linalg.lapack_lite._ilp64
 
 _OLD_PROMOTION = lambda: np._get_promotion_state() == 'legacy'
 
-
-def import_nose():
-    """ Import nose only when needed.
-    """
-    nose_is_good = True
-    minimum_nose_version = (1, 0, 0)
-    try:
-        import nose
-    except ImportError:
-        nose_is_good = False
-    else:
-        if nose.__versioninfo__ < minimum_nose_version:
-            nose_is_good = False
-
-    if not nose_is_good:
-        msg = ('Need nose >= %d.%d.%d for tests - see '
-               'https://nose.readthedocs.io' %
-               minimum_nose_version)
-        raise ImportError(msg)
-
-    return nose
+IS_MUSL = False
+# alternate way is
+# from packaging.tags import sys_tags
+#     _tags = list(sys_tags())
+#     if 'musllinux' in _tags[0].platform:
+_v = sysconfig.get_config_var('HOST_GNU_TYPE') or ''
+if 'musl' in _v:
+    IS_MUSL = True
 
 
 def assert_(val, msg=''):
@@ -99,62 +88,6 @@ def assert_(val, msg=''):
         raise AssertionError(smsg)
 
 
-def gisnan(x):
-    """like isnan, but always raise an error if type not supported instead of
-    returning a TypeError object.
-
-    Notes
-    -----
-    isnan and other ufunc sometimes return a NotImplementedType object instead
-    of raising any exception. This function is a wrapper to make sure an
-    exception is always raised.
-
-    This should be removed once this problem is solved at the Ufunc level."""
-    from numpy.core import isnan
-    st = isnan(x)
-    if isinstance(st, type(NotImplemented)):
-        raise TypeError("isnan not supported for this type")
-    return st
-
-
-def gisfinite(x):
-    """like isfinite, but always raise an error if type not supported instead
-    of returning a TypeError object.
-
-    Notes
-    -----
-    isfinite and other ufunc sometimes return a NotImplementedType object
-    instead of raising any exception. This function is a wrapper to make sure
-    an exception is always raised.
-
-    This should be removed once this problem is solved at the Ufunc level."""
-    from numpy.core import isfinite, errstate
-    with errstate(invalid='ignore'):
-        st = isfinite(x)
-        if isinstance(st, type(NotImplemented)):
-            raise TypeError("isfinite not supported for this type")
-    return st
-
-
-def gisinf(x):
-    """like isinf, but always raise an error if type not supported instead of
-    returning a TypeError object.
-
-    Notes
-    -----
-    isinf and other ufunc sometimes return a NotImplementedType object instead
-    of raising any exception. This function is a wrapper to make sure an
-    exception is always raised.
-
-    This should be removed once this problem is solved at the Ufunc level."""
-    from numpy.core import isinf, errstate
-    with errstate(invalid='ignore'):
-        st = isinf(x)
-        if isinstance(st, type(NotImplemented)):
-            raise TypeError("isinf not supported for this type")
-    return st
-
-
 if os.name == 'nt':
     # Code "stolen" from enthought/debug/memusage.py
     def GetPerformanceAttributes(object, counter, instance=None,
@@ -198,7 +131,7 @@ elif sys.platform[:5] == 'linux':
 
         """
         try:
-            with open(_proc_pid_stat, 'r') as f:
+            with open(_proc_pid_stat) as f:
                 l = f.readline().split(' ')
             return int(l[22])
         except Exception:
@@ -225,7 +158,7 @@ if sys.platform[:5] == 'linux':
         if not _load_time:
             _load_time.append(time.time())
         try:
-            with open(_proc_pid_stat, 'r') as f:
+            with open(_proc_pid_stat) as f:
                 l = f.readline().split(' ')
             return int(l[13])
         except Exception:
@@ -398,8 +331,8 @@ def assert_equal(actual, desired, err_msg='', verbose=True):
 
     # Inf/nan/negative zero handling
     try:
-        isdesnan = gisnan(desired)
-        isactnan = gisnan(actual)
+        isdesnan = isnan(desired)
+        isactnan = isnan(actual)
         if isdesnan and isactnan:
             return  # both nan, so equal
 
@@ -409,7 +342,7 @@ def assert_equal(actual, desired, err_msg='', verbose=True):
         if (array_actual.dtype.char in 'Mm' or
                 array_desired.dtype.char in 'Mm'):
             # version 1.18
-            # until this version, gisnan failed for datetime64 and timedelta64.
+            # until this version, isnan failed for datetime64 and timedelta64.
             # Now it succeeds but comparison to scalar with a different type
             # emits a DeprecationWarning.
             # Avoid that by skipping the next check
@@ -478,7 +411,7 @@ def print_assert_equal(test_string, actual, desired):
 
 
 @np._no_nep50_warning()
-def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True):
+def assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True):
     """
     Raises an AssertionError if two items are not equal up to desired
     precision.
@@ -590,9 +523,9 @@ def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True):
         # If one of desired/actual is not finite, handle it specially here:
         # check that both are nan if any is a nan, and test for equality
         # otherwise
-        if not (gisfinite(desired) and gisfinite(actual)):
-            if gisnan(desired) or gisnan(actual):
-                if not (gisnan(desired) and gisnan(actual)):
+        if not (isfinite(desired) and isfinite(actual)):
+            if isnan(desired) or isnan(actual):
+                if not (isnan(desired) and isnan(actual)):
                     raise AssertionError(_build_err_msg())
             else:
                 if not desired == actual:
@@ -605,7 +538,8 @@ def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True):
 
 
 @np._no_nep50_warning()
-def assert_approx_equal(actual,desired,significant=7,err_msg='',verbose=True):
+def assert_approx_equal(actual, desired, significant=7, err_msg='',
+                        verbose=True):
     """
     Raises an AssertionError if two items are not equal up to significant
     digits.
@@ -690,9 +624,9 @@ def assert_approx_equal(actual,desired,significant=7,err_msg='',verbose=True):
         # If one of desired/actual is not finite, handle it specially here:
         # check that both are nan if any is a nan, and test for equality
         # otherwise
-        if not (gisfinite(desired) and gisfinite(actual)):
-            if gisnan(desired) or gisnan(actual):
-                if not (gisnan(desired) and gisnan(actual)):
+        if not (isfinite(desired) and isfinite(actual)):
+            if isnan(desired) or isnan(actual):
+                if not (isnan(desired) and isnan(actual)):
                     raise AssertionError(msg)
             else:
                 if not desired == actual:
@@ -709,7 +643,8 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',
                          precision=6, equal_nan=True, equal_inf=True,
                          *, strict=False):
     __tracebackhide__ = True  # Hide traceback for py.test
-    from numpy.core import array, array2string, isnan, inf, bool_, errstate, all, max, object_
+    from numpy.core import (array2string, isnan, inf, bool_, errstate,
+                            all, max, object_)
 
     x = np.asanyarray(x)
     y = np.asanyarray(y)
@@ -835,10 +770,10 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',
                     max_abs_error = max(error)
                     if getattr(error, 'dtype', object_) == object_:
                         remarks.append('Max absolute difference: '
-                                        + str(max_abs_error))
+                                       + str(max_abs_error))
                     else:
                         remarks.append('Max absolute difference: '
-                                        + array2string(max_abs_error))
+                                       + array2string(max_abs_error))
 
                     # note: this definition of relative error matches that one
                     # used by assert_allclose (found in np.isclose)
@@ -850,10 +785,10 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',
                         max_rel_error = max(error[nonzero] / abs(y[nonzero]))
                     if getattr(error, 'dtype', object_) == object_:
                         remarks.append('Max relative difference: '
-                                        + str(max_rel_error))
+                                       + str(max_rel_error))
                     else:
                         remarks.append('Max relative difference: '
-                                        + array2string(max_rel_error))
+                                       + array2string(max_rel_error))
 
             err_msg += '\n' + '\n'.join(remarks)
             msg = build_err_msg([ox, oy], err_msg,
@@ -1066,15 +1001,15 @@ def assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True):
 
     """
     __tracebackhide__ = True  # Hide traceback for py.test
-    from numpy.core import number, float_, result_type, array
+    from numpy.core import number, float_, result_type
     from numpy.core.numerictypes import issubdtype
     from numpy.core.fromnumeric import any as npany
 
     def compare(x, y):
         try:
-            if npany(gisinf(x)) or npany( gisinf(y)):
-                xinfid = gisinf(x)
-                yinfid = gisinf(y)
+            if npany(isinf(x)) or npany(isinf(y)):
+                xinfid = isinf(x)
+                yinfid = isinf(y)
                 if not (xinfid == yinfid).all():
                     return False
                 # if one item, x and y is +- inf
@@ -1114,8 +1049,6 @@ def assert_array_less(x, y, err_msg='', verbose=True):
     compared, no assertion is raised if both objects have NaNs in the same
     positions.
 
-
-
     Parameters
     ----------
     x : array_like
@@ -1130,15 +1063,13 @@ def assert_array_less(x, y, err_msg='', verbose=True):
     Raises
     ------
     AssertionError
-      If actual and desired objects are not equal.
+      If x is not strictly smaller than y, element-wise.
 
     See Also
     --------
     assert_array_equal: tests objects for equality
     assert_array_almost_equal: test objects for equality up to precision
 
-
-
     Examples
     --------
     >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
@@ -1305,42 +1236,21 @@ def rundocs(filename=None, raise_on_error=True):
         raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg))
 
 
-def raises(*args):
-    """Decorator to check for raised exceptions.
-
-    The decorated test function must raise one of the passed exceptions to
-    pass.  If you want to test many assertions about exceptions in a single
-    test, you may want to use `assert_raises` instead.
-
-    .. warning::
-       This decorator is nose specific, do not use it if you are using a
-       different test framework.
-
-    Parameters
-    ----------
-    args : exceptions
-        The test passes if any of the passed exceptions is raised.
-
-    Raises
-    ------
-    AssertionError
-
-    Examples
-    --------
-
-    Usage::
-
-        @raises(TypeError, ValueError)
-        def test_raises_type_error():
-            raise TypeError("This test passes")
+def check_support_sve():
+    """
+    gh-22982
+    """
+    
+    import subprocess
+    cmd = 'lscpu'
+    try:
+        output = subprocess.run(cmd, capture_output=True, text=True)
+        return 'sve' in output.stdout
+    except OSError:
+        return False
 
-        @raises(Exception)
-        def test_that_fails_by_passing():
-            pass
 
-    """
-    nose = import_nose()
-    return nose.tools.raises(*args)
+_SUPPORTS_SVE = check_support_sve()
 
 #
 # assert_raises and assert_raises_regex are taken from unittest.
@@ -1352,8 +1262,10 @@ class _Dummy(unittest.TestCase):
     def nop(self):
         pass
 
+
 _d = _Dummy('nop')
 
+
 def assert_raises(*args, **kwargs):
     """
     assert_raises(exception_class, callable, *args, **kwargs)
@@ -1380,7 +1292,7 @@ def assert_raises(*args, **kwargs):
 
     """
     __tracebackhide__ = True  # Hide traceback for py.test
-    return _d.assertRaises(*args,**kwargs)
+    return _d.assertRaises(*args, **kwargs)
 
 
 def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs):
@@ -1531,7 +1443,7 @@ def assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,
 
     Given two array_like objects, check that their shapes and all elements
     are equal (but see the Notes for the special handling of a scalar). An
-    exception is raised if the shapes mismatch or any values conflict. In 
+    exception is raised if the shapes mismatch or any values conflict. In
     contrast to the standard usage in numpy, NaNs are compared like numbers,
     no assertion is raised if both objects have NaNs in the same positions.
 
@@ -2453,6 +2365,7 @@ def assert_no_gc_cycles(*args, **kwargs):
     with _assert_no_gc_cycles_context(name=func.__name__):
         func(*args, **kwargs)
 
+
 def break_cycles():
     """
     Break reference cycles by calling gc.collect
@@ -2548,7 +2461,7 @@ def _get_mem_available():
 
     if sys.platform.startswith('linux'):
         info = {}
-        with open('/proc/meminfo', 'r') as f:
+        with open('/proc/meminfo') as f:
             for line in f:
                 p = line.split()
                 info[p[0].strip(':').lower()] = int(p[1]) * 1024
@@ -2585,7 +2498,7 @@ def _no_tracing(func):
 def _get_glibc_version():
     try:
         ver = os.confstr('CS_GNU_LIBC_VERSION').rsplit(' ')[1]
-    except Exception as inst:
+    except Exception:
         ver = '0.0'
 
     return ver
@@ -2593,3 +2506,4 @@ def _get_glibc_version():
 
 _glibcver = _get_glibc_version()
 _glibc_older_than = lambda x: (_glibcver != '0.0' and _glibcver < x)
+
diff --git a/contrib/python/numpy/py3/numpy/testing/overrides.py b/contrib/python/numpy/py3/numpy/testing/overrides.py
new file mode 100644
index 00000000000..edc7132c204
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/testing/overrides.py
@@ -0,0 +1,83 @@
+"""Tools for testing implementations of __array_function__ and ufunc overrides
+
+
+"""
+
+from numpy.core.overrides import ARRAY_FUNCTIONS as _array_functions
+from numpy import ufunc as _ufunc
+import numpy.core.umath as _umath
+
+def get_overridable_numpy_ufuncs():
+    """List all numpy ufuncs overridable via `__array_ufunc__`
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    set
+        A set containing all overridable ufuncs in the public numpy API.
+    """
+    ufuncs = {obj for obj in _umath.__dict__.values()
+              if isinstance(obj, _ufunc)}
+    return ufuncs
+    
+
+def allows_array_ufunc_override(func):
+    """Determine if a function can be overridden via `__array_ufunc__`
+
+    Parameters
+    ----------
+    func : callable
+        Function that may be overridable via `__array_ufunc__`
+
+    Returns
+    -------
+    bool
+        `True` if `func` is overridable via `__array_ufunc__` and
+        `False` otherwise.
+
+    Notes
+    -----
+    This function is equivalent to ``isinstance(func, np.ufunc)`` and
+    will work correctly for ufuncs defined outside of Numpy.
+
+    """
+    return isinstance(func, np.ufunc)
+
+
+def get_overridable_numpy_array_functions():
+    """List all numpy functions overridable via `__array_function__`
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    set
+        A set containing all functions in the public numpy API that are
+        overridable via `__array_function__`.
+
+    """
+    # 'import numpy' doesn't import recfunctions, so make sure it's imported
+    # so ufuncs defined there show up in the ufunc listing
+    from numpy.lib import recfunctions
+    return _array_functions.copy()
+
+def allows_array_function_override(func):
+    """Determine if a Numpy function can be overridden via `__array_function__`
+
+    Parameters
+    ----------
+    func : callable
+        Function that may be overridable via `__array_function__`
+
+    Returns
+    -------
+    bool
+        `True` if `func` is a function in the Numpy API that is
+        overridable via `__array_function__` and `False` otherwise.
+    """
+    return func in _array_functions
diff --git a/contrib/python/numpy/py3/numpy/testing/tests/test_doctesting.py b/contrib/python/numpy/py3/numpy/testing/tests/test_doctesting.py
deleted file mode 100644
index 92c2156d814..00000000000
--- a/contrib/python/numpy/py3/numpy/testing/tests/test_doctesting.py
+++ /dev/null
@@ -1,57 +0,0 @@
-""" Doctests for NumPy-specific nose/doctest modifications
-
-"""
-#FIXME: None of these tests is run, because 'check' is not a recognized
-# testing prefix.
-
-# try the #random directive on the output line
-def check_random_directive():
-    '''
-    >>> 2+2
-    <BadExample object at 0x084D05AC>  #random: may vary on your system
-    '''
-
-# check the implicit "import numpy as np"
-def check_implicit_np():
-    '''
-    >>> np.array([1,2,3])
-    array([1, 2, 3])
-    '''
-
-# there's some extraneous whitespace around the correct responses
-def check_whitespace_enabled():
-    '''
-    # whitespace after the 3
-    >>> 1+2
-    3
-
-    # whitespace before the 7
-    >>> 3+4
-     7
-    '''
-
-def check_empty_output():
-    """ Check that no output does not cause an error.
-
-    This is related to nose bug 445; the numpy plugin changed the
-    doctest-result-variable default and therefore hit this bug:
-    http://code.google.com/p/python-nose/issues/detail?id=445
-
-    >>> a = 10
-    """
-
-def check_skip():
-    """ Check skip directive
-
-    The test below should not run
-
-    >>> 1/0 #doctest: +SKIP
-    """
-
-
-if __name__ == '__main__':
-    # Run tests outside numpy test rig
-    import nose
-    from numpy.testing.noseclasses import NumpyDoctest
-    argv = ['', __file__, '--with-numpydoctest']
-    nose.core.TestProgram(argv=argv, addplugins=[NumpyDoctest()])
diff --git a/contrib/python/numpy/py3/numpy/testing/tests/test_utils.py b/contrib/python/numpy/py3/numpy/testing/tests/test_utils.py
index 052cc3aa13d..0aaa508ee5d 100644
--- a/contrib/python/numpy/py3/numpy/testing/tests/test_utils.py
+++ b/contrib/python/numpy/py3/numpy/testing/tests/test_utils.py
@@ -8,13 +8,12 @@ import weakref
 import numpy as np
 from numpy.testing import (
     assert_equal, assert_array_equal, assert_almost_equal,
-    assert_array_almost_equal, assert_array_less, build_err_msg, raises,
+    assert_array_almost_equal, assert_array_less, build_err_msg,
     assert_raises, assert_warns, assert_no_warnings, assert_allclose,
     assert_approx_equal, assert_array_almost_equal_nulp, assert_array_max_ulp,
     clear_and_catch_warnings, suppress_warnings, assert_string_equal, assert_,
     tempdir, temppath, assert_no_gc_cycles, HAS_REFCOUNT
     )
-from numpy.core.overrides import ARRAY_FUNCTION_ENABLED
 
 
 class _GenericTest:
@@ -191,8 +190,6 @@ class TestArrayEqual(_GenericTest):
         self._test_not_equal(a, b)
         self._test_not_equal(b, a)
 
-    @pytest.mark.skipif(
-        not ARRAY_FUNCTION_ENABLED, reason='requires __array_function__')
     def test_subclass_that_does_not_implement_npall(self):
         class MyArray(np.ndarray):
             def __array_function__(self, *args, **kwargs):
@@ -793,41 +790,6 @@ class TestArrayAssertLess:
         self._assert_func(-ainf, x)
 
 
-@pytest.mark.skip(reason="The raises decorator depends on Nose")
-class TestRaises:
-
-    def setup_method(self):
-        class MyException(Exception):
-            pass
-
-        self.e = MyException
-
-    def raises_exception(self, e):
-        raise e
-
-    def does_not_raise_exception(self):
-        pass
-
-    def test_correct_catch(self):
-        raises(self.e)(self.raises_exception)(self.e)  # raises?
-
-    def test_wrong_exception(self):
-        try:
-            raises(self.e)(self.raises_exception)(RuntimeError)  # raises?
-        except RuntimeError:
-            return
-        else:
-            raise AssertionError("should have caught RuntimeError")
-
-    def test_catch_no_raise(self):
-        try:
-            raises(self.e)(self.does_not_raise_exception)()  # raises?
-        except AssertionError:
-            return
-        else:
-            raise AssertionError("should have raised an AssertionError")
-
-
 class TestWarns:
 
     def test_warn(self):
diff --git a/contrib/python/numpy/py3/numpy/testing/utils.py b/contrib/python/numpy/py3/numpy/testing/utils.py
deleted file mode 100644
index 20a88330434..00000000000
--- a/contrib/python/numpy/py3/numpy/testing/utils.py
+++ /dev/null
@@ -1,29 +0,0 @@
-"""
-Back compatibility utils module. It will import the appropriate
-set of tools
-
-"""
-import warnings
-
-# 2018-04-04, numpy 1.15.0 ImportWarning
-# 2019-09-18, numpy 1.18.0 DeprecatonWarning (changed)
-warnings.warn("Importing from numpy.testing.utils is deprecated "
-              "since 1.15.0, import from numpy.testing instead.",
-              DeprecationWarning, stacklevel=2)
-
-from ._private.utils import *
-from ._private.utils import _assert_valid_refcount, _gen_alignment_data
-
-__all__ = [
-        'assert_equal', 'assert_almost_equal', 'assert_approx_equal',
-        'assert_array_equal', 'assert_array_less', 'assert_string_equal',
-        'assert_array_almost_equal', 'assert_raises', 'build_err_msg',
-        'decorate_methods', 'jiffies', 'memusage', 'print_assert_equal',
-        'raises', 'rundocs', 'runstring', 'verbose', 'measure',
-        'assert_', 'assert_array_almost_equal_nulp', 'assert_raises_regex',
-        'assert_array_max_ulp', 'assert_warns', 'assert_no_warnings',
-        'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings',
-        'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY',
-        'HAS_REFCOUNT', 'suppress_warnings', 'assert_array_compare',
-        'assert_no_gc_cycles'
-        ]
diff --git a/contrib/python/numpy/py3/numpy/tests/test_numpy_config.py b/contrib/python/numpy/py3/numpy/tests/test_numpy_config.py
new file mode 100644
index 00000000000..82c1ad70b93
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/tests/test_numpy_config.py
@@ -0,0 +1,44 @@
+"""
+Check the numpy config is valid.
+"""
+import numpy as np
+import pytest
+from unittest.mock import Mock, patch
+
+pytestmark = pytest.mark.skipif(
+    not hasattr(np.__config__, "_built_with_meson"),
+    reason="Requires Meson builds",
+)
+
+
+class TestNumPyConfigs:
+    REQUIRED_CONFIG_KEYS = [
+        "Compilers",
+        "Machine Information",
+        "Python Information",
+    ]
+
+    @patch("numpy.__config__._check_pyyaml")
+    def test_pyyaml_not_found(self, mock_yaml_importer):
+        mock_yaml_importer.side_effect = ModuleNotFoundError()
+        with pytest.warns(UserWarning):
+            np.show_config()
+
+    def test_dict_mode(self):
+        config = np.show_config(mode="dicts")
+
+        assert isinstance(config, dict)
+        assert all([key in config for key in self.REQUIRED_CONFIG_KEYS]), (
+            "Required key missing,"
+            " see index of `False` with `REQUIRED_CONFIG_KEYS`"
+        )
+
+    def test_invalid_mode(self):
+        with pytest.raises(AttributeError):
+            np.show_config(mode="foo")
+
+    def test_warn_to_add_tests(self):
+        assert len(np.__config__.DisplayModes) == 2, (
+            "New mode detected,"
+            " please add UT if applicable and increment this count"
+        )
diff --git a/contrib/python/numpy/py3/numpy/tests/test_public_api.py b/contrib/python/numpy/py3/numpy/tests/test_public_api.py
index 32356d2aa4d..856167aee22 100644
--- a/contrib/python/numpy/py3/numpy/tests/test_public_api.py
+++ b/contrib/python/numpy/py3/numpy/tests/test_public_api.py
@@ -34,7 +34,6 @@ def test_numpy_namespace():
     # None of these objects are publicly documented to be part of the main
     # NumPy namespace (some are useful though, others need to be cleaned up)
     undocumented = {
-        'Tester': 'numpy.testing._private.nosetester.NoseTester',
         '_add_newdoc_ufunc': '_multiarray_umath._add_newdoc_ufunc',
         'add_docstring': '_multiarray_umath.add_docstring',
         'add_newdoc': 'numpy.core.function_base.add_newdoc',
@@ -65,7 +64,7 @@ def test_numpy_namespace():
 
 @pytest.mark.skip
 @pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
-@pytest.mark.parametrize('name', ['testing', 'Tester'])
+@pytest.mark.parametrize('name', ['testing'])
 def test_import_lazy_import(name):
     """Make sure we can actually use the modules we lazy load.
 
@@ -139,6 +138,8 @@ PUBLIC_MODULES = ['numpy.' + s for s in [
     "doc",
     "doc.constants",
     "doc.ufuncs",
+    "dtypes",
+    "exceptions",
     #"f2py",
     "fft",
     "lib",
@@ -161,6 +162,7 @@ PUBLIC_MODULES = ['numpy.' + s for s in [
     "polynomial.polynomial",
     "random",
     "testing",
+    "testing.overrides",
     "typing",
     "typing.mypy_plugin",
     "version",
@@ -195,6 +197,7 @@ PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [
     "core.umath",
     "core.umath_tests",
     "distutils.armccompiler",
+    "distutils.fujitsuccompiler",
     "distutils.ccompiler",
     'distutils.ccompiler_opt',
     "distutils.command",
@@ -248,7 +251,6 @@ PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [
     "distutils.numpy_distribution",
     "distutils.pathccompiler",
     "distutils.unixccompiler",
-    "dual",
     #"f2py.auxfuncs",
     #"f2py.capi_maps",
     #"f2py.cb_rules",
@@ -280,7 +282,6 @@ PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [
     "lib.utils",
     "linalg.lapack_lite",
     "linalg.linalg",
-    "ma.bench",
     "ma.core",
     "ma.testutils",
     "ma.timer_comparison",
@@ -290,7 +291,6 @@ PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [
     "random.mtrand",
     "random.bit_generator",
     "testing.print_coercion_tables",
-    "testing.utils",
 ]]
 
 
@@ -322,6 +322,7 @@ SKIP_LIST = [
     "numpy.core.code_generators.generate_ufunc_api",
     "numpy.core.code_generators.numpy_api",
     "numpy.core.code_generators.generate_umath_doc",
+    "numpy.core.code_generators.verify_c_api_version",
     "numpy.core.cversions",
     "numpy.core.generate_numpy_api",
     "numpy.distutils.msvc9compiler",
@@ -362,6 +363,7 @@ SKIP_LIST_2 = [
     'numpy.matlib.char',
     'numpy.matlib.rec',
     'numpy.matlib.emath',
+    'numpy.matlib.exceptions',
     'numpy.matlib.math',
     'numpy.matlib.linalg',
     'numpy.matlib.fft',
@@ -472,6 +474,10 @@ def test_api_importable():
 
 
 @pytest.mark.xfail(
+    hasattr(np.__config__, "_built_with_meson"),
+    reason = "Meson does not yet support entry points via pyproject.toml",
+)
+@pytest.mark.xfail(
     sysconfig.get_config_var("Py_DEBUG") is not None,
     reason=(
         "NumPy possibly built with `USE_DEBUG=True ./tools/travis-test.sh`, "
@@ -506,3 +512,17 @@ def test_array_api_entry_point():
         "does not point to our Array API implementation"
     )
     assert xp is numpy.array_api, msg
+
+
+@pytest.mark.parametrize("name", [
+        'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
+        'ComplexWarning', 'TooHardError', 'AxisError'])
+def test_moved_exceptions(name):
+    # These were moved to the exceptions namespace, but currently still
+    # available
+    assert name in np.__all__
+    assert name not in np.__dir__()
+    # Fetching works, but __module__ is set correctly:
+    assert getattr(np, name).__module__ == "numpy.exceptions"
+    assert name in np.exceptions.__all__
+    getattr(np.exceptions, name)
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/fail/einsumfunc.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/fail/einsumfunc.pyi
index f0e3f1e9571..2d1f3741851 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/fail/einsumfunc.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/fail/einsumfunc.pyi
@@ -4,12 +4,9 @@ import numpy as np
 AR_i: np.ndarray[Any, np.dtype[np.int64]]
 AR_f: np.ndarray[Any, np.dtype[np.float64]]
 AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]
-AR_O: np.ndarray[Any, np.dtype[np.object_]]
 AR_U: np.ndarray[Any, np.dtype[np.str_]]
 
 np.einsum("i,i->i", AR_i, AR_m)  # E: incompatible type
-np.einsum("i,i->i", AR_O, AR_O)  # E: incompatible type
 np.einsum("i,i->i", AR_f, AR_f, dtype=np.int32)  # E: incompatible type
-np.einsum("i,i->i", AR_i, AR_i, dtype=np.timedelta64, casting="unsafe")  # E: No overload variant
 np.einsum("i,i->i", AR_i, AR_i, out=AR_U)  # E: Value of type variable "_ArrayType" of "einsum" cannot be
 np.einsum("i,i->i", AR_i, AR_i, out=AR_U, casting="unsafe")  # E: No overload variant
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/fail/numerictypes.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/fail/numerictypes.pyi
index a5c2814ef11..ce5662d5e66 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/fail/numerictypes.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/fail/numerictypes.pyi
@@ -9,5 +9,3 @@ np.maximum_sctype(1)  # E: No overload variant
 np.issubsctype(1, np.int64)  # E: incompatible type
 
 np.issubdtype(1, np.int64)  # E: incompatible type
-
-np.find_common_type(np.int64, np.int64)  # E: incompatible type
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/dtype.py b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/dtype.py
index e849cfdd4e1..6ec44e6b06c 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/dtype.py
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/dtype.py
@@ -15,7 +15,7 @@ np.dtype({"names": ["a", "b"], "formats": [int, float]})
 np.dtype({"names": ["a"], "formats": [int], "titles": [object]})
 np.dtype({"names": ["a"], "formats": [int], "titles": [object()]})
 
-np.dtype([("name", np.unicode_, 16), ("grades", np.float64, (2,)), ("age", "int32")])
+np.dtype([("name", np.str_, 16), ("grades", np.float64, (2,)), ("age", "int32")])
 
 np.dtype(
     {
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/ndarray_misc.py b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/ndarray_misc.py
index 19a1af9e292..6beacc5d7cc 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/ndarray_misc.py
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/ndarray_misc.py
@@ -150,7 +150,7 @@ A.argpartition([0])
 A.diagonal()
 
 A.dot(1)
-A.dot(1, out=B0)
+A.dot(1, out=B2)
 
 A.nonzero()
 
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/numerictypes.py b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/numerictypes.py
index 7f1dd094543..63b6ad0e22e 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/numerictypes.py
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/numerictypes.py
@@ -8,7 +8,7 @@ np.issctype("S8")
 
 np.obj2sctype(list)
 np.obj2sctype(list, default=None)
-np.obj2sctype(list, default=np.string_)
+np.obj2sctype(list, default=np.bytes_)
 
 np.issubclass_(np.int32, int)
 np.issubclass_(np.float64, float)
@@ -17,17 +17,12 @@ np.issubclass_(np.float64, (int, float))
 np.issubsctype("int64", int)
 np.issubsctype(np.array([1]), np.array([1]))
 
-np.issubdtype("S1", np.string_)
+np.issubdtype("S1", np.bytes_)
 np.issubdtype(np.float64, np.float32)
 
 np.sctype2char("S1")
 np.sctype2char(list)
 
-np.find_common_type([], [np.int64, np.float32, complex])
-np.find_common_type((), (np.int64, np.float32, complex))
-np.find_common_type([np.int64, np.float32], [])
-np.find_common_type([np.float32], [np.int64, np.float64])
-
 np.cast[int]
 np.cast["i8"]
 np.cast[np.int64]
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/simple.py b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/simple.py
index 03ca3e83f74..80116870287 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/pass/simple.py
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/pass/simple.py
@@ -33,13 +33,13 @@ np.dtype(two_tuples_dtype)
 three_tuples_dtype = [("R", "u1", 2)]
 np.dtype(three_tuples_dtype)
 
-mixed_tuples_dtype = [("R", "u1"), ("G", np.unicode_, 1)]
+mixed_tuples_dtype = [("R", "u1"), ("G", np.str_, 1)]
 np.dtype(mixed_tuples_dtype)
 
 shape_tuple_dtype = [("R", "u1", (2, 2))]
 np.dtype(shape_tuple_dtype)
 
-shape_like_dtype = [("R", "u1", (2, 2)), ("G", np.unicode_, 1)]
+shape_like_dtype = [("R", "u1", (2, 2)), ("G", np.str_, 1)]
 np.dtype(shape_like_dtype)
 
 object_dtype = [("field1", object)]
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/einsumfunc.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/einsumfunc.pyi
index d5f9301494f..5f6415f275c 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/einsumfunc.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/einsumfunc.pyi
@@ -1,5 +1,6 @@
 from typing import Any
 import numpy as np
+import numpy.typing as npt
 
 AR_LIKE_b: list[bool]
 AR_LIKE_u: list[np.uint32]
@@ -7,10 +8,12 @@ AR_LIKE_i: list[int]
 AR_LIKE_f: list[float]
 AR_LIKE_c: list[complex]
 AR_LIKE_U: list[str]
+AR_o: npt.NDArray[np.object_]
 
-OUT_f: np.ndarray[Any, np.dtype[np.float64]]
+OUT_f: npt.NDArray[np.float64]
 
 reveal_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b))  # E: Any
+reveal_type(np.einsum("i,i->i", AR_o, AR_o))  # E: Any
 reveal_type(np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u))  # E: Any
 reveal_type(np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i))  # E: Any
 reveal_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f))  # E: Any
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/linalg.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/linalg.pyi
index 19e13aed692..13035186431 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/linalg.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/linalg.pyi
@@ -33,9 +33,9 @@ reveal_type(np.linalg.cholesky(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
 reveal_type(np.linalg.cholesky(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
 reveal_type(np.linalg.cholesky(AR_c16))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
 
-reveal_type(np.linalg.qr(AR_i8))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]]
-reveal_type(np.linalg.qr(AR_f8))  # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]]
-reveal_type(np.linalg.qr(AR_c16))  # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]
+reveal_type(np.linalg.qr(AR_i8))  # E: QRResult
+reveal_type(np.linalg.qr(AR_f8))  # E: QRResult
+reveal_type(np.linalg.qr(AR_c16))  # E: QRResult
 
 reveal_type(np.linalg.eigvals(AR_i8))  # E: Union[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{complex128}]]]
 reveal_type(np.linalg.eigvals(AR_f8))  # E: Union[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]
@@ -45,17 +45,17 @@ reveal_type(np.linalg.eigvalsh(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
 reveal_type(np.linalg.eigvalsh(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
 reveal_type(np.linalg.eigvalsh(AR_c16))  # E: ndarray[Any, dtype[floating[Any]]]
 
-reveal_type(np.linalg.eig(AR_i8))  # E: Union[Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]], Tuple[ndarray[Any, dtype[{complex128}]], ndarray[Any, dtype[{complex128}]]]]
-reveal_type(np.linalg.eig(AR_f8))  # E: Union[Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]], Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]]
-reveal_type(np.linalg.eig(AR_c16))  # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]
+reveal_type(np.linalg.eig(AR_i8))  # E: EigResult
+reveal_type(np.linalg.eig(AR_f8))  # E: EigResult
+reveal_type(np.linalg.eig(AR_c16))  # E: EigResult
 
-reveal_type(np.linalg.eigh(AR_i8))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]]
-reveal_type(np.linalg.eigh(AR_f8))  # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]]
-reveal_type(np.linalg.eigh(AR_c16))  # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]
+reveal_type(np.linalg.eigh(AR_i8))  # E: EighResult
+reveal_type(np.linalg.eigh(AR_f8))  # E: EighResult
+reveal_type(np.linalg.eigh(AR_c16))  # E: EighResult
 
-reveal_type(np.linalg.svd(AR_i8))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{float64}]]]
-reveal_type(np.linalg.svd(AR_f8))  # E: Tuple[ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[floating[Any]]]]
-reveal_type(np.linalg.svd(AR_c16))  # E: Tuple[ndarray[Any, dtype[complexfloating[Any, Any]]], ndarray[Any, dtype[floating[Any]]], ndarray[Any, dtype[complexfloating[Any, Any]]]]
+reveal_type(np.linalg.svd(AR_i8))  # E: SVDResult
+reveal_type(np.linalg.svd(AR_f8))  # E: SVDResult
+reveal_type(np.linalg.svd(AR_c16))  # E: SVDResult
 reveal_type(np.linalg.svd(AR_i8, compute_uv=False))  # E: ndarray[Any, dtype[{float64}]]
 reveal_type(np.linalg.svd(AR_f8, compute_uv=False))  # E: ndarray[Any, dtype[floating[Any]]]
 reveal_type(np.linalg.svd(AR_c16, compute_uv=False))  # E: ndarray[Any, dtype[floating[Any]]]
@@ -72,9 +72,9 @@ reveal_type(np.linalg.pinv(AR_i8))  # E: ndarray[Any, dtype[{float64}]]
 reveal_type(np.linalg.pinv(AR_f8))  # E: ndarray[Any, dtype[floating[Any]]]
 reveal_type(np.linalg.pinv(AR_c16))  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
 
-reveal_type(np.linalg.slogdet(AR_i8))  # E: Tuple[Any, Any]
-reveal_type(np.linalg.slogdet(AR_f8))  # E: Tuple[Any, Any]
-reveal_type(np.linalg.slogdet(AR_c16))  # E: Tuple[Any, Any]
+reveal_type(np.linalg.slogdet(AR_i8))  # E: SlogdetResult
+reveal_type(np.linalg.slogdet(AR_f8))  # E: SlogdetResult
+reveal_type(np.linalg.slogdet(AR_c16))  # E: SlogdetResult
 
 reveal_type(np.linalg.det(AR_i8))  # E: Any
 reveal_type(np.linalg.det(AR_f8))  # E: Any
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/modules.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/modules.pyi
index ba830eb0d33..4191c564af8 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/modules.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/modules.pyi
@@ -16,6 +16,8 @@ reveal_type(np.random)  # E: ModuleType
 reveal_type(np.rec)  # E: ModuleType
 reveal_type(np.testing)  # E: ModuleType
 reveal_type(np.version)  # E: ModuleType
+reveal_type(np.exceptions)  # E: ModuleType
+reveal_type(np.dtypes)  # E: ModuleType
 
 reveal_type(np.lib.format)  # E: ModuleType
 reveal_type(np.lib.mixins)  # E: ModuleType
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/npyio.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/npyio.pyi
index 68605cf9460..2c62d8d21ed 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/npyio.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/npyio.pyi
@@ -75,13 +75,13 @@ reveal_type(np.fromregex(str_path, re.compile("test"), dtype=np.str_, encoding="
 reveal_type(np.fromregex(pathlib_path, "test", np.float64))  # E: ndarray[Any, dtype[{float64}]]
 reveal_type(np.fromregex(bytes_reader, "test", np.float64))  # E: ndarray[Any, dtype[{float64}]]
 
-reveal_type(np.genfromtxt(bytes_file))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.genfromtxt(bytes_file))  # E: ndarray[Any, dtype[Any]]
 reveal_type(np.genfromtxt(pathlib_path, dtype=np.str_))  # E: ndarray[Any, dtype[str_]]
 reveal_type(np.genfromtxt(str_path, dtype=str, skip_header=2))  # E: ndarray[Any, dtype[Any]]
-reveal_type(np.genfromtxt(str_file, comments="test"))  # E: ndarray[Any, dtype[{float64}]]
-reveal_type(np.genfromtxt(str_path, delimiter="\n"))  # E: ndarray[Any, dtype[{float64}]]
-reveal_type(np.genfromtxt(str_path, ndmin=2))  # E: ndarray[Any, dtype[{float64}]]
-reveal_type(np.genfromtxt(["1", "2", "3"], ndmin=2))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.genfromtxt(str_file, comments="test"))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.genfromtxt(str_path, delimiter="\n"))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.genfromtxt(str_path, ndmin=2))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.genfromtxt(["1", "2", "3"], ndmin=2))  # E: ndarray[Any, dtype[Any]]
 
 reveal_type(np.recfromtxt(bytes_file))  # E: recarray[Any, dtype[record]]
 reveal_type(np.recfromtxt(pathlib_path, usemask=True))  # E: ma.mrecords.MaskedRecords[Any, dtype[void]]
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/numerictypes.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/numerictypes.pyi
index e1857557d90..d4399e2b121 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/numerictypes.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/numerictypes.pyi
@@ -21,8 +21,6 @@ reveal_type(np.issubclass_(1, 1))  # E: Literal[False]
 reveal_type(np.sctype2char("S8"))  # E: str
 reveal_type(np.sctype2char(list))  # E: str
 
-reveal_type(np.find_common_type([np.int64], [np.int64]))  # E: dtype[Any]
-
 reveal_type(np.cast[int])  # E: _CastFunc
 reveal_type(np.cast["i8"])  # E: _CastFunc
 reveal_type(np.cast[np.int64])  # E: _CastFunc
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/scalars.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/scalars.pyi
index b7fc75acce4..965aa5ace44 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/scalars.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/scalars.pyi
@@ -1,4 +1,3 @@
-import sys
 import numpy as np
 
 b: np.bool_
@@ -151,8 +150,7 @@ reveal_type(round(u8, 3))  # E: {uint64}
 reveal_type(round(f8))  # E: int
 reveal_type(round(f8, 3))  # E: {float64}
 
-if sys.version_info >= (3, 9):
-    reveal_type(f8.__ceil__())  # E: int
-    reveal_type(f8.__floor__())  # E: int
+reveal_type(f8.__ceil__())  # E: int
+reveal_type(f8.__floor__())  # E: int
 
 reveal_type(i8.is_integer())  # E: Literal[True]
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/testing.pyi b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/testing.pyi
index 5440af80065..5c35731d3ba 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/testing.pyi
+++ b/contrib/python/numpy/py3/numpy/typing/tests/data/reveal/testing.pyi
@@ -113,7 +113,6 @@ reveal_type(np.testing.rundocs())  # E: None
 reveal_type(np.testing.rundocs("test.py"))  # E: None
 reveal_type(np.testing.rundocs(Path("test.py"), raise_on_error=True))  # E: None
 
-@np.testing.raises(RuntimeError, RuntimeWarning)
 def func3(a: int) -> bool: ...
 
 reveal_type(func3)  # E: def (a: builtins.int) -> builtins.bool
@@ -175,4 +174,3 @@ reveal_type(np.testing.assert_no_gc_cycles(func3, 5))  # E: None
 reveal_type(np.testing.break_cycles())  # E: None
 
 reveal_type(np.testing.TestCase())  # E: unittest.case.TestCase
-reveal_type(np.testing.run_module_suite(file_to_run="numpy/tests/test_matlib.py"))  # E: None
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/test_generic_alias.py b/contrib/python/numpy/py3/numpy/typing/tests/test_generic_alias.py
deleted file mode 100644
index 861babd4b3a..00000000000
--- a/contrib/python/numpy/py3/numpy/typing/tests/test_generic_alias.py
+++ /dev/null
@@ -1,188 +0,0 @@
-from __future__ import annotations
-
-import sys
-import copy
-import types
-import pickle
-import weakref
-from typing import TypeVar, Any, Union, Callable
-
-import pytest
-import numpy as np
-from numpy._typing._generic_alias import _GenericAlias
-from typing_extensions import Unpack
-
-ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True)
-T1 = TypeVar("T1")
-T2 = TypeVar("T2")
-DType = _GenericAlias(np.dtype, (ScalarType,))
-NDArray = _GenericAlias(np.ndarray, (Any, DType))
-
-# NOTE: The `npt._GenericAlias` *class* isn't quite stable on python >=3.11.
-# This is not a problem during runtime (as it's 3.8-exclusive), but we still
-# need it for the >=3.9 in order to verify its semantics match
-# `types.GenericAlias` replacement. xref numpy/numpy#21526
-if sys.version_info >= (3, 9):
-    DType_ref = types.GenericAlias(np.dtype, (ScalarType,))
-    NDArray_ref = types.GenericAlias(np.ndarray, (Any, DType_ref))
-    FuncType = Callable[["_GenericAlias | types.GenericAlias"], Any]
-else:
-    DType_ref = Any
-    NDArray_ref = Any
-    FuncType = Callable[["_GenericAlias"], Any]
-
-GETATTR_NAMES = sorted(set(dir(np.ndarray)) - _GenericAlias._ATTR_EXCEPTIONS)
-
-BUFFER = np.array([1], dtype=np.int64)
-BUFFER.setflags(write=False)
-
-def _get_subclass_mro(base: type) -> tuple[type, ...]:
-    class Subclass(base):  # type: ignore[misc,valid-type]
-        pass
-    return Subclass.__mro__[1:]
-
-
-class TestGenericAlias:
-    """Tests for `numpy._typing._generic_alias._GenericAlias`."""
-
-    @pytest.mark.parametrize("name,func", [
-        ("__init__", lambda n: n),
-        ("__init__", lambda n: _GenericAlias(np.ndarray, Any)),
-        ("__init__", lambda n: _GenericAlias(np.ndarray, (Any,))),
-        ("__init__", lambda n: _GenericAlias(np.ndarray, (Any, Any))),
-        ("__init__", lambda n: _GenericAlias(np.ndarray, T1)),
-        ("__init__", lambda n: _GenericAlias(np.ndarray, (T1,))),
-        ("__init__", lambda n: _GenericAlias(np.ndarray, (T1, T2))),
-        ("__origin__", lambda n: n.__origin__),
-        ("__args__", lambda n: n.__args__),
-        ("__parameters__", lambda n: n.__parameters__),
-        ("__mro_entries__", lambda n: n.__mro_entries__([object])),
-        ("__hash__", lambda n: hash(n)),
-        ("__repr__", lambda n: repr(n)),
-        ("__getitem__", lambda n: n[np.float64]),
-        ("__getitem__", lambda n: n[ScalarType][np.float64]),
-        ("__getitem__", lambda n: n[Union[np.int64, ScalarType]][np.float64]),
-        ("__getitem__", lambda n: n[Union[T1, T2]][np.float32, np.float64]),
-        ("__eq__", lambda n: n == n),
-        ("__ne__", lambda n: n != np.ndarray),
-        ("__call__", lambda n: n((1,), np.int64, BUFFER)),
-        ("__call__", lambda n: n(shape=(1,), dtype=np.int64, buffer=BUFFER)),
-        ("subclassing", lambda n: _get_subclass_mro(n)),
-        ("pickle", lambda n: n == pickle.loads(pickle.dumps(n))),
-    ])
-    def test_pass(self, name: str, func: FuncType) -> None:
-        """Compare `types.GenericAlias` with its numpy-based backport.
-
-        Checker whether ``func`` runs as intended and that both `GenericAlias`
-        and `_GenericAlias` return the same result.
-
-        """
-        value = func(NDArray)
-
-        if sys.version_info >= (3, 9):
-            value_ref = func(NDArray_ref)
-            assert value == value_ref
-
-    @pytest.mark.parametrize("name,func", [
-        ("__copy__", lambda n: n == copy.copy(n)),
-        ("__deepcopy__", lambda n: n == copy.deepcopy(n)),
-    ])
-    def test_copy(self, name: str, func: FuncType) -> None:
-        value = func(NDArray)
-
-        # xref bpo-45167
-        GE_398 = (
-            sys.version_info[:2] == (3, 9) and sys.version_info >= (3, 9, 8)
-        )
-        if GE_398 or sys.version_info >= (3, 10, 1):
-            value_ref = func(NDArray_ref)
-            assert value == value_ref
-
-    def test_dir(self) -> None:
-        value = dir(NDArray)
-        if sys.version_info < (3, 9):
-            return
-
-        # A number attributes only exist in `types.GenericAlias` in >= 3.11
-        if sys.version_info < (3, 11, 0, "beta", 3):
-            value.remove("__typing_unpacked_tuple_args__")
-        if sys.version_info < (3, 11, 0, "beta", 1):
-            value.remove("__unpacked__")
-        assert value == dir(NDArray_ref)
-
-    @pytest.mark.parametrize("name,func,dev_version", [
-        ("__iter__", lambda n: len(list(n)), ("beta", 1)),
-        ("__iter__", lambda n: next(iter(n)), ("beta", 1)),
-        ("__unpacked__", lambda n: n.__unpacked__, ("beta", 1)),
-        ("Unpack", lambda n: Unpack[n], ("beta", 1)),
-
-        # The right operand should now have `__unpacked__ = True`,
-        # and they are thus now longer equivalent
-        ("__ne__", lambda n: n != next(iter(n)), ("beta", 1)),
-
-        # >= beta3
-        ("__typing_unpacked_tuple_args__",
-         lambda n: n.__typing_unpacked_tuple_args__, ("beta", 3)),
-
-        # >= beta4
-        ("__class__", lambda n: n.__class__ == type(n), ("beta", 4)),
-    ])
-    def test_py311_features(
-        self,
-        name: str,
-        func: FuncType,
-        dev_version: tuple[str, int],
-    ) -> None:
-        """Test Python 3.11 features."""
-        value = func(NDArray)
-
-        if sys.version_info >= (3, 11, 0, *dev_version):
-            value_ref = func(NDArray_ref)
-            assert value == value_ref
-
-    def test_weakref(self) -> None:
-        """Test ``__weakref__``."""
-        value = weakref.ref(NDArray)()
-
-        if sys.version_info >= (3, 9, 1):  # xref bpo-42332
-            value_ref = weakref.ref(NDArray_ref)()
-            assert value == value_ref
-
-    @pytest.mark.parametrize("name", GETATTR_NAMES)
-    def test_getattr(self, name: str) -> None:
-        """Test that `getattr` wraps around the underlying type,
-        aka ``__origin__``.
-
-        """
-        value = getattr(NDArray, name)
-        value_ref1 = getattr(np.ndarray, name)
-
-        if sys.version_info >= (3, 9):
-            value_ref2 = getattr(NDArray_ref, name)
-            assert value == value_ref1 == value_ref2
-        else:
-            assert value == value_ref1
-
-    @pytest.mark.parametrize("name,exc_type,func", [
-        ("__getitem__", TypeError, lambda n: n[()]),
-        ("__getitem__", TypeError, lambda n: n[Any, Any]),
-        ("__getitem__", TypeError, lambda n: n[Any][Any]),
-        ("isinstance", TypeError, lambda n: isinstance(np.array(1), n)),
-        ("issublass", TypeError, lambda n: issubclass(np.ndarray, n)),
-        ("setattr", AttributeError, lambda n: setattr(n, "__origin__", int)),
-        ("setattr", AttributeError, lambda n: setattr(n, "test", int)),
-        ("getattr", AttributeError, lambda n: getattr(n, "test")),
-    ])
-    def test_raise(
-        self,
-        name: str,
-        exc_type: type[BaseException],
-        func: FuncType,
-    ) -> None:
-        """Test operations that are supposed to raise."""
-        with pytest.raises(exc_type):
-            func(NDArray)
-
-        if sys.version_info >= (3, 9):
-            with pytest.raises(exc_type):
-                func(NDArray_ref)
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/test_runtime.py b/contrib/python/numpy/py3/numpy/typing/tests/test_runtime.py
index 44d06900632..c32c5db3266 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/test_runtime.py
+++ b/contrib/python/numpy/py3/numpy/typing/tests/test_runtime.py
@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import sys
 from typing import (
     get_type_hints,
     Union,
@@ -24,10 +23,7 @@ class TypeTup(NamedTuple):
     origin: None | type
 
 
-if sys.version_info >= (3, 9):
-    NDArrayTup = TypeTup(npt.NDArray, npt.NDArray.__args__, np.ndarray)
-else:
-    NDArrayTup = TypeTup(npt.NDArray, (), None)
+NDArrayTup = TypeTup(npt.NDArray, npt.NDArray.__args__, np.ndarray)
 
 TYPES = {
     "ArrayLike": TypeTup(npt.ArrayLike, npt.ArrayLike.__args__, Union),
diff --git a/contrib/python/numpy/py3/numpy/typing/tests/test_typing.py b/contrib/python/numpy/py3/numpy/typing/tests/test_typing.py
index 5011339b56f..bcaaf5250c9 100644
--- a/contrib/python/numpy/py3/numpy/typing/tests/test_typing.py
+++ b/contrib/python/numpy/py3/numpy/typing/tests/test_typing.py
@@ -431,7 +431,7 @@ def test_extended_precision() -> None:
     output_mypy = OUTPUT_MYPY
     assert path in output_mypy
 
-    with open(path, "r") as f:
+    with open(path) as f:
         expression_list = f.readlines()
 
     for _msg in output_mypy[path]:
diff --git a/contrib/python/numpy/py3/numpy/version.py b/contrib/python/numpy/py3/numpy/version.py
index d5657d0d08e..d9d2fe1b761 100644
--- a/contrib/python/numpy/py3/numpy/version.py
+++ b/contrib/python/numpy/py3/numpy/version.py
@@ -4,12 +4,20 @@ from ._version import get_versions
 
 __ALL__ = ['version', '__version__', 'full_version', 'git_revision', 'release']
 
+
+_built_with_meson = False
+try:
+    from ._version_meson import get_versions
+    _built_with_meson = True
+except ImportError:
+    from ._version import get_versions
+
 vinfo: dict[str, str] = get_versions()
 version = vinfo["version"]
 __version__ = vinfo.get("closest-tag", vinfo["version"])
-full_version = vinfo['version']
 git_revision = vinfo['full-revisionid']
 release = 'dev0' not in version and '+' not in version
-short_version = vinfo['version'].split("+")[0]
+full_version = version
+short_version = version.split("+")[0]
 
 del get_versions, vinfo
diff --git a/contrib/python/numpy/py3/tests/ya.make b/contrib/python/numpy/py3/tests/ya.make
index d7415e6ffb8..7750eb421ce 100644
--- a/contrib/python/numpy/py3/tests/ya.make
+++ b/contrib/python/numpy/py3/tests/ya.make
@@ -24,6 +24,8 @@ TEST_SRCS(
     array_api/tests/test_creation_functions.py
     array_api/tests/test_data_type_functions.py
     array_api/tests/test_elementwise_functions.py
+    array_api/tests/test_indexing_functions.py
+    array_api/tests/test_manipulation_functions.py
     array_api/tests/test_set_functions.py
     array_api/tests/test_sorting_functions.py
     array_api/tests/test_validation.py
@@ -115,9 +117,11 @@ TEST_SRCS(
     #f2py/tests/test_assumed_shape.py
     #f2py/tests/test_block_docstring.py
     #f2py/tests/test_callback.py
+    #f2py/tests/test_character.py
     #f2py/tests/test_common.py
     #f2py/tests/test_compile_function.py
     #f2py/tests/test_crackfortran.py
+    #f2py/tests/test_docs.py
     #f2py/tests/test_f2cmap.py
     #f2py/tests/test_f2py2e.py
     #f2py/tests/test_kind.py
@@ -135,6 +139,7 @@ TEST_SRCS(
     #f2py/tests/test_size.py
     #f2py/tests/test_string.py
     #f2py/tests/test_symbolic.py
+    #f2py/tests/test_value_attrspec.py
     #f2py/tests/util.py
     fft/tests/__init__.py
     fft/tests/test_helper.py
@@ -209,20 +214,19 @@ TEST_SRCS(
     random/tests/test_seed_sequence.py
     random/tests/test_smoke.py
     testing/tests/__init__.py
-    #testing/tests/test_doctesting.py
     testing/tests/test_utils.py
     tests/__init__.py
     tests/test__all__.py
     #tests/test_ctypeslib.py
     tests/test_lazyloading.py
     tests/test_matlib.py
+    tests/test_numpy_config.py
     tests/test_numpy_version.py
     tests/test_public_api.py
     tests/test_reloading.py
     #tests/test_scripts.py
     tests/test_warnings.py
     typing/tests/__init__.py
-    typing/tests/test_generic_alias.py
     #typing/tests/test_isfile.py
     #typing/tests/test_runtime.py
     typing/tests/test_typing.py
diff --git a/contrib/python/numpy/py3/ya.make b/contrib/python/numpy/py3/ya.make
index 54b9fdd61c2..253c01869b6 100644
--- a/contrib/python/numpy/py3/ya.make
+++ b/contrib/python/numpy/py3/ya.make
@@ -4,7 +4,7 @@ LICENSE(BSD-3-Clause)
 
 PROVIDES(numpy)
 
-VERSION(1.24.4)
+VERSION(1.25.2)
 
 NO_COMPILER_WARNINGS()
 NO_EXTENDED_SOURCE_SEARCH()
@@ -51,9 +51,9 @@ ENDIF()
 
 NO_CHECK_IMPORTS(
     numpy._pyinstaller.*
+    numpy.core.umath_tests
     numpy.distutils.command.*
     numpy.distutils.msvc9compiler
-    numpy.testing._private.noseclasses
 )
 
 NO_LINT()
@@ -77,12 +77,14 @@ PY_SRCS(
     numpy/_typing/_char_codes.py
     numpy/_typing/_dtype_like.py
     numpy/_typing/_extended_precision.py
-    numpy/_typing/_generic_alias.py
     numpy/_typing/_nbit.py
     numpy/_typing/_nested_sequence.py
     numpy/_typing/_scalars.py
     numpy/_typing/_shape.py
     numpy/_typing/_ufunc.pyi
+    numpy/_utils/__init__.py
+    numpy/_utils/_inspect.py
+    numpy/_utils/_pep440.py
     numpy/_version.py
     numpy/array_api/__init__.py
     numpy/array_api/_array_object.py
@@ -91,6 +93,7 @@ PY_SRCS(
     numpy/array_api/_data_type_functions.py
     numpy/array_api/_dtypes.py
     numpy/array_api/_elementwise_functions.py
+    numpy/array_api/_indexing_functions.py
     numpy/array_api/_manipulation_functions.py
     numpy/array_api/_searching_functions.py
     numpy/array_api/_set_functions.py
@@ -100,8 +103,6 @@ PY_SRCS(
     numpy/array_api/_utility_functions.py
     numpy/array_api/linalg.py
     numpy/compat/__init__.py
-    numpy/compat/_inspect.py
-    numpy/compat/_pep440.py
     numpy/compat/py3k.py
     numpy/core/__init__.py
     numpy/core/__init__.pyi
@@ -201,6 +202,7 @@ PY_SRCS(
     numpy/distutils/fcompiler/sun.py
     numpy/distutils/fcompiler/vast.py
     numpy/distutils/from_template.py
+    numpy/distutils/fujitsuccompiler.py
     numpy/distutils/intelccompiler.py
     numpy/distutils/lib2def.py
     numpy/distutils/line_endings.py
@@ -217,7 +219,10 @@ PY_SRCS(
     numpy/doc/__init__.py
     numpy/doc/constants.py
     numpy/doc/ufuncs.py
-    numpy/dual.py
+    numpy/dtypes.py
+    numpy/dtypes.pyi
+    numpy/exceptions.py
+    numpy/exceptions.pyi
     numpy/fft/__init__.py
     numpy/fft/__init__.pyi
     numpy/fft/_pocketfft.py
@@ -274,7 +279,6 @@ PY_SRCS(
     numpy/linalg/linalg.pyi
     numpy/ma/__init__.py
     numpy/ma/__init__.pyi
-    numpy/ma/bench.py
     numpy/ma/core.py
     numpy/ma/core.pyi
     numpy/ma/extras.py
@@ -309,15 +313,11 @@ PY_SRCS(
     numpy/testing/__init__.py
     numpy/testing/__init__.pyi
     numpy/testing/_private/__init__.py
-    numpy/testing/_private/decorators.py
     numpy/testing/_private/extbuild.py
-    numpy/testing/_private/noseclasses.py
-    numpy/testing/_private/nosetester.py
-    numpy/testing/_private/parameterized.py
     numpy/testing/_private/utils.py
     numpy/testing/_private/utils.pyi
+    numpy/testing/overrides.py
     numpy/testing/print_coercion_tables.py
-    numpy/testing/utils.py
     numpy/typing/__init__.py
     numpy/typing/mypy_plugin.py
     numpy/version.py
@@ -364,6 +364,7 @@ SRCS(
     numpy/core/src/multiarray/dlpack.c
     numpy/core/src/multiarray/dragon4.c
     numpy/core/src/multiarray/dtype_transfer.c
+    numpy/core/src/multiarray/dtype_traversal.c
     numpy/core/src/multiarray/dtypemeta.c
     numpy/core/src/multiarray/einsum.c
     numpy/core/src/multiarray/einsum_sumprod.c
@@ -401,8 +402,8 @@ SRCS(
     numpy/core/src/multiarray/typeinfo.c
     numpy/core/src/multiarray/usertypes.c
     numpy/core/src/multiarray/vdot.c
-    numpy/core/src/npymath/_signbit.c
-    numpy/core/src/npymath/halffloat.c
+    numpy/core/src/npymath/arm64_exports.c
+    numpy/core/src/npymath/halffloat.cpp
     numpy/core/src/npymath/ieee754.c
     numpy/core/src/npymath/ieee754.cpp
     numpy/core/src/npymath/npy_math.c
@@ -448,18 +449,24 @@ ENDIF()
 SRCS(
     numpy/core/src/_simd/_simd.dispatch.c
     numpy/core/src/multiarray/argfunc.dispatch.c
-    numpy/core/src/npysort/x86-qsort.dispatch.cpp
+    numpy/core/src/npysort/simd_qsort.dispatch.cpp
+    numpy/core/src/npysort/simd_qsort_16bit.dispatch.cpp
     numpy/core/src/umath/_umath_tests.dispatch.c
     numpy/core/src/umath/loops_arithm_fp.dispatch.c
     numpy/core/src/umath/loops_arithmetic.dispatch.c
+    numpy/core/src/umath/loops_autovec.dispatch.c
     numpy/core/src/umath/loops_comparison.dispatch.c
     numpy/core/src/umath/loops_exponent_log.dispatch.c
     numpy/core/src/umath/loops_hyperbolic.dispatch.c
+    numpy/core/src/umath/loops_logical.dispatch.c
     numpy/core/src/umath/loops_minmax.dispatch.c
     numpy/core/src/umath/loops_modulo.dispatch.c
     numpy/core/src/umath/loops_trigonometric.dispatch.c
     numpy/core/src/umath/loops_umath_fp.dispatch.c
+    numpy/core/src/umath/loops_unary.dispatch.c
+    numpy/core/src/umath/loops_unary_complex.dispatch.c
     numpy/core/src/umath/loops_unary_fp.dispatch.c
+    numpy/core/src/umath/loops_unary_fp_le.dispatch.c
 )
 
 IF (ARCH_X86_64)
@@ -470,15 +477,16 @@ IF (ARCH_X86_64)
     SRC_C_AVX2(numpy/core/src/multiarray/argfunc.dispatch.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/multiarray/argfunc.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC(numpy/core/src/multiarray/argfunc.dispatch.sse42.c)
-    SRC(numpy/core/src/npysort/x86-qsort.dispatch.avx512_skx.cpp $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
+    SRC(numpy/core/src/npysort/simd_qsort.dispatch.avx512_skx.cpp $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
+    SRC(numpy/core/src/npysort/simd_qsort_16bit.dispatch.avx512_icl.cpp $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS -mavx512vbmi2)
     SRC_C_AVX2(numpy/core/src/umath/_umath_tests.dispatch.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/umath/_umath_tests.dispatch.sse41.c)
-    SRC_C_AVX2(numpy/core/src/umath/loops_arithm_fp.dispatch.avx2.c $F16C_FLAGS)
-    SRC(numpy/core/src/umath/loops_arithm_fp.dispatch.avx512f.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
+    SRC_C_AVX2(numpy/core/src/umath/loops_arithm_fp.dispatch.fma3.avx2.c $F16C_FLAGS)
     SRC_C_AVX2(numpy/core/src/umath/loops_arithmetic.dispatch.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/umath/loops_arithmetic.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC(numpy/core/src/umath/loops_arithmetic.dispatch.avx512f.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC(numpy/core/src/umath/loops_arithmetic.dispatch.sse41.c)
+    SRC_C_AVX2(numpy/core/src/umath/loops_autovec.dispatch.avx2.c $F16C_FLAGS)
     SRC_C_AVX2(numpy/core/src/umath/loops_comparison.dispatch.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/umath/loops_comparison.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC(numpy/core/src/umath/loops_comparison.dispatch.avx512f.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
@@ -488,12 +496,19 @@ IF (ARCH_X86_64)
     SRC_C_AVX2(numpy/core/src/umath/loops_exponent_log.dispatch.fma3.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/umath/loops_hyperbolic.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC_C_AVX2(numpy/core/src/umath/loops_hyperbolic.dispatch.fma3.avx2.c $F16C_FLAGS)
+    SRC_C_AVX2(numpy/core/src/umath/loops_logical.dispatch.avx2.c $F16C_FLAGS)
+    SRC(numpy/core/src/umath/loops_logical.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC_C_AVX2(numpy/core/src/umath/loops_minmax.dispatch.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/umath/loops_minmax.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC(numpy/core/src/umath/loops_trigonometric.dispatch.avx512f.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
     SRC_C_AVX2(numpy/core/src/umath/loops_trigonometric.dispatch.fma3.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/umath/loops_umath_fp.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
+    SRC_C_AVX2(numpy/core/src/umath/loops_unary.dispatch.avx2.c $F16C_FLAGS)
+    SRC(numpy/core/src/umath/loops_unary.dispatch.avx512_skx.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
+    SRC(numpy/core/src/umath/loops_unary_complex.dispatch.avx512f.c $AVX_CFLAGS $F16C_FLAGS $AVX2_CFLAGS $AVX512_CFLAGS)
+    SRC_C_AVX2(numpy/core/src/umath/loops_unary_complex.dispatch.fma3.avx2.c $F16C_FLAGS)
     SRC(numpy/core/src/umath/loops_unary_fp.dispatch.sse41.c)
+    SRC(numpy/core/src/umath/loops_unary_fp_le.dispatch.sse41.c)
 ELSEIF (ARCH_ARM64)
     SRC(numpy/core/src/umath/_umath_tests.dispatch.asimdhp.c)
 ENDIF()