# mypy: allow-untyped-defs
from dataclasses import dataclass
from typing import Callable

import torch
import torch.fx.node
import torch.utils._pytree as pytree
from torch._ops import HigherOrderOperator


def is_graphable(val) -> bool:
    """Definition: a graphable type is a type that that is an acceptable input/output type to a FX node."""
    return isinstance(val, torch.fx.node.base_types)


def is_graphable_type(typ) -> bool:
    """Return whether the given type is graphable"""
    return issubclass(typ, torch.fx.node.base_types)


def to_graphable(stuff):
    """Flattens stuff into a flat list of graphable types."""
    # We can consider preserving things like List[int] to improve
    # perf and readability (right now that is all flattened out)
    flat_args, spec = pytree.tree_flatten(stuff)
    for arg in flat_args:
        if not is_graphable(arg):
            raise RuntimeError(
                f"Expected all pytree.tree_leaves of (args, kwargs) to be graphable types, but found "
                f"non-fx-graphable type {type(arg)}. If this type is meant to be constant, mark it as "
                f"via pytree.register_constant; otherwise, register it as a pytree."
            )
    return flat_args, spec


def from_graphable(flat_args, spec):
    """The inverse of to_graphable."""
    stuff = pytree.tree_unflatten(flat_args, spec)
    return stuff


def func_to_graphable(func):
    """
    Pack and flatten a function type into graphable types.
    This is useful for legalizing the function argument of `flat_apply`.
    """
    return pytree.tree_flatten(_ConstantFunction(func))


@dataclass(frozen=True)
class _ConstantFunction:
    func: Callable

    def __call__(self, *args, **kwargs):
        return self.func(*args, **kwargs)


pytree.register_constant(_ConstantFunction)

_op_types = (
    torch._ops.OpOverload,
    torch._ops.OpOverloadPacket,
    torch._ops.HigherOrderOperator,
)


class FlatApply(HigherOrderOperator):
    def __init__(self) -> None:
        super().__init__("flat_apply")

    def __call__(self, func, in_spec, *flat_args, **_unused):
        """
        Functions that take in non-graphable types cannot directly be put into FX graph.

        Given func(*args, **kwargs), if all of the non-graphable types are pytrees,
        then we're able to store a call to flat_apply(func, in_spec, *flat_args) in the FX graph.

        The semantics of flat_apply(func, in_spec, *flat_args) are roughly equivalent to:

        >>> def flat_apply_impl(func, in_spec, *flat_args):
        >>>     args, kwargs = pytree.tree_unflatten(flat_args, in_spec)
        >>>     output = func(*args, **kwargs)
        >>>     return output

        flat_apply supports the following two cases:
        - an input type is a container type (e.g. of tensors) registered as a pytree.
        We'll tree_flatten the input type and store the spec.
        - an input type is a constant type (i.e. torch.compile will specialize on it)
        registered with pytree.register_constant. The constant type goes directly
        into the spec.

        """
        assert isinstance(func, _op_types) or pytree._is_constant_holder(func)
        assert len(_unused) == 0
        return impl(func, in_spec, *flat_args)


def impl(func, in_spec, *flat_args):
    if not isinstance(func, _op_types):
        # assume _ConstantFunction
        func = pytree._retrieve_constant(func)
        assert isinstance(func, _ConstantFunction)

    args, kwargs = from_graphable(flat_args, in_spec)
    out = func(*args, **kwargs)

    # Right now, all outputs must either be graphable or lists/tuples of graphables.
    #
    # TODO: The following can be updated to support non-graphable outputs and pytrees.
    # For non-graphable constant outputs: the assumption would be that they are constant
    # (everytime the function runs those MUST be the same)
    # For pytree outputs:
    # I'm not sure if we need to return (flat_output, spec) or just (flat_output,):
    # in the latter case the tracers need to carry out the output specs
    # (they need to know how to reconstruct the object from just the flat_output).
    def is_valid_output(x):
        if isinstance(x, (tuple, list)):
            return all(map(is_valid_output, x))
        return is_graphable(x)

    assert is_valid_output(out)
    return out


flat_apply = FlatApply()

# mypy: allow-untyped-defs
from dataclasses import dataclass
from typing import Callable

import torch
import torch.fx.node
import torch.utils._pytree as pytree
from torch._ops import HigherOrderOperator


def is_graphable(val) -> bool:
    """Definition: a graphable type is a type that that is an acceptable input/output type to a FX node."""
    return isinstance(val, torch.fx.node.base_types)


def is_graphable_type(typ) -> bool:
    """Return whether the given type is graphable"""
    return issubclass(typ, torch.fx.node.base_types)


def to_graphable(stuff):
    """Flattens stuff into a flat list of graphable types."""
    # We can consider preserving things like List[int] to improve
    # perf and readability (right now that is all flattened out)
    flat_args, spec = pytree.tree_flatten(stuff)
    for arg in flat_args:
        if not is_graphable(arg):
            raise RuntimeError(
                f"Expected all pytree.tree_leaves of (args, kwargs) to be graphable types, but found "
                f"non-fx-graphable type {type(arg)}. If this type is meant to be constant, mark it as "
                f"via pytree.register_constant; otherwise, register it as a pytree."
            )
    return flat_args, spec


def from_graphable(flat_args, spec):
    """The inverse of to_graphable."""
    stuff = pytree.tree_unflatten(flat_args, spec)
    return stuff


def func_to_graphable(func):
    """
    Pack and flatten a function type into graphable types.
    This is useful for legalizing the function argument of `flat_apply`.
    """
    return pytree.tree_flatten(_ConstantFunction(func))


@dataclass(frozen=True)
class _ConstantFunction:
    func: Callable

    def __call__(self, *args, **kwargs):
        return self.func(*args, **kwargs)


pytree.register_constant(_ConstantFunction)

_op_types = (
    torch._ops.OpOverload,
    torch._ops.OpOverloadPacket,
    torch._ops.HigherOrderOperator,
)


class FlatApply(HigherOrderOperator):
    def __init__(self) -> None:
        super().__init__("flat_apply")

    def __call__(self, func, in_spec, *flat_args, **_unused):
        """
        Functions that take in non-graphable types cannot directly be put into FX graph.

        Given func(*args, **kwargs), if all of the non-graphable types are pytrees,
        then we're able to store a call to flat_apply(func, in_spec, *flat_args) in the FX graph.

        The semantics of flat_apply(func, in_spec, *flat_args) are roughly equivalent to:

        >>> def flat_apply_impl(func, in_spec, *flat_args):
        >>>     args, kwargs = pytree.tree_unflatten(flat_args, in_spec)
        >>>     output = func(*args, **kwargs)
        >>>     return output

        flat_apply supports the following two cases:
        - an input type is a container type (e.g. of tensors) registered as a pytree.
        We'll tree_flatten the input type and store the spec.
        - an input type is a constant type (i.e. torch.compile will specialize on it)
        registered with pytree.register_constant. The constant type goes directly
        into the spec.

        """
        assert isinstance(func, _op_types) or pytree._is_constant_holder(func)
        assert len(_unused) == 0
        return impl(func, in_spec, *flat_args)


def impl(func, in_spec, *flat_args):
    if not isinstance(func, _op_types):
        # assume _ConstantFunction
        func = pytree._retrieve_constant(func)
        assert isinstance(func, _ConstantFunction)

    args, kwargs = from_graphable(flat_args, in_spec)
    out = func(*args, **kwargs)

    # Right now, all outputs must either be graphable or lists/tuples of graphables.
    #
    # TODO: The following can be updated to support non-graphable outputs and pytrees.
    # For non-graphable constant outputs: the assumption would be that they are constant
    # (every time the function runs those MUST be the same)
    # For pytree outputs:
    # I'm not sure if we need to return (flat_output, spec) or just (flat_output,):
    # in the latter case the tracers need to carry out the output specs
    # (they need to know how to reconstruct the object from just the flat_output).
    def is_valid_output(x):
        if isinstance(x, (tuple, list)):
            return all(map(is_valid_output, x))
        return is_graphable(x)

    assert is_valid_output(out)
    return out


flat_apply = FlatApply()
