# mypy: allow-untyped-defs
import inspect
from collections import defaultdict
from collections.abc import Callable, Sequence
from functools import lru_cache, partial, wraps
from itertools import chain
from typing import Optional, TYPE_CHECKING, TypeVar, Union
from typing_extensions import ParamSpec


if TYPE_CHECKING:
    from torch.export.decomp_utils import CustomDecompTable

import torch
import torch.library
from torch._ops import HigherOrderOperator, OperatorBase, OpOverload, OpOverloadPacket
from torch._prims_common import CustomOutParamAnnotation
from torch._subclasses.functional_tensor import FunctionalTensor
from torch.utils import _pytree as pytree


__all__ = [
    "decomposition_table",
    "pre_autograd_decomposition_table",
    "meta_table",
    "register_decomposition",
    "get_decompositions",
    "core_aten_decompositions",
    "_should_decompose_because_unsafe_op",
]

_T = TypeVar("_T")
_P = ParamSpec("_P")

# TODO: relax key type here; torch registrations should be possible to; but
# right now this type is accurate
global_decomposition_table: dict[str, dict[torch._ops.OperatorBase, Callable]] = (
    defaultdict(dict)
)

decomposition_table = global_decomposition_table["post_autograd"]
pre_autograd_decomposition_table = global_decomposition_table["pre_autograd"]
meta_table = global_decomposition_table["meta"]


def _should_decompose_because_unsafe_op(op: torch._ops.OperatorBase) -> bool:
    """
    Returns True if the op must always decompose in export/compile tracing system

    In export, we always decompose certain CIA ops that are tagged with
    maybe_aliasing_or_mutating because we statically need to know if the op is
    mutating or not. But these CIA ops could have different behaviour in runtime.

    native_batch_norm is a prim op which has a wrong schema and it needs to be replaced
    with correct schema. But until then, we will force decompose it via this tag.
    """
    if not isinstance(op, torch._ops.OpOverload):
        return False
    if torch.Tag.maybe_aliasing_or_mutating in op.tags:
        return True
    return op is torch.ops.aten.native_batch_norm.default


def _add_op_to_registry(registry, op, fn):
    """
    This is an internal API for adding an op to the decomposition table.

    If op is OpOverload, it will be added to the registry directly.
    If op is OpOverloadPacket, all the valid op_overloads in the packet will be added to the registry.
    """
    overloads: list[Union[torch._ops.OperatorBase]] = []
    if isinstance(op, HigherOrderOperator):
        # There's no concept of overloads for HigherOrderOperator
        registry[op] = fn
        return
    elif isinstance(op, OpOverload):
        overloads.append(op)
    else:
        if not isinstance(op, OpOverloadPacket):
            raise AssertionError(f"expected OpOverloadPacket, got {type(op)}")
        for ol in op.overloads():
            overloads.append(getattr(op, ol))

    for op_overload in overloads:
        if op_overload in registry:
            raise RuntimeError(f"duplicate registrations for {op_overload}")
        # TorchScript dumps a bunch of extra nonsense overloads
        # which don't have corresponding dispatcher entries, we need
        # to filter those out, e.g aten.add.float_int
        if torch._C._dispatch_has_kernel(op_overload.name()):
            registry[op_overload] = fn


def _convert_out_params(f):
    out_annotation = f.__annotations__.get("out")

    # If there are no out params, do not wrap the function.
    if not out_annotation:
        return f

    # Hack to detect when out is a Tuple. There seems to be no pretty way of doing this
    if getattr(out_annotation, "__origin__", None) is tuple:
        sig = inspect.signature(f)
        out_names = sig.return_annotation._fields
        # If out is a tuple, we need to register a function that unpacks all the out
        # elements as this is what native_functions.yaml expects

        @wraps(f)
        def _fn(*args, **kwargs):
            out_kwargs = tuple(kwargs.pop(o, None) for o in out_names)
            # Either all of the out kwargs are set or none of them
            is_none = out_kwargs[0] is None
            if not all((o is None) == is_none for o in out_kwargs):
                raise AssertionError(
                    f"all out kwargs must be set or none of them, got {out_kwargs}"
                )
            return f(*args, **kwargs, out=None if is_none else out_kwargs)

        out_params = [
            inspect.Parameter(
                o,
                kind=inspect.Parameter.KEYWORD_ONLY,
                default=None,
                annotation=t,
            )
            for o, t in zip(out_names, out_annotation.__args__)
        ]
        # Drop the out parameter and concatenate the new kwargs in the signature
        params = chain((v for k, v in sig.parameters.items() if k != "out"), out_params)
        _fn.__signature__ = inspect.Signature(  # type: ignore[attr-defined]
            parameters=params,  # type: ignore[arg-type]
            return_annotation=sig.return_annotation,
        )
        # Drop the out parameter and concatenate the new kwargs in the annotations
        _fn.__annotations__ = {k: v for k, v in f.__annotations__.items() if k != "out"}
        for o in out_params:
            _fn.__annotations__[o.name] = o.annotation

        # Propagate that this function is wrapped by `out_wrapper`
        _fn._torch_decompositions_out_wrapper = f._torch_decompositions_out_wrapper  # type: ignore[attr-defined]

        return _fn

    # Alternatively, there may be a single tensor out parameter with a name
    # other than "out". This will need special treatment and is indicated by an
    # annotation, which we will remove here so it is not exposed after wrapping.
    custom_out_param_name = f.__annotations__.pop(CustomOutParamAnnotation, None)
    if custom_out_param_name:

        @wraps(f)
        def _fn(*args, **kwargs):
            out_kwarg = kwargs.pop(custom_out_param_name, None)
            return f(*args, **kwargs, out=out_kwarg)

        out_param = inspect.Parameter(
            custom_out_param_name,
            kind=inspect.Parameter.KEYWORD_ONLY,
            default=None,
            annotation=out_annotation,
        )

        # Drop the out parameter and concatenate the new kwarg in the signature
        sig = inspect.signature(f)
        params = chain(
            (v for k, v in sig.parameters.items() if k != "out"), (out_param,)
        )
        _fn.__signature__ = inspect.Signature(  # type: ignore[attr-defined]
            parameters=params,  # type: ignore[arg-type]
            return_annotation=sig.return_annotation,
        )

        # Drop the out parameter and concatenate the new kwargs in the annotations
        _fn.__annotations__ = {k: v for k, v in f.__annotations__.items() if k != "out"}
        _fn.__annotations__[out_param.name] = out_param.annotation

        return _fn

    return f


def register_decomposition(
    aten_op, registry=None, *, type="post_autograd", unsafe=False
) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
    """
    A decorator to register a function as a decomposition to the Python
    decomposition table.  Use it like this::

        @register_decomposition(torch.ops.aten.clamp_min)
        def clamp_min(x):
            return torch.clamp(self, min=min)

    If you are writing a new decomposition, consider contributing it
    directly to PyTorch in torch._decomp.decompositions.

    This API is experimental; we are almost certainly going to extend
    the API when we make decompositions eligible for use in transforms (e.g.,
    autograd) and not just backend tracing, where we then need to know if a
    decomposition can be used to simulate a transform.

    By default, we also will register it to the Meta key of dispatcher,
    and replace the c++ Meta implementation if there is already one.

    unsafe kwarg is for reuse of this function for registering non-function
    things
    """

    if type not in {"post_autograd", "pre_autograd", "meta"}:
        raise AssertionError(
            f"type must be one of post_autograd, pre_autograd, or meta, got {type}"
        )

    def decomposition_decorator(fn: Callable[_P, _T]) -> Callable[_P, _T]:
        orig_fn = fn
        if not unsafe:
            fn = _convert_out_params(fn)

        nonlocal registry
        if registry is None:
            registry = global_decomposition_table[type]

        def register(op):
            _add_op_to_registry(registry, op, fn)

        # To handle allowing multiple aten_ops at once
        pytree.tree_map_(register, aten_op)
        return orig_fn

    return decomposition_decorator


def get_decompositions(
    aten_ops: Sequence[Union[torch._ops.OperatorBase, OpOverloadPacket]],
    type: str = "post_autograd",
) -> dict[torch._ops.OperatorBase, Callable]:
    """
    Retrieve a dictionary of decompositions corresponding to the list of
    operator overloads and overload packets passed as input.  Overload
    packets will include all decomposed overloads in the packet.  If there is
    no decomposition for a requested operator, it is silently ignored.

    This API is experimental; we are almost certainly going to give an alternate,
    more recommended formulation, where a user provides the set of operators
    they know how to implement, and we provide decompositions for everything
    not in this set.
    """
    if type not in {"post_autograd", "pre_autograd", "meta"}:
        raise AssertionError(
            f"type must be one of post_autograd, pre_autograd, or meta, got {type}"
        )

    registry = global_decomposition_table[type]
    packets_to_overloads = defaultdict(list)

    for opo in registry:
        if isinstance(opo, (OpOverload, OpOverloadPacket)):
            packets_to_overloads[opo.overloadpacket].append(opo)
    decompositions: dict[torch._ops.OperatorBase, Callable] = {}
    for op in aten_ops:
        if isinstance(op, OpOverloadPacket) and op in packets_to_overloads:
            for op_overload in packets_to_overloads[op]:
                decompositions[op_overload] = registry[op_overload]
        elif isinstance(op, (torch._ops.OperatorBase)) and op in registry:
            decompositions[op] = registry[op]
    return decompositions


def remove_decompositions(
    decompositions: dict[torch._ops.OperatorBase, Callable],
    aten_ops: Sequence[Union[OpOverload, OpOverloadPacket]],
) -> None:
    """
    Given a dictionary of decompositions obtained from get_decompositions(), removes
    operators associated with a list of operator overloads and overload packets passed
    as input. If the decomposition dictionary does not contain a decomposition that is
    specified to be removed, it is silently ignored.
    """
    for op in aten_ops:
        if isinstance(op, OpOverloadPacket):
            for overload_name in op.overloads():
                opo = getattr(op, overload_name)
                decompositions.pop(opo, None)
        elif isinstance(op, OpOverload):
            decompositions.pop(op, None)


# populate the table
import torch._decomp.decompositions
import torch._refs


def core_aten_decompositions() -> "CustomDecompTable":
    from torch.export.exported_program import default_decompositions

    return default_decompositions()


# See NOTE [Core ATen Ops]
#
# list was copied from torch/_inductor/decomposition.py
# excluding decompositions that results in prim ops
# Resulting opset of decomposition is core aten ops
def _core_aten_decompositions_post_autograd() -> dict[
    torch._ops.OperatorBase, Callable
]:
    aten = torch.ops.aten
    return get_decompositions(
        [
            aten.addcdiv,
            aten.addcdiv_,
            aten.addcmul,
            aten.addcmul_,
            aten.addr,
            aten.affine_grid_generator,
            aten.alias_copy,
            aten.all,
            aten.aminmax,
            aten.arange.default,
            aten.arange.start,
            aten.avg_pool2d_backward,
            aten.baddbmm,
            aten.binary_cross_entropy,
            aten.binary_cross_entropy_backward,
            aten.binary_cross_entropy_with_logits,
            aten.block_diag,
            aten.bernoulli.p,
            aten.bernoulli.default,
            aten.celu,
            aten.celu_,
            aten.channel_shuffle,
            aten.clamp_max,
            aten.clamp_min,
            aten.col2im,
            aten.count_nonzero,
            aten.linalg_cross,
            aten.cudnn_batch_norm,
            aten.cudnn_batch_norm_backward,
            aten.miopen_batch_norm_backward,
            aten.deg2rad,
            aten.deg2rad_,
            aten.detach,
            aten.diag_embed,
            aten.diagonal_backward,
            aten.diagonal_copy,
            aten.dot,
            aten.vdot,
            aten.elu_,
            aten.elu_backward,
            aten._embedding_bag,
            aten.embedding_dense_backward,
            aten.empty_like,
            aten._euclidean_dist.default,
            aten.expand_as,
            aten.expand_copy,
            aten.eye,
            aten.fill,
            aten.fill_,
            aten.floor_divide,
            aten.frac,
            aten.frac_,
            aten._fused_moving_avg_obs_fq_helper,
            aten.gelu_,
            aten.gelu_backward,
            aten.glu,
            aten.glu_backward,
            aten.hardshrink,
            aten.hardsigmoid,
            aten.hardsigmoid_,
            aten.hardsigmoid_backward,
            aten.hardswish,
            aten.hardswish_,
            aten.hardswish_backward,
            aten.hardtanh_,
            aten.hardtanh_backward,
            aten.heaviside,
            aten.heaviside_,
            aten.huber_loss,
            aten.huber_loss_backward,
            aten.im2col,
            aten.index_add.out,
            aten.index_add.default,
            aten.index_add_,
            aten.index_copy.out,
            aten.index_copy.default,
            aten.index_copy_,
            aten.index_fill.int_Scalar,
            aten.index_fill.int_Tensor,
            aten.index_fill.int_Scalar_out,
            aten.index_fill.int_Tensor_out,
            aten.index_fill_,
            aten.isin,
            aten.isneginf,
            aten.isposinf,
            aten.l1_loss,
            aten._lazy_clone,
            aten._test_parallel_materialize,
            aten.leaky_relu_,
            aten.leaky_relu_backward,
            aten.lerp,
            aten.lerp_,
            aten.linspace,
            aten.logaddexp,
            aten.logaddexp2,
            aten.logit,
            aten.logit_,
            aten.logit_backward,
            aten.log_sigmoid_backward,
            aten.log_sigmoid_forward,
            aten._log_softmax_backward_data,
            aten.logspace,
            aten.logsumexp.default,
            aten.masked_fill,
            aten.masked_fill_,
            aten.max_unpool2d,
            aten.max_unpool3d,
            aten.mish,
            aten.mish_,
            aten.mish_backward,
            aten.mse_loss,
            aten.mse_loss_backward,
            aten.multi_margin_loss,
            aten.multilabel_margin_loss_forward,
            aten.mv,
            aten.mvlgamma,
            aten.mvlgamma_,
            aten.nansum,
            aten.nan_to_num,
            aten.nan_to_num_,
            aten.narrow,
            aten.native_batch_norm_backward,
            aten.native_dropout_backward,
            aten.native_group_norm_backward,
            aten.native_layer_norm_backward,
            aten._fused_rms_norm,
            aten._fused_rms_norm_backward,
            aten.new_empty,
            aten.new_full,
            aten.new_ones,
            aten.new_zeros,
            aten.nll_loss2d_forward,
            aten.nll_loss2d_backward,
            aten.nll_loss_backward,
            aten.nll_loss_forward,
            aten.norm.ScalarOpt_dtype,
            aten.norm.Scalar,
            aten.norm.ScalarOpt_dim_dtype,
            aten.norm.ScalarOpt_dim,
            aten.norm.dtype_out,
            aten.norm.out,
            aten.norm.names_dtype_out,
            aten.norm.names_out,
            aten.norm.ScalarOpt_dtype_out,
            aten.norm.Scalar_out,
            aten.ones,
            aten.ones_like,
            aten.pixel_shuffle,
            aten.pixel_unshuffle,
            aten._prelu_kernel,
            aten._prelu_kernel_backward,
            aten._reshape_alias,
            aten.rad2deg,
            aten.rad2deg_,
            aten.reflection_pad1d,
            aten.reflection_pad1d_backward,
            aten.reflection_pad2d,
            aten.reflection_pad2d_backward,
            aten.reflection_pad3d,
            aten.reflection_pad3d_backward,
            aten.replication_pad1d,
            aten.replication_pad2d,
            aten.replication_pad3d,
            aten.renorm,
            aten.renorm_,
            aten.replication_pad2d,
            aten.resize_as,
            aten.roll,
            aten.rot90,
            aten.rrelu_with_noise,
            aten.rrelu_with_noise_,
            aten.rsub,
            aten._safe_softmax,
            aten._scaled_dot_product_flash_attention_for_cpu.default,
            aten.select_backward,
            aten.select_scatter,
            aten.sgn,
            aten.sgn_,
            aten.sigmoid_backward,
            aten.silu,
            aten.silu_,
            aten.silu_backward.grad_input,
            aten.silu_backward,
            aten.sinc,
            aten.sinc_,
            aten.slice_backward,
            aten.smooth_l1_loss,
            aten.smooth_l1_loss_backward,
            aten.soft_margin_loss,
            aten.soft_margin_loss_backward,
            aten._softmax_backward_data,
            aten.softplus,
            aten.softplus_backward,
            aten.softshrink,
            aten.special_entr,
            aten.special_log_ndtr,
            aten.special_xlog1py,
            aten.split.Tensor,
            aten.split_with_sizes_copy,
            aten.squeeze_copy,
            aten.squeeze.default,
            aten.squeeze.dim,
            aten.std.correction,
            aten.std.out,
            aten.std.correction_out,
            aten.std.names_out,
            aten.std.correction_names_out,
            aten.std_mean.correction,
            aten.std_mean.correction_out,
            aten.stack,
            aten.sum.default,
            aten.sum.out,
            aten.t,
            aten.t_copy,
            aten.take,
            aten.tanh_backward,
            aten.threshold,
            aten.threshold_,
            aten.threshold_backward,
            aten.trace,
            aten.transpose.int,
            aten.transpose_copy,
            aten.tril,
            aten.tril_,
            aten.triu,
            aten.triu_,
            aten.unbind,
            aten.unfold_backward,
            aten.unfold_copy,
            aten._unsafe_index,
            aten._unsafe_index_put,
            aten._unsafe_masked_index,
            aten._unsafe_masked_index_put_accumulate,
            aten.unsafe_split.Tensor,
            aten.unsafe_split_with_sizes,
            aten.unsqueeze_copy,
            aten._unsafe_view,
            aten.upsample_linear1d,
            aten.upsample_bilinear2d.out,
            aten.upsample_trilinear3d.out,
            aten.upsample_nearest2d_backward,
            aten.view_as_complex,
            aten.xlogy,
            aten.xlogy_,
            aten.zero,
            aten.zero_,
            aten.zeros,
            aten.zeros_like,
            aten._chunk_cat,
            aten._weight_norm_interface,
        ]
    )
