"""
Note [Opaque Objects]

Opaque objects are the way we allow custom operators to accept a user-defined
"black box" object as an input.

There are two kinds of opaque types: VALUE type and REFERENCE type.
The distinction determines how torch.compile handles the object.

REFERENCE TYPES (default):

Reference-typed opaque objects represent mutable stateful objects and are
treated as black boxes. In torch.compile, since torch.compile cannot optimize
the anything (including tensors) within the object, the object must be an
input to the graph.

You can register a custom class as being a reference-based opaque object class
through `register_opaque_type(MyClass, typ="reference")`.

VALUE TYPES:

Value-typed opaque objects represent constant values.
In torch.compile, the graph specializes on the object like how other constants
are. Therefore there are a couple of methods on the class that must be
implemented before registering it as a value-typed opaque object class:
  - __eq__: torch.compile will create guards based on the equality of this
  object, meaning that a recompilation will happen if __eq__ returns False.
  - __hash__: This must be implemented for Fake Tensor caching
  - __fx_repr__: This must be implemented to provide an evaluable representation
    for FX graph codegen. It should return a tuple of (repr_string, dict[str, type])
    where repr_string can reconstruct the object and the dict maps names used in
    repr_string to their corresponding types.

You can register a custom class as being a reference-based opaque object class
through `register_opaque_type(MyClass, typ="value")`.
"""

from dataclasses import dataclass
from typing import Any, Literal, NewType
from weakref import WeakKeyDictionary

import torch

from .fake_class_registry import register_fake_class


@register_fake_class("aten::OpaqueObject")
class FakeOpaqueObject:
    def __init__(self) -> None:
        pass

    @classmethod
    def __obj_unflatten__(cls, flattened_ctx: dict[str, Any]) -> None:
        raise RuntimeError(
            "FakeOpaqueObject should not be created through __obj_unflatten__ "
            "and should be special handled. Please file an issue to Github."
        )


OpaqueTypeStr = "__torch__.torch.classes.aten.OpaqueObject"

OpaqueType = NewType("OpaqueType", torch._C.ScriptObject)


@dataclass
class _OpaqueTypeInfo:
    class_name: str
    opaque_typ: Literal["reference", "value"]


# Mapping of type -> (string name, reference/value type)
_OPAQUE_TYPES: WeakKeyDictionary[Any, _OpaqueTypeInfo] = WeakKeyDictionary()
# Mapping of class_name -> (type, reference/value type)
_OPAQUE_TYPES_BY_NAME: dict[str, _OpaqueTypeInfo] = {}


def get_opaque_type_name(cls: Any) -> str:
    """
    Gets the registered opaque type name for a given class.

    Args:
        cls (type): The class to get the type name for.

    Returns:
        str: The registered type name for the class.

    Raises:
        ValueError: If the class is not registered as an opaque type.
    """
    if cls not in _OPAQUE_TYPES:
        raise ValueError(
            f"Class {cls} is not registered as an opaque type. "
            f"Call register_opaque_type({cls.__name__}) first."
        )
    return _OPAQUE_TYPES[cls].class_name


def register_opaque_type(cls: Any, *, typ: str) -> None:
    """
    Registers the given type as an opaque type which allows this to be consumed
    by a custom operator.

    The type name will be automatically generated from the class's fully
    qualified name (ex. my_module.MyClass).

    Args:
        cls (type): The class to register as an opaque type.
        typ (str): Either "reference" or "value". See Note [Opaque Objects] for
            more details.
    """
    import torch.utils._pytree as pytree

    # Prevent registration of built-in types (int, str, list, dict, etc.) and torch.Tensor
    if cls.__module__ == "builtins" or cls is torch.Tensor:
        raise ValueError(
            f"Unable to register built-in type {cls} as an opaque type. "
            "Please wrap it in a custom class and register the custom class as opaque."
        )

    if cls in pytree.SUPPORTED_NODES:
        raise ValueError(
            f"{cls} cannot be registered as an opaque object as it has been "
            "registered as a pytree. Opaque objects must be pytree leaves."
        )

    assert typ in ["reference", "value"], (
        "Opaque type must be either 'reference' or 'value'"
    )

    if typ == "value":
        if cls.__eq__ is object.__eq__:  # type: ignore[comparison-overlap]
            raise TypeError(
                f"Value-type opaque object of type {cls} is "
                "expected to have a non-default `__eq__` "
                "implementation as we will use this in torch.compile "
                "to guard on the equality of objects."
            )

        # Class with a custom `__eq__` without `__hash__` won't inherit the default
        # `__hash__` from object; see https://stackoverflow.com/a/1608907.
        if cls.__hash__ is None:  # type: ignore[comparison-overlap]
            raise TypeError(
                f"Value-type opaque object of type {cls} is "
                "expected to have a non-default `__hash__` "
                "implementation as we will use this in torch.compile "
                "for FakeTensor caching."
            )

        if not hasattr(cls, "__fx_repr__"):
            raise TypeError(
                f"Value-type opaque object of type {cls} is "
                "expected to have a `__fx_repr__` method "
                "implementation as we will use this to reconstruct "
                "the object in the FX codegen. __fx_repr__ should return "
                "a tuple of (repr_string, set_of_types)."
            )

    # Generate a fully qualified name by combining module and qualname
    name = f"{cls.__module__}.{cls.__qualname__}"

    type_info = _OpaqueTypeInfo(name, typ)
    _OPAQUE_TYPES[cls] = type_info
    _OPAQUE_TYPES_BY_NAME[name] = type_info

    torch._C._register_opaque_type(name)


def is_opaque_type(cls: Any) -> bool:
    """
    Checks if the given type is an opaque type.
    """
    if isinstance(cls, str):
        return torch._C._is_opaque_type_registered(cls)

    if cls not in _OPAQUE_TYPES:
        return False

    return torch._C._is_opaque_type_registered(_OPAQUE_TYPES[cls].class_name)


def is_opaque_value_type(cls: Any) -> bool:
    """
    Checks if the given type is an opaque **value** type.
    See Note [Opaque Objects] for more information.
    """
    if not is_opaque_type(cls):
        return False

    if isinstance(cls, str):
        return _OPAQUE_TYPES_BY_NAME[cls].opaque_typ == "value"

    return _OPAQUE_TYPES[cls].opaque_typ == "value"


def is_opaque_reference_type(cls: Any) -> bool:
    """
    Checks if the given type is an opaque **reference** type.
    See Note [Opaque Objects] for more information.
    """
    if not is_opaque_type(cls):
        return False

    if isinstance(cls, str):
        return _OPAQUE_TYPES_BY_NAME[cls].opaque_typ == "reference"

    return _OPAQUE_TYPES[cls].opaque_typ == "reference"


def get_opaque_obj_repr(obj: Any) -> tuple[str, dict[str, type]]:
    """
    Get the FX-evaluable repr for an opaque object and collect required globals.

    Objects must implement __fx_repr__() which should return:
        (repr_string, dict_mapping_name_to_type)

    where repr_string is an evaluable string representation and
    dict_mapping_name_to_type maps the names used in repr_string to their types.

    For example, if repr_string is "Foo(bar=Bar(1))", the dict should be:
        {"Foo": Foo, "Bar": Bar}
    """
    if not hasattr(obj, "__fx_repr__"):
        raise TypeError(
            f"Value-type opaque object of type {obj} is "
            "expected to have a `__fx_repr__` method "
            "implementation as we will use this to reconstruct "
            "the object in the FX codegen. __fx_repr__ should return "
            "a tuple of (repr_string, dict[str, type])."
        )

    repr_str, globals_dict = obj.__fx_repr__()

    if not isinstance(repr_str, str):
        raise TypeError(
            f"__fx_repr__ for {type(obj).__name__} must return a string as the "
            f"first element, got {type(repr_str).__name__}"
        )

    if not isinstance(globals_dict, dict):
        raise TypeError(
            f"__fx_repr__ for {type(obj).__name__} must return a dict as the "
            f"second element, got {type(globals_dict).__name__}"
        )

    return repr_str, globals_dict