import torch
from torch._prims_common import (
    Number,
    NumberType,
    TensorLike,
    TensorLikeType,
    ShapeType,
    ELEMENTWISE_TYPE_PROMOTION_KIND,
)
import torch._prims_common as utils
from torch.utils._pytree import tree_flatten, tree_unflatten

from typing import Callable, Sequence, Tuple, NamedTuple, Optional, overload
import inspect
from functools import wraps
import warnings
from itertools import chain

@overload
def _maybe_convert_to_dtype(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType:
    pass

@overload
def _maybe_convert_to_dtype(a: NumberType, dtype: torch.dtype) -> NumberType:
    pass

@overload
def _maybe_convert_to_dtype(a: Sequence, dtype: torch.dtype) -> Sequence:
    pass

@overload
def _maybe_convert_to_dtype(a: None, dtype: torch.dtype) -> None:
    pass

# TODO: implement ref.cast with an option to enforce safe casting
def _maybe_convert_to_dtype(a, dtype):
    if isinstance(a, TensorLike):
        if a.dtype != dtype:
            return a.to(dtype)
        return a
    if isinstance(a, Number):
        return utils.dtype_to_type_ctor(dtype)(a)  # type: ignore[arg-type]
    if isinstance(a, Sequence):
        return tuple(_maybe_convert_to_dtype(x, dtype) for x in a)
    # Passthrough None because some functions wrapped with type promotion
    # wrapper might have optional args
    if a is None:
        return None

    raise ValueError(
        "Received type {0} that is neither a tensor or a number!".format(type(a))
    )


def _maybe_convert_to_type(a: NumberType, typ: type) -> NumberType:
    if not isinstance(a, Number):
        msg = "Found unknown type {0} when trying to convert scalars!".format(type(a))
        raise ValueError(msg)
    if not utils.is_weakly_lesser_type(type(a), typ):
        msg = "Scalar {0} of type {1} cannot be safely cast to type {2}!".format(
            a, type(a), typ
        )
        raise ValueError(msg)

    return typ(a)


def _annotation_has_type(*, typ, annotation):
    if hasattr(annotation, "__args__"):
        for a in annotation.__args__:
            if _annotation_has_type(typ=typ, annotation=a):
                return True
        return False

    return typ is annotation


class elementwise_type_promotion_wrapper:
    """
    Adds elementwise type promotion to a Python reference implementation.

    Takes two kwargs, type_promoting_args and type_promotion_kind.

    type_promoting_args must be a string Sequence specifiying the argument names of all
    arguments that participate in type promotion (and should be type promoted). If the
    arg specifies a Sequence-type then every element of the Sequence will participate in
    type promotion.

    type_promotion_kind must be one of the kinds specified by ELEMENTWISE_TYPE_PROMOTION_KIND.
    See its documentation for details.

    Other type promotion behavior, like validating the Python type of scalar arguments, must
    be handled separately.
    """

    def __init__(
        self,
        *,
        type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND,
        type_promoting_args: Optional[Sequence[str]] = None,
    ):
        self.type_promoting_arg_names = type_promoting_args
        self.type_promotion_kind = type_promotion_kind

    def __call__(self, fn: Callable) -> Callable:
        sig = inspect.signature(fn)

        @wraps(fn)
        def _fn(*args, **kwargs):
            bound = sig.bind(*args, **kwargs)
            type_promoting_args = tuple(
                bound.arguments[x]
                for x in self.type_promoting_arg_names  # type: ignore[union-attr]
                if x in bound.arguments.keys()
            )

            flattened_type_promoting_args = tree_flatten(type_promoting_args)[0]
            compute_dtype, result_dtype = utils.elementwise_dtypes(
                *flattened_type_promoting_args,
                type_promotion_kind=self.type_promotion_kind,
            )

            promoted_args = {
                x: _maybe_convert_to_dtype(bound.arguments[x], compute_dtype)
                for x in self.type_promoting_arg_names  # type: ignore[union-attr]
                if x in bound.arguments.keys()
            }
            bound.arguments.update(promoted_args)

            result = fn(**bound.arguments)

            if isinstance(result, TensorLike):
                return _maybe_convert_to_dtype(result, result_dtype)
            if isinstance(result, Sequence):
                return tuple(_maybe_convert_to_dtype(x, result_dtype) for x in result)
            raise AssertionError(f"Unhandled result type: {type(result)}")

        _fn.__signature__ = sig  # type: ignore[attr-defined]
        return _fn


# Returns True if resize is necessary
def _resize_output_check(out: TensorLikeType, shape: ShapeType):
    # If the shapes are correct there's nothing to do
    if utils.same_shape(out.shape, shape):
        return False
    if out.numel() != 0:
        msg = (
            f"An output with one or more elements was resized since it had shape {str(out.shape)} "
            "which does not match the required output shape {str(shape)}. "
            "This behavior is deprecated, and in a future PyTorch release outputs will not "
            "be resized unless they have zero elements. "
            "You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0)."
        )
        warnings.warn(msg)
    return True


# TODO: handle tuples of tensors
def _maybe_resize_out(out: TensorLikeType, shape: ShapeType):
    if _resize_output_check(out, shape):
        return out.resize_(shape)
    else:
        return out


def _safe_copy_out(
    *, copy_from: TensorLikeType, copy_to: TensorLikeType, exact_dtype: bool = False
):
    # Checks same device
    if copy_from.device != copy_to.device:
        msg = "Attempting to copy from device {0} to device {1}, but cross-device copies are not allowed!".format(
            copy_from.device, copy_to.device
        )
        raise RuntimeError(msg)

    # Checks safe cast
    if exact_dtype:
        torch._check(
            copy_from.dtype == copy_to.dtype,
            lambda: f"Expected out tensor to have dtype {copy_from.dtype} "
            f"but got {copy_to.dtype} instead",
        )
    else:
        torch._check(
            utils.can_safe_cast_to(cast_from=copy_from.dtype, cast_to=copy_to.dtype),
            lambda: f"Attempting to cast from {copy_from.dtype} to out tensor with dtype {copy_to.dtype}, "
            "but this can't be cast because it is not safe!",
        )

    return copy_to.copy_(copy_from)


def out_wrapper(*out_names: str, exact_dtype: bool = False):
    is_tensor = len(out_names) == 0
    assert is_tensor or len(out_names) >= 2

    def _out_wrapper(fn: Callable) -> Callable:
        """
        Adds the out parameter to a Python reference.
        """
        out_type = (
            TensorLikeType
            if is_tensor
            else Tuple[tuple(TensorLikeType for _ in range(len(out_names)))]
        )
        return_type = (
            TensorLikeType
            if is_tensor
            else NamedTuple(
                f"return_types_{fn.__name__}", [(o, TensorLikeType) for o in out_names]
            )
        )

        sig = inspect.signature(fn)
        factory_kwargs = ("device", "dtype")
        is_factory_fn = all(p in sig.parameters for p in factory_kwargs)

        @wraps(fn)
        def _fn(*args, out=None, **kwargs):
            if is_factory_fn and out is not None:
                for k in factory_kwargs:
                    out_attr = getattr(out, k)
                    if k not in kwargs:
                        kwargs[k] = out_attr

            result = fn(*args, **kwargs)
            assert (
                isinstance(result, TensorLike)
                and is_tensor
                or isinstance(result, Tuple)  # type: ignore[arg-type]
                and len(result) == len(out_names)
            )
            if out is not None:
                # Naively you might expect this assert to be true, but
                # it's not:
                #
                #   assert type(out) == type(result)
                #
                # The reason is that functions under this wrapper can
                # get registered to the Meta dispatch key, and that
                # means they can be executed in a context where tensor
                # subclasses are disabled (with no_dispatch), which is a
                # handy way for an is-a tensor subclass (e.g.,
                # FakeTensor) to have the normal meta backend create a
                # meta tensor, to be wrapped once it gets returned.
                # In this situation, you will get a FakeTensor as
                # the output tensor, but not the result--which will
                # be a normal meta tensor, but this is perfectly
                # harmless.
                if is_tensor:
                    assert isinstance(out, TensorLike)
                    # These two operations are done in-place
                    _maybe_resize_out(out, result.shape)
                    _safe_copy_out(copy_from=result, copy_to=out, exact_dtype=exact_dtype)  # type: ignore[arg-type]
                else:
                    assert isinstance(out, Tuple)  # type: ignore[arg-type]
                    torch._check_type(
                        len(out) == len(result),
                        lambda: f"expected tuple of {len(result)} elements but got {len(out)}",
                    )
                    for r, o in zip(result, out):
                        # These two operations are done in-place
                        _maybe_resize_out(o, r.shape)
                        _safe_copy_out(copy_from=r, copy_to=o, exact_dtype=exact_dtype)  # type: ignore[arg-type]
            else:
                out = result
            # mypy does not see through  the definition of out_type given that it's in a different scope
            return out if is_tensor else return_type(*out)  # type: ignore[operator]

        out_param = inspect.Parameter(
            "out",
            kind=inspect.Parameter.KEYWORD_ONLY,
            default=None,
            annotation=out_type,
        )
        # Mark that the function now returns a tuple
        assert sig.return_annotation in (sig.empty, out_type)
        params = chain(sig.parameters.values(), (out_param,))
        _fn.__signature__ = inspect.Signature(  # type: ignore[attr-defined]
            parameters=params, return_annotation=return_type  # type: ignore[arg-type]
        )
        _fn.__annotations__ = fn.__annotations__
        _fn.__annotations__["out"] = out_type
        _fn.__annotations__["return"] = return_type
        return _fn

    return _out_wrapper


def backwards_not_supported(prim):
    def redispatch_prim(args, kwargs):
        with torch._C._AutoDispatchBelowAutograd():
            old = torch._C._dispatch_tls_is_dispatch_key_excluded(torch._C.DispatchKey.ADInplaceOrView)
            return prim(*args, **kwargs)

    class BackwardsNotSupported(torch.autograd.Function):
        @staticmethod
        def forward(ctx, args_spec, *flat_args):
            args, kwargs = tree_unflatten(flat_args, args_spec)  # type: ignore[arg-type]
            return redispatch_prim(args, kwargs)

        @staticmethod
        def backward(ctx, *args):
            raise RuntimeError("backwards not supported on prim")

    @wraps(prim)
    def _autograd_impl(*args, **kwargs):
        flat_args, args_spec = tree_flatten((args, kwargs))
        if torch.is_grad_enabled() and any(a.requires_grad for a in flat_args if isinstance(a, torch.Tensor)):
            # TODO: There is a subtle bug here: prims like copy_to
            # return their input argument after mutating it; and custom
            # autograd function will incorrectly turn the result into
            # a view which will fail test_python_ref_executor tests.
            # At the moment, we sidestep this by observing that the
            # unit tests don't ever try to run the executor with
            # autograd, so we don't exercise the buggy case, but if
            # you ever want to feed autograd through this, be aware
            # of it!  We need a way of properly implementing autograd
            # for mutating operations in Python to do this.
            return BackwardsNotSupported.apply(args_spec, *flat_args)
        else:
            return redispatch_prim(args, kwargs)

    return _autograd_impl


# TODO: when tracing this will add torch tensors and not TensorMeta objects
# to the trace -- we should fix this by adding a tracing context and NumberMeta classes
# TODO: this wrapper is currently untested
def elementwise_unary_scalar_wrapper(fn: Callable) -> Callable:
    """
    Allows unary operators that accept tensors to work with Python numbers.
    """
    sig = inspect.signature(fn)

    @wraps(fn)
    def _fn(*args, **kwargs):
        if len(args) > 0 and isinstance(args[0], Number):
            dtype = utils.type_to_dtype(type(args[0]))
            args_ = list(args)
            args_[0] = torch.tensor(args[0], dtype=dtype)
            result = fn(*args_, **kwargs)
            assert isinstance(result, torch.Tensor)
            return result.item()

        return fn(*args, **kwargs)

    _fn.__signature__ = sig  # type: ignore[attr-defined]
    return _fn
