Intermediate changes

3 files changed, 1564 insertions, 3 deletions

diff --git a/contrib/python/numpy/py3/numpy/random/.gitignore b/contrib/python/numpy/py3/numpy/random/.gitignore
deleted file mode 100644
index fea3f955ac..0000000000
--- a/contrib/python/numpy/py3/numpy/random/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# generated files
-_bounded_integers.pyx
-_bounded_integers.pxd
diff --git a/contrib/python/numpy/py3/numpy/random/_bounded_integers.pxd b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pxd
new file mode 100644
index 0000000000..7e41463a90
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pxd
@@ -0,0 +1,29 @@
+from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
+                          int8_t, int16_t, int32_t, int64_t, intptr_t)
+import numpy as np
+cimport numpy as np
+ctypedef np.npy_bool bool_t
+
+from numpy.random cimport bitgen_t
+
+cdef inline uint64_t _gen_mask(uint64_t max_val) nogil:
+    """Mask generator for use in bounded random numbers"""
+    # Smallest bit mask >= max
+    cdef uint64_t mask = max_val
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+    mask |= mask >> 32
+    return mask
+
+cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
diff --git a/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx
new file mode 100644
index 0000000000..fe9731c287
--- /dev/null
+++ b/contrib/python/numpy/py3/numpy/random/_bounded_integers.pyx
@@ -0,0 +1,1535 @@
+#!python
+#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True
+
+import numpy as np
+cimport numpy as np
+
+__all__ = []
+
+np.import_array()
+
+
+cdef extern from "numpy/random/distributions.h":
+    # Generate random numbers in closed interval [off, off + rng].
+    uint64_t random_bounded_uint64(bitgen_t *bitgen_state,
+                                   uint64_t off, uint64_t rng,
+                                   uint64_t mask, bint use_masked) nogil
+    uint32_t random_buffered_bounded_uint32(bitgen_t *bitgen_state,
+                                            uint32_t off, uint32_t rng,
+                                            uint32_t mask, bint use_masked,
+                                            int *bcnt, uint32_t *buf) nogil
+    uint16_t random_buffered_bounded_uint16(bitgen_t *bitgen_state,
+                                            uint16_t off, uint16_t rng,
+                                            uint16_t mask, bint use_masked,
+                                            int *bcnt, uint32_t *buf) nogil
+    uint8_t random_buffered_bounded_uint8(bitgen_t *bitgen_state,
+                                          uint8_t off, uint8_t rng,
+                                          uint8_t mask, bint use_masked,
+                                          int *bcnt, uint32_t *buf) nogil
+    np.npy_bool random_buffered_bounded_bool(bitgen_t *bitgen_state,
+                                             np.npy_bool off, np.npy_bool rng,
+                                             np.npy_bool mask, bint use_masked,
+                                             int *bcnt, uint32_t *buf) nogil
+    void random_bounded_uint64_fill(bitgen_t *bitgen_state,
+                                    uint64_t off, uint64_t rng, np.npy_intp cnt,
+                                    bint use_masked,
+                                    uint64_t *out) nogil
+    void random_bounded_uint32_fill(bitgen_t *bitgen_state,
+                                    uint32_t off, uint32_t rng, np.npy_intp cnt,
+                                    bint use_masked,
+                                    uint32_t *out) nogil
+    void random_bounded_uint16_fill(bitgen_t *bitgen_state,
+                                    uint16_t off, uint16_t rng, np.npy_intp cnt,
+                                    bint use_masked,
+                                    uint16_t *out) nogil
+    void random_bounded_uint8_fill(bitgen_t *bitgen_state,
+                                   uint8_t off, uint8_t rng, np.npy_intp cnt,
+                                   bint use_masked,
+                                   uint8_t *out) nogil
+    void random_bounded_bool_fill(bitgen_t *bitgen_state,
+                                  np.npy_bool off, np.npy_bool rng, np.npy_intp cnt,
+                                  bint use_masked,
+                                  np.npy_bool *out) nogil
+
+
+cdef object format_bounds_error(bint closed, object low):
+    # Special case low == 0 to provide a better exception for users
+    # since low = 0 is the default single-argument case.
+    if not np.any(low):
+        comp = '<' if closed else '<='
+        return f'high {comp} 0'
+    else:
+        comp = '>' if closed else '>='
+        return f'low {comp} high'
+
+
+
+
+cdef object _rand_uint32_broadcast(np.ndarray low, np.ndarray high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for smaller integer types
+
+    This path is simpler since the high value in the open interval [low, high)
+    must be in-range for the next larger type, uint64. Here we case to
+    this type for checking and the recast to uint32 when producing the
+    random integers.
+    """
+    cdef uint32_t rng, last_rng, off, val, mask, out_val, is_open
+    cdef uint32_t buf
+    cdef uint32_t *out_data
+    cdef uint64_t low_v, high_v
+    cdef np.ndarray low_arr, high_arr, out_arr
+    cdef np.npy_intp i, cnt
+    cdef np.broadcast it
+    cdef int buf_rem = 0
+
+    # Array path
+    is_open = not closed
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+    if np.any(np.less(low_arr, 0)):
+        raise ValueError('low is out of bounds for uint32')
+    if closed:
+        high_comp = np.greater_equal
+        low_high_comp = np.greater
+    else:
+        high_comp = np.greater
+        low_high_comp = np.greater_equal
+
+    if np.any(high_comp(high_arr, 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))
+
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.uint32)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.uint32)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint32_t *>np.PyArray_DATA(out_arr)
+    cnt = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        for i in range(cnt):
+            low_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
+            high_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
+            # Subtract 1 since generator produces values on the closed int [off, off+rng]
+            rng = <uint32_t>((high_v - is_open) - low_v)
+            off = <uint32_t>(<uint64_t>low_v)
+
+            if rng != last_rng:
+                # Smallest bit mask >= max
+                mask = <uint32_t>_gen_mask(rng)
+
+            out_data[i] = random_buffered_bounded_uint32(state, off, rng, mask, use_masked, &buf_rem, &buf)
+
+            np.PyArray_MultiIter_NEXT(it)
+    return out_arr
+
+cdef object _rand_uint16_broadcast(np.ndarray low, np.ndarray high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for smaller integer types
+
+    This path is simpler since the high value in the open interval [low, high)
+    must be in-range for the next larger type, uint32. Here we case to
+    this type for checking and the recast to uint16 when producing the
+    random integers.
+    """
+    cdef uint16_t rng, last_rng, off, val, mask, out_val, is_open
+    cdef uint32_t buf
+    cdef uint16_t *out_data
+    cdef uint32_t low_v, high_v
+    cdef np.ndarray low_arr, high_arr, out_arr
+    cdef np.npy_intp i, cnt
+    cdef np.broadcast it
+    cdef int buf_rem = 0
+
+    # Array path
+    is_open = not closed
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+    if np.any(np.less(low_arr, 0)):
+        raise ValueError('low is out of bounds for uint16')
+    if closed:
+        high_comp = np.greater_equal
+        low_high_comp = np.greater
+    else:
+        high_comp = np.greater
+        low_high_comp = np.greater_equal
+
+    if np.any(high_comp(high_arr, 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))
+
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.uint16)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.uint16)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint16_t *>np.PyArray_DATA(out_arr)
+    cnt = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        for i in range(cnt):
+            low_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
+            high_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
+            # Subtract 1 since generator produces values on the closed int [off, off+rng]
+            rng = <uint16_t>((high_v - is_open) - low_v)
+            off = <uint16_t>(<uint32_t>low_v)
+
+            if rng != last_rng:
+                # Smallest bit mask >= max
+                mask = <uint16_t>_gen_mask(rng)
+
+            out_data[i] = random_buffered_bounded_uint16(state, off, rng, mask, use_masked, &buf_rem, &buf)
+
+            np.PyArray_MultiIter_NEXT(it)
+    return out_arr
+
+cdef object _rand_uint8_broadcast(np.ndarray low, np.ndarray high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for smaller integer types
+
+    This path is simpler since the high value in the open interval [low, high)
+    must be in-range for the next larger type, uint16. Here we case to
+    this type for checking and the recast to uint8 when producing the
+    random integers.
+    """
+    cdef uint8_t rng, last_rng, off, val, mask, out_val, is_open
+    cdef uint32_t buf
+    cdef uint8_t *out_data
+    cdef uint16_t low_v, high_v
+    cdef np.ndarray low_arr, high_arr, out_arr
+    cdef np.npy_intp i, cnt
+    cdef np.broadcast it
+    cdef int buf_rem = 0
+
+    # Array path
+    is_open = not closed
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+    if np.any(np.less(low_arr, 0)):
+        raise ValueError('low is out of bounds for uint8')
+    if closed:
+        high_comp = np.greater_equal
+        low_high_comp = np.greater
+    else:
+        high_comp = np.greater
+        low_high_comp = np.greater_equal
+
+    if np.any(high_comp(high_arr, 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))
+
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.uint8)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.uint8)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint8_t *>np.PyArray_DATA(out_arr)
+    cnt = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        for i in range(cnt):
+            low_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
+            high_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
+            # Subtract 1 since generator produces values on the closed int [off, off+rng]
+            rng = <uint8_t>((high_v - is_open) - low_v)
+            off = <uint8_t>(<uint16_t>low_v)
+
+            if rng != last_rng:
+                # Smallest bit mask >= max
+                mask = <uint8_t>_gen_mask(rng)
+
+            out_data[i] = random_buffered_bounded_uint8(state, off, rng, mask, use_masked, &buf_rem, &buf)
+
+            np.PyArray_MultiIter_NEXT(it)
+    return out_arr
+
+cdef object _rand_bool_broadcast(np.ndarray low, np.ndarray high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for smaller integer types
+
+    This path is simpler since the high value in the open interval [low, high)
+    must be in-range for the next larger type, uint8. Here we case to
+    this type for checking and the recast to bool when producing the
+    random integers.
+    """
+    cdef bool_t rng, last_rng, off, val, mask, out_val, is_open
+    cdef uint32_t buf
+    cdef bool_t *out_data
+    cdef uint8_t low_v, high_v
+    cdef np.ndarray low_arr, high_arr, out_arr
+    cdef np.npy_intp i, cnt
+    cdef np.broadcast it
+    cdef int buf_rem = 0
+
+    # Array path
+    is_open = not closed
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+    if np.any(np.less(low_arr, 0)):
+        raise ValueError('low is out of bounds for bool')
+    if closed:
+        high_comp = np.greater_equal
+        low_high_comp = np.greater
+    else:
+        high_comp = np.greater
+        low_high_comp = np.greater_equal
+
+    if np.any(high_comp(high_arr, 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))
+
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT8, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_UINT8, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.bool_)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.bool_)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <bool_t *>np.PyArray_DATA(out_arr)
+    cnt = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        for i in range(cnt):
+            low_v = (<uint8_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
+            high_v = (<uint8_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
+            # Subtract 1 since generator produces values on the closed int [off, off+rng]
+            rng = <bool_t>((high_v - is_open) - low_v)
+            off = <bool_t>(<uint8_t>low_v)
+
+            if rng != last_rng:
+                # Smallest bit mask >= max
+                mask = <bool_t>_gen_mask(rng)
+
+            out_data[i] = random_buffered_bounded_bool(state, off, rng, mask, use_masked, &buf_rem, &buf)
+
+            np.PyArray_MultiIter_NEXT(it)
+    return out_arr
+
+cdef object _rand_int32_broadcast(np.ndarray low, np.ndarray high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for smaller integer types
+
+    This path is simpler since the high value in the open interval [low, high)
+    must be in-range for the next larger type, uint64. Here we case to
+    this type for checking and the recast to int32 when producing the
+    random integers.
+    """
+    cdef uint32_t rng, last_rng, off, val, mask, out_val, is_open
+    cdef uint32_t buf
+    cdef uint32_t *out_data
+    cdef uint64_t low_v, high_v
+    cdef np.ndarray low_arr, high_arr, out_arr
+    cdef np.npy_intp i, cnt
+    cdef np.broadcast it
+    cdef int buf_rem = 0
+
+    # Array path
+    is_open = not closed
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+    if np.any(np.less(low_arr, -0x80000000LL)):
+        raise ValueError('low is out of bounds for int32')
+    if closed:
+        high_comp = np.greater_equal
+        low_high_comp = np.greater
+    else:
+        high_comp = np.greater
+        low_high_comp = np.greater_equal
+
+    if np.any(high_comp(high_arr, 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))
+
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.int32)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.int32)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint32_t *>np.PyArray_DATA(out_arr)
+    cnt = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        for i in range(cnt):
+            low_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
+            high_v = (<uint64_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
+            # Subtract 1 since generator produces values on the closed int [off, off+rng]
+            rng = <uint32_t>((high_v - is_open) - low_v)
+            off = <uint32_t>(<uint64_t>low_v)
+
+            if rng != last_rng:
+                # Smallest bit mask >= max
+                mask = <uint32_t>_gen_mask(rng)
+
+            out_data[i] = random_buffered_bounded_uint32(state, off, rng, mask, use_masked, &buf_rem, &buf)
+
+            np.PyArray_MultiIter_NEXT(it)
+    return out_arr
+
+cdef object _rand_int16_broadcast(np.ndarray low, np.ndarray high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for smaller integer types
+
+    This path is simpler since the high value in the open interval [low, high)
+    must be in-range for the next larger type, uint32. Here we case to
+    this type for checking and the recast to int16 when producing the
+    random integers.
+    """
+    cdef uint16_t rng, last_rng, off, val, mask, out_val, is_open
+    cdef uint32_t buf
+    cdef uint16_t *out_data
+    cdef uint32_t low_v, high_v
+    cdef np.ndarray low_arr, high_arr, out_arr
+    cdef np.npy_intp i, cnt
+    cdef np.broadcast it
+    cdef int buf_rem = 0
+
+    # Array path
+    is_open = not closed
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+    if np.any(np.less(low_arr, -0x8000LL)):
+        raise ValueError('low is out of bounds for int16')
+    if closed:
+        high_comp = np.greater_equal
+        low_high_comp = np.greater
+    else:
+        high_comp = np.greater
+        low_high_comp = np.greater_equal
+
+    if np.any(high_comp(high_arr, 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))
+
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_INT32, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.int16)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.int16)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint16_t *>np.PyArray_DATA(out_arr)
+    cnt = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        for i in range(cnt):
+            low_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
+            high_v = (<uint32_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
+            # Subtract 1 since generator produces values on the closed int [off, off+rng]
+            rng = <uint16_t>((high_v - is_open) - low_v)
+            off = <uint16_t>(<uint32_t>low_v)
+
+            if rng != last_rng:
+                # Smallest bit mask >= max
+                mask = <uint16_t>_gen_mask(rng)
+
+            out_data[i] = random_buffered_bounded_uint16(state, off, rng, mask, use_masked, &buf_rem, &buf)
+
+            np.PyArray_MultiIter_NEXT(it)
+    return out_arr
+
+cdef object _rand_int8_broadcast(np.ndarray low, np.ndarray high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for smaller integer types
+
+    This path is simpler since the high value in the open interval [low, high)
+    must be in-range for the next larger type, uint16. Here we case to
+    this type for checking and the recast to int8 when producing the
+    random integers.
+    """
+    cdef uint8_t rng, last_rng, off, val, mask, out_val, is_open
+    cdef uint32_t buf
+    cdef uint8_t *out_data
+    cdef uint16_t low_v, high_v
+    cdef np.ndarray low_arr, high_arr, out_arr
+    cdef np.npy_intp i, cnt
+    cdef np.broadcast it
+    cdef int buf_rem = 0
+
+    # Array path
+    is_open = not closed
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+    if np.any(np.less(low_arr, -0x80LL)):
+        raise ValueError('low is out of bounds for int8')
+    if closed:
+        high_comp = np.greater_equal
+        low_high_comp = np.greater
+    else:
+        high_comp = np.greater
+        low_high_comp = np.greater_equal
+
+    if np.any(high_comp(high_arr, 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))
+
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
+    high_arr = <np.ndarray>np.PyArray_FROM_OTF(high, np.NPY_INT16, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.int8)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.int8)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint8_t *>np.PyArray_DATA(out_arr)
+    cnt = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        for i in range(cnt):
+            low_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 0))[0]
+            high_v = (<uint16_t*>np.PyArray_MultiIter_DATA(it, 1))[0]
+            # Subtract 1 since generator produces values on the closed int [off, off+rng]
+            rng = <uint8_t>((high_v - is_open) - low_v)
+            off = <uint8_t>(<uint16_t>low_v)
+
+            if rng != last_rng:
+                # Smallest bit mask >= max
+                mask = <uint8_t>_gen_mask(rng)
+
+            out_data[i] = random_buffered_bounded_uint8(state, off, rng, mask, use_masked, &buf_rem, &buf)
+
+            np.PyArray_MultiIter_NEXT(it)
+    return out_arr
+
+
+cdef object _rand_uint64_broadcast(object low, object high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for 64-bit integer types
+
+    Requires special treatment since the high value can be out-of-range for
+    the largest (64 bit) integer type since the generator is specified on the
+    interval [low,high).
+
+    The internal generator does not have this issue since it generates from
+    the closes interval [low, high-1] and high-1 is always in range for the
+    64 bit integer type.
+    """
+
+    cdef np.ndarray low_arr, high_arr, out_arr, highm1_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
+
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+
+    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)
+    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)
+        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
+            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
+
+    if np.any(np.greater(low_arr, highm1_arr)):
+        raise ValueError(format_bounds_error(closed, low_arr))
+
+    high_arr = highm1_arr
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_UINT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.uint64)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.uint64)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint64_t *>np.PyArray_DATA(out_arr)
+    n = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        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)
+            off = <uint64_t>(<uint64_t>low_v)
+
+            if rng != last_rng:
+                mask = _gen_mask(rng)
+            out_data[i] = random_bounded_uint64(state, off, rng, mask, use_masked)
+
+            np.PyArray_MultiIter_NEXT(it)
+
+    return out_arr
+
+cdef object _rand_int64_broadcast(object low, object high, object size,
+                                       bint use_masked, bint closed,
+                                       bitgen_t *state, object lock):
+    """
+    Array path for 64-bit integer types
+
+    Requires special treatment since the high value can be out-of-range for
+    the largest (64 bit) integer type since the generator is specified on the
+    interval [low,high).
+
+    The internal generator does not have this issue since it generates from
+    the closes interval [low, high-1] and high-1 is always in range for the
+    64 bit integer type.
+    """
+
+    cdef np.ndarray low_arr, high_arr, out_arr, highm1_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
+
+    low_arr = <np.ndarray>low
+    high_arr = <np.ndarray>high
+
+    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)
+    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)
+        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
+            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
+
+    if np.any(np.greater(low_arr, highm1_arr)):
+        raise ValueError(format_bounds_error(closed, low_arr))
+
+    high_arr = highm1_arr
+    low_arr = <np.ndarray>np.PyArray_FROM_OTF(low, np.NPY_INT64, np.NPY_ALIGNED | np.NPY_FORCECAST)
+
+    if size is not None:
+        out_arr = <np.ndarray>np.empty(size, np.int64)
+    else:
+        it = np.PyArray_MultiIterNew2(low_arr, high_arr)
+        out_arr = <np.ndarray>np.empty(it.shape, np.int64)
+
+    it = np.PyArray_MultiIterNew3(low_arr, high_arr, out_arr)
+    out_data = <uint64_t *>np.PyArray_DATA(out_arr)
+    n = np.PyArray_SIZE(out_arr)
+    mask = last_rng = 0
+    with lock, nogil:
+        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)
+            off = <uint64_t>(<int64_t>low_v)
+
+            if rng != last_rng:
+                mask = _gen_mask(rng)
+            out_data[i] = random_bounded_uint64(state, off, rng, mask, use_masked)
+
+            np.PyArray_MultiIter_NEXT(it)
+
+    return out_arr
+
+
+cdef object _rand_uint64(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_uint64(low, high, size, use_masked, *state, lock)
+
+    Return random `np.uint64` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.uint64` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.uint64
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint64. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint64_t rng, off, out_val
+    cdef uint64_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.uint64)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < 0x0ULL:
+            raise ValueError("low is out of bounds for uint64")
+        if high > 0xFFFFFFFFFFFFFFFFULL:
+            raise ValueError("high is out of bounds for uint64")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint64_t>(high - low)
+        off = <uint64_t>(<uint64_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint64_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.uint64(<uint64_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.uint64)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint64_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint64_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_uint64_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_uint32(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_uint32(low, high, size, use_masked, *state, lock)
+
+    Return random `np.uint32` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.uint32` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.uint32
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint32. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint32_t rng, off, out_val
+    cdef uint32_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.uint32)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < 0x0UL:
+            raise ValueError("low is out of bounds for uint32")
+        if high > 0XFFFFFFFFUL:
+            raise ValueError("high is out of bounds for uint32")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint32_t>(high - low)
+        off = <uint32_t>(<uint32_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint32_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.uint32(<uint32_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.uint32)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint32_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint32_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_uint32_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_uint16(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_uint16(low, high, size, use_masked, *state, lock)
+
+    Return random `np.uint16` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.uint16` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.uint16
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint16. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint16_t rng, off, out_val
+    cdef uint16_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.uint16)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < 0x0UL:
+            raise ValueError("low is out of bounds for uint16")
+        if high > 0XFFFFUL:
+            raise ValueError("high is out of bounds for uint16")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint16_t>(high - low)
+        off = <uint16_t>(<uint16_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint16_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.uint16(<uint16_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.uint16)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint16_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint16_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_uint16_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_uint8(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_uint8(low, high, size, use_masked, *state, lock)
+
+    Return random `np.uint8` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.uint8` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.uint8
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint8. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint8_t rng, off, out_val
+    cdef uint8_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.uint8)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < 0x0UL:
+            raise ValueError("low is out of bounds for uint8")
+        if high > 0XFFUL:
+            raise ValueError("high is out of bounds for uint8")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint8_t>(high - low)
+        off = <uint8_t>(<uint8_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint8_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.uint8(<uint8_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.uint8)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint8_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint8_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_uint8_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_bool(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_bool(low, high, size, use_masked, *state, lock)
+
+    Return random `np.bool_` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.bool_` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.bool_
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for bool. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef bool_t rng, off, out_val
+    cdef bool_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.bool_)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < 0x0UL:
+            raise ValueError("low is out of bounds for bool")
+        if high > 0x1UL:
+            raise ValueError("high is out of bounds for bool")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <bool_t>(high - low)
+        off = <bool_t>(<bool_t>low)
+        if size is None:
+            with lock:
+                random_bounded_bool_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.bool_(<bool_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.bool_)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <bool_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_bool_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_bool_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_int64(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_int64(low, high, size, use_masked, *state, lock)
+
+    Return random `np.int64` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.int64` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.int64
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint64. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint64_t rng, off, out_val
+    cdef uint64_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.int64)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < -0x8000000000000000LL:
+            raise ValueError("low is out of bounds for int64")
+        if high > 0x7FFFFFFFFFFFFFFFL:
+            raise ValueError("high is out of bounds for int64")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint64_t>(high - low)
+        off = <uint64_t>(<int64_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint64_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.int64(<int64_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.int64)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint64_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint64_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_int64_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_int32(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_int32(low, high, size, use_masked, *state, lock)
+
+    Return random `np.int32` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.int32` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.int32
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint32. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint32_t rng, off, out_val
+    cdef uint32_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.int32)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < -0x80000000L:
+            raise ValueError("low is out of bounds for int32")
+        if high > 0x7FFFFFFFL:
+            raise ValueError("high is out of bounds for int32")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint32_t>(high - low)
+        off = <uint32_t>(<int32_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint32_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.int32(<int32_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.int32)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint32_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint32_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_int32_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_int16(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_int16(low, high, size, use_masked, *state, lock)
+
+    Return random `np.int16` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.int16` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.int16
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint16. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint16_t rng, off, out_val
+    cdef uint16_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.int16)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < -0x8000L:
+            raise ValueError("low is out of bounds for int16")
+        if high > 0x7FFFL:
+            raise ValueError("high is out of bounds for int16")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint16_t>(high - low)
+        off = <uint16_t>(<int16_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint16_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.int16(<int16_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.int16)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint16_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint16_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_int16_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)
+
+cdef object _rand_int8(object low, object high, object size,
+                             bint use_masked, bint closed,
+                             bitgen_t *state, object lock):
+    """
+    _rand_int8(low, high, size, use_masked, *state, lock)
+
+    Return random `np.int8` integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution in the
+    interval [`low`, `high`).  If `high` is None (the default),
+    then results are from [0, `low`). On entry the arguments are presumed
+    to have been validated for size and order for the `np.int8` type.
+
+    Parameters
+    ----------
+    low : int or array-like
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is the *highest* such
+        integer).
+    high : int or array-like
+        If provided, one above the largest (signed) integer to be drawn from the
+        distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    use_masked : bool
+        If True then rejection sampling with a range mask is used else Lemire's algorithm is used.
+    closed : bool
+        If True then sample from [low, high].  If False, sample [low, high)
+    state : bit generator
+        Bit generator state to use in the core random number generators
+    lock : threading.Lock
+        Lock to prevent multiple using a single generator simultaneously
+
+    Returns
+    -------
+    out : python scalar or ndarray of np.int8
+          `size`-shaped array of random integers from the appropriate
+          distribution, or a single such random int if `size` not provided.
+
+    Notes
+    -----
+    The internal integer generator produces values from the closed
+    interval [low, high-(not closed)].  This requires some care since
+    high can be out-of-range for uint8. The scalar path leaves
+    integers as Python integers until the 1 has been subtracted to
+    avoid needing to cast to a larger type.
+    """
+    cdef np.ndarray out_arr, low_arr, high_arr
+    cdef uint8_t rng, off, out_val
+    cdef uint8_t *out_data
+    cdef np.npy_intp i, n, cnt
+
+    if size is not None:
+        if (np.prod(size) == 0):
+            return np.empty(size, dtype=np.int8)
+
+    low_arr = <np.ndarray>np.array(low, copy=False)
+    high_arr = <np.ndarray>np.array(high, copy=False)
+    low_ndim = np.PyArray_NDIM(low_arr)
+    high_ndim = np.PyArray_NDIM(high_arr)
+    if low_ndim == 0 and high_ndim == 0:
+        low = int(low_arr)
+        high = int(high_arr)
+        # Subtract 1 since internal generator produces on closed interval [low, high]
+        if not closed:
+            high -= 1
+
+        if low < -0x80L:
+            raise ValueError("low is out of bounds for int8")
+        if high > 0x7FL:
+            raise ValueError("high is out of bounds for int8")
+        if low > high:  # -1 already subtracted, closed interval
+            raise ValueError(format_bounds_error(closed, low))
+
+        rng = <uint8_t>(high - low)
+        off = <uint8_t>(<int8_t>low)
+        if size is None:
+            with lock:
+                random_bounded_uint8_fill(state, off, rng, 1, use_masked, &out_val)
+            return np.int8(<int8_t>out_val)
+        else:
+            out_arr = <np.ndarray>np.empty(size, np.int8)
+            cnt = np.PyArray_SIZE(out_arr)
+            out_data = <uint8_t *>np.PyArray_DATA(out_arr)
+            with lock, nogil:
+                random_bounded_uint8_fill(state, off, rng, cnt, use_masked, out_data)
+            return out_arr
+    return _rand_int8_broadcast(low_arr, high_arr, size, use_masked, closed, state, lock)