add ymake export to ydb

1 files changed, 901 insertions, 0 deletions

diff --git a/contrib/tools/cython/Cython/Compiler/FusedNode.py b/contrib/tools/cython/Cython/Compiler/FusedNode.py
new file mode 100644
index 0000000000..26d6ffd3d6
--- /dev/null
+++ b/contrib/tools/cython/Cython/Compiler/FusedNode.py
@@ -0,0 +1,901 @@
+from __future__ import absolute_import
+
+import copy
+
+from . import (ExprNodes, PyrexTypes, MemoryView,
+               ParseTreeTransforms, StringEncoding, Errors)
+from .ExprNodes import CloneNode, ProxyNode, TupleNode
+from .Nodes import FuncDefNode, CFuncDefNode, StatListNode, DefNode
+from ..Utils import OrderedSet
+
+
+class FusedCFuncDefNode(StatListNode):
+    """
+    This node replaces a function with fused arguments. It deep-copies the
+    function for every permutation of fused types, and allocates a new local
+    scope for it. It keeps track of the original function in self.node, and
+    the entry of the original function in the symbol table is given the
+    'fused_cfunction' attribute which points back to us.
+    Then when a function lookup occurs (to e.g. call it), the call can be
+    dispatched to the right function.
+
+    node    FuncDefNode    the original function
+    nodes   [FuncDefNode]  list of copies of node with different specific types
+    py_func DefNode        the fused python function subscriptable from
+                           Python space
+    __signatures__         A DictNode mapping signature specialization strings
+                           to PyCFunction nodes
+    resulting_fused_function  PyCFunction for the fused DefNode that delegates
+                              to specializations
+    fused_func_assignment   Assignment of the fused function to the function name
+    defaults_tuple          TupleNode of defaults (letting PyCFunctionNode build
+                            defaults would result in many different tuples)
+    specialized_pycfuncs    List of synthesized pycfunction nodes for the
+                            specializations
+    code_object             CodeObjectNode shared by all specializations and the
+                            fused function
+
+    fused_compound_types    All fused (compound) types (e.g. floating[:])
+    """
+
+    __signatures__ = None
+    resulting_fused_function = None
+    fused_func_assignment = None
+    defaults_tuple = None
+    decorators = None
+
+    child_attrs = StatListNode.child_attrs + [
+        '__signatures__', 'resulting_fused_function', 'fused_func_assignment']
+
+    def __init__(self, node, env):
+        super(FusedCFuncDefNode, self).__init__(node.pos)
+
+        self.nodes = []
+        self.node = node
+
+        is_def = isinstance(self.node, DefNode)
+        if is_def:
+            # self.node.decorators = []
+            self.copy_def(env)
+        else:
+            self.copy_cdef(env)
+
+        # Perform some sanity checks. If anything fails, it's a bug
+        for n in self.nodes:
+            assert not n.entry.type.is_fused
+            assert not n.local_scope.return_type.is_fused
+            if node.return_type.is_fused:
+                assert not n.return_type.is_fused
+
+            if not is_def and n.cfunc_declarator.optional_arg_count:
+                assert n.type.op_arg_struct
+
+        node.entry.fused_cfunction = self
+        # Copy the nodes as AnalyseDeclarationsTransform will prepend
+        # self.py_func to self.stats, as we only want specialized
+        # CFuncDefNodes in self.nodes
+        self.stats = self.nodes[:]
+
+    def copy_def(self, env):
+        """
+        Create a copy of the original def or lambda function for specialized
+        versions.
+        """
+        fused_compound_types = PyrexTypes.unique(
+            [arg.type for arg in self.node.args if arg.type.is_fused])
+        fused_types = self._get_fused_base_types(fused_compound_types)
+        permutations = PyrexTypes.get_all_specialized_permutations(fused_types)
+
+        self.fused_compound_types = fused_compound_types
+
+        if self.node.entry in env.pyfunc_entries:
+            env.pyfunc_entries.remove(self.node.entry)
+
+        for cname, fused_to_specific in permutations:
+            copied_node = copy.deepcopy(self.node)
+            # keep signature object identity for special casing in DefNode.analyse_declarations()
+            copied_node.entry.signature = self.node.entry.signature
+
+            self._specialize_function_args(copied_node.args, fused_to_specific)
+            copied_node.return_type = self.node.return_type.specialize(
+                                                    fused_to_specific)
+
+            copied_node.analyse_declarations(env)
+            # copied_node.is_staticmethod = self.node.is_staticmethod
+            # copied_node.is_classmethod = self.node.is_classmethod
+            self.create_new_local_scope(copied_node, env, fused_to_specific)
+            self.specialize_copied_def(copied_node, cname, self.node.entry,
+                                       fused_to_specific, fused_compound_types)
+
+            PyrexTypes.specialize_entry(copied_node.entry, cname)
+            copied_node.entry.used = True
+            env.entries[copied_node.entry.name] = copied_node.entry
+
+            if not self.replace_fused_typechecks(copied_node):
+                break
+
+        self.orig_py_func = self.node
+        self.py_func = self.make_fused_cpdef(self.node, env, is_def=True)
+
+    def copy_cdef(self, env):
+        """
+        Create a copy of the original c(p)def function for all specialized
+        versions.
+        """
+        permutations = self.node.type.get_all_specialized_permutations()
+        # print 'Node %s has %d specializations:' % (self.node.entry.name,
+        #                                            len(permutations))
+        # import pprint; pprint.pprint([d for cname, d in permutations])
+
+        # Prevent copying of the python function
+        self.orig_py_func = orig_py_func = self.node.py_func
+        self.node.py_func = None
+        if orig_py_func:
+            env.pyfunc_entries.remove(orig_py_func.entry)
+
+        fused_types = self.node.type.get_fused_types()
+        self.fused_compound_types = fused_types
+
+        new_cfunc_entries = []
+        for cname, fused_to_specific in permutations:
+            copied_node = copy.deepcopy(self.node)
+
+            # Make the types in our CFuncType specific.
+            type = copied_node.type.specialize(fused_to_specific)
+            entry = copied_node.entry
+            type.specialize_entry(entry, cname)
+
+            # Reuse existing Entries (e.g. from .pxd files).
+            for i, orig_entry in enumerate(env.cfunc_entries):
+                if entry.cname == orig_entry.cname and type.same_as_resolved_type(orig_entry.type):
+                    copied_node.entry = env.cfunc_entries[i]
+                    if not copied_node.entry.func_cname:
+                        copied_node.entry.func_cname = entry.func_cname
+                    entry = copied_node.entry
+                    type = entry.type
+                    break
+            else:
+                new_cfunc_entries.append(entry)
+
+            copied_node.type = type
+            entry.type, type.entry = type, entry
+
+            entry.used = (entry.used or
+                          self.node.entry.defined_in_pxd or
+                          env.is_c_class_scope or
+                          entry.is_cmethod)
+
+            if self.node.cfunc_declarator.optional_arg_count:
+                self.node.cfunc_declarator.declare_optional_arg_struct(
+                                           type, env, fused_cname=cname)
+
+            copied_node.return_type = type.return_type
+            self.create_new_local_scope(copied_node, env, fused_to_specific)
+
+            # Make the argument types in the CFuncDeclarator specific
+            self._specialize_function_args(copied_node.cfunc_declarator.args,
+                                           fused_to_specific)
+
+            # If a cpdef, declare all specialized cpdefs (this
+            # also calls analyse_declarations)
+            copied_node.declare_cpdef_wrapper(env)
+            if copied_node.py_func:
+                env.pyfunc_entries.remove(copied_node.py_func.entry)
+
+                self.specialize_copied_def(
+                        copied_node.py_func, cname, self.node.entry.as_variable,
+                        fused_to_specific, fused_types)
+
+            if not self.replace_fused_typechecks(copied_node):
+                break
+
+        # replace old entry with new entries
+        try:
+            cindex = env.cfunc_entries.index(self.node.entry)
+        except ValueError:
+            env.cfunc_entries.extend(new_cfunc_entries)
+        else:
+            env.cfunc_entries[cindex:cindex+1] = new_cfunc_entries
+
+        if orig_py_func:
+            self.py_func = self.make_fused_cpdef(orig_py_func, env,
+                                                 is_def=False)
+        else:
+            self.py_func = orig_py_func
+
+    def _get_fused_base_types(self, fused_compound_types):
+        """
+        Get a list of unique basic fused types, from a list of
+        (possibly) compound fused types.
+        """
+        base_types = []
+        seen = set()
+        for fused_type in fused_compound_types:
+            fused_type.get_fused_types(result=base_types, seen=seen)
+        return base_types
+
+    def _specialize_function_args(self, args, fused_to_specific):
+        for arg in args:
+            if arg.type.is_fused:
+                arg.type = arg.type.specialize(fused_to_specific)
+                if arg.type.is_memoryviewslice:
+                    arg.type.validate_memslice_dtype(arg.pos)
+
+    def create_new_local_scope(self, node, env, f2s):
+        """
+        Create a new local scope for the copied node and append it to
+        self.nodes. A new local scope is needed because the arguments with the
+        fused types are already in the local scope, and we need the specialized
+        entries created after analyse_declarations on each specialized version
+        of the (CFunc)DefNode.
+        f2s is a dict mapping each fused type to its specialized version
+        """
+        node.create_local_scope(env)
+        node.local_scope.fused_to_specific = f2s
+
+        # This is copied from the original function, set it to false to
+        # stop recursion
+        node.has_fused_arguments = False
+        self.nodes.append(node)
+
+    def specialize_copied_def(self, node, cname, py_entry, f2s, fused_compound_types):
+        """Specialize the copy of a DefNode given the copied node,
+        the specialization cname and the original DefNode entry"""
+        fused_types = self._get_fused_base_types(fused_compound_types)
+        type_strings = [
+            PyrexTypes.specialization_signature_string(fused_type, f2s)
+                for fused_type in fused_types
+        ]
+
+        node.specialized_signature_string = '|'.join(type_strings)
+
+        node.entry.pymethdef_cname = PyrexTypes.get_fused_cname(
+                                        cname, node.entry.pymethdef_cname)
+        node.entry.doc = py_entry.doc
+        node.entry.doc_cname = py_entry.doc_cname
+
+    def replace_fused_typechecks(self, copied_node):
+        """
+        Branch-prune fused type checks like
+
+            if fused_t is int:
+                ...
+
+        Returns whether an error was issued and whether we should stop in
+        in order to prevent a flood of errors.
+        """
+        num_errors = Errors.num_errors
+        transform = ParseTreeTransforms.ReplaceFusedTypeChecks(
+                                       copied_node.local_scope)
+        transform(copied_node)
+
+        if Errors.num_errors > num_errors:
+            return False
+
+        return True
+
+    def _fused_instance_checks(self, normal_types, pyx_code, env):
+        """
+        Generate Cython code for instance checks, matching an object to
+        specialized types.
+        """
+        for specialized_type in normal_types:
+            # all_numeric = all_numeric and specialized_type.is_numeric
+            pyx_code.context.update(
+                py_type_name=specialized_type.py_type_name(),
+                specialized_type_name=specialized_type.specialization_string,
+            )
+            pyx_code.put_chunk(
+                u"""
+                    if isinstance(arg, {{py_type_name}}):
+                        dest_sig[{{dest_sig_idx}}] = '{{specialized_type_name}}'; break
+                """)
+
+    def _dtype_name(self, dtype):
+        if dtype.is_typedef:
+            return '___pyx_%s' % dtype
+        return str(dtype).replace(' ', '_')
+
+    def _dtype_type(self, dtype):
+        if dtype.is_typedef:
+            return self._dtype_name(dtype)
+        return str(dtype)
+
+    def _sizeof_dtype(self, dtype):
+        if dtype.is_pyobject:
+            return 'sizeof(void *)'
+        else:
+            return "sizeof(%s)" % self._dtype_type(dtype)
+
+    def _buffer_check_numpy_dtype_setup_cases(self, pyx_code):
+        "Setup some common cases to match dtypes against specializations"
+        if pyx_code.indenter("if kind in b'iu':"):
+            pyx_code.putln("pass")
+            pyx_code.named_insertion_point("dtype_int")
+            pyx_code.dedent()
+
+        if pyx_code.indenter("elif kind == b'f':"):
+            pyx_code.putln("pass")
+            pyx_code.named_insertion_point("dtype_float")
+            pyx_code.dedent()
+
+        if pyx_code.indenter("elif kind == b'c':"):
+            pyx_code.putln("pass")
+            pyx_code.named_insertion_point("dtype_complex")
+            pyx_code.dedent()
+
+        if pyx_code.indenter("elif kind == b'O':"):
+            pyx_code.putln("pass")
+            pyx_code.named_insertion_point("dtype_object")
+            pyx_code.dedent()
+
+    match = "dest_sig[{{dest_sig_idx}}] = '{{specialized_type_name}}'"
+    no_match = "dest_sig[{{dest_sig_idx}}] = None"
+    def _buffer_check_numpy_dtype(self, pyx_code, specialized_buffer_types, pythran_types):
+        """
+        Match a numpy dtype object to the individual specializations.
+        """
+        self._buffer_check_numpy_dtype_setup_cases(pyx_code)
+
+        for specialized_type in pythran_types+specialized_buffer_types:
+            final_type = specialized_type
+            if specialized_type.is_pythran_expr:
+                specialized_type = specialized_type.org_buffer
+            dtype = specialized_type.dtype
+            pyx_code.context.update(
+                itemsize_match=self._sizeof_dtype(dtype) + " == itemsize",
+                signed_match="not (%s_is_signed ^ dtype_signed)" % self._dtype_name(dtype),
+                dtype=dtype,
+                specialized_type_name=final_type.specialization_string)
+
+            dtypes = [
+                (dtype.is_int, pyx_code.dtype_int),
+                (dtype.is_float, pyx_code.dtype_float),
+                (dtype.is_complex, pyx_code.dtype_complex)
+            ]
+
+            for dtype_category, codewriter in dtypes:
+                if dtype_category:
+                    cond = '{{itemsize_match}} and (<Py_ssize_t>arg.ndim) == %d' % (
+                                                    specialized_type.ndim,)
+                    if dtype.is_int:
+                        cond += ' and {{signed_match}}'
+
+                    if final_type.is_pythran_expr:
+                        cond += ' and arg_is_pythran_compatible'
+
+                    if codewriter.indenter("if %s:" % cond):
+                        #codewriter.putln("print 'buffer match found based on numpy dtype'")
+                        codewriter.putln(self.match)
+                        codewriter.putln("break")
+                        codewriter.dedent()
+
+    def _buffer_parse_format_string_check(self, pyx_code, decl_code,
+                                          specialized_type, env):
+        """
+        For each specialized type, try to coerce the object to a memoryview
+        slice of that type. This means obtaining a buffer and parsing the
+        format string.
+        TODO: separate buffer acquisition from format parsing
+        """
+        dtype = specialized_type.dtype
+        if specialized_type.is_buffer:
+            axes = [('direct', 'strided')] * specialized_type.ndim
+        else:
+            axes = specialized_type.axes
+
+        memslice_type = PyrexTypes.MemoryViewSliceType(dtype, axes)
+        memslice_type.create_from_py_utility_code(env)
+        pyx_code.context.update(
+            coerce_from_py_func=memslice_type.from_py_function,
+            dtype=dtype)
+        decl_code.putln(
+            "{{memviewslice_cname}} {{coerce_from_py_func}}(object, int)")
+
+        pyx_code.context.update(
+            specialized_type_name=specialized_type.specialization_string,
+            sizeof_dtype=self._sizeof_dtype(dtype))
+
+        pyx_code.put_chunk(
+            u"""
+                # try {{dtype}}
+                if itemsize == -1 or itemsize == {{sizeof_dtype}}:
+                    memslice = {{coerce_from_py_func}}(arg, 0)
+                    if memslice.memview:
+                        __PYX_XDEC_MEMVIEW(&memslice, 1)
+                        # print 'found a match for the buffer through format parsing'
+                        %s
+                        break
+                    else:
+                        __pyx_PyErr_Clear()
+            """ % self.match)
+
+    def _buffer_checks(self, buffer_types, pythran_types, pyx_code, decl_code, env):
+        """
+        Generate Cython code to match objects to buffer specializations.
+        First try to get a numpy dtype object and match it against the individual
+        specializations. If that fails, try naively to coerce the object
+        to each specialization, which obtains the buffer each time and tries
+        to match the format string.
+        """
+        # The first thing to find a match in this loop breaks out of the loop
+        pyx_code.put_chunk(
+            u"""
+                """ + (u"arg_is_pythran_compatible = False" if pythran_types else u"") + u"""
+                if ndarray is not None:
+                    if isinstance(arg, ndarray):
+                        dtype = arg.dtype
+                        """ + (u"arg_is_pythran_compatible = True" if pythran_types else u"") + u"""
+                    elif __pyx_memoryview_check(arg):
+                        arg_base = arg.base
+                        if isinstance(arg_base, ndarray):
+                            dtype = arg_base.dtype
+                        else:
+                            dtype = None
+                    else:
+                        dtype = None
+
+                    itemsize = -1
+                    if dtype is not None:
+                        itemsize = dtype.itemsize
+                        kind = ord(dtype.kind)
+                        dtype_signed = kind == 'i'
+            """)
+        pyx_code.indent(2)
+        if pythran_types:
+            pyx_code.put_chunk(
+                u"""
+                        # Pythran only supports the endianness of the current compiler
+                        byteorder = dtype.byteorder
+                        if byteorder == "<" and not __Pyx_Is_Little_Endian():
+                            arg_is_pythran_compatible = False
+                        elif byteorder == ">" and __Pyx_Is_Little_Endian():
+                            arg_is_pythran_compatible = False
+                        if arg_is_pythran_compatible:
+                            cur_stride = itemsize
+                            shape = arg.shape
+                            strides = arg.strides
+                            for i in range(arg.ndim-1, -1, -1):
+                                if (<Py_ssize_t>strides[i]) != cur_stride:
+                                    arg_is_pythran_compatible = False
+                                    break
+                                cur_stride *= <Py_ssize_t> shape[i]
+                            else:
+                                arg_is_pythran_compatible = not (arg.flags.f_contiguous and (<Py_ssize_t>arg.ndim) > 1)
+                """)
+        pyx_code.named_insertion_point("numpy_dtype_checks")
+        self._buffer_check_numpy_dtype(pyx_code, buffer_types, pythran_types)
+        pyx_code.dedent(2)
+
+        for specialized_type in buffer_types:
+            self._buffer_parse_format_string_check(
+                    pyx_code, decl_code, specialized_type, env)
+
+    def _buffer_declarations(self, pyx_code, decl_code, all_buffer_types, pythran_types):
+        """
+        If we have any buffer specializations, write out some variable
+        declarations and imports.
+        """
+        decl_code.put_chunk(
+            u"""
+                ctypedef struct {{memviewslice_cname}}:
+                    void *memview
+
+                void __PYX_XDEC_MEMVIEW({{memviewslice_cname}} *, int have_gil)
+                bint __pyx_memoryview_check(object)
+            """)
+
+        pyx_code.local_variable_declarations.put_chunk(
+            u"""
+                cdef {{memviewslice_cname}} memslice
+                cdef Py_ssize_t itemsize
+                cdef bint dtype_signed
+                cdef char kind
+
+                itemsize = -1
+            """)
+
+        if pythran_types:
+            pyx_code.local_variable_declarations.put_chunk(u"""
+                cdef bint arg_is_pythran_compatible
+                cdef Py_ssize_t cur_stride
+            """)
+
+        pyx_code.imports.put_chunk(
+            u"""
+                cdef type ndarray
+                ndarray = __Pyx_ImportNumPyArrayTypeIfAvailable()
+            """)
+
+        seen_typedefs = set()
+        seen_int_dtypes = set()
+        for buffer_type in all_buffer_types:
+            dtype = buffer_type.dtype
+            dtype_name = self._dtype_name(dtype)
+            if dtype.is_typedef:
+                if dtype_name not in seen_typedefs:
+                    seen_typedefs.add(dtype_name)
+                    decl_code.putln(
+                        'ctypedef %s %s "%s"' % (dtype.resolve(), dtype_name,
+                                                 dtype.empty_declaration_code()))
+
+            if buffer_type.dtype.is_int:
+                if str(dtype) not in seen_int_dtypes:
+                    seen_int_dtypes.add(str(dtype))
+                    pyx_code.context.update(dtype_name=dtype_name,
+                                            dtype_type=self._dtype_type(dtype))
+                    pyx_code.local_variable_declarations.put_chunk(
+                        u"""
+                            cdef bint {{dtype_name}}_is_signed
+                            {{dtype_name}}_is_signed = not (<{{dtype_type}}> -1 > 0)
+                        """)
+
+    def _split_fused_types(self, arg):
+        """
+        Specialize fused types and split into normal types and buffer types.
+        """
+        specialized_types = PyrexTypes.get_specialized_types(arg.type)
+
+        # Prefer long over int, etc by sorting (see type classes in PyrexTypes.py)
+        specialized_types.sort()
+
+        seen_py_type_names = set()
+        normal_types, buffer_types, pythran_types = [], [], []
+        has_object_fallback = False
+        for specialized_type in specialized_types:
+            py_type_name = specialized_type.py_type_name()
+            if py_type_name:
+                if py_type_name in seen_py_type_names:
+                    continue
+                seen_py_type_names.add(py_type_name)
+                if py_type_name == 'object':
+                    has_object_fallback = True
+                else:
+                    normal_types.append(specialized_type)
+            elif specialized_type.is_pythran_expr:
+                pythran_types.append(specialized_type)
+            elif specialized_type.is_buffer or specialized_type.is_memoryviewslice:
+                buffer_types.append(specialized_type)
+
+        return normal_types, buffer_types, pythran_types, has_object_fallback
+
+    def _unpack_argument(self, pyx_code):
+        pyx_code.put_chunk(
+            u"""
+                # PROCESSING ARGUMENT {{arg_tuple_idx}}
+                if {{arg_tuple_idx}} < len(<tuple>args):
+                    arg = (<tuple>args)[{{arg_tuple_idx}}]
+                elif kwargs is not None and '{{arg.name}}' in <dict>kwargs:
+                    arg = (<dict>kwargs)['{{arg.name}}']
+                else:
+                {{if arg.default}}
+                    arg = (<tuple>defaults)[{{default_idx}}]
+                {{else}}
+                    {{if arg_tuple_idx < min_positional_args}}
+                        raise TypeError("Expected at least %d argument%s, got %d" % (
+                            {{min_positional_args}}, {{'"s"' if min_positional_args != 1 else '""'}}, len(<tuple>args)))
+                    {{else}}
+                        raise TypeError("Missing keyword-only argument: '%s'" % "{{arg.default}}")
+                    {{endif}}
+                {{endif}}
+            """)
+
+    def make_fused_cpdef(self, orig_py_func, env, is_def):
+        """
+        This creates the function that is indexable from Python and does
+        runtime dispatch based on the argument types. The function gets the
+        arg tuple and kwargs dict (or None) and the defaults tuple
+        as arguments from the Binding Fused Function's tp_call.
+        """
+        from . import TreeFragment, Code, UtilityCode
+
+        fused_types = self._get_fused_base_types([
+            arg.type for arg in self.node.args if arg.type.is_fused])
+
+        context = {
+            'memviewslice_cname': MemoryView.memviewslice_cname,
+            'func_args': self.node.args,
+            'n_fused': len(fused_types),
+            'min_positional_args':
+                self.node.num_required_args - self.node.num_required_kw_args
+                if is_def else
+                sum(1 for arg in self.node.args if arg.default is None),
+            'name': orig_py_func.entry.name,
+        }
+
+        pyx_code = Code.PyxCodeWriter(context=context)
+        decl_code = Code.PyxCodeWriter(context=context)
+        decl_code.put_chunk(
+            u"""
+                cdef extern from *:
+                    void __pyx_PyErr_Clear "PyErr_Clear" ()
+                    type __Pyx_ImportNumPyArrayTypeIfAvailable()
+                    int __Pyx_Is_Little_Endian()
+            """)
+        decl_code.indent()
+
+        pyx_code.put_chunk(
+            u"""
+                def __pyx_fused_cpdef(signatures, args, kwargs, defaults):
+                    # FIXME: use a typed signature - currently fails badly because
+                    #        default arguments inherit the types we specify here!
+
+                    dest_sig = [None] * {{n_fused}}
+
+                    if kwargs is not None and not kwargs:
+                        kwargs = None
+
+                    cdef Py_ssize_t i
+
+                    # instance check body
+            """)
+
+        pyx_code.indent() # indent following code to function body
+        pyx_code.named_insertion_point("imports")
+        pyx_code.named_insertion_point("func_defs")
+        pyx_code.named_insertion_point("local_variable_declarations")
+
+        fused_index = 0
+        default_idx = 0
+        all_buffer_types = OrderedSet()
+        seen_fused_types = set()
+        for i, arg in enumerate(self.node.args):
+            if arg.type.is_fused:
+                arg_fused_types = arg.type.get_fused_types()
+                if len(arg_fused_types) > 1:
+                    raise NotImplementedError("Determination of more than one fused base "
+                                              "type per argument is not implemented.")
+                fused_type = arg_fused_types[0]
+
+            if arg.type.is_fused and fused_type not in seen_fused_types:
+                seen_fused_types.add(fused_type)
+
+                context.update(
+                    arg_tuple_idx=i,
+                    arg=arg,
+                    dest_sig_idx=fused_index,
+                    default_idx=default_idx,
+                )
+
+                normal_types, buffer_types, pythran_types, has_object_fallback = self._split_fused_types(arg)
+                self._unpack_argument(pyx_code)
+
+                # 'unrolled' loop, first match breaks out of it
+                if pyx_code.indenter("while 1:"):
+                    if normal_types:
+                        self._fused_instance_checks(normal_types, pyx_code, env)
+                    if buffer_types or pythran_types:
+                        env.use_utility_code(Code.UtilityCode.load_cached("IsLittleEndian", "ModuleSetupCode.c"))
+                        self._buffer_checks(buffer_types, pythran_types, pyx_code, decl_code, env)
+                    if has_object_fallback:
+                        pyx_code.context.update(specialized_type_name='object')
+                        pyx_code.putln(self.match)
+                    else:
+                        pyx_code.putln(self.no_match)
+                    pyx_code.putln("break")
+                    pyx_code.dedent()
+
+                fused_index += 1
+                all_buffer_types.update(buffer_types)
+                all_buffer_types.update(ty.org_buffer for ty in pythran_types)
+
+            if arg.default:
+                default_idx += 1
+
+        if all_buffer_types:
+            self._buffer_declarations(pyx_code, decl_code, all_buffer_types, pythran_types)
+            env.use_utility_code(Code.UtilityCode.load_cached("Import", "ImportExport.c"))
+            env.use_utility_code(Code.UtilityCode.load_cached("ImportNumPyArray", "ImportExport.c"))
+
+        pyx_code.put_chunk(
+            u"""
+                candidates = []
+                for sig in <dict>signatures:
+                    match_found = False
+                    src_sig = sig.strip('()').split('|')
+                    for i in range(len(dest_sig)):
+                        dst_type = dest_sig[i]
+                        if dst_type is not None:
+                            if src_sig[i] == dst_type:
+                                match_found = True
+                            else:
+                                match_found = False
+                                break
+
+                    if match_found:
+                        candidates.append(sig)
+
+                if not candidates:
+                    raise TypeError("No matching signature found")
+                elif len(candidates) > 1:
+                    raise TypeError("Function call with ambiguous argument types")
+                else:
+                    return (<dict>signatures)[candidates[0]]
+            """)
+
+        fragment_code = pyx_code.getvalue()
+        # print decl_code.getvalue()
+        # print fragment_code
+        from .Optimize import ConstantFolding
+        fragment = TreeFragment.TreeFragment(
+            fragment_code, level='module', pipeline=[ConstantFolding()])
+        ast = TreeFragment.SetPosTransform(self.node.pos)(fragment.root)
+        UtilityCode.declare_declarations_in_scope(
+            decl_code.getvalue(), env.global_scope())
+        ast.scope = env
+        # FIXME: for static methods of cdef classes, we build the wrong signature here: first arg becomes 'self'
+        ast.analyse_declarations(env)
+        py_func = ast.stats[-1]  # the DefNode
+        self.fragment_scope = ast.scope
+
+        if isinstance(self.node, DefNode):
+            py_func.specialized_cpdefs = self.nodes[:]
+        else:
+            py_func.specialized_cpdefs = [n.py_func for n in self.nodes]
+
+        return py_func
+
+    def update_fused_defnode_entry(self, env):
+        copy_attributes = (
+            'name', 'pos', 'cname', 'func_cname', 'pyfunc_cname',
+            'pymethdef_cname', 'doc', 'doc_cname', 'is_member',
+            'scope'
+        )
+
+        entry = self.py_func.entry
+
+        for attr in copy_attributes:
+            setattr(entry, attr,
+                    getattr(self.orig_py_func.entry, attr))
+
+        self.py_func.name = self.orig_py_func.name
+        self.py_func.doc = self.orig_py_func.doc
+
+        env.entries.pop('__pyx_fused_cpdef', None)
+        if isinstance(self.node, DefNode):
+            env.entries[entry.name] = entry
+        else:
+            env.entries[entry.name].as_variable = entry
+
+        env.pyfunc_entries.append(entry)
+
+        self.py_func.entry.fused_cfunction = self
+        for node in self.nodes:
+            if isinstance(self.node, DefNode):
+                node.fused_py_func = self.py_func
+            else:
+                node.py_func.fused_py_func = self.py_func
+                node.entry.as_variable = entry
+
+        self.synthesize_defnodes()
+        self.stats.append(self.__signatures__)
+
+    def analyse_expressions(self, env):
+        """
+        Analyse the expressions. Take care to only evaluate default arguments
+        once and clone the result for all specializations
+        """
+        for fused_compound_type in self.fused_compound_types:
+            for fused_type in fused_compound_type.get_fused_types():
+                for specialization_type in fused_type.types:
+                    if specialization_type.is_complex:
+                        specialization_type.create_declaration_utility_code(env)
+
+        if self.py_func:
+            self.__signatures__ = self.__signatures__.analyse_expressions(env)
+            self.py_func = self.py_func.analyse_expressions(env)
+            self.resulting_fused_function = self.resulting_fused_function.analyse_expressions(env)
+            self.fused_func_assignment = self.fused_func_assignment.analyse_expressions(env)
+
+        self.defaults = defaults = []
+
+        for arg in self.node.args:
+            if arg.default:
+                arg.default = arg.default.analyse_expressions(env)
+                defaults.append(ProxyNode(arg.default))
+            else:
+                defaults.append(None)
+
+        for i, stat in enumerate(self.stats):
+            stat = self.stats[i] = stat.analyse_expressions(env)
+            if isinstance(stat, FuncDefNode):
+                for arg, default in zip(stat.args, defaults):
+                    if default is not None:
+                        arg.default = CloneNode(default).coerce_to(arg.type, env)
+
+        if self.py_func:
+            args = [CloneNode(default) for default in defaults if default]
+            self.defaults_tuple = TupleNode(self.pos, args=args)
+            self.defaults_tuple = self.defaults_tuple.analyse_types(env, skip_children=True).coerce_to_pyobject(env)
+            self.defaults_tuple = ProxyNode(self.defaults_tuple)
+            self.code_object = ProxyNode(self.specialized_pycfuncs[0].code_object)
+
+            fused_func = self.resulting_fused_function.arg
+            fused_func.defaults_tuple = CloneNode(self.defaults_tuple)
+            fused_func.code_object = CloneNode(self.code_object)
+
+            for i, pycfunc in enumerate(self.specialized_pycfuncs):
+                pycfunc.code_object = CloneNode(self.code_object)
+                pycfunc = self.specialized_pycfuncs[i] = pycfunc.analyse_types(env)
+                pycfunc.defaults_tuple = CloneNode(self.defaults_tuple)
+        return self
+
+    def synthesize_defnodes(self):
+        """
+        Create the __signatures__ dict of PyCFunctionNode specializations.
+        """
+        if isinstance(self.nodes[0], CFuncDefNode):
+            nodes = [node.py_func for node in self.nodes]
+        else:
+            nodes = self.nodes
+
+        signatures = [StringEncoding.EncodedString(node.specialized_signature_string)
+                      for node in nodes]
+        keys = [ExprNodes.StringNode(node.pos, value=sig)
+                for node, sig in zip(nodes, signatures)]
+        values = [ExprNodes.PyCFunctionNode.from_defnode(node, binding=True)
+                  for node in nodes]
+
+        self.__signatures__ = ExprNodes.DictNode.from_pairs(self.pos, zip(keys, values))
+
+        self.specialized_pycfuncs = values
+        for pycfuncnode in values:
+            pycfuncnode.is_specialization = True
+
+    def generate_function_definitions(self, env, code):
+        if self.py_func:
+            self.py_func.pymethdef_required = True
+            self.fused_func_assignment.generate_function_definitions(env, code)
+
+        for stat in self.stats:
+            if isinstance(stat, FuncDefNode) and stat.entry.used:
+                code.mark_pos(stat.pos)
+                stat.generate_function_definitions(env, code)
+
+    def generate_execution_code(self, code):
+        # Note: all def function specialization are wrapped in PyCFunction
+        # nodes in the self.__signatures__ dictnode.
+        for default in self.defaults:
+            if default is not None:
+                default.generate_evaluation_code(code)
+
+        if self.py_func:
+            self.defaults_tuple.generate_evaluation_code(code)
+            self.code_object.generate_evaluation_code(code)
+
+        for stat in self.stats:
+            code.mark_pos(stat.pos)
+            if isinstance(stat, ExprNodes.ExprNode):
+                stat.generate_evaluation_code(code)
+            else:
+                stat.generate_execution_code(code)
+
+        if self.__signatures__:
+            self.resulting_fused_function.generate_evaluation_code(code)
+
+            code.putln(
+                "((__pyx_FusedFunctionObject *) %s)->__signatures__ = %s;" %
+                                    (self.resulting_fused_function.result(),
+                                     self.__signatures__.result()))
+            code.put_giveref(self.__signatures__.result())
+            self.__signatures__.generate_post_assignment_code(code)
+            self.__signatures__.free_temps(code)
+
+            self.fused_func_assignment.generate_execution_code(code)
+
+            # Dispose of results
+            self.resulting_fused_function.generate_disposal_code(code)
+            self.resulting_fused_function.free_temps(code)
+            self.defaults_tuple.generate_disposal_code(code)
+            self.defaults_tuple.free_temps(code)
+            self.code_object.generate_disposal_code(code)
+            self.code_object.free_temps(code)
+
+        for default in self.defaults:
+            if default is not None:
+                default.generate_disposal_code(code)
+                default.free_temps(code)
+
+    def annotate(self, code):
+        for stat in self.stats:
+            stat.annotate(code)