"""
This file contains auxiliary Ops, used during the compilation phase and Ops
building class (:class:`FromFunctionOp`) and decorator (:func:`as_op`) that
help make new Ops more rapidly.

"""

import copy
import pickle
import warnings
from typing import Dict, Tuple

from aesara.graph.basic import Apply
from aesara.graph.op import Op
from aesara.link.c.op import COp
from aesara.link.c.type import CType


def register_view_op_c_code(type, code, version=()):
    """
    Tell ViewOp how to generate C code for an Aesara Type.

    Parameters
    ----------
    type : Aesara type
        It must be the Aesara class itself and not an instance of the class.
    code : C code
        Returns a view for the Aesara type 'type'. Use %(iname)s and %(oname)s
        for the input and output C variable names respectively.
    version
        A number indicating the version of the code, for cache.

    """
    ViewOp.c_code_and_version[type] = (code, version)


class ViewOp(COp):
    """
    Returns an inplace view of the input. Used internally by Aesara.

    """

    view_map = {0: [0]}
    # Mapping from Type to C code (and version) to use.
    # In the C code, the name of the input variable is %(iname)s,
    # the output variable is %(oname)s.
    c_code_and_version: Dict = {}
    __props__: Tuple = ()
    _f16_ok: bool = True

    def make_node(self, x):
        return Apply(self, [x], [x.type()])

    def perform(self, node, inp, out):
        (x,) = inp
        (z,) = out
        z[0] = x

    def __str__(self):
        return f"{self.__class__.__name__}"

    def c_code(self, node, nodename, inp, out, sub):
        (iname,) = inp
        (oname,) = out
        fail = sub["fail"]

        itype = node.inputs[0].type.__class__
        if itype in self.c_code_and_version:
            code, version = self.c_code_and_version[itype]
            return code % locals()

        # Else, no C code
        raise NotImplementedError()

    def c_code_cache_version(self):
        version = []
        # If any of the c code is unversioned, we have to return ()
        # Else, we will return a list of (type name, version) pairs.
        for t, (c, v) in sorted(
            self.c_code_and_version.items(), key=lambda pair: str(pair[0])
        ):
            if not v:
                warnings.warn(
                    f"Type {t} has C code for ViewOp, but it has no "
                    "version. You should add a 'version' keyword "
                    "arg when calling register_view_op_c_code.",
                    stacklevel=2,
                )
                return ()
            version.append((str(t), v))

        return tuple(version)

    def infer_shape(self, fgraph, node, input_shapes):
        return input_shapes

    def grad(self, args, g_outs):
        return g_outs


view_op = ViewOp()


class OutputGuard(ViewOp):
    """
    This op is used only internally by Aesara.

    Only the AddDestroyHandler optimizer tries to insert them in the graph.

    This Op is declared as destructive while it is not destroying anything.
    It returns a view. This is used to prevent destruction of the output
    variables of an Aesara function.

    There is a mechanism in Aesara that should prevent this, but the use
    of OutputGuard adds a safeguard: it may be possible for some optimization
    run before the add_destroy_handler phase to bypass this mechanism, by
    making in-place optimizations.

    TODO: find a current full explanation.

    """

    destroy_map = {0: [0]}

    check_input = False


_output_guard = OutputGuard()


def register_deep_copy_op_c_code(typ, code, version=()):
    """
    Tell DeepCopyOp how to generate C code for an Aesara Type.

    Parameters
    ----------
    typ : Aesara type
        It must be the Aesara class itself and not an instance of the class.
    code: C code
        Deep copies the Aesara type 'typ'. Use %(iname)s and %(oname)s for the
        input and output C variable names respectively.
    version
        A number indicating the version of the code, for cache.

    """
    DeepCopyOp.c_code_and_version[typ] = (code, version)


class DeepCopyOp(COp):
    # Mapping from Type to C code (and version) to use.
    # In the C code, the name of the input variable is %(iname)s,
    # the output variable is %(oname)s.
    c_code_and_version: Dict = {}

    check_input: bool = False
    __props__: Tuple = ()
    _f16_ok: bool = True

    def __init__(self):
        pass

    def make_node(self, x):
        return Apply(self, [x], [x.type()])

    def perform(self, node, args, outs):
        if hasattr(args[0], "copy"):
            # when args[0] is a an ndarray of 0 dimensions,
            # this return a numpy.dtype and not an ndarray
            # So when the args have a copy attribute we use it
            # as this don't have this problem
            outs[0][0] = args[0].copy()
        else:
            outs[0][0] = copy.deepcopy(args[0])

    def c_code_cache_version(self):
        version = []
        # If any of the c code is unversioned, we have to return ()
        # Else, we will return a list of (type name, version) pairs.
        for t, (c, v) in sorted(
            self.c_code_and_version.items(), key=lambda pair: str(pair[0])
        ):
            if not v:
                warnings.warn(
                    f"Type {t} has C code for DeepCopyOp, but it has "
                    "no version. You should add a 'version' keyword"
                    " arg when calling "
                    "register_deep_copy_op_c_code.",
                    stacklevel=2,
                )
                return ()
            version.append((str(t), v))

        if version:
            version.append(1)
        return tuple(version)

    def c_code(self, node, name, inames, onames, sub):
        (iname,) = inames
        (oname,) = onames
        fail = sub["fail"]

        itype = node.inputs[0].type.__class__
        if itype in self.c_code_and_version:
            code, version = self.c_code_and_version[itype]
            return code % locals()

        # Else, no C code
        raise NotImplementedError()

    def infer_shape(self, fgraph, node, input_shapes):
        return input_shapes


deep_copy_op = DeepCopyOp()


def load_back(mod, name):
    __import__(mod)
    import sys

    module = sys.modules[mod]
    obj = getattr(module, name)
    return obj


class FromFunctionOp(Op):
    """
    Build a basic Aesara Op around a function.

    Since the resulting Op is very basic and is missing most of the
    optional functionalities, some optimizations may not apply.  If you
    want to help, you can supply an infer_shape function that computes
    the shapes of the output given the shapes of the inputs.

    Also the gradient is undefined in the resulting op and Aesara will
    raise an error if you attempt to get the gradient of a graph
    containing this op.

    """

    def __init__(self, fn, itypes, otypes, infer_shape):
        self.__fn = fn
        self.itypes = itypes
        self.otypes = otypes
        self.__infer_shape = infer_shape
        if self.__infer_shape is not None:
            self.infer_shape = self._infer_shape

    def __eq__(self, other):
        return type(self) == type(other) and self.__fn == other.__fn

    def __hash__(self):
        return hash(type(self)) ^ hash(self.__fn)

    def __str__(self):
        return "FromFunctionOp{%s}" % self.__fn.__name__

    def perform(self, node, inputs, outputs):
        outs = self.__fn(*inputs)
        if not isinstance(outs, (list, tuple)):
            outs = (outs,)
        assert len(outs) == len(outputs)
        for i in range(len(outs)):
            outputs[i][0] = outs[i]

    def __reduce__(self):
        mod = self.__fn.__module__
        name = self.__fn.__name__
        try:
            obj = load_back(mod, name)
        except (ImportError, KeyError, AttributeError):
            raise pickle.PicklingError(
                f"Can't pickle as_op(), not found as {mod}.{name}"
            )
        else:
            if obj is not self:
                raise pickle.PicklingError(
                    f"Can't pickle as_op(), not the object at {mod}.{name}"
                )
        return load_back, (mod, name)

    def _infer_shape(self, fgraph, node, input_shapes):
        return self.__infer_shape(fgraph, node, input_shapes)


def as_op(itypes, otypes, infer_shape=None):
    """
    Decorator that converts a function into a basic Aesara op that will call
    the supplied function as its implementation.

    It takes an optional infer_shape parameter that should be a callable with
    this signature:

        def infer_shape(fgraph, node, input_shapes):
            ...
            return output_shapes

    Here `input_shapes` and `output_shapes` are lists of tuples that represent
    the shape of the corresponding inputs/outputs.

    This should not be used when performance is a concern since the very basic
    nature of the resulting Op may interfere with certain graph optimizations.

    Examples
    --------
    @as_op(itypes=[aesara.tensor.fmatrix, aesara.tensor.fmatrix],
           otypes=[aesara.tensor.fmatrix])
    def numpy_dot(a, b):
        return numpy.dot(a, b)

    """
    if not isinstance(itypes, (list, tuple)):
        itypes = [itypes]
    if not all(isinstance(t, CType) for t in itypes):
        raise TypeError("itypes has to be a list of Aesara types")
    if not isinstance(otypes, (list, tuple)):
        otypes = [otypes]
    if not all(isinstance(t, CType) for t in otypes):
        raise TypeError("otypes has to be a list of Aesara types")

    # make sure they are lists and not tuples
    itypes = list(itypes)
    otypes = list(otypes)

    if infer_shape is not None and not callable(infer_shape):
        raise TypeError("infer_shape needs to be a callable")

    def make_op(fn):
        return FromFunctionOp(fn, itypes, otypes, infer_shape)

    return make_op


class UpdatePlaceholder(Op):
    """A placeholder for a `SharedVariable` update that hasn't been set.

    These will appear in `FunctionGraph.outputs` and represent potential updates
    (i.e. the `updates` argument to `aesara.function` and/or updates specified via
    `SharedVariable.default_update`s) that could be specified by rewrites.

    When present, these should be removed by non-rewrite steps in the
    compilation pipeline (and before any thunks are created for them).

    .. note::

        One reason these can't be removed during the rewrite passes is that the
        `FunctionGraph.outputs` list entries containing them need to be
        entirely removed, and we don't want to add/remove
        `FunctionGraph.outputs` during rewriting.

    """

    view_map = {0: [0]}

    def make_node(self, x):
        return Apply(self, [x], [x.type()])

    def perform(self, node, inp, out):  # pragma: no cover
        (x,) = inp
        (z,) = out
        z[0] = x

    def __str__(self):  # pragma: no cover
        return f"{self.__class__.__name__}"

    def infer_shape(self, fgraph, node, input_shapes):
        return input_shapes

    def grad(self, args, g_outs):  # pragma: no cover
        return g_outs


update_placeholder = UpdatePlaceholder()
